I (D - I rq World Mlorc6> \q% 97 s Development 9 g I n d Indicators The world by income A,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ . >444.~ ~~~ . This new volume-together with Its companion publications, the World Bank Atlas and the Ubrld Development Indicators CD-ROM-has been totally redesigned. based on extensive consultation with partners inside and outside the World Bank and In close collaboration with the Bank's sec- tor specialists and research staff. We have expanded the coverage of the Itbrld Development Indi- cators to provide an overview of the main themes of development that are the focus of the World Bank's work: the welfare of people, the use and preservation of the environment, the growth and structure of the economy, the complementary roles of states and markets, and the growing llnks between economies. Guiding our selection of indicators is a desire to present the most useful . | and Interesting information available on trends In development. Over the coming years we expect * . X the coverage to evolve and grow, reflecting emerging issues in the global economy and greater availability of data. Previously published as an appendix to the World Development Report, the World Develop- ment lndcators now takes its place as the World Bank's principal statistical survey of world devel- opment. It contains most of the data previously published in the Bank's frld Tables and Social Indicators of Development. Unlike the World Tables, which presented tables for each economy containing long time series of macroeconomic data. the World Development Indicators follows a cross-sectional format, with tables arranged topically. Most tables show Indicators for a recent year and an earlier year, covering 148 economies with populations of more than one million. Where possible, the Indicators are in growth rates or shares. This format Is designed to facilitate comparative analysis of important economic trends across countries and over time. For researchers who need time series of primary data, the fWrld Developmnent Indicators CD-ROM contains time series of all Indicators available in the World Devel- opment Indicators database. And for those who want a brief overview of the world economy. the World Bank Atlas summarizes 34 current indicators for 209 economies In maps, graphs, and tables. We have trled to ensure that the Indicators are consistent with international standards and are reasonably comparable across economies and over time. But the state of intemational sta- tistics is not good. Many statistical offices are underfunded, and their staffs undertrained. In some areas the importance of accurate measures of social and economic aggregates for policy formulation is not recognized-or worse, statistics are manipulated to support policy. It is not possible to correct for all possible sources of error. but where difficultles are known in collecting and reporting data, we have-In what is another new feature-provided wamrings in general notes or in specific footnotes to the tables. When data have been found to be unreliable or to deviate seriously from accepted norms, they have been omitted. We have llmited our selection of Indica- tors to those that are available for most of the principal economies. We welcome your suggestions and comments on these new products. Please write to us at World Bank headquarters or send us email at info@worldbank.org. Masood Ahmed Director Intemational Economics Department Shaida Badiee Senior Manager Development Data Group World Development Indicators 1997 vil Contents Foreword v 4 5 Key agricultural inputs 146 Acknowledgments vi 46 Structure of manufacturing 150 Preface vii 4 7 Growth of merchandise trade 154 Acronyms and abbreviations xi 4.8 Structure of merchandise exports 158 Partners xii 4 9 Structure of merchandise imports 162 Users guide xx 4 10 Structure of service exports 166 Statistical methods xxiv 4 11 Structure of service imports 170 Primary data documentation xxvii 4 12 Structure of demand 174 4 13 Growth of consumption and investment 178 . 4 14 Structure of consumption in PPP terms 182 4 15 Macroeconomic indicators 186 Introduction 3 4 16 Central government finances 190 11 The quality of life 6 4:17 Central government revenues 194 1 2 Gender dimensions of development 10 4 18 Central government expenditures 198 1 3 Structural transformation 14 4 19 Monetary indicators 202 Our changing world 20 4 20 Inflation 206 4 21 Balance of payments current account 210 4 22 Balance of payments capital and financial account 214 4 23 External debt 218 Introduction 29 424 External debt management 222 2 1 Population 34 2 2 Population dynamics 38 2 3 Labor force structure 42 0 2 4 Employment 46 Introduction 227 2 5 Poverty 50 5 1 Credit, investment, and expenditure 232 2 6 Distribution of income or consumption 54 5 2 Private capital flows 236 2 7 Education policy and infrastructure 58 5 3 Stock markets 240 2 8 Access to education 62 5 4 State-owned enterprises 244 2 9 Educational attainment 66 5 5 Relative prices and exchange rates 248 2 10 Gender and education 70 5 6 Trade policies 252 211 Health spending and personnel 74 5 7 Export competitiveness 256 2 12 Access to health services 78 5 8 Tax policies 260 2 13 Risk factors in health 82 5 9 Portfolio investment regulation and risk 264 2 14 Mortality 86 5 10 Financial depth and efficiency 268 5 11 Power and communications 272 . 5 i2 Transport infrastructure 276 5 13 Science and technology 280 Introduction 91 5 14 The information age 284 3 1 Land use and deforestation 94 3 2 Biodiversity and protected areas 98 3 3 Freshwater 102 3 4 Energy production and use 106 Introduction 289 3 5 Energy efficiency, dependency, and emissions 110 6 1 Integration with the global economy 292 3 6 Urbanization 114 6 2 Direction of OECD trade 296 3 7 Traffic and congestion 120 6 3 OECD trade with low- and middle-income economies 298 3 8 Air pollution 121 6 4 Uruguay Round tariff reductions 300 3.9 Government commitment 122 6 5 Commodity prices 302 6 6 Net financial flows from Development Assistance Committee countries 304 . 6 7 Aid flows from Development Assistance Introduction 127 Committee countries 306 4 1 Growth of output 130 6 8 Financial terms of official development 4 2 Structure of output 134 assistance commitments 308 4 3 Agricultural production 138 6 9 Distnbution of net aid by Development 4 4 Food crops 142 Assistance Committee countries 310 vill World DeveloDment Indicators 1997 6 10 Aid dependency 314 4 lla Services as a share of total trade, 1980-95 173 6 11 Net concessional flows from multilateral institutions 318 4 12a Structure of demand, 1995 177 6 12 Net resource flows from international financial Institutions 322 4 13a Private consumption per capita, 1980-95 181 6 13 Foreign labor and population in OECD countries 326 4 14a Expenditure on services in nominal and PPP terms as a share of total expenditure in selected economies 185 414b Expenditure on food in nominal and PPP terms as a share Credits 328 of total expenditure in selected economies 185 Bibliography 330 4 20a Range of annual inflation rates, by region, 1980-90 206 Index of indicators 335 4 20b Range of annual nflation rates, by region, 1990-95 207 Readers survey 341 4 20c Average annual rates of inflation, by region, 1980-95 208 5a GNP per capita and stock market capitalization in emerging markets, 1995 229 5b Implementing the private sector development agenda 230 la Targeting development 4 5 2a Net private capital flows to developing economies, 4a The pitfalls of measunng national income 128 by region, 1980-95 239 5a Private capital flows prove resilient 228 5 9a Country rating of top 10 developing economy recipients of pnvate capital flows, 1996 267 5 Ila GNP per capita and telephone density in developing economies. 1995 275 2a Regional population shares, 1980 and 2030 30 5 13a High-technology exports from the top 10 high-technology 2 2a Composition of population by age and sex in low- and exporters among developing economies, 1995 283 high-income economies, 1995 and 2025 39 5 14a The 10 developing economies with the most TV sets 2 2b Population growth in low- and middle-income per capita, 1995 287 economies, 1990-2035 40 5 14b The 10 developing economies with the most personal computers 2 2c Population growth in Sub-Saharan Afnca, 1990-2035 40 per capita, 1995 287 2 2d Population growth in Asia, 1990-2035 40 6 3a Manufactures in high-income OECD country imports 2 2e Population growth in the Middle East from low- and middle-income economies, 1970-95 298 and North Africa, 1990-2035 41 6 5a Weighted index of pnmary commodity prices for low- and 2 2f Population growth in Latin America middle-income economies, 1970-95 302 and the Caribbean. 1990-2035 41 6 7a Net ODA from DAC countnes as a share of GNP, 1994 307 2 2g Population growth in Europe and Central Asia, 1990-2035 41 6 7b Net ODA from DAC countnes, 1994 307 2 14a Infant mortality, by region, 1970, 1980, and 1995 89 6 8a Share of grants in net bilateral ODA 3a Global water use, by sector, 1900-2000 93 from DAC countnes, 1960-95 309 3b Energy use, by income group, 1994 93 6 9a Distribution of net bilateral ODA and official aid 3 la Land use in low-income economies, 1980 and 1994 97 from the five largest donors, 1994 313 3 lb Land use in middle-income economies, 1980 and 1994 97 6 12a Net IBRD and IDA lending, 1970-95 325 3 Ic Land use in high-income economies, 1980 and 1994 97 6 13a Stocks of foreign population by nationality 3 2a Protected areas, globally and by income group, 1994 98 in selected OECD countries 327 3 Sa Carbon dioxide emissions per capita, by income group, 1980 and 1992 113 3 5b Carbon dioxide emissions, by income group, 1992 113 _ _ 3 6a Urban population, by region. 1970-95 117 2a Population living on less than $1 a day 3 6b Urban population, by income group, 1980-95 117 in developing economies, 1987 and 1993 31 3 7a Motor vehicle registration, 1930-90 118 2b Estimated illiterate population aged 15 3 7b Motor vehicle production, 1950-90 119 and above, 1980 and 1995 32 3 9a Global atmospheric concentration 2 3a Unemployment and underemployment in three countries 45 of chlorofluorocarbons, 1978-94 124 2 5a Poverty gap in various regions, 1987 and 1993 53 4 3a Food production, by region, 1980 and 1995 141 2 6a Income shares of lowest and highest quintiles, 1960s-1990s 57 4 6a Five largest developing manufactunng economies, 1994 153 2 7a Public education spending per pupil, 4 6b Shares of manufactured goods produced, by income group. 1994 153 by level of schooling, 1985 and 1992 61 4 7a Net barter terms of trade, 1980-93 157 2 13a Cost-effectiveness of public health interventions and essential 4 8a Merchandise exports from developing economies, 1980-94 161 clinical services n low-income economies, 1990 83 4 9a Merchandise imports of developing economies, 1980-95 165 2 13b Prevalence of child malnutrition, 1985, 1990, and 1995 84 4 1Oa World trade in goods and services, 1980-95 169 3 2a Countnes with largest shares of protected areas 99 World Development Indicators 1997 ix 3.6a Urban agulomeratJons wlth populations of 10 million or more, 2015 117 3.9a Status of national environmental action plans 123 4a Average annual growth of world trade and GDP, 1950-95 129 4b The emerging giants of the developing world, 1995 129 4.14a Structure of consumption in Pm terms, by Income group, 1993 184 4.14b Structure of consumption in nominal local currency terms, by Income group. 1993 184 5.6a OECD imports covered by nontariff barriers before and after the Uruguay Round 255 5.7a Average annual growth of exports and export growth factors. 1983-94 258 5.7b Correlation of export growth factors vwth export growth. 1983-94 259 6a Global environment for developing economies. 1974-2006 290 x World Development Indicators 1997 Acronyms and abbreviations Ib Barrel ADS Aslan Development Bank btu British thermal units AIDS African Development Bank COCN Customs Cooperation Council Nomenclature APEC Asia-Pacific Economic Cooperation CFC Chlorofluorocarbon CDC Centers for Disease Control and Prevention e.ll Cost, insurance, and freight CDIAC Carbon Dioxide Information Analysis Center cDnI Convention on Intemational Trade in Endangered CEC Commission of the European Communities Species of Wild Flora and Fauna DAC Development Assistance Committee DO0 Carbon dioxide uED European Bank for Reconstruction and Development COMTRADE Commodity Trade database EDF European Development Fund CPI Consumer price index EFIA European Free Trade Area e. m Cubic meter EIB European Investment Bank DHS Demographic and Health Survey EU European Union DUTU Dry metric ton unit EUROSTAR Statistical Office of the European Communities DPI Diphtheria. pertussis. and tetanus FAO Food and Agriculture Orgenizatlon DMS Debtor Reporting System Sam General Agreement on Tariffs and Trade AW Enhanced Structural Adjustment Facility OEF Global Environment Facility FM Foreign direct investment IED International Bank for Reconstruction and Development te.a Free on board CoCO International Cocoa Organization =WE Former Yugoslav Republic 100 International Coffee Organization 54 France. Germany. Japan, United Kingdom. and United States ICP International Comparison Programme :-7 G-5 plus Canada and Italy IDA International Development Association GDP Gross domestic product IDB InterAmerican Development Bank lIES Global Environment Monitoring System IEA Intemational Energy Agency aF Govemment Fnance Statistics IFC Intemational Finance Corporation MS Geographic information system ILO International Labour Organisatlon _NI Gross national income IMF International Monetary Fund SMP Gross national product IRF International Road Federation ha Hectare ITU Intemational Telecommunication Union HIV Human immunodeficiency virus IUCN World Conservation Union IOs International Country Risk Guide LME London Metals Exchange IDlE Intemational Classifiation of Status in Empioyment MIOA Multilateral Investment Guarantee Agency IFS International Financial Statisics OECD Organization for Economic Cooperation and Development OCW Intemational Standard Classfcation of Education PAO Pan American Health Organization ISIC Intemational Standard Industrial Classiflcation U.N. United Nations kg Kilogram UNAIDS Joint United Nations Programme on HIV/AIDS klm Kilometer UNCED United Nations Conference on Environment and Development kwh Kilowatt-hour UNCTAD United Nations Conference on Trade and Development Ml Narrow money (currency and demand deposits) UNDP United Nations Development Programme M1 Money plus quasi money UNECE United Nations Economic Commission for Europe Ma Broad money or liquid liabilities UNEP United Nations Environment Programme rrsbtu Millions of British thermal units UNESCO United Nations Educational, Scientifc, and Cultural Organization mt Metric ton UNFPA United Nations Population Fund MFA MuitMflbre Arrangement UNICEF United Nations Children's Fund MUV Manufactures unit value UNIDO United Nations Industrial Development Organization NAFrA North American Free Trade Agreement UNMISD United Nations Research Institute for Social Development NEAP National environmental action plan UNSO United Nations Statistical Office mOO Nongovemmental organization WCMC World Conservation Monitoring Centre ODA Offcial development assistance WFP World Food Programme P/E Price-eamings ratio WHO World Health Organization PPP Purchasing power party wro World Trade Organization RID Research and development WEW World Wide Fund for Nature SAF Structural Adjustment Facillty SOR Special drawing right slnc Standard Intemational Trade Classification SNA U.N. System of National Accounts SO Sulfur dioxide q. kmn Square kilometer TFP Total factor productivity TRAINS Trade Analysis and Information System TSP Triple superphosphate World Development Indicators 1997 xi Partners Defining, gathering, and disseminating international statistics is a collective effort of many peo- ple 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 have administered censuses and household surveys in every part of the world to the committees and working parties of the national and international statistical agencies that have developed the nomenclature, classifi- cations, and standards that are fundamental to an international statistical system. Non- governmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a con- tinuing dialogue about the quality and interpretation of statistical indicators. What all these contributors have in common is a strong belief that accurate data, readily available, will improve the quality of public and private decisionmaking. Statistical indicators are a classic example of a public good. Like all information, they are costly to collect, but once available in a convenient form, they can be shared widely at little additional cost and with no diminution of their value. Indeed, it is of benefit for all to have good information widely shared. One of the consequences of the public nature of statistical indica- tors, however, is that they are often taken for granted and the work of those who developed them goes unacknowledged. In the new World Development Indicators we want to take a first step toward correcting this situation by identifying the organizations that have contributed data directly to this volume. We recognize that such a list omits many others whose work is no less vital. In the future we hope to see our list of partners grow. In the meantime we wish to acknowl- edge our debt and gratitude to all those whose efforts have helped to build a base of compre- hensive, quantitative information about the world and its people. Food and Agriculture Organization The Food and Agriculture Organization (FAO) was founded in October 1945 with a mandate to raise nutrition levels and living standards, to improve agricultural productivity, and to better the condi- tion of rural populations. Since its inception the FAO has worked to alleviate poverty and hunger by promoting agricultural development, improved nutrition, and the pursuit of food security-the access of all people at all times to the food they need for an active and healthy life. The organi- zation provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments, and serves as an international forum for debate on food and agricultural issues. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fer- tilizer Yearbook The FAO makes much of its data available on diskette through its Agrostat PC system FAO publications can be ordered from national sales agents or directly from the FAO Distri- bution and Sales Section, Viale delle Terme di Caracalla, 00100 Rome, Italy. Website: http://www.fao.org/default htm International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, was founded with the signing of the Convention on International Civil Aviation on December 7, 1944 It is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. xii World Development Indicators 1997 The ICAO promotes the adoption of safety measures, establishes visual and instrument flight rules for pilots and crews, develops aeronautical charts, coordinates aircraft radio frequencies, and sets uniform regulationsfortheoperation of airservices and customs procedures The ICAO's membership consists of 185 countries To obtain ICAO publications contact ICAO. Document Sales Unit, 999 University Street, Mon- treal, Quebec H3C 5H7, Canada; telephone (514) 954 8022, fax (514) 954 6769: email: sales_unit@icao org, Website: http://www.cam org/-icao. International Labour Organisation The International Labour Organisation (ILO) is the United Nations specialized agency that seeks the promotion of social Justice and internationally recognized human and labor rights. Founded in 1919, it is the only surviving major creation of the Treaty of Versailles, which brought the League of Nations into being. It became the first specialized agency of the United Nations in 1946 The ILO has a structure that is unique within the United Nations system, a tripartite structure that has workers and employers participating as equal partners with governments in the work of its governing organs As part of its mandate, the ILO maintains an extensive statistical publication program Its most comprehensive collection of labor force statistics is the Yearbook of Labour Statistics. Publications can be ordered from the International Labour Office, 4 route des Morillons, CH-1211 Geneva 22, Switzerland, or from sales agents and major booksellers throughout the world and ILO offices in many countries. Fax: (41 22) 798 86 85, Website: http.//www unicc org/ilo/index html. International Monetary Fund The International Monetary Fund (IMF) was established at a conference held in Bretton Woods, New Hampshire, U.S.A., on July 1-22, 1944, a conference that also established the World Bank. The IMF came into official existence on December 27, 1945, when representatives of 29 coun- tries signed its articles of agreement. The IMF commenced financial operations on March 1, 1947. It currently has 181 member countries. The statutory purposes of the IMF are to promote international monetary cooperation, to facil- itate the expansion and balanced growth of international trade, to promote exchange rate stabil- ity, to assist in the establishment of a multilateral system of payments, to make the general resources of the Fund temporarily available to its members under adequate safeguards, and to shorten the duration and lessen the degree of disequilibrium in the international balances of pay- ments of members. In furtherance of its purposes the IMF maintains an extensive program for the development and compilation of international statistics. The IMF is responsible for collecting and reporting sta- tistics on international financial transactions and the balance of payments In April 1996 it under- took an important initiative aimed at improving the quality of international statistics, establishing the Special Data Dissemination Standard (SDDS) to guide members that have or seek access to international capital markets in providing economic and financial data to the public. Major statistical publications of the IMF include International Financial Stafistics, Balance of Payments Statistics Yearbook, Govemment Finance Statistics Yearbook, and Direction of Trade Statistics. For more information on IMF statistical publications contact International Monetary Fund, Pub- lications Services, Catalog Orders, 700 19th Street, N.W, Washington, D.C. 20431, U.S A tele- phone: (202) 623 7430, fax (202) 623 7201; telex RCA 248331 IMF UR, email pubweb@imf org, Website: http://www imf org, SDDS bulletin board http //dsbb imf org World Development Indicators 1997 xiii Intematlonal Tebc_munleatlen Union Founded in Pads in 1885 as the Intemational Telegraph Union, the Intemational TelecommunIcaton ; Union (ITU) took its present name in 1934 and became a specialized agency of the United Nations in 1947. It \ The ITU is an intergovernmental organization within which the public and private sectors cooper- ate for the development of telecommunications. The ITU adopts international regulations and treaties govemring all terrestrial and space uses of the frequency spectrum and the use of the geostationary- satellite orbit. It also develops standards for the interconnection of telecommunications systems worldwide. The ITU fosters the development of telecommunications in developing countries by estab- lishing mediumterm development policies and strategies in consultation with other partners in the sector and providing specialized technical assistance in management, telecommunications policy, human resource management, research and development, technology choice and transfer, network installation and maintenance. and Investment financing and resource mobilization. The major statistical publication of the ITU is the Telecommunications )barbook. Publications can be ordered from ITU Sales and Marketing Service, Place des Nations, CH-1211 Geneva 20, Switzerland: telephone: (41 22) 730 6141 (English). (41 22) 730 6142 (French), and (41 22) 730 6143 (Spanish); fax: (41 22) 730 5194: email (Internet): sales.onlineOitu.ch and (X.400): S=sales; P=ltu: A=400net; C=ch; telex: 421 000 uit ch; telegram: ITU GENEVE. Oig.ibtin fw nenie Cooperation mad Develpn The Organization for Economic Cooperation and Development (OECD) was originally set up in 1948 as the Organization for European Economic Cooperation (OEEC) to administer Marshall Plan funding OC D on the European side. In 1960, when the Marshall Plan had completed its task, the member coun- tires agreed to bring in the United States and Canada to form an organization to coordinate policy among the Western industrial countries. OCDE The OECD is the International organization of the industrialized, market economy countries. At OECD. representatives from member countries meet to exchange information and harmonize policy with a view to maximizing economic growth in member countries and helping nonmember countries develop more rapidly. The present members of the OECD are Australia, Austria, Belgum, Canada, the Czech Republic, Denmark, hnland, France, Germany, Greece, Hungary, Iceland, Ireland, italy, Japan, the Republic of Korea, Luxembourg, Mexico. the Netherlands, New Zealand, Norway, Poland, Portu- gal, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States. Membership for the Slovak Republic Is under consideration. Associated with the OECD are several agencies or bodies that have their own governing statutes, including the International Energy Agency (IEA) and the Centre for Cooperation with the Economies In Transition. To further its alms, the OECD has set up a number of specialized committees. One of these Is the Development Assistance Committee (DAC), whose members have agreed to coordinate their poli- cles on assistanre to developing countries and economies in transition. Major statistical publications of the OECD include National Accounts of OECD Countries, Labour Force Statistics, Revenue Statistics of OECD Member Countnes, Intemnational Direct lnvestnent Sta- tistics eaibook. Basic Science and Technolgy Statistics, Industnal Structure Statistics, and Ser- vices: Statistics on International Transactions. The OECD operates five publications and information centers: in Bonn. Mexico D.F, Paris, Tokyo, and Washington, D.C. These centers promote OECD publications and documents nationally and make them available to a large public. The OECD designates a depository library in every country, usually the national library. and supplies it with free copies of publications and working documents. For information on OECD publications contact OECD, 2, rue Andre-Pascal. 75775 Paris Cedex 16, Franrce; telephone: (33 1) 45 24 82 00; fax: (33 1) 45 24 85 00; Websites: http://www. oecd.org and http://www.oecdwash.org. xlv World Development Indicators 1997 The United Natins The United Nations and its specialized agencies maintain a number of programs for the collec- tion of international statistics, some of which are described elsewhere In this book. At United Nations headquarters the Statistics Division of the Department of Economic and Social Infor- mation and Policy Analysis provides a wide range of statistical outputs and services for produc- ers and users of statistics worldwide. By increasing the global availability and use of official statistics, the division's work facilitates national and international policy formulation, imple- mentation, and monitoring. The Statistics Division publishes statistics in the fields of intemational trade, national accounts, demography and population, gender, industry, energy, environment. human settlements, and disability. Major statistical publications of the Statistics Division include the Intemational Trade Statistics Yearbook, the Yearbook of National Accounts, and the Monthly Bulletin of Statis- tics, along with general statistics compendiums such as the Statistical Yearbook and World Sta- tIstis Pocketbook. For publications contact the United Nations Sales Section, DC240853, New York, N.Y. 10017, U.S.A.; fax: (212) 963 3489; email: statistics@un.org; Website: http://www.un.org. United Nabns Children's Fund The United Nations Children's Fund (UNICEF), the only organization of the United Nations dedi- cated exclusively to children, works with other United Nations bodies and with governments and nongovemmental organizations to improve children's lives in more than 140 developing countries - e through community-based services In primary health care, basic education, and safe water and uniceF sanitation. According to its mission statement. adopted in 1996, UNICEF Ns guided by the Con- vention on the Rights of the Child and strives to establish children's rights as enduring ethical principles and international standards of behavior towards children.' Major publications of UNICEF Include The State of the Worfd's Children and The Progress of Nations. UNICEF publications are available through UNICEF field offices in developing countries and through UNICEF national committees in Industrial countries. Many UNICEF publications are also available on the Intemet. For information on UNICEF publications contact UNICEF House, 3 United Nations Plaza, New York, N.Y. 10017, U.S.A.; telephone: (212) 326 7000; fax: (212) 888 7465; telex: RCA-239521; Website: http://www.unicef.org. United Ntiens Coniormne on Trade and Develeopm The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. It was established as a permanent intergovernmental body in 1964 in Geneva with a view to accelerating economic growth and development, particularly In developing countries. UNCTAD discharges Its mandate through pol- UNCTAD Icy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. UNCTAD's 188 member governments aim to achieve steady, sustained growth in all countries and to accelerate the development of devel- oping countries, so that all people can enjoy economic and social wel-being. UNCTAD has a maJor program of publications In trade and economic staUstics, including the Handbook of International Trade and Developrnent Statistics. For Information contact UNCTAD, Palais des Nations, CH-1211 Geneva 10, Switzerland; tele- phone: (41 22) 907 12 34 or 917 12 34; fax: (41 22) 907 00 57; telex: 42962: Website: http://www.unicc.org/unctad. World Development Indicators 1997 xv United Nations Educational, Scientific, and Cultural Organization The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a special- ized agency of the United Nations established in 1945 to promote aims set out in the United Nations charter. "to contribute to peace and security by promoting collaboration among nations through education, science, and culture in order to further universal respect forjustice, for the rule of law, and for the human rights and fundamental freedoms . for the peoples of the world, without distinction of race, sex, language, or religion . . ." The principal statistical publications of UNESCO are the Statistical Yearbook, World Edu- cation Report (biennial), and Basic Education and Literacy World Statistical Indicators For publications contact UNESCO Publishing, Promotion, and Sales Division, 1, rue Miol- lis F, 75732 Paris Cedex 15, France, fax (33 1) 45 68 57 41, email c.laje@unesco org; Web- site http://www unesco org United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations v and people to improve their quality of life without compromising that of future generations The _ UNEP was established as the environmental conscience of the United Nations system and has been creating a basis for comprehensive consideration and coordinated action within the United Nations on the problems of the human environment. UNEP publications include Global Environment Outlook and Our Planet (a bimonthly magazine) For information contact UNEP, P 0 Box 30552, Nairobi, Kenya; telephone (254 2) 62 1234 or 3292; fax: (254 2) 62 3927 or 3692; Website. http //unep.unep no. United Nations Industrial Development Organization The United Nations Industrial Development Organization (UNIDO) was established in 1966 by the General Assembly to act as the central coordinating body for industrial activities within the United Nations system and to promote industrial development and cooperation at global, UNIDO regional, national, and sectoral levels In 1985 UNIDO became the sixteenth specialized agency % 7W of the United Nations As the youngest such agency, it was given a mandate that recognizes the economic realities of industrial development in today's world UNIDO's constitution calls for the organization to assist in development, scientific, and technological plans and programs for industrialization in the public, cooperative, and private sectors UNIDO's databases and information services include the Industrial Statistics Database (INDSTAT), Commodity Balance Statistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information Among its pub- lications is the International Yearbook of Industnal Statistics For information contact UNIDO Public Information Section, Vienna International Centre, PO Box 300, A-1400 Vienna, Austria, telephone: (43 1) 211 31 5021 or 5022, fax: (43 1) 209 2669, email, unido-pinfo@unido org, Website. http.//www.unido.org. World Health Organization The constitution of the World Health Organization (WHO) was adopted on July 22, 1946, bythe Interna- tional Health Conference, convened in New York by the Economic and Social Council. The WHO's VI objective is the attainment by all people of the highest possible level of health, defined as a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity In support of its main objective, the WHO carries out a wide range of functions, including coor- dinating international health work, helping governments strengthen health services, furnishingtech- xvi World Development Indicators 1997 nical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promot- ing and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceutical, and similar products; and standardizing diagnostic procedures. The WHO publishes the World Health Statistics Annual and many other technical and statisti- cal publications. For publications contact Distribution and Sales (DSA), Division of Publishing, Language, and Library 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, Website: http://www.who.ch. World Trade Organization The World Trade Organization (WTO), established on January 1, 1995, is the successor to the Gen- . ORLD eral Agreement on Tariffs and Trade (GATT). It is now the legal and institutional foundation of the ,,T,RADE multilateral trading system and embodies the results of the Uruguay Round trade negotiations ;,RGANIZATION concluded with the Marrakesh Declaration of April 15, 1994. The essential functions of the WTO are administering and implementing the multilateral trade agreements that make up the WTO, serving as a forum for multilateral trade negotiations, seeking to resolve trade disputes, overseeing national trade policies, and cooperating with other international institutions involved in global economic policymaking. The Statistics and Information Systems Divisions of the WTO compile statistics on world trade and maintain the Integrated Database, which contains the basic records of the outcome of the Uruguay Round. The WTO Annual Report includes a statistical appendix For publications contact World Trade Organization, Publications Services, Centre William Rap- pard, 154 rue de Lausanne, CH-1211 Geneva, Switzerland, telephone: (41 22) 739 5208 or 5308; fax. (41 22) 739 5458; email: publications@wto.org, Website http://www.wto org. Currency Data & Intelligence, Inc. Currency Data & Intelligence, Inc is a research and publishing firm that produces currency-related products and undertakes research for international agencies and universities worldwide. Its flag- ship product, the World Currency Yearbook, is the most comprehensive source of information on & Intelligencey nca currency. It includes official and unofficial exchange rates and discussions of economic, social, and political issues that bear on the value of currencies in world markets. A second publication, the monthly newsletter Global Currency Report, covers devaluations and other critical develop- ments in exchange rate restrictions and valuation and provides parallel market exchange rates. For information contact Currency Data & Intelligence, Inc., 328 Flatbush Avenue, Suite 344, Brooklyn, N.Y. 11238, U.S.A; telephone: (718) 230 7176; fax: (718) 230 1992; email: Curncydata@AOL.com. Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business infor- mation. The monthly Euromoney magazine includes a semiannual rating of country creditworthiness. For information contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, U K.; telephone: (44 171) 779 8999, fax: (44 171) 779 8617, Website: http://www.euromoney.com. World Development Indicators 1997 xvii Institutional Investor, Inc. Insttutional Investor magazine is published monthly by Institutional Investor, Inc, which develops country-by-country credit ratings every six months based on information provided by leading international banks. For information contact Institutional Investor, Inc., 488 Madison Avenue, New York, N.Y. 10022, U S A.; telephone: (212) 224 3300 International Road Federation The International Road Federation (IRF) is a not-for-profit, nonpolitical service organization repre- senting the views and interests of all road-related industries across the world. The IRF has more than 600 corporate and institutional members in approximately 100 countries around the world- S companies, associations, research institutes, and administrations concerned with modernizing and developing road infrastructure To encourage better road and transport systems worldwide, the IRF assists in the transfer and application of technology and management practices that will produce maximum economic and social returns from national road investments, through its consultative status with the United Nations and the OECD and its advisory capacity with the European Union The IRF publishes World Road Statistics For information contact International Road Federation, 63 rue de Lausanne, CH-1202 Geneva, Switzerland; telephone: (41 22) 731 71 50; fax (41 22) 731 71 58, email IRD@dial eunet.ch, Website http://www eunet ch/Customers/irf. 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 secu- iM1i Moodys Investors Service rities 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 elec- tronic form Clients of Moody's Investors Service include investment banks, brokerage firms, insur- ance companies, public utilities, research libraries, manufacturers, and government agencies and departments. Moody's publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers For information contact Moody's Investors Service, 99 Church Street, New York, N.Y. 10007, U.S A. Website: http //www dnb-dc.com/moodys.html. Political Risk Services Political Risk Services is a global leader in political and economic risk forecasting and market A ._ analysis and has served international companies large and small for nearly 20 years The data it contributed to this year's World Development Indicators come from the International Country Risk Gulde, a monthly publication that monitors and rates political, financial, and economic risk in 130 iUlCAIH countries. 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 Political Risk Services, 6320 Fly Road, Suite 102, P0. Box 248, East Syracuse, N.Y 13057, U.S.A; telephone: (315) 431 0511, fax (315) 431 0200; email: custserv@polrisk com. xviii World Development Indicators 1997 Price Waterhouse Price Waterhouse is one of the world's largest international organizations of accountants and con- sultants Founded in 1849, it now consists of a network of 27 individual practice firms in 119 countries and territories. Staffed with knowledgeable professionals committed to client service, it is uniquely equipped to advise on matters relating to international operations, not only in indi- vidual countries but also on a regional or global basis For information contact Price Waterhouse World Firm Services BV, Inc ,1251 Avenue of the Amer- icas, New York, N Y 10020, U S A; telephone: (212) 819 5000: fax- (212) 790 6620, telex. 362196. Standard and Poor's Rating Services Standard and Poor's Sovereign Ratings provides issuer and local and foreign currency debt ratings for sovereign governments and for sovereign-supported and supranational issuers worldwide. Standard & Poor's Rating Services monitors the credit quality of $1.5 trillion worth of bonds and other financial instruments and offers investors global coverage of debt issuers Standard & Poor's also has ratings on commercial paper, mutual funds, and the financial condition of insurance companies worldwide For information contact The McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, N.Y. 10020, U S.A.; subscriber services (212) 208 1146, Website. http //www.ratings standardpoor.com World Conservation Monitoring Centre The World Conservation Monitoring Centre (WCMC) provides information services on the conser- vation and sustainable use of the world's living resources and helps others to develop informa- tion systems of their own It works in close collaboration with a wide range of organizations and people to increase access to the information necessary for wise management of the world's liv- WORLD CONSERVATION ing resources. Committed to the principle of data exchange with other centers and noncommer- MONITORING CENTRE cial users, the WCMC, whenever possible, places the data it manages in the public domain. For information contact World Conservation Monitoring Centre, 219 Huntingdon Road, Cam- bridge CB3 ODL, U.K; telephone: (44 12) 23 27 73 14, fax (44 12) 23 27 71 36; Website http://www.wcmc.org uk World Resources Institute The World Resources Institute is an independent center for policy research and technical assis- tance on global environmental and development issues Because people are inspired by ideas, empowered by knowledge, and moved to change by greater understanding, the institute provides- and helps other institutions provide-objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development The institute's current areas of work include trade, forests, energy, economics, technology, biodiver- sity, human health, climate change, sustainable agriculture, resource and environmental infor- mation, and national strategies for environmental and resource management. For information contact World Resources Institute, 1709 New York Avenue, N.W, Washington, D C 20006, U S.A., telephone (202) 638 6300; fax. (202) 638 0036; telex 64414 WRIWASH; Website http //www.wn.org World Development Indicators 1997 xix Users guide Indicators Indicators are shown for the most recent year for which data are available and, in most tables, for an earlier year or period. I T Principal sections Sectioni1 World view x4 < S tn i Wrve2.3 Labor force structure Section 2 S n People Section 3 Environment SectSon 4 Economy Al1a- 2 2 I 2 2 21 Section 5 w States and markets 2 1 2 -1720 3 0 Austml~~~~~~~~~~~~~1 11 10 12 71 3. 0 03 3 4 _~~~~~~~~~~~~~~~~~~~~442 0um 5 3 4 4 05 00 40 41 0 0 4 5 4 4 4 1 2 1 7 4 7 44 0 I~1 Section 6 w Global links 04 41 90 1 25 20 42 42 3 30 3 6o,P 6 7 4 4 4 04 - 2 1 44 10 0 0 4247142744017902o,71~2 2 2100 44 33 1~ 0 9sn n 2 3 2 2 4 2 7 ~~~~~~~~~2 3 47 43 30 2 9sllwa 7 4 2 4 4 6-4 -2 3 4 37 44 3ssmasndH67tegowns 2 ~2 2 16 3 .3 3 1 40 The tables C407n1 4 S 7 6 7 27 20 5 3 30 27 Within each section the tables display the identi- Ce1 20 4 1 1 2 43 0 fying icons and are numbered by section and -14792e7bllc 2 2 2 21 71 43 47 34 33 4 2 4 5 ~~~~23 20 42 4 2 4 table number. Countries and economies are 0711 7 0 4 0 0 20 4 43 0 0 listed in alphabetical order Section 1-World C2 00 22 6 13 42 46 22 42 47 2 73 -* 07303 1 1 1~~~~ ~~~~ 1 2 2 41 20 20 43 27 view displays data for 209 economies with pop- 21 7402 212 3 2 3302 1 40 ulations of more than 30,000. The full tables in 0u47 22 0° 3 3 sections 2-6 contain data for 148 economies 4ooooooooo; 0 7 7 3 5 04 ° 47 47 3 3 with populations of more than one million. In W-n24 3 5 2 3 0 4 5 223 250 14 245 1 some cases shorter tables are presented. 9 P 23 4 2 23 23 2 1 27 1 lt somecase 0247402 2 3 4 2 4 20 20 2 4 1411 When available, aggregate measures for income 03014 1203247 7 43 0 and regional groups appear at the bottom of 077204 22 4 7 20 40 27 2t 42 44 47 47 each table. The term country, used interchange- 5s4732 3432242322 0 4 24 13 ably with economy, does not imply political inde- 7h90400 0 0 0 1 1 3 4 2 44 44 37 pendence or official recognition by the World 1937 52 .3 47 40 21 00 31 40 42 0 0 Bank, but refers to any territory for which I,00a 0 7 4 4 S 11 o 24 30 S 0 1147941414 4 7 ~~ ~~2 3 1 3 0 2 7 0 authorities report separate social or economic 11170 2 3 2 4 5 3 237 47 47 41 34 Guln A73 999s 0 1 0 1 1 1 9 ~~~~~2 1 40 4013 3 statistics 44111 3 4 43 41 14 45 44 40 23 33700144 2 3 1 2 4 30 39 20 43 0 14 9 07s747 Ksn 7 2 43 151 056 437 40 0 41 773, d711011 '17lpmn 141711 1417 Statistics Section 1-World view includes data for 209 economies (those with more than 30,000 people). Data in the remaining sections are pre- sented for 148 economies (those with more than one million people whose governments disseminate data on a regular basis), plus Tai- wan, China, in selected tables Data are shown for economies as they were constituted in 1995, and historical data are revised to reflect current political arrangements Throughout the tables, excep- tions are noted. Data for China do not include data for T-aiwan, China, unless other- wise noted. xx World Development Indicators 1997 __ ~2 In 1991 the Union of Soviet Socialist Republics was formally dissolved into 15 countries (Arme- nia, Azerbaijan, Belarus, Estonia, Georgia, 2.3 _ Kazakstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Russian Federation, Tajikistan, Turk- t53.343480 1311 ~"'~'~'menistan, Ukraine, and Uzbekistan) Whenever possible, data are shown for the individual coun- 330325 33333.33333353R 43s g 'z tries. l,dl 39 62 3 9 9 17 I, 32 1 ~14 333 520 35 39 23 8 32 34 1 S 2 Data shown for the Republic of Yemen refer to Non l1l33.oRSp 20 34 12 19 35 3.3 39 20 24 14 5 1.1--p 27 21 41 5 9 26 36 to 3that country from 1990 onward; data for previ- l0Rla- 2 2 1 1 2 0 3 3 Isl2 3 2 35 27 25 34 30 0 ous years refer to the former People's Democra- R3 ly35 39 i3 25 25 070 01 33 33 2 tic Republic of Yemen and the former Yemen Jamats 1 2 t~~~ 1 23 0 16 43 46 0 I 0 Jap6n 79 87 0 57 30 67 10 01 35 41 0 0 Arab Republic, unless otherwise noted. Jordan353 1 20 0 1 3 30 32 15 21 5 1 Ke,3 2a 3 i i 3 13 3 33 2 35 43 30 45 33 R=3 Rep 1 12 15 i9 15 45 45 3 0 Whenever possible, data are shown for the indi- K44,3,R3P is 3i 16 22 26 2 2 13 33 40 0 silal, I 1 5 1 1 33 13 33 0i 0 ovidual countries formed from the former 5amPDR 2 3 a is4 25 30 43 47 31 27 Yugoslavia-Bosnia and Herzegovina, Croatia, lewsGdon 2 2 1 1 2 33 23 5 the former Yugoslav Republic of Macedonia, les4ho 1 t 1 1 1 2 3 24 33~~~~ 37 23 2 Soa i 5 1 2 3 5 33 35 3 21 s s Slovenia, and the Federal Republic of ..W.onb P7R I 1 _ i1 1 s1 12 s 3 Yugoslavia. All references to the Federal Repub- Mavwl 3 5 3 3 7 23 24 51 49 45 35 lic of Yugoslavia in the tables are to the Federal Ml6 3 3 3 24 22 61 Republic of Yugoslavia (Serbia/Montenegro) n I I81 1 2 2 2 i s 43 Ii 3 5 Maurbus I I 0 O 1 ~21 1 3 26 2 Mi,i35 35 65 22 i33 52 32 25 2 3 3 7 338031 3 5 2 2 2 02 05 33 42 Additional information about the data is Mong lla i I L 1 ~~~2 30 2 S 5 4 M0344 32 16 7 10 15 25 is 34 33 21 6 recorded in Primary data documentation. This 38333334 3 17 23 20 1 26 section gives an overview of some of the 831333 34 i1 is Ii 1 20 i4 2 5 0 01 i4 i2 . 6 12 7 O 14 24 i4 33 49 56 national and international efforts to improve Iwzm43,3, 2 2 8 7 3 1 lO basic data collection and provides information 5133,31 3 3 2 3 5 5 33 51 23 33 38 4_ on primary sources, census years, fiscal years, 1813 33 34 30 44 62 is 238330 33 20G and other background Statistical methods pro- P3335333 44 70 23 iG 08 31 34 s2 2i 23 13 vides technical information on some of the gen- 313431 1i 2 1 1 1 29 21 30 34 6 4 eral calculations and formulas used throughout P1833334331333 2 i 2 2 3 ii2 2 1 42 42 23 19 833433,3 3 3 1 2 5 i9 i3 07 2 1 the book. 0.13 39 14 5 3 13 51 27 24 2 P3[I,831333 28 40 33 i6 33 275 38 35 57 Ii 3 P2an9 16 26 3 ~16 21 03 04 4B 46 O O Por8ull 6 7 6 s 6 04 02 i 3 s 3 i 2 Discrepancies in data presented in different Pu.. Rl.R i 2 3 1 2 3B 35 32 38 5 5 Rom333 16 33 38 3 333 -01 31 43 a4 s s editions of the World Development Indicators reflect not only updates by the countries, but also revisions to historical series and changes in methodology. Readers are therefore advised not to compare data series between editions of the World Development Indicators or between different World Bank publications. Consistent time-series data are available in the World Data are shown whenever possible for the individual countries Development Indicators CD-ROM. formed from the former Czechoslovakia-the Czech Republic and the Slovak Republic Except where noted, growth rates are in real terms. (See Statistical methods for information Data are shown for Eritrea whenever possible; in most cases prior to on the methods used for calculating growth 1992, however, it is covered in the data for Ethiopia. rates.) Data for some economic indicators for some economies are presented in fiscal years Data shown for Germany refer to the unified Germany, unless other- rather than calendar years; see Pnmary data wise noted documentation. All dollar figures are current U S. dollars unless otherwise stated The meth- Data shown for Jordan refer to the East Bank only, unless otherwise ods used for converting national currencies are noted described in Statistical methods. World Development Indicators 1997 xxi The World Bank's classification of economies For operational and analytical purposes the World 3 Bank's main criterion for classifying economies is gross national product (GNP) per capita Every country is classified as low income, middle income (subdivided into lower middle and upper middle), or high income. Consult the front cover 2.3 flap to check a country's income classification. Note that classification by income does not nec- P4442-542 . 84b 44'54 essarily reflect development status IW 48117744 Because GNP per capita changes with time, the Rwanda 3 1 9 4 2 28 27 48 30: 4 4 country composition of income groups may deal l 3 817535481 54 change from one edition to the next Once the 287255238 3 3 3 27 2b 3 2 classification is fixed for an edition, all historical b 07 2 4 data presented are based on the same country 15 13 88 l e 3 2 23 35337 1 0 grouping. The income-based country groups are 33481 4 8 5 1 12 22 88 47 353 33 29 defined using 1995 GNP per capita a 5s 02 34 4 7 57,1834,an 2 3 2 2 3 2 2 9 37 44 0 0 Low-income economies are those with a GNP per I1:1:11 3 45 2 1 3 23 2 52 43 212 14 capita of $765 or less in 1995. nmd 1 2 1 29 1 6134315 22 Middle-income economies are those with GNP per 21I4.4 3 5 1 22 2 27 3 7 capita of more than $765 but less than $9,386 I I 22 27 22 2 25 33 4 14 capita 81442418143 2 3 1 3 ~~ ~~~ ~~~ ~~~~~~ ~~~~~~~~~~~~~~~2 1 34 47 4 Lower-middle-income and upper-middle-income 47424 3 34 22 22 15 -02 -d 3 43 8 -,52d3851,4 - 2 I47 21 5 3 8 8 economies are divided at GNP per capita of -5344d2448 34 38 27 22 27 25 2 23 41 5 775427415 85 172 11 132 882 83 8 2 2 $3,035. -5-> 2 I 111842 40 2 High-income economies are those with a GNP per 39554388n 13 3 83 33 7 27 22 4 8 capita of $9,386 or more. 223745 74 43 85 77 4 24 82 3 48 33 8 72,454 Fed R217 6 22 17 12 25 33 38 42 8 I Aggregate measures for regions 8384338 2 3 5 31 21 44 34 32 29 The aggregate measures for regions refer only . . ; to low- and middle-income economies The coun- _5Lowmoome 8382t 8934t I155755 2I55 21w 185 3040 35 2414 w try composition of regions is based on the Mddbm5ome 717 5137 855' lIst 404 82s 385 381 124 World Bank's analytical regions and may differ -25482 111 1522 2 42 88 23s 341 11 34 124-dl54 235 4311 2255 52387I 4,4523 2133 205 17 38 35 4 154 from common geographic usage For regional 5485584 t 573t 1119t 2151 49 Is 19w 03 4d 45w5 354 74 classifications see the map on the inside back _ 45,5,574414,8 2857 2937 1382 57 2 29w Ow 47w 44w 12w 12,, 38447474 8155 - 47 88 54-2 27 3' 25 124 3, cover and the lists on the back cover flap 535 14 58 72323 2427 434371521, 25, 34w' 324 54w 1152 I.,14 52 53I23747 22 u 05 38 24 s Aggregate measures for income groups The aggregate measures for income groups include the 209 economies shown in the sum- mary tables of section 1 wherever data are available. To maintain consistency in the aggre- Footnotes gate measures over time and between tables, The most recent available data are presented for each of the indica- missing data are Imputed. Most aggregates are tors shown. Although international standards of coverage, definition, totals (designated by a t) or median values (m) an casfctoapltom tsaitcsrotebyoures an or are weighted averages (w) Weighting may and classification apply to most inevstably differences In coverages result in discrepancies between subgroup intentnal ancies, there are ietaldincesoin overa, aggregates and overall totals. See Statistical World Bank staff review the dnta toe are competing Sources of datar methods for further details.WolBaksafrvethdaatenuehttemsteibeae presented. Known deviations from standard definitions or breaks in comparability over time or across countries are noted in the tables. When available data are deemed to be too weak to provide reliable measures of levels and trends or do not adequately adhere to interna- tional standards, the data are not shown. xxii World Development Indicators 1997 Commentary For some tables brief discussions are included on the overall theme of the table, explaining why the indicators shown are useful for measuring development 42.3 0Definitions 2.o to to the taboo foroal _____________________Definitions provide short descriptions of the Thetabbrhneeleet bAIO pde ePdte-etMM U11'1111 principal indicators in each table. These are In pt t. i,tsifeutt -ntthntee ePed alrtn -e c e-ec be eILOe s l b the late thttedtietl t mP necessarely triet ane may omit some details. aboetetelY esp city In de-eteting oount.e. ce.nen eoseyot enuitc g c- -fe a -totn foc- de -en pe-e And t meebYuetW d ff0ete to etdecrly ceApe the e Ott of th IL te trcc e mhe uotret IL0 defirte cn ret e c employedue ofe nt ecertora etettn ter abreret ace eeitrepepuroecieecccnoetospt.tlcenthcrtt According be the ltternaeeecl Lbbtoetu t Psee and neeerel tdtaae oc no eeet uile trde oeetedeameoce eeturnase cneeerd Oegen nSekn .ILO dtn tier, the uneplye e -ceeretb 0 teceece | d'rercecee amntg pe receIpt b th the ceip - te u -he these MbhO t M M t eterlecle her end -eeceree oaeend -ep.Mt retmeec Pi-eced Whteetat ecn treDecee e im-t the treat Notes about the data beoktAke WMdCounbtesewnth utempteAemeee doreteneens (eecaamP ttde er eeeth tteclett tmen-. theetthereuce asetteattteetettoe ad oe to houStene sttes ohen bact ereatne et ettetfge Petectmpeate a reeesoneperte e ceae ena crorceceeorb otetetal the -or About the data provides a general discussion of ueoereemycete eetheecnernda. P e la. s and -d-ete-cete ermnt-eo t -Iecttectcetferceeern e f thus rtlfy tna the etc tee-kmegt tee and t -aocn hecete en ecet it ate can rirtrme"ce ete dot tnudes --met-I international data standards, data collection ths ysems dO not c...t d-Lubged -W -1 -nrtn so ce oSIff.- snsmcutesesadte nacbee n cAl lw hn o ah , -med ee -,tt rth de ee - -ede-D me n -at reu en e ert eee e cts > E ate ttc o r t areee aet e c i ctls m e t h o d s , a n d s o u r c e s o f p o t e n t ia l e r r o r s a n d Ab_ ntdon-9--istt7asun-I -o1- --y -, -ho-- ec nt e to - t eetetdene -inconsistencies. Readers are urged to take the ehetteb tu -en e.tteed In de-otpig --dac -merea edmthte --terentct,e e ,d -ice een.enraIMaona.eo elate, time to read these notes to gain an understand- OonmelUtwmnmaytttnot D ruetdeM drdneemeenentritcrthraete tet eetreme xbo tenehbMsm theratenetimtcbdiitctttntner bate n prtectte ttch eoednenicgte- sensget 1leen co neneenmoa-em eheeereit etI c ten b. e.-enheien * hie etsotabo,oo ing of the reliability and limitations of the data agnleurtca aterte ee tttela nsetl coeeenceneio Peareretetche mtctnthehrotttte aeWopetaten oatenteete bor B unempl -eetp-Ifientalled ttnreon peo mo ie I -totIcurttent tete net e c . t -re presented. eunempte-tent -eceetl ette-nieta ten ane electtenet- e deto entelatcrtce eeotcl ehthenetea operdeet en teen meteett seeeLOlrue ladtee hter ecoe apten ee ntreetL _ en any ete - s o nenM s at tem- cac.y enemeenereaprccmr For a full discussion of data collection methods ttemnlctrad-eeteeeeh abt asemyeyerc leek Ihs Ticeotrcted ceu lnt geribd40pca a foodct-c ustter Ott ttr bhe nght w rert rend otorth ten treviettt,etteoncene~ctoceeh -S | cficetae by here and definitions readers should consult the tech- eteettehe IncPentenoc reb met nemeicypten peturceen tut teant'sw teen eotOeeccct L t__ e isncp,Ineinecteenata msurahEorObherformsotsoclEdasustanp It dr-nndrowlfuloprtm n ncnflnsome - I LImcs panical documentation provided by the original - dmt t neenro manta r epl teefnd t- eene .alIAen,ty,e t e .. a ntre ote neta c o m p ile rs c ite d In th e D a ta so u rc e s feP tenIof uned Instead fte ,- et te eecr t- edomeeaatrte in tetmtetet e lecihraersttte ttto teesetcnera:m ect a tetit ccunoraper urJtmo b segeacen Tak g inte -cpnt the unde-nmplymd tre -l-rge t-mbet emotoesistie meetit pe1tet ccii; tree hePtenter teneoengeeedttrh eaten eu-h c-ateet ce crtamiyoten prseccerut pet sahett e te95Pe c rl te td carte tti-tettoct rmpleyedrneebsteeq nglenrtq-a IeAlIone Cter t,eiaet Ietm ertenttr m-ttsA- maae e .'tme ter- e .et-nemert then theyq ht Ul-Ield .da hIgher -st-met -,te -hteheC aeit to ceentepcc mito ttInt it potuatlen pe-tI rh- tetceduh tmet ne ef aeebtr ud-utoetrn Householdeue.tyt -- lat Ientenen t In t--mcet-e-obotursethe the -tAm-e unemploy-mnt ape endoram csoiptrment en tad enter cit tAc tomthta trsrpetnrtcpcsPbesdt,ce Prttmetr etnnhnm that unemPIOPmemt egemoA ebtt n etterere te UN nCE ny tihe Stg e -the icyim tiras t onee atsssc.a tlcacnet ysbetlcablrundmsma)snde tmecittethud hIshkn97mnyoAeeeenttbolw 5aedteo ubst to retrethent ngteatutssttceyed TtoN 2 as grmpotmetteot amd net ttieetciildtepntasnegdir ourecu tuto r huen eearaietcporoetmdt to fore oeteetcar reiecerltocscsw ththeniin ylesv telneraict en pmtoct aaethtetnet bw5 trt teat en a ten ten gt mre wci tcch Mtn tm e i tng m e-ent- cuhdeietemetee en Proo tMp1 5 bettduc curt me do nct necenuricf' brentng ae geat toot ..eee. r c at. cotetod unreogararee doestect hs pc me tends deng uremP eta,. eoct-etSctStetc hO>hOdc necnetectecrtbettbette the 5enrarb u 1wm . ISS . PoIn .45 mrkoutdebehom PData presentation conventions -,9-r.d W-Ad dan 9 12an d symbols The cutoff date for data is February 1, 1997 The symbol means that data are not available or that aggregates cannot be calculated _________ _______________ because of missing data in the year shown. A blank means not applicable or that an aggre- gate is not analytically meaningful. The numbers 0 and 0.0 mean zero or less than half the unit shown. Sources Billion is 1,000 million The World Bank collects development data from Tnillion is 1,000 billion international organizations, government agen- The symbol / in dates, as in 1990/91, means cies, and other public and private organizations that the period of time, usually 12 months, to improve its understanding of and advice on straddles two calendar years and refers to a development issues ranging from health and crop year, a survey year, or a fiscal year. education to privatization. These partners are Figures in italics indicate data that are for years identified in the Data sources section following or penods other than those specified. Data for each table, and key publications of the partners years that are more than three years from the drawn on for the table are listed and sometimes range shown are footnoted. displayed. For a description of the partners and Dollars are current U.S. dollars unless other- information on their data publications see the wise stated. Partners section. World Development Indicators 1997 xxiii Statistical methods This section describes some of the statistical procedures used in preparing the World Develop- ment Indicators It covers the methods employed for calculating regional and income group aggre- gates and for calculating growth rates, and it describes the World Bank's Atlas method for deriving the conversion factor used to estimate GNP and GNP per capita in U.S dollars. Other statistical procedures and calculations are described in the About the data sections that follow each table. Aggregation rules Because of missing data, aggregations of data for groups of economies should be treated as approx- imations to unknown totals or average values. The regional and income group aggregates at the end of most of the tables are based on the largest available set of data, including the values for the 148 economies shown in the main tables, the smaller economies shown in tables 1.1, 1.2, and 1.3, and Taiwan, China. The aggregation rules are intended to yield estimates for a consistent set of economies from one time period to the next and for all indicators. However, small differences between the values of subgroup aggregates and overall totals and averages may occur because of the approximations used. In addition, discrepancies due to data reporting and accounting practices may cause differences in such theoretically identical aggregates as world exports and world imports. How group aggregates in the World Development Indicators are calculated depends on the nature of the indicator. In group and world totals (indicated in the tables by t) missing data are imputed using a suitable proxy variable in a benchmark year, usually 1987. The imputed value is calculated so that it (or its proxy) bears the same relationship to the total of available data as it did in the benchmark year. Imputed values are not calculated if missing data account for more than one-third of the total in the benchmark year. Proxy variables are selected from a set of vari- ables for which complete data are available for 1987. The variables used as proxies are GNP in U.S. dollars, GNP per capita in U S. dollars, total population, exports and imports of goods and services in U S. dollars, and value added in agriculture, industry, manufacturing, and services in local currency Aggregates of ratios are generally calculated as weighted averages of the ratios (indicated by w) using the value of the denominator or, in some cases, another indicator as a weight. The aggre- gate ratios are based on the available data, including data for economies not shown in the main tables Missing values are assumed to have the same average value as the available data. If miss- ing data account for approximately one-third of the total value of the weights in the benchmark year, no aggregate is calculated. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according to the rules for computing totals. Aggregates calculated as medians of the available data are indicated by m Aggregate growth rates are generally computed as the weighted averages of growth rates. In a few cases growth rates may be computed from time series of group totals (see the discussion below on methods of computing growth rates) Growth rates are not calculated if more than one- third of the observations in a period are missing. Exceptions to the rules occur throughout the book. Depending on the judgment of World Bank analysts, the aggregates may be based on as little as 60 percent of the available data. In other cases, where missing or excluded values are judged to be small or irrelevant, aggregates are based only on the data shown in the tables. Growth rates Growth rates shown in the World Development Indicators are calculated as annual averages and represented as percentages. Except where noted, growth rates of values are computed from con- stant price or real value series Three main methods are used to calculate growth rates: the least squares, the exponential endpoint, and the geometric endpoint Rates of change from one period to the next are calculated as proportional changes from the earlier period Note, however, that the annual changes in the speed of integration indicators in table 6 1 are not proportional growth rates but average annual differences. xxiv World Development Indicators 1997 Least-squares growth rate. Least-squares growth rates are used wherever there is a sufficiently long time series to permit a reliable calculation. If more than one-half of the observations in a period are missing, no growth rate is calculated. The least-squares growth rate, r, is estimated by fitting a linear regression trend line to the logarithmic annual values of the variable in the relevant period. The regression equation takes the form log X, = a + bt, which is equivalent to the logarithmic transformation of the compound growth equation, Xt = X. (1 + r)t . In this equation X is the variable, t is time, and a = log X. and b = log (1 + r) are the para- meters to be estimated. If b* is the least-squares estimate of b, the average annual growth rate, r, is obtained as [antilog (b*) - 1] and multiplied by 100 for expression as a percentage. The calculated growth rate is an average rate that is representative of the available observa- tions over the period. It does not necessarily match the actual growth rate between any two periods. Exponential growth rate. The growth rate between two points in time for labor force and popu- lation indicators is calculated from the equation r= ln(pn/p1)/n where pn and p, are the last and first observations in the period, n is the number of years in the period, and In is the natural logarithm operator. This growth rate is based on a model of continuous, exponential growth between two points in time. It does not take into account the intermediate values of the series. Geometric growth rate. The geometric growth rate is applicable to compound growth over dis- crete periods, such as the payment and reinvestment of interest or dividends. Although continu- ous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at intervals for which the compound growth model is appropriate. The average growth rate over n periods is calculated as r= exp{[ln(pn/p,)]/n} - 1. World Bank Atlas method In calculating GNP in U.S. dollars and GNP per capita for certain operational purposes, the World Bank uses a synthetic exchange rate commonly called the Atlas conversion factor. The purpose of this conversion factor is to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of a country's exchange rate (or alter- native conversion factor) for that year and its exchange rates for the two preceding years, after adjustment for differences between the inflation rate in the country and the inflation rate in the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). The coun- try's rate of inflation is measured by its GNP deflator. The inflation rate for G-5 countries is mea- sured by changes in the deflator for the SDR (special drawing right, the International Monetary Fund's unit of account). The SDR deflator is calculated as a weighted average of the G-5 coun- tries' GDP deflators in SDR terms. The weights are determined by the amount of each currency included in one SDR unit. Weights vary over time both because the IMF changes the composition World Development Indicators 1997 xxv of the SDR and because the SDR exchange rate for each currency changes. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas con- version factor. This three-year averaging smooths annual fluctuations in prices and exchange rates for each country. The Atlas conversion factor is applied to the country's GNP The resulting GNP in U.S. dol- lars is divided by the midyear population for the latest of the three years to derive GNP per capita. When official exchange rates are deemed to be unreliable or unrepresentative of the effective exchange rate during a period, an alternative estimate of the exchange rate is used in the Atlas formula (see below). The following formulas describe the computation of the Atlas conversion factor for year t: e, et2 t / t1)+ et-:, Pt / t$+ et] Pt-2 Pt-2 ) Pt-I Pt-I) and for calculating GNP per capita in U.S. dollars for year t Yt$ = (Yl/Nt)let* where et* is the Atlas conversion factor (national currency to the U.S dollar) in year t, et is the aver- age annual exchange rate (national currency to the U S dollar) for year t, Pt is the GNP deflator for year t, ps$ is the SDR deflator in U.S. dollar terms for year t, Y1$ is the Atlas GNP in U.S dollars in year t, Yt is current GNP (local currency) for year t, and Nt is the midyear population for year t Alternative conversion factors The World Bank systematically assesses the appropriateness of official exchange rates as con- version factors. An alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transac- tions of foreign currencies and traded products, the case for only a small number of countries (see Primary data documentation). Alternative conversion factors are used in the Atlas method and elsewhere in the World Development Indicators as single-year conversion factors. xxvi World Development Indicators 1997 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 socioeconomic data, however, the World Bank places par- ticular emphasis on data documentation to inform users of data in economic analysis and policy- making. The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in the World Development Indicators. Differences in the methods and conventions used by the primary data collectors-usually national statistical agencies, central banks, and customs services-may give rise to significant discrepancies over time both among and within countries. Delays in reporting data and the use of old surveys as the base for current estimates may severely compromise the quality of national data. Although data quality is improving in some countries, many developing countries lack the resources to train and maintain the skilled staff and obtain the equipment needed to measure and report demographic, economic, and environmental trends in an accurate and timely way. The World Bank recognizes the need for reliable data to measure living standards, track and evaluate economic trends, and plan and monitor development projects. Thus, in concert with bilateral and other multilateral agencies, it has funded and participated 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 surveys with country partner statistical agencies. * National accounts and inflation. : Price and expenditure surveys for the International Comparison Programme. : Statistical improvement projects in the economies of the former Soviet Union. * External debt management. * Environmental and economic accounting. At the institutional level the World Bank undertook a major renewal program, forming a new unit, the Development Data Group, to concentrate on collecting, enhancing the quality of, and dis- seminating development indicators. New initiatives include: * Improving documentation of data collected by the World Bank. * Enhancing dialogue with primary data producers in the field and improving data partnerships within the Bank and with other agencies that are major data producers. * Upgrading systems and technology to improve data collection and management and to dis- seminate data to a broader audience through CD-ROMs and the Internet. * Strengthening partnerships with other multilateral agencies to improve coordination of sta- tistical capacity building activities in developing countries. World Development Indicators 1997 xxvii Metadata for macroeconomic indicators Eoeonoy Natlionali currncy Finel NatbMal eemeub e blaee o vement year Paiesta lame mNd md trade Balance of Alternative PPP Payments Reporting SNA price conversion survey Manual System Accounting I perlod Base year valuation fctor year In use of trade concept Albania Albanian lek Dec. 31 CY 1993 VAP BPM5 G Algeria Algerian dinar Dae. 31 CY 1980 VAB BPMS S Angola Angolan kwanza reajustado Dec. 31 CY 1970 VAP BPM5 S Argentina Argentine peso Dec. 31 CY 1986 VAP 1972-81 BPM5 S C Armenia Armenian dram Dec. 31 CY 1993 VAB 1990-95 BPM5 Australia Australian dollar Jun. 30 CY 1989 VAP 1993 BPM5 a C Austria Austrian schilling Der. 31 CY 1990 VAP 1993 BPM5 S C Azarbaijan Azerbailannian manst Dec. 31 CY 1987 VAP 1990-95 BPM4 Bengladesh Bangiadeshi take Jun. 30 FY 1985 VAP 1985 BPM4 G Belarus Belarussian ruble Der. 31 CY 1990 VAB 1990-95 1993 BPM4 C Belgum Belgian franc Dec. 31 CY 1985 VAP 1993 BPM5 S C Benin CFA franc Der. 31 CY 1985 YAP 1985 BPM5 S BoliMa Boliviano Der. 31 CY 1980 VAP 1974-85 BPM5 S C Bosnia and Herzegovina Bosnian and Herzegovinian dinar Dec. 31 CY 1987 VAP BPM5 Botswana Botswana pula Ma. 31 CY 1986 VAP 1985 BPM5 G B Brazil Brazilian real Dec. 31 CY 1980 VAB BPM5 S C Bulgaria Bulgarian lev Dec. 31 CY 1990 VAP 1991-95 1993 BPM5 B C Burkina FPso CFA franc Dec. 31 CY 1985 VAB BPM5 S C Burundi Burundi franc Dec. 31 CY 1980 VAB BPM5 S Cambodia Cambodian rlel Dec. 31 CY 1960 VAP BPM5 S Cameroon CFA franc Jun. 30 FY 1980 VAP 1985 BPM4 S C Canada Canadian dollar Mar. 31 CY 1986 VAB 1993 BPM5 a C Central African Republic CFA franc Dec. 31 CY 1987 VAB BPM5 S Chad CFA franc Dec. 31 CY 1977 VAB BPM5 S C Chile Chilean peso Dec. 31 CY 1986 VAP BPM5 S C China Chinese yuan Dec. 31 CY 1990 VAP BPM5 G B Colomoia Colombian peso Dec. 31 CY 1975 VAP 1993-95 BPM5 S C Congo CFA franc Dec. 31 CY 1978 VAP 1985 BPM5 S Costa Rica Costa Rican colon Dec. 31 CY 1987 VAP BPM5 S C COte d'ivori CFA franc Dec. 31 CY 1986 VAB 1985 BPM5 S C Croatia Croatian kuna Dec. 31 CY 1994 VAB 1993 BPM5 C Cuba Cuban peso Dec. 31 CY .. .. S Czech Republic Czech koruna Dec. 31 CY 1984 VAP 1993 BPM5 G C Denmark Danish krone Dec. 31 CY 1980 VAB 1993 BPM5 a C Dominican Republic Dominican peso Dec. 31 CY 1970 VAP 1992-95 BPM5 a C EcuaJor Ecuadorian sucre Dec. 31 CY 1975 VAP BPM5 G B Egypt. Arab Rep. Egyptan pound Jun. 30 FY 1987 VAB 1985 BPM4 S C El Salvador Salvadoran colon Dec. 31 CY 1962 VAP 1990-95 BPM5 S B Eritrea Ethiopian binr Dec. 31 CY 1992 VAB BPM4 Estonia Estonian kroon Dec. 31 CY 1993 VAB 1990-95 BPM5 C Ethiopia Ethiopian birr Jul. 7 FY 1981 VAB 1985 BPM4 G B Finland Finnish markka Dec. 31 CY 1990 VAB 1993 BPM5 a C France French franc Dec. 31 CY 1980 VAP 1993 BPM5 S C Gabon CFA franc Dec. 31 CY 1989 VAP BPM5 S B Gambla, The Gambian dalasi Jun. 30 FY 1976 VAB BPM5 a B xxvE World Development Indicetors 1997 Data sources and years Eco_ Lat Laet hamahold oer Vltal Ltt Lt Lt Lt Lt popdiuan drb_srphlb survey reglelratimn agylultural ndratrlal watr iurv ot maurvy d cener eemnpbt en dat wIthdrawal Iaentlsx expsdere data and for engIneers R&D engaged Inl R&D Albania 1989 i 1990 1970 Algeria 1987 PAPCHILD. 1992 1993 1990 Anpla 1970 1987 Argentina 1991 1988 1993 1976 1988 1992 Armenia 1989 V 1991 1989 Australia 1991 Vi 1990 1992 1985 1990 1990 Austria 1991 / 1990 1994 1991 1989 1989 Azerbalijan 1989 V 1989 Bangladesh 1991 DHS. 1994 1992 1987 Belarus 1989 / 1989 1992 1992 Belgium 1991 / 1990 1994 1980 1990 1990 Benin 1992 WFS. 1981 1992 1981 1994 1989 1989 Bolivia 1992 DHS. 1994 1994 1987 1991 1991 Bosnia and Harzegovina 1991 / 1991 Botswana 1991 DHS, 1988 1994 1992 Brazil 1991 DHS, 1991 1992 1990 1985 1985 Bulgaria 1992 / 1994 1988 1992 1991 Burkina Faso 1985 SDA, 1995 1983 1992 Burundl 1990 1991 1987 1989 1989 Cambodla 1962 1987 Canmeroon 1987 DHS. 1991 1994 1987 Canada 1991 .f 1991 1994 1991 1991 1992 Central Aflcan Republic 1988 DHS, 1994-95 1992 1987 Chad 1993 1987 Chile 1992 1994 1975 19B8 1992 China 1990 Population, 1995 1994 1980 Colombia 1993 DHS, 1995 1988 1994 1987 1982 1982 Congo 1984 1988 1987 1984 1984 Coats Rica 1984 CDC. 1993 i 1994 1970 1992 1986 COte d'lvoire 1988 OHS. 1994 1993 1986 Croatna 1991 J 1992 1992 1992 Cuba 1981 V 1989 1975 1992 1992 Czech Repubic 1991 CDC, 1993 i 1991 1992 1992 Denmark 1991 VI 1989 1992 1990 1992 1992 Dominican Republic 1993 DHS, 1991 1985 1987 Ecuador 1990 DHS, 1994 1994 1987 1990 1990 Egypt. Arb Rep. 1986 DHS. 1995 / 1993 1992 1991 1991 El Salvador 1992 CDC, 1994 1994 1975 1992 1992 Eritrea 1984 Estonla 1989 i 1989 Ethiopia 1994 Family and fertility. 1990 1989-92 1990 1987 Finland 1990 V 1994 1991 1991 1991 France 1990 lnccma, 1989 i 1988 1994 1990 1991 1991 Gabon 1993 1982 1987 1986 Ganbia. The 1993 1982 1982 World Development Indicators 1997 xxlx Metadata for macroeconomic indicators Economy National currency Fiscal National accounts Balance of Government year payments flnance end and trade Balnnce of Alternative PPP Payments Reporting SNA price conversion survey Manual Systern Accounting period& Base year valuation factor year in use of trade concept Georgia Georgian lari Deoc 31 CY 1987 VAB 1990-95 BPM4 Germany Deutsche mark Dec 31 CY 1990 VAP 1993 BPM5 S C Ghana Ghanaian cedi Dec 31 CY 1975 VAP 1994 BPM5 G C Greece Greek drachma Dec 31 CY 1970 VAB 1993 BPM5 S C Guatemala Guatemalan quetzal Dec 31 CY 1958 VAP BPM5 S B Guinea Guinean franc Dec 31 CY 1989 VAP BPM5 S C Guinea-Bissau Guinea-Bissau peso Dec 31 CY 1986 VAP 1972-86 BPM5 S Haiti Haitian gourde Sep 30 FY 1976 VAP BPM5 G Honduras Honduran lempira Dec 31 CY 1978 VAB BPMS S Hong Kong Hong Kong dollar Dec 31 CY 1990 VAB 1985 BPM4 G Hungary Hungarian forint Dec 31 CY 1991 VAB 1993 BPM5 G C India Indian rupee Mar 31 FY 1980 VAB 1985 BPM4 G C Indonesia Indonesian rupiah Mar 31 CY 1993 VAP BPM5 S C Iran, Islamic Rep Iranian rial Mar 20 FY 1982 VAB 1985 BPM5 S C Iraq Iraqi dinar Dec 31 CY 1969 VAB S Ireland Irish pound Dec 31 CY 1985 VAB 1993 BPM5 G C Israel Israeli new sihelkel Dec 31 CY 1990 VAB BPM5 S C Italy Italian lira Dec 31 CY 1985 VAP 1993 BPM5 S C Jamaica Jamaica dollar Dec 31 CY 1986 VAP BPM5 G Japan Japanese yen Mar 31 CY 1985 VAP 1993 BPM5 G C Jordan Jordan dinar Dec 31 CY 1990 VAB BPM4 G 8 Kazakstan Kazak tenge Dec 31 CY 1993 VAB 1990-95 BPM4 Kenya Kenya shilling Jun 30 CY 1982 VAB 1985 BPM5 G B Korea, Dem Rep Democratic Republic of Korea won Dec 31 CY 1990 VAP BPM5 Korea, Rep Korean won Dec 31 CY 1990 VAP 1985 BPM5 S C Kuwait Kuwaiti dinar Jun 30 CY 1984 VAP BPM5 S C Kyrgyz Republic Kyrgyz som Dec 31 CY 1987 VAB 1990-94 BPM4 Lao PDR Lao kip Dec 31 CY 1990 VAP BPM5 Latvia Latvian lat Dec 31 CY 1993 VAB 1990-95 BPM5 C Lebanon Lebanese pound Dec 31 CY 1990 VAB BPM4 G Lesotho Lesotho loti Mar 31 CY 1980 VAB BPM5 G C Libya Libyan dinar Dec 31 CY 1975 VAR BPM5 G Lithuania Lithuanian litas Dec 31 CY 1992 VAB 1990-95 BPM5 C Macedonia, FYR Macedonian denar Dec 31 CY 1990 VAP Madagascar Malagasi franc Dec 31 CY 1984 VAB 1985 BPM5 S C Malawi Malawi kwacha Mar 31 CY 1978 VAB 1985 BPM5 G B Malaysia Malaysian ringgit Dec 31 CY 1978 VAP BPM5 G C Mali CFA franc Dec 31 CY 1987 VAB 1985 BPM5 S Mauritania Mauritanian ouguiya Dec 31 CY 1985 VAB BPM5 S Mauritius Mauritian rupee Jun 30 CY 1992 VAB 1985 BPM5 G C MeXico Mexican peso Dec 31 CY 1980 VAP BPM5 G C Moldova Moldovan leu Dec 31 CY 1993 VAB 1990-93 1993 BPM5 Mongolia Mongolian tugrik Dec. 31 CY 1986 VAB 1993 BPM5 C Morocco Moroccan dirham Dec 31 CY 1980 VAP 1985 BPM5 S C Mozambique Mozambican meticai Dec 31 CY 1987 VAB BPM5 S xxx World Development Indicators 1997 Data sources and years Economy Latest Latest household or Vital Latest Latest Latest Latest Latest populatlon demographic survey registration agricultural industrial water survey of survey of census complete census data withdrawal sclentists expenditure data and for engineers R&D engaged In R&D Georgia 1989 V 1989 Germany V 1993 1991 1989 1989 Ghana 1984 DHS, 1993 1987 1970 Greece 1991 V 1994 1980 1986 1986 Guatemala 1994 DHS, 1995 1990 1970 1988 1988 Guinea 1983 SDA, 1994-95 1989 1987 1984 1984 Guinea-Bissau 1991 SDA, 1991 1988 1991 Haiti 1982 DHS, 1994-95 1987 Honduras 1988 DHS, 1994 1993 1994 1992 Hong Kong 1991 1994 Hungary 1990 Income, 1995 V 1994 1991 1992 1992 India 1991 National family health, 1992-93 1986 1993 1975 Indonesia 1990 DHS, 1994 1994 1987 Iran, Islamic Rep. 1991 Demographic, 1995 1988 1993 1975 Iraq 1987 1992 1970 Ireland 1991 v 1991 1994 1980 1988 1988 Israel 1983 V 1993 1989 Italy 1991 V 1990 1991 1990 1990 1990 Jamaica 1991 LSMS, 1994 V 1992 1975 1986 1986 Japan 1990 v 1990 1994 1990 Jordan 1994 DHS,1990 1994 1975 Kazakstan 1989 V 1989 Kenya 1989 DHS, 1993 1994 1990 Korea, Dem. Rep. 1993 1987 Korea, Rep. 1990 1992 Kuwait 1985 V 1993 1974 Kyrgyz Republic 1989 LSMS,1994 / 1989 Lao PDR 1985 1987 Latvia 1989 1 1989 1992 1992 Lebanon 1970 1975 Lesotho 1986 DHS, 1991 1985 1987 Libya 1984 1987 1989 1994 1980 1980 Lithuania 1989 V 1989 1992 1992 Macedonia, FYR 1991 V 1991 1991 Madagascar 1993 SDA, 1993 1988 1984 1989 1988 Malawi 1987 DHS, 1992 1994 Malaysia 1991 V 1975 Mali 1987 DHS, 1987 1981 1987 Mauritania 1988 PAPCHILD, 1990 1985 Mauritius 1990 CDC, 1991 1 1974 1992 1992 Mexico 1990 DHS. 1987 1991 1984 1984 Moldova 1989 s 1989 Mongolia 1989 1987 Morocco 1994 DHS, 1995 1992 Mozambique 1980 1992 World Development Indicators 1997 xxxi Metadata for macroeconomic indicators reenmy Mtal currec FMcal eme macaue tiamia o vanue Sr~~~~~~~~~~~~~~pnlat I Bnaisa1 an iand tuda jlernative PMp Reputing SM price oonversion survey Manuel System Accounting paI Blo as Bayewr valuation factor yew In use of Vade concept Myanmar Myanmar kyat Mar. 31 FY 1985 VAP BPM4 G C Nwnibia Namibla dollar Mar. 31 CY 1990 VAB BPM5 C Nepal Napaleae rupee Jul. 14 FY 1985 VAB BPM4 G C Netherlands Netherlands guilder Dec. 31 CY 1990 VAP 1993 BPM5 S C Nw Zealand New Zealand dollar Jun. 30 CY 1982 VAP 1993 OPM5 G B Nicaragua Nicaraguan gold cordoba Dec. 31 CY 1980 VAP OPM5 G C Niger CFA franc Dec. 31 CY 1987 VAP BPM5 S Nigeria NIerian naira Dec. 31 CY 1987 VAB 1992-95 1985 BPM5 G Norway Norwegan krone Dec. 31 CY 1990 VAP 1993 OPM5 G C Oman RIal Omani Dec. 31 CY 1978 VAP BPM5 G B PakIstan Pakistan rupee Jun. 30 FY 1981 VAB 1985 BPM4 G C Panama Panamanian balboa Dec. 31 CY 1992 VAB BPM5 S C Papua New Guinea Papua New Guinea kina Dec. 31 CY 1983 VAP BPM5 G B Paraguy Paraguayan guaranl Dec. 31 CY 1982 VAP BPM4 S C Peru Peruvian new sol Dec. 31 CY 1979 VAP 1987-91 BPM5 S C Philippines Philippine peso Dec. 31 CY 1985 VAP 1985 BPM5 G B Poland Pollsh zloty Dec. 31 CY 1990 VAP 1993 BPM5 G C Portugal Portuguese escudo Dec. 31 CY 1985 VAP 1993 BPM5 S C Puerto Rico U.S. dollar Dec. 31 CY Romanla Romanian leu Dec. 31 CY 1993 VAB 1992 1993 BPM5 G C Rusalan Fedwation Russlan ruble Dec. 31 CY 1993 VAB 1990-95 1993 BPM4 C Rwanda Rwanda franc Dec. 31 CY 1985 VAB 1985 BPM5 G C SaudI Arabia Saudi Arabian rlyal Hri year H3rl year 1970 VAP BPM5 S Senegal CFA franc Dec. 31 CY 1987 VAP 1985 BPM5 S Siae Leone Sierra Leonean lone Jun. 30 CY 1985 VAB 1985 BPM5 G 0 Slrgapore Sirgapore dollar Mar. 31 CY 1985 VAP BPM5 G c Slovak Republc Sklwak korune Dec. 31 CY 1993 YAP 1993 BPM5 Slovenla Slovenian tolwr Dec. 31 CY 1992 VAB 1993 BPM5 South Africa South African rend Mar. 31 CY 1990 VAB BPM5 C Spain Spanish peseta Dec. 31 CY 1996 VAP 1993 BPM5 S C Sri Lanka Sri Lanka rupae Dec. 31 CY 1982 VAB 1985 BPM5 a C Sudan Sudanewa pound Jun. 30 FY 1982 VAB BPM4 a Sweden Swedish krona Jun. 30 CY 1990 VAB 1993 BPM5 0 C Swlzerland Swlaa franc Dec. 31 CY 1990 VAP 1993 BPM5 S C Syrian Arab Repubic Sydan pound Dec. 31 CY 1985 YAP BPM5 S c TIWkistan iWik ruble Dec. 31 CY 1993 YAP 1990-94 BPM4 Tanznia Taznisa ahilling Jun. 30 FY 1992 VAB 1985 BPM5 G Thalland Thai bat Sep. 30 CY 1988 VAP 1985 BPM5 G C Togo CFA Ihnc Dec. 31 CY 1978 YAP BPM5 S Trinidad and Tobap Trinidad and Tobago dollar Dec. 31 CY 1985 VAB BPM5 S Tunisia Tunisian diner Dec. 31 CY 1990 VAP 1985 BPM5 G C Turkey TUrkish lra Dec. 31 CY 1994 VAB 1993 BPM5 S C Turkmenistan Tudiren manat Dec. 31 CY 1987 VAP 1990-95 BPM4 Uganda Uganda shilling June 30 FY 1991 VAB BPM4 0 Ukraine Ukrainian hrtvnwa Dec. 31 CY 1990 YAB 1990-95 1993 BPM5 xxxii World Development Indicators 1997 Data sources and years Mm , lt L alt heslld or Vitl i lt lt lt l lat pe am"VI dn_le urn reuyt |Ialbm= Induel w_te manet surey a eer_ ee~~~~~~~~~~ompbta e r_ ddX wItmh_a r I *dt xpmd tum data and hir I e'~~~~~~~~~~o -nm R&D Myanmar 1983 1993 1987 Namibia 1991 DHS. 1992 1991 Nepal 1991 1992 1987 Netherlands 1971 V 1989 1991 1991 1991 New Zealand 1991 VJ 1990 1992 1991 1990 1990 Nicaragua 1971 LSMS, 1993 1985 1975 1987 1987 Niger 1988 Household budget and consumpton, 1993 1982 1988 Nigeria 1991 Consumption expenditure. 1992 1990 1987 1987 1987 Norway 1990 v 1989 1994 1985 1991 1991 Oman 1993 Child health, 1989 1975 Pakistan 1981 LSMS, 1991 1990 1975 Panama 1990 1990 1975 Papuo New Guinea 1989 1989 1987 Paraguay 1992 CDC, 1992 1991 1981 1987 Peru 1993 LSMS, 1994 1987 1981 1984 Philippines 1990 DHS, 1993 1975 Poland 1988 V 1990 1991 1992 1992 Porugal 1991 J 1989 1990 1990 1990 Puerto Rico 1990 1987 Romania 1992 LSMS, 1995 J 1994 1992 1992 Russlan Federation 1989 LSMS. 1994 V 1991 1991 1988 Rwanda 1991 DHS. 1992 1986 1993 1985 1985 Saudi Araa 1992 Matamal and child health. 1993 J 1989 1975 Senegal 1988 DHS, 1992-93 1987 1981 1981 Sierra Leone 1985 SHEHEA, 1989-90 1993 1987 Singapore 1990 v 1975 1987 1987 Slovak Repubic 1991 J 1991 r9wvenia 1991 J 1992 1992 South Africa 1991 LSMS. 1993 1990 1991 1991 Spain 1991 V 1989 1994 1991 1990 1990 Sri Lanka 1993 DHS. 1993 J 1970 1985 1984 Sudan 1993 DHS. 1989-0 1995 Sweden 1990 J 1991 1991 1991 Swtzerand 1990 V* 1990 1991 1989 1989 Syrlan Arab Republic 1981 1992 1978 Tajlkistan 1989 */ 1989 Tanzenis 1988 LSMS, 1993 1988 1994 Thallend 1990 DHS. 1987 1988 1991 1987 1991 1991 Top 1981 OHS. 1988 1984 1987 Thlnided and Tobago 1990 DHS. 1987 , 1975 1984 1984 Tunisia 1994 1990 1992 1992 Turkey 1990 Population and health. 1983 1991 1991 1991 1991 Turkmenistan 1989 Vt 1989 Uganda 1991 DHS. 1995 1991 1989 1970 Ukralne 1991 VI 1989 1989 1989 World Development Indicators 1997 xxxill Metadata for macroeconomic indicators Economy National currency Fiscal National accounts Balance of Government year payments finance end and trade Balance of Alternative PPP Payments Reporting SNA price conversion survey Manual System Accounting period' Base year valuation factor year in use of trade concept United Arab Emirates U.A.E. dirham Dec. 31 CY 1985 VAB BPM4 G B United Kingdom Pound sterling Dec. 31 CY 1990 VAB 1993 BPM5 G C United States U.S. dollar Sep. 30 CY 1985 VAP 1993 BPM5 G C Uruguay Uruguayan peso Dec. 31 CY 1983 VAP BPM5 S C Uzbekistan Uzbek sum Dec. 31 CY 1987 VAB 1990-95 BPM4 Venezuela Venezuelan bollvar Dec. 31 CY 1984 VAP BPM5 G C Vietnam Vietnamese dong Dec. 31 CY 1989 VAP BPM4 West Bank and Gaza Israeli new shekel Dec. 31 CY .. VAP Yemen, Rep. Yemen rial Dec. 31 CY 1990 VAB 1990-95 BPM4 G C Yugoslavia, Fed. Rep. Yugoslav new dinar Dec. 31 CY 1984 VAP 1985 S Zaire New zaire Dec. 31 CY 1987 VAP BPM5 S C Zambia Zambian kwacha Dec. 31 CY 1977 VAP 1985 BPM5 G C Zimbabwe Zimbabwe dollar Jun 30 CY 1980 VAB 1985 BPM5 G C Note: For explanation of the abbreviations used in the table see the notes. a. Also applies to balance of payments reporting. *||lif>&;lf *a n- osz * * gates, such vs the GDP deflator, express the price level rela- f rst arrival, as imports; underthe special trade system, goods tive to prices ,n the base year. Constant price data reported in are recorded as imports when declared for domestic con- * Fiscal year end is the date of the end of the fisca year for the World Barik are partially rebased to a common 1987 base sumption whether at time of entry or on withdrawal from cus- the central governmenit, Fiscal years for other levels of gov- year. See the notes to table 4.1 for further discussion. e SNA toms storage. Exports under the gerrerar system comprise ernment and the reporting years for statistica surveys may d f price valuation shows whether value added in the national outward moving goods: ra) nat onal goods wholly or partly pro- fer, but if a country is designated as a fiscal year reporter in accounts is roported at bas c or producers' prices (VAB) or at duced in the country; (b) foreign goods, ne ther transformed the following co umn, the date shown s the end of ts nationa purchasers' prices (VAP). Purchasers' prices include the value nor declared for domestic consumption in the country, that accounts reporting period. * Reporting period for national of taxes levied on value added and collected from consumers move outward from customs storage; and (cf nationalized accounts and balance of payments data is des gnated as and thus tenri to overstate the actua va ue added in produc goods that have been declared from domestic consumption either calendar year basis, (CY! or fiscal year rFY!. Most tion. See the notes to table 4.2 for further discussion of and move outward without having been transformed. Under the economies report their national accounts and balance of pay nationa accounts valuation. 0 Altemative conversion factor special system of trade, exports comprise categories (a) and ments data using calendar years, but a limited number use f s- dentifies the countries and years for wnich a World Bank- xc). In some compilations categories (b) and (c) are classified cal years, which straddle two calendar years. In the Worod estimated cou version factor has been used in place of the ofti- as re-exports. Direct transit trade, consist ng of goods enter- Development Indicators fiscal year data are ass gned to the cial fIFS line rf) exchange rate. See Statistical methods for ing or teaving for transport purposes only, is excluded from calendar year that contains the arger share of the fiscal year: further dOscur sion of the use of alternative conversion factors. both import and export statistics. See the notes to tables 4.8 if a country's fiscal year ends before June 30, the data are * PPP survey year refers to the latest ava able survey year and 4.9 for further discussion. a Government finance shown in the earlier year of the fiscal period: if the fiscal year for the Interr ational Comparison Programme's estimates of accounting concept describes the accounting basis for report- ends on or after June 30, the data are shown in the second purchasing power paritbes (PPPs). See the notes to tables 4,14 ing central government financial data. For most countries gov- year of the period. Note that Saudi Arabia follows a lunar year and 5.5 for falther details. 0 Balance of Payments Manual In ernment finance data have been consolidated (C) into one set whose starting and ending dates change with respect to the use refers to the classification system used for compiling and of accounts capturing ai fiscal activities of the central govern- solar year. Because the Internationa Monetary Fund (IMF) reporting dati on balance of payments items in tables 4.21 ment. Budgetary central government accounts (B) exclude cen- reports most balance of payments data on a calendar year and 4.22. BPM4 refers to the fourth edition of the IMF's Bal- tra government units. See the notes to table 4.16 for further basis, balance of paynnents data for fiscal year reporters in the ance of Payn ents Manuai (1977), and BPM5 to the f fth edi- details. World Development Indicators are based on fiscal year esti- tion (1993). Since 1995 the IMF has adjusted a balance of mates provided by World Banh operational staff. These esti payments data to BPM5 conventions, but some countries con mates may differ from IMF data but allow consistent tinue to repoa using the older system. * System of trade comparisons between national accounts and balance of pay refers to the general trade system (G) or the special trade sys ments data. * Base year is the year used as the base period tem (S) For imports under the genera trade system, both for constant price calculations n the country's natiorial goods enterir g directly for domest c consumpt on and goods accounts. Price indexes derived from national accounts aggre entered into customs storage are recorded, at the time of their xxxiv World Development Indicators 1997 Data sources and years Economy Latest Latest household or Vital Latest Latest Latest Latest Latest population demographic survey registration agricultural Industrial water survey of survey of census complete census data withdrawal scIentIsts expenditure data and for engineers R&D engaged in R&D United Arab Emirates 1980 1985 1980 United Kingdom 1991 V 1993 1994 1991 1991 1991 United States 1990 Current population, 1994 / 1987 1990 1988 1988 Uruguay 1985 1990 1994 1965 Uzbekistan 1989 1989 1992 1992 Venezuela 1990 LSMS, 1993 1993 1970 Vietnam 1989 Intercensal demographic, 1995 1992 1985 1985 West Bank and Gaza Demographic, 1995 Yemen, Rep 1994 DHS, 1991-92 1987 Yugoslavia, Fed Rep 1991 V 1992 1992 Zaire 1984 1990 1990 Zambia 1990 SDA, 1993 1990 1994 Zimbabwe 1992 DHS, 1994 1987 Note: For explanation of the abbreviations used in the table see the notes lection effort by UNESCO in science and technology and research and development (R&D) See the notes to table 5 13 * Latest population census shows the most recent year in for more information which a census was conducted 0 Latest household or demo- graphic survey gives information on the surveys used In com- piling household and demographic data presented in section 2 PAPCHILD is the Pan Arab Project for Child Development, DHS is Demographic and Health Survey, WFS is World Fertility Study, LSMS is Living Standards Measurement Study, SDA is Social Dimensions of Adjustment, CDC is Centers for Disease Control and Prevention, and SHEHEA is Survey of Household Expenditure and Household Economic Activities * Vital reg- istration complete identifies countries judged to have com- plete registries of vital statistics (/) by the United Nations Department of Economic and Social Information and Policy Analysis, Statistical Division, and reported in Population and Vital Statistics Reports Countries with complete vital statis- tics registries may have more accurate and more timely demo- graphic indicators * Latest agricultural census shows the most recent year in which an agricultural census was con- ducted and reported to the Food and Agriculture Organization * Latest Industrial data refer to the most recent year for which manufacturing value added data at the three-digit level of the International Standard Industrial Classification (rev 2 or rev 3) are available in the UNIDO database * Water withdrawal survey refers to the most recent year for which data have been compiled from a variety of sources See the notes to table 33 for more information * Latest surveys of scientists and engi- neers engaged in R&D and expenditures on R&D refer to the most recent year for which data are available from a data col- P PP jP '''P ![''i1rF'PP' II IL IL  II I I 011111 III liii III d No word might be used but what marks either number, weight or measure. -Sir William Petty, 1676 The organization and coverage of the World Development Indicators reflect the priori- r tes of an institution dedicated to promoting economic development, The focus is on people, the environment, the economy, the relative roles of states and markets, and the links between industrial and developing economies But what is development? And how do we measure it? Life's quality Since the 17th century economists have viewed development as a means of improving standards of living and the quality of life in very broad terms. Sir William Petty, one of the first development economists, was interested not only in national income but also in such factors as "the Common Safety" and "each Man's particular Happiness" (cited in Sen 1988). "Ultimately," Amartya Sen argues that "the assessment of development achieved cannot be a matter only of quantification of the means of that achieve- ment. The concept of development has to take note of the actual achievements themselves" (1988, p. 15). These achievements-Sen labels them as "function- ings"-include the length of life (life expectancy) and the "nature of life" and what people value as important to their well-being (nourishment, good health, clean air and water, the ability to move about freely). True, these values differ greatly from individual to individual, reflecting different aspirations, conceptions, abilities, and tastes-and from society to society, reflecting culture and tradition. Yet there clearly are certain basic needs common to all mankind for food, health, shelter, and personal freedoms, which if met constitute development (Dasgupta 1993). In measunng development, it helps to distnguish between indicators that measure the "constituents" of development (such outcomes as health and literacy) and those that measure its "determinants," the goods and services that produce development or well- being, such as food, shelter, safe drinking water, clean air, education, health care, and real national income (Dasgupta and Weale 1992). Partha Dasgupta and Martin Weale show a strong correlation between the rankings of 48 developing countries for GNP per capita (adjusted for purchasing power parity) and their rankings for five other indicators (life expectancy, infant mortality, adult literacy, political rights, and civil nghts). This leads Dasgupta to observe that "recent suggestions that national income is a vastly misleading index are not borne out by this exercise. We can do better than merely rely on national income, but we wouldn't have been wildly off the mark as regards an ordinal comparison of countries had we relied exclusively on national income per head" (1993, p. 115). Not the same for all One reason to go beyond national income is to capture inequalities in access to resources. Life expectancy, infant mortality, and adult literacy measure outcomes, but they also say much about differential access to assets and income and about other per- vasive forms of differentiation based on gender. The causes of gender inequality, linked as they are to the decisions in households, are particularly complex. Regardless of how such decisions are made, they clearly are influenced by market signals and institutional and cultural norms that do not capture the full benefits to society of investing in women. Limited education and training, poor health and nutrition, and denied access to resources depress women's quality of World Development Indicators 1997 3 life-and hinder economic efficiency and growth. This is dis- turbing, because women are agents of change, shaping the welfare of future generations. Social and environmental sustainability The environment can no longer be thought of as a source of free goods and services. Environmental values and costs * * - should be included when measuring the quality of life. Although this is easier said than done, we cannot ignore the damage that people do to their environment or the damage done to themselves through environmental degradation. The poor often suffer the most. They cannot escape the polluted . - air in the streets of cities or from the open fires burning wood or dung in rural areas. They cannot protect themselves - - - - . from contaminated water, and their farmlands are more likely to suffer from soil erosion. Development may be a cure - - * * I for environmental ills-the rich can afford to maintain a -: - - - - . * * - s * healthy environment-but it is also associated with greater * * -.- consumption of natural resources, particularly energy, and potentially destabilizing alterations in natural balances. The --a - -.. challenge for governments and citizens alike is to find devel- IS! S I opment strategies that are sustainable at a local and a global -- - 0 I level. From farming to computer programming The process of development is sometimes referred to as "struc- - -- = _ tural transformation," the change in patterns of consumption, _. production, technology, foreign trade, and resource use. And - - * so the developed market economies have perhaps seen more pro- _ __ found change in this century than at any other time in human . -_ - -._--- history (Drucker 1994). At the beginning of the 20th century farmers constituted the largest single group in today's industrial economies. Today they make up no more than 5 percent of the workforce. Even - - _ a blue-collar workers, who dominated the labor force of high- income economies in the 1950s, have retreated to proportions - , common at the beginning of the century. East Asian economies are now experiencing a similar-pos- sibly even faster-structural transformation. But they are at _- one extreme. At the other are many countries and parts of _ __ countries that remain untouched by major change. Fueling the structural transformation is economic - -. . . - -- growth, the "expansion in productive resources and the increase in the efficiency of their use" (Syrquin 1988). This . _ . - -n growth comes through increases in inputs and through tech- _ _ _ nical change. A proxy for technical change is the growth of what is called total factor productivity (TFP)-that is, the contribution to economic growth by the increase in produc- tivity of all factors combined-of such inputs as land, natural resources, and human and physical capital. In practice, how- ever, it is difficult to disentangle technical change from the factors of production, because that change is embodied in labor (through education, training, and experience) and in . j. . capital (through the innovations embodied by machinery and equipment). 4 World Development Indicators 1997 Developing countries nevertheless appear to experience inelastic demand for food. So, food prices fall relative to other slower TFP growth than do the industrial countries-and consumer prices. countries that grow faster typically enjoy faster TFP growth * In household budgets food's share declines with sustained (World Bank 1991b). progress, facilitating the diversification of spending and choices. A large number of cross-country studies of the post-World Changes in the structure of demand therefore tend to drive War II period throw light on the process of structural transfor- changes in producton. mation (Syrquin 1988): * International trade is an important pathway for structural * Savings and investment tend to rise as a share of GDP during transformation, especially for small economies. Small economies periods of rapid transformation. All the evidence points to have small domestic markets and must specialize-their share of strong correlations between growth rates and investment rates. trade (exports plus imports) in GDP is high. If they lack natural * Rapid agricultural growth has been generally associated with resources, they must develop competitive exports of manufac- successful industrialization and sustained gains in overall output tured goods and, increasingly, services. Large economies with and productivity. Such growth usually reflects improvements in large domestic markets may have a relatively smaller share of yield (output per unit of land) and is often associated with the trade in GDP, but they are still likely to be important world use of modern inputs (fertilizers, agricultural machinery). traders. Production for domestic consumption provides them * While agriculture provides the initial stimulus for transfor- with a base for developing an efficiently sized and competitive mation, its importance declines with development. The share export sector. For all economies, regardless of size, access to of agriculture in output and employment tends to fall with imports from world markets is important for sustaining efficient industrialization, but farm productivity outstrips the relatively production processes and high levels of consumption. The tables in this section provide an overview of the quality of life, gender dif- ferences, and economic strudtute i_Fthe 209 ecornoies for which data are available. (In the rest of this bookdaWtrs'e Shown oriyIfor 148 economies whose populations exceed one fitMMon4 Fbiog the-tables is a set of charts that summarize key development trends taplngthe world economy. World Development Indicators 1997 5 . ~1.1 The quality of life Population GNP per Poverty Infant Total Adult Access density capitaa mortality rate fertility rate illilteracy rate to sanitation % of people living on % of people people pppb less than $1 per 1.000 births It. and above % of per sq km, $ $ a day (PPP) live births per woman Male Female population 1995 1995 1995 1981-95 1970 1995 1970 1995 1995 1995 1994-95 Afghanistan 36 C . 198 158 7.1 6 9 53 85 8 Albania 119 670 -. 66 30 5.2 2 6 100 Algeria 12 1,600 5,300 d 1 6 139 34 7.4 3.5 263 51 American Samoa 285 e Andorra 142 Angola 9 410 1,310d 178 124 6.5 6 9 16 Antigua and Barbuda 157 e . 18 2.6 1.7 Argentina 13 8,030 8,310 52 22 3.1 2.7 4 4 89 Armeniag 133 730 2,260 .. 16 3.2 1 8 Aruba 421f Australia 2 18,720 18,940 .18 6 2.9 1 9 90 Austria 97 26,890 21,250 .26 6 2.3 1 5 .100 Azerbaijang 87 480 1,460 . . 25 4.7 2 3 Bahamas, The 28 11,940 14,71lod 35 15 3 5 2 0 :2 2 98 Bahrain 836 7,840 13,400~ d 64 19 6 5 3.1 11L 21 100 Bangladesh 920 240 1,380 . 140 79 7.0 3 5 5 1 74 30 Barbados 607 6,560 10,620d ..38 13 3.0 1 8 :2 3 Belarusg 50 2,070 4,220 . .. 13 2.4 1 4 . . 100 Belgium 24,710 21,660 ..21 8 2.2 1 6 100 Belize 9 2,630 5,400~ d 36 6.9 3 9 . 57 Benin 49 370 1,760 . 155 95 6.9 6.0 51 74 22 Bermuda 1.260 f Bhutan 15 420 1,260d 44 72 22 Bolivia 7 800 2,540 7 1 153 69 6.5 4 5 10D 24 44 Bosnia and Herzegovina .. Botswana 3 3,020 5,580 34.7 95 56 6.9 4 4 20 40 55 Brazil 19 3,640 5,400 28.7 95 44 5.0 2.4 17 17 73 Brunei 54 f~ 57 9 5.6 2 9 7 17 Bulgaria 76 1,330 4,480 2.6 27 15 2.2 1.2 . ..99 Burkina Faso 38 230 7BO0 . 141 99 7.0 6 7 71 91 14 Burundi 244 160 630~ d 138 98 6.8 6 5 51L 78 48 Cambodia 57 270 . . 161 108 5.8 4 7 20 4 7 Cameroon 29 650 2,110 .. 126 56 5.8 5 7 25 48 40 Canada 3 19,380 21,130 .19 6 2.3 1 7 . .85 Cape Verde 94 960 1,87O0d 86 46 7.0 4 0 19 36 24 Cayman Islands 127 . f . Central African Republic 5 340 l,07O0d . 139 98 5.7 5 1 32 48 Chad 5 180 700~ d 171 117 6.0 5 9 38 65 32 Channel Islands -.f . 19 7 2 0 1 7 Chile 19 4,160 9,520 15 0 77 12 4.0 2 3 5 5 71 China 129 620 2,920 29.4 69 34 5 8 1 9 10 27 Colombia 35 1,910 6,130 7.4 74 26 5 3 2.8 9D 9 70 Comoros 224 470 1,320d 87 . 5 9 36 50 83 Congo 8 680 2,050 . 101 90 5.9 6 0 17 33 9 Costa Rica 67 2,610 5,850 18.9 62 13 4.9 2 8 5 5 99 C6te dIlvoire 44 660 1,580 17.7 135 86 7 4 5 3 50 70 54 Croatia 85 3,250 16 .. 1.5 .68 Cuba 100 h39 9 3 8 1 7 4 5 66 Cyprus 79 f. 29 8 2 6 2 2 .100 Czech Republic 134 3,870 9,770 3.1 21 8 1 9 1 3 Denmark 123 29,890 21,230 .14 6 2 0 1 8 .100 Djibouti 27 , h . 159 108 6.6 5.8 40 67 Dominica 99 2,990 . . 17 5 5 2.3 Dominican Republic 162 1,460 3,870 19.9 98 37 6 1 2 9 183 18 85 Ecuador 41 1,390 4,220 30.4 100 36 6 3 3 2 8 12 64 6 World Development Indicators 1997 4w Population GNP per Poverty Infant Total Adult Access density capItoa mortality rate fertility rate Iiiiteracy raFte to sanitation % of people living on % of people people pppb less than $1 per 1,000 births 15 and above % Of per sq km $ $ a day (PPP) live births per woman Male Female population 1995 1995 1995 1981-95 1970 1995 1970 1995 1995 1995 1994-95 Egypt, Arab Rep 58 790 3,820 7 6 158 56 5 9 3 4 36 61 El Salvador 271 1,610 2,610 103 36 6 3 3.7 27 30 73 Equatorial Guinea 14 380 ..163 ill 5 7 5 9 10 32 50 Eritrea Estoniag 35 2,860 4,220 6.0 20 14 2 2 1.3 Ethiopia 56 100 450 33.8 158 112 5 8 7 0 55 75 1 0 Faeroe Islands 32 f~ .. Fij i 42 2,440 5,7800 d 49 21 4 1 2.7 6 11 Finland 17 20,580 17,760 13 5 1 8 1.8 .100 France 106 24,990 21,030 . 8 6 2 5 1 7 . ..96 French Guiana 2 . .. 44 French Polynesia 61 . .17 5 6 3.0 Gabon 4 3.490 .138 89 4 2 5 2 26 47 76 Gambia, The Ill 320 9300 d 185 126 6 5 5.3 47 75 34 Georgiag 7 7 440 1,470 18 2 7 1 9 Germany 234 27,510 20,070 ..23 6 2 0 1.2 .100 Ghana 75 390 1,990d ill1 73 6.7 5 1 24 47 29 Greece 81 8,210 11,710 30 8 2 3 1.4 96 Greenland 0 f~ ... Grenada 268 2,980 Guadeloupe 253 . 42 11 4.5 2.1 Guam 273 f ~ . 22 9 4 7 2.7 Guatemala 98 1,340 3,340 53.3 100 44 6 5 4 7 38 51 71 Guinea 27 550 . 26.3 181 128 6 0 6 5 50 78 6 Guinea-Bissau 38 250 7908 87 0 185 136 5.9 6.0 32 58 20 Guyana 4 590 2,420d .80 60 5 4 2 4 1 3 90 Haiti 260 250 gl0d 141 72 6.0 4 4 52 58 24 Honduras 53 600 1,900 46 5 110 45 7 2 4 6 27 27 68 Hong Kong 6,252 22,990 22,950'1 19 5 3 3 1 2 4 12 Hungary ill 4,120 6,410 0.7 36 11 2.0 1 6 94 Iceland 3 24,950 20,460 13 4 2 8 2 1 95 India 313 340 1,400 52.5 137 68 5.8 3 2 35 62 29 Indonesia 107 980 3,800 14.5 118 51 5 5 2 7 10 22 55 Iran, Islamic Rep 39 h . 131 45 6.7 4 5 22 34 82 Iraq 46 h . 102 108 7 1 5 4 29 55 36 Ireland 52 14,710 15,680 .20 6 3 9 1 9 . 100 Isle of Mane Israel 268 15,920 16,490 25 8 3 8 2 4 .70 Italy 195 19,020 19,870 30 7 2.4 1 2 .100 Jamaica 233 1,510 3,540 4.7 43 13 5 3 2 4 19 11 74 Japan 333 39,640 22,110 13 4 2 1 1 5 .85 Jordan 47 1.510 4,060d8 2.5 . 31 4 8 7 21 30 Kazakstang 6 1,330 3,010 . 27 3 4 2 3 Kenya 47 280 1,380 50 2 102 58 8 1 4 7 14 30 43 Kiribati 107 920 ..105 55 3.8 .100 Korea, Dem Rep 198 hn 51 26 6 2 2.2 1 3 100 Korea, Rep 454 9,700 11,450 46 10 4 3 1.8 1 3 100 Kuwait 93 17,390 23,790d 48 11 7 1 3 0 18 25 Kyrgyz Republicg 24 700 1,800 18 9 30 4 9 3.3 . .53 Lao PDR 21 350 .146 90 6 2 6.5 31 56 30 Latviag 41 2,270 3,370 21 16 1 9 1 3 Lebanon 391 2,660 ..50 32 5 4 2 8 5 10 Lesotho 65 770 1,780d 50 4 134 76 5.7 4 6 19 38 35 Liberia 29 C 178 172 6 8 6.5 46 78 24 Libya 3 e 122 61 7 5 6 1 12 37 World Development Indicators 1997 7 e ~1.1 Population GNP per Poverty Infant Total Adult Access density capitaa mortality rate fertility rate illiteracy rate to sanitation % of people living on % of people people pppb less than $1 per 1,000 births 15 and above % of per sq km $ $ a day (PPP) live births per woman Male Female population 1995 1995 1995 1981-95 1970 1995 1970 1995 1995 1995 1994-95 Liechtenstein 194 f Lithuaniag 57 1,900 4,120 2.1 24 14 2 4 15 Luxembourg 41,210 37,930 25 6 2 0 1 7 100 Macao 20,750 f 7 4 5 1 8 Macedonia, FYR 83 860 23 3 1 2 2 Madagascar 23 230 640 72 3 181 89 6 6 5 8 17 Malawi 104 170 750 193 133 7 8 6 6 28 58 63 Malaysia 61 3,890 9,020 5 6 45 12 5 5 3 4 1.1 22 94 Maldives 850 990 3,080d 127 52 7 0 6 6 7 7 40 Mali 8 250 550 204 123 7 1 6 8 61 77 44 Malta 1,163 e 28 9 2 0 19 . 100 Marshall Islands h Martinique 364 f 38 7 4 4 2 0 Mauritania 2 460 1,540d 31 4 148 96 6 5 5 2 50 74 64 Mauritius 556 3,380 13,210 60 16 3.7 2 2 13 21 100 Mayotte e Mexico 48 3,320 6,400 14 9 72 33 6 5 3 0 8 13 70 Micronesia, Fed Sts. h 32 . 4 6 39 Moldovag 132 920 6 8 22 2 6 2 0 50 Monaco f . . 100 Mongolia 2 310 1,950d 102 55 5 8 3 4 Morocco 60 1,110 3,340 1 1 128 55 7 0 3 4 43 69 63 Mozambique 21 80 810d . 171 113 6.5 6 2 42 77 23 Myanmar 69 c 128 83 5 9 3 4 11 22 42 Namibia 2 2,000 4,150d 118 62 6.0 5 0 . 36 Nepal 157 200 1,170d 53 1 166 91 6.4 5 3 59 86 6 Netherlands 456 24,000 19,950 . 13 6 2.6 1 6 . 100 Netherlands Antilles 250 f . 24 11 2.9 2 1 New Caledonia 10 f 15 4.3 2 5 New Zealand 13 14,340 16,360 . 17 7 3.2 2 1 Nicaragua 36 380 2,000d 43 8 106 46 6.9 4 1 35 33 Niger 7 220 750 d 61 5 170 119 7 2 7 4 79 93 15 Nigeria 122 260 1,220 28 9 139 80 6.9 5 5 33 53 63 Northern Mariana Islands f Norway 14 31,250 21,940 . 13 5 2.5 1 9 . 100 Oman 10 4,820 8,140d . 119 18 8.4 7 0 . 72 Pakistan 169 460 2,230 11 6 142 90 7.0 5 2 50 76 30 Panama 35 2,750 5,980 25 6 47 23 5.2 2 7 9 10 87 Papua New Guinea 9 1,160 2,420d . 112 64 6.1 4 8 19 37 26 Paraguay 12 1,690 3,650 . 55 41 6.0 4 0 7 9 30 Peru 19 2,310 3,770 49 4 108 47 6.0 3 1 6 17 47 Philippines 230 1,050 2,850 27 5 66 39 6 4 3 7 5 6 75 Poland 127 2,790 5,400 6 8 33 14 2 2 1 6 100 Portugal 108 9,740 12,670 56 7 2 8 1.4 100 Puerto Rico 420 e 29 11 3 2 2 1 Qatar 59 11,600 17,690d 68 18 6.8 3 9 21 20 100 R6union 3 f 54 8 4.3 2 2 Romania 99 1,480 4,360 17 7 49 23 2 9 1 4 49 Russian Federationg 9 2,240 4,480 1 1 18 2 0 1 4 Rwanda 259 180 540 45 7 142 133 8 2 6 2 30 48 S5o Tome and Principe 172 350 . 60 . 4 8 21 Saudi Arabia 9 7,040 . 119 21 7.3 6 2 29 50 86 Senegal 44 600 1,780 54 0 135 62 6.5 5 7 57 77 Seychelles 169 6,620 . 15 2 4 92 Sierra Leone 59 180 580 :197 179 6.5 6 5 55 82 8 World Development Indicators 1997 -1.1O Population GNP per Poverty Infant Total Adult Access density capitaa mortality rate fertility rate illiteracy rate to sanitation % of people living on % of people people pppb less than $1 per 1,000 births 15 and above % of per sq km $ $ a day (PPP) live births per woman Male Female population 1995 1995 1995 1981-95 1970 1995 1970 1995 1995 1995 1994-95 Singapore 4,896 26,730 22,770d 20 4 3.1 1 7 4 14 100 Slovak Republic 112 2,950 3,610 12 8 25 11 2 4 1 5 . 51 Slovenia 99 8,200 24 7 2 2 1 3 90 Solomon Islands 13 910 2,190d 41 5 1 Somalia 14 ..c 158 128 7 0 7 0 South Africa 34 3,160 5,030d 23 7 79 50 5 7 3 9 18 18 46 Spain 78 13,580 14,520 28 7 2 8 1.2 97 Sri Lanka 280 700 3,250 4 0 53 16 4 3 2 3 7 13 66 St Kitts and Nevis 114 5,170 9,410d , 31 35 2 4 St Lucia 272 3,370 . 17 55 29 St. Vincent and the Grenadines 285 2,280 . . 19 5 0 2 3 Sudan 11 ,c 118 77 6 7 4.8 42 65 Suriname 3 880 2,250 .. 53 33 5 6 2 6 5 9 56 Swaziland 55 1,170 2,880 139 69 6 5 4 6 22 24 63 Sweden 21 23,750 18,540 11 4 1 9 1 7 100 Switzerland 178 40,630 25,860 15 6 2 1 1 5 100 Syrian Arab Republic 77 1,120 5,320 96 32 7 7 4 8 14 44 78 Tajikistang 42 340 920 .. 42 6 8 4 2 62 Tanzania) 34 120 640 16 4 129 82 6 8 5 8 21 43 86 Thailand 114 2,740 7,540 0.1 73 35 5 5 1.8 4 8 87 Togo 75 310 1,130d 134 88 66 64 33 63 20 Tonga 144 1,630 18 3 3 100 Trinidad and Tobago 251 3,770 8,610d 52 13 3.6 2 1 1 3 56 Tunisia 58 1,820 5,000 3.9 121 39 64 2 9 21 45 72 Turkey 79 2,780 5,580 144 48 5 3 2 7 8 28 94 Turkmenistang 10 920 .. 4.9 . 46 6 3 3 8 .. 60 Uganda 96 240 1,470d 50.0 109 98 7 1 6 7 26 50 60 Ukraineg 89 1,630 2,400 22 15 2 0 1 5 49 United Arab Emirates 29 17,400 16,470d 87 16 6 5 3 6 21 20 95 United Kingdom 242 18,700 19,260 19 6 2 4 1 7 96 United States 29 26,980 26,980 20 8 2.5 2 1 85 Uruguay 18 5,170 6,630 46 18 2 9 2 2 3 2 82 Uzbekistang 55 970 2,370 . 30 5 7 3 7 18 Vanuatu 14 1,200 2,290d . 41 5 0 Venezuela 25 3,020 7,900 11.8 53 23 5 3 3 1 8 10 55 Vietnam 226 240 . 104 41 5 9 31 4 9 21 Virgin Islands (U.S.) 291 f.. 19 5 3 2 4 West Bank and Gaza h . 28 6 2 Western Samoa 59 1,120 2,030d 22 6 7 4.2 Yemen, Rep. 29 260 . 186 100 7 7 7 4 51 Yugoslavia, Fed Rep. 103 h 53 18 2 4 1 9 100 Zaire 19 120 490d 131 6 2 13 32 9 Zambia 12 400 930 84 6 106 109 6 8 5 7 14 29 42 Zimbabwe 28 540 2,030 41 0 96 55 7 7 3.8 10 20 58 a Calculated using the World Bank Atlas method b PPP is purchasing power parity See the notes following these tables c Estimated to be low income ($765 or less) d The estimate is based on regression, others are extrapolated from the latest International Comparison Programme benchmark estimates e Upper middle income ($3,036 to $9,385) f Estimated to be high income ($9,386 or more) g Estimates for the economies of the former Soviet Union are preliminary, and their classification will be kept under review h Estimated to be lower middle income ($766 to $3,035) i References to GNP relate to GDP j Data cover mainland Tanzania only World Development Indicators 1997 9 . 1.2 Gender dimensions of development Population Gross primary enrollment Female labor force Life expectancy at birth sex ratio Male Female women per % of relevant % of relevant Male Female 100 men age group age group % of total years years 1970 1995 1970 1993 1970 1993 1970 1995 1970 1995 1970 1995 Afghanistan 95 96 47 46 8 16 7 35 37 44 37 45 Albania 98 95 109 95 102 97 40 41 66 70 69 76 Algeria 105 98 93 111 58 96 6 24 52 68 54 71 American Samoa Andorra Angola 104 103 98 53 47 46 36 45 39 48 Antigua and Barbuda 116 65 72 69 78 Argentina 99 104 105 108 106 107 25 31 64 69 70 76 Armenia 105 106 87 93 46 48 69 68 75 74 Aruba Australia 98 99 115 108 115 107 31 43 68 74 75 80 Austria 112 107 104 103 103 103 38 41 67 74 74 80 Azerbaijan 106 104 91 87 45 44 64 66 72 75 Bahamas, The 102 99 100 103 40 46 63 70 69 77 Bahrain 86 77 114 109 84 112 5 19 60 71 64 75 Bangladesh 93 97 72 128 35 105 5 42 45 57 43 58 Barbados 113 106 103 101 40 46 66 73 71 78 Belarus 118 112 96 95 51 49 68 64 76 75 Belgium 104 104 103 99 104 100 30 40 68 73 75 80 Belize 102 100 111 107 19 22 57 73 61 76 Benin 103 105 51 88 22 44 49 48 43 49 45 52 Bermuda Bhutan 99 128 11 1 40 Bolivia 103 102 91 62 32 37 44 59 48 62 Bosnia and Herzegovina Botswana 115 107 63 113 67 120 53 46 50 50 54 53 Brazil 100 101 22 35 57 63 61 71 Brunei 94 92 111 104 20 34 66 73 68 78 Bulgaria 100 104 101 87 100 84 44 48 69 68 74 75 Burkina Faso 102 103 17 47 10 30 49 47 39 45 42 47 Burundi 108 103 42 76 20 63 51 50 42 45 45 48 Cambodia 100 108 35 26 49 53 41 52 44 54 Cameroon 103 101 103 75 37 38 43 55 46 58 Canada 100 102 101 106 100 104 32 45 69 76 76 82 Cape Verde 110 115 119 110 28 38 55 65 58 67 Cayman Islands Central African Republic 109 106 88 92 41 49 47 40 46 45 51 Chad 104 102 52 80 17 38 42 44 37 47 40 50 Channel Islands 106 74 82 Chile 102 102 107 99 107 98 22 32 59 72 66 78 China 94 94 120 116 42 45 61 68 63 71 Colombia 101 100 107 118 110 120 23 37 59 67 63 73 Comoros 102 46 96 21 81 43 42 46 54 47 58 Congo 105 104 41 43 43 49 49 54 Costa Rica 98 98 110 106 109 105 18 30 65 74 69 79 Cote d'lvoire 97 97 71 80 45 58 33 33 43 53 46 56 Croatia 107 105 38 43 70 78 Cuba 96 98 121 104 121 104 20 38 68 74 72 78 Cyprus 102 100 101 101 33 38 69 75 73 80 Czech Republic 107 105 99 100 46 47 70 77 Denmark 102 103 95 97 97 98 36 46 71 72 76 78 Djibouti 103 105 41 31 39 48 42 51 Dominica 100 71 75 Dominican Republic 97 97 100 95 100 99 22 29 57 68 61 73 Ecuador 99 99 99 124 95 122 16 26 57 67 60 72 10 World Development Indicators 1997 -1.2 Population Gross primary enrollment Female labor force Life expectancy at birth sex ratio Male Female women per % of relevant % of relevant Male Female 100 men Ege group age group % of total years years 1970 1995 1970 1993 1970 1993 1970 1995 1970 1995 1970 1995 Egypt, Arab Rep 98 97 87 105 57 89 8 29 50 64 52 66 El Salvador 99 104 87 79 83 80 21 34 56 66 60 72 Equatorial Guinea 105 104 86 67 37 35 38 48 42 51 Entrea .. 52 41 47 47 Estonia 119 112 84 83 51 49 66 65 74 76 Ethiopia 102 99 23 27 10 19 42 41 39 47 42 51 Faeroe Islands Fiji 96 98 106 128 103 127 12 27 63 70 66 74 Finland 107 104 84 100 79 100 44 48 66 73 74 80 France 105 105 118 107 117 105 36 44 68 74 76 82 French Guiana French Polynesia 95 96 127 123 67 73 Gabon 104 103 89 81 46 44 43 53 46 56 Gambia, The 103 102 34 84 15 61 45 45 35 45 38 48 Georgia 113 110 48 46 69 78 Germany 112 105 97 98 39 42 67 73 74 79 Ghana 102 101 73 83 54 70 51 51 48 57 51 61 Greece 105 103 108 106 26 36 70 75 74 81 Greenland Grenada 107 Guadeloupe 104 105 38 45 64 72 70 79 Guam 77 90 70 76 Guatemala 97 98 62 89 51 78 12 26 51 63 54 68 Guinea 101 99 45 61 21 30 48 47 36 44 37 45 Guinea-Bissau 103 105 57 23 39 40 35 42 37 45 Guyana 101 101 100 98 96 98 20 33 58 61 62 67 Haiti 104 103 46 43 46 54 49 57 Honduras 99 98 87 :11 87 112 14 30 51 64 55 69 Hong Kong 97 92 118 115 35 37 67 76 73 81 Hungary 106 108 98 95 97 95 40 44 67 66 73 74 Iceland 98 99 94 102 100 98 34 44 71 77 77 81 India 93 94 90 113 56 91 29 32 50 62 49 63 Indonesia 102 100 87 116 73 112 30 40 47 62 49 66 Iran, Islamic Rep 98 96 93 109 52 101 13 24 55 68 54 69 Iraq 97 97 95 98 41 83 7 18 55 60 56 62 Ireland 99 100 107 103 106 103 26 33 69 74 73 79 Isle of Man Israel 98 99 96 95 95 96 30 40 70 75 73 79 Italy 104 105 112 98 109 99 29 38 69 75 75 81 Jamaica 105 102 119 109 119 108 43 46 66 72 70 77 Japan 104 103 99 102 99 102 39 41 69 77 75 83 Jordan 95 92 94 95 6 21 69 72 Kazakstan 108 106 86 86 47 47 64 74 Kenya 100 101 67 92 48 91 45 46 48 57 52 60 Kiribati 95 56 . 61 Korea, Dem Rep 105 103 46 45 58 67 62 74 Korea, Rep. 99 99 104 100 103 102 32 40 58 68 62 76 Kuwait 76 77 101 76 8 28 64 74 68 79 Kyrgyz Republic 109 104 49 47 63 72 Lao PDR 98 103 66 123 40 92 45 47 39 51 42 54 Latvia 119 114 83 . 82 51 50 66 63 74 75 Lebanon 99 109 130 117 112 114 28 62 68 66 71 Lesotho 109 104 71 90 101 105 39 37 47 57 51 60 Liberia 99 98 75 36 38 39 45 46 48 46 Libya 91 92 110 110 19 21 50 63 53 67 World Development Indicators 1997 II S 1.2 G e n dhea r ensionzs of development Population Gross primary enrollment Female labor force Life expectancy at birth sex ratio Male Female women per % of relevant % of relevant Male Female 100 men age group age group % of total years years 1970 1995 1970 1993 1970 1993 1970 1995 1970 1995 1970 1995 Liechtenstein Lithuania 113 111 95 90 49 48 67 63 75 75 Luxembourg 103 104 112 112 27 37 67 73 74 80 Macao 103 23 39 58 75 62 80 Macedonia, FYR 97 96 88 87 30 41 71 75 Madagascar 103 99 99 75 82 72 45 45 44 56 47 59 Malawi 108 103 84 77 51 49 40 43 41 44 Malaysia 98 98 91 93 84 93 31 37 60 69 63 74 Maldives 89 95 136 133 38 42 51 64 49 63 Mali 104 106 30 38 15 24 47 46 36 48 39 51 Malta 108 103 107 109 106 106 21 27 68 75 72 79 Marshall Islands Martinique 104 105 38 47 66 73 71 80 Mauritania 103 102 20 76 8 62 47 44 41 51 44 54 Mauritius 100 101 94 107 93 106 20 32 60 68 65 75 Mayotte Mexico 100 101 106 114 101 110 19 31 59 69 64 75 Micronesia, Fed Sts 89 95 30 63 66 Moldova 109 78 77 52 42 65 73 Monaco Mongolia 101 98 46 46 52 64 54 66 Morocco 100 100 66 85 36 60 14 35 50 64 53 68 Mozambique 104 104 69 51 49 48 40 45 44 48 Myanmar 100 101 88 78 44 43 47 58 50 61 Namibia 104 101 134 138 39 41 47 55 49 57 Nepal 97 96 44 129 8 87 39 40 43 57 42 56 Netherlands 100 101 101 96 102 99 26 40 71 75 77 81 Netherlands Antilles 101 104 33 42 61 75 67 80 New Caledonia 93 97 125 124 71 75 New Zealand 100 100 Ill 102 109 101 29 44 69 73 75 79 Nicaragua 101 99 79 101 81 105 19 36 52 65 55 70 Niger 104 102 19 35 10 21 45 44 37 44 40 49 Nigeria 103 103 47 105 27 82 37 36 41 51 45 54 Northern Mariana Islands Norway 101 101 85 99 94 99 29 46 71 75 77 81 Oman 98 89 6 87 1 82 6 15 46 68 49 73 Pakistan 93 92 57 80 22 49 9 26 50 62 49 64 Panama 96 97 101 97 25 34 64 71 67 76 Papua New Guinea 92 94 63 80 39 67 42 42 47 56 47 58 Paraguay 100 98 115 114 103 110 21 29 63 67 68 72 Peru 98 101 114 99 22 29 52 65 56 68 Philippines 99 99 33 37 56 64 59 68 Poland 106 104 103 98 99 97 45 46 67 67 74 76 Portugal 111 107 99 122 96 118 25 43 64 72 71 79 Puerto Rico 104 105 27 36 69 72 75 80 Qatar 54 60 11l 92 86 87 4 12 59 70 63 75 R6union 108 104 23 42 59 70 67 79 Romania 104 104 1l 87 113 86 44 44 67 66 71 74 Russian Federation 120 112 107 107 51 49 . 58 72 Rwanda 102 102 76 78 60 76 49 49 43 38 46 40 Sao Tome and Principe 102 66 72 Saudi Arabia 94 81 61 78 29 73 5 13 51 69 54 71 Senegal 100 100 51 67 32 50 42 42 42 49 44 51 Seychelles 95 69 76 Sierra Leone 104 104 40 27 36 36 33 35 36 38 12 World Development Indicators 1997 1.2 Population Gross primary enrollment Female labor force Life expectancy at birth sex ratio Male Female women per % of relevant % of relevant Male Female 100 men age group age group % of total years years 1970 1995 1970 1993 1970 1993 1970 1995 1970 1995 1970 1995 Singapore 95 98 109 101 26 38 65 74 70 79 Slovak Republic 103 105 101 101 41 48 68 76 Slovenia 107 106 97 97 36 46 66 70 73 78 Solomon Islands 89 95 102 87 46 46 62 63 Somalia 102 101 17 5 44 43 39 47 42 50 South Africa 100 101 100 111 99 110 33 37 50 61 56 67 Spain 105 104 121 104 125 105 19 36 70 74 75 81 Sri Lanka 92 100 104 106 94 105 25 35 64 70 66 75 St Kitts and Nevis 116 67 72 St Lucia 110 61 68 64 73 St Vincent and the Grenadines 102 62 69 64 76 Sudan 100 99 47 59 29 41 20 28 42 52 44 55 Suriname 100 104 129 122 22 31 62 66 66 73 Swaziland 103 103 91 123 83 116 33 37 44 57 48 61 Sweden 100 101 93 100 95 100 36 48 72 76 77 81 Switzerland 105 103 . 100 . 102 34 40 70 75 76 82 Syrian Arab Republic 95 97 95 111 59 99 12 26 54 66 57 71 Tajikistan 103 100 . 91 . 88 45 44 60 66 65 66 Tanzania 103 102 41 71 27 69 51 49 44 50 47 52 Thailand 101 100 86 98 79 97 48 46 56 67 61 72 Togo 104 102 98 122 44 81 39 40 43 49 46 52 Tonga . 96 67 72 Trinidad and Tobago 103 104 106 94 107 94 30 36 63 70 68 75 Tunisia 102 98 121 123 79 113 13 30 54 68 55 70 Turkey 97 95 124 107 94 98 38 35 55 66 59 71 Turkmenistan 103 102 46 42 57 . 64 Uganda 102 103 46 99 30 83 48 48 49 44 51 44 Ukraine 121 114 87 . 87 51 49 66 64 74 74 United Arab Emirates 60 58 112 112 71 108 4 13 59 74 63 76 United Kingdom 106 104 104 112 104 113 36 43 69 74 75 79 United States 105 104 107 . 106 37 46 67 74 75 80 Uruguay 101 105 115 109 109 108 26 40 66 70 72 77 Uzbekistan 105 102 80 . 79 48 46 Vanuatu 93 88 105 . 107 63 65 Venezuela 97 98 94 95 94 97 21 33 63 70 68 75 Vietnam 106 103 . 48 49 54 65 57 70 Virgin Islands (U.S.) . 106 72 79 West Bank and Gaza . 98 Western Samoa 93 89 67 71 Yemen, Rep 103 99 38 7 8 29 41 53 42 54 Yugoslavia, Fed Rep 102 101 108 72 103 73 36 42 66 70 70 75 Zaire 106 102 110 78 65 58 45 44 44 47 Zambia 102 102 99 109 80 99 45 45 45 45 48 46 Zimbabwe 101 100 81 123 66 114 44 44 49 56 52 58 World Development Indicators 1997 13 O 1.3 Structural transformation GNP per Labor force Agriculture Investment Trade Central Money and capita In agriculture government quasi money revenue (M2) average annual % % % % % % groath % of GDP of GDP of GDP of GDP of GDP 1970-95 1970 1990 1970 1995 1970 1995 1970 1995 1980 1995 1980 1995 Afghanistan .. 66 52 . 5 . 22 . . 27 Albania . 66 55 56 16 . 52 . 23 . 47 Algeria 0.6 47 26 11 13 37 32 51 57 53 39 American Samoa Andorra Angola . 78 75 12 27 132 Antigua and Barbuda 4.9 4 22 217 . 46 62 Argentina -0.4 16 12 10 6 24 18 10 16 16 . 19 19 Armenia -0.3 27 17 44 9 85 Aruba .. . .. . 51 Australia 1.4 8 5 6 3 27 23 29 40 22 25 36 61 Austria 2.2 15 8 7 2 30 27 61 77 35 36 73 90 Azerbaijan 35 31 27 . 16 66 . . . 9 Bahamas, The 1 7 7 5 . . . 20 16 30 53 Bahrain -2.2 7 2 1 27 191 34 28 40 71 Bangladesh 1 5 81 64 55 31 11 17 21 37 11 18 36 Barbados 1.9 17 7 11 5 26 13 138 96 27 38 59 Belarus . 35 20 13 25 . . . 10 Belgium 1.9 5 3 3 2 23 18 101 143 44 45 45 80 Belize 3.0 40 33 20 26 109 . 26 43 Benin 0.1 81 62 36 34 12 20 50 64 .. 17 25 Bermuda 1.2 Bhutan 5.5 95 40 .. 32 84 9 15 . 28 Bolivia -0 7 55 47 20 24 15 49 47 .. 18 16 45 Bosnia and Herzegovina . 50 11 Botswana 7 3 80 46 33 5 42 25 86 101 34 57 28 26 Brazil 45 23 12 14 21 22 14 15 23 26 11 26 Brunei 13 2 3 Bulgana 0.2 35 14 . 13 21 94 37 Burkina Faso 1 5 92 92 35 34 12 22 23 45 12 14 22 Burundi 0.6 94 92 71 56 5 11 22 43 14 14 20 Cambodia 79 74 51 13 19 14 36 . . 8 Cameroon 1.5 85 70 31 39 16 15 51 46 16 14 21 16 Canada 1 7 8 3 5 22 19 43 71 19 21 45 59 Cape Verde 5.7 46 31 13 45 75 . 53 76 Cayman Islands Central African Republic -1.6 89 80 35 44 19 15 71 46 16 19 21 Chad -0.1 92 81 47 44 18 9 54 46 .. .. 20 14 Channel Islands Chile 1.8 24 19 7 19 27 29 54 32 21 21 34 China 6.9 78 74 34 21 28 40 5 40 . 6 33 92 Colombia 1.9 41 25 25 14 20 20 30 35 12 17 17 19 Comoros -0 6 82 97 39 17 64 14 . 20 Congo 1.7 66 48 18 10 24 27 93 128 35 15 15 Costa Rica 0.7 43 26 23 17 21 25 63 81 18 26 39 32 Cote d'lvoire -1.9 76 60 40 31 22 13 65 76 23 27 26 Croatia .. 51 15 12 14 93 . 45 . 22 Cuba . 30 18 Cyprus 5 5 39 14 5 22 99 21 32 61 90 Czech Republic 17 11 6 25 108 41 . 81 Denmark 1.7 11 6 7 4 26 16 59 64 36 41 43 58 Djibouti 3 12 101 . .. . 71 Dominica 2.5 21 26 109 . 50 61 Dominican Republic 1.4 48 25 23 15 19 20 42 55 14 17 18 24 Ecuador 1.5 51 33 24 12 18 19 33 56 13 16 21 27 14 World Development Indicators 1997 1.3S GNP per Labor force Agriculture Investment Trade Central Money and capita in agriculture government quasi money revenue (M2) average annual % % % % % % growth % of GDP of GDP of GDP of GDP of GDP 1970-95 1970 1990 1970 1995 1970 1995 1970 1995 1980 1995 1980 1995 Egypt, Arab Rep 3 7 52 43 29 20 14 17 33 54 46 41 52 97 El Salvador -1 0 57 36 40 14 13 19 49 55 11 12 28 36 Equatorial Guinea 81 74 50 20 23 81 112 11 Eritrea 85 79 11 20 104 Estonia -1 8 18 14 8 27 160 35 23 Ethiopia 91 80 57 17 39 16a 14 22a 42 Faeroe Islands Fiji 0 9 52 45 29 22 14 100 104 32 50 Finland 1 9 20 8 17 6 30 16 53 68 27 33 40 57 France 1 8 14 5 2 27 18 31 43 40 41 72 64 French Guiana French Polynesia Gabon -2 8 80 61 19 32 26 88 101 36 15 15 Gambia, The 0.2 86 82 33 28 5 21 66 103 23 23 21 23 Georgia -1.9 37 26 67 3 46 Germany 9 4 . 21 46 . 32 62 Ghana -1.2 60 60 47 46 14 19 44 59 7 17 16 15 Greece 1 5 42 23 29 21 28 19 28 57 31 28 50 53 Greenland Grenada 11 32 47 63 77 Guadeloupe 28 7 Guam Guatemala -0 3 62 52 25 13 17 36 47 9 8 20 24 Guinea 92 87 24 . 15 46 9 Guinea-Bissau 0 1 89 85 47 46 30 16 34 48 14 Guyana -2 6 32 22 19 36 23 19 113 159 35 49 56 Haiti -1 3 74 68 44 11 2 31 17 11 24 43 Honduras 0 2 65 40 32 21 21 23 62 80 15 21 25 Hong Kong 5 7 4 1 0 20 35 181 297 . 61 Hungary 11 25 15 8 34 23 63 67 53 43 Iceland 3 3 17 11 24 15 87 70 25 30 20 38 India 2 4 71 64 45 29 17 25 8 27 12 13 35 46 Indonesia 4 7 66 57 45 17 16 38 28 53 21 18 13 Iran, Islamic Rep -2 4 44 41 25 29 39 22 25 54 35 Iraq 47 16 Ireland 2 6 26 14 . 13 79 136 35 37 44 50 Isle of Man Israel 2 0 10 4 25 24 70 69 50 39 15 67 Italy 2 4 19 9 8 3 27 18 33 49 31 40 71 62 Jamaica -0 8 33 24 7 9 32 17 71 145 29 33 44 Japan 3 2 20 7 6 2 39 29 20 17 12 21 83 113 Jordan 28 21 8 26 121 . 27 104 Kazakstan 27 22 12 . 22 69 Kenya 1 0 86 80 33 29 24 19 60 72 22 22 30 38 Kiribati 11 83 Korea, Dem Rep 55 38 Korea, Rep 10 0 49 18 25 7 24 37 37 67 17 20 29 41 Kuwait -3 5 2 1 0 0 12 12 84 104 89 33 78 Kyrgyz Republic 36 32 44 . 16 58 Lao PDR 81 78 52 13 Latvia 01 19 16 9 21 91 . 27 25 Lebanon 5 7 . 29 70 15 118 Lesotho 2 3 43 41 35 10 12 87 65 138 34 51 40 29 Liberia -3.0 81 76 24 22 98 18 Libya -4.8 24 11 2 17 89 35 World Development Indicators 1997 15 *31.3 GNP per Labor force Agriculture Investment Trade Central Money and capita in agriculture government quasi money revenue (M2) average annual % % % % % % growth % of GDP of GDP fGDP of GDP of GDP 1970-95 1970 1990 1970 1995 1970 1995 1970 1995 1980 1995 1980 1995 Liechtenstein Lithuania 31 18 11 19 108 25 23 Luxembourg 3 0 8 4 3 23 162 184 45 47 Macao . . .. . 31 .. 111 Macedonia, FYR 55 22 15 86 Madagascar -2 4 84 84 24 34 10 11 41 54 13 8 18 18 Malawi -0 2 91 95 44 42 26 15 63 69 19 18 15 Malaysia 4 0 54 27 29 13 22 41 80 194 26 25 46 85 Maldives 67 33 14 35 36 43 Mali 0.2 93 93 66 46 16 26 33 60 11 18 20 Malta 6 1 7 3 7 3 33 29 129 198 35 35 114 139 Marshall Islands Martinique 25 7 Mauritania -0.7 85 55 29 27 22 15 74 104 21 19 Mauritius 0 5 34 17 16 9 10 25 85 120 21 21 40 73 Mayotte Mexico 0 9 44 28 12 8 21 15 15 48 15 17 25 31 Micronesia, Fed Sts 43 Moldova 54 33 50 7 78 12 Monaco Mongolia 48 32 . 27 26 Morocco 1 8 58 45 20 14 18 21 39 62 23 38 65 Mozambique -0 2 86 83 33 60 102 Myanmar 12 78 73 38 63 14 12 14 4 16 7 24 Namibia 63 49 14 20 110 35 39 Nepal 1 3 94 95 67 42 6 23 13 60 8 11 22 34 Netherlands 1 5 7 5 3 22 89 99 49 46 67 82 Netherlands Antilles 14 52 New Caledonia New Zealand 0 8 12 10 12 25 24 48 62 34 35 26 78 Nicaragua -5 3 50 28 25 33 18 18 55 76 23 25 24 30 Niger -2.6 93 91 65 39 10 6 29 30 14 13 14 Nigeria -0.9 71 43 41 28 15 20 24 25 Northern Mariana Islands Norway 2 9 12 6 5 28 23b 77 71 37 40 47 56 Oman 3 3 57 48 16 14 17 93 89 38 32 14 31 Pakistan 2 9 59 56 37 26 16 19 22 36 16 19 39 41 Panama 0 7 42 26 11 24 79 27 28 33 68 Papua New Guinea 0 2 86 79 37 26 42 24 72 106 23 22 33 30 Paraguay 21 53 39 32 24 15 23 31 82 11 14 20 26 Peru -11 48 36 19 7 16 17 34 30 17 16 17 17 Philippines 0 6 58 45 30 22 21 23 43 80 14 18 22 45 Poland 1.2 39 27 6 17 53 41 57 32 Portugal 2 5 32 18 28 b 49 66 26 34 70 78 Puerto Rico 0 6 14 4 3 1 29 17 107 Qatar -5 9 10 3 17 67 R4union 37 7 Romania 0.0 49 24 .. 21 26 60 45 30 33 20 Russian Federation 1 3 19 14 7 . 25 44 17 12 Rwanda -0.2 94 92 37 9 13 27 32 13 14 16 S3o Tome and Principe -1 0 . 23 18 50 76 108 Saudi Arabia -2 9 64 20 4 12 20 89 70 . 14 50 Senegal -0 6 83 76 24 20 16 16 59 69 24 27 20 Seychelles 3 3 4 26 129 . 50 29 45 Sierra Leone -1 1 76 67 29 42 16 6 62 40 16 13 19 10 16 World Development Indicators 1997 1.33* GNP per Labor force Agriculture Investment Trade Central Money and capita In agriculture government quasi money revenue (M2) average annual %%% % growth %of GDP of GDP of GOP of GDP of GOP 1970-95 1970 1990 1970 1995 1970 1995 1970 1995 1980 1995 1980 1995 Singapore 5.7 3 0 2 0 39 33 225 .. 25 26 58 83 Slovak Republic .. 19 12 .. 6 .. 28 .. 124 .. . . 63 Slovenia .. 50 5 .. 5 .. 22 .. 113 .. . . 32 Solomon Islands 2.8 80 77 . .. . .. . . . .. 41 27 Somalia -0.3 81 84 59 .. 12 .. 28 .. . 18 South Africa -0.6 31 14 8 5 30 18 48 44 23 27 50 52 Spain 2.0 26 12 .. 3 .. 21 27 47 24 32 75 79 Sri Lanka 3.2 55 49 218 23 19 25 54 83 20 21 28 32 St. Kitts and Nevis 5.2 .. .. 6 .. 39 .. . . 32 69 73 St. Lucia ... ... 11 .. 25 .. 141 25 .. 44 60 St. Vincent and the Grenadines 3.7 .. . . 11 . .. . .. . .. 62 58 Sudan -0.7 77 69 43 .. 14 - 31 . 14 .. 28 20 Suriname 4.3 26 21 7 26 21 23 115 11 . .. 40 23 Swaziland 1.2 64 37 .33 9 19 17 127 186 . .. 28 31 Sweden 1.1 .. . . 2 .. 14 48 77 35 38 Switzerland 1.1 8 6 . .. 28 23 67 68 20 22 107 126 Syrian Arab Republic 1.4 50 34 20 .. 14 .. 39 .. 27 23 41 63 Tajikistan .. 46 41 .. . . 17 .. 228 Tanzania .. 90 84 .. 58 .. 31 .. 96 18 .. 41 31 Thailand 5.2 80 64 26 11 26 43 34 90 14 19 35 74 Togo -1.1 74 66 34 38 15 14 88 65 30 .. 29 29 Tonga ... ... 36 . .. . .. 23 .. 23 32 Trinidad and Tobago -0.1 19 11 5 3 26 14 84 68 43 .. 27 40 Tunisia 2.3 42 28 17 12 21 24 47 93 31 .. 38 44 Turkey 1.7 71 53 40 16 14 25 10 45 18 18 14 25 Turkmenistan .. 38 37 . . . . . . . Uganda .. 90 93 54 50 13 16 41 33 3 .. 13 10 Ukraine .. 31 20 .. 18 .. . . . . . . 0 United Arab Emirates -4.0 9 7 .. 2 . 27 .. 139 . .. 19 54 United Kingdom 1.8 3 2 3 2 20 160 45 57 35 36 United States 1.7 4 3 .. 2 18 16 11 24 20 21 60 59 Uruguay 0.2 19 14 18 9 .. 14 30 41 22 30 31 33 Uzbekistan .. 43 34 .. 33 .. 23 .. 125 .. . Vanuatu .. . .. . . . . . 16 .. 73 118 Venezuela -1.1 26 12 6 5 33 16 38 49 22 19 29 23 Vietnam .. 77 72 .. 28 .. 27 .. 83 .. . Virgin Islands (U.S.) .. .. . .. . .. .. West Bank and Gaza . . . . . . . . . . . Western Samoa .. . .. . . . . . . . 17 Yemen, Rep. .. 70 58 .. 22 .. 12 .. 88 .. 21 .. 47 Yugoslavia, Fed. Rep. .. 50 29 . . . . . . . . . Zaire -4.0 75 68 15 .. 15 .. 34 .. . .. . Zambia -2.6 79 75 11 22 28 12 90 71 25 14 28 13 Zimbabwe -0.3 77 69 15 15 20 22 .. 74 24 .. 29 26 a Inclades Eritrea. b. includes statistical discrepancy. World Development Indicators 1997 17 _______________________________ _ Table 1.2 Table 1.3 Differences ri the t,pportunities and resources avail No single set of indicators can fully descnbe all the TOMB 1I able to me-' and women exist throughout the world, relevant characteristics of an economy. Those The indicators in this table provide an overvsew of oLt are mo-.t prevalent in ponr develnping countries. included here were chosen because they cover a the quality of life in the 209 economies with popula- This pattern begins at an early age: for example. boys broad range of issues and afford reasonably com- tions greater than 30.000 that are inrcluded rn the receive a hil!her share of educationi anti health spend- plate coverage. World Development Inidicators. Except fti pupulaticn ing than girl . do. Such inequalities in tile allocation of For the indicators in this table. as for others in this density. GNP per capita. and the illiteracy iate. all resources r latter- because education, health. and 40ook, differences in definition. collection methods. these indicators appear elsewhere in the book. nutrition art strongly associatea with well-being. eco- timreliness. and the capabilities of reporting agen- Population density is computed from 1995 mii- nomic effici. ncy. and growth. cies may affect accuracy. consistency. and compara- year population estimates divided bv the surfare Girls in m.ally developing countries are allowed less bility. For more information on the indicators see the area of the country or economy. Different results edLication b their families than boys are, as reflected tables indicated: GNP per capita (table 1.1). share of may be obtained depending on whether lancl area or in lower fen ale priniarv school enrolinient and higher labor force in agriculture ttable 4.5). value added in arable land area is used. feniale illite acy rate-s 'see tahle 1.11. They theretore agriculture itable 4.3). gross domestic investment Gross national product rGNP) is the broadest me.i- hatv fewcr eomplominent opportuiities, especially in itables 4.12 and 4.15). exports arid imports of sure of national income (see Definitionst. GNP per the forial sector. Women who work outside the iome goods and services [tables 4.8-4.122. government capita in U.S. dollars is used by the World Bank !o do so in a Idition lo taking on grieling houselhold revenues (table 4.181. and monev supply itable classify countries for analytical purposes and fo chores and :hildbearing and child-rearing responsibili- 4.21). For additional inforimation on national determine borrowinig eligibility. For definitions of the ties. often ailiin prey to the debi'itriting effects of accounts indicators see the introduction to section 4 income groups used in this book. see the Use's undernouris iment and ill health. and the notes to tables 4.1 and 4.2. guide. In caiculatirig GNP in U.S. dollars from GNP Femaile n orbidity anti mortality rates ofteni exceed reported in national currencies. the World Bank fol niale rates. particLrla'ly during early childhood and lows its Atlas conversion method. This involves usirg the reprou:uctive years. Life expiectancy has a three-year average of exchange iates to sirtoothi increased f ir both men and wonten in all regions. the effects of transitory exchange rate fluctuations. BLit wlile i i high incomieu coLlitrnes woomen tend to For further discussioii of thie Atlas inetlicrd see outlive iern tby six to eight years oni average. in low- Statistical methods. income cou itries tire difference is mLich narrower- Relative prices ot goods and services not traded aboLit two ti three vears. on the international market tend to vary substantially This tem. le disacivantlage is also reflected in the from one country to anotther. leading to big differ overall sex atio of the populiation. Although the nat- ences in the relative purchasing power of currencies ural sex rat- at birih of 105 femnles lier 100 males and thus in welfare as measured by GNP per capit,i. inicicatrs ic-niale biological advantage. sex ratios in To capture these differences in the relative purchas- many count res are below 1(K). In dceveloping coun ing power of currenicies over equivalent goods and tfles tins g-nerally iniplies Ihigher feiiiale mortality. services. table 1.1 also shows GNP per capita esti although migration zannot be rulecd out as a tactor. mates that are converted into international dollais Ratios exce, -ding 105 caii be attributed to male migra- using purchasing power parities IPPPsi. For an expla- tion in seart h of jobs or. increasinghN in some parts of nation of PPPs see the notes to table 4.14 the world. tc war losses. Data on sex ratios shotuld be Illiteracy rates are problematic statistics. Literacy interpreted ,ith caution. because it is not possible to and illiteracy are difficult both to define arid in nie.i- isolate imortility aicl migration effects. For more infor sure. The definition here is based on the concept uf t iation on tUese incicators see the tables indicated: -functional- literacy. To IiieaSLiie literacy Lising sex ranio :tanOle 2.li. gross piiirarv enrollment (table such a definition requires census or sample survey 2.8). tenialei as a percentage of thie labor force (table nieasurements under controlled conditions. lii prac- 2.3i. arid lif. expectancy at birth {table 2.14). tice, many countries estimate the number of illiten- For infornmation on other aspects ot gender differ- ate adults from selfreported data or from esti- ences see ables 2.1 lpopulation:. 2 2 ipupulation mates of school completion. Becduse of triese dynamics). i.3 ilabtor force siructuireh. 2.4 (emp!oy- problems comparisons across countries-and eveni rent. 2.9 u Educational attainnient;. 2.10 (gender and over time for one country--shoUld be itiade wit:l edLucationi. -mnd 2.14 arortalityr. See table 2.12 for caution. data oii the sliare o births attended by health staff. For additional information on the other indicators see the notes to the tables indicated: population itable 2.11. people living on less than $1t a day (table 2.5). infant mortality rate itable 2.141. total fertility rate itable 2.2i. and acct!ss to saniltation1 Itable 2.121. 18 World Development Indicators 1997 grovwth rate is compute-d using the least-squares rrv1thoc andt constant 1987 prices. * Labor force In Tabia LI ag1culture is the proportion of the tntal labor force * Population density is the nidyear resident pnpu- uecordf-il as working in agi culture. huniting. forestry. lation dividedl by thi! surface area in square kilorme- .id fishing (ISIC major division 1). e Agriculture is ters. e GNP is the suti of gross value added by all the valne added of the agricultural sf-ctor (ISIC major resident pr iducers pius any taxes lt:ss subsidies) divisioii 1t. * Investment is gross domestic invest- that are no included in the valua: on of OUitpit plus mient. hich comprises gioss add titrs to the fixea net receipt! of prinrary income ite-ployee coinpen- ciapital stock plus net uh*inges in inventories. sation ant prope ty ncoriiu front nnrJesicdent * Trade is the sunm of exports and imports of goods sources. a GNP per capita is the gross national arid services. a Central government revenue product. converted to U.S. dollars u-ring the World includes all revenuLe to th.- central government from Bank Atlas method. divided by the nirdyear popula- laxes aind nonrepayable receipts (otic-r than grants). tion. a GNP per capita In PPP terms is the gross a Money and quasi money (M2) iS tie suni of cur- national prc duct converted to international dollars by- rertcy outside banks arid cdemand deposits other adjustrng fr r purchasing powe r parity. divide d hy the Ifian those of the central Invlernniont. plus the time. midyear po ulation. * Poverty is MiEasLiredi as the savings. anci foreign currency deposits of resident proportion - If the population living on less than S1 a sector!. other than the central government. This mea- day measured at 1985 prices. acilusted for purchas sure of thc money suppiv is commnioy called M2. ing power parity. * Infant mortality rate is the number of leaths if infants under ore year of ago m. per 1.000 yive births in a giverr year. a Total fertilfty rate is the number of children thmt wiuld hi! borim tn Thr inklicators here andt througholt the rest of the a woman if she were to live to thit erd of tier child- borok 1 ave been compiled by World Bank staff from bearing yea-s and bear children at earh age in accor- primarv and secondary sourc:es. For most of the dance witf. prevailing age-slecitic fertility raites. indicators ihown in the tables in this section. the a Adult Illiteracy rate is thu* proportion uif dults scrurcts are cited in the notes tu thit! tables referred aged 15 ar.d above who cannot. we-th undersitanding. to in Ahbout tie dara. Data on sturface area are from read arid v rite a short. simple statitment Oii their lire Food arid AgricultLire Cirganization Isee Data everyday lile. e Access to sanitation refers to the snurres for table 3.1). GNP per capita is estimated share of the population with at loast adequate exc- bv World Bank staff based orn nationial accounts reta dispor-al facilities that can t ff-ctively prevent dtna c:llected by World Bank staff during economic human. arimal. arid insect contacl withl excreta. irrissiolis or reported by riational statistical offices Suitable fa. ilities range tront similpit but protected to othIer international organizations such as the pit latrines to flush toilets with sewerage. OECD. For high-income OECD economies the data come from the OECD. Data on illiteracy rates are TUsb 1.2 stipplimrd by UNESCO and published in its Statistical a Populatiom ax rsatio is the numilber of women per Yesarbiook isee Data souwces for table 2.9). 100 nien iii the population. Differences in this ratio across countrnes reflect differen:es in sex ratios at birth and in patturns of migration and mortality. a Gros primary enrolimant i,s thcr ratio of total enroll- nient, regaidless ol age. to the population of the age group that officially corresponds tn the prinuary level of educatici1. * Female labor force as a perceurtsge of the total is the oroportioFn of c-cornomicnlly active females in the lalior force as dinfirred by nitional authorrites a Life expectancy at birth indri:ates the number of fears a newborn infait wcruld live if pre- vailing patterns of mortality at the title of its birtil were to stI V the same throug0loLlt its life. Table 1.3 * GNP per capita is the gross natiornal product con- verted to i.S. dollars using the World Bank Atlas method. crrvided by the midyear Iopulation. The World Development Indicators 1997 19 -wa,s~~~~~~~~ fiG § ~~~~~~~~~~~~~~~1000 'r' 1D. - 6 35 9 55 64 2.5 90 107 1,000 60 Infant mortality rate, 1970 and 1995 Urban population as a share of the total, 1980 and 1995 per 1,000 live births millions 140 1,706 120 100 1,360 19 1,243 1970 1980 80~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 604 0 \ ~ 1 903 40 9 5 437 ~~~~~~~~~~~~~~~~~~~488 478 583 78~~~38 20 urban D l ena 31 0 East Asia Europe & Latin America Middle East South Asia Sub-Saharan East Asia Europe & Latin Amenca Middle East South Asia Sub-Saharan & Pacific Central Asia & Caribbean & N. Africa Africa & Pacific Central Asia & Caribbean & N. Africa Africa 2 1980 1995 21 29 52 61 78 World Development Indicators 1997 21 World's poor by region, 1993 Illiteracy, 1990 and 1995 percentage of people aged 15 and above 60 Europe & Latin America 50 Central Asia & Caribbean East Asia Middle East 40 & Pacific & N. Africa 30 1990 * South Asia 20 195 Wo,ld Bank Pa .i Crtr~aff na Aia Sbm Sub-Saharan * 0 o tlnate e Africa 0 East Asia Europe Latin South Sub- & Pac2ic & Central Amedca Asia Sahatan Asia & Carib. Africa 22 World Developmnent Indicators 1997 740 690 1980 $1.10 $1.20 5,100 4,600 $3.40 1980 Per capit. enn y u", by regon, 1980 Per capta ergy le, by Income group, Ar bei land, 1994 md 1994 1980 and 1994 _ O v oal mid kg ad oil equivaen thousands 1g of oil equivalenT thouands 3.0 3.0 40 2.5 2.5 1990 1994 30 2.0 2.0 1.5 1.5 20 1.0 1.0 130~~~~~1 Eo.:jEp anMfl ot u- o rm Middle lmoe H~IM lcom E':Aou, ain.k. mf Sb le E~~~~~~~~~~~~~~~~EstAhErp ath M Aiddl Smili &Pacfic A Cen"a America East & AWsa Sahace & Pacnc & Cantul America Es saSwr Asia & Carib. N. Aftti Afric Asia & C01db. N. Attcs Africa 130 10 45 6 World Development Indicators 1997 23 Per cipita GNP, by rehgn, 197595 GNP growth raft In delOping economle, Share of labor force In sgculture, 1970 Index 11975 =100) 1970-95 and 1990 pernt pm 350 EAy 5 As & ~~41370 250 6030* K O *, 300~~~~~~~~~~~~~~~~~~~~~~~~~~~19 250 90~~~~~~~~~~ 200 40 Sut dSnh Adlt=6N t,.i :e*> 0 h20Ai -1 100 50 -2 1975 1900 1985 1990 1995 1970 1975 1900 1905 1990 1996 Low and middle Est Asia SubSehow Nfit. Excludes the ecaKnines of the romie.r Scndt Union. nci fc Auc 24 World Development Indicators 1997 Foreign direct investment as a share of GDP, Trade to GDP ratio, by region, 1980-95 981483 and 1991-93 percent Region 1981-83 1991-93 East Asia & Pacific 0 20 0.60 Middle East & N. Africa Europe & Central Asia 0.00 0.43 Latin America & Canb. 0.32 0649 0.6 Middle East & N. Africa 0 16 0.00 LSubSaharan Africa South Asia 0.00 0.02 Sub-Saharan Africa 0 11 0.o7 0 4 High income 0 29 0.61 Latin America & Canb. 0.2 South Asia 0 1980 1985 1990 1995 44 $1 $4 $2 $5 third World Development Indicators 1997 25 Net foreign direct investment, by region, Net private capital flows, by region, Top 12 recipients of net private capital, 1995 1990 and 199S 1990 and 1995 billions of U S. dollars billions of U S. dollars 50 50 U Share of Economy $ billions total (%) China 44.3 24.1 40 40 Brazil 19.1 10 4 Mexico 13 1 7 1 1995 Malaysia 11 9 6 5 30 30 Indonesia 11.6 6 3 i ; ~ * Thailand 9.1 4 9 * i1 i * Hungary 7 8 4.2 20 1995 i120 _ 111Argentina 7 2 3 9 f iii i _ - - Czech Republic 5.6 3.0 10 1.0 _ ±gs Poland 5 1 2.8 Es _0 Philippines 4.6 2.5 _ s91 * _ _ _ _ _ _ I | ~~~~~~~~~ ~ ~~~~~~~~~~~~~Chile 4.2 2.3 O_ ~ < * o ~ ~ _ _Total 143.6 78.0 East Asla Europe natin Middle South Su9 East Asia Europe Latin Middle South Sub- & Pacific & Central Amnerica East & Asia Saharan & Pacific & Central America East & Asia Saharan Asia & Carnb. N. Africa Afnca Asia & Carib N. Africa Africa 26WrdDvlpen_niaos19 $32 27 1994 $100 $24 Net aid receved per person, 1994 Net ODA flows, 1980-95 U.S. doilars billions of 1994 U.S. dollars 30 60 50 20 40 30 10 20 0.11 11 1 0!lEi 10 East Asia Europe Latin Middle South Sub- i980 1985 1990 1995 & Paciffc & Central Amenca East & Asia Saharan Asia & Canb. N. Africa Africa 12.6 1994 1.0 World Development Indicators 1997 27 t - :~~I4 ~-~~ * The ultimate aim of development is to improve human well-being in a substantial and sustainable way. Human capital development-the product of education and improvements in health and nutrition-is both a part of and a means of achieving this goal The importance of investing in human capital has become clearer in recent years, with increasing evidence on how and to what extent such investments interact with other factors in development as forces for change. This secton allows readers to eval- uate how well different economies are doing in building human capital and extending human welfare Living standards have been improving all over the world. Globally, real GNP per capita has increased by more than 3 percent a year on average since the mid-1960s. But while only the East Asian miracle economies have been able to sustain this (or even higher) income growth for long periods, improvements in social indicators have been sustained in all regions for much of the past 25 years Many developing countries have succeeded in reducing poverty, a few by as much as 50 percent (World Bank 1990). Average infant mortality rates for low- and middle-income countries have declined from 107 per 1,000 live births in 1970 to 60 in 1995; life expectancy at birth increased from an average 55 years to 64 years The world today is healthier, better educated, and better fed than it was 25 years ago. These achievements nevertheless mask vast dispanties across regions and countries. Infant mortality remains above 90 per 1,000 live births in Sub-Saharan Africa and 70 in South Asia, compared with 40 for EastAsia. Average life expectancy at birth is only 52 years in Africa, compared with more than 60 for other regions. Primary school enrollment in some African countries has declined, and secondary school enrollment is only 24 percent, compared with over 50 percent for some other developing regions. And as the world approaches the turn of the century, more than 1.3 billion people are living on less than $1 a day, and another 2 billion are only slightly better off. Most of the poor-about 60 per- cent-live in South Asia and Sub-Saharan Africa, which together account for 14 percent of the aggregate GDP of developing countnes and 3 percent of the world's. Poverty reduction requires action simultaneously to stimulate growth through sound economic policies that promote sustainable, equitable development and to invest heavily in human capital through improvements in education, health, nutrition, and other social services (World Bank 1990). While this section focuses on human cap- ital, sections 4 and 5 look at economic growth and its preconditions. Why does human capital matter? Because the poor's most significant asset is their labor, the most effective way to improve their welfare is to increase their employment opportunities and the productivity of their labor through investments in human capital. Often, the poor are unable to finance such investments. So the challenge is to create an enabling environment and to mobilize resources for human capital investments. Human capital is critical in raising the living standards of the poor. Health care and good nutrition reduce sickness and mortality and improve labor productivity. Literacy and numeracy widen horizons, making it easier for people to learn new skills through- out their working lives, and thus ensure full participation in social and economic life. By raising productivity, investments in education stimulate growth, and by opening eco- nomic opportunities to more people, they help reduce income inequality. In turn, faster World Development Indicators 1997 29 growth and greater equality increase the supply of, and demand for, education. Better education and health enable couples to make more informed decisions about the number and spacing of their chil- dren and about their schooling, and to protect maternal and child health. The improved health of educated people motivates them to make still more investments in their education and health. The relatonship between investments and outcomes is thus mutually reinforcing, justifying investments in human cap- ital on both economic and equity grounds. Developing countries have already made big investments in human capital development, assisted by private and official devel- opment agencies More recently, governments have taken new ini- tiatives to address social issues and identified specific goals, based on agreed targets, to measure progress (see box 1 a in section 1). The poverty of data Many indicators have been proposed to measure progress in building human capital Yet there are continuing problems with the quality of data that cast doubts on the reliability of the indi- cators and raise concerns about their use in decisionmaking (Srinivasan 1994). Data often suffer from conceptual problems, measurement biases and errors, and lack of comparability over time and across countries. The specialized United Nations agen- cies that standardize concepts and methods of data collecton are underfinanced, and government commitment to data collection, including allocation of sufficient human and financial resources, often falls prey to budget cuts. Not surprisingly, population censuses often become the main source of information on most aspects of human capital, sup- plemented by official estimates based on the censuses and sur- veys or broad generalizations. The problem is that censuses are infrequent (usually decennial), have long processing times, and fail to reflect such aspects of human capital development as access to and quahty of social services. Important supplements to population censuses are specialized household surveys that gather information on the level and extent of poverty and on the impact of government policies on poor and vulnerable popula- tions. Household surveys have other advantages. They can be conducted frequently and more cheaply than censuses and are increasingly used to measure living standards. The indicators reported here share many of these shortcom- ings (detailed notes on their quality are presented with each table). With these caveats, the data nevertheless help quantify the consequences of poverty, allow intertemporal and cross- country comparisons, and highlight social problems that need to be resolved. Population and the labor force Worldwide, policymakers have tended to focus on the high pop- ulation growth in many developing countries. Although the fer- tility rate has declined by one birth per woman in the past 15 years, average population growth rates have fallen less (tables 2.1 and 2.2). Between 1980 and 2030 the population of developing countries will more than double-to 7.2 billion, compared with 30 World Development Indicators 1997 1 billhon for high-income countnes-further increasing the is little disagreement on one aspect of the threshold-the developing country share of world population (figure 2a). income needed to buy a minimum standard of living and nutri- Will this population boom depress economic growth and tion-what constitutes minimum basic needs is more subjective. living standards? In the short run an increase in population So the perception of poverty varies by culture, and criteria for means lower per capita income growth. High birth rates and distinguishing the poor from the nonpoor reflect national pri- young populations increase dependency ratios, making it more orities. In general, as countries become richer, their perception difficult to invest in human resources-the key to boosting of what constitutes an acceptable level of consumption changes, labor productivity. But in the long run the larger number of and so does the poverty line. workers may accelerate growth. According to the World Bank's To allow comparisons across countries the World Bank has World Development Report 1995: Workers in an Integrating World, established an international poverty hne based on $1 a day per poor labor outcomes may have little to do with the growth of person in 1985 purchasing power parity prices (table 2.5). labor supply. In the past 30 years growth in the working-age pop- According to this measure, inroads have been made in reducing ulation (15-64) was remarkably similar across regions-ranging poverty, although overall gains have been modest While the pro- between 2.5 percent and 3.2 percent a year-but there were portion of poor in the world's population dechned slightly, from wide differences in GDP growth. In East Asia output growth 30 percent to 29 percent, between 1987 and 1993, the number exceeded expansion of the working-age populaton by about 5 of people living on less than $1 a day increased, from 1.2 billion percentage points a year; in Sub-Saharan Africa growth of the to 1.3 billion (table 2a). working-age population (roughly 3 percent a year) exceeded Where are the world's poor? South Asia is home to a quarter GDP growth. The dilemma remains, however, of what to do with of the world's population but contains 39 percent of its poor and future labor supply when economic growth is stagnant, popu- has the highest aggregate poverty level (43 percent) East Asia lations continue to grow quickly, and most countries restrict accounts for roughly a third of the world's poor, most in China immigration. It usually takes about a generation for a decline and Indochina, with the incidence of poverty showing a modest in fertility to appreciably slow growth in the labor supply. decline between the late 1980s and the early 1990s. And while At current and projected fertility rates, developing countries Sub-Saharan Africa has only 17 percent of the world's poor, the are expected to add about 640 million people to the world's incidence of poverty has remained unchanged at around 39 per- labor force in the next 15 years (table 2.3). The fastest increases cent. Its people remain among the poorest in the world, with are expected in Sub-Saharan Africa and the Middle East and poor access to productive resources and services, few employ- North Africa. In many countries almost all men between the ages ment opportunities, limited access to assets (particularly for of 25 and 54 are in the labor force (World Bank 1995g). The par- women), and inadequate education, health, and water and san- ticipation of women, however, often changes significantly as itation services. development proceeds. Female participation tends to be higher Does economic growth reduce poverty? Analysis of a new when an economy is organized around family-based production World Bank development database finds a strong positive rela- in agriculture, as in many Sub-Saharan African countries, and at tonship between growth and poverty reduction (Ravallion and higher levels of per capita income, when labor market options Chen 1996; and Deininger and Squire 1996). This finding is con- for women increase. Child labor, a sign of poverty, is pervasive firmed by a World Bank research project on India, which esti- in many parts of the world. While child labor may reduce house- mated that a 10 percentage point increase in mean consumption hold poverty, it is always at the expense of children's educaton resulted in a 12-13 percent decline in the proportion of the pop- and well-being. Income uncertainty, household size, and lack of ulation living below the poverty line (Ravallion 1996). schooling opportunities are strongly correlated with child labor. While the relationship between growth and poverty is unam- How is the labor force deployed? In developing countries most biguous, that between growth and inequality is less clear. Income workers are self-employed or work in family enterprises (table 2.4). inequality, with its political and economic overtones, stubbornly As economies grow, however, more work for wages. In middle- income countries better educational opportunities and lower fer- tility have led more women to enter the labor market, and they now Table 2a Population living on less than $1 a day in developing account for a growing share of wage employment. In low-income economies, 1987 and 1993 countries unpaid work and family responsibilities, as well as lack of Share of population Millions % investment in women's education, are strongly associated with Region 1987 1993 1987 1993 their limited participation m the labor force. East Asia and the Pacific 464 0 445 8 28 8 26 0 Europe and Central Asia 2 2 14 5 0 6 3 5 How much poverty is there? Latin America and the Caribbean 91 2 109 6 22 0 23 5 Middle East and North Africa 10 3 10 7 4 7 4 1 Without material assets or human capital, the poor hire out their South Asia 479 9 514 7 45 4 43 1 labor, mostly in unskilled work. Poverty is the result when people Sub-Saharan Africa 179 6 218 6 38 5 39 1 cannot earn enough from their labor to maintain a minimal Total 1,227 1 1.313 9 30 1 29 4 standard of living. An estimate of the number of poor depends, Source World Bank 1996f of course, on the choice of a poverty threshold. And while there World Development Indicators 1997 31 persists in many countries (table 2.6). The queston of whether, imbalance is that almost two-thirds of the world's illiterate and under what conditions, economic growth is associated with adults-565 million-are women (table 2b). changes in inequality has led to much empincal work-and to Why does girls' education matter? Social returns to invest- Kuznets's famous hypothesis that inequality increases with ments in female education are significantly greater than for income at the early stages of development and decreases at similar investments in males, while private returns are the same higher levels of income. Lack of time-series data over a long or slightly higher (Heyneman 1996, Hill and King 1993; and period prevents the tesnng of this hypothesis. However, recent Psacharopoulos 1994). Gender differences in persistence to work at the World Bank suggests that there is no strong rela- grade 4 and in progression to secondary school are marginal nonship between growth and changes in aggregate income in most countries, with rates for girls increasingly exceeding inequality as measured by the Gini coefficient-that is, the Gini those for boys (table 2.9). Girls who enter school are more coefficient appears to change little with changes in income likely than boys to do so because of a strong motivation to (Deimnger and Squire 1996). obtain schooling or their parents' desire that they be edu- cated-and so are more likely to complete their schooling. Big payoffs from investing in education Women are not easily able to translate their educational Investments in education create economic opportunities. The achievements into social and economic gains, however. They poor benefit most from basic education-rates of return are are increasingly concentrated at the lower end of the profes- higher for primary education than for secondary. And devel- sional ladder, often filling vacancies left by men as they move oping countries are spending more on education, particularly to betterjobs (table 2.10). primary educaton (table 2.7). Between 1980 and 1992 spend- ing on prnmary education as a share of GDP increased in Changing needs in health and nutrition roughly four of every 10 countries. The impact of education Along with education, improvements in health status and nutri- spending depends on how and on what it is spent Governments ton directly address the worst aspects of poverty. Access of the spend little on instructional materials, however, even though poor to health services is important both for increasing their they have been shown to have a consistently positive effect on income (illness reduces people's capacity to work) and for rais- student achievement in developing countries (Lockheed, ing living standards even if income remains at poverty levels. Verspoor, and associates 1991). Of the countries for which The public sector has been dominant in health improve- information is available, 90 percent direct less than 5 percent ments-training medical personnel, investing in clinics and hos- of pnmary and secondary education spending to teaching pitals, running subsidy and insurance schemes, and directly materials. providing medical care. Government efforts have helped Because most students in postprimary education come from increase the number of doctors, nurses, and hospital beds better-off families, skewing education spending toward primary throughout the developing world (table 2.11). But in many low- education can increase the access of the poor to education. But income countries private spending on health exceeds public despite increased expenditures on education in recentyears, par- spending, reflecting the inefficiency of the public system (with ticularly primary educaton, many countries stlll suffer from low the distribution of political power explaining much of the allo- enrollment rates (table 2.8). In primary schools low enrollment caton of resources) and ineffective social insurance systems typically reflects underenrollment of the poor, but it also has (World Bank 1993c). The weakness of the health network means gender dimensions, reflectng mainly cultural norms and the that patients seek care in hospitals or from private practitioners. value of female contributions to the household (Schultz 1993; Because the poor have worse access to health care, they gener- and Hill and King 1993). One consequence of this long-standing ally use fewer health services. Table 2b Estimated illiterate population aged 15 and above, 1980 and 1995 millions Female Female Total Female % Total Female % Region or group 1980 1980 1980 1l995 1995 1995 East Asia, including Oceania 276 1 186 3 67 5 209 9 149 5 71 2 Latin America and the Caribbean 44 1 24 7 56 1 42 9 23 4 54 7 Middle East and North Africa 55 8 34 5 61 8 65 5 41 2 41 2 South Asia 345 9 207 2 59 9 415 5 256 1 616 Sub-Saharan Africa 125 9 76 2 60 5 140 5 87 1 62 0 Least developed countries 135 4 81 2 59 9 166 0 101 0 60 8 Developing countries 848 4 530 6 62 5 871 8 556 7 63 9 Developed countries 29 0 20 9 72 0 12 9 7 9 61 6 Note: Some of the increase in the estimated illiterate population from 1980 to 1995 may reflect better reporting The regional groupings are based on the United Nations country classif- cation Bulgaria, the former Czechoslovakia, Romania, and the former Soviet Union are included with developed countries Source UNESCO 1995b 32 World Development Indicators 1997 Under the Health for All by the Year 2000 initiative adopted affects the economy Adult mortality rates are highest in Sub- by the World Health Assembly in 1981, many developing coun- Saharan Africa and South Asia, where poverty is also the worst. tries are taking an important step in reducing inequities by Communicable diseases are still common. AIDS has emerged as emphasizing primary health care, including immunization, san- a serious threat in developing countries, especially among itaton, access to safe water, family planning services, and safe people between the ages of 15 and 54. The rising consumption motherhood ininatives (table 2.12). Even so, much remains to of tobacco in developing countries also adds to ill health. And be done. Malnutrition, especially in women and children, as populations age, health care systems have to cope more with remains a burden. And although the rate of measles immuniza- noncommunicable diseases, such as heart attacks and strokes, tion worldwide is 80 percent, ranging between 60 percent in Sub- which are expensive to treat and can absorb considerable public Saharan Africa and 89 percent in the Middle East and North health care resources for relatively small gains in overall health Afnca, more than one million children are killed by the disease status (tables 2.13 and 2.14). every year. Another 43 million cases occur annually, leaving many of the survivors prey to malnutriuon and other debilitat- The answer requires more than money ing conditions Much has already been done to increase investments in human While most public health efforts have emphasized infant and capital worldwide. WVhere necessary and possible, governments child health, adult health is becoming a new issue for public will also have to effectively mobilize private resources, while con- health policy in developing countries. More than a third of the tinuing to play a major role themselves if progress is to be sus- population in developing countries is between the ages of 15 and tained. But results do not depend on more resources alone; they 60. The loss of an adult income earner not only may affect the also depend on how well resources are used and how successfully family-by pushing the whole household into poverty-it also intersectoral linkages are exploited. World Development Indicators 1997 33 O 2.1 Population Total Average annual Age dependency Population aged Women per 100 men growth rate ratio 60 and above aged 60 and above dependents as % of working- millions % age population % of total 1980 1995 2010 1980-95 1995-2010 1980 1995 1995 2010 1995 2010 Albania 3 3 4 1.3 1 0 0 7 0 6 9 12 119 117 Algeria 19 28 36 2 7 1 7 1 0 0 7 6 7 112 113 Angola 7 11 16 2 9 2 8 0 9 1 0 5 4 121 120 Argentina 28 35 40 1 4 1 0 0 6 0 6 13 14 133 135 Armenia 3 4 4 1.3 0 8 0 6 0 6 11 12 133 145 Australia 15 18 20 1.4 0 8 0.5 0 5 15 18 121 116 Austria 8 8 8 0 4 0 1 0.6 0 5 20 24 156 129 AzerbaUjan 6 8 9 13 1 0 0.7 0 6 9 11 145 161 Bangladesh 87 120 150 22 1 5 10 09 5 6 81 96 Belarus 10 10 10 0 5 0 1 0 5 0 5 17 19 182 181 Belgium 10 10 10 0 2 0 1 0 5 0 5 21 24 136 131 Benin 3 5 8 3 1 2 6 0 9 1 0 4 4 122 121 Bolivia 5 7 10 2 2 2.1 0 9 0 8 6 6 119 122 Bosnia and Herzegovina Botswana 1 1 2 3 2 17 1 0 0 8 4 4 174 167 Brazil 121 159 190 1 8 1 2 0 7 0 6 7 9 115 128 Bulgaria 9 8 8 -0 3 -0 3 0 5 0 5 21 23 125 135 Burkina Faso 7 10 15 2 7 2.6 0 9 1 0 5 4 109 140 Burundi 4 6 9 2 8 2.5 0 9 1.0 4 3 150 143 Cambodia 6 10 14 2 9 2 0 0 9 0 9 4 5 161 162 Cameroon 9 13 20 2 8 2 9 0 9 0 9 5 5 118 119 Canada 25 30 32 1 2 0 6 0 5 0 5 16 20 127 119 Central African Republic 2 3 4 2.3 2 0 0 9 0 9 6 5 132 137 Chad 4 6 9 2.4 2 4 0 8 0 9 6 6 119 119 Chile 11 14 17 1 6 1 1 0 6 0 6 9 13 138 134 China 981 1,200 1,347 1 3 0 8 0 7 0 5 10 12 102 100 Colombia 28 37 45 1 8 1 3 0 8 0 6 8 9 108 127 Congo 2 3 4 3 0 2 6 0 9 10 6 4 146 150 Costa Rica 2 3 4 2.7 1 5 0.7 0 7 7 10 113 114 Cote d'lvoire 8 14 19 3 6 2 2 1.0 10 5 5 94 92 Croatia 5 5 5 0 3 0 0 0 5 0 5 20 24 149 138 Cuba 10 11 12 0 8 0 6 0 7 0.5 12 17 105 113 Czech Republic 10 10 10 0 1 0 0 0 6 0 5 18 22 149 141 Denmark 5 5 5 0 1 0.1 0 5 0 5 19 23 131 124 Dominican Republic 6 8 9 2 1 1 3 0 8 0 6 6 9 104 119 Ecuador 8 11 15 2 4 1 7 0 9 0 7 6 8 114 120 Egypt,ArabRep 41 58 73 23 1 6 08 07 6 7 118 111 El Salvador 5 6 8 1 4 2 0 1 0 0 8 6 6 132 146 Eritrea Estonia 1 1 1 0 0 -0 5 0 5 0 5 19 23 175 174 Ethiopia 38 56 86 2.7 2.8 1.0 1.0 4 4 126 113 Finland 5 5 5 0.4 0 2 0 5 0 5 19 24 154 132 France 54 58 60 0 5 0 3 0.6 0 5 20 23 141 131 Gabon 1 1 2 3 0 2 3 0 6 0 8 9 8 121 118 Gambia, The 1 1 2 3 7 2 1 0 8 0 8 5 5 108 115 Georgia 5 5 6 0.4 01 0 5 0 5 17 20 157 165 Germany 78 82 82 0.3 0 0 0.5 0.5 20 25 153 125 Ghana 11 17 25 31 2 4 0.9 0 9 5 5 117 119 Greece 10 10 11 0 5 0 2 0 6 0 5 22 25 121 124 Guatemala 7 11 15 29 2.5 10 09 5 5 110 118 Guinea 4 7 10 2 6 2 8 0 9 1 0 4 4 110 98 Guinea-Bissau 1 1 1 1 9 2 1 0 8 0 9 5 4 126 135 Haiti 5 7 9 19 1 3 0 8 0 8 6 6 123 129 Honduras 4 6 9 3 2 2.5 10 0 9 5 5 111 117 Hong Kong 5 6 6 1 4 0.2 0 5 0 4 14 19 104 98 34 World Development Indicators 1997 2.1 0 Total Average annual Age dependency Population aged Women per 100 men growth rate ratio 60 and above aged 60 and above dependents as % of working- millions % age population % of total 1980 1995 2010 1980-95 1995-2010 1980 1995 1995 2010 1995 2010 Hungary 11 10 10 -03 -02 05 05 19 21 150 150 India 687 929 1,127 20 13 07 0.7 8 9 101 104 Indonesia 148 193 235 18 1 3 08 06 7 8 113 117 Iran, Islamic Rep 39 64 91 3 3 2 4 0 9 0 9 6 6 92 100 Iraq 13 20 31 2 9 3 0 0.9 0 9 4 5 106 107 Ireland 3 4 4 0 4 0 7 0 7 0 5 15 18 123 119 Israel 4 6 7 24 1 4 07 06 11 13 122 116 Italy 56 57 56 01 -01 0.5 0 5 22 27 134 130 Jamaica 2 3 3 1 1 10 0.9 0 6 9 10 120 125 Japan 117 125 128 0 5 01 0.5 0 4 20 29 129 123 Jordan 2 4 6 4 4 2 4 1.1 0 8 4 6 77 97 Kazakstan 15 17 18 0 7 0 7 0.6 0 6 11 13 160 165 Kenya 17 27 37 3 2 2 2 1 1 10 4 4 113 116 Korea, Dem Rep 18 24 29 1 8 13 0 8 0 5 7 10 178 138 Korea, Rep 38 45 50 1 1 0 8 0 6 0 4 9 14 146 138 Kuwait 1 2 2 13 2 6 0 7 0 6 4 8 69 97 Kyrgyz Republic 4 5 5 1 5 1 1 0 8 0 8 9 9 152 156 Lao PDR 3 5 7 2 8 2 8 0.8 0 9 5 5 120 115 Latvia 3 3 2 -01 -0 7 0 5 0 5 20 24 180 184 Lebanon 3 4 5 2 3 1 3 0 8 0 7 8 8 120 136 Lesotho 1 2 3 2 5 21 0 8 0 8 6 7 129 120 Libya 3 5 9 3.8 3 2 1 0 0 9 4 5 81 83 Lithuania 3 4 4 0.6 -0 1 0 5 0 5 17 20 171 173 Macedonia, FYR 2 2 2 0.8 0 8 0 6 0 5 13 17 120 119 Madagascar 9 14 21 3.0 2 8 0 9 0 9 5 5 117 116 Malawi 6 10 14 3.1 24 10 10 4 4 119 107 Malaysia 14 20 26 2.5 18 08 07 6 8 116 115 Mali 7 10 15 2 6 3 0 1 0 10 4 4 130 132 Mauritania 2 2 3 2.6 2 3 0 9 0 9 5 5 125 118 Mauritius 1 1 1 1 0 10 06 05 8 11 124 129 Mexico 67 92 114 21 1 5 0 9 0 7 6 8 124 130 Moldova 4 4 5 0 5 0 3 0 5 0 6 14 16 156 162 Mongolia 2 2 3 2 6 1 8 0 9 0 7 6 7 119 114 Morocco 19 27 34 21 1 6 0 9 0 7 6 7 112 126 Mozambique 12 16 23 1 9 2 4 0 9 0 9 5 5 123 121 Myanmar 34 45 57 1.9 16 0 8 0 7 7 7 116 120 Namibia 1 2 2 2 7 2 3 0 9 0 9 6 6 121 122 Nepal 15 21 30 2 5 2 2 0 9 0 8 5 6 92 102 Netherlands 14 15 16 0.6 0 3 0 5 0 5 18 23 131 118 New Zealand 3 4 4 1.0 0 8 0 6 0 5 15 17 122 120 Nicaragua 3 4 6 30 24 1 0 1 0 5 5 113 111 Niger 6 9 15 33 32 10 10 4 4 121 130 Nigeria 71 111 164 3 0 2 6 0 9 0 9 4 5 131 122 Norway 4 4 5 0 4 0 3 0.6 0 5 20 22 130 119 Oman 1 2 4 4 6 38 0.9 10 4 5 107 85 Pakistan 83 130 190 3 0 2 5 0.9 0 9 5 5 96 101 Panama 2 3 3 2 0 13 0.8 0 6 7 10 103 107 Papua NewGuinea 3 4 6 2 2 2 0 0.8 0 7 5 6 97 102 Paraguay 3 5 7 2 9 21 0.8 0 8 5 7 123 114 Peru 17 24 30 21 1 6 0.8 06 6 8 118 115 Philippines 48 69 90 2 3 18 0.8 0 7 5 7 116 117 Poland 36 39 40 0 5 0 3 0 9 0 5 16 18 149 151 Portugal 10 10 10 01 0 1 0.6 0 5 19 21 143 147 Puerto Rico 3 4 4 1 0 0 8 0.7 0 6 15 17 139 156 Romania 22 23 22 01 -0 1 0.6 0 5 17 20 131 135 Russian Federation 139 148 145 0.4 -0 1 0 5 0.5 16 17 194 177 World Development Indicators 1997 35 O 2.1 Total Average annual Age dependency Population aged Women per 100 men growth rate ratio 60 and above aged 60 and above dependents as % of working- millions % age population % of lotal 1980 1995 2010 1980-95 1995-2010 1980 1995 1995 2010 1995 2010 Rwanda 5 6 11 14 3 5 10 1 1 4 3 122 131 Saudi Arabia 9 19 31 4 7 3 3 0 9 09 4 5 89 Senegal 6 8 12 2 8 2 5 0 9 1 0 4 4 116 112 Sierra Leone 3 4 6 1 7 2 7 0 9 0 9 4 3 130 140 Singapore 2 3 3 1 8 1 0 0 5 0 4 9 15 113 112 Slovak Republic 5 5 6 0 5 0 2 0.6 0 5 15 17 144 148 Slovenia 2 2 2 0 3 0 0 0 5 0 4 17 22 161 138 South Africa 29 41 55 2 3 1 9 0.8 0 7 7 8 119 132 Spain 37 39 39 0 3 0 0 0 6 0 5 20 23 133 136 Sri Lanka 15 18 21 14 1 1 0 7 0 6 8 12 103 116 Sudan 19 27 37 24 22 09 09 5 5 118 116 Sweden 8 9 9 0 4 0 2 0 6 0 6 22 25 126 119 Switzerland 6 7 7 0 7 0 2 0 5 0 5 20 26 135 126 SyrianArab Republic 9 14 21 3 2 2 7 1 1 1 0 4 5 107 120 Tajikistan 4 6 8 2 6 19 0 9 0 9 7 6 134 127 Tanzania 19 30 44 3 1 2 6 10 0 9 4 4 120 114 Thailand 47 58 65 15 0 7 0 8 0 5 7 10 123 122 Togo 3 4 6 3 0 2 7 0 9 10 5 5 122 125 Trinidad and Tobago 1 1 1 12 1 0 0 7 0 6 8 11 121 125 Tunisia 6 9 11 2 3 1 5 0 8 0 6 7 8 101 116 Turkey 44 61 75 2 1 1 4 0 8 0 6 8 10 103 110 Turkmenistan 3 5 6 3 0 2 3 0 8 0 7 6 6 145 138 Uganda 13 19 28 2 7 2 6 1 0 11 4 2 108 110 Ukraine 50 52 50 0 2 -0.2 0 5 0 5 20 22 175 169 United Arab Emirates 1 2 3 5 7 2 2 0 4 0 5 3 10 48 United Kingdom 56 59 60 0 3 0 2 0 6 0 5 21 23 132 124 United States 228 263 297 1 0 0 8 0 5 0 5 16 19 138 128 Uruguay 3 3 3 0 6 0 6 0 6 0 6 17 18 131 146 Uzbekistan 16 23 31 2 4 2 0 0 9 0 8 7 7 138 139 Venezuela 15 22 28 2 5 17 0.8 0 7 6 9 117 120 Vietnam 54 73 93 21 16 0 9 0 7 7 7 133 140 West Bank and Gaza 1 2 4 3 9 4 1 0 9 0 9 4 4 102 116 Yemen, Rep 9 15 25 3 9 3 3 1:1 10 4 4 117 139 Yugoslavia, Fed Rep 10 11 11 0 7 0 2 0 5 0 5 16 19 127 125 Zaire 27 44 3 2 10 Zambia 6 9 12 3 0 21 11 10 4 3 100 106 Zimbabwe 7 11 14 30 16 10 08 4 5 111 105 Low income 2,378 t 3,180 t 3,971 t 19w 15w 08w 0 7 w 8w 9 w 104 w 105 w Excl China&lndia 709t 1050t 1,479t 26w 23w 09w 09w 5w 5w 115w 117w Middle income 1,236 t 1,591 t 1,916 t 17 w 1 2 w 0 7 w 06w 10 w 12 w 137 w 131 w Lower middle income 905 t 1,153 t 1,378 t 1 6 w 1 2 w 0 7 w 06w 9 w 10 w 135 w 133 w Upper middle income 331 t 438 t 539 t 1 9 w 14w 0 7 w 06w 13 w 15 w 140 w 127 w Low & middle income 3,614 t 4,771 t 5,887 t 19w 14w 08w 06w 8 w 9 w 116 w 115 w East Asia & Pacific 1,360 t 1,706 t 1,974 t 1 5 w low 0 7 w 05w 9 w 11w 106 w 106 w Europe & Central Asia 437 t 488 t 511 t 0 7 w 03w 06w 05w 15 w 17 w 159 w 142 w Latin America & Carib 358 t 478 t 587 t 19w 14w 08w 06w 8 w 9 w 119 w 125 w Middle East& N Africa 175 t 272 t 383 t 30w 2 3 w 09w 08w 5 w 6 w 99 w 102 w South Asia 903t 1,243 t 1,572 t 21 w 16 w 08w 0 7 w 7w 8w 99 w 104w Sub-Saharan Africa 381 t 583 t 860 t 28w 26w 09w 09w 5 w 5 w 122 w 120 w High income 816t 902t 963t 07w 04w 05w 05w 18w 22w 133w 125w 36 World Development Indicators 1997 Population and development I_ Population growth rates have started to decline in many countries, but the absolute Population estimates are based on national cen- * Total population is based on the de facto definition numbers continue to increase, and more suses Precensus and postcensus estimates are of population, which counts all residents regardless of people will be added to the world's population interpolations or projections The international com- legal status or citizenship Refugees not permanently in the 1990s than in any previous decade The parability of population indicators is limited by dif- settled in the country of asylum are generally consid- world's population was estimated to be 5.7 bil- ferences in the concepts, definitions, data collection ered to be part of the population of their country of lion in mid-1995-and growing by 230,000 procedures, and estimation methods used by origin Theindicatorsshownaremidyearestimatesfor people a day national statistical agencies and other organizations 1980 and 1995 and projections for 2010 * Average Sub-Saharan Africa is projected to grow at that collect the data In addition, the frequency and annual growth rate is calculated as the exponential 2 6 percent a year, Europe at a barely notice- quality of coverage of population censuses vary by change for the period indicated See Statistical meth- able 0 1 percent a year Such regional dis- country and region ods for more information * Age dependency ratio is parities will gradually change the relative Of the 148 economies here, 117 conducted a calculatedastheratioofdependents-thepopulation distribution of people, with fast-growing conti- census between 1987 and 1995 The proportion is under age 15 and above age 65-to the working-age nents dramatically increasing their share of 80 percent for high-income countries and 86 percent population-those aged 15-64 * Population aged the global population for low- and middle-income countries The recentness 60 and above represents the proportion of the popu- In low-income economies more than a third of a census, along with the availability of comple- lation that is aged 60 and above * Women per 100 of the population is under age 15, compared mentary data from surveys or registration systems, is men aged 60 and above is the ratio of women to men with less than a fifth in high-income economies one of the many objective means forjudging the qual- in that age group In high-income economies there are roughly ity of demographic data (see Pnrmary data documen- two people of working age to support each tation for the most recent census or survey year and l person who is either too young or too old to for registration completion status) work In low-income economies this ratio is For developing countries that lack recent census- Population estimates are based population data, population figures are esti- produced by the World mates provided by national statistical offices or the Bank's Human Develop- United Nations Population Division Population pro- ment and International Nine of every 10 people added to jections require fertility, mortality, and net migration Economics Departments in the world's population over the next estimates based on demographic data collected consultation with World from sample surveys, some of which may be small t Bank countrydepartments 15 years will be in developing in size or have limited coverage These estimates Important inputs to the economies are the product of demographic modeling and are - - - World Bank's demographic susceptible to biases and errors due to shortcom- work come from the following sources around 1.5 to 1 because of the large popula- ings of the model and the data * Population censuses tion under 15 But the transition to lower pop- The governmental and political climate also affects * United Nations Department of Economic and Social ulation growth also poses social and economic the quality of offcial demographic data The trust and Information and Policy Analysis, World Population problems. As growth slows, the average age of cooperation of the public, the government's commit Prospects The 1996 Edition and Population and Vital the population rises, and the proportion of ment to full and accurate enumeration, the confiden- Statistics Report elderly people eventually increases. Many low- tiality and protection against misuse accorded to * Eurostat, Demographic Statistics growth economies already face crises in their census data, and the independence of census agen- * Council of Europe, Recent Demographic pension and social security systems. cies from unreasonable political influence all affect Developments in Europe and North Amenca (1995) the quality and reliability of the data * U S Bureau of the Census, World Population The population projections here are based on World Profile 1996 Bank staff estimates using the cohort component * Projections are based on the method discussed in method Mortality, fertility, and net migration are pro- Bos and others, World Population Projections jected separately by age-sex group and applied to the 1994-95 1990 base year age-sex structure For countries in which fertility has begun to decline to replacement level (countries making the "fertility transition"), this trend is assumed to continue For countries in which this event has not yet occurred, the current fertility rate is assumed to decline atthe average rate of coun- tries making the fertility transition Countries with below-replacement fertility are assumed to have con- stant fertility rates in 1995-2000 and to regain replacement fertility by 2030 World Development Indicators 1997 37 O 2.2 Population dynamics Crude death Crude birth Total fertility Contraceptive Population rate rate rate prevalence momentum rate % of per 1,000 per 1,000 births women people people per woman 15-49 1980 1995 1980 1995 1980 1995 1989-95 1995 Albania 6 6 29 21 3 6 2 6 1 5 Population momentum Algeria 12 5 42 26 6 7 3.5 51 1 6 Over the next several decades the popula- Angola 23 19 50 49 6 9 6.9 1 5 tions of low- and middle-income countries will Argentina 9 8 24 20 3.3 2 7 1.4 continue to grow. The rates of growth will Armenia 6 7 23 14 2.3 1 8 1.3 decline, but the absolute increases will be Australia 7 7 15 15 1 9 19 1.2 large-and accompanied by substantial shifts Austria 12 :10 12 1 1 6 1 5 1 0 in the age structure Even when fertility Azerbaijan 7 7 25 21 3 2 2 3 14 reaches the replacement level of about two Bangladesh 18 10 44 28 6 1 3 5 45 1 6 Belarus 10 12 16 11 2 0 14 1 1 children per couple, the number of births will Belgium 12 11 13 12 1 7 1 6 10 remain high--and population growth will not Benin 19 15 49 43 6 5 60 1 6 stop for several decades. This phenomenon, Bolivia 15 10 39 35 5.5 4 5 45 1 6 called "population momentum," is a facet of Bosnia and Herzegovina . the youthful age structures typical of popula- Botswana 14 12 48 34 6 7 4 4 1 7 tions of developing countries. It occurs Brazil 9 7 31 21 3 9 2 4 1 5 because large cohorts born in previous years Bulgaria 11 13 15 10 2 1 1.2 . 10 move through the reproductive ages, generat- Burkina Faso 20 18 47 46 7 5 6.7 8 1 5 Burundi 18 17 46 44 6.8 6 5 * 1.5 ing more births than are offset by deaths in Cambodia 27 13 39 40 4.7 4 7 1.5 the smaller, older cohorts. Here populaton Cameroon 15 11 47 41 6 5 5.7 16 1 5 momentum is measured as the ratio of the Canada 7 7 15 13 1 7 1.7 11 population when zero growth has been Central African Republic 19 17 43 38 5 8 5.1 15 1 4 achieved to the population in 1995, assuming Chad 22 18 44 43 5.9 5 9 1.4 that fertility is at replacement level in 1995 Chile 7 6 24 20 2.8 2 3 1.4 and remains at that level China 6 7 18 17 2 5 1.9 83 1 3 Because of population momentum, the full Colombia 7 7 30 23 3 8 2 8 72 1 5 effect of lower fertility on the growth and age Congo 16 16 46 47 6 2 6 0 ±5 structure of a population takes several Costa Rica 4 4 30 25 3 7 2 8 75 1 7 decade of a population pyral C6te d'lvoire 16 12 51 37 7 4 5 3 16 decades to be felt As the population pyra- Croatia 11 11 1 5 10 mids for low-income countries show, before Cuba 6 7 14 14 2 0 1 7 1 3 the smaller birth cohorts born recently make Czech Republic 13 11 15 10 2 1 1 3 69 10 their way through the age structures, the Denmark 11 12 11 13 1 5 18 . 10 larger birth cohorts from the past will mean Dominican Republic 7 5 33 24 4 2 2 9 56 1 6 large increases in the number of women of Ecuador 9 6 36 27 50 32 57 1 6 reproductive age (figure 2 2a). In high-income Egypt, Arab Rep 13 8 39 26 5 1 3 4 47 1 5 countries, where fertility rates are well below El Salvador 11 6 39 30 5 3 3 7 53 1 7 replacement level and age structures are Entrea Estonia 12 14 15 10 2 0 1 3 1 0 older, population will Increase much less Ethiopia 20 17 47 47 6 6 7.0 4 1 5 A longer-term effect of momentum is the Finland 9 10 13 13 1 6 1.8 . 1 0 large increase in absolute population size pro- France 10 9 15 12 1 9 1 7 1 1 jected for developing regions during the next Gabon 18 15 33 39 4 5 5 2 1 4 century In China, where replacement fertility Gambia, The 24 18 48 41 6.5 5 3 12 1 4 was reached around 1990, the population is Georgia 9 9 18 11 2.3 1 9 1.1 expected to grow by another 400 million people Germany 12 11 11 9 1.6 1 2 0 9 before stabilizing. In India the combination of Ghana 15 10 45 37 6 5 5.1 20 1 6 Greece 9 9 15 10 2 2 1.4 1 1 above-replacement fertility and momentum is Guatemala 11 7 43 35 6.2 4 7 31 1 7 projected to double Its current population, Guinea 24 20 46 48 6 1 6 5 1.4 which will surpass China's in 50 years. Guinea-Bissau 25 25 43 45 6 0 6 0 1 3 In low- and middle-income economies Haiti 15 12 37 35 59 44 18 15 slightly more than 85 percent of the projected Honduras 10 6 43 35 65 46 47 1 7 increase between 1995 and 2035 is from Hong Kong 5 5 17 11 2 0 1 2 1 1 population momentum and mortality decline-and the rest, from fertility above 38 World Development Indicators 1997 2.2 0 Crude death Crude birth Total fertility Contraceptive Population rate rate rate prevalence momentum rate % Of per 1,000 per 1,000 births women people people per woman 15-49 1980 1995 1980 1995 1980 1995 1989-95 1995 opopulation Hungary 14 14 14 11 19 1 6 ., 1.0 Figure 2.2a Composition of Induatia by age and sex in low- and high-income India 13 9 35 26 5 0 3 2 43 1 4 economies, 1995 and 2025 Indonesia 12 8 34 23 4.3 2 7 55 1.4 Iran, Islamic Rep 11 6 44 32 6 1 4 5 . 17 percent Iraq 9 8 41 38 6 4 5 4 . 1.7 Ireland 10 9 22 14 3 2 19 60 1 3 Israel 7 6 24 20 32 24 1.5 1995 75+ Italy 10 10 11 9 16 12 1.0 3 70-74 _6es-39 Jamaica 7 6 28 22 3 7 2 4 67 1.6 6 80-64 _ 55-59 Japan 6 7 14 10 1 8 1 5 . 10 5-549 Jordan 5 . 31 6.8 4 8 35 1.8 40-44 Kazakstan 8 9 24 18 2 9 2 3 59 1.3 35-3S Kenya 13 9 51 35 7 8 4.7 33 1.7 2_25 29 Korea, Dem Rep 6 6 22 22 3 0 2 2 1.4 20-24 1_49 _Korea, Rep 6 6 22 16 2 6 1 8 79 1.2 s-a _ Kuwait 4 3 37 22 5 3 3 0 . 1 5 _-4 Kyrgyz Republic 9 8 30 25 4 1 3.3 . 1.5 2025 75+ _ Lao PDR 20 14 45 44 6 7 6 5 15 70-74 Latvia 13 16 15 9 2 0 1 3 0 9 _60-64 _ Lebanon 9 8 30 26 4 0 2 8 . 15 55-59 Lesotho 15 11 41 33 5.6 46 23 15 _ s45-4 Libya 12 7 46 41 7 3 6.1 . 17 35-39 Lithuania 10 12 16 11 2 0 15 10 30-34 Macedonia, FYR 7 7 21 16 2 5 2 2 1.2 25-29 20-24 Madagascar 16 11 46 41 6.5 5 8 17 1 5 i5-1w 10-14 Malawi 23 20 57 47 7.6 6 6 13 1.5 0-4 _ Malaysia 6 5 31 26 4.2 3 4 16 6 4 2 0 0 2 4 6 Mali 22 17 49 49 7.1 6 8 1.6 Men Women Mauritania 19 14 43 38 6.3 5 2 4 15 Mauritius 6 7 24 19 2 7 2.2 75 13 Mexico 7 5 33 26 4.5 30 . 1 7 1995 75+ Moldova 10 11 20 14 2.4 2.0 1 2 70-74 Mongolia 11 7 38 27 54 34 . 1 6 60-64 Morocco 12 7 38 27 5.4 3 4 50 1 6 55&-5 Mozambique 20 18 46 44 6.5 6.2 1 5 50-54 4t5-4 Myanmar 14 10 36 28 5.1 3.4 15 40-44 _ 35-39 Namibia 14 12 41 37 59 5.0 29 15 _325-34 Nepal 18 12 44 36 6 4 5.3 23 1 4 25-29 20-24 _ Netherlands 8 9 13 12 1 6 1.6 1 0 1.0-14 New Zealand 9 8 16 16 2 1 21 1.2 - _ -8 0_ Nicaragua 11 6 45 33 6 2 4 1 44 1.8 0-4- Niger 23 19 51 52 7.4 7 4 4 1 5 2 025 75+-= _ Nigeria 18 13 50 42 6.9 5 5 6 1 6 e5-e9 Norway 10 10 12 14 1.7 1 9 11 I5s-s Oman 10 4 45 44 9.9 7 0 0 1 7 50-5s4 Pakistan 15 8 47 38 7.0 5 2 12 1 6 - 45-49 - 40-44 Panama 6 5 29 23 3.7 2 7 1.6 3 5-39 - 30-34 Papua New Guinea 14 10 37 33 5.7 48 14 25-29 Paraguay 7 5 36 31 4.8 4.0 48 1 6 -20-24 I [5-X9 _ Peru 11 6 35 26 4 5 3 1 59 1 5 _ 5_9 = Philippines 9 7 35 29 4.8 3 7 40 1.5 - 0-4 Poland 10 10 19 13 2 3 1 6 1.1 6 4 2 0 0 2 4 6 Portugal 10 10 16 11 2 2 1 4 . 11 Men Women Puerto Rico 6 8 23 17 2 6 2.1 1 3 Source: World Bank staff estimates Romania 10 12 18 11 2 4 1.4 57 1 1 Russian Federation 11 15 16 9 1 9 1 4 10 World Development Indicators 1997 39 022.2 Crude death Crude birth Total fertility Contraceptive Population replacement level (figure 2 2b). Regional dis- rate rate rate prevalence momentum parities among developing countries remain rate Slightly less than half the increase in popula- % of tion growth in Sub-Saharan Africa is from high per 1,000 per 1,000 births women people people per woman 15-49 fertility (figure 2 2c), compared with only 7 1980 1995 1980 1995 1980 1995 1989-95 1995 percent in Asia (figure 2.2d). Rwanda 19 22 51 41 8 3 6.2 21 1.5 In Europe and Central Asia fertility is below Saudi Arabia 9 5 43 36 7 3 6 2 1 6 replacement level In that region the increase Senegal 20 14 46 40 6 7 5 7 7 1 4 Sierra Leone 29 29 49 48 6 5 6.5 14 Singapore 5 5 17 16 17 1 7 i2 Slovak Republic 10 10 19 12 2 3 1 5 1 1 Slovenia 10 10 15 10 2 1 1 3 1 0 b South Africa 12 8 36 30 4 9 3 9 15 Spain 8 9 15 10 2 2 1 2 1 1 8 Sri Lanka 6 6 28 i9 3.5 2 3 1 4 Sudan 17 12 45 35 6 5 4.8 9 1 5 7 Sweden 11 10 12 13 1 7 1 7 1 16 Switzerland 9 9 12 12 1 6 1 5 1 0 Syrian Arab Repubiic 9 5 46 39 7 4 4 8 i 8 5 momentum Tajikistan 8 7 37 28 5 6 4 2 1 7 Tanzania 15 14 47 42 6 7 5 8 20 1 5 4 Thailand 8 6 28 17 3 5 1 8 1 4 1990 2005 2020 2035 Togo 16 15 45 44 6 6 6 4 1 6 Source: World Bank staff estimates Trinidad and Tobago 7 6 29 19 3 3 2 1 14 Tunisia 9 6 35 24 5 2 2 9 1 5 Turkey 10 7 32 23 4 3 2 7 63 1 5 Turkmenistan 8 7 34 31 4 9 3 8 16 6 Uganda 18 19 49 49 7 2 6 7 15 Ukraine 11 14 15 10 2 0 1 5 1 0 United Arab Emirates 5 3 30 20 5 4 3 6 1 2 United Kingdom 12 11 13 13 1 9 1 7 1 0 2 0 United States 9 8 16 15 1 8 2 1 1 2 Uruguay 10 10 19 16 2 7 2 2 1 2 1 5 Uzbekistan 8 6 34 29 4 8 3 7 1 7 1 0 Venezuela 6 5 33 25 41 31 1 6 Vietnam 8 7 36 26 5.0 3 1 49 1 6 0 5 _ a / West Bank and Gaza 6 45 6 2 Populaton momentum Yemen, Rep 19 13 53 48 7 9 7 4 10 1 6 0 Yugoslavia, Fed Rep 10 10 18 14 2 3 1 9 1 1 1990 2005 2020 2035 Zaire 17 48 6 6 . :16 Zaire 17 48 6 6 1 6 ~~~~~~~~~~~~~~~~~~~~~~~Source: World Bank staff estimates Zambia 15 18 50 45 7 0 5 7 15 15 Zimbabwe 13 10 49 31 68 38 48 1 6 ; ; , i~~~~~ I; ^ cr:. FT i' _ - Lowincome 13w 10w 31w 26w 43w 32w 14w Excl China& India 17 w 13 w 45w 37 w 6 3 w 50w 1 5 w Middle income 10 w 8 w 29w 22 w 38w 30w 1.4 w Lower middle income lO w 8 w 28w 22 w 3 7 w 30w i4w Upper middle income 9 w 7 w 30 w 23 w 39w 29w i 5 w Ferti Low & middle income 12 w 9 w 30 w 25 w 41w 31w 1.4 w 4 East Asia & Pacific 8 w 7 w 22w 19 w 31w 22w 13w l'opaiation Europe & Central Asia 10 w 11 w 20w 14 w 2 5 w 20w 1 1 w entUm Latin America & Carib 8 w 7 w 31 w 24 w 41w 28w 1 5 w 3 Middle East& N Africa 12 w 7 w 41 w 32 w 61 w 42w 16w SouthAsia 14w 9w 37w 28w 53w 35w 14w 2 Sub-Saharan Africa 18 w 15 w 47 w 41 w 6 7 w 5 7 w 1 5 w 1990 2005 2020 2035 High income 9w 8w 15w 13w 19w 17w i11w Source. World Bank staff estimates 40 World Development Indicators 1997 2.22 in population results from the dynamics of the ._ __1_ _ current age structure, which produces more births than deaths (figure 2.2g). Population dynamics indicators, or vital rates, are * Crude death rate and crude birth rate indicate based on data derived from registration systems, the number of deaths and the number of live births censuses, and sample surveys conducted by occurring during the year, per 1.000 midyear popu- national statistical offices As with the basic demo- lation The difference between the crude death and graphic data in table 2 1, international comparisons birth rates is the rate of natural increase 0 Total are limited by differences in definitions, data collec- fertility rate represents the number of children that tion, and estimation methods would be born to a woman if she were to live to the Figure 2.2e Population growth in the Registration systems in many developing coun- end of her childbearing years and bear children in Middle East and North Africa, tries in Africa, Asia, and Latin America are incom- accordance with prevailing age-specific fertility 1L990-2035 plete because of deficiencies in geographic rates 0 Contraceptive prevalence rate is the per- billions coverage, in coverage of population groups, or both centage of women who are practicing, or whose 0 6 For these countries vital rates are estimated by sexual partners are practicing, any form of contra- applying various estimation techniques to incom- ception and is usually measured for women aged 0 5 plete vital registration data or to data from demo- 15-49 0 Population momentum is measured as 0.4 > graphic surveys the ratio of the population when zero growth has uFtpWation The crude death, crude birth, and total fertility been achieved to the population in year t, given the 0.3 momentum rates for 1995 are often based on projections from assumption that fertility remains at replacement censuses or surveys from earlier years (see Primary level from year t onward 0 2 data documentation for the most recent census or 1990 2005 2020 2035 survey year and registration completion status) p - Source: world Bank staff estimates Contraceptive prevalence rates are obtained mainly from demographic and health surveys and contra- Vital rates estimates are ceptive prevalence surveys (see Primary data docu- produced by the World Figure 2.2f Population growth in Latin mentation for the most recent survey year) Bank's Human Develop- America and the Caribbean, 1990-2035 ment and International Economics Departments in billions cnultation with World 1 2 Bank country departments - - ~~~~Important inputs come from 1.0 f f the following sources Population * Population censuses momntum 0.8 * Eurostat, Demographic Statistics K United Nations Department of Economic and 0.6 Social Information and Policy Analysis, World 1990 2005 2020 2035 Population Prospects- The 1996 Edition and Population and Vital Statistics Report Source: World sank stuff estimates * Demographic and health surveys from national sources Figure 2.2g Population growth in Europe and Central Asia, 1990-2035 billions 10 Population momerntum 09 08 1990 2005 2020 2035 Source: Word sank staff estimates World Development Indicators 1997 41 O 2.3 Labor force structure Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 millions millions % % of labor force % of age group 1980 1995 1980 1995 2010 1980-95 1995-2010 1980 1995 1980 1995 Albania 2 2 1 2 2 2.0 13 39 41 4 1 Algeria 9 16 5 9 15 3.8 3 7 21 24 7 2 Angola 4 5 3 5 8 2 4 2 9 47 46 30 27 Argentina 17 21 11 14 18 16 2.0 28 31 8 5 Armenia 2 2 1 2 2 14 1.3 48 48 0 0 Austratia 10 12 7 9 10 2 0 0.9 37 43 0 0 Austria 5 5 3 4 4 05 0 0 40 41 0 0 AzerbaUjan 4 5 3 3 4 12 19 47 44 0 0 Bangladesh 44 64 41 60 81 2 5 2 0 42 42 35 30 Belarus 6 7 5 5 6 04 0 3 50 49 0 0 Belgium 6 7 4 4 4 0 3 -01 34 40 0 0 Benin 2 3 2 2 4 2 7 2 6 47 48 30 28 Bolivia 3 4 2 3 4 2 6 2.6 33 37 19 14 Bosnia and Herzegovina 2 2 2 16 0 5 33 38 1 0 Botswana 0 1 0 1 1 31 21 50 46 26 17 Brazil 71 101 48 71 87 2.6 1.3 28 35 19 16 Bulgaria 6 6 5 4 4 -0 4 -0 5 45 48 0 0 Burkina Faso 4 5 4 5 8 2 0 2 2 48 47 71 51 Burundi 2 3 2 3 5 2 6 2 9 50 49 50 49 Cambodia 3 5 3 5 7 2 7 2 5 56 53 27 25 Cameroon 5 7 4 5 9 2 6 31 37 38 34 25 Canada 17 20 12 15 17 1 6 0 5 40 45 0 0 Central African Republic 2 1 2 2 1 7 1 9 48 47 39 31 Chad 2 3 2 3 5 2 3 2 5 43 44 42 38 Chile 7 9 4 6 8 2 5 20 26 32 0 0 China 586 811 539 709 802 1 8 0 8 43 45 30 12 Colombia 16 22 9 16 22 3 5 2 2 26 37 12 7 Congo 1 1 1 1 2 2 9 2 8 43 43 27 26 Costa Rica 1 2 1 1 2 3.4 21 21 30 10 5 Cote d'lvoire 4 7 3 5 7 2 8 2 2 32 33 28 20 Croatia 3 3 2 2 2 0 2 0 0 40 43 0 0 Cuba 6 8 4 5 6 2 2 10 31 38 0 0 Czech Republic 6 7 5 6 5 0 3 -01 47 47 0 0 Denmark 3 4 3 3 3 0 5 -0 3 44 46 0 0 Dominican Republic 3 5 2 3 5 2 9 2 2 25 29 25 16 Ecuador 4 7 3 4 6 34 2 8 20 26 9 5 Egypt, Arab Rep 23 34 14 21 31 2 5 2.6 26 29 18 11 El Salvador 2 3 2 2 3 2 3 3.1 26 34 17 15 Entrea 1 2 3 2 6 3.0 47 47 44 40 Estonia 1 1 1 1 0 0 -0.5 51 49 0 0 Ethiopia 19 28 17 25 38 2 7 2.7 42 41 46 42 Finland 3 3 2 3 2 0 5 -0.3 46 48 0 0 France 34 38 24 26 27 0 5 0.4 40 44 0 0 Gabon 0 1 0 1 1 2 0 1.8 45 44 29 18 Gambia, The 0 1 0 1 1 34 2 4 45 45 44 37 Georgia 3 4 3 3 3 0 3 0 3 49 46 0 0 Germany 52 56 37 40 41 0 5 0 1 40 42 0 0 Ghana 6 9 5 8 12 3 0 2 7 51 51 16 13 Greece 6 7 4 4 5 1 1 0 5 29 36 5 0 Guatemala 4 6 2 4 6 31 3 4 22 26 19 16 Guinea 2 3 2 3 5 2 2 2 7 47 47 41 34 Guinea-Bissau 0 1 0 1 1 1 5 21 40 40 43 39 Haiti 3 4 3 3 4 1 4 1 4 45 43 33 25 Honduras 2 3 1 2 4 3 6 3 6 25 30 14 9 Hong Kong 3 4 2 3 3 15 0 6 34 37 6 0 42 World Development Indicators 1997 2.33 Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 militons mililons % % of labor force % of age group 1980 1995 1980 1995 2010 1980-95 1995-2010 1980 1995 1980 1995 Hungary 7 7 5 5 5 -0 5 -0 3 43 44 0 0 India 394 562 300 398 518 1 9 17 34 32 21 14 Indonesla 83 120 59 89 123 2 8 2 2 35 40 13 10 Iran, Islamic Rep 20 34 12 19 35 3 3 3 9 20 24 14 5 Iraq 7 11 4 5 9 2 8 3 6 17 18 11 3 Ireland 2 2 1 1 2 0 8 13 28 33 1 0 Israel 2 3 1 2 3 2 7 2 5 34 40 0 0 Italy 36 39 23 25 25 0 7 -0 1 33 38 2 0 Jamaica 1 2 1 1 2 2 0 16 46 46 0 0 Japan 79 87 57 66 67 1 0 01 38 41 0 0 Jordan 1 2 1 1 2 5 0 3 9 15 21 4 1 Kazakstan 9 10 7 8 9 0 9 10 48 47 0 0 Kenya 8 14 8 13 19 3 3 2 5 46 46 45 41 Korea, Dem. Rep 10 16 8 12 15 2 8 1 5 45 45 3 0 Korea, Rep 24 32 16 22 26 2 2 1 3 39 40 0 0 Kuwait 1 1 0 1 1 3 3 21 13 28 0 0 Kyrgyz Republic 2 3 2 2 3 1 5 1 8 48 47 0 0 Lao PDR 2 3 2 2 4 2 5 3 0 45 47 31 27 Latvia 2 2 1 1 1 -0 2 -0 6 51 50 0 0 Lebanon 2 2 1 1 2 3 3 2 7 23 28 5 0 Lesotho 1 1 1 1 1 2 3 2 4 38 37 28 22 Libya 2 3 1 2 3 3 3 3 5 18 21 9 0 Lithuania 2 2 2 2 2 0 4 -0 1 50 48 0 0 Macedonia, FYR 1 1 1 1 1 12 1 0 36 41 1 0 Madagascar 5 7 4 6 10 2 7 3 0 45 45 40 36 Malawi 3 5 3 5 7 2 8 2 4 51 49 45 35 Malaysla 8 12 5 8 12 2 8 2 7 34 37 8 3 Mali 3 5 3 5 8 2 4 2 9 47 46 61 55 Mauritania 1 1 1 1 2 2 2 2 5 45 44 30 24 Mauritius 1 1 0 0 1 21 1 3 26 32 5 3 Mexico 35 55 22 36 52 3 2 2 5 27 31 9 7 Moldova 3 3 2 2 2 0 2 0 5 38 42 3 0 Mongolia 1 1 1 1 2 3 0 2 5 46 46 4 2 Morocco 10 16 7 10 15 2 6 2 5 34 35 21 6 Mozambique 6 8 7 8 12 16 2 4 49 48 39 34 Myanmar 19 27 17 23 30 20 1 8 44 43 28 25 Namibia 1 1 0 1 1 24 2 5 40 41 34 22 Nepal 8 12 7 10 14 2 4 2 4 38 40 56 45 Netherlands 9 11 6 7 7 1 5 01 31 40 0 0 New Zealand 2 2 1 2 2 1 9 1 1 34 44 0 0 Nicaragua 1 2 1 2 3 3 3 3 6 29 36 19 14 Niger 3 4 3 4 7 3 0 3 2 45 44 48 45 Nigeria 38 58 30 44 67 2 7 2 8 36 36 29 26 Norway 3 3 2 2 2 0 9 0 3 40 46 0 0 Oman 1 1 0 1 1 4 0 4 6 7 15 6 1 Pakistan 44 70 29 46 77 3 1 3 4 23 26 23 18 Panama 1 2 1 1 1 2 9 21 30 34 6 4 Papua New Guinea 2 2 2 2 3 2 2 2 1 42 42 28 19 Paraguay 2 3 1 2 3 2 9 2 8 27 29 15 8 Peru 9 14 5 9 13 3 1 2 7 24 29 4 2 Philippines 27 40 19 28 41 2 7 2 5 35 37 14 8 Poland 19 26 19 19 21 0 3 0 4 45 46 0 0 Portugal 6 7 5 5 5 04 0 2 39 43 8 2 Puerto Rico 2 2 1 1 2 1 8 1 5 32 36 0 0 Romania 14 15 11 11 11 -0 1 01 46 44 0 0 Russian Federation 95 99 76 77 79 0 1 0 1 49 49 0 0 World Development Indicators 1997 43 0 .2.3 Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 millions millions % % of labor force % of age group 1980 i995 1980 1995 2010 1980-95 1995-20i0 1980 1995 1980 1995 Rwanda 3 3 3 4 6 2 8 2 7 49 49 43 42 Saudi Arabia 5 10 3 6 10 5 4 3 4 8 13 5 0 Senegal 3 4 3 4 6 2 7 2 6 42 42 43 31 Sierra Leone 2 2 1 2 2 1 9 2 5 36 36 19 15 Singapore 2 2 1 1 2 2 1 1 0 35 38 2 0 Slovak Republic 3 4 2 3 3 0 8 0 3 45 48 0 0 Slovenia 1 1 1 1 1 0 3 -0 2 46 46 0 0 South Afnca 16 24 11 16 23 2 6 2 3 35 37 1 0 Spain 23 27 14 17 18 1 2 04 28 36 0 0 Sn Lanka 9 12 5 8 10 2 2 18 27 35 4 2 Sudan 10 14 7 10 15 2 5 2 6 27 28 33 29 Sweden 5 6 4 5 5 0 8 0 0 44 48 0 0 Switzerland 4 5 3 4 4 13 0 2 37 40 0 0 Synan Arab Republic 4 7 2 4 7 3 2 3 6 23 26 14 6 Tajikistan 2 3 2 2 3 2 2 2 9 47 44 0 0 Tanzania 9 15 10 15 23 3:1 2 7 50 49 43 39 Thailand 26 39 24 34 38 2 2 08 47 46 25 16 Togo 1 2 1 2 3 2 6 2 9 39 40 36 29 Trinidad and Tobago 1 1 0 1 1 1 4 2 0 32 36 1 0 Tunisia 3 5 2 3 5 2 8 2 7 29 30 6 0 Turkey 25 38 19 28 37 2 6 2 0 35 35 21 24 Turkmenistan 2 3 1 2 3 2 9 3 4 47 45 0 0 Uganda 6 9 7 9 14 2 4 2 7 48 48 49 45 Ukraine 33 34 26 26 26 -0 2 -0 1 50 49 0 0 United Arab Emirates 1 2 1 1 2 4 7 2 1 5 13 0 0 United Kingdom 36 38 27 29 30 0 5 0 2 39 43 0 0 United States 151 172 110 133 152 1 3 0 9 42 46 0 0 Uruguay 2 2 1 1 2 1 4 10 31 40 4 2 Uzbekistan 9 12 6 9 14 2 4 2 9 48 46 0 0 Venezuela 8 13 5 8 13 3 3 2 7 27 33 4 1 Vietnam 28 43 26 37 48 2 4 1 7 48 49 22 9 West Bank and Gaza 1 Yemen, Rep 4 8 2 5 9 4 1 4 2 33 29 26 20 Yugoslavia, Fed Rep 6 7 4 5 5 0 9 0 3 38 42 0 0 Zaire 14 22 12 18 30 2 9 3 3 45 44 33 30 Zambia 3 5 2 4 6 3 0 2 6 45 45 19 16 Zimbabwe 3 6 3 5 7 3 1 21 44 44 37 29 Low income 1,352 t 1,934 t 1,156 t 1,575 t 2,005 t 2 1 w 1.6 w 40 w 41 w 28 w 19 w Excl China& India 371t 563 t 317 t 467 t 685 t 2.6 w 2 5 w 40w 41 w 33 w 28w Middle income 717 t 981 t 513 t 688 t 895 t 20w 1.7 w 36w 38 w 13 w 8 w Lower middle income 527 t 712 t 387 t 507 t 657 t 1.8 w 17w 38w 40 w 13 w 8 w Upper middle income 191 t 269 t 126 t 182 t 238 t 24w 1 8 w 29w 34 w 11 w 8 w Low & middle income 2,069 t 2,916 t 1,669 t 2,263 t 2,899 t 20w 1 7 w 38 w 40 w 24 w 16 w East Asia & Pacific 796 t 1,119 t 704 t 951 t 1,126 t 2.0 w 1 1 w 43 w 45 w 25 w 11 w Europe & Central Asia 277 t 317 t 219 t 238 t 262 t 06w 06w 46 w 46 w l0 w 11 w Latin America & Carib 201 t 293 t 130 t 197 t 266 t 28w 20w 27w 33 w 13 w 10 w Middle East& N Afnca 91 t 151 t 54 t 88 144 32w 33w 24w 26w 13w 5 w South Asia 508 t 732 t 389 t 532 t 716 t 21 w 20w 34 w 33w 24 w 17 w Sub-Saharan Africa 196 t 305 t 173 t 257 t 385 t 2 7 w 2 7 w 42 w 42 w 35w 30 w High income 522 t 605 t 368 t 432 t 465 t 11 w 05w 39 w 42 w 0 w 0 w 44 World Development Indicators 1997 2.33 Who is in the labor force? . ___ The labor force includes both people who are cur- rently employed and those who are unemployed. Data on the labor force, or economically active pop- * Population aged 15-64 is the number of people In practice, it is difficultto countthe unemployed ulation, are collected by the ILO from the latest who could potentiallybe economically active, exclud- accurately, especially in developing countries census or survey of countries ing children * Total labor force comprises people And itmaybejustasdifficultto knowwho isfully Despite the efforts of the ILO to encourage the who meet the ILO definition of the economically employed use of international standards, labor force and active population all peoplewho supply laborforthe According to the International Labour employment and unemployment data are not fully production of goods and services during a specified Organisation (ILO) definition, the unemployed comparable because of differences among period It includes both the employed and the unem- are those "without work but available for and economies, and sometimes within economies, in ployed While national practices vary in the treat- seeking work " Countries with unemployment definitions (for example. daily or weekly rates) and ment of such groups as the armed forces and insurance systems often base estimates of coverage Data comparability is also hampered by seasonal or part-time workers, in general the labor unemployment on those who file claims and differences in methods of collection, classification, force includes the armed forces, the unemployed, thus certify that they are seeking work BUt and tabulation The reference period is another and first-time job-seekers, but excludes homemak- these systems do not count discouraged work- important source of differences in some countries ers and other unpaid caregivers and workers in the ers who have given up theirjob search because census data refer to the status of each person on informal sector * Average annual growth rate of the they believe that no employment opportunities the day of the census or survey or during a specific labor force is computed using the exponential end- exist or do not register as unemployed aftertheir period before the inquiry date, while in others the point method See Statistical methods for more infor- benefits have been exhausted In developing data are recorded without reference to any period mation * Females as a percentage of the labor economies rural women may not be counted as And in some countries the statistics on labor force force shows the extent to which women are active in part of the labor force during seasons of low relate to people above a specific age, while in others the labor force * Children 10-14 in the labor force agricultural activity. there is no specific age provision For a review of the is the share of that age group that is active in the labor Some unemployment-often called "frictional problems relating to definitions, methods of collec- force unemployment"-occurs in all economies as a tion, and classification of data on the labor force, result of the normal operation of labor markets. see ILO l1990a) and the chapter notes in the ILO At any time, some workers are temporarily Yearbook of Labour Statistics unemployed-between jobs as employers look The estimated population aged 15-64 typically a- M Labor force estimates are for the right workers and workers search for provides a rough estimate of the economically active tP calculated by the World betterjobs In countries without unemployment population But in many developing economies chil- , Bank's International Eco- insurance or other forms of social assistance, it dren under 15 work full or part time And in some ANNUAOUE5 nomics Department by may not be feasible to remain without work for high-income countries many workers postpone DU,T.VAil applying sex-specific activ- 1995 a prolonged period Instead, people find some retirement past age 65 ity rates from the ILO data- form of work, often in informal or unrecorded Estimates of women in the labor force are not base, Estimates and activities. comparable internationally, because in many coun- Projections of the Economi- Taking into account the underemployed- tries large numbers of women assist on farms or in - cally Active Population, those engaged for only a few hours a week or other family enterprises without pay, and countries 1950-2010, to the World Bank's population esti- employed in jobs requiring lower qualifications differ in the criteria used to determine the extent to mates to create a labor force series consistent with than they have-would yield a higher estimate which such workers are to be counted as part of the its population estimates This procedure sometimes of labor underutilization. Household surveys labor force results in estimates of the absolute size of the labor that examine unemployment and underem- Reliable estimates of child labor are hard to force that differ slightly from those published by the ployment confirm that unemployment figures obtain According to UNICEF's The State of the ILO in its Yearbook of Labour Statistics alone may seriously underestimate the under- World's Children 1997, in many countries child labor utilization of labor (table 2 3a) is officially presumed not to exist, and so is not included in surveys or covered in official data Data are also subject to underreporting because they do Table 2.3a Unemployment and not include children engaged in agricultural or house- underemployment in three countries hold activitieswiththeirfamilies Availablestatistics percentage of the labor force suggest that more boys than girls work, but the Dis- number of working girls is often underestimated Un- couraged Under- because surveys do not include girls working as Country Year employed workers employed unregistered domestic help or those doing full-time Ghana 1988-89 16 15 241 household work in order to enable their parents to Ukraine 1994 0 4 14 5 work outside the home Vietnam 1992-93 1 3 3 B 10 0 Source: World Bank 1995g World Development Indicators 1997 45 O3 2.4 Employment Employers and own-account workers Employees Unpaid family workers Male Female Male Female Male Female %of % of %of %of %of %of economically economically economically economically economically economically active population active population active population active population active population active population 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Albania Algeria Angola Argentina .. .. . . . .. Armenia Australia 10.0 9 7 4.2 4.6 49.2 413 29 3 32 9 0 2 0 3 02 0 5 Austria 7 9 6.3 3.2 3.4 52 6 50 9 31 8 36 5 0 9 0 8 3.5 2 1 AzerbaUjan Bangladesh 36 9 238 2.0 2 5 43.0 9 5 23 2 0 15 0 135 08 32 7 Belarus Belgium 8.8 93 30 35 499 508 261 256 05 05 26 26 Benin . 31.2 27 2 . 4 2 1.1 18.3 12 2 Bolivia . 18 4 17 2 351 17 8 2 3 . 34 Bosnia and Herzegovina Botswana 1 9 3.3 1.2 3.2 28.3 39.8 12 7 22.7 24.6 11.1 20 9 6 0 Brazil Bulgaria Burkina Faso Burundi 25.8 .. 9 8 .. 5.1 0.5 . 15.9 42 5 Cambodia Cameroon 382 . 220 13.3 . 1 3 . 5 7 .. 123 Canada 6.1 6.1 2 6 3 5 53 4 48 2 36 4 40 8 0 2 01 0 8 0 4 Central African Republic Chad Chile 171 193 50 66 323 439 16.1 22.7 6.5 1 5 34 1 6 China Colombia 181 98 . 34.2 . 22 7 .. 04 . 0.9 Congo Costa Rica 181 5 6 . 49 5 22 8 2 2 1.2 C6te d'lvoire Croatia Cuba 45 03 . 633 .. 308 . 02 . 0.0 Czech Republic 1 7 0.5 499 .. 46 4 . 00 . 0.0 Denmark 9.5 6 8 1 3 1.5 45 1 46 3 39.6 43 5 0 0 0 1 2.5 1.6 Dominican Republic 29.3 7 2 .. 331 . 18.2 1 9 1.4 Ecuador 31.8 5.4 . 35 8 11.8 4 7 1 1 Egypt, Arab Rep 27.3 29 2 1 0 2 0 484 45 4 5.5 51 12 5 14 2 0.2 0.5 ElSalvador 16.0 156 123 115 405 422 18.6 190 82 86 27 27 Eritrea Estonia Ethiopia Finland 6.0 8.6 4.1 3 9 45 7 42 6 40 2 41 1 0 9 0 4 1 1 0 3 France ., Gabon Gambia, The . 43 6 34 4 0 4 0 1 5 2 91 Georgia Germany Ghana Greece 30.4 26.1 6 0 6 4 33.0 32.6 132 19.0 2.5 2 8 10 9 8 3 Guatemala 27 4 35.3 10 3 3 8 41.3 39 4 8 3 9 0 9.1 11 8 1 6 0 8 Guinea Guinea-Bissau 50 7 .. 0 6 13.2 . 1 5 . 23.0 . 0 9 Haiti 36 1 . 23 3 9.1 . 7 5 . 6.5 . 4 0 Honduras . .. Hong Kong 8 3 9.1 15 12 53.7 52.6 30 9 34 3 0 4 0 1 1 0 0 7 46 World Development Indicators 1997 2.4 Employers and own-account workers Employees Unpaid family workers Male Female Male Female Male Female %of %of %of %of %of %of economically economically economically economically economically economically active population active population active population active population active population active population 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Hungary 1.6 7 2 0.6 3.8 445 45 8 353 40 9 02 06 26 1.7 India Indonesia 37.5 28.4 15.0 110 20.2 24.5 7 5 12.8 8.0 9 1 9 6 11.7 Iran, Islamic Rep Iraq Ireland 16.5 18.7 1.7 2.0 453 46 3 23.7 23 0 1 8 2 0 0.4 1.2 Israel 15.0 11.0 4.2 3 7 45.3 41 9 28.5 32.7 0 6 0 2 1.6 0.6 Italy 16.3 16.4 4.6 52 45.7 40.4 21 1 23 5 1 6 1 4 3 2 2.3 Jamaica 16.5 .. 5 4 . 26.1 .. 18.3 . 0 7 .. 0.5 Japan 116 8.5 5.2 38 463 483 24.0 304 2.0 1.1 87 52 Jordan 22.5 . 0.3 610 . 6.3 . 0 7 .. 0.0 Kazakstan Kenya Korea, Dem Rep. Korea,Rep 23.7 202 84 74 30 6 367 14.3 225 43 12 13.6 92 Kuwait 9.9 00 758 126 01 00 Kyrgyz Republic Lao PDR Latvia . . .. Lebanon Lesotho Libya Lithuania Macedonia, FYR 44.6 . 27.2 64 .. 2 6 1 0 . 21 Madagascar Malawi . . Malaysia 204 16.4 83 47 377 469 167 245 47 24 5.6 51 Mali .. Mauritania . . Mauritius Mexico 203 22.9 67 72 323 363 120 175 35 83 1 9 53 Moldova Mongolia Morocco 24.2 27.6 2 9 3.1 33 4 38 3 7.1 10.2 12 2 14 8 5 4 6 0 Mozambique Myanmar Namibia Nepal Netherlands 8.1 6.0 1 5 2 8 55 6 510 25.0 317 0 3 0 2 1.7 1 3 New Zealand 37.4 33 9 13.0 5 2 0 4 0 6 Nicaragua Niger .. . Nigena . .. Norway 80 6.3 16 20 494 443 35.8 402 08 03 2.3 08 Oman Pakistan .. .. .. .. . .. Panama ,, Papua New Guinea Paraguay 36.6 16.1 6.6 11.8 30.4 37.4 7.4 27.6 8.2 0 8 1.0 1 2 Peru 21.5 .. 14.4 . 37 1 . 17 7 1 2 2 2 Philippines 25.7 25.6 9.1 110 25.6 25.8 14.4 148 97 63 104 77 Poland 8.9 13.3 4 3 8.7 42.2 36.6 31.8 32 1 2.9 2 5 9 2 3 5 Portugal 12.5 13.6 3.5 9 5 42 6 40.5 23.7 33 7 2 8 0 8 10 2 1 0 Puerto Rico 11.8 11.7 1.5 2 3 52 7 48.6 31.2 35 4 0 4 0 1 0 8 0 6 Romania 5 8 8.1 . 46 4 .. 33.3 . 0 6 .. 1.3 Russian Federation . . . .. . .. World Development Indicators 1997 47 042.4 Employers and own-account workers Employees Unpaid family workers Male Female Male Female Male Female %of %of %of %of %of %of economically economically economically economically economically economically active population active population active population active population active population active population 1980 1993 1980 1993 1980 1993 1980 1993 1-980 1993 1980 1993 Rwanda Saudi Arabia Senegal Sierra Leone Singapore 10.3 10.3 18 2 0 516 47 8 30 7 36 3 11 0.1 1.4 0.7 Slovak Republic 7 8 . 5 5 40 5 37 2 . 0.7 1.2 Slovenia South Africa Spain 154 127 41 44 510 461 176 249 23 14 51 22 Sri Lanka 20.5 22 1 3.2 4 9 39 7 34 3 14 7 18 1 4 7 2 9 3.7 3 7 Sudan Sweden 55 71 18 24 482 396 420 422 01 02 04 03 Switzerland 8 4 13 55 5 34 9 .. 0 8 2:1 Syrian Arab Republic Tajiklstan Tanzania Thailand 22 9 23 8 8 2 7 3 13 7 12 4 7 9 7 3 15 6 16 3 30 1 32 1 Togo Trinidad and Tobago Tunisia Turkey 24 9 2 6 34 1 7 4 8 3 19 4 Turkmenistan Uganda Ukraine United Arab Emirates 6 8 0 0 87 8 4 9 0 0 0 0 United Kingdom 8 4 2 9 39 9 36 8 0 2 0 4 United States 5 9 5 6 2 1 2 6 517 48 8 38 8 421 0 1 0 1 0 5 0 2 Uruguay 14 9 8.0 410 313 0 7 1.6 Uzbekistan Venezuela Vietnam West Bank and Gaza Yemen, Rep. 33 8 3 1 371 2 5 8 9 14 6 Yugoslavia, Fed Rep 121 51 42 5 232 2.9 . 7 5 Zaire Zambia Zimbabwe 48 World Development Indicators 1997 2.44 Women in the workplace ,* _ Labor force participation rates show how many women are in the labor force but do not Data on employment are drawn from the same The ICSE classifies workers with respect to the type show what work they do Differences between sources as the labor force data in table 2 3 and are of explicit or implicit contract of employmentthey have women and men in where they work and what subject to the same caveats for quality and interna- with other people or organizations The basic criteria they do are as important as differences in tional comparability fordefiningclassificationgroupsaretypeofeconomic their participation rates The ILO defines employment categories using the risk and type of authority over establishments and According to a recent International Labour International Classification of Status in Employment otherworkers thatthejob incumbent has orwili have Organisation (ILO) report, women's activities (ICSE) Until 1993 the main ICSE groups were * Employers operate, alone or with one or more part- in developing countries remain highly concen- employers, own-account workers, employees, mem- ners, their own economic enterprise, or engage inde- trated in low-wage, low-productivity, and pre- bers of producers cooperatives, and unpaid family pendently in a profession or trade, and hire one or carious forms of employment that tend to be workers In 1993 the group own-account workers more employees on a continuous basis The definition outside the purview of labor regulations and was expanded to include people working in a family of "on a continuous basis" is determined by national therefore more prone to exploitation (Lim enterprise with the same degree of commitment as circumstances Partners may or may not be members 1996). A high percentage of women work in the head of the enterprise These people, usually of the same family or household * Own-account the informal sector or in agriculture, where women, were formerly considered unpaid family workers operate, alone or with one or more partners, wages are generally among the lowest. workers their own economic enterprise, or engage indepen- Women remain concentrated in certain occu- According to the ILO. "experience has shown that dently in a profession or trade, and hire no employ- pations in all regions, whatever the level of because of the way countries measure 'status in ees on a continuous basis As with employers, development. In the industrial sector women employment,' the content of the groups is not easily partners may or may not be members of the same tend to be concentrated in a limited number of comparable across countries" (Yearbook of Labour family or household * Employees are people who manufacturing jobs-such as in the garment Statistics 1995, p 4) Managers and directors of workforapublicorprivateemployerandreceiveremu- industry, where more than two-thirds of the incorporated enterprises are classified as employees neration in wages, salary. commission, tips, piece in most countries, but in some they are classified as rates, or pay in kind * Unpaid family workers (also employers Similarly, family members who regularly referred to as contributing family workers) work with- receive remuneration in the form of wages, salaries, out pay in an economic enterprise operated by a Fewer than 6 percent of senior commissions, piece rates, or pay in kind are classi- related person living in the same household and management positions worldwide fied as employees in most countries, but some coun- cannot be regarded as a partner because their com- tries classify them as unpaid family workers mitment in terms of working time or other factors is are held by women *In many instances the type of information needed not at a level comparable to that of the head of the to classify workers is not collected in labor force sur- establishment In countries where it is customary for global workforce is female and which accounts veys Some countries are unable to measure the young people to work without pay in an enterprise for more than one-fifth of the female labor employment status of unpaid family workers And operated by a related person, the requirement of force in manufacturing. many cannot distinguish between own-account work- living in the same household is often eliminated Most women in manufacturing are catego- ers and employers in their basic observations, so The above categories should add up to lOD per- rized as laborers, operators, and clerical work- only the sum of those two groups can be presented cent Where they do not, the difference was not clas- ers According to the ILO report, most women Differences between countries in the treatment of sifiable by status (see About the data for outside the agricultural sector earn on average unemployed people are particularly pronounced In composition and treatment) about three-fourths of the male wage for the general, unemployed people with previous job expe- same work in both industrial and developing rience are included with employees and classified |_ countries, and the gap is not narrowing according to their last job In some countries, how- In all regions, according to the ILO report, ever, they and unemployed people seeking their first ! v - Employment data are com- women work longer hours for lower wages job form much of the group persons not classifiable ^ - piled by the World Bank's than their male counterparts In industrial bystatus.andarenotincludedinthetable Thecon- International Economics countries women work at least two hours cept of unemployment is also poorly defined for the L Department using an ILO more a week than men, and differences of self-employed, the largest category of employment 1995 database corresponding to 5-10 hours are not unusual The same pat- in many developing countries. 4 table 2a in the ILO's tern prevails in the home. In developing coun- Yearbook of Labour tries women spend 31-42 hours a week in l Statstics unpaid work in the home, while men spend 5-15 hours in unpaid work World Development Indicators 1997 49 O 2.5 Poverty National poverty line International poverty line Population below Population the poverty line below Poverty Survey Rural Urban National Survey $1 a day gap year % % % year % % Albania 1996 19.6 Poverty lines-difficult to compare Algeria 1988 16 0 4 International comparisons of poverty data Angola entail both conceptual and practical prob- Argentina 1991 25 5 lems Different countries have different Armenia definitions cif poverty, and consistent com- Australia parisons between countries can be difficult. Austria -Local poverty lines tend to have higher pur- Azerbaijan Bangladesh 1991-92 47 6 46 7 47 5 chasing power In rch countres, where more Belarus generous standards are used than in poor Belgium countries Benin 1995 33 0 Is it reasonable to treat two people with the Bolivia 1990-91 7 1 1 1 same standard of living differently-in terms Bosnia and Herzegovina of their command over commodities- Botswana . 1985-86 34 7 13 3 because one happens to live in a better-off Brazil 1990 32 6 13.1 17 4 1989 28 7 11 6 country? It can be argued that to make con- Bulgarina 1992 2 6 0 8 sistent international comparisons, we should Burkina Faso Burundi 1990 . 36 2 try to hold the real value of the poverty line Cambodia constant, just as is typical when making com- Cameroon 1984 32 4 44 4 40 0 parisons over time. Canada The poverty measures given under the Central African Republic international poverty line attempt to do this Chad . Here the poverty line is set for all countries at Chile 1992 15 0 4 9 $1 a person per day, in 1985 international China 1990 11 5 0 4 8 6 1993 29 4 9 2 prices, and adjusted to local currency using Colombia 1992 31 2 9 9 18 8 1991 7 4 2 3 exchange rates aimed at assuring purchasing congo power parity for consumption The figure of $1 Costa Rica 1989 18 9 7 2 CMte d'lvoire 1 988 17.7 4 3 a day was chosen for the World Bank's World Croatia Development Report 1990 Poverty because it Cuba is typical of the poverty lines in low-income coun- Czech Republic 1993 31 0 4 tries. Of course, by the same token, it is lower- Denmark often much lower-than the poverty lines found Dominican Repubiic 1992 20.6 1989 19 9 6 0 in middle- or high-income countries Ecuador 1994 47 0 25 0 35.0 1994 30 4 9 1 Currency conversions can be problematic, Egypt, Arab Rep 1990-91 7 6 1 1 however Using standard purchasing power El Salvador 1992 55 7 43.1 483 ,, Eritrel Salvador 1992 7 43.1 483parity (PPP) exchange rates, such as those Eritrea Estonia 1994 14.7 6 8 89 1993 6 0 1.6 from the Penn World Table, is clearly prefer- Ethiopia . 1981-82 33 8 8 0 able to using official exchange rates, because Finland many commodities are not traded interna- France tionally. But PPP rates were designed not for Gabon making international poverty comparisons, Gambia, The 1992 640 but for comparing aggregates from national Georgia accounts Itwould be betterto design special- Germany purpose PPP rates for the poor. But with no Ghana 1992 34 3 26 7 3:14 - such rates now available, the standard PPP Greece Guatemala .1t989 53.3 28 5 rates for consumption appear to be the best Guinea 1991 26.3 12 4 option Guinea-Bissau 1991 60 9 24 1 48 8 1991 87 0 57 8 Just as there are problems in comparing a Haiti 1987 65 0 poverty measure for one country with that for Honduras 1992 460 56 0 500 1992 46 5 20 1 another, there can also be problems in com- Hong Kong paring poverty measures within countries. For example, the cost of living is typically higher in 50 World Development Indicators 1997 2.55 National poverty line International poverty line Population below Population the poverty line below Poverty Survey Rural Urban National Survey $1 a day gap year % % % year % % urban than in rural areas (Food staples, for Hungary 1993 . 25.3 1993 0 7 0 3 example, tend to be more expensive in urban India 1992 52.5 15 6 areas.) So the urban poverty line should be Indonesia 1990 143 16 8 15 1 1993 14 5 2.0 higher than the rural poverty line But it is not Iran, Islamic Rep always clear that the difference between Iraq urban and rural poverty lines properly reflects Ireland the difference in the cost of living. Israel For some countries the urban poverty line in Y Jamaica 1992 34 2 1993 4.7 0.9 common use has a higher real value-meaning Japan that it allows poor people to buy more com- Jordan 1991 15 0 1992 2.5 0 5 modities for consumption-than does the rural Kazakstan . . povertyline.Sometimesthedifferencehasbeen Kenya 1992 46 4 29 3 42 0 1992 50 2 22 2 so large asto imply that the incidence of poverty Korea, Dem Rep is greater in urban than in rural areas, even Korea, Rep though the reverse is found when adjustments Kuwait are made for differences in the cost of living As Kyrgyz Republic 1993 52 2 32 0 45.4 1993 18.9 5.0 with international comparisons, when the real Lao PDR 1993 53 0 24 0 46.1 Latvia Lebanon Lesotho 1993 53 9 27 8 49 2 1986-87 50 4 24 8 Libya About 1.3 billion people live on less Lithuania 1993 21 05 than $1 a day @ Macedonia, FYR 1990 28 0 24 0 than $1 a day Madagascar 1993 72 3 33 2 Malawi value ofthe poverty line varies, itis not clear how Malaysia 1989 15 5 1989 5.6 0 9 meaningful such urban-rural comparisons are Mali Mauritania 1990 57 0 1988 31 4 15 2 The problems of making poverty compar- Mauritius 1992 10 6 isons do not end there. Further issues arise in Mexico 1988 10 1 1992 14 9 3 8 measuring household living standards. The Moldova 1992 6 8 1.2 choice between income and consumption as a Mongolia 1995 33 1 38 5 36 3 welfare indicator is one issue. Incomes are Morocco 1990-91 18 0 7 6 13 1 1990-91 1 1 0 1 generally more difficult to measure accurately, Mozambique and it can also be argued that consumption Myanmar accords better with the idea of the standard of Namibia Nepal ~1995-96 44.0 23 0 42 0 1995-96 53 1 16 9 living than does income, which can vary over Nepal Netherlands time even if the standard of living does not But New Zeaiand consumption data are not always available, Nicaragua 1993 76.1 31 9 50 3 1993 43.8 18 0 and when they are not there is little choice but Niger 1992 615 22.2 to use income. Nigeria 1992-93 36.4 30 4 34 1 1992-93 28 9 11 7 There are still other problems In some Norway countries an allowance is made for differ- Oman ences in household size and composition Pakistan 1991 36.9 280 340 1991 11 6 26 when determining who is poor, while in others Panama 1989 25 6 12 6 no allowance is made. Household survey Papua New Guinea Paraguay 1991 28.5 19 7 21 8 questionnaires can also differ widely, for Peru 1991 68.0 50 3 54 0 1994 49 4 20 5 example, in the number of distinct categories Philippines 1991 71.0 39 0 54 0 1988 27 5 6 9 of consumer goods they identify and in the Poland 1993 238 1993 68 44 order in which questions are asked. Survey Portugal quality varies, and even similar surveys may Puerto Rico not be strictly comparable. Romania 1994 28 0 15 6 21 5 1992 17 7 4 2 Comparisons across countries at different Russian Federation 1994 30.9 1993 1.1 0 1 World Development Indicators 1997 51 022.5 National poverty line International poverty line levels of development also pose a potential problem, because of differences in the relative importance of consumption of nonmarket Population below Population goods. The local market value of all consump- the poverty line below Poverty Survey Rural Urban National Survey $1 a day gap tion in kind (including consumption from own year % % % year % % production, particularly important in underde- Rwanda 1993 51 2 1983-85 45.7 11 3 veloped rural economies) should be included Saudi Arabia in the measure of total consumption expendi- Senegal 1991-92 54 0 25 5 ture Similarly, the imputed profit from produc- Sierra Leone . tion of nonmarket goods should be included in Singapore income. This is not always done, though such Slovak Republic 1992 12 8 2 2 omissions were a far bigger problem in sur- Slovenia veys before the 1980s Survey data now rou- South Africa 1993 23 7 6 6 Spain itnely Include valuations for consumpbton or Sri Lanka 1991 244 183 22 4 1990 4 0 1 0 Income from own producton Nonetheless, Sudan the methods of valuation vary-for example, Sweden some surveys use the price at the nearest Switzerland market, while others use the average farm- Syrian Arab Republic gate selling price Talikistan Tanzania 1991 51 1 1993 16 4 3 7 Thailand 1992 15 5 10 2 13 1 1992 0 1 0 0 Togo 1987-89 32 3 Trinidad and Tobago 1992 21 0 Tunisia 1990 14 1 1990 3 9 0 9 Turkey Turkmenistan 1993 4 9 0 5 Uganda 1992-93 . 55 0 1989-90 50 0 14 7 Ukraine 1995 31 7 United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela 1989 31 3 1991 11 8 3 1 Vietnam 1993 57 2 25 9 50.9 West Bank and Gaza Yemen, Rep. 1992 19.2 18 6 19 1 Yugoslavia, Fed Rep Zaire Zambia 1993 86 0 1993 84 6 53 8 Zimbabwe 1990-91 25.5 1990-91 41 0 14 3 52 World Development Indicators 1997 2.55 Table 2.5a Poverty gap in various regions, 1987 and 1993 It is impossible to create a data set on poverty and * Survey year is the year in which the underlying data distribution that is strictly comparable across coun- were collected. * Rural poverty rate is the percent- Region 1987 1993 tries. But the poverty measures given under the age of the rural population deemed poor * Urban East Asia and the Pacific 8 3 7 8 international poverty line are designed to reduce the poverty rate isthe percentage of the urban population Europe and Central Asia 0 2 1 1 comparability problems in several ways. Nationally deemed poor. * National poverty rate is the percent- Latin America and the Caribbean 8 2 9 1 representative surveys have been used, surveys age of the population living below the poverty line Middle East and North Africa 0 9 0 6 conducted either by national statistical offices or by deemed appropriate for the country by its authorities South Asia 14 1 12 6 private agencies under government or international National estimates are based on population-weighted Sub-Saharan Africa 1t4 4 :15 3 agency supervision subgroup estimates from household surveys Total 9 5 9 2 The poverty measures are based on the most * Population below $1 a day is the percentage of the Note. The aggregates were derived by adjusting esti- recent purchasing power parity (PPP) estimates, from population living on less than $1 a day at 1985 mates from national surveys closest to 1987 and 1993 by the growth rate of real private per capita consump- the latest version of the Penn World Table (PWT 5 6) international prices, adjusted for purchasing power tion from national accounts The sample of countries covered by the surveys was assumed to be representa- These estimates include revisions to PPP exchange parity * Poverty gap is the mean shortfall below the tive of the region This assumption is less robust for the rates in the previous version of the table (PWT 5 0) poverty ine (counting the nonpoor as having zero Middle East and North Africa and Sub-Saharan Africa For further detaiis on data and methodology see to incorporate better data The revisions resulted in shortfall) expressed as a percentage of the poverty Ravalion and Chen 1996 Source World Bank 1996f significant changes, the most striking relating to line This measure reflects the depth of poverty as well China. Usingthe updated PPP exchange rates for con- as its incidence sumption from PWT 5 6 produces an estimate of the percentage of China's population living on less than ,, $1 a day (in international prices) in 1992 nearly triple that estimated using the PPP rates from PWT 5 0, Poverty measures are with the same distribution data For India, however, prepared by the Poverty the revised PPP rates result in a lower estimate for and Human Resources this indicator. Such changes in the estimated inci- | Division of the World dence of poverty occur because a large change in the Bank's Policy Research PPP for a country can produce dramatically different Department. National pov- poverty lines in local currency erty lines are based on the Whenever possible, consumption has been used World Bank's country pov- as the welfare indicator for deciding who is poor A erty assessments Inter- person is said to be poor if he or she lives in a house- national poverty lines are based on primary household hold whose total consumption per person is less surveydataobtainedfromgovernmentstatisticalagen- than the poverty line. The measure of consumption cies and World Bank country departments is generally comprehensive, Including that from own The World Bank has prepared an annual review of production as well as all food and nonfood goods poverty trends since 1993. The most recent is Poverty purchased. When only household incomes are avail- Reduction and the World Bank (1996f) able, the average level of income has been adjusted to accord with either a survey-based estimate of mean consumption (when available) or an estimate based on consumption data from national accounts This procedure adjusts only the mean, however, nothing can be done to correct for the difference in Lorenz (income distribution) curves between con- sumption and income Empirical Lorenz curves were weighted by house- hold size, so they are based on percentiles of popu- lation, not households In all cases the measures of poverty have been calculated from primary data sources (tabulations or household data) rather than existing estimates Estimation from tabulations requires an interpolation method, the method chosen was Lorenz curves with flexible functional forms, which have proved reliable in past work. World Development Indicators 1997 53 O ~2.6 Distribution of income or consumption Survey year Ginl Index Percentage share of Income or consumption Lowest Lowest Second Third Fou rth Highest Highest 10% 20% 20% 20% 20% 20% 10% Albania Algeria 1988a,b 38.7 2.8 6.9 11 0 ±5.1 20 9 46.1 31.5 Angola Argentina . Armenia Australia 1985 e,f 4.4 11.1 17.5 24.8 42 2 25.8 Austria AzerbaUan....* Bangladesh 1992 enb 28.3 4.1 9.4 13.5 17.2 22.0 37.9 23.7 Belarus 1993 C.id 21.6 4 9 11 1 15 3 ±8.5 22.2 32.9 19.4 Belgium 1978-79 e,f .. 7.9 13 7 18.6 23.8 36 0 21 5 Benin Bolivia 1990 C,d 42 0 2.3 5.6 9.7 14.5 22.0 48.2 31.7 Bosnia and Herzegovina Botswana . .. .. Brazil 1989 1, 63 4 0.7 2.1 4 9 8.9 16.8 67.5 51 3 Bulgaria 1992 I.d 30.8 3 3 8.3 13.0 17.0 22 3 39 3 24 7 Burkina Faso... Burundi Cambodia Cameroon Canada 197ef57 11.8 17.7 24 6 40 2 24 1 Central African Republic Chad Chile 1994 C,d 56.5 1 4 3.5 6.6 10.9 18.1 61 0 46.1 China 195Cd 41 5 2 2 5 5 9.8 14.9 22.3 47 5 30.9 Colombia 191Cd 51 3 1 3 3.6 7.6 12.6 20.4 55 8 39.5 Congo Costa Rica 1989C1,d 46.1 1.2 4.0 9 1 14.3 21.9 50 7 34 1 MSe dIlvoire 1988 1.b 36.9 2 8 6.8 11.2 15.8 22.2 44 1 28 5 Croatia Cuba Czech Republic 1993C,d 26.6 4.6 10.5 13 9 16 9 21.3 37.4 23 5 Denmark l981~ . 5.4 12 0 184 25.6 38.6 22 3 Dominican Republic 1989 C,d 50 5 1.6 4.2 7.9 12 5 19.7 55.7 39 6 Ecuador 1994 eSb 46 6 2.3 5.4 8 9 13.2 19.9 52.6 37 6 Egypt, Arab Rep. 1991 eSb 32 0 3.9 8.7 12.5 16.3 21.4 41.1 26 7 El Salvador . .. Eritrea Estonia 1993C.d 39.5 2.4 6.6 10.7 15 1 21 4 46.3 31 3 Ethiopia Finland 1981e. . . 6.3 12 1 18.4 25.5 37.6 21.7 France 1989e, . 5.6 11.8 17.2 23 5 41 9 26.1 Gabon Gambia, The . ... Georgia Germany 1988e1,i 7.0 11.8 17.1 23 9 40 3 24.4 Ghana 1992d,b 33.9 3 4 7 9 12.0 16.1 21.8 42 2 27.3 Greece Guatemala 1989 1, 59 6 0 6 2 1 5.8 10.5 18.6 63 0 46.6 Guinea 1991a,b 46 8 0 9 3 0 8.3 14.6 23.9 50 2 31 7 Guinea-Bissau 1991ae,b 56 2 0 5 2 1 6.5 12.0 20.6 58 9 42 4 Haiti Honduras 1992C,d 52 7 1 5 3 8 7 4 12.0 19.4 57.4 41 9 Hong Kong 198Oe.f . 5.4 10 B 15 2 21.6 47.0 31 3 54 World Development Indicators 1997 2.60 Survey year Gini Index Percentage share of Income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Hungary 19930,b 27.0 4.0 9.5 14.0 17.6 22.3 36.6 22.6 India 19920,b 33.8 3 7 8.5 12.1 15.8 21.1 42.6 28.4 Indonesia 1993a,b 31 7 3.9 8.7 12.3 16.3 22.1 40.7 25.6 Iran, Islamic Rep. Iraq Ireland Israel 1979ef . .. 6.0 12 1 17 8 24.5 39.6 23 5 Italy 1986e. .. 6.8 12 0 16.7 23.5 41 0 25.3 Jamaica 1991e,b 41.1 2 4 5.8 10.2 14 9 21.6 47.5 31 9 Japan 1979e f 8.7 13.2 17.5 23.1 37 5 22.4 Jordan 19910,b 43.4 2 4 5.9 9 8 13 9 20 3 50 1 34 7 Kazakstan 19930,d 32 7 3 1 7.5 12.3 16 9 22 9 40.4 24.9 Kenya 1992a,b 57 5 1 2 3.4 6 7 10 7 17 0 62 1 47.7 Korea, Dem Rep. Korea, Rep Kuwait Kyrgyz Republic Lao PDR 1992 ab 30.4 4.2 9 6 12 9 16.3 21 0 40 2 26.4 Latvia 1993 C,d 27.0 4 3 9.6 13 6 17 5 22.6 36.7 22.1 Lebanon Lesotho 1986-87 a 56 0 0 9 2 8 6 5 11.2 19.4 60.1 43.4 Libya Lithuania 1993 Cd 33.6 3 4 8 1 12.3 16 2 21 3 42.1 28.0 Macedonia, FYR Madagascar 1993a,b 43.4 2.3 5 8 9 9 14.0 20.3 50.0 34 9 Malawi Malaysia 1989 Cd 48.4 1.9 4.6 8 3 13.0 20.4 53.7 37 9 Malt Mauritania 1988 a,b 42 4 0.7 3.6 10.6 16.2 23.0 46 5 30.4 Mauritius .. .. . .. Mexico 1992a,b 50.3 1 6 4 1 7.8 12.5 20.2 55 3 39 2 Moldova 1992 C,d 34.4 2.7 6.9 11.9 16.7 23.1 41 5 25 8 Mongolia Morocco 1990 91a,b 39.2 2.8 6.6 10.5 15.0 21.7 46 3 30 5 Mozambique Myanmar Namibia Nepal 1995-96 a,b 36 7 3.2 7.6 11.5 15.1 21.0 44.8 29 8 Netherlands 1988e,f .. 8 2 13 1 18.1 23 7 36.9 21 9 New Zealand 1981-82ef .. 5.1 10.8 16 2 23.2 44 7 28 7 Nicaragua 1993a,b 50 3 1.6 4.2 8 0 12.6 20.0 55.2 39 8 Niger l992a,b 36 1 3 0 7.5 11.8 15 5 21.1 44 1 29 3 Nigena 1992-93afb 37 5 1 3 4.0 8.9 14 4 23.4 49 3 31.3 Norway 1979 ef ,. 6.2 12 8 18 9 25 3 36 7 21.2 Oman Pakistan 1991 as 31 2 3 4 8.4 12 9 16 9 22 2 39 7 25.2 Panama 1989c.d 56 6 0 5 2.0 6 3 11 6 20 3 59 8 42.2 Papua New Guinea Paraguay Peru 1994a,b 44.9 1 9 4 9 9 2 14 1 21 4 50 4 34.3 Philippines 1988 d.b 40.7 2 8 6 5 10 1 14 4 21 2 47 8 32.1 Poland 1992 adb 27.2 4 0 9 3 13 8 17 7 22 6 36.6 22.1 Portugal Puerto Rico .. Romania 1992 C d 25 5 3 8 9 2 14 4 18 4 23 2 34 8 20.2 Russian Federation 1993 a1 49.6 1 2 3 7 8 5 13 5 20 4 53 8 38.7 World Development Indicators 1997 55 062.6 Survey year Gini index Percentage share of income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Rwanda 1983-85asb 28 9 4 2 9 7 13 2 16 5 21 6 39 1 24 2 Saudi Arabia Senegal 1991ab 54 1 1 4 3 5 7 0 11 6 19 3 58 6 42 8 Sierra Leone Singapore 1982-83 e0, 5 1 9 9 14 6 21 4 48 9 33 5 Slovak Republic 19921,d 19 5 5 1 11 9 15 8 18 8 22 2 31 4 18 2 Slovenia 1993e,d 28 2 4 1 9 5 13 5 171 21 9 37 9 23.8 South Africa 1993ab 58 4 1 4 3 3 5 8 9 8 17 7 63 3 47 3 Spain 1988ef 8 3 13 7 18 1 23 4 36 6 21 8 Sn Lanka 1990a,b 301 3 8 8 9 13 1 16 9 21 7 39 3 25 2 Sudan Sweden 1981 eJ 8 0 13 2 17 4 24 5 36 9 20 8 Switzerland 1982of 5 2 11 7 16 4 22 1 44 6 29 8 Syrian Arab Republic Ta]ikistan Tanzania 19930,0 38 1 2 9 6 9 10 9 15 3 21 5 45 4 30 2 Thailand 199201 46 2 2 5 5 6 8 7 13 0 20.0 52 7 37 1 Togo Tnnidad and Tobago Tunisia 19901,0 40 2 2 3 5 9 10 4 15 3 22 1 46 3 30 7 Turkey Turkmenistan 19930d 35 8 2 7 6 7 11 4 16 3 22 8 42 8 26 9 Uganda 1992-93 a,b 40 8 3 0 6 8 10 3 14 4 20 4 48 1 33 4 Ukraine 199200 25 7 4 1 9 5 14 1 18 1 22 9 35 4 20 8 United Arab Emirates United Kingdom 1988e.f 4 6 10 0 16 8 24 3 44 3 27 8 United States 1985ef 4 7 11 0 17 4 25 0 41 9 25 0 Uruguay Uzbekistan Venezuela 1990g,0 53 8 1 4 3 6 7 1 11 7 19 3 58 4 42 7 Vietnam 19930b 35 7 3 5 7.8 11 4 15 4 21 4 44 0 29 0 West Bank and Gaza Yemen, Rep Yugoslavia, Fed Rep Zaire Zambia 19930,b 46 2 1 5 3 9 8 0 13 8 23 8 50 4 31.3 Zimbabwe 1990 56 8 1 8 4 0 6 3 10 0 17 4 62 3 46 9 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 a Refers to income shares by percentiles of households f Ranked by household income 56 World Development Indicators 1997 2.6 0 _=_________________________________ Because the underlying household surveys differ in method and in the type of data collected, the distribu- Inequality in the distribution of income is reflected in the tion indicators are not strictly comparable across coun- * Survey year is the year in which the underlying data percentage share of income or consumption accruing tries These problems are diminishing as survey were collected * Gini index measures the extent to to segments ofthe population ranked by income or con- methods improve and become more standardized, but which the distribution of income (or, in some cases, sumption levels The segments ranked lowest by per- achieving strict comparability is still impossible (see consumption expenditures) among individuals or sonal or family income typically receive the smallest the notes to table 2 5) households within an economy deviates from a per- share of total income The Gini index provides a conve- The following sources of noncomparability should fectly equal distribution A Lorenz curve plots the cumu- nient summary measure of the degree of inequality be noted First, the surveys differ in whether they use lative percentages of total income received against the Data on personal or household income or consump- income or consumption expenditure as the living stan- cumulative number of recipients, startingwith the poor- tion come from nationally representative household dard indicator For 37 of the 66 low- and middle- est individual or household The Gini index measures surveys The data sets referto differentyears between income economies the data refer to consumption the area between the Lorenz curve and a hypothetical 1985 and 1994 Footnotes to the survey year indicate expenditure Income is typically more unequally dis- line of absolute equality, expressed as a percentage of whether the rankings are based on per capita income tributed than consumption In addition, the definitions the maximum area under the line. Thus a Gini index of or consumption, or, in the case of high-income of income used in surveys are usually very different zero represents perfect equality while an index of 100 economies, household income Where the original data from the economic definition of income (the maximum percent implies perfect inequality * Percentage from the household survey were available, they have level of consumption consistent with keeping produc- share of income or consumption is the share that been used to directly calculate the income (or con- tive capacity unchanged) For these reasons, con- accrues to subgroups of population indicated by sumption) shares by quintile Otherwise, shares have sumption is usually a much better measure Second, deciles or quintiles Percentage shares by quintiles been estimated from the best available grouped data the surveys differ in whether they use the household may not add up to 100 because of rounding The distribution indicators for low- and middle- or the individual as their unit of observation Further, income economies have been adjusted for household household units differ in size (number of members) size, providing a more consistent measure of per and in extent of income sharing among members capita income or consumption No adjustment has Individuals differ in age and consumption needs Data on distribution for low- and middle-income been made for spatial differences in cost of living Where households are used as the observation unit, economies are compiled by the Poverty and Human withincountries,becausethedataneededforsuchcal- the deciles or quintiles refer to the percentage of Resources Division of the World Bank's Policy culations are generally unavailable For further details households rather than of population Third, the sur- Research Department, using primary household on the estimation method for low-and middle-income veysdifferaccordingtowhethertheyranktheunitsof survey data obtained from government statistical economies, see Ravallion and Chen (1996) observation by household or per capita income (or agencies and World Bank country departments consumption) Data for high-income economies are from national World Bank staff have made an effort to assure sources, supplemented by Table 2.6a Income shares of lowest and that the data for low- and middle-income economies * Luxembourg Income Study database, 1990 highest quintiles, 1960s-1990s are as comparable as possible Whenever possible, * Eurostat, Statisfical Yearbook percent consumption has been used rather than income * United Nations, National Accounts Statistics, Households have been ranked by consumption or Compendium of Income Distribution Statistics Region or group i960s ±970s ±si80s ±99so Lowest quintile income per capita in forming the percentiles, and the (1985) East Asia and the Pacific 6 4 6 0 6 3 6 9 percentiles are of population, not households The Europe and Central Asia 9 7 9 8 9 8 8 8 comparability of the data for high-income economies Latin America and the Is more limited, because the observation unit is usu- Caribbean 3 4 3 7 3 7 4 5 ally a household unadjusted for size and households Middie East and North Africa 5 7 66 6a9 are ranked according to total household income South Asia 7 4 7 8 7 9 8 8 rather than income per household member These Sub-Saharan Africa 2 8 51 5 7 5 2 data are presented pending the publication of Industrial and high-income improved data from the Luxembourg Income Study, developing economies 6 4 6 3 6 7 6 3 which ranks households by the average disposable income per adult equivalent The estimates in the Highest quintiie table should therefore be treated with considerable East Asia and the Pacific 45 9 46 5 45 5 44 3 Europe and Central Asia 36 3 34 5 34 6 37 8 caution Latin America and the Canbbean 616 54 2 54 9 52 9 Middle East and North Africa 49 0 46 7 45.4 South Asia 44 1 42.2 42.6 39.9 Sub-Saharan Africa 62.0 55.8 48 9 52 4 Industrial and high-income developing economies 31.2 41 1 39 9 39 8 Source: Deininger and Squire 1996 World Development Indicators 1997 57 O 2.7 Education policy and infrastructure Primary Duration of Public spending on education Spending on Primary school school primary teaching materials pupil-teacher starting education ratio age Primary Secondary Primary Secondary Tertiary % of total % of total pupils per % of GDP % of GDP % of GDP for level for level teacher years years 1980 1992 1980 1992 1980 1992 1992 1992 1980 1993 Albania 6 8 21 17 Algeria 6 6 1 4 1 7a 1 3 3 2a 0 9 35 27 Angola 6 4 34b . 0lb ° 7b 32 Argentina 6 7 0 9 1 6 0 6 0 8 0 5 0 5 . 20 16 Armenia 7 3 Australia 6 6 3 3 3 0 1 1 1 4 19 17 Austria 6 4 0 8 0 9 2.5 2 4 0 7 0 9 15 12 Azerbaijan 7 4 4 Bangladesh 6 5 0 5 b 08b 0 4b 08b O lb O.1b 54 63 Belarus 6 4 3 0 1.0 0 7 . 5 Belgium 6 6 1.5 b 1 2b 28b 2.1 b 1.0, 0.8b 18 10 Benin 6 6 48 49 Bolivia 6 8 2 4 0 5 0 7 20 Bosnia and Herzegovina Botswana 7 7 2 5 1 9g 1 4 30b 0 6 0.7b 2 4b 1 5b 32 27 Brazil 7 8 1 6a 3a 0 70 26 23 Bulgaria 7 8 1 9 3 0 0 6 0 8 19 14 Burkina Faso 7 6 0 7 0 4 0 7 , 54 58 Burundi 7 6 1 2 6 16 1 lb 1 O 0 70 0gb 37 63 Cambodia 6 6 Cameroon 6 6 1 6b 2 1 0.5b 0 3b 52 48 Canada 6 6 4 3C 4 60 18 g 2 10 16 Central African Republic 6 6 2 4 b 1.3b 0 5" 0 4" 0 7b 05b L.7b 6.0 60 Chad 6 6 1.0 0 5 0 2 1 3 2.7 61 Chile 6 8 1 8 1 2 0 8 0 4 1 4 0.5 34 26 China 7 5 0 6 0.6 0 70 0 6d 0 4 0.3 27 22 Colombia 6 5 0 8b 1 3.,b 0 5b 1 1.0,b 04b 0.6.,b 31 28 Congo 6 6 2 1 1 7 1.5 Costa Rica 6 6 1 9 1 5 1 5 0.9 1.8 15 28 32 e6t d'lvoire 6 6 2 7 2 1 0 9 39 39 Croatia 7 8 . 19 18 Cuba 6 6 2.5 12 17 13 Czech Republic 6 4 17 1.2 0 6 20 9 29 3 21 Denmark 7 6 3 2 14 1 1 2 9 1.0 1.2 10 Dominican Republic 7 8 0 6b 0 4b 0 4b 01 b 0.4" 0 lb 64 34 Ecuador 6 6 10 07 09 07 08 05 36 31 Egypt, Arab Rep 7 5 2 1 2 5 0 9 1 4 34 27 El Salvador 7 9 2 2 0 2 0 5 48 40 Entrea 7 5 . . 39 Estonia 7 6 31 0 9 . 18 Ethiopia 7 6 1 1 1 5 0 7 0 7 04 0 3 0.8 14 64 30 Finland 7 6 1 6' 1.8 20" 26' 0 9 200 46' 310 14 France 6 5 1 0 0.9 1 9 21 0 6 0 7 1 4 0 6 24 19 Gabon 6 6 1.00 1 3e 0.4' 0 3e Gambia, The 7 6 1 4 1.1 0 6 0 5 0 3 02 7.6 152 24 30 Georgia Germany 6 4 . , 16 Ghana 6 6 0.5 0.8 0 7 0 9 0 0" 0 3 29 28 Greece 6 6 08 08 09 1.3 05 05 24 Guatemala 7 6 0.6" 0.4" 02b 0 2b 0 3b 0.3 b 34 32 Guinea 7 6 0 8a 0 7a 0.4a 36 49 Guinea-Bissau 7 6 3 0 0 6 0.1 .. 23 Haiti 6 6 0.7 0 8 0 2 0.3 0 1 0.1 0 0 0.0 44 Honduras 7 6 1 7 18 0 5 0.6 0 5 0 7 37 37 HongKong 6 6 07 07 08 10 05 0.8 30 27 58 World Development Indicators 1997 2.77 Primary Duration of Public spending on education Spending on Primary school school primary teaching materials pupil-teacher starting education ratio age Primary Secondary Primary Secondary Tertiary % of total % of total pupils per % of GDP % of GDP % of GDP for level for level teacher years years 1980 1992 1980 1992 1980 1992 1992 1992 1980 1993 Hungary 6 8 15 2 59 0 8 15 0 8 10 15 11 India 6 5 1 0 1 4 0 7 1 0 0 4 0 5 55 64 Indonesia 7 6 32 23 Iran, Islamic Rep 6 5 2 8 1 3 2 5 1 7 0 5 0 6 27 32 Iraq 6 6 1 4 0 5 0 7 28 22 Ireland 6 6 1 4 1 5 2 1 2 1 0 9 1 1 0 3 0 2 29 24 Israel 6 8 2 4 1 79 2 1 1 6 1 8 0 9 1639 16.8 15 16 Italy 6 5 11 1 0 1 6 1 8 0 3 12h 04 0 2 16 11 Jamaica 6 6 2 1 b l 0 b 23b 1 o0 1 20b 80 41 40 Japan 6 6 1 5' 14' 0 4 25 19 Jordan 6 10 2 9 32 22 Kazakstan 7 4 18 Kenya 6 8 3 6 2 8 0 9 0 8 0 7 0 8 38 31 Korea, Dem. Rep 6 4 Korea.Rep 6 6 15 14 10 13 0 3 0 2 1 5 1 6 48 31 Kuwait 6 4 0 8 3,80 11 1 7a 0 4 5 50 19 Kyrgyz Republic Lao PDR 6 5 0 7 0 8 0 1 30 30 Latvia 7 4 1 8 2 9 0 4 0 6 14 Lebanon 6 5 18 6 Lesotho 6 7 2 7 3 8 2 3 2 3 1.5 1 2 48 49 Libya 6 9 0 4 18 12 Lithuania 7 4 2 6 1 1 25 17 Macedonia, FYR 7 8 2 9 1 2 0 9 23 20 Madagascar 6 5 1 5 0 7e 0 9 0.50 1 0 44 40 Malawi 6 8 0 9 1 9 0 4 0 4 0 7 0 7 1 8 9 2 65 68 Malaysia 6 6 1 7 b 1 6 1 7b 0 6 0 7b 27 20 Mall 8 6 1 4 0 9 0 9 42 60 Mauntania 6 6 1 7 2 4 0 6 41 53 Mauntius 5 6 2 1 1 2 1 7 1 2 0 4 0 5 20 21 Mexico 6 6 0 9 0 9b 0 7b 08b 04b 05b 0 ob 0 o0 39 29 Moldova 6 4 23 Mongolia 8 3 4 98 1 88 32 Morocco 7 6 1 7 b 1 6 b 2 2b 2 5b 09b 0 8 0 38 28 Mozambique 7 5 1 7J 0 5- 0 3J 81 55 Myanmar 5 5 52 Namibia 7 7 32 Nepal 6 5 11 13 0 5 0 6 0 8 38 39 Netherlands 6 6 1 3 0. 09C,k 2 30,k 2 1Ch 90 C 8C 1 60,k 0 7C,k 23 16 New Zealand 5 6 1.8 1 5 1 5 1 4 1 4 2 5 17 16 Nicaragua 7 6 1 2 170 0 7 0 58e 0 3 0 38.e 090,e 35 37 Niger 7 6 1.0 1.4b 1 3 0.9b 0 5 41 34 Nigeria 6 6 0.9 2 0 1 2 37 37 Norway 7 6 2 8Ck 2 7C,k 1 4.,k 1 9C,k 0 80 1.2' 2 00,k 2 8"k 8 6 Oman 6 6 1 6 1 3 0 2 2 3 2 7 23 27 Pakistan 5 5 0 6 0 4 0 3 36 45 Panama 6 6 2 0 1 6 1 0 1 0 0 6 1 3 27 23 Papua New Guinea 7 6 31 33 Paraguay 7 6 1 2 0 5 0 6 27 24 Peru 6 6 13 0 6 0 1 37 29 Philippines 7 6 1.0 0 3 0 4 30 33 Poland 7 8 0 9 19 0 7 10 0 8 0 9 21 17 Portugal 6 6 1 7 b 1.8b 0 80 1 8b 0.30 0 7 18 12 Puerto Rico 5 8 Romania 6 4 1 30 Cga 05a 21 21 Russian Federation 7 3 28 20 World Development Indicators 1997 59 072.7 Primary Duration of Public spending on education Spending on Primary school school primary teaching materials pupil-teacher starting education ratio age Primary Secondary Primary Secondary Tertiary % of total % of total pupils per % of GDP % of GDP % of GDP for level for level teacher years years 1980 1992 1980 1992 1980 1992 1992 1992 1980 1993 Rwanda 7 7 1 5 .. 0 5 . 0 2 59 58 Saudi Arabia 6 6 5 1 0 7 13 18 14 Senegal 7 6 1 8 1 7 1.2 10 1 1 0 9 46 54 Sierra Leone 5 7 33 34 Singapore 6 6 0 8 1 0 0 4 . 31 26 Slovak Republic 6 4 1 5 1 0 0 8 . 22 Slovenia 7 4 1 2 . 2 3 1 0 0 9 0 4 16 South Africa 6 7 5 3 0 8 3 2 27 27 Spain 6 5 1.3 0 9 0 4 1 9 0 3 0 7 20 Sri Lanka 5 5 2 lb 1 7b 0.2b 0 3b 16 29 Sudan 7 6 21 13 0 9 34 34 Sweden 7 6 3 4k 3 0,,k 0 k 1 3,k 0 7 1 2 3 5k 7 9 10 Switzerland 7 6 22k 3 4 1,2k 0 9 0.9 34k 6 O0 Syrian Arab Republic 6 6 1.4 1 6a 10 0 90 1 2a 08a 1o0 1 7a 28 24 Tajikistan 7 4 22 Tanzania 7 7 2.1 0 8 0.4 . 41 37 Thailand 6 6 1 4 1.6 0 4 0.6 0.5 0.5 3 3 8.3 25 19 Togo 6 6 15 19 1 6 17 1 6 0 7 1 0 1 1 55 53 Trinidad and Tobago 5 7 1.4 1 4 1 0 1.2 0 3 0 4 24 27 Tunilsa 6 6 1.9 2 2 1 7 1 9 0 9 1 0 39 26 Turkey 6 5 0 8 1 5 0 4 0 7 0.5 0 0 0 1 27 27 Turkmenistan Uganda 6 7 03b 11 03b 34 31 Ukraine 7 4 1 5 2.1 0 8 0 9 0 7 0 7 45 20 United Arab Emirates 6 6 16 17 United Kingdom 5 6 14 1 3 2 1 2 1 1 2 1 0 5.2 329 19 20 UnitedStates 6 6 2 500, 1 9 170,0 1 8 2 60,0 1 2 14 Uruguay 6 6 :10' 0 9 0 7 0 7 0 3 0 6 22 21 Uzbekistan 7 4 .. 20 Venezuela 6 9 06m 0 5 06'm 01 1 5m 1 0 34 23 Vietnam 6 5 . 39 35 West Bank and Gaza Yemen, Rep. Yugoslavia, Fed Rep 7 4 8 7 15 1 22 Zaire 6 6 1.0 1 7 0.8 44 Zambia 7 7 1 8 1 0 0 7 49 44 Zimbabwe 7 7 4.2 5 0 1 4 2 6 0 5 1.6 1 2 2 3 Low income 09w low 0.8w 08w 04w 05w .. 34w 33w Excl. China & India 41 w 39 w Middle income .. . 07w . 28 w 23 w Lowermiddle income 14w . low 07w .. 29w 23w Upper middle income . 2 2w 06w . . 22 w Low & middle income 12w .. 0.6w .. 32 w 28 w East Asia & Pacific 08w 07w 04w 28 w 23 w Europe & Central Asia 1 1w . 07w . 17 w Latin America & Carib 12w . 05w . 07w 30 w 24 w Middle East& N Africa 28w 21w 07w lOw 29 w 24 w South Asia 09w 14w 06w low 03w 05w Sub-Saharan Africa 39 w 36w High income 19w . 19w 16w a Includes capital expenditure b Ministry of education expenditure only c Includes both public and private expenditure d Excludes expenditure on specialized secondary and technical and vocational schools e Ministry of primary and secondary education expenditure only f Excludes expenditure on universities g Includes special education h Includes capital expenditure on universities i EKcludes public subsidies to private education j Includes foreign aid for education k Expenditure on primary education covers six grades of primary and the first three grades of secondary education Expenditure on secondary education covers only the last three grades of secondary education (upper secondary) I Includes expenditure on special and adult education m Central government expenditure only 60 World Development Indicators 1997 2.77 -__ The comparability of pupil-teacher ratios is affected by differences in whether both full- and part- Statistics on education are compiled by the United time teachers are included and in whether teachers * Primary school starting age is the age at which Nations Educational, Scientific, and Cultural are assigned nonteaching duties and by differences children are officially accepted for primary school edu- Organization (UNESCO) from official replies to sur- in class size by grade and in number of hours taught cation * Duration of primary education is the mini- veys and from reports provided by education author- Moreover, the underlying enrollment levels are sub- mum number of grades (years) a child is expected to ities in each country. Because coverage, definitions, ject to a variety of reporting errors (see About the coverinprimaryschooling * Publicspendingonedu- and data collection methods differ across countries data for table 2.8 for further discussion of enroll- cation is the ratio of public expenditures on public and may change over time within a country, caution ment data) While the pupil-teacher ratio is often education plus subsidies to private education at pri- should be exercised when using education statis- used to compare the quality of schooling across mary, secondary, and tertiary levels to current GDP tics. Although exceptions are noted in the table. it is countries, it is not strongly related to the value * Spending on teaching materials is the ratio of advisable to consult the country- and indicator-spe- added of schooling systems (Behrman and public expenditure on teaching materials to total cific notes in the source cited below See Behrman Rosenzweig 1994) public expenditure on primary or secondary educa- and Rosenzweig (1994) for a general discussion of tion Expenditure on teaching materials includes pur- the reliability of data on education chases of textbooks, books, and other scholastic For many countries the primary school starting supplies * Primary school pupil-teacher ratio is the age and duration of primary education changed number of pupils enrolled in primary school divided by between 1980 and 1993 (see the notes to table 2 8 the number of primary school teachers (regardless of for definitions of primary, secondary, and tertiary their teaching assignment) levels) As a result the relative size of public spend- ing on education by level and primary pupil-teacher l= ratios also may have changed These changes also may affect the comparability of school enrollment International statistics on ratios over time and across countries (see table education are compiled by 2.8). UNESCO's Division of The data on public spending on education exclude Statistics, in cooperation foreign aid received for education They may also with the National Commis- exclude expenditures by religious schools, which sions for UNESCO and play a significant role in many developing countries national statistical ser- Data for some countries and for some years refer to vices Data reported in this expenditures of the ministry of education only table were compiled using (excluding education expenditures by other min- a UNESCO electronic database corresponding to var- istries and departments, local authorities, and so ious tables in UNESCO's Statistical Yearbook 1995. on) Data for a few countries include private expen- ditures (all such cases are noted), although national practices vary with respect to whether parents or schools pay for books, uniforms, and other supplies Table 2.7a Public education spending per pupil, by level of schooling, 1985 and 1992 percentage of per capita GNP Pre- Pre- primary primary and and All levels primary Secondary Tertiary All levels primary Secondary Tertiary Region or group i985 1985 1985 1985 1992 1992 1992 1992 East Asia, including Oceania 14 3 7 6 18 1 129 2 14 4 8 2 18 9 90 1 Latin America and the Caribbean 12 2 6 3 12 7 43 5 14 2 8 2 12 9 48 6 Middle East and North Africa 23 6 17 2a 115 1 18 8 15 00 75 7 South Asia 18 3 10 9 18 2 79 8 19 6 10 7 22 9 76 3 Sub-Saharan Africa 23 5 14 0 42 0 423 7 27 9 15 1 53 7 507 8 Least developed countries 18 3 10 6 27 9 155 3 18 1 10 1 32 3 142 8 Developing countries 16 4 11 3a 98 1 18 0 13 00 84 9 Developed countries 20 7 16 8a 34 1 21 4 17 7a 29 4 Note: The regional groupings are based on the United Nations country classification Bulgaria, the former Czechoslovakia, Romania, and the former Soviet Union are included with developed countries a Data are for preprimary, primary, and sec- ondary levels Source UNESCO 199Sb World Development Indicators 1997 61 O3 2.8 Access to education Gross enrollment Age efficiency ratio ratio Primary Secondary Preprimary Primary Secondary Tertiary net enrollment net enrollment % of relevant % of relevant % of relevant % of relevant as % of as % of age group age group age group age group gross enrollment gross enrollment 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Albania 113 96 67 8 10 Algeria 3 94" 103" 33 61" 6 11 86h 91" 57 54c Angola 53 88 20 14 0 1 39 . 74 Argentina 47 106 107 56 72 22 41 88 81 Armenia 90 .. 85 30 49 Australia 71 111 108 71 84 25 42 91 92 98 96 Austria 75 99 103 93 107 22 43 88 87 70 67 Azerbaijan 23 89 88 25 26 Bangladesh 62 111 18 19 3 69 . 88 Belarus 79 . 96 98 92 39 44 Belgium 111 104" 99 91 103 26 94 96 90 85 Benin 3 64d 64d 16a 12 2 . 80d Bolivia . 87 37b 16 23 91 44b Bosnia and Herzegovina Botswana 91 116 19 19 1 3 84 83 80 80 Brazil 36 99d 1ll, 34a 43b 11 12 82d 80" 44a 451b Bulgaria 56 98 92 84 72 16 23 99 96 90 91 Burkina Faso 18" 38" 3b 8e 0 . 83c 81" 79e Burundi 26e 72d 3b 7 1 1 74 74 80 72 Cambodia Cameroon 17 98e 87 18c 32 2 2 72e 78c Canada 58 99 105 88 88 52 103 91 94 69 64 Central African Republic 71" 14e 1 2 80e Chad 59 9 1 Chile 86 109 98 52 70c 12 27 89 79" China 27 112 109 46 52 1 6 81 Colombia 22 124 1190 41 62a 9 10 . 71" 66a Congo Costa Rica 66 105a 105o 48b 47b 21 30 85a 86a 83b C6ted'lvoire 1 79d 69e 19b 25" 3 71" Croatia 26 91 85 . 83 27 Cuba 94 106" 104 81 77 17 18 90" 96 76 Czech Republic 88 103 86 18 16 Denmark 98 96 98 105 114 28 41 100 100 84 77 Dominican Republic 20 118c 97 42 37 .. 60" 63 . 81 Ecuador 23 117b 123 53" 55 35 , 77b Egypt,ArabRep 7 73" 97" 50 76b 16 17 76a 81b El Salvador 25 75 79" 24 29 4 15 92 90" Eritrea 4 47d 14d 55d 74d Estonia 69 83 92 43 38 95 .. 80 Ethiopia 1 34 23" 8 1ll 0 1 75" Finland 37 96 100 100 119 32 63 France 84 111 106 85 106b 25 50 90 94 93 85b Gabon . . . 3 Gambia, The 24 51b 73b 11 19 .. .. 95 82 b 96 Georgia 30 Germany 101 97 . 101 36 84 .. 71 Ghana 80 76 41 36 2 Greece 103 . 81 17 94 96 90 88 Guatemala 31 71" 84" 18 24 8 82" . 68 Guinea 36d 46d 17" 12d 5 42d Guinea-Bissau . 68 . 6 6 .. 69e .. 45 Haiti 76 .. 14"a ,,1 . 50 .. .. Honduras 20 98c 112b 30 32b 8 9 79c 81" b 62b Hong Kong 81 107 102 64a 10 21 89 . 95a 62 World Development Indicators 1997 2.8 Gross enrollment Age efficiency ratio ratio Primary Secondary Preprimary Primary Secondary Tertiary net enrollment net enrollment % of relevant % of relevant % of relevant % of relevant as % of as % of age group age group age group age group gross enrollment gross enrollment 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Hungary 114 96 95 70 81 14 17 98 96 93 India . 83 105 30 . 5 . . Indonesia 35 1070 114a 29 43 10 82a 85a 86 Iran, Islamic Rep 7 87 105a 42 660 15 87a Iraq 8 113 91 57 44e 9 87 87 82 85e Ireland 106 100 103 90 105 18 34 90 87 75 Israel 81 95 95 73 87 29 35 Italy 95 100 98 72a 81a 27 37 Jamaica 73 103 109 67 66 7 6 93 94 96 98 Japan 49 101 102 93 96 31 30 100 100 Jordan 25 104 94 76 53 27 19 97 94 89 70 Kazakstan . 86 90 34 42 . Kenya 34 115b 91 20 25 1 79b Korea, Dem Rep. Korea, Rep 71 110 101 78 93 15 48 64 60 90 94 Kuwait 45 102 . .. b1 . 830 84 81b 77 Kyrgyz Republic . . . .. 28 21 Lao PDR 7 113 1070 21 25a 0 2 63e 72a Latvia 38 .. 83 . 87 45 39 95 74 Lebanon 73 111 115 59 76 30 29 Lesotho 102d 98d 18 26 2 2 64d 67 d 73 63 Libya 1250 110 76b 97 8 16 99 96 Lithuania 34 92 . 78 49 39 Macedonia, FYR 21 100 87 61 54 28 16 Madagascar . 136 73e 14d 3 4 Malawi 600 800 3 4 1 1 72c 77C 38 Malaysia 35 93 93 48 59 4 Mali 2 27e 31e ge ge 1 6ge 70e Mauritania 37b 690 11l 15b 4 Mauritius , 93 106a 50 59c 1 4 Mexico 65 122b 112a 48 58 14 14 . 88a Moldova 59 77 . 69 29 35 Mongolia .. 107 91 78 .. 93 95 Morocco 62 83e 73 260 350 6 10 74 76 78 840 Mozambique gge 600 5 7e 0 0 49e 680 Myanmar .. 91 22 Namibia 13 136d , 55 3 66d 55 Nepal 84 107 e 21 21a 3 6 Netherlands 98 100 97 93b 93 29 45 92 96 88 70 New Zealand 77 ll1 102 83 104 27 58 96 97 98 93 Nicaragua 15 990 1030 42 410 13 9 750 770 54 63a Niger 2 25b 290 50 7 0 1 83b 79a Nigeria . 119 93 21a 29 2 Norway 113 100 99 94 116 26 54 99 100 89 79 Oman 3 52 85a 12 61 b 5 84 86a 86 88b Pakistan . 39 65 14 Panama 106b 105 61 64 21 23 83b 75 Papua New Guinea 0 59 74 12 12 2 Paraguay 41 104b 1120 26 37 8 10 84b 86 82 85 Peru 34 114c 119 59a 65 17 40 760 74 78a 81 Philippines 12 113 111 64 79 24 26 89 88 70 Poland 42 100 98 77 84 18 26 98 98 Portugal 48 123 120 37 81 11 23 80 85 Puerto Rico .. .. .. . 48 Romania 76 102 95 71 82a 12 12 100 870 Russian Federation 63 102 107 96 88 46 45 . 90 World Development Indicators 1997 63 0 .2.8 Gross enrollment Age efficiency ratio ratio Primary Secondary Preprimary Primary Secondary Tertiary net enrollment net enrollment % of relevant % of relevant % of relevant % of relevant as % of as % of age group age group age group age group gross enrollment gross enrollment 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Rwanda 63a 50 3 10 0 88 93 78 Saudi Arabia 8 610 75a 290 290 7 14 790 81 a 720 710 Senegal 2 46 540 11jb 110 3 3 770 83c Sierra Leone 52 14 1 Singapore 1080 107 58b 78 8 920 ggb Slovak Republic 77 101 89 17 Slovenia 61 97 89 28 South Africa 24 . 770 13 82 65c Spain 67 1090 104 87b 113 23 41 93 95 85 82 Sri Lanka 103b 1060 55a 74b 3 6 Sudan 23 50 50 16 20 2 2 Sweden 67 97 100 88 99 31 38 99 99 93 94 Switzerland 91 101 91 18 31 94 92 71 69 Syrian Arab Republic 6 1000 1050 46b 47i 17 18 90a 910 Tajikistan 16 89 100 24 25 Tanzania 93 70 3 5 73 72 Thailand 50 990 98 29 37 13 19 Togo 3 1180 1020 33e 27e 2 3 67e 68e Trinidad and Tobago 9 99 94 70 76 4 8 90 93 78 71 Tunisia 9 1030 1180 27b 520 5 11 800 83c 67 59 Turkey 5 96 103a 35 61 5 16 920 Turkmenistan 23 Uganda 50 91 5 13 1 1 74 Ukraine 54 102 87 94 80 42 46 United Arab Emirates 64 890 110 520 890 3 11 83a 91 830 90 United Kingdom 27 103 112 83 92 19 37 94 85 95 92 United States 62 100 107 91 97 56 81 80 Uruguay 33 1070 1090 62 81 17 30 87a Uzbekistan 80 94 30 33 Venezuela 43 93 96 b 210 35 21 29 96 96 Vietnam 30 109 111 42 35 2 2 87 West Bank and Gaza Yemen, Rep Yugoslavia, Fed Rep 25 99 72 82 65 19 Zaire 1 920 680 24 24e 1 79d 670 Zambia 90 104 16 2 86 Zimbabwe 85 119 8 45 1 6 Low income 92w 105 w 33 w 3w Excl China& India 75w 83 w 19 w 3 w Middleincome 28w 101w 104w 52w 62w 20w 20w Lower middle income 22w 101w 104w 54w 64w 24w 23w Upper middle income 41 w 104 w 107 w 44 w 55w 13 w 17 w Low & middle income 95 w 104 w 39 w 8 w East Asia & Pacific 26 w 110 w 117 w 43 w 55w 3 w 5m Europe & Central Asia 30w 101 w 97 w 81 w 86 w 31 w 32w Latin America & Carib 43w 106 w 110 w 42 w 51 w 14 w 15 w Middle East& N Africa 13w 87w 97 w 42 w 59w 11 w 14 w South Asia 76 w 98 w 27 w 5w Sub-Saharan Africa 80 w 72 w 15 w 24 w 1 w High income 70w 93w 103 w 86 w 97w 35w 55w Percentage of repeaters in total enrollment a 5-9 b 10-14 c 15-19 d 20-25 e More than 25 64 World Development Indicators 1997 2.88 jections (see the discussion of demographic data in the notes to table 2 1) School enrollment data are important indicators of the In using enrollment data, it is important to consider * Gross enrollment ratio is the ratio of total enroll- size and capacity of the education system and may be repetition rates, which are quite high in some devel- ment, regardless of age, to the population of the age useful measures of outcomes, but they are notoriously oping countries, leading to a substantial number of group that officially corresponds to the level of edu- rifewitherrors ThedataherearereportedtotheUnited overage children enrolled in each grade The gross cation shown Estimates are based on UNESCO's Nations Educational, Scientific, and Cultural Organizea- enrollment ratios here provide an indication of the classification of education levels, as follows tion (UNESCO) by national education authorities on the capacity of each level of education relative to the age Preprimary provides education for children not old basis of annual enrollment surveys, typically conducted group that should be enrolled at that level But a high enough to enter school at the first level Primary, or at the beginning of the school year They do not reflect ratio does not necessarily indicate a successful edu- first level, provides the basic elements of education actual rates of attendance or the nonattendance of cation system The quality of schools varies widely at elementary or primary school (see table 2 7 for dropouts during the school year Furthermore, school within countries, between countnes, and over time duration of primary school) Secondary provides gen- administrators may have incentives to exaggerate Thus a lower enrollment ratio could be consistent with eral or specialized instruction at middle, secondary, enrollments Behrman and Rosenzweig (1994), com- greater aggregate, quality-adjusted educational capital or high schools, teacher training schools, and voca- paring official school enrollment data for Malaysia in if quality more than compensates for quantity tional or technical schools, this level of education is 1988 with gross school attendance rates from a house- The gross enrollment ratios for primary and sec- based on at least four years of instruction at the first hold survey, found that the official statistics system- ondary levels have been calculated by taking into level Tertiary requires, as a minimum condition of atically overstated enrollment account different national systems of education with admission, the successful completion of education at Overage or underage enrollments may occur, par- different durations of schooling at the pnmary and the second level or evidence of attainment of an ticularly when parents prefer for cultural or economic secondary levels For the tertiary level the ratios are equivalent level of knowledge and is provided at a uni- reasons to have children start school at other than expressed as a percentage of the population in the versity, teachers college, or higher-level professional the official age, Children's age at enrollment may also five-year age group following the secondary school school .Ageefficiencyratioistheratioofnetenroll- be inaccurately estimated or misstated, especially in leaving age The population estimates used to calcu- ment to gross enrollment at the pnmary and sec- communities where registration ofbirths is not strictly late gross enrollment ratios are from the United ondary levels Net enrollment ratio is the ratio of the enforced Parents who choose to enroll their under- Nations and are midyear estimates, while enroll- number of children of official school age enrolled in age children in primary school may do so by over- ments refer to the beginning of the school year school to the number of children of official school age reporting the age of the child And in education The age efficiency ratio, a useful complement to the in the population systems where the authorities are willing to alter gross enrollment ratio, reflects the extent to which school records, ages for children repeating a grade planned (net) enrollments match actual (gross) enroll- l_ may be deliberately underreported ments It does not measure the cost-efficiency of the Other problems affecting cross-country compar- system Nor does it reflectthe quality of the education Gross enrollment ratios are isons of enrollment data stem from measurement provided However, lower efficiency ratios are likelyto from UNESCO's Stahstical errors in estimates of school-age populations Age- be associated with higher direct costs of schooling Yearbook 1995 Age effi- sex structures from censuses or vital registration sys- because of the cost of providing teachers, materials, ciency ratios were com- tems, the pnmary sources for school-age population and classrooms for repeaters, and with higher oppor- piled by World Bank staff data, are commonly subject to underenumeration tunity costs of providing schooling to overage stu- using the UNESCO data- (especially of young children) in order to circumvent dents Windham (1988) provides a general discussion base on enrollment by laws or regulations or from age heaping resulting from of efficiency indicators level, age, and gender parents' rounding up children's ages While adjust- In general, the enrollment data in this table cover ments for age bias are commonly made in census both public and private schools but may exclude cer- data, such adjustments are rarely made for data from tain specialized schools and training programs inadequate vital registration systems Compounding Interested readers should consult the notes to the these problems, pre- and post-census year estimates appropriate tables in UNESCO's Statistical Yearbook of school-age children are either interpolations or pro- 1995 World Development Indicators 1997 65 O 2.9 Educational attainment Percentage of cohort Progression to Expected years of reaching grade 4 secondary school schooling Male Female Male Female % % Male Female 1980 1990 1980 1990 1980 1990 1980 1990 1980 1992 1980 1992 Albania 97 96 Algeria 92b 96b 91 95a 55J 77 62' 83f 9 11 6 9 Angola 49 37 8 7 Argentina 73 76 13 14 Armenia Australia 94 97 12 13 12 14 Austria 92 97 97 99 11 15 11 14 Azerbaijan Bangladesh 29 44 30 46 Belarus Belgium 78d 810 14 14 13 14 Benin 77e 58 73e 58 62J 48J Bolivia 52 50 9 11 8 9 Bosnia and Herzegovina Botswana 910 98, 84' 85' 7 10 8 11 Brazil 98 98 9 9 Bulgaria 98 93a 95 91 11 11 11 12 Burkina Faso 79b 86b 79b go, 27J 271 2 3 1 2 Burundi 83b 79d 83b 79d 8 7 3 5 2 4 Cambodia Cameroon 81d 81d 240 199 8 6 Canada 94 95 97 98 15 17 15 18 Central African Republic 38' 35J Chad 74d 65d Chile 78a 810 79 80 12 12 China Colombia 420 72a 46a 74 Congo 9le 910 861 80 Costa Rica 80c 900 84 gla 52 66 53 67 10 10 10 9 C6te d'lvoire 94c 850 g9d 82d 31J 27 Croatia 16 28 11 11 Cuba 91b 96 12 13 Czech Republic Denmark 14 15 14 15 Dominican Republic 10 10 Ecuador 78b 76 Egypt, Arab Rep 75 83 82h 859 11 9 El Salvador 52a 55a 28 22 26 17 9 9 Eritrea 84 70 Estonia 91 96 12 13 Ethiopia 42 48 82 77 Finland 99 98 99 98 France 13 14 13 15 Gabon 82d 79d Gambia, The 41J 42' 5 6 3 Georgia Germany 96 98 99 99 15 14 Ghana 87 82 Greece 98 98 12 13 12 13 Guatemala 66b 56 Guinea 810 78d 51J 47' 4 2 Guinea-Bissau 63e 470 719 46h 6 3 Haiti 63 60 64 60 38 45 Honduras Hong Kong 100 99 87 93 12 12 66 World Development Indicators 1997 2.99 Percentage of cohort Progression to Expected years of reaching grade 4 secondary school schooling Male Female Male Female % % Male Female 1980 1990 1980 1990 1980 1990 1980 1990 1980 1992 1980 1992 Hungary 96 96 14 15 28 29 9 12 10 12 India 57 52 Indonesia 10 9 Iran, Islamic Rep. 94a 93 859 83f 10 8 Iraq 88 85 46 51 . 12 9 9 7 Ireland 97 98 100 99 . 11 13 I1 13 Israel 97 98 98 97 Italy 98 99 98 100 Jamaica 98 100 . . 10 11 11 11 Japan 100 100 100 100 100 1oo 100 100 13 . 12 Jordan 95a 99 95a 97 88' 60 88' 75 12 11 12 12 Kazakstan Kenya 84 85 Korea, Dem. Rep Korea, Rep 96 100 96 100 99 . 96 12 14 11 13 Kuwait 98 97 98 97 11 11 Kyrgyz Republic Lao PDR 31 31 70h 66h 8 6 Latvia Lebanon Lesotho 61d 75d 77c 85C 7 8 10 10 Libya 99 93 . 77 76 Lithuania Macedonia, FYR Madagascar 680 72e 35 35J Malawi 62a 73b 55a 68a 6 5 Malaysia 98 99 Mali 73e 77e 4J1 64J 36J 60' . 2 1 Mauritania 960 82b 86 83C 34J 28i Mauritius 97 97 47 49J 47 54J Mexico 91i 65 86 81 Moldova 96 97 Mongolia Morocco 90e 83 89e 81 79' 841 8 8 5 6 Mozambique 60d 54d 25 39 23 39 5 4 4 3 Myanmar Namibia 76 . 72h 12 13 Nepal Netherlands 97 . 100 65 75 14 16 13 15 New Zealand 98 99 . 14 15 13 16 Nicaragua 510 55a 8 8 9 9 Niger 820 79c 381 30 . . 3 . Nigeria 74 76 Norway 99 100 . 13 15 13 16 Oman 74 88 83 93 5 8 2 7 Pakistan 53 55 41 45 . Panama 87b 85 88a 88 . . I 11 11 11 Papua New Guinea 77 72 85 70 . . . Paraguay 79a 78. 9 . 8 Peru 85 .. 83 .. 81' 78' . 11 .. 10 Philippines . 10 11 11 11 Poland 12 12 12 12 Portugal Puerto Rico Romania 93 94 11 . 11 Russian Federation .. World Development Indicators 1997 67 02 92.9 Percentage of cohort Progression to Expected years of reaching grade 4 secondary school schooling Male Female Male Female % % Male Female 1980 1990 1980 1990 1980 1990 1980 1990 1980 1992 1980 1992 Rwanda 73b 73b 74b 765 5 2 6 6 Saudi Arabia 91c 90gb 85 94 7 9 5 8 Senegal 93 900b 6 4 Sierra Leone Singapore 96 98 72h 78h 11 11 Slovak Republic 98 99 Slovenia South Africa 12 . 12 Spain 92 94 13 14 12 15 Sri Lanka Sudan 83 71 Sweden 99 100 12 14 13 14 Switzerland 92 94 42 45 42 46 14 15 13 14 Syrian Arab Republic 94a 910 76g 69' 76 62 11 10 8 9 Tajikistan Tanzania 90 89b 89 gob Thailand Togo goe 870 840 82e 39J 34J 11 6 Trinidad and Tobago 83 96 89 97 11 11 11 11 Tunisia 94d 950 90c 93b 31i 57J 31J 59J 10 11 7 10 Turkey 98 98 47 33 Turkmenistan Uganda 83 74 Ukraine United Arab Emirates 940 93 97 99 8 :1 7 12 United Kingdom 13 15 13 15 United States 14 16 15 16 Uruguay 93 98a 99 98a Uzbekistan Venezuela 840 83 69 70 10 11 Vietnam 71 67 West Bank and Gaza Yemen, Rep Yugoslavia, Fed Rep Zaire 730 700 250 309 7 4 Zambia Zimbabwe 67 64 Percentage of repeaters in grade 4 a 5-9 b 10-14 c 15-19 d 20-24 e More than 24 Percentage of repeaters in final primary grade f 5-9 g 10-14 h 15-19 i 20-24 j More than 24 68 World Development Indicators 1997 2.9 - 3=_ tion, and the availabil Ity of special programs and other C_ alternatives to the general secondary education Indicators of persistence or grade progression pro- system Enrollment in the final grade of primary * Percentage of cohort reaching grade 4 is the vide a measure of how successful an education school may be systematically overestimated because proportion of children enrolled in primary school in system is in maintaining a flow of students from one enrollment as reported at the beginning of the school 1980 and 1990 who reach grade 4 in 1983 and grade to the next and thus of imparting a particular year includes dropouts who may leave school during 1993, respectively The estimate is based on the level of education Although school attendance is the year reconstructed cohort method (see About the data) mandatory in most countries, at least through the Expected years of schooling estimates the total * Progression to secondary school is the number primary level, students drop out of school for a van- number ofyears ofschoolingthat an average child will of new entrants in the first grade of secondary (gen- ety of reasons, discouragement over poor perfor- receive It may also be interpreted as an indicator of eral) school divided by the number of children mance. the cost of schooling, and the opportunity the total education resources, measured in school enrolled in the final grade of primary school in the cost of additional time spent in school are frequently years, that a child will require over his or her " lifetime" previous year (according to the country's duration of cited In addition, the progress of students to higher in school Because the calculation of this indicator primary education, as shown in table 2 7) grades may be limited bythe availabilityofteachers, assumes that the probability of a child's being * Expected years of schooling are the average classrooms, and educational materials enrolled in school at anyfuture age is equal tothe cur- number of years of formal schooling that a child is Persistence measures the proportion of a single- rent enrollment ratio forthat age, changes and trends expected to receive, including university education year cohort of students that eventually reaches a par- in future enrollment ratios are not accounted for and years spent in repetition They are the sum of ticular grade or level of schooling Tracking data for the age-specific gross enrollment ratios for primary, individual students are not available. so calculations secondary, and tertiary education are based on the reconstructed cohort method This method uses data on average promotion, repetition, =_ and dropout rates to calculate the flow of students from one grade to the next Other flows caused by new Estimates of the percent- entrants, reentrants, grade skipping, migration, or l age of cohort reaching school transfers during the school year are not con- grade 4 and progression sidered The reconstructed cohort method makes I to secondary school were three simpiifying assumptions dropouts never return compiled using the to school, promotion, repetition, and dropout rates UNESCO database on remain constant over the entire period in which the enrollment by level, grade cohort is enrolled in school, and the same rates apply - or field, and gender Data to all pupils enrolled in a given grade, regardless of on expected years of whether they previously repeated a grade schooling are from UNESCO's Statistical Yearbook, Because UNESCO data do not include dropouts or supplemented by information from UNESCO's World dropout rates, the number of dropouts was estimated Education Report as the difference between enrollments in successive grades in successive years, after netting out repeaters The remaining students are assumed to be promoted Repeated application of the same calcula- tions leads to an estimate of the number of students entering each successive grade (Fredricksen 1991) The percentage of the cohort reaching grade 4, rather than some other grade, is shown for two rea- sons First, because of differences among countries in the duration of primary schooling, which ranges from three to 10 grades (see table 2 7 for each country's duration of primary schooling), grade 4 estimates are more comparable across countries than are estimates for other grades Second, using grade 4 minimizes the effect of repetition at or close to the final grade of pri- mary education Progression to secondary school measures the pro- portion of students in the final grade of primary school who enter the first year of the general secondary system. The comparability of this indicator across time and between countries may be affected by changes in the definition of the primary and sec- ondary levels, rules governing repetition and promo- World Development Indicators 1997 69 O ~2.10 Gender and education Primary educationi Secondary general Secondary vocatlonal Teachers Pupils Teachers Pupils Teachers Pupils % female % female % female % female % female % female 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Albania 50 60 47 49 46 58 59 56 32 41 36 Algeria 37 43 42 46 44 39 46 19 21 34 Angola 47 33 21 Argentina 92 49 49 75 64 47 Armenia .50 Australia 70 74 49 49 45 51 50 49 Austria 75 84 49 49 54 61 49 49 36 44 41 43 AzerbaUjan 60 47 48 32 38 Bangladesh 8 37 7 .. 24 5 2 Belarus 49 51 Belgium 59 78 49 49 Benin 23 32 34 ..26 28 26 Bolivia 48 47 ... .... Bosnia and Herzegovina . Botswana 72 77 55 51 35 43 56 53 45 32 25 30 Brazil 85 .. 49 ... 52 ..47 Bulgaria 72 79 49 48 64 75 68 67 49 58 40 38 Burkina Faso 20 23 37 39 . 18 33 34 . 21 40 49 Burundi 47 47 39 45 18 .. 25 38 10 14 18 39 Cambodia . 36 45 .. . 37 Cameiroon 20 32 45 47 18 20 34 40 24 25 39 41 Canada . 69 49 48 . 53 49 49 Central African Republic 25 37 ..12 .. 25 .25 49 Chad . 7 32 4 17 Chile 74 72 49 49 .. 55 54 . 47 47 China 37 46 45 47 25 33 40 44 25 38 34 48 Colombia 79 80 50 50 41 .. 50 51 42 45 59 Congo 25 34 48 48 8 .. 40 41 . 23 54 47 Costa Rica 79 80 49 49 57 .. 54 51 50 50 49 M6e dIlvoire 15 20 40 42 . .. 28 34 . .. 49 Croatia 73 75 49 49 69 65 .. 57 .. 46 Cuba 75 78 48 49 50 60 51 54 25 34 46 46 Czech Republic .. 93 .. 49 . 66 . 52 .. 40 . 40 Denmark . 58 49 49 .. 51 52 .41 46 Dominican Republic .. 71 40 50 57 . 64 Ecuador 65 65 49 49 38 44 48 47 37 60 55 Egypt, Arab Rep. 47 54 40 45 35 42 36 44 21 35 38 45 El Salvador 65 71 49 49 24 .. 43 48 32 48 53 Eritrea 35 44 .. 12 . 43 ... 10 Estonia . 96 49 49 83 53 .. 72 . 49 Ethiopia 22 27 35 38 10 10 36 46 .. 18 Finland . . 49 49 .. 53 53 42 42 47 54 France 68 78 48 48 58 58 49 51 42 . 68 45 Gabon 27 49 50 28 27 42 .. 7 28 Gambia, The 34 31 35 41 27 .. 30 ..20 19 Georgia Germany . 85 49 48 . 50 .. 35 . 44 Ghana 42 35 44 46 21 . 38 39 21 25 31 Greece 48 55 48 48 55 56 50 50 24 44 20 34 Guatemala 62 45 46 . 43 ...39 Guinea 14 22 33 33 14 28 24 .. 25 Guinea-Bissau 24 32 20 . 22 -. 3 14 Haiti 49 . 46 11 47 . Honduras 74 73 50 50 - 50 53 .49 56 Hong Kong 73 48 51 ..32 70 World Development Indicators 1997 2.10 0 Primary education Secondary general Secondary vocational Teachers Pupils Teachers Pupils Teachers Pupils % female % female % female % female % female % female 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Hungary 80 83 49 49 61 67 65 64 40 39 44 India 26 30 39 43 30 34 32 38 ..32 13 Indonesia . 52 46 48 . 39 36 46 . 27 27 40 Iran, Islamic Rep. 57 55 40 47 45 39 44 10 16 16 24 Iraq 48 68 46 45 42 55 32 39 24 52 29 26 Ireland 74 77 49 49 . 51 50 .. 72 49 Israei 77 84 49 49 57 53 . 46 45 Italy 87 92 49 49 64 71 48 50 45 51 41 43 Jamaica 87 93 50 49 67 . 52 56 . 65 Japan 57 60 49 49 34 50 50 28 47 45 Jordan 59 60 48 49 44 53 46 55 28 37 30 35 Kazakstan 97 . 49 74 52 Kenya 29 44 47 49 24 36 42 44 Korea, Dem. Rep. .. Korea, Rep. 37 54 49 48 28 39 46 47 20 25 44 53 Kuwait 56 71 48 49 50 54 46 49 23 .. 33 Kyrgy(z Republic 88 80 49 50 58 67 49 51 33 50 50 Lao PDR 30 42 45 43 26 39 38 39 26 28 31 Latvia 92 49 49 .. 80 . 52 . . 45 Lebanon 48 49 .. 51 53 .. 8 40 40 Lesotho 75 80 59 54 48 50 60 59 47 . 56 46 Libya 47 67 47 49 24 40 39 53 12 25 Lithuania 97 98 49 48 85 82 52 . .. 41 Macedonia, FYR . 52 . 48 61 ... 44 Madagascar . 56 49 49 . 50 11 34 Malawi 32 34 41 48 .29 39 . . 4 Malaysia 44 59 49 49 46 54 48 51 22 34 29 27 Mali 20 23 36 39 . 17 29 34 .. .8 34 Mauritania 9 18 35 45 8 11 21 36 .. 4 7 23 Mauritius 43 47 49 49 39 57 48 51 17 .. 22 Mexico .49 48 43 48 .66 59 Moldova 96 96 49 49 51 51 .. 57 Mongolia 82 49 53 . 52 41 . 62 Morocco 30 38 37 41 .. 38 41 .. . 23 38 Mozambique 22 23 43 42 27 20 29 40 15 24 17 25 Myanmar 54 66 48 48 61 45 . Namibia 65 50 . 46 55 20 .. 21 Nepal 10 16 28 39 .. Netherlands 46 56 49 50 26 30 52 52 31 41 41 New Zealand 66 80 49 49 41 54 49 49 .. 82 48 Nicaragua 78 84 51 50 . 55 52 53 . . 56 49 Niger 30 34 35 38 22 18 29 33 15 12 8 13 Nigeria 33 45 43 44 8 .. 36 .38 .. 17 Norway 56 63 49 49 . 51 51 . . 47 41 Oman 34 49 34 48 27 46 25 47 . . 0 17 Pakistan 32 .. 33 31 30 . 26 32 20 42 17 33 Panama 80 .. 48 55 - 51 47 .. 54 Papua New Guinea 27 35 41 45 34 35 32 40 31 31 32 Paraguay 55 48 48 51 ..44 Peru 60 .. 48 46 - 46 40 Philippines 80 .. 49 50 . 53 . Poland 83 . 49 49 69 .. 71 71 47 . 44 42 Portugal . 48 48 59 . 48 . 31 Puerto Rico. . Romania 70 84 49 49 53 65 65 52 41 52 45 42 Russian Federation 98 98 49 49 76 79 51 52 World Development Indicators 1997 71 O 2.10 Primary education Secondary general Secondary vocational Teachers Pupils Teachers Pupils Teachers Pupils % female % female % female % female % female % female 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Rwanda 38 47 48 50 28 38 55 44 Saudi Arabia 39 49 39 47 33 43 37 44 15 12 Senegal 24 25 40 43 16 14 34 35 25 35 Sierra Leone 42 30 Singapore 66 48 56 51 24 23 Slovak Republic 91 49 75 51 61 47 Slovenia 93 49 76 52 56 44 South Africa 58 50 64 53 28 Spain 67 74 49 48 43 53 51 51 31 43 46 52 Sri Lanka 80 48 48 64 51 51 Sudan 31 52 40 43 26 33 37 45 10 15 21 17 Sweden 79 49 49 51 51 52 45 Switzerland 49 49 49 50 39 41 Syrian Arab Republic 54 64 43 47 22 43 37 44 15 32 Tajikistan 50 49 35 47 Tanzania 37 42 47 49 28 20 33 43 Thailand 49 48 49 57 46 50 Togo 21 16 38 40 13 11 24 26 Trinidad and Tobago 66 74 50 49 52 56 50 51 Tunisia 29 48 42 47 39 46 Turkey 41 43 45 47 36 40 35 39 34 39 Turkmenistan Uganda 30 31 43 44 20 29 38 Ukraine 97 98 49 49 51 United Arab Emirates 54 69 48 48 48 45 51 United Kingdom 78 77 49 49 49 54 49 49 57 52 United States 84 49 49 54 49 49 Uruguay 49 49 58 Uzbekistan 78 80 49 49 48 48 46 49 Venezuela 83 75 50 50 58 Vietnam 65 47 58 47 West Bank and Gaza Yemen, Rep Yugoslavia, Fed Rep 75 49 51 Zaire 24 42 43 30 Zambia 40 47 35 3 Zimbabwe 38 42 48 48 36 32 42 44 Low income 32 w 38 w 42w 44 w 26 w 33 w 36 w 41 w 30w 31w Excl China & India 30 w 40 w 42 w 33 w Middle income 47w 48 w 46 w 49w Lower middle income 70w 47w 48 w 45 w 49 w Upper middle income 48 w Low & middle income 43 w East Asia & Pacific 41 w 46 w 45 w 47 w 28 w 35 w 40 w 44 w 25w 36w 33w 45w Europe & Central Asia 84 w 84 w 49 w 49 w 67w 53w 52 w Latin America & Carib 49 w Middle East& N Africa 53 w 41 w 46 w 44w 37w 45w 26w 24w 30 w SouthAsia 24w 31w 38w 41w 27w 34w 31w 38w 27w 15w Sub-Saharan Africa 29 w 37w 42 w 44 w 34 w 41 w High income 77w 49 w 49 w 50w 49 w 49 w 72 World Development Indicators 1997 2.10 0 Progress-and gaps Despite progress in raising both male and female enrollment rates in all regions during The data on female enrollment suffer from the same * Female teachers as a percentage of total teachers the past three decades, school enrollment problems affecting general school enrollment data includes full-time and part-time teachers * Female remains lower among girls than among boys discussed in the notes to table 2 8 To the extent pupils as a percentage of total pupils includes enroll- This gap is widest in South Asia, the Middle that boys or girls may be more likely to drop out of ments in public and private schools but may exclude East, and Sub-Saharan Africa and reflects both school or repeat grades, male and female enroll- certain specialized schools and training programs cultural norms and the value of girls' contribu- ment rates may misrepresent the actual pattern of tion to household work. Moreover, female attendance in some countries enrollment rates often mask high absenteeism Data on teachers may not reflect the functions and dropout rates. Low female enrollment is, they perform That is, teachers may be employed by The estimates in this table in part, a "bootstrap" problem. literate par- schools in many capacities outside the classroom, were compiled using ents, especially mothers, are more likely than and the responsibilities assigned to male and UNESCO's electronic data- illiterate ones to enroll their daughters in female teachers may differ systematically base on institutions, school, and once enrolled, these girls are as teachers, and pupils likely as boys to remain in school Most education systems do not prepare boys and girls equally for occupations. Studies of learning styles in industrial coun- tries have found that women leave school with fewer opportunities for continuing their education and poor prospects of translating their higher-level education into social and The widest enrollment gaps between boys and girls are in South Asia, the Middle East, and Sub-Saharan Africa- economic advancement At the postsec- ondary and higher levels, where the gap in enrollment between women and men is wider, there is implicit "gender streaming,' or sex segregation by field of study. This phenome- non, widespread in both developing and industrial countries, discourages women from acquiring training in a variety of fields, especially the "hard" sciences, mathemat- ics, and engineering. In the teaching profession, one of the largest occupational fields requiring advanced train- ing, women are well represented in many coun- tries, often constituting more than half of the total qualified employed. But a hierarchical pat- tern of occupational segregation leads to inequality between women and men at both the top and the bottom of the profession. In many countries men move upto better-paid and more prestigious positions in secondary and higher education as these levels expand, while women predominate at the primary level. World Development Indicators 1997 73 O 2.11 Health spending and personnel Health expenditure People per physician People per nurse People per hospital bed Total Public Private % of GDP % of GDP % of GDP 1990-95, b 1990-95a 1990-95' 1980 1993 1980 1993 1980 1993 Albania 2 7 735 248 Algeria 4 6 3 3 1 3 1,062 330 390 Angola 4 0 23,725 774 Argentina 10 6 4 3 6 3 330 1,783 218 Armenia 7 8 3 1 4 7 284 261 122 101 119 120 Australia 8 4 5 8 2 7 559 . 146 178 Austria 9 7 6 2 3 6 440 231 170 90 95 Azerbaijan 7 5 1 4 6 1 298 257 118 106 103 96 Bangladesh 2 4 1 2 1 3 8,424 12,884 14,750 11,549 4,702 5,479 Belarus 6 4 5 3 1 1 295 236 102 89 80 80 Belgium 8 2 7 2 1 0 401 274 130 107 121 Benin 1 7 16,980 14,216 2,157 4,182 684 4,182 Bolivia 5 0 2 7 2 4 1,911 2,348 7,048 709 Bosnia and Herzegovina Botswana 1 9 8,122 5,151 700 480 421 635 Brazil 7 4 2 7 4 7 1,301 844 1,140 3,379 299 Bulgaria 4 0 . 407 306 190 162 90 101 Burkina Faso 5 5 2 3 3 2 54,819 34,804 3,073 9,649 3,300 Burundi 0 9 17,153 4,778 . 1,519 Cambodia 7 2 0 7 6 5 9,374 1,231 . 453 Cameroon 1 4 1 0 0 4 11,996 1,999 . 381 Canada 9 8 7 0 2 7 560 464 122 107 . 150 Central African Republic 1 7 23,364 25,920 1,826 11,309 642 1,140 Chad 1 8 30,030 64,403 1,373 Chile 6 5 2 5 4 0 942 3,800 293 320 China 3.8 1 8 1 9 1,100 1,063 2,100 1,490 500 612 Colombia 7 4 3 0 4 4 1,105 2,717 627 732 Congo 6 8 3 6 3 2 8,425 3,713 595 1,401 306 Costa Rica 8.5 6 3 2 2 1,133 2,213 302 CMte d'lvoire 3 4 1 4 2 0 11,739 3,244 . 1,223 Croatia 101 8 5 1 6 Cuba 7 9 721 275 180 184 Czech Republic 9 9 7 8 2 1 273 122 Denmark 6 6 5 5 1 1 420 360 140 153 177 Dominican Republic 5 3 2 0 3 3 949 1,239 9,423 506 Ecuador 5 3 2 0 3 2 652 1,853 524 608 Egypt, Arab Rep 4 9 939 1,316 762 489 493 517 El Salvador 5 0 1 2 3 8 3,046 1,515 3,233 680 Eritrea 1 1 Estonia 5 9 239 253 95 127 80 104 Ethiopia 1 1 88,124 32,499 4,998 13,628 3,384 4,141 Finland 8 3 6 2 2 1 530 406 100 101 64 93 France 9 7 7 6 2 1 462 334 110 . 109 Gabon 0 5 2,184 1,987 225 1,173 305 Gambia, The 1 8 1,642 Georgia 0 3 208 182 90 85 94 95 Germany 9 5 7 0 2 5 452 367 118 Ghana 1 0 22,970 621 3,608 685 Greece 6 4 411 312 370 403 161 197 Guatemala 2 7 0 9 1 7 . 3,999 1,360 7,087 1,191 Guinea 0 9 45,457 7,445 5,056 5,166 1,712 Guinea-Bissau I 1 7,491 . 1,130 562 671 Haiti 3 6 1 3 2 3 9,079 10,855 8,945 1,350 1,251 Honduras 5 6 2 8 2 8 3,100 1,266 4,582 775 1,276 Hong Kong 4 3 1 9 2 5 1,211 795 249 234 74 World Development Indicators 1997 2.11 0 Health expenditure People per physician People per nurse People per hospital bed Total Public Private % of GDP % of GDP % of GDP 1990-95m 199095a 1990-95a 1980 1993 1980 1993 1980 1993 Hungary 7 3 6 8 0 5 400 306 157 321 110 97 India 3 5 0 7 2 8 2,694 2,459 4,674 3,323 1,299 1,371 Indonesia 1 5 0 7 0 8 12,458 7,028 2,732 1,423 Iran, Islamic Rep 4 8 2 8 2 0 2,949 3,142 1,179 675 724 Iraq 1,776 1,659 2,195 1,398 514 584 Ireland 7 9 6 0 1 9 784 632 141 153 103 101 Israel 4 1 401 130 197 164 Italy 8 3 5 9 2 5 750 207 250 333 131 Jamaica 5 4 3 0 2 3 2,786 6,420 489 476 Japan 7 0 5 5 1 5 740 608 210 89 64 Jordan 7 9 3 7 4 2 1,272 554 875 548 795 533 Kazakstan 2 2 312 254 99 91 76 75 Kenya 1 9 10,071 21,970 983 8,675 602 Korea, Dem Rep. 419 Korea, Rep 5 4 1 8 3 6 1,690 951 454 586 300 Kuwait 7 0 589 182 241 335 Kyrgyz Republic 3 5 343 303 115 105 83 92 Lao PDR 2.6 0 8 1 4 4,446 493 405 Latvia 3 7 242 278 102 118 73 82 Lebanon 5 3 2 1 3 3 550 537 2,971 606 Lesotho 3 5 24,095 2,040 Libya 750 957 355 340 207 246 Lithuania 4 8 255 235 92 92 83 84 Macedonia, FYR 7 7 6 8 0 9 427 189 Madagascar 10 9,891 8,385 1,721 3,736 1,072 Malawi 2 3 53,605 44,205 3,024 28,951 1,184 Malaysia 1 4 3,917 2,441 570 480 439 437 Mali 1 3 25,444 18,376 2,322 5,297 Mauritania 1 5 15,772 2,261 1,486 Mauritius 2 2 1,920 1,165 627 392 320 347 Mexico 5 3 2 8 2 6 1,149 615 1,704 Moldova 5 1 320 250 105 90 83 80 Mongolia 4 7 4 4 0 4 101 371 211 219 89 87 Morocco 3 4 1 6 1 7 18,558 4.665 898 814 775 Mozambique 4 6 39,142 36,225 4,629 4,937 918 1,156 Myanmar 0 9 0 5 0 4 4,952 12,528 4,943 1,227 1,171 1,605 Namibia 7 6 3 9 3 7 4,328 317 207 Nepal 5 0 1 2 3 8 30,062 13.634 7,783 2,257 5,728 4,210 Netherlands 8.8 6 9 2 0 480 399 168 123 80 170 New Zealand 7 5 5 7 1 7 638 518 81 149 Nicaragua 7 8 4 3 3 5 2,308 2,039 598 549 Niger 2 2 53,986 3,765 Nigeria ,, , 8,853 5,208 1,089 1,450 1,154 157 Norway 7 3 6 9 0 4 524 308 70 73 67 210 Oman .. 2.5 . 2,142 1,131 907 617 398 Pakistan 0 8 3,500 1,923 5,870 3,330 1,742 1,769 Panama 7 5 5 4 2 0 1,010 562 1,069 Papua New Guinea . 2 8 16,073 12,754 1,000 1,569 180 290 Paraguay 4 3 1 0 3 3 1,746 1,231 . 7,098 762 Peru 4.9 2 6 2 3 1,395 939 664 Philippines 2 4 1 3 1 0 7,848 8.273 2,591 589 780 Poland 4 6 560 451 227 189 178 180 Portugal 7 6 4 3 3 4 494 353 227 Puerto Rico 6 0 Romania . 3 3 678 538 280 114 127 Russian Federation 4 8 4 1 0 6 248 222 88 90 77 77 World Development Indicators 1997 75 O3 2.11 Health expenditure People per physician People per nurse People per hospital bed Total Public Private % of GDP % of GDP % of GDP 1990-95' 1990-95' 1990-95a 1980 1993 1980 1993 1980 1993 Rwanda 1 9 . 31,482 24,967 10,314 8,133 654 1,152 Saudi Arabia 2 2 1,819 749 738 329 686 401 Senegal 1 6 12,683 18,192 1,931 13,174 1,923 Sierra Leone 1 6 17,305 1,869 823 Singapore 3 5 1 1 2 4 1,111 714 321 239 275 Slovak Republi(c . 6 3 287 105 11 Slovenia 7 9 143 167 South Africa 7 9 3 6 4.3 Spain 7 4 5 8 1 6 361 261 281 262 209 Sri Lanka 1 9 1 4 0 4 7,172 6,843 1,262 1,745 340 365 Sudan 0 3 8,803 1,408 1,086 919 Sweden 7.7 6 4 1 3 454 394 107 108 68 161 Switzerland 9 6 6 9 2 7 580 128 93 Syrian Arab Republic 2,243 1,159 1,372 1,047 905 920 Tajikistan 6 4 422 424 152 139 100 95 Tanzania 2 8 716 976 Thailand 5 3 1 4 3 9 6,803 4,416 2,280 1,067 651 765 Togo 1 7 18,813 11,385 1,225 3,060 664 Trinidad and Tobago 3 9 2 6 1 3 1,377 1,520 381 247 308 Tunisia 5 9 3 0 2 9 3,694 1,549 956 411 470 350 Turkey 4 2 2 7 1 5 1,642 976 1,239 1,098 445 403 Turkmenistan 2 8 349 306 125 100 94 93 Uganda 3 9 1 8 2 2 21,405 22,399 2,009 6,762 661 760 Ukraine 5 4 274 227 97 87 80 77 United Arab Emirates 2 2 1 9 0 3 936 1,208 410 718 351 United Kingdom 6 9 5 8 1 1 611 207 202 107 161 United States 14 3 6 3 7 9 549 421 196 121 171 194 Uruguay 8 5 2 0 6 5 501 221 Uzbekistan 3 5 . 347 282 118 86 87 106 Venezuela 7 1 2 3 4 8 1,188 633 329 2,969 385 Vietnam 5 2 1 1 4 1 4,151 2,279 1,241 1,149 286 261 West Bank and Gaza 6 2 3 4 2 7 Yemen, Rep 2 6 1 1 1 5 6,912 4,498 2,014 1,833 1,196 Yugoslavia, Fed Rep 290 232 810 71 73 Zaire 15,150 1,355 702 Zambia 3 3 2 6 0 7 13,221 10,917 1,693 4,937 289 Zimbabwe 2 1 6,105 7,384 921 1,594 Low income 4,913 w 1,088 w 1,152 w Excl China & India 5,410 w 1,953 w 2,131 w Middle income 3,599w 472w Lower middle income 4,287w . 385 w 402 w Upper middle income 1,341 w 826 w 635 w Low & middle income 4,243W . 921 w 950w East Asia & Pacific 1,834 w 1,063 w 2,084 w 1,490 w 518 w 612 w Europe & Central Asia 478w 371w 1l1w 260w 133w 140w Latin America & Carib 1,458 w 970 w 688 w Middle East& N Africa 4,235w 1,117w 624w 661w South Asia 3,857w 2,847w 5,788w 3,313w 1,731w 1,889w Sub-Saharan Africa High income 612w 522w 216w 159w a Data are for most recent year available b Totals may not add up due to rounding 76 World Development Indicators 1997 2.11 0 Most industrial countries have developed systems * Health expenditure includes outlays for the provi- for tracking and comparing public and private health sion of health services (preventive and curative), pop- care expenditures over the past two decades ulation activities, nutrition activities, and emergency By contrast, in developing countries data are aid designated for health It does not include water rarely tabulated in national health accounts, which and sanitation * Public health expenditure com- is necessary to ensure consistency and complete- prises recurrent and capital governmentexpenditures ness Compiling complete information on public on health care (including government and social secu- health care spending has proved difficult in some rity expenditures for medical care) and donor assis- developing countries And estimates of private tance for health services * Private health health spending are often lacking or incomplete expenditure covers direct out-of-pocket expenditures Data are provided here only for countries with actual by households, direct payments by employers for data health services, and expenditures by nongovernmen- Data on physicians and nurses are mainly from the tal and charitable organizations. * Physicians are World Health Organization's (WHO) second evaluation defined as graduates of any faculty or school of med- of progress in implementing national health-for-all icine who are working in the country in any medical strategies Thedatafordevelopingcountriesherehave field (practice, teaching, research) a Nurses are been supplemented by country statistical yearbooks defined as persons who have completed a program of and World Bank sector studies basic nursing education, are qualified and registered Two factors affect the comparability of the WHO's or authorized to provide service for the promotion of physician ratios First, in many developing countries health, prevention of illness, care of the sick, and a significant share of the population, particularly in rehabilitation, and are working in the country The rural areas, receives treatment from practitioners of data do not cover auxiliary and paraprofessional per- indigenous medicine not included in the WHO defin- sonnel * Hospital beds comprise those available in ition of physician Second, the extent to which home- public and private, general and specialized hospitals opaths, osteopaths, and the like are included varies and rehabilitation centers Hospitals are establish- across countries ments permanently staffed by at least one physician Thus these are essentially indicators of availabil- ity, not of quality or use They do not show how well m trained physicians are or how well equipped hospi- tals or medical centers are-nor do they reveal the Country information on use of their services Similarly, data on hospital public health expenditures beds and hospital usage may be misleading in poor IS from national sources. countries, where hospital crowding can result in ...: supplemented by World people sleeping on floors in wards and corridors Bank sector studies, The WHO reviews the international health statis- including tics it compiles for validity and consistency, querying ; i * Goldstein and others. data that - - Trends in Health Status, * Vary by more than a reasonable amount from the Services, and Finance, value reported for the previous period vol 1 * Are not consistent with data reported for other * Chellaraj and others, Trends in Health Status, indicators Services, and Finance, vol 2 * Are not consistent with data from other sources * Klugman and Schieber, A Survey of Health Reform Inconsistent or grossly inaccurate data are not in Central Asia retained Data were also drawn from World Bank public expen- diture reviews, the Pan American Health Organization, the International Monetary Fund's Govemment Finance Statistics, and other studies Data for private expenditure are largely from household surveys, World Bank poverty assessments and sector studies, and other studies Data on public and private health expen- ditures for industrial countries and Turkey are from the OECD Data for physicians, nurses, and hospital beds come from government statistical yearbooks, the World Bank, the OECD, and the WHO World Development Indicators 1997 77 O 2.12 Access to health services Health care Safe water Sanitation Child immunization Births attended by health staff % of % of % of Measles DPT population population population % of children % of children with access with access with access under 12 months under 12 months % of total 1980 1993 1980 1994-95 1980 1994-95 1980 1995 1980 1995 1985 1990 Albania 100 92 100 90 91 94 97 99 Algeria 77 17 69 33 75 Angola 70 24 32 16 32 21 15 16 Argentina 64 89 76 46 66 Armenia 95 83 Australia 99 100 99 95 99 90 86 95 99 Austria 100 100 85 100 90 60 90 90 Azerbaijan 91 90 Bangladesh 80 74 83 30 96 91 7 Belarus 100 50 100 96 90 100 Belgium 100 99 100 70 97 100 Benin 42 70 22 72 79 34 51 Bolivia 60 44 83 88 36 29 Bosnia and Herzegovina 57 67 Botswana 86 70 55 68 64 78 52 Brazil 92 73 78 69 73 Bulgaria 100 96 99 98 93 97 100 100 Burkina Faso 35 5 14 23 55 2 47 33 Burundi 80 58 48 30 44 38 57 26 Cambodia 13 75 79 Cameroon 20 15 41 40 47 31 20 48 25 Canada 99 97 100 60 85 98 80 93 99 100 Central African Republic 13 16 19 70 21 40 Chad 26 29 32 24 18 21 Chile 95 96 71 93 92 95 China 46 89 93 51 Colombia 88 87 96 70 77 91 51 Congo 60 9 49 39 42 50 Costa Rica 97 100 99 94 85 C6te d'lvoire 60 20 82 17 54 28 57 42 40 Croatia 96 68 90 87 Cuba . 100 61 94 31 66 100 67 100 99 100 Czech Republic 96 96 Denmark 100 100 100 100 100 20 88 85 89 Dominican Republic 79 85 100 100 98 44 Ecuador 80 70 64 100 26 80 27 Egypt, Arab Rep 100 99 90 84 70 82 82 24 El Salvador 62 73 94 100 35 Eritrea 45 45 Estonia 81 84 Ethiopia 55 4 27 10 7 54 6 57 58 Finland 100 100 100 100 80 98 92 100 France 100 85 96 30 76 79 89 Gabon 87 67 76 35 50 48 48 92 Gambia, The 90 42 61 34 70 88 88 93 54 65 Georgia 63 58 Germany 100 28 75 56 80 Ghana 25 56 42 68 22 71 73 42 Greece 96 70 31 78 Guatemala 60 64 71 84 78 22 Guinea 45 49 12 6 44 69 4 73 76 Guinea-Bissau 30 80 24 57 20 30 68 24 74 16 Haiti 45 28 24 24 30 20 Honduras 62 . 70 68 90 38 96 50 63 Hong Kong 42 84 83 78 World Development Indicators 1997 2.12 0 Health care Safe water Sanitation Child immunization Births attended by health staff % of % of % of Measles DPT population populabon population % of children % of children with access with access with access under 12 months under 12 months % of total 1980 1993 1980 1994-95 1980 1994-95 1980 1995 1980 1995 1985 1990 Hungary 94 99 100 99 100 India 50 63 29 84 92 33 75 Indonesia 43 63 55 89 91 31 Iran, Islamic Rep 50 73 50 89 60 82 95 29 97 70 Iraq . 98 74 45 36 95 13 91 24 74 Ireland 100 60 78 36 65 Israel . 100 99 70 94 84 92 99 Italy . 99 99 100 50 50 100 Jamaica 70 74 82 39 93 89 88 Japan 100 95 85 68 85 o00 100 Jordan 90 89 89 76 30 92 100 75 86 Kazakstan 72 80 Kenya 49 . 43 73 84 Korea, Dem Rep 100 . 100 100 98 52 96 100 Korea, Rep. 100 89 100 89 92 61 93 65 95 Kuwait 100 100 100 100 . 93 54 100 99 Kyrgyz Republic 75 53 89 83 Lao PDR 41 30 65 7 51 Latvia . 85 65 Lebanon 92 59 . 88 92 45 Lesotho 80 18 57 12 35 82 56 56 28 Libya 100 100 90 30 70 18 89 91 76 Lithuania 94 96 Macedonia, FYR 85 87 Madagascar 65 32 17 59 67 62 71 Malawi 40 80 54 63 99 98 59 41 Malaysia 88 90 75 94 81 58 90 82 92 Mali 20 . 44 44 11 49 18 46 27 Mauritania 72 64 53 50 23 Mauritius 100 99 100 100 53 85 88 89 84 91 Mexico 51 91 87 . 70 90 92 45 Moldova . 50 98 96 Mongolia 90 100 . 54 85 88 100 100 Morocco 62 32 59 50 63 92 44 93 26 Mozambique 30 9 28 10 23 51 71 38 57 28 29 Myanmar 30 20 39 20 42 66 5 69 25 94 Namibia 57 36 57 61 71 Nepal 10 11 48 0 22 78 77 10 Netherlands 100 100 100 100 100 93 95 97 97 New Zealand 100 87 71 87 72 84 99 100 Nicaragua 57 81 23 85 42 Niger . 30 57 15 16 38 5 19 47 21 Nigeria 40 67 43 38 20 50 24 44 45 Norway 100 100 100 100 80 93 90 92 100 Oman 75 89 15 56 72 98 9 99 60 90 Pakistan 65 85 38 60 16 30 53 55 24 70 Panama 82 82 87 84 49 86 83 85 Papua New Guinea . 96 31 26 35 28 50 34 20 Paraguay 8 30 76 28 77 22 Peru 60 47 97 18 94 44 Philippines 84 75 86 51 85 76 Poland 100 100 67 50 100 89 96 95 95 Portugal . 57 41 100 70 94 90 93 Puerto Rico Romania . 77 50 49 93 98 99 Russian Federation 91 72 World Development Indicators 1997 79 O ~2.12 Health care Safe water Sanitation Child immunization Births attended by health staff % Of % Of % of Measles DPT population population population % of children % of children with access with access with access under 12 months under 12 months ft of total 1980 1993 1980 1994-95 1980 1994-95 1980 1995 1980 1995 1985 1990 Rwanda 48 32 28 Saudi Arabia 85 98 91 93 76 86 94 53 97 79 Senegal 40 80 80 Sierra Leone 26 13 44 41 25 Singapore 100 100 100 100 57 88 87 95 100 100 Slovak Republic 43 51 99 99 Slovenia 90 91 98 South Africa .46 76 73 Spain 98 99 95 97 90 88 96 Sri Lanka 90 90 57 66 88 45 91 87 85 Sudan 70 77 55 1 74 76 20 Sweden 100 85 100 47 96 94 99 100 Switzerland 100 100 85 100 83 89 100 Syrian Arab Republic 99 71 87 45 78 98 14 100 37 80 Tajikistan 62 90 95 Tanzania 72 93 49 86 37 75 58 79 74 Thailand 30 59 81 87 86 93 59 71 Togo - 67 20 65 9 73 Trinidad and Tobago 99 .. 82 . 56 . 87 52 81 90 Tunisia 95 90 72 86 46 72 89 36 90 60 Turkey 100 67 92 10 94 45 75 64 86 78 Turkmenistan .. .85 . 60 . 90 87 Uganda . 71 42 . 60 10 79 2 79 Ukraine . 100 97 50 49 -. 96 . 94 100 United Arab Emirates 96 90 100 98 75 95 90 45 90 96 97 United Kingdom 100 96 60 92 44 92 98 United States 90 98 85 96 89 37 94 99 Uruguay ..34 82 80 .. 86 100 Uzbekistan ...18 71 .. 65 Venezuela 88 55 94 .. 63 .. 82 Vietnam 75 97 38 21 . 95 93 West Bank and Gaza .. Yemen, Rep. 16 . 52 51 49 . 52- Yugoslavia, Fed Rep . .58 100 89 75 90 79 Zaire 80 59 . 25 9 41 35 Zambia 75 .. 47 .. 42 55 78 47 76 . 43 Zimbabwe 55 .10 74 5 58 51 78 32 80 67 Low income .. 53 w . ... 77w 80 w Excl. China &India .. . Sw . 31 w . 65 w 63 w Middle income ... . . 86 w 86 w Lower middle income .. .87 w .. 89 w Upper middle income . .. 86 w . 75w . 83 w . 79 w Low & middle income . .. 56 w ... 80 w 82 w East Asia &Pacific . ..49 w ... 88 w . 91 w Europe & Central Asia .. . .83 w .. 90 w Latin America &Carib . ..80 w 67w 84 w . 80 w 68 w Middle East& N Africa . 85 w . .89 w . 91 w 58 w South Asia .63 w . 29 w 80 w . 84 w 29 w Sub-Saharan Africa .. 47 w 48w . 60 w . 58 w High income . 94 w 92 w 83 w . 89 w 80 World Development Indicators 1997 2.12 0 Implementing primary health care ,,* , The Global Strategy for Health for All by the Year 2000, adopted by the World Health Data reported here are provided to the World Health * Percentage of population with access to health Assembly in 1981, marked a radical change Organization (WHO) by member states in the context care is the share of the population covered for treat- in the orientation of health development. The of monitoring and evaluating their progress in imple- ment of common diseases and injuries, including avail- strategy was aimed at attaining a socially and menting national health-for-all strategies Reliable, ability of essential drugs on the national list, within one economically productive life for all people by observation-based statistical data forthe indicators hour'swalk ortravel * Percentageof populationwith redirecting national health systems toward an do not exist in many developing countries, so in most access to safe water is the share of the population approach based on primary care. Equity in the cases the data are estimates Such assessments with reasonable access to an adequate amount of safe availability of health services is an underlying often may be biased by a country's inflated or water (including treated surface water and untreated principle of primary care and thus a critical deflated estimates designed to show either progress but uncontaminated water, such as from springs, san- element in monitoring progress in implement- or a need for international assistance Thus the itary wells, and protected boreholes) In urban areas ing the strategy resulting data cannot be used for analytical pur- the source may be a public fountain or standpost The strategy includes a list of indicators for poses-and are of limited use for monitoring located not more than 200 meters away In rural areas global monitoring and evaluation (WHO 1995, progress in development efforts, national or interna- the definition implies that members of the household annex 2) The health-for-all global indicator of tional do not have to spend a disproportionate part of the day primary health care is expressed as the per- Access indicators measure the supply of services fetching water An adequate amount of water is that centage of the population with access to at but reveal little about benefits or rate of use For needed to satisfy metabolic, hygienic. and domestic least the following: example, access to health care provides no infor- requirements, usually about 20 liters of safe water a * Safe water in the home or within 15 minutes' mation on the quality of health care or on how the person per day The definition of safe water has walking distance. consumption of services differs among groups changed over time * Percentage of population with - Adequate sanitary facilities in the home or within a country, region, or community Moreover, access to sanitation refers to the share of the popu- immediate vicinity such indicators, unless based on survey statistics, lation with at least adequate excreta disposal facilities are becoming increasingly less informative in many that can effectively prevent human, animal, and insect developing countries For the poor and for many in contact with excreta Suitable facilities range from rural areas, services by nongovernmental organiza- simple but protected pit latrines to flush toilets with Each year 43 million cases of tions play an increasingly important role, widening sewerage To be effective, all facilities must be cor- measles occur-and one million the gap between official statistics and the actual rectly constructed and properly maintained * Child production and consumption of many essential ser- immunization measures the rate of vaccination cover- deaths from the disease * vices It is not known, however, whether such ser- age of children under one year of age for four dis- vices truly replace publicly provided services, and if eases-measles and DPT (diphtheria, pertussis or * Immunization against the major infectious so, howtheydiffer in quantityand qualityfrom public whooping cough, and tetanus) A child is considered diseases. services Health care facilities also tend to be con- adequately immunized against measles after receiving * Local health care, including the availability of centrated in urban areas Separate figures for rural one dose of vaccine, and against DPT after receiving at least 20 essential drugs within one hour's areas show much lower levels of coverage and twoorthreedosesofvaccine,dependingontheimmu- walk or travel, trained personnel for attending access nization scheme * Births attended by health staff pregnancy and childbirth, and family planning Similarly, while information on access to safe water refer to the percentage of deliveries attended by per- services. is widely used, it may have different meanings in dif- sonnel trained to give the necessary supervision, care, Access to primary health care has been ferent countries, despite the official WHO definition and advice to women during pregnancy, labor, and the examined through demographic and health sur- (see Definitions) In many countries child immuniza- postpartum period, to conduct deliveries on their own, veys in a limited number of countries These tion is difficult to measure because of data recording and to care for the newborn and the infant studies note a wide discrepancy between the practices Data on births attended by health staff are proportion of the population considered to have from the WHO, supplemented by data from UNICEF m. access to services and the rate of utilization of They are based on national sources, derived from offi- these services. This discrepancy indicates cial community and hospital records, some reflect , The table was produced problems of community knowledge, perceived only births in hospitals and other medical institutions - The using information pro- need, or motivation to use the services. The Sometimes smaller private and rural hospitals are World Health vided to the WHO by coun- widest discrepancy between accessibility and excluded, and sometimes even relatively primitive -, Report tries as part of their use is noted for family planning services. But local facilities are included Thus the coverage is not 1996 responsibility for monitor- many of the constraints on the use of family always comprehensive, and the figures should be Figh i Ing progress toward planning services-transportation costs, diffi- treated with extreme caution No cross-country com- ' @ "3'"' Thealth for all" and culty of access, quality of service-also affect parison should be attempted for any of the indicators d reported in the WHO's the use of other health services. ' .., World Health Report Most countries have made significant 1996 DatafordeliverycarearefromWHO,Progress progress in providing access to primary health Towards Health for All, Statistics of Member States care; in others there has been little improve- ment or even a deterioration World Development Indicators 1997 81 O 2.13 Risk factors in health Low- Prevalence of Adult HIV-1 Tobacco birthweight child seto- consumption babies malnutrition prevalence % of per 100 kilograms a year % of births children under 5 adults per adult 1980-82 1990 1989-95 1994 1984-86 1995 Albania 0 0 Strengthening public health programs Algeria 9 0 1 1 7 1.9 Public health programs typically serve needs Angola 15 20 1 o that cannot be met by private or market-based Argentina 6 0 4 2 0 1 9 activities Their objective is to prevent dis- Armenia 0 0 ease or injury and to provide information on Australia 0 1 2 1 1 8 self-cure and the importance of seeking care Austria 6 0 2 2 1 1 9 By contrast, clinical services respond to Azerbaijan 0 0 demand from individuals who are already sick, Bangladesh 34 840 0 o 9 - 0 and they are often provided, partly or entirely, Belgium 6 0 2 through private resources. Providing essential Benin 10 36 1 2 clinical services is often the responsibility of Bolivia 10 9 13 0 1 . public health programs, however Bosnia and Herzegovina 00 . Governments face difficult choices in the Botswana 18 0 274 8 242 8 use of public money devoted to health. The Brazil 15 18 0 7 World Bank's World Development Report Bulgaria 6 0 0 3.9 4 4 1993. Investing in Health identified six par- Burkina Faso 21 12 6 7 ticularly cost-effective public health activities Burundi 2 7 Cambodia 2 0 providing population-based services, such as Cameroon 13 14 3 0 i Immunization and mass screening for wide- Canada 6 6 . 0 2 2 8 2.3 spread diseases; improving diet and nutrition; Central African Republic 23 18 . 5 8 providing family planning and maternal health Chad 11 . 2 7 . care; reducing the abuse of tobacco, alcohol, Chile 7 1 0 1 0 9 0.9 and other drugs; improving household and China 6 17 0 0 2 5 2 8 external environments, including mitigating Colombia 3 17 10 0 2 1 8 2.0 occupational hazards; and preventing AIDS Congo 15 7 2 (table 2.13a) The report recommended that Costa Rica 2 0 5 C61te dIlvoire 14 15 6 8 1 1 1 0 public health programs in developing coun- Croatia 00 tries include components in most or all of Cuba 7 0 0 4 3 4 7 these six areas, depending on local epidemi- Czech Republic 0 0 ological conditions. The criterion for including Denmark 5 . 0 2 2 7 2 4 a service should be its cost-effectiveness in Dominican Republic 14 10 1 0 dealing with major threats to health. The Ecuador 45 0 3 report identified care for sick children, prena- Egypt, Arab Rep 7 12 9 0.0 18 1 8 tal and delivery care, treatment of sexually El Salvador 9 8 22 0.6 1 1 1 1 transmitted diseases, and short-course ther- Estrea 3.2 apy for tuberculosis as the most cost-effec- Estonia O O Ethiopia 47 2.5 tive essential clinical services. Finland 4 5 0.0 1 7 1 5 Government action in many areas of public France 5 . 0.3 2 4 2 2 health has already had important payoffs in Gabon 10 2.3 developing countries Immunization saves an Gambia, The 35 10 2 1 estimated 3 million lives a year, and diarrheal Georgia 0 0 disease control more than one million. Germany 0 L 2 4 2 1 Contraceptive use has increased in developing Ghana 5 27 2.3 countries from about 10 percent of married cou- Greece 6 9 0.1 2 9 3 0 ples In the mid-1960s to 53 percent In 1990 Guatemala 10 . 0.4 Guinea 18 11 18 0.6 (WHO 1996b), enabling women to space or Guinea-Bissau 13 12 3.1 avoid pregnancies But governments need to Haiti 15 27 4.4 expand their efforts and move forward with Honduras 9 19 1,6 public health initiatives, especially in the areas Hong Kong 1 6 of child malnutrition, tobacco use, and AIDS. The last two are high-risk factors in developing 82 World Development Indicators 1997 2.13 0 Low- Prevalence of Adult HIV-1 Tobacco birthweight child serG- consumption babies malnutrition prevalence % of per 100 kilograms a year % of births children under 5 adults per adult 1980-82 1990 1989-95 1994 1984-86 1995 Table 2.13a Cost-effectiveness of public Hungary 10 0 1 3 2 3 5 health interventions and essential clinical India 30 53 0 4 0 8 0 8 services in low-income economies, 1990 Indonesia 8 39 0 0 1 4 1 5 Iran, Islamic Rep 4 12 16 0 0 0 9 0 8 Total Iraq 6 15 0 0 31 31 global Annual Ireland 4 0 1 2 6 2 4 disease cost Israel 0 1 2 4 2 2 burden per averted capita Italy 7 . 0 3 1 9 1 9 Program % $ Jamaica 10 11 10 0 9 Care for Japan 6 3 0 0 2 6 21 sick children 14 16 Jordan 10 17 0 0 Immunization' 6 0 5 Kazakstan 0 0 Prenatal and delivery care 4 3 8 Kenya 18 15 23 8 3 Family planning 3 0 9 Korea, Dem Rep 0 0 0 AIDS prevention 2 17 Korea, Rep. 9 4 0 0 2 7 3 2 Treatment of sexually Kuwait 7 0 1 transmitted diseases 1 0 2 Kyrgyz Republic 00 Short-course chemotherapy Lao PDR 13 40 0 0 for tuberculosis 1 0 6 l School health 0 1 03 Latvia 00 Discouraging tobacco and Lebanon 0 1 alcohol use 0 1 03 Lesotho 8 10 21 3 1 Libya 5 5 01 a Refers to the Expanded Programme of Immunization, Lithuania 0 O which focuses on preventing selected childhood dis- eases and, through support to national immunization Macedonia, FYR 0 0 programs, aims to achieve 90 percent immunization Madagascar 10 32 0 1 coverage of children born each year Malawi 22 10 27 13 6 0 4 0 4 Source: World Bank 1993c Malaysia 10 8 23 0.3 17 19 Mali 13 10 1.3 Mauritania 0 7 countries and are expected to be among the Mauritius 8 0 1 main causes of death and disability in the next Mexico 5 0 4 1 1 11 few decades Moldova 0 0 Mongolia 5 10 0 0 Child malnutrition Morocco 9 9 1 8 1.8 Either directly or in association with Such Mozambique 16 11 5 8 0 4 0 4 infectious diseases as measles, diarrhea, or Myanmar 13 31 1 5 2 9 3 1 respiratory diseases, malnutrition accounts Namibia 14 65 Nepal 26 70 0.1 for about a quarter of deaths among children Netherlands 4 0 0 3.1 2 8 under age five. According to World Health New Zealand 5 6 0 1 2 3 2 0 Organization (WHO) estimates, about a third Nicaragua 8 12 0 1 of the children in developing countries are Niger . . 1.0 malnourished (table 2 13b). Because chronic Nigeria 18 17 43 2 2 0 5 0 4 malnutrition is mostly a consequence of Norway 4 5 0 1 2 0 1 9 poverty, governments need to ensure food Oman 8 0 1 distribution, especially during periods of sea- Pakistan 30 40 0 1 1.6 18 sonal variability, and control infectious dis- Panama 8 7 06 eases But equally important is the need to Papua New Guinea 23 0 2 encourage more healthy eating by providing Peru 9 16 0 2 information on improving diets Philippines 30 01 1 5 16 Poland 8 8 . 01 44 3 7 Tobacco Portugal 8 5 02 18 20 Tobacco causes more deaths than all other Puerto Rico psychoactive substances combined (World Romania 0 0 19 21 Bank 1993c) About 3 million premature Russian Federation 00 World Development Indicators 1997 83 O 2.13 Low- Prevalence of Adult HIV-1 Tobacco birthweight child sero- consumption Table 2.13b Prevalence of child babies malnutrition prevalence malnutrition, 1985, 1990, and 1995 percentage of children under 5 % of per 100 kilograms a year % of births children under 5 adults per adult Region 1985 ±990 ±995 1980-82 1990 1989-95 1994 1984-86 1995 Asia 417 36 8 37 3 Rwanda 28 7 2 Latin America and the Caribbean 10 5 9 3 7 7 Saudi Arabia .0.0 1.9 1 8 Middle East and North Africa 14 2 12 1 12 4 Senegal 10 20 1 4 Sub-Saharan Africa 29 2 29 7 31 2 Sierra Leone 13 23 3.0 Singapore 8 7 14 0 1 3.6 3 2 Note, Data refer to 93 countries and are based on Slovak Republic 0 0 World Bank regional groupings Source' WHO estimates Slovenia 0 O South Africa 3 2 1 5 1 2 Spain 1 0.6 2 3 2 5 SrainLanka025 22 3820.1deaths a year (6 percent of the world total In Sr Lanka 25 22 38 0.1 Sudan 17 1.0 1990) are attributable to smoking. If current Sweden 5 0 1 1 6 14 trends continue, annual deaths related to Switzerland 5 0.3 3 1 2 4 tobacco smoking are projected to reach 10 Syrian Arab Republic 10 8 0.0 3 3 34 million by 2020, with most of the increase in Tajikistan 0.0 developing countries. Effectively discouraging Tanzania 16 28 6.4 0 6 0 6 tobacco use involves slow changes, and public Thailand 12 10 13 2.1 1 9 2 0 education is central to this process. Togo 32 8.5 Information on the risks of smoking-and Trinidad and Tobago 13 0.9 Tunisia 7 4 0.0 taxes on tobacco-are changing behavior In Turkey 8 . 0.0 2 0 2 2 some countries, although so far mostly in Turkmenistan 0.0 richer ones. Uganda 23 14.5 Ukraine 6 5 0 0 . AIDS United Arab Emirates 8 . 0 2 AIDS has killed about 6 million people and United Kingdom 0 1 2.0 1 7 infected 28 million (WHO 1996b). More than United States 7 7 . 0 5 2 9 2.3 80 percent of those infected in 1990 lived in Uruguay . 8 0 3 developing countries, by 2000 this share is Uzbekistan . . 0 Venezuela . 10 6 0 3 1 4 1.6 expected to increase to 95 percent. AIDS is Vietnam 17 45 0 1 1 0 1.1 the largest cause of death in many African West Bank and Gaza cities, and it is likely to become a major cause Yemen, Rep 30 0 o of death in Sub-Saharan Africa and in India Yugoslavia, Fed Rep 0 1 . . and other Asian countries unless action is Zaire 13 10 35 3 7 05 0 5 taken now (Bobadilla and others 1994) A Zambia . 27 17 1 combination of strategies is required to stem Zimbabwe 15 6 16 17 4 0 6 0 6 the spread of AIDS Most crucial is providing information on how to avoid infection and pro- Low income 0.8 w 18w moting condom use, which has proved suc- ExcL China& India 23w cessful in Uganda and Zaire (World Bank Middle income 0 3 w 1993c). Lower middle income 02w Upper middle income 0 7 w Low & middle income 06w 19 w East Asia & Pacific 01w 25w Europe & Central Asia 00w Latin America & Carib. 05w Middle East & N. Africa 0.0 w 1 8 w South Asia 03w low Sub-Saharan Africa 43w High income 03w 2 2 w 84 World Development Indicators 1997 2.13 0 rentlyinvolvestwoHIVviruses HIV-1andHIV-2 HIV- 1 is the dominant type worldwide HIV-2 is found The limited availability of data on health status is a principally in West Africa, but cases have been * Low-birthweight babies are children born weigh- major constraint to assessing the health situation in reported in East Africa, Europe, Asia, and Latin ing less than 2,500 grams, with the measurement developing countries Surveillance data are lacking America There are at least 10 different genetic sub- taken within the first hours of life, before significant for a number of major public health concerns types of HIV-1, but their biological and epidemiolog- postnatal weight loss has occurred * Prevalence of Estimates of prevalence and incidence are available ical significance is unclear at present While the child malnutrition is the percentage of children for only a few diseases and a handful of countries, routes of transmission for the two viruses are the under 5 whose weight for age is less than minus 2 and are notoriously unreliable and variable National same, HIV-2 appears to be less easily transmitted standard deviations from the median of the refer- health authorities differ widely in their capability and than HIV-1 and the progression from HIV-2 infection ence population * Adult HIV-1 seroprevalence willingness to collect or report information Even to AIDS appears to be slower than that for HIV-1 reflects the estimated rate of infection in each coun- when intentions are good, reporting is based on def- AIDS is late-stage infection characterized by a try's adult population (age 15 and older) * Tobacco initions that may vary widely across countries or over severely weakened immune system that can no consumption is kilograms of dry-weight tobacco con- time To compensate for the paucity of data and longer ward off life-threatening opportunistic infec- sumed per adult (aged 15 and older) per year ensure a reasonable degree of reliability and interna- tions and cancers Surveys of HIV seroprevalence tional comparability, the World Health Organization are not based on national samples. Most HIV data |= (WHO) prepares estimates in accordance with epi- originate from diagnostic centers or screening pro- demiological and statistical procedures grams and are therefore subject to selection (usu- Data presented here are drawn from a variety of Low birthweight is associated with maternal mal- ally high-risk groups) and participation bias The sources In order of their appearance in the table, nutrition, raises the risk of infant mortality, and extent of bias in the estimates is determined by how these are leads to poor growth in infancy and childhood, thus different the sampled population group or geo- * WHO, World Health Statistics Annual increasing the incidence of other forms of retarded graphical area is from the general population * United Nations, Update on the Nutrition Situation development Estimates of low-birthweight infants Tobacco consumption, where raw-leaf equivalents * WHO are drawn from hospital records and community sur- are not available, is derived from Food and * FAO, Tobacco Supply, Demand and Trade veys But since many births in developing countries Agriculture Organization (FAD) data by converting Projections 1995 and 2000 take place at home without assistance from formal data on consumption or sale of products In some medical practitioners and are seldom recorded, cases consumption is calculated from production of these data should be treated with caution and net trade in leaf and products Estimates for Estimates of child malnutrition, here defined by 1995 are based on assumptions on the growth of weight for age, are from survey data The minimum private consumption expenditure to derive per capita criterion for including a survey in the global analysis demand for tobacco The demand function and elas- is that it be at least a national survey Weight for age ticities were based on an analysis of recent national is a composite indicator of both weight for height family budget surveys and previous time-series data (wasting) and height for age (stunting) The disad- on consumption Antismoking campaigns and other vantage of this indicator is that it cannot indicate preventive activities that have influenced tobacco whether the malnutrition is due to stunting or wast- consumption were also considered for some coun- ing This indicator is nevertheless useful for com- tries through a trend factor, independent of income parisons with earlier surveys, as weight for age was and price the first anthropometric measure in general use Methods of assessment vary, but the indicator used here is less than minus 2 standard deviations from the median weight for age of the U S National Center of Health Statistics reference population aged 0-59 months The reference population, adopted by the WHO in 1983, is based on children from the United States who are assumed to be well nourished Where this indicator could not be esti- mated (because a different age range or assess- ment method was used), priority was given to deriving identically defined prevalence comparable within the country across time This approach has minor effects on the estimated rates, which are con- sidered generally comparable across countries by the WHO Adult HIV-1 seroprevalence rates reflect the rate of HIV-1 infection estimated by WHO for each coun- try's adult population The global HIV pandemic cur- World Development Indicators -1997 85 O3 2.14 Mortality Life expectancy Infant Under-5 Adult Maternal Mortality rate by broad cause at birth mortality mortality mortality mortality per 100,000 population rate rate rate ratio Non- Male Female per 1,000 per 1,000 per 100,000 Communi- communi- Injuries and years years live btrths per 1,000 Male Female live births cable cable accidents 1980 1995 1980 1995 1995 1995 1995 1995 1989-95 1985-90 1985-90 1985-90 Albania 68 70 72 76 30 37 143 77 23a Algeria 58 68 60 71 34 42 177 133 1403 Angola 40 45 43 48 124 209 493 406 Argentina 66 69 73 76 22 27 176 84 140a 107 530 59 Armenia 70 68 76 74 16 24 209 108 35a 60 580 66 Austra(lia 71 74 78 80 6 8 129 63 31 424 48 Austria 69 74 76 80 6 7 130 64 30 437 55 Azerbaijan 64 66 72 75 25 31 231 91 29a 110 595 46 Bangladesh 49 57 48 58 79 115 314 292 887b Belarus 66 64 76 75 13 20 301 100 25a 28 625 90 Belgium 70 73 77 80 8 10 135 63 52 459 68 Benin 46 49 49 52 95 156 472 399 Bolivia 50 59 54 62 69 96 292 237 373c Bosnia and Herzegovina Botswana 56 50 60 53 56 74 212 153 220b Brazil 60 63 66 71 44 57 181 123 2000 Bulgaria 69 68 74 75 15 19 213 106 200 73 619 64 Burkina Faso 43 45 45 47 99 164 426 340 939b Burundi 45 45 49 48 98 162 481 403 1,327 b Cambodia 39 52 42 54 108 158 370 298 Cameroon 49 55 52 58 56 86 413 341 Canada 71 76 78 82 6 8 113 55 39 395 48 Central African Republic 43 46 49 51 98 160 505 406 649b Chad 41 47 44 50 117 197 470 385 1,594b Chile 66 72 73 78 12 15 162 77 131 444 88 China 66 68 68 71 34 43 186 142 115d 117 696 88 Colombia 63 67 68 73 26 31 214 118 107b Congo 47 49 53 54 90 144 405 313 822b Costa Rica 71 74 75 79 13 16 115 68 C6te d'lvoire 50 53 53 56 86 138 392 333 887 Croatia 66 70 74 78 16 18 176 78 100 Cuba 72 74 75 78 9 10 122 78 360 73 472 82 Czech Republic 67 70 74 77 8 10 195 88 120 Denmark 71 72 77 78 6 7 148 80 Dominican Republic 62 68 66 73 37 44 155 100 Ecuador 61 67 65 72 36 45 179 100 Egypt, Arab Rep 54 64 57 66 56 76 278 238 El Salvador 51 66 63 72 36 42 229 154 Eritrea Estonia 64 65 74 76 14 16 284 95 410 Ethiopia 39 47 42 51 112 188 442 352 1,528b Finland 69 73 77 80 5 5 146 64 43 450 76 France 70 74 78 82 6 9 130 51 40 362 70 Gabon 47 53 50 56 89 145 386 322 483 Gambia, The 39 45 42 48 126 213 511 419 Georgia 67 69 75 78 18 21 189 77 550 69 591 56 Germany 69 73 76 79 6 7 140 69 35 468 45 Ghana 51 57 54 61 73 116 320 253 742b Greece 72 75 77 81 8 10 113 61 51 393 48 Guatemala 56 63 60 68 44 58 245 166 464b Guinea 39 44 40 45 128 220 498 497 8800 Guinea-Bissau 37 42 40 45 136 233 584 572 Haiti 50 54 54 57 72 101 391 329 6000 Honduras 58 64 62 69 45 59 166 111 Hong Kong 71 76 77 81 5 6 109 57 71 354 28 86 World Development Indicators 1997 2.14 0 Life expectancy Infant Under-5 Adult Maternal Mortality rate by broad cause at birth mortality mortality mortality mortality per 100,000 population rate rate rate ratio Non- Male Female per 1,000 per 1,000 per 100,000 Communi- communi- Injuries and years years live births per 1,000 Male Female live births cable cable accidents 1980 1995 1980 1995 1995 1995 1995 1995 1989-95 1985-90 1985-90 1985-90 Hungary 66 66 73 74 11 14 276 116 loa 55 690 90 India 55 62 54 63 68 95 229 219 437a Indonesia 53 62 56 66 51 75 262 205 390a Iran, Islamic Rep 59 68 61 69 45 59 158 149 Iraq 61 60 63 62 108 145 182 143 Ireland 70 74 75 79 6 7 134 72 . 57 526 39 Israel 70 75 76 79 8 9 114 72 64 444 53 Italy 71 75 77 81 7 8 123 57 . 38 425 39 Jamaica 69 72 73 77 13 15 144 90 Japan 73 77 79 83 4 6 101 47 . 51 306 41 Jordan 69 . 72 31 33 171 120 132 b Kazakstan 62 64 72 74 27 35 271 99 53a 86 700 103 Kenya 53 57 57 60 58 90 362 295 Korea, Dem. Rep 64 67 70 74 26 32 215 102 48a Korea, Rep. 64 68 70 76 10 14 230 96 30a Kuwait 69 74 73 79 11 14 126 68 18a Kyrgyz Republic 61 63 70 72 30 42 276 120 80a 124 651 95 Lao PDR 44 51 47 54 90 147 444 375 Latvta 64 63 74 75 16 20 328 102 Lebanon 63 68 67 71 32 40 191 135 Lesotho 51 57 55 60 76 121 347 258 598 b Libya 56 63 59 67 61 75 215 166 Lithuania 66 63 76 75 14 19 304 97 16a 25 598 107 Macedonia, FYR 71 75 23 31 144 92 12a Madagascar 49 56 52 59 89 127 445 384 Malawi 43 43 45 44 133 225 553 487 620c Malaysia 65 69 69 74 12 14 194 123 34e Mali 41 48 43 51 123 192 412 326 1,249b, Mauritania 45 51 48 54 96 158 467 396 . Mauritius 63 68 69 75 16 20 222 116 112a Mexico 64 69 70 75 33 41 162 89 . 168 490 102 Moldova 62 65 69 73 22 26 275 128 34a 54 704 104 Mongolia 57 64 59 66 55 74 221 182 Morocco 56 64 60 68 55 75 213 163 3721 Mozambique 42 45 46 48 113 190 431 339 1,512b Myanmar 51 58 54 61 83 119 308 252 518b Namibia 52 55 54 57 62 78 356 304 Nepal 49 57 47 56 91 131 327 354 515. Netherlands 72 75 79 81 6 8 121 59 . 40 416 36 New Zealand 70 73 76 79 7 9 137 70 . 50 487 58 Nicaragua 56 65 62 70 46 61 177 130 .. Niger 40 44 43 49 119 200 510 401 593c Nigeria 44 51 48 54 80 176 450 377 Norway 73 75 79 81 5 8 118 59 52 399 53 Oman 58 68 61 73 18 22 201 134 Pakistan 55 62 56 64 90 127 208 228 Panama 68 71 72 76 23 28 139 88 Papua New Guinea 51 56 52 58 64 95 371 339 Paraguay 65 67 69 72 41 52 158 108 180 a Peru 57 65 61 68 47 62 211 157 Philippines 59 64 63 68 39 53 254 189 208C Poland 67 67 75 76 14 16 228 89 10a 73 603 80 Portugal 68 72 75 79 7 11 163 76 70 429 78 Puerto Rico 70 72 77 80 11 15 147 61 21a 78 447 59 Romania 67 66 72 74 23 29 224 :11 48a 93 685 65 Russian Federation 62 58 73 72 18 21 430 143 520 47 704 115 World Development Indicators 1997 87 O 2.14 Life expectancy I Infant Uncler-5 Adult Maternal Mortality rate by broad cause at birth mortality mortality mortality mortality per 100,000 population rate rate rate ratio Non- Male Female per 1,000 per 1,000 per 100,000 Communi- communi- Injuries and years years live births per 1,000 Male Female live births cable cable accidents 1980 1995 1980 1995 1995 1995 1995 1995 1989-95 1985-90 1985-90 1985-90 Rwanda 44 38 48 40 133 200 542 461 1,512 b Saudi Arabia 60 69 62 71 21 31 181 149 180 Senegal 44 49 46 51 62 97 561 496 Sierra Leone 34 35 37 38 179 236 589 470 Singapore 69 74 74 79 4 6 143 82 114 498 39 Slovak Republic 67 68 74 76 11 15 221 93 8a Slovenia 66 70 75 78 7 8 188 81 5 0 South Africa 54 61 60 67 50 67 281 173 4045 Spain 73 74 79 81 7 9 132 57 45 410 42 Sri Lanka 66 70 70 75 16 19 172 108 300 Sudan 47 52 50 55 77 109 445 378 Sweden 73 76 79 81 4 5 112 57 41 397 46 Switzerland 73 75 79 82 6 7 118 53 Syrian Arab Republic 60 66 63 71 32 40 217 154 179a Tajikistan 64 66 69 66 42 61 200 197 390 182 558 53 Tanzania 48 50 52 52 82 133 485 417 748b Thailand 61 67 66 72 35 42 199 119 Togo 48 49 51 52 88 128 377 311 626b Trinidad and Tobago 66 70 71 75 13 18 177 108 Tunisia 61 68 62 70 39 50 171 148 139b Turkey 59 66 64 71 48 63 158 111 183. Turkmenistan 61 62 68 69 46 65 250 122 43a 216 737 68 Uganda 48 44 49 44 98 160 622 558 506 Ukraine 65 64 74 74 15 21 294 112 330 32 673 93 United Arab Emirates 66 74 70 76 16 19 122 92 20b United Kingdom 71 74 77 79 6 7 128 69 49 478 31 United States 70 74 78 80 8 10 131 63 54 447 58 Uruguay 67 70 74 77 18 21 174 83 98 519 67 Uzbekistan 64 66 71 72 30 48 209 101 430 137 601 65 Venezuela 65 70 71 75 23 25 173 94 2000 Vietnam 61 65 65 70 41 49 206 136 1050 West Bank and Gaza 28 149 102 Yemen, Rep 47 53 50 54 100 145 384 311 1,471b Yugoslavia, Fed Rep 68 70 73 75 18 22 170 99 Zaire 47 51 Zambia 49 45 52 46 109 180 534 494 Zimbabwe 53 56 57 58 55 83 391 393 Low income 57w 62w 59 w 64 w 69 w 104 w 244 w 211 w Exci Chma& India 50w 55 w 52 w 57w 89 w 143 w 353 w 303 w Middle income 61w 65w 67w 71w 39w 53 w 235 w 139 w Lower middle income 60w 64 w 66 w 70w 41 w 56 w 253 w 148 w Upper middle income 62w 66 w 68 w 73w 35w 45 w 187 w 113 w Low & middle income 56w 63w 59 w 66 w 60w 88w 241 w 186w East Asia & Pacific 63w 66 w 66 w 70w 40 w 53 w 203 w 154 w Europe & Central Asia 64 w 64 w 72w 73w 26 w 35 w 289 w 116 w Latin America & Carib 62w 66 w 68 w 72w 37w 47w 183w 114w Middle East& N Africa 57w 65 w 60 w 68 w 54 w 72 w 212 w 176 w South Asia 54 w 61 w 54 w 62 w 75w 106 w 239 w 230 w Sub-Saharan Africa 46w 50 w 49w 53 w 92 w 157 w 434w 359w High income 70w 74 w 77 w 81 w 7 w 9 w 132 w 62w a Official estimate b UNICEF-WHO estimate based on statistical modeling c Indirect estimate based on sample survey d Based on a survey covering 30 provinces e Based on civil registra- tion f Based on sample survey 88 World Development Indicators 1997 2.14 0 *==_ main problem lies in determining the cause of death. C_ In many developing countries, particularly in rural Mortality statistics and the indicators derived from areas, trained medical personnel are not available * Life expectancy at birth indicates the number of them, such as life expectancy and infant mortality, to certify the cause of death In such cases the years a newborn infant would live if prevailing patterns are often cited as measures of a population's wel- cause of death is determined by a layperson, usually of mortalityatthe time of its birth were to staythe same fare or quality of life They may be used to compare a (rural) health worker The accuracy of such report- throughout its life * Infant mortality rate is the levels of socioeconomic development or to identify ing is clearly lower than for cases that have been number of infants who die before reaching one year of populations In need Cause-specific mortality rates medically certified Incomplete reporting introduces age, per 1,000 live births in a given year * Under-5 are useful both for placing the current health status other potential biases, as does the use of hospital- mortality rate is the probabilitythat a newborn babywill of a population in an epidemiological context and for based information to impute the health situation of die before reaching age 5, if subject to current age- objective evaluation and planning in the health a country as a whole. specificmortalityrates Aswithotherdemographicdata sector As with all demographic indicators, mortality Life expectancy and age-specific mortality rates (see notes to tables 2 1 and 2 2), 1995 estimates are statistics should be used cautiously, with an aware- for 1995 are generally estimates based on the most often projected on the basis of the most recent census ness of the many difficulties involved in collecting recent census or survey (see Primary data docu- or survey (see Pnmary data documentafion) * Adult and reporting them mentation) Maternal mortality ratios are drawn from mortality rate is the probability of dying between the In developing countries mortality statistics from diverse national sources Where national adminis- ages of 15 and 60, that is, the percentage of 15-year- civil registers are notably defective Estimates are trative systems are weak, estimates are derived olds who will die before their sixtieth birthday derived by applying indirect estimation techniques to from demographic and health surveys using indirect * Maternal mortality ratio is the number of female registration data, or from censuses or surveys, estimation techniques orfrom other national sample deaths that occur during pregnancy and childbirth per which also are subject to errors and biases (See the surveys For a number of countries maternal mor- 100,000 live births * Mortality rate by broad cause notes to tables 2 1 and 2 2 for further discussion of tality ratios are derived by WHO and UNICEF (1996) is standardized for age using the world population as demographic data ) Mothers may be reluctant to talk using statistical modeling Cause-specific mortality the reference population * Deaths from communica- about children who have died, and may over- or rates are standardized usingthe direct method age- ble diseases include deaths from infectious diseases underestimate the length of a year when answering specific mortality rates are applied to the age distri- listed in the WHO's Intemational Classificafion of survey questions about child deaths in the past 12 bution of a standard population-in this case the Diseases, Ninth Revision (1977), plus influenza and months (UNRISD 1977) And because many preg- world-and the average is computed. This approach pneumonia, nutritional disorders and anemia, and nant women die from lack of suitable health care, eliminates national differences in cause-specific maternal (includingabortion) and perinatal (occurringat many maternal deaths go unrecorded, particularly in rates due solely to the age distribution of the popu- about the time of childbirth) causes of death * Deaths countries with remote rural populations This may lation Cases in which the cause of death was ill from noncommunicable diseases include all causes of account for some ofthe low maternal mortality ratios defined are distributed among the three groups of death other than communicable diseases and injuries in the table, especially for African countries causes of death in proportion to the number of and accidents * Deaths from injuries and accidents Differences in definitions may also affect the deaths in each group include deaths from all violent causes, whether inten- comparability of mortality data over time and across tional, unintentional, or unknown countries The available cause-specific mortality data are wholly inadequate, selected indicators are shown here to convey a sense of their potential utility The Mortality estimates are produced by the World Bank's Human Development and International Economics Departments in consultation with World Bank country Figure 2.14a Infant mortality, by region, departments Important inputs came from the fol- 1970, 1980, and 1995 lwn ore lowing sources per 1,000 live births * Bos and others, World Population Projections 1994-95. 150 - Eurostat, Demographic Statistics 120 * United Nations Department of Economic and Social Information and Policy Analysis, World Population ±995 90 19 Prospects The 1996 Edition and Population and Vital l i i Statistics Report 60 . * Demographic and health surveys from national 30IL-sources 3 UNICEF, The State of the World's Children 1997 0 Maternal mortality ratios are drawn from East A.,a Europe Lahr, Middle South Sub- rid the and America Easitand Asia Saharan * WHO, Matemal Mortality, A Global Factbook P.ifc Centrai and the North Afnca WHO and UNICEF, Revised 1990 Estimates on As,. Caribbean Afnca Matemal Mortality A New Approach Source. World Bank staff estimates Mortality rates by cause are from WHO, World Health Statistics Annual World Development Indicators 1997 89 IIt MIl Today some 1.5 billion people live exposed to dangerous levels of air pollution, 1 bil- lion live without clean water, and 2 billion live without sanitation. Although food production doubled over the past quarter-century and outstripped population growth, the gains may have come at the cost of lost crop diversity and natural habitats-and more chemical contamination. Some estimates suggest that a seventh of the world's tropical forests have been lost in the past 25 years. These problems are not just local or national-they are global, as evidenced by growing regional pollution, epidemics of disease, the loss of biodiversity, potential global climate effects, and the possibility of "environmental refugees" leaving severely degraded areas. Poverty arising from lack of economic development is at the root of many environ- mental problems. Only with accelerated economic development in poor countries can environmental problems be tackled. True, economic growth can make some environ- mental problems worse, but without growth environmental problems will be harder to address. So it is not useftil to think of development and the environment as involving a tradeoff. The only sensible approach is to ensure-through better environmental stew- ardship-that future economic development is socially and environmentally sustainable. As we have come to better understand the links between economic development and the environment, it has become broadly accepted that inappropriate economic policies have a high cost for the environment, that poverty and environmental prob- lems are closely linked, that environmental values have to be incorporated in the prices that guide economic growth, and that regional and global actions are essential to deal with environmental problems that cross national borders. Broad acceptance of these propositions does not mean, however, that they have been translated into effective policies. Indeed, environmental problems continue to worsen in many countries. But growing national awareness of environmental issues and the way economic activities affect the environment is at last influencing the thinking of policymakers. For example, a few counthes have reduced per capita carbon dioxide (CO2) emissions over the past decade, and several others, mostly high-income coun- tries, have exceeded the informal objective of designating 10 percent of total land area as protected areas. We are learning more about how economic and environmental systems are inter- connected-and how actions in one system can have important effects on the other. The environment can no longer be thought of as a source of "free" environmental goods and services-free forests, free fish, free freshwater. Nor can it be thought of as just a sink for disposing of waste products from homes, industries, and other sources. Measuring the environment The environment is a cross-cutting issue, and this must be reflected in environmental indicators Some indicators deal with environmental "goods," such as protected areas or biodiversity. Others measure deforestation or soil loss, or pollution of the air or water. And still others monitor the effects of environmental degradation-such as waterborne disease, species loss, or number of threatened species. Such indicators are important because the links between the environmental and economic worlds are direct and immediate. Growth at the expense of the environment, or of the health of a nation's population, is likely to be unsustainable. World Development Indicators 1997 91 Many relevant indicators are not presented here, however, Agriculture is typically responsible for 60-80 percent of annual because of weaknesses in country coverage and concerns about withdrawals of freshwater, but industrial and domestic uses are the quality and comparability of data Depletion issues in partic- much more important and produce more value per cubic meter. ular are inadequately captured. This lack of adequate and timely In this century global water withdrawal has increased almost data of acceptable quahty is a serious constraint on measuring the tenfold, with an increasing share going to industrial and domes- state of the environment and designing sound policies. While new tic uses (figure 3a). Greater efficiency in the use of water within techniques, such as geographic information systems (GIS), are sectors and reallocation among sectors are needed to balance now being used to analyze the environment, information on many supply and demand. aspects of the environment is sparse. The data available are usu- Linked to the shortage of freshwater is the question of reliabil- ally of uneven quality, relate to different periods, and are some- ity of supply. In many developing countries water supply is handled times out of date. As a result data are not only inadequate for largely by public utilities that are not operationally viable, resulting policymaking, but may not always be comparable across countries. in a water supply of both poor quality and limited availability. (Specific issues relatng to each indicator are discussed in the Total water available is an imperfect indication of the envi- About the data sections following each table.) ronmental and health consequences of the water supply, since Another problem in measuring the state of the environment water-short countries can, with proper management, do better is that many environmental indicators are not meaningful at the than water-rich countries with inappropriate policies. This is national level. Although the world is organized into nation-states especially true in agriculture, where water wastage is a costly and with sovereign governments, activites in one nation may some- persistent problem. Raising water prices can usually help the times have consequences for other nations. Air and water pol- environment without harming agricultural production. lution do not observe national boundanes. On the other hand, some environmental issues are highly localized and location-spe- Energy use and pricing cific. So in many cases global, regional, or city indicators are The link between economic growth and increased energy con- more meaningful than national aggregates. This is the direction sumption is direct and positive-and only at the highest income environmental indicators are moving. levels are there signs of decreased per capita energy consump- tion despite economic growth. Per capita energy use in Land use and biodiversity Germany has declined from 4,600 kilograms oil equivalent in With growth and development, there isa tendency for forestland 1980 to 4,100 in 1994, while energy use in the United States and to be converted to agricultural land and urban land. But as devel- Canada has been stable, around 7,850 kilograms oil equivalent opment proceeds, some low-productivity agricultural land can in recent years (table 3.4). During this period low-income coun- revert to forests. This is less common, however, and for most tries increased their per capita consumption from 250 to 370 developing countries the loss of forestland is a major issue. Of kilograms oil equivalent (excluding China and India, the more direct importance for the environment is how land is used increase would be from 115 to 135 kilograms oil equivalent). and whether agricultural and forestry practces are sustainable But low-income countries use only 14 percent of total world (table 3.1). Sustainability, however, is not captured well by cur- energy (1.7 percent, excluding China and India), while high- rent national indicators. income countries use 57 percent of the total (figure 3b). Closely linked to changes in land use are changes in pro- Fortunately, high-income countries now use energy more effi- tected areas and in biodiversity. The extent of protected areas ciently: their GDP per unit of energy use, measured in constant and their management reveal how a country is protecting its bio- 1987 dollars per kilogram oil equivalent, increased from $2.90 logical resources. Many countries have an unofficial goal of pro- in 1980 to $3.40 in 1994 (table 3.5). The energy efficiency of tecting about 10 percent of their land area. But only some middle-income countries, however, declined slightly during the countmes have achieved this goal (table 3.2). Protected areas in same period. high-income countries approach 12 percent of their land area, Energy use has important environmental consequences at all while in low- and middle-income countries protected areas rep- stages of production and consumption, not all of which are resent 3.0-6.5 percent of their land area. Protection has costs as reflected in the prices paid by users of energy or in the costs well as benefits. Without appropriate analysis of both, it is hard borne by producers of energy. These consequences can be miti- to be certain whether protecting a specified percentage of land gated by pricing commercial energy (through taxes or subsidies) area is the right goal. so as to encourage efficiency in energy use and by increasing reliance on renewable energy. A major byproduct of energy gen- Water supply eration is emissions of CO2, the principal greenhouse gas. In The availability and quality of water are crucial to economic China 82 percent of greenhouse gas emissions were generated by growth and development. For water the problem is often "too energy use. The United States and China are the largest contrib- little, too much, or too dirty." Some countries have abundant utors to CO2 emissions, accounting for some 35 percent of global untapped water to support growth far into the future. Others, emissions. On a per capita basis CO2 emissions declined about 4 such as Yemen, have already used up almost all sources, and percent during 1980-92 in high-income countries, notably as a major increases in supplies will be expensive (table 3.3). result of lower emissions in Germany. Total emissions in high- 92 World Development Indicators 1997 income countries increased only 4 percent during this period, with Germany reducing its emissions by 18 percent. Urbanization and air pollution In most countries urbanization is a natural consequence of eco- nomic growth. Rapid urbanization can yield important social benefits as people gain easier access to schools, medical care, Figure 3a Global water use, and cultural facilities. But it can also lead to negative environ- by sector, 1900-2000 mental consequences requiring a policy response Forty-five percent of the world's populaton lives in urban 5,000 areas: two out of five people in low- and middle-income coun- 4,000 5=U7t tries and four out of five in high-income countries (table 3.6) 4,000 And the urban population grew faster (2.5 percent) than total population (1.7 percent) during 1980-95. It is easy to forget that "'3,000 a many parts of the developing world are very urban. Most of Latin ^ 2,000 America is as urban as Europe, with 74 percent of the popula- tion living in urban areas. Asia is urbanizing rapidly, and even 1 ooo ,00 . such traditonally rural countries as China and India now have hundreds of millions of people in towns and cities. 0 5 8 6 Increased urbanization usually means increases in air and 1900 1950 2000 water pollution-increases that can overwhelm the natural Note: Water withdrawal estimates, margins of error capacities of air and water to absorb pollution. The costs of con- notwithstanding, provide good indications of the trolling pollution and treating problems can be enormous. And dynamics of water use in this century. Source: Shiklovanov 1993. - pollution exposes people to severe health hazards. Several major urban air pollutants-suspended particulate matter, lead, sulfur dioxide-are known to be harmful to health Especially harmful is particulate matter, which contributes to respiratory diseases. Many of those pollutants come from vehi- cles, whose numbers are strongly linked to rising income (tables 3.7 and 3.8). Government commitment Figure 3b Energy use, A crucial variable in all this, but one that is very difficult to mea- by income group, 1994 by Income group, 1994 sure, is a government's commitment to a cleaner environment China and India and to better management of environmental resources (table Low income 12% 3.9). The strength of environmental policies in any country (excluding China and India) reflects the priority assigned by its government to problems of 2% environmental degradation-and that priority reflects the bene- Middle income fits expected from using scarce financial resources that have com- V | | Mddleinco29% peting uses. In addition to national environmental problems, governments are increasingly concerned about global environ- mental issues To address these issues, agreements have been reached, and treaties signed, on areas related to the quality of life High income on earth. Many recent agreements resulted from the 1992 United 57% Nations Conference on Environment and Development in Rio dejaneiro, which attracted representatives of almost every coun- try. The conference produced Agenda 21, which proposes an array of actions to address environmental challenges. But per- Source: Table 3.4. haps more important, the conference caused countries to develop comprehensive environmental policy frameworks. Government policies can make a difference, stimulating pos- itive links between economic growth and the environment. And monitoring what is happening to the environment can guide policy toward a future that is more economically and environ- mentally sustainable. World Development Indicators 1997 93 3.1 Land use and deforestation Land area Rural Land use Forest Annual population area deforestation density share thousand arable people Cropland Permanent pasture Other thousand thousand sq km % per sq km % of land area % of land area % of land area sq km sq. km % change 1994 1994 1994 1980 1994 1980 1994 1980 1994 1990 1980-90 1980-90 Albania 27 21 349 26 26 15 15 59 59 14 -0 0 -0.0 Algena 2,382 3 165 3 3 15 13 82 83 41 0 3 0.8 Angola 1,247 2 239 3 3 43 43 54 54 231 1.7 0.7 Argentina 2,737 9 17 10 10 52 52 38 38 592 0.9 0.1 Armenia 28 . .. .. . 3 0.2 3.9 Australia 7,644 6 6 6 6 57 54 37 40 1,456 -0 0 -0.0 Austna 83 17 252 20 18 25 24 56 57 39 -0 1 -0.4 Azerbaijan 87 18 207 22 10 26 11 52 80 10 0 1 1.3 Bangladesh 130 73 1,026 70 74 5 5 25 21 8 0 4 4.1 Belarus 207 30 50 22 31 12 14 66 55 63 -0 3 -0 4 Belgium 33 23 39 .. 24 .. 21 .. 55 6 -0.0 -0.3 Benin 111 13 219 16 17 4 4 80 79 49 0.7 1.3 Bolivia 1,084 2 145 2 2 25 24 73 73 493 6.3 1.2 Bosnia and Herzegovina 51 12 379 16 .. 24 .. 61 23 0.0 0.1 Botswana 567 1 238 1 1 45 45 54 54 143 0 8 0.5 Brazil 8,457 5 82 6 6 20 22 74 72 5,611 36 7 0.6 Bulgaria 111 36 63 38 38 18 16 44 46 37 -0 9 -0.2 Burkina Faso 274 13 212 10 13 22 22 68 65 44 0 3 0.7 Burundi 26 39 567 46 46 39 39 15 15 2 0.0 0.6 Cambodia 177 22 204 12 22 3 8 85 70 122 1.3 1.0 Cameroon 465 13 121 15 15 4 4 81 81 204 1 2 0.6 Canada 9,221 5 15 5 5 3 3 92 92 4,533 -47.1 -1.1 Central African Republic 623 3 101 3 3 5 5 92 92 306 1 3 0.4 Chad 1,259 3 152 3 3 36 36 62 62 114 0 9 0.7 Chile 749 5 50 6 6 17 18 77 76 88 -0 1 -0 1 China 9,326 10 910 11 10 36 43 53 47 1,247 8 8 0.7 Colombia 1,039 4 257 5 5 37 39 58 56 541 3 7 0 7 Congo 342 0 747 0 0 29 29 70 70 199 0.3 0.2 Costa Rica 51 6 589 10 10 39 46 51 44 14 0.5 3.0 C6te d'lvoire 318 8 318 10 12 41 41 49 47 109 1.2 1.0 Croatia 56 20 158 29 22 28 20 42 59 20 0.0 0.2 Cuba 110 24 102 30 31 24 27 46 42 17 0 2 1.0 Czech Republic 77 41 114 41 44 13 12 45 45 26 -0 0 -0.0 Denmark 42 56 33 63 56 6 7 31 37 5 0 0 0 0 Dominican Republic 48 21 274 29 31 43 43 27 26 11 0 4 2 9 Ecuador 277 6 293 9 11 15 18 77 71 120 2.4 1 8 Egypt, Arab Rep 995 3 1,012 2 4 0 0 98 96 0 0.0 0 0 El Salvador 21 27 536 35 35 29 29 36 35 1 0.0 2.3 Eritrea 101 4 661 5 .. 69 . 26 .. Estonia 42 27 36 24 27 8 7 68 66 19 -0 2 -1.2 Ethiopia 1,000 10 455 11 .. 20 .. 69 142 0 4 0 3 Finland 305 9 73 8 9 1 0 91 91 234 -0 1 -0 0 France 550 33 86 34 35 23 19 42 45 135 -0 1 -0 1 Gabon 258 1 183 2 2 18 18 80 80 182 1.2 0 6 Gambia, The 10 17 471 16 17 19 19 65 64 1 0 0 0.8 Georgia 70 11 286 17 16 39 24 44 60 28 0.2 0.7 Germany 349 34 95 36 34 17 15 47 51 107 -0 5 -0.5 Ghana 228 12 381 15 19 37 37 48 44 96 1 4 1.4 Greece 129 19 152 30 27 41 41 29 32 60 0 0 0 0 Guatemala 108 12 450 16 18 12 24 72 58 42 0 8 1 8 Guinea 246 2 750 3 3 44 44 54 53 67 0.9 1 2 Guinea-Bissau 28 11 273 10 12 38 38 51 50 20 0.2 0 8 Haiti 28 20 864 32 33 18 18 49 49 0 0.0 5 2 Honduras 112 15 181 16 18 13 14 71 68 46 1 1 2.2 Hong Kong 1 6 5,239 7 7 1 1 92 92 0 -0 0 -0.5 94 World Development Indicators 1997 3.1@ Land area Rural Land use Forest Annual population area deforestation density share thousand arable people Cropland Permanent pasture Other thousand thousand sq. km % per sq km % of land area % of land area % of land area sq km sq km % change 1994 1994 1994 1980 1994 1980 1994 1980 1994 1990 1980-90 1980-90 Hungary 92 51 77 58 54 14 12 28 34 17 -0 1 -0 5 India 2,973 56 404 57 57 4 4 39 39 517 3 4 0 6 Indonesia 1,812 9 738 14 17 7 7 79 77 1,095 12.1 1 1 Iran, Islamic Rep 1,636 10 157 8 11 27 27 65 62 180 -0 0 -0.0 Iraq 437 13 84 12 13 9 9 78 78 19 0 0 01 Ireland 69 19 116 16 19 67 45 17 36 4 -0 -1 2 Israel 21 17 20 21 6 7 74 72 1 -0 0 -0.3 Italy 294 28 229 42 38 17 15 40 47 86 Jamaica 11 14 728 22 20 24 24 54 56 2 0 3 7 8 Japan 377 11 702 13 12 2 2 85 87 238 0 0 0 0 Jordan 89 4 374 4 5 9 9 87 87 1 -0 0 -1 1 Kazakstan 2,671 13 20 11 13 57 70 32 17 Kenya 569 7 476 8 8 37 37 55 55 12 01 0 6 Korea, Dem Rep. 120 14 538 16 17 0 0 84 83 90 0 0 0 0 Korea, Rep 99 19 478 22 21 1 1 77 78 65 0.1 01 Kuwait 18 0 1,043 0 0 8 8 92 92 0 0 0 0.0 Kyrgyz Republic 192 7 196 10 7 62 44 28 48 7 0 1 1 2 Lao PDR 231 4 428 3 4 3 3 94 93 132 1.3 0 9 Latvia 62 28 41 28 28 12 13 60 59 28 -01 -0.2 Lebanon 10 21 245 30 30 1 1 69 69 1 0 0 0 6 Lesotho 30 11 470 10 11 66 66 24 24 Libya 1,760 1 42 1 1 7 8 91 91 7 -0 1 -1 4 Lithuania 65 35 46 49 47 8 7 43 46 20 -0 0 -0 0 Macedonia, FYR 25 24 140 . 26 25 49 9 0 0 0 1 Madagascar 582 4 379 5 5 41 41 54 53 158 1 4 0 8 Malawi 94 18 493 14 18 20 20 66 62 35 0 5 1 4 Malaysia 329 6 508 15 23 1 1 85 76 176 4 2.1 Mali 1,220 2 280 2 2 25 25 74 73 121 1.1 0 8 Mauritania 1,025 0 515 0 0 38 38 62 62 6 0 0 0 0 Mauritius 2 49 661 53 52 3 3 44 44 1 0 0 0 2 Mexico 1,909 12 98 13 13 39 39 48 48 486 6.8 1 3 Moldova 33 53 122 67 66 11 13 23 21 4 -0 2 -6.7 Mongolia 1,567 1 75 1 1 79 75 20 24 139 1 3 0 9 Morocco 446 19 155 18 21 47 47 35 32 90 -1 2 -1 4 Mozambique 784 4 356 4 4 56 56 40 40 173 1 4 0 8 Myanmar 658 14 341 15 15 1 1 84 84 289 4 0 13 Namibia 823 1 145 1 1 46 46 53 53 126 0 4 0 3 Nepal 137 17 782 17 17 14 15 69 68 50 0 5 10 Netherlands 34 27 185 24 28 35 31 41 41 3 -0 0 -0 3 New Zealand 268 9 23 13 14 53 50 34 35 75 0 0 0 0 Nicaragua 121 9 151 10 10 40 45 50 44 60 1.2 1 9 Niger 1,267 3 188 3 3 8 8 90 89 24 0 1 0 4 Nigeria 911 33 220 33 36 44 44 23 20 156 1 2 0 7 Norway 307 3 130 3 3 0 0 97 97 96 -1 2 -1 4 Oman 212 0 11,439 0 0 5 5 95 95 41 0.0 0 0 Pakistan 771 27 400 26 28 6 6 67 66 19 0 8 3 4 Panama 74 7 232 7 9 17 20 75 71 31 0 7 1 9 Papua New Guinea 453 0 8,840 1 1 0 0 99 99 360 1 1 0 3 Paraguay 397 6 101 4 6 40 55 56 40 129 4 0 2 8 Peru 1,280 3 178 3 3 21 21 76 76 679 2 7 0 4 Philippines 298 19 569 29 31 3 4 67 65 78 3 2 3 5 Poland 304 47 96 49 48 13 13 38 39 87 -0 1 -01 Portugal 92 24 292 34 32 9 11 57 58 31 -01 -0 5 Puerto Rico 9 4 3,095 11 9 38 26 51 65 3 0 0 0 0 Romania 230 41 110 46 43 19 21 35 36 63 0 0 0 0 Russian Federation 16,889 8 31 8 8 5 .. 87 7,681 15.5 0 2 World Development Indicators 1997 95 @3.1 Land area Rural Land use Forest Annual population area deforestation density share thousand arable people Cropland Permanent pasture Other thousand thousand sq km % per sq km % of land area % of land area % of land area sq km sq km % change 1994 1994 1994 1980 1994 1980 1994 1980 1994 1990 1980-90 1980-90 Rwanda 25 35 674 41 47 28 28 30 24 2 0 0 0 2 Saudi Arabia 2,150 2 109 1 2 40 56 60 42 12 0.0 0.0 Senegal 193 12 206 12 12 30 30 58 58 75 0 5 0 7 Sierra Leone 72 7 540 7 8 31 31 62 62 19 0 1 0.6 Singapore 1 2 0 13 2 0 0 87 98 0 0 0 2 3 Slovak Republic 48 31 150 41 34 13 17 45 49 18 0 0 0 1 Slovenia 20 12 317 14 25 61 10 0 0 0 0 South Africa 1,221 10 162 11 11 67 67 22 23 45 -0 4 -0 8 Spain 499 31 59 41 40 22 21 37 38 256 -0.0 -0 0 Sr Lanka 65 14 1,531 29 29 7 7 64 64 17 0 3 1 4 Sudan 2,376 5 152 5 5 41 46 54 48 430 4 8 1.1 Sweden 412 7 53 7 7 2 1 91 92 280 -0.1 -0 0 Switzerland 40 10 673 10 11 40 29 49 60 12 -0 1 -0.6 Syrian Arab Republic 184 26 134 31 30 46 45 24 25 7 -0 3 -4 3 Tajikistan 141 6 476 13 6 50 25 37 69 5 0.0 0 6 Tanzania 884 3 732 3 4 40 40 57 56 336 4 4 1 2 Thailand 511 34 263 36 41 1 2 63 58 127 5 2 3.5 Togo 54 38 133 43 45 4 4 53 52 14 0 2 1 5 Trinidad and Tobago 5 15 557 23 24 2 2 75 74 2 -0.0 -2 1 Tunisia 155 19 128 30 32 22 20 48 48 7 -0 1 -1 9 Turkey 770 32 78 37 36 13 16 50 48 202 0 0 0 0 Turkmenistan 470 3 173 7 3 82 64 11 33 41 0 6 1.4 Uganda 200 25 323 28 34 9 9 63 57 63 0.7 1 0 Ukraine 579 57 47 61 59 12 13 27 28 92 -0.2 -0.3 United Arab Emirates 84 0 2,833 0 0 2 2 97 97 0 0 0 0.0 United Kingdom 242 25 104 29 25 47 46 24 29 24 -0 2 -1 1 United States 9,159 20 34 21 21 26 26 53 53 2,960 3.2 0 1 Uruguay 175 7 25 8 7 78 77 14 15 7 -0 0 -0 6 Uzbekistan 414 10 321 11 11 59 50 31 39 14 1 5.5 Venezuela 882 4 51 4 4 20 20 76 75 457 6 1 2 Vietnam 325 18 969 20 21 1 1 79 78 83 1 4 1.5 West Bank and Gaza Yemen, Rep. 528 3 691 3 3 30 30 67 67 41 0 0 0.0 Yugoslavia, Fed Rep. 102 37 125 . 40 21 39 29 0 1 0.2 Zaire 2,267 3 415 3 3 7 7 90 90 1,133 7 3 0 6 Zambia 743 7 91 7 7 40 40 53 53 323 3.6 1 1 Zimbabwe 387 7 269 7 7 44 44 49 48 89 0 6 0.7 iba;^^;~~~~ S e * * .6 i; S a_ Low income 39,442 t 12 w 636 w 12 w 12 w 31 w 32 w 57 w 55 w 7,916 t 65 5 t 0.8 w Exci China& India 27,143 t 7 w 536 w 8 w 8 w 32 w 32 w 60 w 60 w 6,152 t 53.3 t 08w Middle income 59,999 t 9 w 435 w 9 w 10 w 28 w 23w 62 w 67w 20,913 t 114 4 t 05w Lower middle income 39,649 t 10 w 454 w 10 w 11 w . 18 w 71 w 13,525 t 65 6 t 05w Upper middle income 20,350 t 6 w 352 w 7 w 7 w 30 w 32w 63 w 60 w 7,387 t 48 8 t 0.6 w Low & middle income 99,441 t 10 w 593 w 10 w 11 w 29w 27w 60 w 63 w 28,828 t 179 8 t 06w East Asia & Pacific 15,869 t 10 w 854 w 11 w 12 w 30w 34 w 59 w 54 w 3,986 t 43.5 t 1.0 w Europe & Central Asia 24,114 t 12 w 115 w 13 w 13 w .. 16 w 71 w 8,630 t 16.7 t 0.2 w Latin America & Carib. 20,064 t 6 w 224 w 7 w 7 w 28 w 29w 65w 63 w 9,786 t 748t 0.7 w Middle East & N. Africa 10,992 t 5 w 609 w 5 w 6 w 21 w 24 w 74 w 70w 446 t -1 4 t -0.3 w South Asia 4,775 t 43 w 493 w 44 w 45 w 11 w 10 w 45 w 45 w 658 t 5 5 t 0.8 w Sub-Saharan Africa 23,628 t 6 w 358 w 6 w 7 w 34 w 34 w 60 w 59 w 5,322 t 40.7 t 0 7 w High income 30,872 t 12 w 230 w 12 w 12 w 25 w 24 w 62w 63 w 10,766 t -46 4 t -0.5 w 96 World Development Indicators 1997 3.10 Figure 3.1a Land use in low-income economies, 1980 and 1994 The data indicate major differences in resource * Land area is the total area of the country, exclud- percentage of land area endowments and uses among countries, but true ing area under inland water bodies Arable land 40 comparability is limited because of variations in defi- refers to land under temporary crops. temporary nitions, statistical methods, and the quality of data meadows for mowing or pasture, and land under 30 collection For example, countries sometimes use dif- market and kitchen gardens * Rural population den- ferent definitions for land use The Food and sity is the rural population divided by the arable land Agriculture Organization (FAO), the primary compiler area * Land use is broken into three categories 20- of these data, often adjusts the definitions of land use Cropland includes land under temporary and perma- categories-and sometimes substantially revises nent crops, temporary meadows, market and kitchen lo0 - _ _ _earlier data Because the data thus reflect changes in gardens, and land temporarily fallow Permanent 0 _ _ data reporting procedures as well as actual changes crops are those that do not need to be replanted after Cropland Permanent Forest and Other in land use, apparent trends should be interpreted each harvest, excluding trees grown for wood or pasture woodland with caution Increasingly sophisticated satellite timber Permanent pasture is land used for five or images show land use different from that given by moreyearsforforage, including natural and cultivated Source: Table 3 1 ground-based measures in terms of both total area crops Other land uses include forest and woodland, under cultivation and type of land use Furthermore, as well as logged-over areas to be forested in the near land use data in countries such as India are based on future Also included are uncultivated land, grassland Figure 3.1b Land use in middle-income reporting systems that were geared to the collection not used for pasture, wetlands, wastelands, and built- economies, 1980 and 1994 of land revenue With land revenue no longer a major up areas-residential, recreational, and industrial percentage of land area source of government revenue, the quality and cover- lands and areas covered by roads and other fabri- 40 age of land use data (except for cropland) have cated infrastructure * Forest area refers to land declined Data on forest area may be particularly under natural or planted stands of trees, whether pro- unreliable. ductive or not (see About the data) * Annual defor- 30 1980 Estimates of forest area are derived from country estation refers to the permanent conversion of statistics assembled by the FAO and the United natural forest area to other uses, including shifting 20 h Nations Economic Commission for Europe (UNECE) cultivation, permanent agriculture, ranching, settle- In 1993 new assessments were published for tropi- ments. or infrastructure development Deforested cal countries by the FAO and for temperate zones areas do not include areas logged but intended for jointly by the UNECE and FAO-but with different def- regeneration or areas degraded by fuelwood gather- O initions The FAO defines natural forest in tropical ing, acid precipitation, or forest fires Negative num- Cropland Permanent Forest and Other pasture woodland countries either as closed forest, where trees cover a bers indicate an increase in forest area large portion of the ground with no continuous grass Source, Table 3 1 cover, or as open forest, a mix of forest and grass- , lands with at least 10 percent tree cover and a con- tinuous grass layer on the forest floor The UNECE-FAO -FAQ acii * Data on land area and land Figure 3.1c Land use in high-income assessment defines a forest as land where tree use are from the FAO's economies, 1980 and ±994 crowns cover more than 20 percent of the area Also .diu.0- electronic files They are percentage of land area included are open forest formations, forest roads and also published in the FAO's Prodiuwion 40 - firebreaks, small, temporarily cleared areas, young ---- Production Yearbook The stands expected to achieve at least 20 percent crown FAO gathers these data cover on maturity, and windbreaks and shelter belts * from national agencies 30 ' ee a994 _ The land use data here are based on the FAO defini- -, through annual question- tion of area under forests, and the forestry data on -M r naires and by analyzing the 20 the UNECE-FAO definition results of national agricultural censuses Forestry data are from the World Resources Institute, which 10 f compiles data from the FAO and the UNECE 0 Cropland Permanent Forest and Other pasture woodland Source: Table 3 1 World Development Indicators 1997 97 . 3.2 Biodiversity and protected areas Nationally Mammals Birds Higher plants' protected areas % of thousand total Threatened Threatened Threatened sq km land area Species species Species species Species species 1994b 19940 1994b 19940 19940 1994" 1994b 1994b Albania 0 3 1 2 68 3 306 5 2,965 50 Habitats for diversity Algeria 119 2 5 0 92 11 375 7 3,100 145 Losses of biodiversity are irreversible, and they Angola 26 4 2 1 276 16 909 13 5,000 25 compromise thie choices of both current and Argentina 43 7 1 6 320 20 976 40 9,000 170 future generations. Biologically diverse ecosys- Armenia 2 1 7 6 1 5 tems often contain economically useful prod- Australia 940 8 12 3 252 43 751 51 15,000 1,597 ucts that can oe harvested or used as inputs Austria 20 8 25 2 83 3 414 3 2,950 22 in production--they provide economically valu- Azerbaijan 1 9 0 9 3 6 1 able services, such as Bangladesh 1 0 0 7 109 16 684 28 5,000 24 service quch as Belarus 2 7 1 3 5 4 * Improving the quality of water available for Belgium 0 8 2 3 58 2 429 3 1,400 3 agriculture, industry, or human consumption. Benin 7 8 7 0 188 7 423 1 2,000 3 * Reducing sedimentation in reservoirs and Bolivia 92 3 8 5 316 21 1,274 27 16,500 49 irrigation works Bosnia and Herzegovina 0 3 0 5 2 * Minimizing iloods, landslides, coastal ero- Botswana 106 6 18 8 164 8 550 5 4 sion, and droughts. Brazil 321 9 3 8 394 45 1,635 103 55,000 463 * Providing recreational opportunities. Bulgaria 3 7 3 3 81 1 374 11 3,505 94 * Filtering excess nutrients Burkina Fsso 26 6 9 7 147 6 453 1 1,100 0 * Providing essential habitats for economically Burundi 0 9 3 5 107 6 596 5 2,500 1 Cambodia 30 0 17 0 123 19 429 16 7 important species. Cameroon 20 5 4 4 297 21 874 14 8,000 74 Ecosystems also are the reservoirs of Canada 823 6 8 9 193 6 578 5 2,920 649 genetic material from which new pharmaceuti- Central African Republic 61 1 98 209 9 662 2 3,600 0 cals and impioved crops are developed. And Chad 114 9 9 1 134 13 532 3 1,600 12 many people value ecosystems even if they do Chile 137 3 18 3 91 11 448 15 5,125 292 not use them China 580 8 6 2 499 94 1,186 183 30,000 1,009 The main cause of biodiversity loss has Colombia 93 8 9 0 359 24 1,695 62 50,000 376 been habitat destruction, driven by such Congo 11 8 34 200 13 569 3 4,350 3 human activites as login and b shifts in Costa Rica 6 5 12 7 205 8 850 10 11,000 456 lan to agri gg ing astry develI C6te d'lvoire 19 9 6 3 230 16 694 11 3,517 66 land use to agrculture, Infrastructure devel- Croatia 3 9 7 0 4 opment, or hLiman settlement. Agriculture has Cuba 11 5 10 5 31 10 342 13 6,004 811 played a maior role in this process as the Czech Republic 10 7 13 8 3 5 human activity that affects the largest portion Denmark 13 9 32 7 43 1 439 2 1,200 6 of the earth's surface and the biggest user of Dominican Republic 10 5 21 7 20 3 254 10 5,000 73 Ecuador 111 1 40 1 302 20 1,559 50 18,250 375 Egypt, Arab Rep 7 9 0 8 98 7 439 10 2,066 84 - El Salvador 0 1 0 2 135 2 420 0 2,500 35 = Eritrea . 112 3 537 percentage of total land area Estonia 4 1 9 7 65 5 330 2 1,630 2 Ethiopia 60 2 6 0 255 21 813 17 6,500 153 12 Finland 27 4 9 0 60 3 425 4 1,040 11 10 France 56 0 10 2 93 5 506 5 4,500 117 Gabon 10 5 4 1 190 12 629 4 6,500 78 8 Gambia, The 0 2 2 3 108 3 504 1 966 0 Georgia 1 9 2 7 3 . 5 1 6 Germany 91 9 263 76 2 503 5 2,600 16 4 Ghana 11 0 4 9 222 12 725 7 3,600 32 I I I Greece 2 2 1 7 95 5 398 9 4,900 539 2fl Guatemala 13 3 12 3 250 5 669 4 8,000 315 0 Guinea 1 6 0 7 190 13 552 11 3,000 35 Upper Low Lower World High Guinea-Bissau 108 5 319 1 1,000 middle income middle income income income Haiti 0 1 0 4 3 3 220 10 4,685 28 Honduras 8 6 7 7 173 5 684 4 5,000 55 Source Table 3 2 Hong Kong 98 World Development Indicators 1-997 3.2 @ Nationally Mammals Birds Higher plants' protected areas % of thousand total Threatened Threatened Threatened sq km land area Species species Species species Species species 19940 19940 1994b 19940 19940 1994b 1994b 1994b freshwater worldwide. Agricultural expansion Hungary 5 7 6 2 72 2 363 7 2,148 24 and intensification are both potentially impor- India 143 4 4 8 316 40 1,219 71 15,000 1,256 tant contributors to habitat and biodiversity Indonesia 185 6 10 2 436 57 1,531 104 27,500 281 losses worldwide. Conversion of land to agri- Iran, Islamic Rep 83 0 5 1 140 9 502 12 1 culture is closely related to logging many Iraq 81 4 381 11 2 logged areas are later cultivated, and roads Ireland 0 5 0 7 25 0 417 1 892 9 built for logging facilitate new settlement Israel 3 1 14 9 92 7 500 8 38 Conversion of habitat can lead directly to the Italy 228 7 7 90 4 490 6 5,463 273 Jamaica 0 0 0 2 24 2 262 7 2.746 371 extinction of species. Even if species survive Japan 27 6 7 3 132 17 583 31 4,700 704 the conversion of part of their habitat, their Jordan 2 9 33 71 8 361 4 2,200 10 long-term survival may be threatened by frag- Kazakstan 9 9 0 4 9 14 mentation and disturbance of the rest. As habi- Kenya 35 0 6 2 359 16 1,068 22 6,000 158 tats become smaller, the number of species Korea, Dem Rep 0 6 0 5 7 390 16 2,898 7 they can support falls, and the populations of Korea, Rep 6 9 7 0 49 6 372 19 2,898 69 wide-ranging species often expand at the Kuwait 0 3 1 5 21 2 321 3 234 0 expense of species with more specialized habi- Kyrgyz Republic 2 8 1 5 4 5 1 Lao PDR ~~~~~172 25 651 23 5 tat requirements Species also are threatened Lao PDR 83 25 251 23 X by toxic chemicals and by changes in water Lebanon 0 0 0 4 54 5 329 5 4 regimes caused by human use. Lesotho 0 1 0 2 33 2 281 3 1,576 7 Habitat conservation is vital for stemming Libya 1 7 0 1 76 8 323 2 1,800 57 the declife of biodiversity Habitat conserva- Lithuania 6 3 9 6 68 4 305 4 1,200 0 tion efforts traditionally have centered on Macedonia, FYR 2 2 8 5 protected areas, which have grown substan- Madagascar 11 2 1 9 105 33 253 28 9,000 189 tially in recent decades, particularly in low- Malawi 10 6 11 3 195 6 645 9 3,600 61 and middle-income countries Almost 7 per- Malaysia 148 4 5 286 20 736 31 15,000 510 cent of the world's land area is protected 40 1 3 3 137 12 622 5 1,741 14 Mauritania 17 5 1 7 61 10 541 3 1,100 3 (figure 32a), with the proportion highest In Mauritius 00 2 0 4 3 81 9 700 222 North and Central America (table 3 2a) Mexico 985 5 2 450 24 1,026 34 25,000 1,048 Although protected areas are important in Moldova 0 1 0 4 68 1 270 6 1 conserving biodiversity, they have limits. Mongolia 61 7 3 9 134 8 390 11 2,272 1 Many are subject to encroachment and dis- Morocco 3 7 0 8 105 7 416 11 3,600 195 turbance. Most were established to protect Mozambique 0 0 0 0 179 9 678 13 5,500 92 scenic or recreational resources, with Myanmar 1 7 0 3 251 20 999 43 7,000 29 Namibia 102 2 12 4 154 12 609 6 3,128 23 Nepal 11.1 8 1 167 23 824 23 6,500 21 Netherlands 4 3 12 6 55 2 456 3 1,170 1 Table 3.2a Countries with largest New Zealand 60 7 22 6 10 3 287 45 2,160 236 shares of protected areas Nicaragua 9 0 7 4 200 6 750 3 7,000 78 Share of Niger 84 2 6 6 131 10 482 2 1,170 0 total land area Nigeria 29 7 3 3 274 22 862 8 4,614 9 Country % Norway 55 4 18 0 54 3 453 3 1,650 20 Ecuador 40 1 Oman 9 9 4 6 56 5 430 5 1,018 4 Denmark 32 7 Pakistan 37 2 4 8 151 10 671 22 4,929 12 Venezuela 29 8 Panama 13 3 17 8 218 11 929 9 9,000 561 Germany 26 3 Papua New Guinea 0 8 0 2 214 33 708 31 10,000 95 Austria 25 2 Paraguay 15 0 3 8 305 8 600 22 7,500 12 New Zealand 22 6 Peru 41 8 3 3 344 29 1,678 60 17,121 377 Dominican Republic 21 7 Philippines 6 1 2 0 153 22 556 86 8,000 371 United Kingdom 211 Poland 30 7 10 1 79 4 421 5 2,300 27 Slovak Republic 21 1 Portugal 5 8 6 3 63 6 441 7 2,500 240 World 6 7 Puerto Rico Source Table 3 2 Romania 10 7 4.7 84 3 368 11 3,175 122 Russian Federation 705 4 4 2 17 35 127 World Development Indicators 1997 99 O 3.2 Nationally Mammals Birds Higher plants' ecosystem protection only recently becoming protected an explicit objective. areas Recognition of the limits of protected areas % of has spurred efforts to foster complementari- thousand total Threatened Threatened Threatened sq km land area Species species Species species Species species ties between biodiversity protection and eco- 19940 1994b 19940 19940 19940 1994b 1994b 1994b nomic activities. Such complementarities are Rwanda 3.3 13 3 151 14 666 6 2,288 0 particularly irnportant for agriculture, which Saudi Arabia 62.0 2 9 77 6 413 10 1,729 6 depends on many services provided by the Senegal 21.8 113 155 9 610 5 2,062 32 environment, such as crop pollination and Sierra Leone 0 8 1 1 147 12 622 12 2,090 12 genes for developing improved crop varieties Singapore 0 0 4 9 45 3 295 6 2,000 14 and livestock breeds Moreover, exploiting Slovak Repubiic 10 2 21 1 3 4 biodiversity could substantially boost agricul- Slovenia 1 1 54 69 3 361 3 11 tural production. At the same time, damage South Africa 69 7 5 7 247 25 790 16 23,000 953 to biodiversity often hurts agnculture. Spain 42 5 8.5 82 7 506 10 896 Sri Lanka 8 0 12.3 88 4 428 11 3,000 436 Reconciling biodiversity conservation with Sudan 938 3 9 267 16 937 9 3,132 8 increased prcoduction to meet the needs of a Sweden 29 8 7.2 60 3 463 4 4,916 19 growing human population will be a major Switzerland 7 3 18.5 75 2 400 3 1,650 9 challenge. Syrian Arab Republic 4 6 10 Tajikistan 0 9 0.6 6 9 0 Tanzania 139 4 15 8 322 16 1,005 30 10,000 406 Thailand 70 2 13 7 265 22 915 44 11,000 382 Togo 6 5 11 9 196 8 558 1 2,000 Trinidad and Tobago 0.2 3.1 100 1 433 2 1,982 16 Tunisia 0 4 0.3 78 5 356 6 2,150 24 Turkey 10 7 1 4 116 4 418 13 8,472 1,827 Turkmenistan 11 1 2 4 8 . 9 . 1 Uganda 19.1 9 6 338 15 992 10 5,000 6 Ukraine 4 9 0 8 4 . 10 2,927 16 United Arab Emirates .. 25 2 360 4 0 United Kingdom 51 1 21 1 50 1 219 2 1,550 28 United States 1,302 1 14 2 428 22 768 46 16,302 1,845 Uruguay 0 3 0 2 81 4 365 9 2,184 11 Uzbekistan 2 4 0.6 7 11 5 Venezuela 263 2 29 8 305 12 1,296 22 20,000 107 Vietnam 13 3 4 1 213 25 761 45 350 West Bank and Gaza Yemen, Rep. 66 4 366 12 149 Yugoslavia, Fed. Rep 3.5 3 4 Zaire 99.2 4 4 415 23 1,096 26 11,000 7 Zambia 63.6 8.6 229 7 736 10 4,600 9 Zimbabwe 30 7 7.9 270 9 648 7 4,200 94 Low income 2,001 1 t 52w Exci China & India 1,276 9 t 49w Middle income 2,994 3 t 50w Lower middle income 2,199 7 t 5 6w Upper middle income 794.6 t 3 9 w Low & middle income 4,995.4 t 5 1 w East Asia & Pacific 966.3 t 6 2 w Europe & Central Asia 860 0 t 3 6 w Latin America & Carib. 1,303.4 t 6 5 w Middle East & N Africa 290.8 t 3.0 w South Asia 212.4 t 44w Sub-Saharan Africa 1,362.5 t 5 8w High income 3,607.9 t 11.9 w a Flowering plants only b Data may refer to earlier years They are the most recent reported by the World Conservation Monitoring centre in 1994 100 World Development Indicators 1997 3.2 The data here are subject to variations in definition * Nationally protected areas are totally or partially and In reporting to the World Conservation Monitoring protected areas of at least 1,000 hectares that are Centre (WCMC)-ajointventure ofthe United Nations designed as scientific reserves with limited public Environment Programme (UNEP), World Wide Fund for access, national parks, natural monuments, nature Nature (WWF), and World Conservation Union reserves or wildlife sanctuaries, and protected land- (IUCN)-which compiles and disseminates them As scapes and seascapes The data do not include sites a result cross-country comparability is limited protected under local or provincial law Total land area Compounding these problems, available data are of is used to calculate the percentage of total area pro- different vintages tected (see table 3 1) * Mammals exclude whales Nationally protected areas are areas of at least and porpoises * Birds are listed for countries 1,000 hectares that fall into one of five management included within their breeding or wintering ranges categories defned by the WCMC * Higher plants refer to native vascular plant * Scientific reserves and strict nature reserves with species * Threatened species refer to species clas- limited public access sified according to the IUCN categories endangered. * National parks of national or international signifi- vulnerable, rare, Indeterminate, out of danger, and cance (not materially affected by human activity) insufficiently known - Natural monuments and natural landscapes with unique aspects C * Managed nature reserves and wildlife sanctuaries. - Protected landscapes and seascapes (which may Data on protected areas are from the Protected Areas include cultural landscapes) Data Unit of the WCMC, and those on species are The first three categories, referred to as "totally from the WCMC's Biodiversity Data Sourcebook, the protected," are areas maintained in a natural state WCMC's Global Biodiversity Status of the Earth's and closed to extractive uses The last two cate- Living Resources, and the IUCN's 1994 Red List of gories, referred to as "partially protected," are areas Threatened Animals, as reported by the World that may be managed for specific uses, such as recre- Resources Institute ation or tourism, or that provide optimum conditions for certain species or communities of wildlife Some extractive use is allowed within these areas Designating land as a protected area does not nec- essarily mean, however, that protection is in force Threatened species are defined according to the IUCN's classification categories endangered (in danger of extinction and survival unlikely if causal fac- tors continue operating), vulnerable (likely to move into the endangered category in the near future if causal factors continue operating), rare (not endan- gered or vulnerable, but at risk), indeterminate (known to be endangered, vulnerable, or rare but not enough information is available to say which), out of danger (formerly included in one of the above cate- gories. but now considered relatively secure because appropriate conservation measures are in effect), and insufficiently known (suspected but not definitely known to belong to any of the above categories) Figures on species are not necessarily comparable across countries because taxonomic concepts and coverage vary And while the number of mammals and birds is fairly well known, it is difficult to make an accurate account of plants World Development Indicators 1997 101 . 3.3 Freshwater Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita biliion % of total % for % for % for % of population % of population 1995 cu ml resources' agriculture0 industry' domestic' 1985 1993 1985 1993 Albanta 6,534c 02d 0 9C 76 18 6 100 95 Algeria 529C 45 30.40 600 15e 25e 100 70 Angola 17,081 0 5 0 3 76e joe 14e 80 . 15 Argentina 28,674c 27.6d 2 8c 73 18 9 63 73 17 17 Armenia 2,2070 3 8 45 80 72 15 13 Australia 18,999 14 6d 4.3 33 2 65 Austria 11,212c 2.4 2 60 9 58 33 100 100 Azerbaijan 3,7280 15 8 56 40 74 22 4 Bangladesh 19,6800 22 5 1.00 96 1 3 29 47 43 85 Belarus 5,397c 3 0 5 40 19 49 32 100 . 100 Belgium 1,2320 9 0 72.20 4 85 11 Benin 4,7120 0.1 0 40 67 e ioe 23e 45 82 9 63 Bolivia 40,464 1 2 0.4 85 5 10 81 82 27 21 Bosnia and Herzegovina 0 0 Botswana 10,138c 01 06c 48e 20e 320 98 100 72 53 Brazil 43,6500 36 5 0 50 59 19 22 .. 99 52 68 Bulgaria 24,3790 13.9 6 80 22 76 3 Burkina Faso 2,698 0 4 1.4 81e oe 190 50 . 26 Burundi 575 0.1 2 8 640e 0 360 33 97 22 55 Cambodia 49,691 0 5 0 1 94 1 5 20 12 Cameroon 20,169 0.4 0 1 35 10ge 460 46 71 30 24 Canada 97,987 45 1 1 6 12 70 18 100 100 Central African Republic 43,053 0 1 0 0 740 5e 21 24 5 Chad 6,6690 0 2 0.4c 82e 20 160 27 . 30 Chile 32,900 16 80 3.6 89 5 6 97 100 22 31 China 2,333 460.0 16 4 87 7 6 Colombia 29,066 5 3 0.5 43 16 41 100 90 76 90 Congo 315,989 0.0 000 o1 1 270 620 42 94 7 8 Costa Rica 27,949 1 4d 1.4 89 7 4 100 82 C6te d'lvoire 5,487 0 7 0 9 670 11 22e 30 97 10 73 Croatia 12,851 . 00 . . . 98 . 74 Cuba 3,133 8 1 d 23.5 89 2 9 100 . 91 Czech Republic 5,633 2.7 4 7 2 57 41 100 . 100 Denmark 2,4900 1 2 9.2c 43 27 30 . 100 Dominican Republic 2,557 3 14 9 89 6 5 72 75 24 40 Ecuador 27,359 5.6 1 8 90 3 7 83 79 33 45 Egypt, Arab Rep 1,0050 56 4 971 c 850 ge 60 93 95 61 74 El Salvador 3,379 lod 5 3 89 4 7 76 95 47 16 Eritrea 2,462 0 0 Estonia 10,4910 3 3 21 20 3 92 5 Ethiopia 1,950 2 2 2.0 860 3e I10 93 90 42 20 Finland 22,114c 2 2 1.9g 3 85 12 99 100 90 100 France 3,4100 37.7 19.10 15 69 16 100 100 100 100 Gabon 152,275 0 1 0 0 6e 220 720 75 80 34 30 Gambia, The 7,188c 00 0 30 91e 2e 7e 100 87 33 86 Georgia 11,3330 4 0 6 5c 42 37 21 Germany 2,0890 46 3 27 1c 20 70 11 Ghana 3,116c 03d 0.60 52e 130 350 57 76 40 46 Greece 5,6080 5 0 86c 63 29 8 100 . 95 Guatemala 10,922 0 7d 0.6 74 17 9 89 84 39 51 Guinea 34,289 0.7 0 3 870 3e 100 91 78 20 51 Gulnea-Bissau 25,234 c 0°0 OOC 36 e 4e 600 21 18 37 47 Haiti 1,535 0 0 0 4 68 8 24 59 55 32 34 Honduras 12,0530 1 5 210 91 5 4 51 90 49 54 Hong Kong . . 0 0 102 World Development Indicators 1997 3.3 3 Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita billion % of total % for % for % for % of population % of population 1995 cu ml resources' agriculture' industry' domestic0 1985 1993 1985 1993 Hungary 11,731c 6 8 5 7c 36 55 9 100 95 India 2,243c 380d 18 2C 93 4 3 80 87 47 85 Indonesia 13,090 16 6 0 7 76 11 13 40 86 31 56 Iran, Islamic Rep 1,833 45 4d 38 6 87 9 4 90 100 52 75 Iraq 4,976 42 8d 42.8 92 5 3 100 100 46 85 Ireland 13,943 o 8d 1.60 10 74 16 100 . 100 Israel 3980 1 9 8410 790 5e 16e Italy 2,919c 56.2 33.7c 59 27 14 100 100 Jamaica 3,291 03d 3 9 86 7 7 99 92 93 48 Japan 4,369 90 8 16 6 50 33 17 Jordan 3320 0.5d 32 1c 65 6 29 100 98 88 94 Kazakstan 7,551 c 37 9 30 2c 79 17 4 Kenya 1,1320 21 7 0c 76e 4e 20e 61 74 21 43 Korea, Dem. Rep 2,807 14 2 21.1 73 16 11 100 100 100 100 Korea, Rep 1,474 27 6 41 8 46 35 19 Kuwait 0 0 5 0.0 4 32 64 100 100 Kyrgyz Republic 10,786 11 7 24.0 90 7 3 Lao PDR 55,305 1.0 0.4 82 10 8 34 36 Latvia 12,719 0 0.7 2.20 14 44 42 Lebanon 1,199 0 8d 15 6 85 4 I1 98 98 Lesotho 2,626 0 1 1 0 56e 22e 22e 37 90 14 40 Libya 111 4 6 766 7 870 2e 11e 92 75 Lithuania 6,2450 4 4 19 o0 3 90 7 Macedonia, FYR 0 0 Madagascar 24,687 16 3 4.8 990 00 1e 81 55 17 10 Malawi 1,9170 0.9 5.00 86e 3e 1oe 70 91 27 41 Malaysia 22,642 9 4d 2.1 47 30 23 100 80 Mali 10,217 1 4 1.4 970 1e 2e 58 42 20 25 Mauritania 5,0130 1.6d 14.00 92 2 6 80 49 16 86 Mauritius 1,950 0 4d 16 4 770 70 16e 100 98 Mexico 3,892 77.6d 21 7 86 8 6 95 90 50 66 Moldova 2,924c 3.7 29 10 23 70 7 Mongolia 9,996 0 6 2 2 62 27 11 100 . 100 Morocco 1,129 10 9 36 2 920 30 50 63 100 2 18 Mozambique 12,8650 0 8 0 30 890 2e ge 82 44 2 17 Myanmar 23,988 4 0 0.4 90 3 7 36 38 21 36 Namibia 29,450c 0 1 0.4c 680 30 29e . 97 37 Nepal 7,923 2.7 1.6 95 1 4 78 60 20 41 Netherlands 5,821c 7.8 8 7c 34 61 5 100 100 100 100 New Zealand 90,808 2.0 0 6 44 10 46 100 100 Nicaragua 40,000 0.9 d 0 5 54 21 25 77 74 13 30 Niger 3,6000 0 3 1 5s 82e 2e 160 48 58 34 54 Nigeria 2,516c 3 6 130 540 150 310 60 69 30 11 Norway 90,032 c 2 0 05c 8 72 20 Oman 911 0 5d 24.0 94 3 3 90 98 55 56 Pakistan 3,6030 153 4d 32 8c 98 1 1 84 85 28 47 Panama 54,732 1.3d 0.9 77 11 12 100 . 64 Papua New Guinea 186,192 0 1 0 0 49 22 29 54 97 10 18 Paraguay 65,037 0 0.4 01 c 78 7 15 49 8 17 Peru 1,679 6.1 15.3 72 9 19 73 76 17 24 Philippines 4,709 29 5d 9 1 61 21 18 . Poland 1,456 c 12 3 21 90 11 76 13 94 . 82 Portugal 7,011 c 7 3 10 50 48 37 15 97 .. 90 Puerto Rico 0.0 Romania 9,166c 26 0 12 50 59 33 8 100 90 Russian Federation 28,813 0 117.0 2.70 23 60 17 World Development Indicators 1997 103 .33.3 Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Utban Rural per capita billion % of total % for % for % for % of population % of population 1995 cu m. resources' agriculture' industry0 domestic0 1985 1993 1985 1993 Rwanda 984 0 8 12 2 94e 2e 5e 55 60 Saudi Arabia 116 3 60 163 6 47 8 45 100 98 68 54 Senegal 4,653 c 1 4 3.50 92e 3e 5e 63 27 SierraLeone 38,141 0 4 0 2 89e 4e 70 58 85 8 Singapore 201 0 20 31 7 4 51 45 100 100 Slovak Republic 5,737 1 8 5 8 Slovenia 0 0 SouthAfrica 1,206c 13.3 26 6c 720 II, 17e Spain 2,839g 30 8 27 6C 62 26 12 100 100 Sri Lanka 2,385 6 3d 14 6 96 2 2 76 87 26 49 Sudan 5,766 17.8 11 6 94 10e 4e 49 89 45 73 Sweden 20,3850 2 9 1 6c 9 55 36 Switzerland 7,1030 1 2 2 4c 4 73 23 100 Syrian Arab Republic 2,516 3 3 9 4 83 10 7 77 95 65 77 Tajikistan 16,330c 12.6 13.2c 88 7 5 Tanzania 3,0020 1 2 1 30 89g 2e 90 85 65 47 45 Thailand 3,0730 31 9 17 80 90 6 4 89 . 72 Togo 2,9380 0°1 08 25e 130 620 68 64 26 54 Trinidad and Tobago 3,963 0 2d 2 9 35 38 27 100 83 93 80 Tunisia 434c 3.1 79 5c 89e 3e ge 98 100 79 67 Turkey 3,1630 33 5 17 30 57 e 19 24e 100 100 70 85 Turkmenistan 15,528c 22.8 32 60 91 8 1 Uganda 3,443c 0 2 0 30 600 80 320 45 12 Ukraine 1,6840 34 7 40 00 30 54 16 100 100 United Arab Emirates 122 0 9 300 80 9 11 100 98 100 98 United Kingdom 1,213 11 8 16.6 3 77 20 100 100 100 100 United States 9,418 0 467 3 18g9 420 45e 130 100 100 Uruguay 38,9450 0 7ud 0 5 91 3 6 95 93 27 Uzbekistan 4,725c 82 2 76 40 84 12 4 Venezuela 60,7720 4 ld 0 30 46 11 43 88 68 65 67 Vietnam 5,117 28.9 7 7 78 9 13 90 100 30 66 West Bank and Gaza Yemen, Rep 164 3.4 136 93 2 5 .. 88 . 17 Yugoslavia, Fed Rep. . . 0.0 Zaire 23,239 0 7 0 0 23e 160 610 43 5 Zambia 12,9200 17 150 770 70 16e 70 76 32 43 Zimbabwe 1,8160 1 2 610 790 70 140 100 99 10 65 Low income 5,069 w . 90 w 5 w 5 w Excl China& India 10,722 w . 92w 4w 4w 64 w 71 w 30 w 45 w Middle income 15,185 w 66w 24 w 11 w Lower middle income 12,248 w . 64 w 26 w 10 w Upper middle income 22,930 w . 73w 15 w 12 w 93 w 56 w 65 w Low & middle income 8,411 w . 80 w 13 w 7 w East Asia & Pacific 5,558 w . 84 w 8 w 7 w Europe & Central Asia 12,927 w . 53 w 37 w 11 w Latin Amenca & Canb 28,340w 77w 1 w 12w 89w 47w 57w Middle East&N Africa 1,384w . 86 w 7w 6 w 84w 98w 45 w 70 w South Asia 4,239 w . 95 w 3 w 2 w 77 w 84 w 43 w 80w Sub-Saharan Africa 9,106 w 85w 4 w 10 w 63 w 28 w High income 9,899 w 39w 46 w 15 w a Refers to any year from 1980 to 1995, unless otherwise noted b Unless otherwise noted, sectoral withdrawal percentages are estimated for t987 c Total water resources include river flows from other countries d Data refer to estimates for years before 1980 (see Pnmary data documentation) e Data refer to years other than the 1987 benchmark (see Primary data doc- umentation) 104 World Development Indicators 1997 3.3 Freshwater's scarcity _ _ Freshwater may well be the oil of the late 20th century-an essential and increasingly The data on freshwater resources hide what can be * Freshwater resources refer to both internal renew- scarce resource. For water, the concept of significant variations in total renewable water able resources and, where noted, river flows from availability transcends physical quantities resources from one year to the next They also fail to other countries Internal renewable water resources alone. Other important dimensions are qual- distinguish between seasonal and geographic vana- include flow of rivers and groundwater from rainfall in ity, accessibility, and reliability of supply tions in water availability within a country Data for the country * Annual freshwater withdrawals refer Although abundant globally, natural fresh- small countries and countries in arid and semiarid to total water withdrawal. not counting evaporation water resources are unevenly distributed zones are less reliable than those for larger countries losses from storage basins Withdrawals also include Because of the central role of water in the and countries with higher rainfall The data on fresh- waterfromdesalinationplantsincountrieswherethat functioning of economic, ecological, and waterresources are based on estimates of runoff into source is a significant part of all water withdrawal social systems, its scarcity raises concerns rivers and recharge of groundwater These estimates Withdrawal data are for single years between 1980 for long-term development prospects in some are based on different sources and refer to different and 1995 Withdrawals can exceed 100 percent of regions. years, so the data on freshwater resources should be renewable suppiies when extractions from nonrenew- Where water is not only scarce but also used with caution when comparing countries Caution able aquifers or desalination plants are considerable shared by more than one region or state, com- is also necessary in comparisons using the data on or if there is significant water reuse Withdrawals for petition for limited supplies is a likely source annual freshwater withdrawal, which are subject to agriculture and industry are the share of total with- of conflict, particularly in the Middle East variation in collection and estimation methods drawal for agriculture (irrigation and livestock produc- Uneven distribution makes it important to While information on access to safe water is tion) and the share for direct industrial use, including identify "hot spots" where pressures on water widely used, it is extremely subjective, and such withdrawals for cooling thermoelectric plants supply are likely to be greatest, as captured terms as "adequate amount" and "safe" may have Withdrawals for domestic uses include drinking water, by annual freshwater withdrawals as a per- very different meanings in different countries municipal use or supply, and use for public services, centage of total water resources. In Saudi despite official World Health Organization (WHO) def- commercial establishments, and homes For most Arabia, the United Arab Emirates, and Yemen, initions (see the definitions for table 2 12) Even in countries sectoral withdrawal data are estimated for for example, withdrawals exceed 100 percent, industrial countries treated water may not always be 1987-95 * Access to safe water refers to the per- indicating a reliance on sources other than safe to drink While access to safe water is equated centage of people with reasonable access to an ade- rivers and groundwater. with connection to a public supply system, this does quate amount of safe drinking water in a dwelling or With expanding populations needing more not take account of variation in the quality and cost located within a convenient distance from the user's water for human consumption and agricul- (broadly defined) of the service once connected dwelling (see About the data) tural, industrial, and commercial uses, it is Thus comparisons across countries must be made important to know which sector places the cautiously Changes over time within countries may p greatest strain on freshwater resources As be a result of definitional or measurement changes the indicators show, in most countries agri- VOR_LO RESOURCES Data are compiled by the culture consumes the lion's share (60-80 World Resources Institute percent in most countries and as much as 90 from various sources and percent in some) l UL published In World Access to reliable sources of freshwater Resources The Departe- depends to a large extent on the ability to ment Hydrogbologie in treat water and transport it to consumers. In Orleans, France, compiles industrial countries water from natural data on water resources sources is treated to render it pollutant-free and withdrawal from pub- and brought to consumers through piped net- lished documents, including natonal, United Nations, works. In developing countries such infra- and professional literature The Institute of Geo- structure may be lacking or poorly maintained graphy at the National Academy of Sciences in As a result, many people in the world still Moscow also compiles global water data on the basis depend on water supplies that are unreliable of published work and, where necessary, estimates in both quantity and quality water resources and consumption from models that use other data, such as area under irrigation, live- stock populations, and precipitation World Development Indicators 1997 105 . 3.4 Energy production and use Commercial energy Commercial energy use Traditional fuel Electricity production production thousand thousand average metric tons of metric tons of average annual kg of oil equivalent % of total annual kwh oil equivalent oil equivalent % growth per capita energy use % growth per capita 1980 1994 1980 1994 1980-90 1990-94 1980 1994 198(1 1993 1980-94 1994 Albania 3,053 1,064 3,058 1,093 -1i0 -15 6 1,145 341 11 1 218 -0 3 1,218 Algeria 66,730 103,833 12,078 24,834 6 2 2 1 647 906 2 7 1 7 7 4 725 Angola 7,700 24,914 937 931 1 2 -1 2 133 89 65 4 59 1 2 4 92 Argentina 36,683 60,625 39,669 51,405 1 1 5 0 1,411 1,504 5 7 5 0 3 2 1,930 Armenia 302 1,071 1,441 24 2 -35 9 346 384 -5 3 1,510 Australia 86,096 174,020 70,399 95,280 2 1 2 2 4,792 5,341 3 6 3 5 4 5 9,359 Austria 7,654 8,920 23,449 26,500 1 6 -0 3 3,105 3,301 1 4 3 0 1 9 6,487 Azerbaijan 14,821 16,065 15,001 16,274 5 2 -10 9 2,433 2,182 . 0 0 1 9 2,360 Bangladesh 1,113 5,460 2,809 7,566 9 0 5 4 32 64 73 5 53 2 11 5 84 Belarus 2,566 2,928 2,385 24,772 33 0 -13 2 247 2,392 0 4 0 7 3,032 Belgium 7,976 11,280 46,122 51,790 1 3 1 3 4,684 5,120 0 2 0 3 3 2 7,058 Benin 10 315 149 107 -1 4 -0 3 43 20 84 9 92 7 0 0 2 Bolivia 3,540 4,339 1,713 2,698 -0 6 8 6 320 373 19 2 17 5 3.4 390 Bosnia and Herzegovina 1,525 1,525 348 3 5 438 Botswana 260 248 384 549 3 1 0 7 426 387 Brazil 25,425 68,248 72,141 112,795 4 3 3 7 595 718 40 9 30 9 4 7 1,659 Bulgaria 7,541 8,969 28,476 20,568 0 3 -5 2 3,213 2,438 0 7 1 6 0 1 4,443 Burkina Faso 0 0 144 160 1 0 0 5 21 16 91.3 93 0 5.2 20 Burundi 1 5 58 143 7 3 5 2 14 23 92 93 14 4 50 5 20 Cambodia 13 22 393 512 2 5 0 5 60 52 72 9 Cameroon 2,855 5,782 774 1,335 4 1 3 2 89 103 7016 67 7 3 9 212 Canada 207,360 337,730 193,170 229,730 1 6 2 3 7,854 7,854 0 6 0 7 2 7 18,944 Central African Republic 17 22 59 93 3 6 0 5 26 29 90 7 89 3 3 4 31 Chad 0 0 93 100 0 3 0.5 21 16 94 7 89 9 2 7 14 Chile 3,882 4,598 7,743 14,155 3 9 5 7 695 1,012 14 5 14 2 5 7 1,806 China 428,690 798,850 413,130 791,040 5 6 4 6 421 664 8 3 6 5 8 4 712 Colombia 13,057 44,825 13,972 22,470 3 7 1 5 501 622 21 4 21 8 5 3 1,199 Congo 3,387 9,428 262 847 0 6 28 6 157 331 56 1 53 6 9 2 172 Costa Rica 181 601 1,292 1,843 3 8 1 5 566 558 27 D 15 5 1,444 CMte d'lvoire 192 425 1,435 1,406 1 6 1 1 175 103 65 4 1 3 170 Croatia . 3,821 6,667 -1 7 1,395 . 3 5 1,733 Cuba 293 1,175 9,645 10,133 1 5 -0 9 992 923 27 6 24 4 1 5 1,000 Czech Republic 37,939 37,140 29,394 39,982 -4 7 2,873 3,868 . 0 9 1 1 5,680 Denmark 646 14,900 19,488 20,700 0 5 2 3 3,804 3,977 0 4 0 7 2 8 7,704 Dominican Republic 147 161 2,083 2,591 0 7 1 5 366 337 27 5 18 9 804 Ecuador 10,774 21,024 4,209 6,345 2 6 1 8 529 565 26 3 17 1 6 1 736 Egypt, Arab Rep 33,374 60,931 15,176 34,071 7 2 2 7 371 600 4 9 3 2 7 7 915 El Salvador 366 608 1,000 2,032 1 9 11 5 220 370 51 8 48 0 585 Eritrea Estonia 3,404 5,560 -15 6 3,709 3 3 -4 4 6,104 Ethiopia 55 156 624 1,193 6 4 1 6 17 22 90 8 90 4 3 6 24 Finland 6,888 12,740 24,998 30,520 2 3 1 4 5,230 5,997 3 8 3 3 3 4 12,880 France 46,999 12,480 190,660 234,160 1 9 1 2 3,539 4,042 1 3 1 0 4 9 8,156 Gabon 9,151 15,998 759 692 -3 6 6 1 1,098 652 27 4 50 7 3 6 876 Gambla, The 0 0 53 60 0 6 1 0 83 56 78 3 78 5 5 7 70 Georgia 4,706 502 3,325 614 . 1 1 -2 4 1,255 Germany 184,240 142,630 359,170 336,490 0 5 -1 4 4,587 4,128 . 1 2 1 3 6,431 Ghana 554 523 1,303 1,542 1 6 0 9 121 93 55 8 70 8 4 1 368 Greece 3,696 8,850 15,973 23,560 3 6 1 4 1,656 2,260 2 8 1 6 4.6 3,873 Guatemala 230 569 1,443 2,165 0 3 11 8 209 210 54 0 63 2 5 2 306 Guinea 38 57 356 418 1 5 0 4 80 65 66 8 69 2 0.6 86 Guinea-Bissau 0 0 31 39 2 0 1 6 38 37 75 5 71 5 11 3 41 Haiti 56 14 240 200 4 9 -13 0 45 29 83 3 84 7 51 Honduras 199 211 843 1,173 2 1 3 0 230 204 54 3 58 5 464 Hong Kong 0 0 5,628 13,243 7 0 6 8 1,117 2,185 0 8 0 4 8 8 4,412 106 World Development Indicators 1997 3.4 4 Commercial energy Commercial energy use Traditional fuel Electricity production production thousand thousand average metric tons of metric tons of average annual kg of oil equivalent % of total annual kwh oil equivalent oil equivalent % growth per capita energy use % growth per capita 1980 1994 1980 1994 1980-90 1990-94 1980 1994 1980 1993 1980-94 1994 Hungary 14,340 13.025 28,322 24,450 0 8 -4 0 2,645 2,383 50 5 1 9 2 3 3,264 India 73,761 180,065 93,907 226,638 6 9 5 2 137 248 34 5 24 4 9 1 423 Indonesia 93,838 153,160 25,028 69,740 7 4 7 7 169 366 51 0 34 3 14 4 281 Iran, Islamic Rep 83,430 222,019 38,347 94,159 7 5 8 1 980 1,505 0 3 08 9 2 1,265 Iraq 136,620 29,912 12,003 23,864 6 9 6 1 923 1,213 0 2 0 1 6 4 1,375 Ireland 1,894 3,530 8,485 11,200 2 1 1 5 2,495 3,137 48 9 0 0 3 6 4,713 Israel 151 531 8,616 14,624 4 5 5 9 2,222 2,717 0 0 0 0 7 4 6,090 Italy 20,027 29,830 139,190 154,600 1 4 -0 1 2,466 2,707 49 0 0 8 1 9 4,004 Jamaica 15 10 2,169 2,703 -0 3 1 7 1,017 1,083 6 2 7 4 3 3 938 Japan 43,247 89,260 347,120 481,850 2.4 2 5 2,972 3,856 0 1 0 1 40 7,650 Jordan 0 179 1,710 4,306 5 8 6 4 784 1.067 0 0 0 0 11 4 1,259 Kazakstan 76,799 70,851 76,799 56,664 3 6 -12 5 5,153 3,371 0 1 1 7 3,950 Kenya 91 488 1,991 2,872 4 2 3 9 120 110 75 1 76 9 6 5 136 Korea, Dem Rep 28,275 23,190 30,932 26,464 2 1 -7 7 1,694 1,129 39 3 3 3 5 1,621 Korea, Rep 9,644 19,059 41,426 132,538 8 5 10 1 1,087 2,982 3 7 0 5 11 5 3,712 Kuwait 79,741 110,720 9,500 13.968 4 1 14 5 6,909 8,622 0 0 0 0 4 6 14,074 Kyrgyz Republic 2,190 1,450 2,755 -26 2 616 0 0 2 6 2,891 Lao PDR 236 215 107 182 18 2 5 33 38 88 4 84 8 Latvia 261 412 3,997 -17 5 1,569 11 2 -0 7 1.743 Lebanon 73 70 2,376 3,790 -0 8 14 0 840 964 2 0 3 4 0 1 1,318 Lesotho 0 0 Libya 96,537 74,658 7,122 13,039 4 4 3 2 2,340 2,499 11 1 3,411 Lithuania 186 2,254 7,555 0 9 -20 9 2,030 2 7 3 0 2,704 Macedonia, FYR 1,517 2,686 1,279 2,624 Madagascar 38 83 391 479 18 0 5 45 36 85 7 80 3 31 47 Malawi 99 152 334 370 10 1 2 54 39 83 1 86 4 5 5 86 Malaysia 15,049 57,011 9,522 33,410 9 4 110 692 1,699 6 5 9 8 1,987 Mali 21 42 164 205 2 1 0 4 25 22 85 0 87 0 10 7 36 Mauritania 0 0 214 229 0 2 0 5 138 103 0 0 0 0 3 3 68 Mauritius 21 34 339 431 3 5 0 5 351 387 47 8 47 2 71 901 Mexico 145,000 208,610 97,434 140,840 2 3 2 2 1,464 1,561 4 4 4 2 5 7 1,640 Moldova 24 4,763 2 1 -16 7 1,095 0 4 -3 1 1,892 Mongolia 1,195 2,167 1,943 2,550 3 1 -1 8 1,168 1,058 45 9 3 6 Morocco 618 463 4,927 8,509 3 6 5 3 254 327 5 1 4 0 5 0 426 Mozambique 1,293 161 1,123 619 -5 8 6 0 93 40 72 5 85 3 -21 5 31 Myanmar 1,940 2,164 1,858 2,181 -0 1 4 5 55 49 71 3 5 6 79 Namibia 0 0 Nepal 15 72 174 582 7 2 20 7 12 28 94 7 92 0 12 6 44 Netherlands 71,830 65,770 65,106 70,440 1 0 1 3 4,601 4,580 50 3 0 1 1 9 5,178 New Zealand 5,592 12,830 9,202 15,070 4 5 2 0 2,956 4,245 0 3 0 0 3 0 9,897 Nicaragua 130 476 756 1.273 2 9 7 2 270 300 49 5 43 6 398 Niger 14 55 210 327 2 3 1 2 38 37 77 9 78 7 0 3 21 Nigeria 105,510 102,138 9,879 17,503 2 9 4 6 139 162 62 7 59 9 5 6 144 Norway 55,743 170,150 18,865 23,060 1 9 1 5 4,611 5,318 65 3 1 1 2 3 26,061 Oman 15,133 44,508 1,346 5,018 12 4 6 2 1,223 2,392 15 8 2,950 Pakistan 7,217 19,429 11,698 32,133 80 6 3 142 254 27 1 19 7 9 6 463 Panama 38 206 1,376 1,597 -1 7 8 2 703 618 26 7 21 8 2 5 1,309 Papua New Guinea 80 2,472 705 990 2 4 2 3 228 236 64 2 59 4 Paraguay 64 3,130 550 1,402 6 8 12 1 175 299 65 6 52 6 45 4 7,759 Peru 11,094 8,515 8,139 8,555 -0 5 4 1 471 367 17 8 19 8 2 7 667 Philippines 2,839 6,081 13,406 21,199 2 6 4 2 277 316 36 0 31 3 3 4 404 Poland 120,720 94,437 124,500 92,537 -0 4 -1 1 3,499 2,401 50 1 0 8 1 1 3,512 Portugal 1,481 2,100 10,291 18,090 4 7 2 6 1,054 1,827 48 3 0 7 6 5 3 165 Puerto Rico 35 41 8,042 7,371 0 2 0 8 2,508 2,000 Romania 51,749 28,822 63,846 39,387 0 3 -9 0 2,876 1,733 53 5 2 8 -1 6 2,426 Russian Federation 749,290 910,609 750,240 595,440 4 2 -8 9 5,397 4,014 1 7 1 5 5,904 World Development Indicators 1997 107 .43.4 Commercial energy Commercial energy use Traditional fuel Electricity production production thousand thousand average metric tons of metric tons of average annual kg of oil equivalent % of total annual kwh oil equivalent oil equivalent % growth per capita E nergy use YO growth per capita 1980 1994 1980 1994 1980-90 1990-94 1980 1994 1980 1993 1980-94 1994 Rwanda 29 46 190 209 3 1 -6 3 37 34 84 7 85 9 3 9 39 Saudi Arabia 518,700 471,344 35,496 83,772 5 8 5 7 3,787 4,566 0 0 9.9 4,961 Senegal 0 0 875 803 08 -2 7 158 97 48 1 57 3 3 7 121 Slerra Leone 0 0 310 323 -0 i 0 5 96 77 84 7 698 -1 5 56 Singapore 0 0 6,049 23,743 7 2 16 0 2,651 8,103 0 4 0 0 8 2 6,843 Slovak Republic 4,898 17,343 3,243 0 7 0 9 4,485 Slovenia 2,536 5,195 2,612 5 0 6,350 South Africa 69,065 117,691 60,511 86,995 3 6 -1 5 2,074 2,146 4 2 4,670 Spain 15,781 29,450 68,692 96,200 2 6 1 7 1,837 2,458 50 3 0 5 3 2 4,104 Sri Lanka 127 352 1,411 1,728 0 6 3 4 96 97 53 '3 51 5 6 1 246 Sudan 68 81 1,150 1,731 48 -03 62 66 87:3 762 11 51 Sweden 16,133 31,340 40,992 50,250 2 1 0 6 4,933 5,723 9 () 5 9 3 5 16,236 Switzerland 7,030 11,030 20,840 25,380 2 1 0 3 3,298 3,629 1 L 1 3 9,271 Syrian Arab Republic 9,495 31,474 5,343 13,675 6 7 4 4 614 997 0 ( 0 0 8 7 1,107 Tajikistan 1,986 1,654 3,542 -12 8 616 2,956 Tanzania 86 165 1,023 975 -0 7 2 9 55 34 89 () 89 3 5 3 66 Thailand 535 17,362 12,093 44,395 9 5 9 7 259 769 40 '3 35 9 12 5 1,234 Togo i 0 195 183 0 7 0 0 75 46 38 0 59 2 -4 2 24 Trinidad and Tobago 13,130 13,002 3,863 6,935 3 9 34 3,570 5,436 1 8 1 2 44 3,190 Tunisia 6,149 4,776 3,083 5,264 4 0 3 9 483 595 15 L 12 8 6 4 759 Turkey 17,190 26,790 31,314 57,580 5 8 2 5 705 957 16 8 3 7 9 8 1,302 Turkmenistan 8,035 30,279 10,401 -10 9 2,361 5 1 2,383 Uganda 153 179 320 425 4 6 -1 0 25 23 94 3 88 5 1 7 44 Ukraine 109,790 85,996 108,290 165,132 69 -10 5 2,164 3,180 0 4 02 3,910 United Arab Emirates 93,782 139,365 8,558 25,137 8 8 5 6 8,205 10,531 8 0 7,905 United Kingdom 197,770 241,300 201,200 220,270 1 0 0 7 3,572 3,772 00 0 0 1 4 5,547 United States 1,547,800 1,651,310 1,801,000 2,037,980 1 3 1 5 7,908 7,819 1 3 1 3 3 0 13,243 Uruguay 235 642 2,208 1,971 -0 9 2 7 758 622 13 '2 25 0 4 0 2,406 Uzbekistan 41,894 41,825 2 5 -1 5 1,869 2 8 2,100 Venezuela 132,920 171,075 35,011 46,300 1 5 2 9 2,354 2,186 1 0 0 9 5 3 3,453 Vietnam 2,728 11,252 4,024 7,267 4 0 7 2 75 101 53 15 51 1 9 6 170 West Bank and Gaza Yemen, Rep 17,148 1,364 3,044 7 8 -0 1 160 206 2 6 11 4 146 Yugoslavia, Fed Rep 10,488 11,681 1,110 1 8 2 3 3,153 Zaire 1,478 1,877 1,487 1,902 2 0 1 1 55 45 80 7 83 5 3 0 131 Zambia 1,146 890 1,685 1,296 -3 0 2 3 294 149 54 9 71 0 -3 1 893 Zimbabwe 2,024 3,567 2,797 4,722 5 5 0 5 399 438 33 5 25 6 7 0 --.r - 'Em. mrT."M~~~ I. I Low income 673,843 t 1,215,554 t 587,124 t i,154,712 t 6 1 t 42 t 248 w 369 w 18 0 w 13 9 w 84 t 518 w Excl China & India 171,392 t 236,639 t 80,087 t 137,034 t 803t 0 t 114 w 134 w 52 1 w 36 4 w 7 8 t 257 w Middle income 2,788,780 t3,447,268 t 1,873,142 t 2,313,337 t 12 5 t -2 8 t 1,537 w 1,475 w 60w 90t 2,114 w Lower middle income 1,779,584 t 2,237,535 t 1,448,776 t 1,647,009 t 16 3 t -4 6 t 1,632 w 1,449 w 48w ii 5 t 2,012 w Upper middle income 1,009,196 t 1,209,733 t 424,366 t 666,328 t 44 t 2 6 t 1,282 w 1,544 w 18 7 w 10 7 w 4 9 t 2,382 w Low & middle income 3,462,623 t 4,662,822 t 2,460,266 t 3,468,049 t 10 4 t -0 8 t 686 w 739 w 86w 8 7 t 1,066 w East Asia & Pacific 575,418 t 1,074,050 t 514,066 t 1,000,586 t 56 t 4 7 t 378 w 593 w 14 2 w 10 9 w 8 3 t 704 w Europe & Central Asia 1,226,858t 1,412,506 t 1,279,071 t 1,288,624 t 21 6 t -7 0 t 3,105 w 2,647 w 15w 12 7 t 3,811 w Latin America & Carib 397,781 t 613,069 t 317,962 t 451,011 t 24 t 3 1 t 888 w 960 w 17 1 w 14 2 w 54 t 1,574 w Middle East& N Africa 971,980 t 1,068,548 t 143,540 t 323,064 t 64t 5 4 t 825 w i,220 w 19w 16w 85t 1,299 w South Asia 84,986 t 208,838 t 110,906 t 271,293 t 7 0 t 5 4 t 123 w 222 w 35 3 w 25 2 w 9 2 t 385 w Sub-Saharan Africa 205,600 t 285,811 t 94,721 t 133,471 t 3 1 t -0 3 t 249 w 237 w 64 9 w 64 2 w 35 t 571 w High income 2,768,760 t3,381,732 t 3,789,479 t 4,543,482 t 1 6 t 1 7 t 4,644 w 5,066 w 58w low 32t 8,914 w 108 World Development Indicators 1997 3.4 Energy use and the environment _ _ _ _ Each stage in the production, transport, and '- use of energy has an impact on the environ- Energy data are compiled by the International Energy * Commercial energy production refers to commer- ment The quantity and mix of energy used in Agency (IEA) and the United Nations Statistical cial forms of primary energy-petroleum (crude oil, a country are an indicator both of potential Division (UNSD) UNSD data are primarily from natural gas liquids, and oil from nonconventional environmental impact and, roughly, of the responses to questionnaires sent to national govern- sources), natural gas, solid fuels (coal, lignite, and country's stage of development. ments, supplemented by official national statistical other derived fuels), and primary electricity-all con- Commercial energy is widely traded, making pubitcations and by data from intergovernmental verted into oil equivalents (see About the data) it necessary to distinguish between its pro- organizations When official data are not available, * Commercial energy use is indigenous production duction and use. The production and transport the UNSD prepares estimates based on the profes- plus imports and stock changes, minus exports and of primary energy have a range of potential sional and commercial literature The variety of international marine bunkers (see About the data) environmental consequences-from direct sources affects the cross-country comparability of * Traditional fuel includes estimates of the con- restructuring of the environment in the case of data sumption of fuelwood, charcoal, bagasse, and animal surface coal mines to the risk of leaks and cat- Commercial energy use refers to domestic prim- and vegetable wastes Total energy use comprises astrophic releases in the extraction and move- ary energy use before transformation to other commercial energy use plus traditional fuel use ment of crude oil and natural gas. Production end-use fuels (such as electricity and refined petro- * Electricity production includes that generated by of secondary energy (refined petroleum prod- leum products) The use of firewood, dried animal nuclear, hydroelectric, geothermal, wind, tide, and ucts and thermal electricity) and energy use in manure, and other traditional fuels is not included wave power sources households, industry, and vehicles are by far All forms of commercial energy-primary energy and the largest sources of air pollutants and C02 primary electricity-are converted into oil equiva- _ emissions (see table 3.5). lents To convert nuclear electricity into oil equiva- Growth in commercial energy use is closely lents, a notional thermal efficiency of 33 percent is Data on commercial energy related to the growth of the modern sectors- assumed, for hydroelectric power. 100 percent effl- Mmay production and use are pri- ciency is assumed For traditional fuel, fuelwood and c manly from the lEA and the charcoal consumption data are estimated from pop- United Nations Ener&' IU99 - 199ZI ulation data and country-specific per capita con- STSTInuES Statistics Yearbook Tradi- People in high-income economies sumption figures by the Food and Agriculture NDIGSPU" tional fuel data are from the use nearly seven times as much Organization (FAO) after an assessment of the avail- World Resources Institute's able consumption data Estimates of bagasse con- World Resources and the commercial energy as do people in sumption, a traditional fuel, are based on sugar . FAO developing economies 0 production data Electricity production includes both public and industry, motorized transport, and urban self-generating power plants Self-generating power areas-in developing countries, but more weakly plants are operated by organizations or companies correlated with growth in more developed coun- to produce electricity for internal operations The tries. Commercial energy use per capita reflects energy source used in generating electricity is very the size of the modern sector as well as eco- important because of the effects different sources nomic factors, such as the relative price of have on air quality, but this aspect of electricity pro- energy, and climatic and geographic factors. duction is not captured here Traditional fuels come from renewable sources, but the management of these resources-such as open-access forests-is often unsustainable. The burning of crop residues and manure reduces the nutrients available for maintaining soil quality And tra- ditional fuel use in the home, combined with inefficient combustion and poorventilation. is a significant source of indoor air pollution and related health problems Electricity production has a large environ- mental impact, whatever the source of energy. Fossil fuel generation is the largest source of air pollution in most countries Hydroelectric and nuclear power generation both have sig- nificant environmental consequences. And solar, wind, and wave generation, relatively benign environmentally, are still rare. World Development Indicators 1997 109 3.5 Energy efficiency, dependency, and emissions GDP per unit Net energy Imports Carbon dioxiide emissions of energy use from Industrial processes 1987 $ per kg % of Total Per capita kg per 1987 $ oil equivalent commercial energy use million metric tons metric tons of GDP 1980 1994 1980 1994 1980 1992 1980 1992 1980 1992 Albania 0 6 2 4 0 3 7 4 2.8 1.2 4.0 18 Algeria 4 1 2.6 -452 -318 66 79 3.5 3 0 1 3 1.2 Angola 7 0 -722 -2,576 5 5 0.8 0 5 0.6 Argentina 2 9 2 7 8 -18 107 117 3 8 3 5 0 9 1.0 Armenia 4.3 1.4 100 79 4 1 1 1.8 Australia 2.4 2.6 -22 -83 203 268 13 8 15 3 1 2 1 2 Austria 4 6 5 4 67 66 52 57 6 9 7.2 0 5 0 4 Azerbaijan 0 2 1 1 64 8 7 . 13 6 Bangladesh 4 5 3 1 60 28 8 17 0.1 0 2 0 6 0 8 Belarus 08 -8 88 102 . 9 9 4 0 Belgium 2 8 3 2 83 78 128 102 13.0 10.1 1 0 0.6 Benin 8.3 18.0 93 -194 0 1 0 1 0 1 0 4 0 3 Bolivia 2 7 2 1 -107 -61 5 7 0 8 10 1 0 13 Bosnia and Herzegovina .. 0 . 15 3 4 Botswana 2.1 4 7 32 55 1 2 1 1 1 6 1.2 0 9 Brazil 3 4 2 8 65 39 184 217 1 5 14 0 7 0 8 Bulgaria 0 7 10 74 56 75 54 8 4 6 4 3 6 2 4 Burkina Faso 11 2 16 0 100 100 0 1 0 1 0 1 0 3 0 2 Burundi 13 9 8 3 98 97 0 0 0.0 0 0 01 01 Cambodia 2 4 97 96 0 0 0 0 0 1 0 4 Cameroon 10 0 6.9 -269 -333 4 2 0.4 0 2 0 5 0.2 Canada 1.7 20 -7 -47 430 410 175 144 1 3 09 Central African Republic 16 2 12 1 71 76 0 0 0 0 0 1 0 1 0 2 Chad 6.2 10.9 100 100 0 0 0 0 0 0 0.4 0 2 Chile 23 23 50 68 27 35 24 26 15 12 China 0 3 0 7 -4 -1 1,489 2,668 1 5 2 3 11.0 6 6 Colombia 2.1 21 7 -99 39 61 1 4 1 8 1 4 1 4 Congo 5.7 2.8 -1,193 -1,013 0 4 0 2 1 6 0 3 16 Costa Rica 3.1 3 4 86 67 2 4 1.1 1.2 0 6 0 7 Cte d'lvoire 6 8 6 8 87 70 5 6 0 6 0 5 0 5 0.6 Croatia 43 16 3 4 Cuba 97 88 31 29 3 2 2 6 Czech Republic 0.8 -29 7 . 136 13 1 4.4 Denmark 4 4 5 5 97 28 63 54 12 3 10 4 0 7 0 5 Dominican Republic 2 0 2 5 93 94 6 10 1 1 1 4 1.5 1 7 Ecuador 2 3 2 2 -156 -231 13 19 1 7 1 8 1 4 14 Egypt, Arab Rep 1 6 12 -120 -79 45 84 1 1 1 5 1 9 2 1 El Salvador 4 5 2 6 63 70 2 4 0 5 0 7 0 5 0 7 Eritrea Estonia 0.7 . 39 0 21 0 3 13 5 0 1 4 9 Ethiopia 6.9 91 87 2 3 0 0 0 1 0.4 Finland 2.9 3.0 72 58 55 41 115 8 2 0.8 0 5 France 4 1 4 4 75 95 484 362 9.0 6.3 0 6 0 4 Gabon 5 0 5 5 -1,106 -2,212 5 6 6 9 5 5 1 3 1 3 Gambia, The 3 5 4 9 100 100 0 0 0 2 0 2 0 9 0 6 Georgia 0 7 85 14 2 5 30 Germany 49 58 1,068 878 13 6 10 9 Ghana 3 6 4 4 57 66 2 4 0.2 0.2 0 5 0.6 Greece 2 8 2.2 77 62 51 74 5 3 7 2 1 2 1 4 Guatemala 5.0 4 3 84 74 4 6 0 6 0 6 0.6 0.7 Guinea . 6 1 89 86 1 1 0 2 0 2 0 4 Guinea-Bissau 3 8 5.8 100 100 0 0 0 2 0 2 1.2 1 0 Haiti 9.5 7 9 77 93 1 1 01 01 0 3 04 Honduras 4 2 4 4 76 82 2 3 0.6 0 6 0 6 0 6 Hong Kong 5 3 5 3 100 100 16 29 3 3 5 0 0 5 0 5 110 World Development Indicators 1997 3.5. GDP per unit Net energy imports Carbon dioxide emissions of energy use from industrial processes 1987 $ per kg % of Total Per capita kg per 1987 $ oil equivalent commercial energy use million metric tons metric tons of GDP 1980 1994 1980 1994 1980 1992 1980 1992 1980 1992 Hungary 0 8 1.0 49 47 82 60 7 7 5 8 3 7 2 6 India 1 9 1 6 21 21 350 769 0 5 0 9 1 9 2 3 Indonesia 21 1.8 -275 -120 95 185 0 6 1 0 1 8 17 Iran, Islamic Rep 3 0 1.9 -118 -136 116 235 3 0 4 0 1 0 1 3 Iraq 7 2 -1,038 -25 44 65 3 4 34 0 5 Ireland 3 1 3 9 78 68 25 31 7 4 8 7 0 9 0 8 Israel 34 3 7 98 96 21 42 5 4 8 1 0 7 0 9 Italy 48 5 5 86 81 372 408 6 6 7 2 06 0 5 Jamaica 1 3 1 5 99 100 8 8 4 0 3 3 3 1 2 1 Japan 5.5 6 2 88 81 934 1,093 8 0 8 8 0 5 0 4 Jordan 1 5 100 96 5 11 2 2 3 0 1 9 Kazakstan 0 3 0 -25 298 17 6 12 6 Kenya 31 33 95 83 6 5 04 02 10 06 Korea, Dem Rep 9 12 126 254 6 9 112 Korea, Rep 18 1 8 77 86 126 290 3 3 6 6 1 7 1 4 Kuwait 2 7 2 0 -739 -693 25 16 18 0 112 1 0 0 8 Kyrgyz Republic 0 9 47 15 3 4 4 3 Lao PDR . 9 1 -121 -18 0 0 0.1 0 1 . 0 2 Latvia 1 2 90 15 5 6 2 6 Lebanon 97 98 6 11 2 2 2 9 Lesotho Libya 5 6 -1,255 -473 27 40 8 8 8:1 0 7 Lithuania 0 8 70 22 5 9 3 1 Macedonia, FYR 44 4 2 0 Madagascar 6 7 5 6 90 83 2 1 0 2 0 1 0 6 0 4 Malawi 32 34 70 59 1 1 01 01 07 05 Malaysia 2.4 17 -58 -71 28 70 2 0 3 8 1 2 :15 Mali 112 115 87 80 0 0 01 00 02 02 Mauritania 3 8 4 8 100 100 1 3 0 4 14 0 8 2 9 Mauritius 3 7 6 3 94 92 1 1 0 6 1 3 0 5 0 5 Mexico 1 3 12 -49 -48 260 333 3 9 3 8 2 0 2 0 Moldova 99 0 14 0 0 3 3 Mongolia 1.2 1 2 38 15 7 9 4 0 4 0 3 0 3 1 Morocco 3.1 2 9 87 95 16 27 0 8 1 1 1 1 1 2 Mozambique 1 4 3 3 -15 74 3 1 0 3 0 1 2 0 0 6 Myanmar -4 1 5 4 01 0 1 Namibia Nepal 12 5 7.3 91 88 1 1 0 0 0 1 0 3 0 3 Netherlands 3 0 3 7 -10 7 153 139 10 8 9 2 0 8 0 5 New Zealand 34 2.8 39 15 18 26 5 7 7 6 0 6 0.7 Nicaragua 5 1 2 7 83 63 2 2 0 7 0 6 0 5 0 7 Niger 12 1 7 3 93 83 1 1 01 0 1 0 2 0 5 Nigena 3 1 2 2 -968 -484 68 97 1 0 0 9 2 2 2 6 Norway 3 9 4 6 -195 -638 40 60 9 8 14 1 0 5 0 6 Oman 2 9 2 4 -1,024 -787 6 10 5 3 5 3 1 5 0 9 Pakistan 1 8 1 5 38 40 32 72 0 4 0 6 1 5 1 6 Panama 3 2 3 9 97 87 4 4 1 9 17 0 8 0 8 Papua New Guinea 3 9 4 8 89 -150 2 2 0 6 0 6 0 7 0 6 Paraguay 60 35 88 -123 1 3 05 06 04 06 Peru 2 5 2 7 -36 0 24 22 14 1 0 1 2 1 2 Philippines 2 4 1 9 79 71 37 50 0 8 0 8 1 1 1 3 Poland 0 5 0 7 3 -2 460 342 12 9 8 9 7 7 6 0 Portugal 3 5 2 8 86 88 27 47 2 8 4 8 0 7 0 9 Puerto Rico 2 4 100 99 14 4 4 0 7 Romania 0 5 027 19 27 191 122 8 6 5 4 5 7 4 5 Russian Federation 0 6 0 5 -53 2,103 14 1 5 5 World Development Indicators 1997 111i e 3.5 GDP per unit Net energy imports Carbon dioxide emissions of energy use from industiial processes 1987 $ per kg % of Total Per zapita kg per 1987 $ oil equivalent commercial energy use million metric tons metr c tons of GDP 1980 1994 1980 1994 1980 1992 1980 1992 1980 1992 Rwanda 9 3 49 85 78 0 0 0 0 01 0 1 0.2 Saudi Arabia 2 7 1 1 -1,361 -463 131 221 14 0 13 1 1 4 2 3 Senegal 4 2 6 3 100 100 3 3 0 5 0 4 0 8 0 6 Sierra Leone 2 3 2 4 100 100 1 0 0 2 01 0 8 0 6 Singapore 2 2 16 100 100 30 50 13 2 17 7 2 2 1 6 Slovah Republic 0 9 . 72 37 7 0 2 5 Slovenia . 51 . 6 2 8 South Africa 1 2 10 -14 -35 213 290 7 3 7.5 2 8 3 5 Spain 36 3 6 77 69 200 223 5 4 5 7 08 0 6 Sri Lanka 3.4 5 1 91 80 3 5 0 2 0 3 0 7 0 6 Sudan 12 7 12 1 94 95 3 3 0.2 0 1 0 2 0 2 Sweden 3 4 3 3 61 38 71 57 8.6 6 6 0 5 0 3 Switzerland 7 3 7 4 66 57 41 44 6 5 6 4 0 3 0 2 Syrian Arab Republic 19 12 -78 -130 19 42 2.2 3 3 1 9 2 8 Tajikistan 0 5 53 4 . 0 7 1 6 Tanzania 4 5 92 83 2 2 0 1 0.1 0.5 Thailand 2 8 2 2 96 61 40 112 0 9 2.0 12 1.3 Togo 63 69 99 100 1 1 02 02 05 06 Trinidad and Tobago 1 5 0 7 -240 -87 17 21 15 4 16.5 3 0 4.4 Tunisia 2 4 2 4 -99 9 9 14 1 5 1 6 1 3 1 1 Turkey 19 1 8 45 53 76 145 1 7 25 13 14 Turkmenistan -191 42 10 5 Uganda 22 6 52 58 1 1 0 1 01 0 1 Ukraine 0 4 -1 48 611 11 7 6 9 United Arab Emirates 3 6 -996 -454 36 71 34 8 33 9 1.2 United Kingdom 2 8 3 5 2 -10 588 566 10 4 9 8 1 0 0 8 United States 2 1 2 6 14 19 4,623 4,881 20.3 19 1 1 2 1 0 Uruguay 34 46 89 67 6 5 20 16 08 06 Uzbekistan 0 3 0 123 . 5 7 8 5 Venezuela 13 1.2 -280 -269 90 116 6 0 5 7 2 0 2 0 Vietnam . 7 5 32 -55 17 22 0 3 0.3 0 5 West Bank and Gaza Yemen, Rep 100 -463 3 10 0 4 0 7 Yugoslavia, Fed Rep 10 38 3 6 Zaire 44 .. 1 1 3 4 01 01 05 07 Zambia 1 3 1.8 32 31 4 2 0 6 0 3 1.6 :10 Zimbabwe 1.5 1 4 28 24 10 19 1.4 1 8 Lowincome 09w 1.1w 2,063t 3,880t 09w 13w 4.2w 36w Excl China&lndia 33w 27w 223t 443t 03w 05w 11 w 13w Middle income 13w 12w 2,831 t 7,221 t 29w 48w 17 w Lower middle income lOw lOw 1,664 t 5,565 t 2.6 w 51w 20w 33w Upper middle income 2.2 w 16w 1,167 t 1,656 t 3 7 w 40w 14w Low & middle income 1 2 w 1 w 4,893 t 1iO,101 t 1 5 w 24w 2.2 w 30w East Asia & Pacific 09w 1,846 t 3,378 t 14w 21w Europe & Central Asia 06w 944 t 4,506 t . 93w 48w Latin America & Carib 23w 20w 855 t 1,029 t 2.4 w 23w 12w 1.2 w Middle East& N Africa 33w 16w 500 t 849 t 2.9 w 3.4 w 1 1 w SouthAsia 20w 17w 395t 866t 04w 07w 1.8w 21w Sub-Saharan Africa 2.2 w 20w 353 t 472 t 0.9 w 09w 17w 1 8 w High income 2 9 w 34w 9,877 t 10,246 t 12 4 w 119 w 09w 0 7 w 112 World Development Indicators 1997 3.55 Moving toward energy efficiency Energy is both a critical factor of production and, through its generation, a major source of pres- The Carbon Dioxide Information Analysis Center * GDP per unit of energy use is the U S dollar esti- sure on the environment. The sustainability of (CDIAC), sponsored by the U S Department of mate of real GDP (at 1987 prices) per kilogram of oil energysupplies and the efficiencyof energyuse Energy, calculates annual anthropogenic emissions equivalent of commercial energy use (see notes to are therefore critical for countries aiming for of CO2 table 3 4 on energy use) * Net energy imports are environmentally sustainable development. Estimates do not include bunker fuels used in calculated as energy use less production, both mea- Carbon dioxide (CO2) emissions, largely a international transport because of the difficulty of sured in oil equivalents A minus sign indicates that byproduct of energy generation and use, are the apportioning these fuels among the countries bene- the country is a net exporter * Carbon dioxide emis- largest source of greenhouse gases associated fiting from that transport Although the estimates of sions from industrial processes are those stemming with global warming. Thus understanding the world emissions are probably within 10 percent of from the burning of fossil fuels and the manufacture links between economic activity and C02 emis- actual emissions (as calculated usingglobal average of cement They include contributions to the carbon sions has become increasingly important. fuel chemistry and use), individual country esti- dioxide flux from solid fuels, liquid fuels, gas fuels, The ratio of real GDP to energy use provides mates may have larger error bounds and gas flaring a measure of energy efficiency. Differences in For information on energy use and production see this ratio over time and across countries are the notes to table 3 4 p. influenced by * Structural changes in the economy. Underlying data on com- - Changes in the energy efficiency of particu- mercial energy use and lar sectors of the economy. _ production are primarily * Differences in fuel mixes Figure 3.5a Carbon dioxide emissions per from International Energy Technological changes in energy-intensive capita, by income group, 1980 and 1992 Agency (IEA) and United industries help increase overall energy effi- metNctons ations sources Data on CO2 emissions are based 15 on several sources as 1290 reported by the World Per dollar of GDP, developing Resources Institute The main source is the Carbon economies produce four times the 9 Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, in carbon dioxide that high-income 6 the state of Tennessee in the United States CDIAC economies do 0 calculations of CO2 emissions are based on data on 3 _ the net apparent consumption of fossil fuels from the ciency. Shifts to thermodynamically efficient O ___ World Energy Data Set maintained by the United fuels such as natural gas also can help. Butthe High Middle Low Low income Nations Statistical Division and from data on world most important factor affecting energy effi- income income income (excluding cement manufacture based on the Cement China ciency is the rapid rise in energy use as coun- and India) Manufacturing Data Set maintained by the U S tries approach middle-income status. The Bureau of Mines Note: Oata refer to emissions from Industriai processes development of heavy industries and the large Source: Table 3 5 increase in private automobile ownership asso- ciated with income growth both increase demand for energy. Offsetting this tendency, Figure 3.5b Carbon dioxide emissions, especially for high-income economies, may be by income group, 1992 the growth of the less energy-intensive ser- vices sector. Growth in services also helps China and India reduce oil-importing countries' dependence on 16% external sources of energy. In the past two decades this dependency has put heavy pres- High Middle sure on these countries' foreign exchange income income earnings and has made their economies vul- 48% 34% nerable to external shocks. Low income Anthropogenic C02 emissions result primarily (excluding from fossil fuel combustion and the manufac- China and India) ture of cement Because fossil fuel consump- 2% tion tends to rise with income, high-income I Source: Table 3 5 countries are the largest emitters per capita. _ World Development Indicators 1997 113 . 3.6 Urbanization Urban population Population in Population in Access to urban agglomerations the largest city sanitation of one million or more in urban areas % of total average annual % of % of % of millions population % growth total population urban population urban population 1995 1980 1995 1980-90 1990-95 i 1980 1995 2015 1980 1995 1985 1993 Albania 1 2 34 37 2 9 -0 9 0 0 0 20 100 100 Algeria 15 6 43 56 4 8 4 0 11 13 16 25 24 95 Angola 3 5 21 32 5 8 5 9 13 20 30 63 64 27 Argentina 30 5 83 88 19 1.7 35 39 37 42 44 76 100 Armenia 2 6 66 69 16 21 34 35 38 51 51 Australia 15 3 86 85 1 4 1 0 47 58 58 25 23 Austria 4 5 55 56 0 2 1 2 27 26 27 49 46 100 100 Azerbaijan 4 2 53 56 1 9 1 6 26 25 27 48 44 Bangladesh 21 9 11 18 5 9 5 0 5 9 15 33 36 21 42 Belarus 7 4 56 71 2 2 16 14 17 18 24 24 100 Belgium 9 8 95 97 0 2 0 5 12 11 11 13 11 100 100 Benin 2 3 32 42 5 2 4 9 0 0 0 9a 45 49 Bolivia 4 3 46 58 4 2 3 2 14 17 19 30 26 51 64 Bosnia and Herzegovina 2 1 36 49 3 2 1 5 Botswana 0 4 15 31 8 9 7 4 0 0 0 34a 79 91 Brazil 124 5 66 78 3 2 2 5 27 33 34 15 13 33 83 Bulgaria 5 9 61 71 10 -0 1 12 16 18 20 23 100 100 Burkina Faso 2 8 9 27 10 7 12.6 0 0 0 42a 38 Burundi 0 5 4 8 6 9 6 5 0 0 0 68 a 90 71 Cambodia 21 12 21 6 8 6 5 63 a Cameroon 6 0 31 45 5 4 5 3 6 10 13 19 22 25 73 Canada 22 7 76 77 14 1 4 29 36 36 16 20 Central African Republic 13 35 39 3 1 3 3 0 0 0 43a 36 Chad 1 4 19 21 3 4 3 5 0 0 0 53a Chile 12 2 81 86 21 1 9 33 36 35 41 41 79 100 China 363 7 19 30 4 8 3 8 8 11 14 6 4 Colombia 26 8 64 73 2 8 2 6 22 28 30 20 21 96 70 Congo 1 5 41 59 5 9 51 0 0 0 67 65 17 11 Costa Rica 17 43 50 3 8 3 3 0 0 0 52 100 Cte d'lvoire 61 35 44 5 4 4 9 15 20 29 44 46 13 60 Croatia 3 1 50 64 2 2 1 5 0 0 0 40 72 72 Cuba 8 4 68 76 1 7 15 20 20 21 29 27 100 Czech Republic 6 8 64 65 0 3 0 1 12 12 12 18 18 Denmark 4 4 84 85 0 2 0 4 27 25 25 32 30 100 100 Dominican Republic 5 1 51 65 4 1 3 4 25 33 37 49 51 72 75 Ecuador 6 7 47 58 4 2 3 6 14 26 30 29 26 79 69 Egypt, Arab Rep 25 9 44 45 2 6 2 5 23 23 26 38 37 95 El Salvador 2 5 42 45 1 6 2 7 0 0 0 89 91 Entrea 0 6 17 Estonia 11 70 73 1 0 -0 9 0 0 0 44a Ethiopia 7 6 11 13 4 7 3 4 3 4 6 30 29 Finland 3 2 60 63 0 7 1 1 0 0 0 22 33 100 100 France 42 3 73 73 0 4 0 6 21 21 20 23 22 100 100 Gabon 0 5 36 50 5 5 5 5 0 0 0 67a 79 Gambia, The 0 3 18 26 6 0 6 8 0 0 0 1000 99 99 Georgia 3 2 52 58 1 6 0 7 22 25 30 42 43 Germany 70 9 83 87 0.4 1 0 38 41 42 10 9 100 100 Ghana 6 2 31 36 4.3 4 3 9 10 14 30 27 47 61 Greece 6 8 58 65 1 3 1 5 31 35 36 54 54 100 100 Guatemala 4 4 37 42 3 4 4 0 0 0 0 29 21 73 82 Guinea 2 0 19 30 5 7 5 8 12 23 34 65 77 54 24 Guinea-Bissau 0 2 17 22 3 5 4 2 0 0 0 37a 21 32 Haiti 2 3 24 32 3 9 3 9 13 18 28 55 56 42 43 Honduras 2 8 36 48 5 4 4 9 0 0 0 30a 22 91 HongKong 5 9 92 95 1 6 1 4 91 90 91 100 95 114 World Development Indicators 1997 3.6 Urban population Population in Population in Access to urban agglomerations the largest city sanitation of one million or more in urban areas % of total average annual % of % of % of millions population % growth total population urban population urban population 1995 1980 1995 1980-90 1990-95 1980 1995 2015 1980 1995 1985 1993 Hungary 6 6 57 65 0 5 0 6 19 20 21 34 30 100 100 India 2491 23 27 3 2 2 9 6 10 12 6 6 30 46 Indonesla 66 3 22 34 5 3 3 9 7 13 17 18 17 30 81 Iran, Islamic Rep 37 8 50 59 5 0 4 0 13 21 21 26 18 95 100 Iraq 15 7 66 78 4.3 3.5 26 22 21 39 29 100 95 Ireland 21 55 58 0 6 0 8 0 0 0 48 44 100 100 Israel 4 3 89 92 37 35 33 41 42a 99 100 Italy 380 67 66 01 0 2 26 20 21 14 11 100 100 Jamaica 1 4 47 55 2 3 21 0 0 0 46a 92 89 Japan 97 2 76 78 0 7 0 4 34 37 39 25 28 Jordan 3 0 60 72 5.1 7 9 29 28 36 49 39 91 91 Kazakstan 99 54 60 19 1 2 6 8 9 12 13 Kenya 7 4 16 28 7 5 6 2 5 8 13 32 28 75 69 Korea, Dem. Rep. 14.6 57 61 2 3 2 4 10 10 11 i7 17 100 100 Korea, Rep 36 5 57 81 3 8 2 9 37 52 54 38 32 100 Kuwait 1.6 90 97 5.1 -11 5 60 66 59 67 68 100 100 Kyrgyz Republic 17 38 39 19 11 0 0 0 37a 81 87 Lao PDR 11 13 22 6 2 6 5 0 0 0 53a 30 13 Latvia 18 68 73 10 -0 6 0 0 0 49 50 Lebanon 3.5 73 87 4 2 2 8 94 Lesotho 0 5 13 23 6 8 6 2 0 0 0 50a 22 50 Libya 4 7 70 86 5 9 4 4 38 61 59 54 70 100 Lithuania 2 7 61 72 2 1 10 0 0 0 23a Macedonia. FYR 13 53 60 1 5 16 0 0 0 Madagascar 3 6 18 27 5 7 5 7 0 0 0 25a 8 12 Malawi 1.3 9 13 6:1 5 9 0 0 0 31 88 82 Malaysia 10 8 42 54 4 4 4 0 7 6 7 16 11 100 100 Mali 2 7 19 27 5 1 5 7 0 0 0 37a 90 Mauritania 12 29 54 7.6 5 5 0 0 0 75 a 7 Mauritius 0 5 42 41 0.4 13 0 0 0 37a 100 Mexico 69.1 66 75 3 2 2 7 27 28 26 31 23 77 81 Moldova 2 2 40 52 2 7 15 0 0 0 96 Mongolia 1.5 52 60 3 9 2 9 0 0 0 45a 100 Morocco 13 0 41 49 3 5 3 0 11 18 22 26 25 85 96 Mozambique 61 13 38 91 7 3 6 14 22 48 36 51 53 Myanmar 12 2 24 27 2 5 3 3 7 9 12 27 32 34 44 Namibia 0 6 23 38 6 2 6 2 0 0 0 35a 36 89 Nepal 2.9 7 14 8.0 7.5 0 0 0 18 0 6 34 Netherlands 13.8 88 89 0 6 0 8 7 14 14 8 8 100 100 NewZealand 3 0 83 84 0 9 1 3 0 0 0 30 31 100 Nicaragua 2 7 53 62 3 9 4 1 23 27 33 42 44 35 Niger 2 1 13 23 7 5 6 9 0 0 0 30a 36 Nigeria 43 7 27 39 5 8 5 3 6 11 15 23 24 30 89 Norway 32 71 73 06 08 0 0 0 220 100 100 Oman 0 3 8 13 8.7 8 6 0 0 0 28a 88 98 Pakistan 45.0 28 35 4 5 4 7 11 18 23 22 22 56 60 Panama 1 5 50 56 28 2 7 0 0 0 56 65 99 Papua New Guinea 0 7 13 16 3 6 3 7 0 0 0 34 a 51 95 Paraguay 2 6 42 54 4 8 4 4 0 0 0 300 66 Peru 17 1 65 72 3 0 2 7 26 31 32 40 44 67 60 Philippines 36 6 38 53 5 2 4 4 12 14 15 33 25 76 Poland 25 0 58 65 1.4 1 0 18 18 19 16 14 100 100 Portugal 3 5 29 36 14 1 2 13 19 24 46 53 100 100 Puerto Rico 2 6 67 71 1 6 1 4 34 30 30 51 42 Romania 12.6 49 55 13 0 0 9 9 10 18 17 100 85 Russian Federation 107 5 70 73 1 2 -0 2 16 i9 19 8 9 World Development Indicators 1997 115 . 3.6 Urban population Population in Population in Access to urban agglomerations the largest city sanitation of one million or more in urban areas % of total average annual % Of % of % of millions population % growth total population urbar population urban population 1995 1980 1995 1980-90 1990-95 1980 1995 2015 1980 1995 1985 1993 Rwanda 0 5 5 8 4.9 4 5 0 0 0 56a 60 Saudi Arabia 14 9 67 79 6 9 4 0 19 21 22 16 17 100 100 Senegal 36 36 42 4 0 4 1 18 23 30 49 55 87 Sierra Leone 1 6 25 39 5 0 4 9 0 0 0 50a 43 90 Singapore 3 0 100 100 1 7 2 0 100 100 100 100 100 85 100 Slovak Republic 3 2 52 59 1 5 1 1 0 0 0 151 Slovenia 1 3 48 64 2 6 1 0 0 0 0 22 95 South Africa 21 1 48 51 2.7 2 9 11 19 19 11 13 Spain 30 0 73 77 0 7 05 20 18 18 16 14 100 100 Sri Lanka 4 0 22 22 1 4 2 1 0 0 0 17a 59 67 Sudan 6 9 20 26 4 0 4 6 6 9 14 31 35 20 85 Sweden 7 3 83 83 0 3 0 6 17 17 18 20 21 100 100 Switzerland 4.3 57 61 1 0 1 5 0 0 0 20 21 100 100 Syrian Arab Republic 7 5 47 53 4 1 4 3 28 28 33 34 27 70 100 Tajlikistan 19 34 32 2 3 2 1 0 0 0 36a 83 Tanzania 7 2 15 24 6.8 6.5 5 6 8 30 24 90 97 Thailand 20.9 17 36 2.8 2.3 10 18 23 59 50 50 - Togo 1 3 23 31 5 3 4 8 0 0 0 51 a 34 56 Trnidad and Tobago 0 9 63 68 1 6 1 8 0 0 0 6a 100 60 Tunisia 5:1 51 57 3 2 2 8 17 23 26 34 40 84 100 Turkey 42 6 44 70 5 8 4 7 17 24 25 23 18 95 99 Turkmenistan 2 0 47 45 2 0 5 7 0 0 0 25- 70 Uganda 2.4 9 12 4 9 5 6 0 0 0 . 41a 40 Ukraine 36 2 62 70 1 2 1 0 14 15 17 7 8 100 70 United Arab Emirates 2 1 72 84 6.1 3 8 0 0 0 45a 93 United Kingdom 52 4 89 90 0 3 0 5 25 23 22 5 4 100 100 United States 200 5 74 76 1 2 1 3 36 39 38 9 8 Uruguay 2 9 85 90 10 0 9 42 42 40 49 46 59 92 Uzbekistan 9.5 41 42 2 5 2.6 11 10 11 28 24 46 Venezuela 20 1 83 93 35 29 16 27 28 20 15 57 55 Vietnam 15 3 19 21 2.5 3 1 5 7 9 27 23 West Bank and Gaza Yemen, Rep 51 20 34 7 0 9 4 0 0 0 151 . 70 Yugoslavia, Fed Rep 5 9 46 57 2 3 1 5 11 13 15 24 24 78 100 Zaire 12.8 29 29 3 1 4 0 28 33 8 Zambia 4 1 40 45 4 2 3 7 9 15 22 23 33 56 76 Zimbabwe 3 5 22 32 6 0 5 2 0 0 0 39 30 Low income 911.7 t 21 w 29 w 4 2 t 3.8 t 7 w 10 w 13 w 12 w 12 w Excl China& India 299 0t 21w 28w 4 6t 4.7 t 6w 9 w 13w 30w 31 w 43w 67 w Middle income 962 3 t 52 w 61 w 3 0 t 2 4 t 16 w 20 w 21 w 22 w 21 w Lower middle income 640 8 t 48 w 56 w 3 0 t 2 3 t 13 w 17 w 19 w 21 w 20 w Upper middle income 3215 t 64 w 73w 3 0 t 2 5 t 24w 28 w 28 w 25 w 22w 60w 86 w Low & middle income 1,874 0 t 32 w 39 w 3 6 t 3 1 t lOw 13 w 16 w 18 w 17 w East Asia & Pacific 537 0 t 21 w 31 w 46t 3 8 t 8 w 11 w 14 w 13 w 10 w Europe & Central Asia 318.7 t 58 w 65 w 2.0 t 1.0 t 14 w 16 w 17 w 16 w 16 w Latin America & Carib 354 2 t 65w 74 w 3 0 t 2 6 t 24 w 28 w 28 w 27 w 24 w 60 w 81 w Middle East& N Africa 153 5 t 48 w 57w 4 4 t 3 8 t 17 w 20 w 22 w 31 w 27w 81 w SouthAsia 3278t 22w 26w 35t 33t 6w lOw 13w 9w 11w 32w 48w Sub-Saharan Africa 182 8 t 23 w 31 w 5 0 t 4 9 t 5 w 8 w 11 w 27w 29 w High income 680.4 t 75w 78 w 09t 0 t 31w 33 w 34 w 17w 17w a Data are for L990. 116 World Development Indicators 1997 3.6 Exploding cities _ __ Urbanization is a companion and stimulus of development, and in developing countries Because the estimates here are based on different * Urban population is the midyear population of more than half of GDP originates in cities. As national definitions of what constitutes a city or met- areas defined as urban in each country The definition the process has accelerated, however, cities ropolitan area and what is urban, cross-country varies slightly from country to country * Population in Africa, Asia, the Middle East, and Latin comparisons should be made with caution To arrive in urban agglomerations of one million or more is the America have also confronted congestion and at estimates of urban population, the United percentage of a country's population living in metro- pollution, with concentrated poverty and Nations ratio of urban to total population is applied politan areas that in 1990 had a population of one uncontrolled sprawl, and problems that at the to the World Bank's estimates of total population million or more people * Population in the largest least impede productive growth and at the (see table 2 1) The resulting series of urban popu- city is the percentage of a country's urban population worst stifle it. lation estimates are also used to compute the pop- living in that country's largest metropoltan area The rapid growth of cities in developing ulation in the largest city as a percentage of the * Access to sanitation in urban areas is the urban countries is nearly universal Whereas less urban population population served by connections to public sewers or than 22 percent of the developing world's pop- household systems such as pit privies, pour-flush ulation was urban in 1960, the proportion latrines, septic tanks, communal toilets, and other averaged 34 percent in 1990. By 2015 it is such facilities expected to exceed 50 percent, with the number of city residents due to reach 4 billion, more than twice today's total. By then there Figure 3.6a Urban population, by region, will be 225 urban agglomerations with popu- 1970-95 Data on urban population, lations of more than 2 million people, and 26 millions population in urban agglomerations with populations exceeding 600 agglomerations, and pop- 10 million (table 3.6a) ulation in the largest city 500 East Aas a, come fromn the United le ~~~~~~~~Nations World Urban- 400 Ez n ization Prospects: The Latin Ametica and 00,_~~~~~~ 1994 Revision Total pop- 300 the Caribbean - w 5 - ulation figures are World Table 3.6a Urban agglomerations with SouthAsa Bank estimates Data on access to sanitation In populations of 10 million or more, 2015 ^ Sub-Saliaran urban areas are from the World Health Organization City Milliiors of people 100 Mfid a - > ~~~~~Mrddie Earst Tokyo. Japan 28 7 and North Africa Bombay, India 27 4 0 Lagos, Nigeria 24 4 1970 1975 1980 1985 1990 1995 Shanghai, China 23 4 Source: United Nations Department of Economic and Jakarta, Indonesia 21 2 Social Information and Policy Analysis data and World S3o Paulo, Brazil 20 8 Bank population estimates Karachi, Pakistan 20 6 BeUing, China 19 4 Dhaka, Bangladesh 19 0 Figure 3.6b Urban population, by Mexico City, Mexico 18 8 income group, 1980-95 New York, U s A 17 6 Calcutta. India 17 6 millions Delhi, India 17 6 1,000 TianJln. China 170 . 74k~~~~~~~~~~~~~~~~~~~iddle intcome - <- Manila, Philippines 14 7 Cairo, Egypt 14 5 Los Angeles, U S A 14 3 Seoul, Rep of Korea 13 1 =P- Income Istanbul, Turkey 12 3 Rio de Janeiro, Brazil 11 6 Lahore, Pakistan 10 8 600 Hyderabad, Pakistan 10 7 Osaka, Japan 10 6 Bangkok, Thailand 10 6 Lima, Peru 10 5 400 Teheran, Islamic Rep of Iran 10 2 1980 1985 1990 1995 Source United Nations 1995 Source: Table 3 6 World Development Indicators 1997 117 . 3.7 Traffic and congestion Vehicles Road traffic Traffic accidents people injured or killed per 1,000 per kilometer million vehicle per 1,000 people of road kilometers vehicles 1980 1994 1980 1994 1980 1994 1980 1994 Albania 15 6 More cars, more traffic Algeria 33 10 1240 44 Traffic congestion in urban areas affects the Angola health of people, their quality of life, and the Argentina 155 181 20 28 productivity of the economy. Household Armenia 4 2 150 income and the ownership of passenger cars Australia 502 574 13 2041 138,501 5 2 have increased, and the expansion of eco- Austria 330 47:1 23 29 35,430b 270 15 nomic activities has been associated with AzerbaUan 49 15 2,207' 12 road transport of more goods and services Belarus over greater distances These developments Belgium 349 446 28 33 45,779e 59,884 25 17 have increased demand for roads and vehi- Benin cles, adding to congestion and air pollution in Bolivia 19 77 3 11 795 1,232 41 urban areas--and increasing health hazards Bosnia and Herzegovina and traffic accidents and injuries. Botswana 27 3 50 The number of vehicles registered worldwide Brazil 85 7 in 1946 was close to 46 million, with 75 per- Bulgaria 214 49 6650 5 cent in the United States. By 1991 this number Burkina Faso had grown to some 600 million, with only 32 Burundi Cambodia 5 1 1,145 6 percent in the United States (figure 3.7a) Cameroon 8 4f 112 During this period the growth of private cars Canada 548 630 19 205,515 14 substantially exceeded that of commercial Central African Republic 8 1 6 1,132 vehicles (figure 3.7b), leading to relatively less Chad 3 1 use of public and mass transportation and Chile 61 8 7,540 38 thus to more pollution. The number of vehicles China 2 8 2 8 2,032 d 165,000 12 22 (excluding motorized two- and three-wheel vehi- Colombia 2,4808 cles) is expecled to reach 820 million by 2010. Congo 18 4 1030 10 costagolca 106 4 4244e 32 In Iran, Kenya, Mexico, the Republic of Korea, CoteadRiire 24 9 and Thailand about half the registered auto- Croatia 158 28 24 mobiles operate in the capital city. Cuba Congestion, the most visible cost of expand- Czech Republic 310 57 38,842 h 12 ing vehicle Ownership, is usually associated Denmark 322 364 24 24 26,300 39,895 10 5 Dominican Republic 36 11 18 Ecuador 38 10 11,851 16 Egypt, Arab Rep 28 32 6,222 h 17 El Salvador 2 17 Eritrea iions Estonia 267 27 2 600 Ethiopia 2 1 1 3 2 38 Finland 288 418 18 27 26,750 41,730 7 4 500 l 'UnitedStates France 402 517 27 37 298,000 461,500 16' 6 01 Rest of the worldX Gabon 400 Gambia, The Georgia 300 GermanyJ 399 51 Ghana 200 Greece 134 282 35 23 510d 23 11 Guatemala 100 Guinea 6 2 118 Guinea-Bissau 01930 1140 1950 1960 1970 1980 1990 Haiti Honduras 30 10 3,288 Source: American Automobile Manufacturers Hong Kong 54 78 234 289 4,407 10,341 77 44 Association 1S95 118 World Development Indicators 1997 3.7 7 Vehicles Road traffic Traffic accidents people injured or killed per 1,000 per kilometer million vehicle per 1,000 people of road kilometers vehicles 1980 1994 1980 1994 1980 1994 1980 1994 with the failure of urban transportation plan- Hungary 108 240 13 23 22 12 ning and, frequently, an Inadequate road net- India 2 6k 1 3k work. But expanding the road network is rarely Indonesia 8 8 59 the solution The problem is that road users Iran, Islamic Rep are heavily subsidized. Road users do not pay Iraq the full cost of building roads or of maintaining Ireland 236 293 9 11 14,917 25,530 11 10 them Until they do, urban traffic and conges- Israel 123 240 114 93 10,4421 27,050 38 29 Italy 334 541 65 226,569 362,647 12' 8 tion will continue to increase. In Asian cities, Jamaica Jamaica for example, rush hourtraffic moves at an aver- Japan 323 520 34 57 389,052 683,754 16' 14 age of just 16 kilometers an hour-in Bangkok Jordan 56 60 25 37 623 31 63 54 the average is closer to 9 kilometers an hour, Kazakstan 14,632 and an average car is estimated to spend the Kenya 8 14 3 5 4,620 74 6 equivalent of 44 days a year stuck in traffic Korea, Dem Rep (World Bank 1996d). Congested city streets Korea, Rep 14 167 11 100 8,728 48,376 212 49 exact a big toll on economic productivity Kuwait 390 . 12,189 7 How much does traffic congestion cost? In Kyrgyz Republic Bangkok the cost is about $400 million a 8 2 85 Latvia 170 7 3,633 10 year-the amount that could be saved just by Lebanon making peak hour traffic move 10 percent Lesotho 10 13 3 4 445 86 faster. The hidden but all-too-real environ- Libya mental tax on urban dwellers is high-the Lithuania 205 12 198c 6 annual costs of dust and lead pollution in Macedonia, FYR 151 35 4,119 11 Bangkok, Jakarta, and Kuala Lumpur com- Madagascar 3 1 bined have been estimated at $5 billion, or Malawi 5 3 about 10 percent of city income (World Bank Malaysia 49 1996a). Another health threat associated Mal Mauritania with vehicles is particulate air pollution-the Mauritius 44 62 23 37 46b 59 dust and soot from vehicle exhaust. This pol- Mexico 131 46 52,640 lution is proving to be far more damaging to Moldova 67 20 8n5d 12m human health than once believed Leaded Mongolia Morocco 42 18 18b 60 Mozambique 2 1 84 Figure 3.7b Motor vehicle production, Myanmar 1950-90 Namibia 87 3 2,149 millions Nepal Netherlands 343 235 65 70,825 98,400 12 4 50 New Zealand 492 554 17 21 16,545 11 9 Total Nicaragua 34 10 122 40 Niger 6 211 63' Nigeria 4 3 123 30 Norway 342 464 17 22 22,646e 8 6 PrPrivate Oman 124 10 11,045h 27 / - cams Pakistan 2 6 5 4 27,547 71 29 20 Panama Papua New Guinea 10 Paraguay Commercial Peru vehicles . - Philippines 10 4 193 1950 1960 1970 1980 1990 Poland 86 223 10 22 44,597 113,000 8 Portugal 145 327 26 27 283b 73,780 31P 19 Source, Amencan Automobile Manufacturers Puerto Rico Association 1995 Romania 105 33 40' 5 Russian Federation World Development Indicators 1997 119 3.7 Vehicles Road traffic Traffic gasoline causes about 90 percent of airborne accidents lead pollution in cities Yet in much of the people world lead additives in gasoline are still used injured or killed in alarmingly large quantities, especially in per 1,000 per kilometer million vehicle per 1,000 people of road kilometers vehicles Africa Lead poisoning does immense damage 1980 1994 1980 1994 1980 1994 1980 1994 to children, affecting more than 90 percent of Rwanda 2 2 the population in African cities and 30 percent Saudi Arabia 163 166 26 43 94,141 12 in Mexico City Senegal 19 13 8 56 Sierra Leone 11 4 29 34 =_ Singapore 154 152 18 Slovak Republic 215 65 6511 10 The data are compiled by the Intemational Road Slovenia 349 47 6,924 9 Federation (IR:) through questionnaires sent to van- South Africa 133 157 18 52,939 23 23 ous national organizations The IRF uses a hierarchy Spain 239 454 120 49 70,489 145,037 13 7 Sri Lanka 25 5 4,119 36 of sourcestogatheras much informaton as possible Sudan The primary sources are national road associations Sweden 370 446 24 28 35,000 7 5 In the absence of such an association in a country, or Switzerland 383 490 38 48 49,294 14 9 in cases of nonresponse, other agencies are con- Syrian Arab Republic tacted, such as road directorates, ministries of trans- Tajikistan 3 1 port or public works, or central statistical offices As Tanzania 3 1 a result the compiled data are of uneven quality In Thailand 13q 70 13q 73 16,824 81,444 29 9 addition, the coverage of each indicator may differ Togo 1 1 1 386' across countnes Trinidad and Tobago Tunisia 38 10 45 Turkey 23 62 4 9 14,785 31,251 26 Turkmenistan Uganda 1 1 479 * Vehicles per 1,000 people exclude buses Ukraine 151 29 3,064d 7 * Vehicles per kilometer of road include cars, United Arab Emirates buses, and freight vehicles but do not include two- United Kingdom 303' 403r 50 63 245,900 413,300 19 13 wheelers Roads referto motorways, highways, main United States 748 25 31 2,418,619 3,504,934a 18 or national roeds, secondary or regional roads, and Uruguay other roads A motorway is a road specially designed Uzbekistan Venezuela 112 - 27 56,900 32 and built for motor traffic Except at special points, Vietnam it provides carriageways separating the traffic flow- West Bank and Gaza ing in opposite directions * Road traffic is the Yemen, Rep 32 8 1,251 10,866 15 number of vehicles multiplied by the distances they Yugoslavia, Fed Rep 118 23 61 travel * Traffic accidents refer to accident-related Zaire 29 8 injuries and to deaths resulting from accidents that Zambia occur within 30 days of the accident Zimbabwe 33 a Passenger cars and goods vehicles only b Buses only c Deaths occurring within three days of accident d Buses and | goods vehicles only e Passenger cars only f Does not include rural trailers g Goods vehicles only h Passenger cars and buses only i Deaths occurring within six days of accident j Data refer to the Federal Republic of Germany before unification k As of April 1 1 As of September 30 m Deaths occurring within seven days of accident n Deaths occurring within 24 hours The data in the table are from the International Road of accident o Only includes accidents after which driving licenses were revoked p Deaths on the spot or during transport to the hospital q Excludes data from Department of Land Transport r As of December 31, not comparable with eariier years Federation's annual World Road Stafishcs, except data for China, which are from Chinese statistical yearbooks 120 World Development Indicators 1997 Air pollution 3.8 Country City Suspended particulate matter Sulfur dioxide annual annual mean average mean average metric tons micrograms annual metric tons micrograms annual per year per cu m % growth per year per cu m % growth 1990' 1987-9O0 1979-90 1990' 1987-900 1979-90 Australia Sydney 114c 2 2 . 28 -109 Belgium Brussels 22 -3.3 42 -11 5 Brazil S5o Paulo 77,000 980 -9.1 122,000 41 -7 5 * Suspended particulate matter refers to smoke, Canada Montreal 61 -1 8 23 -11 0 soot, dust, and liquid droplets from combustion that China Beaijng 1,115,600 3700 -1 6 526,000 115C -1 3 are in the air The amount indicates the quality of the Denmark Copenhagen 34 3 4 .. 30 -0.5 air people are breathing and the state of a country's Finland Helsinki 81 2.0 20 -3 8 technology and pollution control * Sulfur dioxide is Germany Frankfurt 42 0.5 36 -7 2 an air pollutant produced when fossil fuels are Ghana Accra 137c 3 5 Greece Athens 178c -6 0 34 -4 8 burned It contributes to acid rain and can affect Hong Kong Hong Kong 132c 14 9 640 47.3 human health India Calcutta 200,000 3930 -1 0 25,500 54 4 6 Indonesia Jakarta 96,733 2710 2 2 24,700 Iran, Islamic Rep Teheran 261c -2 4 1650 6 9 Japan Tokyo 50 -4 9 20 -8 9 The data in the table are Korea, Rep Seoul 139,000 518,000 drawn from the United Malaysia Kuala Lumpur 1190 -3 9 24 -12 4 IPtt-LINON IN Nations Environment Pro- Mexico Mexico City 451,000 206,000 MEGA( I gILm a1 I HE 'A`Olt I) gramme and World Health Philippines Manila 75,000 900 0 8 148,000 34 -12.0 - Russian Federation Moscow 60,000 130,000 Organization's Urban Air Portugal Lisbon 99c 0 4 27 -3 0 Pollution in Megacities ofthe Thailand Bangkok 80,000 105c -2.4 14 -1.7 World and from the World United Kingdom London 11,000 49,000 ________j -Bank's Environmental Data United States New York 345,000 61 -2 2 404,000 600 -5 8 Book a Estimated emissions b Average concentration observed in the city's various monitoring sites c Exceeds World Health Organization guidelines The burden of air pollution In many towns and cities exposure to air pollu- lar problems for asthmatics And many plants tors The working hypothesis is that acidifi- tion is the main environmental threat to human and trees are susceptible to damage from cation increases the vulnerability of trees to health. Winter smogs-soot, dust, and sulfur ozone exposure, which reduces yields or kills such other stresses as drought and insect dioxide-have long been associated with tem- them off damage porary spikes in the number of deaths. But Emissions of sulfur dioxide and nitrogen Where coal is the primary fuel for power long-term exposure to high levels of soot and oxides lead to the deposition of acidic materials plants, steel mills, industrial boilers, and domes- small particles in the air provokes a wide range as acid rain or acidic compounds over long dis- tic heating, the result is usually high levels of of chronic respiratory diseases and exacer- tances from their sources-often more than urban air pollution-especially particulates and bates heart disease and other conditions The 1,000 kilometers. Lakes in Scandinavia, the sometimes sulfur dioxide-and widespread acid global burden of Ill health caused by particu- northeastern United States, and eastern deposition If the sulfur content of the coal is late pollution-on its own or in combination Canada have lost fish as a result. Such deposi- high Countries such as China, India, Poland, with sulfur dioxide-is very large. According to tion changes the chemical balance of soils and and Turkey fit this pattern today, as many high- conservative estimates in the World Bank's can lead to the leaching of trace minerals and income countries once did World Development Report 1992: Develop- nutrients critical to trees and plants. A second pattern is observed when coal Is ment and the Environment, it includes at least The links between forest damage and acid not an important primary fuel or is used by 500,000 premature deaths a year and 4-5 mil- deposition are complex. Direct exposure to plants with effective dust controls, as in lion new cases of chronic bronchitis. high levels of sulfur dioxide or acid deposition Brazil, Indonesia, Mexico, Thailand, and Summer smogs-from small particles and can cause defoliation and dieback. Extensive many high-income countries. Emissions of ground-level ozone produced by the action of damage to forests in Central and Eastern the worst air pollutants are caused by the the sun on nitrogen oxides and volatile organic Europe is usually thought to be the result of combustion of petroleum products-diesel compounds-are a major problem in southern large emissions of sulfur dioxide from burn- oil, heating oil, and heavy fuel oil Industrial California, Mexico City, Tokyo, and parts of ing poor-quality brown coals and ignites. But plants and vehicles-especially those with Western Europe. Exposure to ozone makes it the effects of soil acidification vary across two-stroke engines-are typically the main difficult for people to breathe, causing particu- species and seem to depend on many fac- offending sources World Development Indicators 1997 121 . 3.9 Government commitment National Country Biological Frequency Participation in treaties conserva- environ- diversity of reporting tion mental profile on trade in strategy profile endangered species % of years Climate Ozone CFC Law of reporteda change layer control the Seab Albania C CP National strategies-international treaties Algeria 50 CP CP CP S Environmental degradation imposes high Angola C S CP costs. Recognizing these costs, governments Argentina 82 CP CP CP CP try to halt degradation by establishing or Armenia CP improving environmental laws and regula- Australia 1988 . 88 CP CP CP CP tions, strengthening environmental manage- Austria . 100 CP CP CP CP ment capacity, adjusting incentives for Azerbaijan 1991 1980 . 80 CP CP CP S polluting agents, creating special funds, and Belarus s CP cP s working more closely with affected groups. Belgium 100 S CP CP s In many countries such efforts have not Benin 0 CP CP CP s succeeded, often primarily because of the Bolivia . 1986 62 CP CP CP CP failure of government to assign priority to Bosnia and Herzegovina CP CP CP problems and interventions, a reflection of Botswana 1990 .. 1991 86 CP CP CP CP competing claims on scarce resources. To Brazil 1988 41 CP CP cP CP address this shortcoming, many countries Bulgaria ° CP CP CP S have prepared national environmental man- Burkina Faso 1982 0 OP OP OP S Burkinadiaso 1981 0 SP CP CP s agement strategies-some focusing narrowly Burundi 1981 0 S S Cambodia 1994 C on environmental issues, others dealing with Cameroon 1981 92 CP CP CP CP the Integration of environmental, economic, Canada 1986 100 CP CP CP s and social concerns. Among such initiatives Central African Republic 50 CP CP s are national conservation strategies and Chad 1990 0 CP CP CP S national environmental action plans. Many Chile 1990 65 CP CP CP s countries have also prepared country envi- China 1990 1990 1994 100 CP CP CP S ronmental profiles and biological diversity Colombia IP 1990 1988 64 CP CP CP S profiles. Congo 100 S CP CP S National conservation strategies-pro- Costa Rica 1990 1982 76 OP OP OP CP Coste dRvire 1991198 76 OP OP OP OP moted by the World Conservation Union Cote d'lvoire 1991 CP CP CP CP Croatia S CP CP CP (IUCN)-provide a comprehensive, cross- Cuba 50 CP CP CP CP sectoral analysis of conservation and Czech Republic . . . 100 CP CP CP s resource management issues to help inte- Denmark . . 100 CP CP CP S grate environmental concerns with the devel- Dominican Republic 1981 80 S CP CP S opment process. Such strategies discuss a Ecuador IP 1987 1988 76 CP CP CP country's current and future needs, institu- Egypt, Arab Rep 1980 0 CP CP CP CP tional capabilities, prevailing technical condi- El Salvador 1985 20 s CP CP s Eritrea ~~~~~~~~~~~~~~~~~~~~tions, and the status of natural resources. Eritona CP National environmental action plans Estonia CP Ethiopia 1990 1991 75 CP CP CP S (NEAPs), supported by the World Bank and Finland 75 CP CP CP s other development assistance agencies, France IP 100 CP OP CP s describe a country's main environmental con- Gabon 67 S CP CP S cerns, identify the principal causes of envi- Gambia, The 1981 20 CP CP CP CP ronmental problems, and formulate policies Georgia CP . and actions to deal with them (table 3 9a). Germany . 100 CP CP CP CP The NEAP is a continuing process in which gov- Ghana 1980 1988 81 CP CP CP CP ernments develop comprehensive environ- Greece C S CP CP CP Guatemala IP 1984 83 s CP CP S mental policies, recommend specific actions, Guinea 1983 1988 45 CP CP CP CP and outline the investment strategies, legis- Guinea-Bissau IP . 1991 0 CP . CP lation, and institutional arrangements Haiti 1985 * C S S required to irnplement them. Honduras 1989 . 29 CP CP CP CP Country environmental profiles identify how Hong Kong national economic and other activities can stay within the constraints imposed by the need to 122 World Development Indicators 1997 ;r-ue 3.9n National Country Biological Frequency Participation in treaties conserva- environ- diversity of reporting tion mental profile on trade in strategy profile endangered species % of years Climate Ozone CFC Law of reporteds change layer control the Sea' Table 3.9a Status of national Hungary 57 S CP CP S environmental action plans IndIa IP 1980 1989 100 CP CP CP CP Indonesla IP 1987 92 CP CP CP CP Completed In preparation Iran, Islamic Rep 31 S CP CP S Albania Bangladesh Iraq , CP Benin Congo Ireland C CP CP CP S Bhutan Dominican Republic Israel 25 S CP CP Bolivia Ecuador Italy 86 CP CP CP CP Burkina Faso Gabon Jamaica 1987 C CP CP CP CP China Haiti Japan 92 CP CP CP S Costa Rica Indonesia Jordan IP 1979 31 CP CP CP CP CBte d'lvoire Korea, Rep Kazakstan CP El Salvador Malaysia Kenya IP 1988 54 CP CP CP CP Ethiopia Nepal Korea, Dem Rep C CP CP CP S Gambia, The Paraguay Korea, Rep C CP CP CP S Grenada St Lucia Kuwait C CP CP CP CP Guinea St Vincent Kyrgyz Republic Guinea-Bissau Tibet Lao PDR 1993 1996 C CP S Guyana Togo Latvia CP CP CP Honduras Uganda Lebanon C CP CP CP CP India Vietnam Lesotho 1982 C CP CP CP S Kenya Libya S CP CP S Lao PDR Lithuania CP CP CP Lebanon Macedonia, FYR CP CP CP Lesotho Madagascar 1984 1991 82 S S Madagascar Malawi IP 1982 70 CP CP CP S Maldives Malaysia 1991 1994 1988 86 CP CP CP S Moldova Mali 1989 C CP CP CP CP Mongolia Mauritania 1987 1981 c CP CP CP S Mozambique Mauritius 88 CP CP CP CP Nicaragua Mexico 100 CP CP CP CP Nigeria Moldova CP Pakistan Mongolia C OP S Philippines Morocco 1980 44 S S S S Rwanda Mozambique 73 CP CP CP S Sao Tome and Principe Myanmar 1994 1989 * CP CP CP S Sierra Leone Namibia 0 CP CP CP CP Sri Lanka Nepal 1987 1979 76 CP CP CP S St KiLts and Nevis Netherlands 100 S CP CP S New Zealand 1985 67 CP CP CP S Source, World Resources Institute, International Institute for Environment and Development, and IUCN 1992 Nicaragua IP 1981 o8 OP OP OP S Niger 1980 41 CP CP CP S Nigeria 1986 1988 18 CP CP CP CP conserve natural resourCes. Some profiles Norway IP 100 CP CP CP S consider issues of equity, justice, and fairness. Oman IP 1981 C CP CP Biological diversity profiles-prepared by the Pakistan 1991 1988 1991 94 OP CP CP S World Conservation Monitoring Centre and the Panama 1980 86 CP CP CP S Papua New Guinea .75 OP OP OP S IUCN-provide basic background on species Paruay diesiy roetdras aos Paraguay 1985 60 CP CP CP CP diversity, protected areas, major ecosystems Peru IP 1986 1988 59 CP CP CP and habitat types, and legislative and admin- Philippines 1990 1992 1988 82 CP CP CP CP istrative support. They identify the status of Poland 0 CP CP CP S sites of critical importance for biodiversity and Portugal 55 CP CP CP S ecosystem conservation, reporting concisely Puerto Rico on the value and conservation needs of these Romania CP CP CP S sites and on the threats to them. Russian Federation . CP CP CP S World Development Indicators 1997 123 . ~3 .9 National Country Biological Frequency Participation in treaties conserva- environ- diversity of reporting l tion mental profile on trade in e . . . . - strategy profile endangered species spejparts per trillion % of years Climate Ozone CFC Law of reported' change layer control the Sea' 600 Rwanda 1987 27 S . .. S Saudi Arabia c CP CP CP S 500 Senegal 1980 1991 80 CP CP CP CP -FO42 Sierra Leone 1985 CP S 400 Singapore 1990 1988 100 S CP CP CP Slovak Republic CP CP CP S Slovenia CP CP CP 300 South Africa 1980 94 S CP CP S CFC--1 Spain IP 100 CP CP CP s 200 Sri Lanka 1988 1988 54 CP CP CP CP Sudan 1989 44 CP CP CP CP 100 Sweden 94 CP CP CP S 1978 1986 1994 Switzerland IP 100 CP CP CP s Synan Arab Repubilc 1981 c Cl' CP Note: CFO-iL and CFC-12 are potent depletors of Syrian Arab Republic 1981 CI Pstratosphent ozone Tajikistan Source: World Resources Institute and others 1996 Tanzania 1986 1988 75 S CP CP CP l Thailand IP 1992 56 CP CP CP S Togo 1985 69 CP CP CP CP To address global issues, many govern- Tunisia 1980 100 CP CP CP CP ments have also signed international treaties Turkey c CP CP and agreements, launched in the wake of the Turkmenistan CP CP CP 1972 United Nations Conference on Human Uganda 1983 1982 1988 o CP CP CP CP Environment in Stockholm and the 1992 Ukraine S CP CP S United Nations Conference on Environment United Arab Emirates 0 CP CP S and Development (UNCED) in Rio de Janeiro. United Kingdom 1990 . 1oo CP CP CP The UNCED Introduced and established pnn- United States 88 OP CP oP ciples covering a wide range of natural Uruguay 59 OP CP OP OP resources and issues of social and economic Uzbekistan CP CP CP Venezuela 79 OP CP CP development. and implementation. Among the Vietnam 1985 1993 OP OP OP OP major treaties and agreements are those on West Bank and Gaza climate change, the ozone layer, chlorofluoro- Yemen, Rep 1982 c S CP carbon control (the Montreal Protocol), the Yugoslavia, Fed Rep IP Sf CP Cpg CP Law of the Sea, and the Convention on Zaire 1984 1981 69 CP CP CP International Trade in Endangered Species. Zambia 1985 1982 45 CP CP CP CP * The Convention on Climate Change aims to Zimbabwe 1987 1982 83 OP OP OP OP stabilize atmospheric concentration of green- CP = Contracting party (has ratified or taken equivalent action). s = Signatory (has signed but not ratified) [P = In prepara- house gases at levels that will prevent human tion a Includes all trade reported by members of the Convention on International Trade in Endangered Species of Wild Flora activities from interfering dangerously with and Fauna (CITES) as of May 1993 b Convention became effective November 16, 1994 c Not a member of CITES as of May the global climate system. 1993 d Data refer to the Federal Republic of Germany before unification e Unless otherwise noted, data refer to the former Yugoslavia f The constituent republics of the former Yugoslavia inherited the status of signatories g. Refers to croatia, * The Vienna Convention for the Protection of Slovenia, and the Federal Republic of Yugoslavia the Ozone Liyer protects human health and the environment by promoting research on the effects of changes in the ozone layer and on alternative substances (such as substitutes for chlorofluorocarbons) and technologies, monitoring the ozone layer, and taking mea- sures to control the activities that produce adverse effects. * The Montreal Protocol for CFC Control requires nations to help protect the earth from excessive levels of ultraviolet radiation by cutting chlorofluorocarbon consumption by 20 percent over their 1986 level by 1994 and 124 World Development Indicators 1997 3.99 by 50 percent over their 1986 level by 1999, I. with allowances for increases in consumption by developing countries (figure 3.9a shows Unlike most other tables in this book, this table pre- * National conservation strategies provide a com- the global atmospheric concentration of sents qualitative rather than quantitative indicators prehensive, cross-sectoral analysis of conservation chlorofluorocarbons in recent years). Government commitment to sound national and and resource management issues to help integrate * The United Nations Convention on the Law international environmental programs is measured environmental concerns with the development of the Sea, which became effective in by the extent to which governments are proactive in process The years shown refer to the year in which a November 1994, establishes a comprehen- preparing national environmental and conservation strategy was completed * Country environmental sive legal regime for the sea and oceans, strategies and the extent to which they have of their profiles identify how national economic and other establishes rules for environmental standards own volition signed international treaties and activities can stay within the constraints imposed by and enforcement provisions, and develops accepted their obligations But the signing of these the need to conserve natural resources The years international rules and national legislation to treaties does not always imply ratification Nor does shown refer to the year in which a profile was com- prevent and control marine pollution. it guarantee that governments will comply with treaty pleted * Biological diversity profiles provide basic * Members of the Convention on International obiigations background on species diversity, major ecosystems Trade in Endangered Species of Wild Flora and and habitat types, protected area systems, and leg- Fauna (CITES) agree to prohibit commercial islative and administrative support * Frequency of trade in endangered species and to closely reporting on trade in endangered species refers to monitor trade in species that may become the percentage of years for which a country has sub- depleted by trade. mitted an annual report to the CITES Secretariat since To help developingcountries complywith their it became a party to the Convention on International obligations under these international agree- Trade in Endangered Species. * Participation in ments, 32 countries created the Global treaties covers four international treaties (see facing Environment Facility (GEF) to focus on global page) Climate change refers to the Convention on improvement in biodiversity, climate change, Climate Change (signed in New York in 1993) Ozone international waters, and ozone layer depletion, layer refers to the Vienna Convention for the Protection of the Ozone Layer (1985) CFC control refers to the Montreal Protocol for CFC Control (for- mally, the Protocol on Substances that Deplete the Ozone Layer, signed in 1987) Law of the Sea refers to the United Nations Convention on the Law of the Sea (signed in Montego Bay, Jamaica, in 1982) Data are from the World Resources Institute's World Resources 1994-95, the World Resources Institute, International Institute for Environment and Develop- ment, and IUCN's 1993 - Directory of Country Envi- ronmental Studies, and the World Bank Environment Department's National Environmental Strategies Leaming from Experience World Development Indicators 1997 125 - EL. = i - In the past 25 years, in the expanding global economy, the developing economies have played an increasingly important role. This trend has four characteristics: * Steady growth and structural transformation, led by low- and middle-income economies that have pursued successful adjustment policies. * Rapid integration of developing economies in the global economy, marked by expanding trade and capital flows. a Improved polcy environments in developzngeconomies, with better macroeconomic man- agement and economic liberalization driving growth and integration * Increasingdisparities within the developingworld, with some regions pulling rapidly ahead (such as East Asia) and others in danger of being marginalized (Sub-Saharan Africa). The indicators presented in this section attempt to measure changes in the global economy and the differential impact of these changes on developing economies. They are mainly indicators that traditionally appear in the World Development Indicators, mea- suring outcomes in the structure and rates of change of output, trade, and aggregate demand and in macroeconomic performance, including central government budgets, money supply, prices, balance of payments, and external debt. Like other data in this book, the data in this section are subject to conceptual and practcal measurement prob- lems that limit their comparability and usefulness (box 4a). Developing economies outpace the world The world economy grew at about 3 percent a year in the 1980s, slowIng to 2 percent in the first half of the 1990s. Low- and middle-income economies, excluding Eastern Europe and the economies of the former Soviet Union, grew more rapidly, averaging 3.4 percent in the 1980s and 5 percent in the first half of the 1990s. Growth among developing countries was dominated by the populous economies of East and South Asia. East Asia's economies grew at almost 8 percent a year in the 1980s and at 10 percent in the 1990s. In South Asia growth was almost 6 percent a year in the 1980s and roughly 5 percent in the 1990s. In the early 1980s Latin America's growth was hurt by the debt crisis and the halt in private capital flows. The debt crisis came to an end in the early 1990s, when these countries began to see much faster growth. In the same period Sub-Saharan Africa and the Middle East and North Africa had disap- pointing growth performance, reflecting poor policies, falling commodity prices, and, in Sub-Saharan Africa, growing official debt. Eastern Europe and the economies of the former Soviet Union-the transition economies-suffered from the collapse of the Soviet system and the end of state trading regimes; in the early 1990s their growth was negative. Developing economies that have done well have some important similarities: * High rates of domestic savings have helped to sustain high rates of investment. The fast- growing economies of EastAsia are investing between 27 percent and 43 percent of GDP, while Chile is investing 27 percent and India 25 percent (tables 4.12 and 4.13). * Rapid growth in trade has increased the share of trade in GDP East Asia's export growth in the 1980s exceeded its GDP growth at around 9 percent a year-almost twice the rate for world exports. By the 1990s East Asian exports were growing at a remarkable 18 per- cent a year (almost three times the rate of growth of world exports). South Asian export growth has also been impressive-7 percent a year in the 1980s and 9 percent in the 1990s. As a result, the share of merchandise trade (exports plus imports) in GDP for developing economies as a group rose from 38 percent in 1980 to 44 percent in 1995. World Development Indicators 1997 127 Most of the increase, however, was in 10 economies, mostly in East Asia (tables 4.8 and 4.9). * Agnculture's zmportance is declining. In China agriculture's share in GDP between 1980 and 1995 fell from 30 percent to 21 per- cent. In India it declined from 38 percent to 29 percent, in Indonesia from 24 percent to 17 percent, in Thailand from 23 percent to 11 percent, in Malaysia from 22 percent to 13 per- cent, and in the Republic of Korea, which moved from middle- income to high-income status during this period, from 15 percent to 7 percent (tables 4.1 and 4.2). * The share of manufactured exports is rising. Further evidence of the structural transformation of these economies is seen in the increasing importance of manufactures in exports, a measure of access to learning, technology transfer, and the ability to produce at world standards. Between 1973-82 and 1983-92 the share of - - @ .* manufactures in merchandise exports rose in East Asia from 35 percent to 50 percent, and in South Asia from 45 percent to 65 . percent (World Bank 1996c, p. 24). The global economy integrates steadily Since World War II international trade has consistently grown 09 - faster than output (table 4a). In the 1950s and 1960s this trend E l reflected recovery from the stagnation of the interwar years and E l a buoyant world economy. In the 1970s and 1980s world trade growth slowed but continued to outpace growth in the global l economy. Led by rapid growth of East Asian exports, world trade in the 1990s is once again outstripping world output. In tandem with sharp growth in merchandise trade, there has been even sharper growth in services, whose share in world trade rose from l 16 percent in 1980 to 18 percent in 1995. Growth in trade has come from increasing trade liberaliza- - - l tion, a result of successive international trade negotiations. Tariffs l l in industrial countries have fallen from 40 percent of imports - - l_ in the 1950s to around 3-4 percent today. Developing coun- l * l l tries, too, have reduced tariffs dramatically. In Latin America l i * 1 0 they have come down to around 15 percent, in Sub-Saharan i Africa and the Middle East and North Africa to 25-30 percent, l 1 and in South Asia, which traditionally had high tariffs, to 45 i percent (Finger, Ingco, and Reincke 1996). Under the Uruguay Round agreements countries are also progressively dismantling nontariff barriers. In some developing countries trade integration goes hand in hand with financial integration, with private capital flows, particularlyforeign direct investment (FDI), rising strongly (tables 4.22 and 5.2). Foreign direct investment to developing economies * _ in 1995 accounted for 33 percent of all FDI, compared with 13 - _ l percent in 1990. Policies make a difference ... Increased openness of economies and progressive dismantling _ of regulations and controls have been features of the economic i _ 1311- policies of developing countries since 1980. Those that have seen the most rapid growth and integration have also had better - _ l l macroeconomic management, as reflected in low inflation rates, i stable real exchange rates, and small budget deficits (table 128 World Development Indicators 1997 Table 4a Average annual growth of world trade and GDP, 1950-95 and North Africa and stagnated in Latin America and the percent Caribbean. The poor outcomes reflected dependence on com- modities and falling world prices (despite the commodity boom 1950-60 1960-70 ±970-80 1980-90 ±990-95 of the early 1990s), the debt crisis in Latin America, poor poli- World tradep 6 5 8 3 5 2 5 0 6 2 cies and weak institutions, and, in Sub-Saharan Africa, political World output 4 2 5 3 3 6 3 1 2 0 Difference 2 3 3 0 1 6 19 4 2 instability and civil wars. In many countries population-weighted trade ratios fell. And while eight countries received two-thirds of a. Exports of goods and services on a national accounts basis all foreign direct investment flows, many have no access to pri- Source: World Bank staff estimates vate foreign capital and must depend heavily on official devel- opinent assistance. 4.15). Indeed, for the fastest growing economies inflation rates In this unequal world a handful of developing economies have been relatively low (around 13 percent), real exchange are emerging as potential giants in the global economy (table rates more stable, and budget deficits around 2-3 percent of 4b). The 10 largest economies accountfor 83 percent of the devel- GDP. By contrast, countries growing more slowly and weak optng world's population, 61 percent of its GNP (69 percent in and slow integrators have seen inflation rates approaching 20 purchasing power parity terms), and 66 percent of its exports. percent, budget deficits of 4-6 percent or more, and volatile Although theyhave not all progressed at the same rate, their share real exchange rates. of world output has grown from about 40 percent to 58 percent. Some smaller economies have higher output per capita, and ... but it is still an unequal world others have grown faster, but the integration of these 10 into a While the economies of Asia fared remarkably well in 1980-95, growing world economy could transform the lives of billions of per capita output fell in Sub-Saharan Africa and the Middle East people in the next century. Table 4b The emerging giants of the developing world, 1995 GNP per Exports of Net foreign Gross GNP in capita in goods and direct International Population GNP PPP terms PPP terms services investment reserves Country or group millions $ billions $ bilions $ $ billions $ billions $ billions China 1,201 745 3,522 2,940 147 38 0 80 India 929 325 1,357 1,460 40 1 3 23 Brazil 162 585 920 5,690 53 3 1 51 Indonesia 193 189 767 3,970 51 4 5 15 Russian Federation 147 328 683 4,640 94 1 5 18 Mexico 92 305 610 6,640 90 4 1 17 Thailand 59 1:60 456 7,760 70 2 3 37 Turkey 62 166 352 5,680 36 1 0 14 Pakistan 130 60 289 2,230 8 0 3 3 Argentina 36 269 288 8,640 24 3 9 16 Total 3,011 3,132 9,244 3,070a 613 60 0 274 Low- and middle-income economies 3,614 5,179 13,439 3,027r 1,395 91 0 515 World 5,673 27,687 31,165 5,929a 6,386 1,735 a Weighted average, data refer only to countries for which PPP data are available Source: Tables 1 1, 21. 4 21, 4 22, and 5 2 and World Bank staff estimates World Development Indicators 1997 129 4.1 Growth of output Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 Albania 30 14 24 76 32 -156 32 59 Algeria 28 01 46 13 23 -11 3.3 -9.0 38 13 Angola 3.7 -4 1 0 5 -1 8 64 09 -11.1 -111 2.2 -10 8 Argentina -03 57 09 05 -09 59 -05 00 64 Armenia 3 3 -212 -3 9 -0 6 5 1 -28 7 4 6 -19 7 Australia 34 35 33 -24 28 33 19 44 37 37 Austria 21 1 9 11 -18 19 17 24 05 23 22 AzerbaUjan -20 2 Bangladesh 4 3 41 2 7 11 4 9 7 3 2 8 7 4 5 7 5.4 Belarus -9.3 -11 2 . -10 9 .. . -6 9 Belgium 1 9 11 18 4.0 2 2 3 0 18 Benin 26 41 51 4.9 21 35 51 53 12 35 Bolivia 00 38 20 -29 -16 -01 Bosnia and Herzegovina Botswana 10.3 4.2 2.2 0 7 114 14 8.8 2.2 110 7 7 Brazil 27 27 28 37 20 17 1.6 1 7 35 36 Bulgaria 4 0 -4 3 -21 -19 5 2 -7 5 7 2 -20 7 BurkinaFaso 37 26 31 46 37 1.4 20 11 47 1.7 Burundi 4.4 -2 3 31 -41 4 5 -5 0 5 7 -7.2 5 4 -1 5 Cambodia 6.4 21 113 6 9 . 8 3 Cameroon 31 -18 22 22 59 -68 12.6 -22 21 -14 Canada 34 18 15 03 29 12 3.2 17 37 18 Central African Republic 1.7 1.0 2 7 1 5 3 1 -4 6 -0 1 -1.6 Chad 63 19 27 69 80 -99 44 -9.2 99 12 Chile 41 73 56 52 37 61 3.4 63 42 84 China 10 2 128 59 43 11 1 181 10.7 172 136 100 Colombia 3 7 4 6 2 9 14 5 0 3.0 3.5 33 31 64 Congo 3.6 -0.6 34 -09 52 12 6.9 -53 25 -21 CostaRica 30 5.1 31 36 28 52 3.0 55 31 56 CMted'lvoire 01 07 -05 03 44 17 46 -22 -13 02 Croatia Cuba Czech Republic 1 7 -2 6 Denmark 24 20 31 03 29 16 14 09 23 13 Dominican Republic 2 7 39 0 4 2 5 2 2 3 3 0.9 36 3 7 4 5 Ecuador 2 0 3 4 4 4 2 5 12 4 9 0.0 3.2 18 2.7 Egypt, Arab Rep 5 0 1 3 15 21 2 6 0 4 . 0.0 8 4 1.5 El Salvador 0 2 6 3 -1.1 1 2 01 2 9 -0.7 0 7 9 3 Eritrea Estonia 21 -9 2 -8 9 -14 9 . -3.8 Ethiopia' 2 3 1 4 1.8 1.2 .. 31 Finland 33 -0.5 -02 00 33 -12 34 21 53 -27 France 2.4 10 2 0 -11 1 1 -10 0.8 -0.9 3 0 15 Gabon 05 -25 17 -02 10 27 98 -02 -03 -100 Gambia,The 34 16 04 26 60 04 7.2 12 39 25 Georgia 0 5 -26 9 0.7 -314 18 -341 0 3 -29.3 -14 -22 3 Germanyb 2 2 . 1.7 1 2 2 9 Ghana 30 43 10 2.4 33 44 3.9 25 64 65 Greece 14 1 1 -01 31 1 3 -0.8 0 5 -1 7 49 0.6 Guatemala 0 8 4 0 23 25 21 4.2 ,, 21 4.9 Guinea-Bissau 4 5 3 5 6 7 4 8 0 4 1 9 0 5 3.3 2 2 Guinea 38 45 23 . 45 Haiti -0 2 -6 5 Honduras 27 35 27 29 33 49 37 32 25 13 Hong Kong 6 9 5 6 130 World Development Indicators 1997 4.1 1 Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 Hungary 16 -10 06 -70 -26 05 36 -46 India 58 46 31 31 71 51 74 54 67 61 Indonesia 61 7.6 3 4 2 9 6 9 10 1 12 6 112 7 0 7 4 Iran, Islamic Rep. 1.5 4 2 4 5 4 8 3 3 3 8 4 5 4 6 -0 4 6 0 Iraq -6 8 Ireland 3 1 4 7 Israel 3 5 64 Italy 24 10 06 1.6 30 03 Jamaica 2 0 2 9 0 6 8.3 2 4 -0.5 2.7 -1 9 1 9 6.0 Japan 4.0 10 13 -22 42 00 48 -09 39 2.3 Jordan -15 82 132 102 -13 79 24 77 -82 62 Kazakstan -11 9 -18 0 -19 2 -23.2 6 1 Kenya 42 1 4 33 -04 39 1 5 49 23 49 31 Korea, Dem. Rep Korea,Rep 94 72 28 13 131 73 132 76 82 79 Kuwait 0.9 12 2 14.7 . 1.0 2 3 0.9 Kyrgyz Republic . -14 7 -7 6 Lao PDR 6 5 Latvia 3 4 -13 7 2 3 -16 4 4 3 -25 1 4 4 -25 1 3 1 -2.1 Lebanon Lesotho 43 75 26 -34 72 123 135 91 52 6.1 Libya -5 7 Lithuania -9 7 Macedonia, FYR Madagascar 1 3 01 2 5 1 6 0 9 0 5 19 2 5 0.8 -0 6 Malawi 2.3 0 7 2.0 1 7 2 9 0.4 3 6 -0 2 3 4 -1 0 Malaysia 52 8.7 38 26 72 110 89 132 42 86 Mali 1.8 25 43 31 27 53 4.1 48 -1 7 12 Mauritania 1.7 4 0 17 4 9 4 9 3 9 -21 1 5 0 4 3 2 Mauritius 62 49 29 -1 4 103 56 11 1 52 54 64 Mexico 10 1.1 06 04 10 05 14 07 1 1 1 5 Moldova Mongolia 5 5 -3 3 2 9 4 6 Morocco 42 12 6.7 -59 30 1 7 41 22 42 28 Mozambique -0 2 7 1 1 6 2 4 -9.8 -2 4 -0 1 15 0 Myanmar 06 5.7 05 51 05 94 -02 70 07 55 Namibia 1 1 3 8 1 8 6.8 -1 1 2 9 5.3 8 4 2 7 4 6 Nepal 4.6 51 40 1 5 60 9 3 3 7 141 4.8 7 2 Netherlands 2 3 1 8 3 4 3.0 16 04 2 3 03 2 6 21 NewZealand 1 8 36 44 0.9 13 38 06 42 1 7 35 Nicaragua -20 11 -22 03 -1 7 -4.4 -31 -0.7 -20 22 Niger -1 1 0 5 18 -33 .- ,, -52 Nigeria 16 16 33 23 -1 0 -1 2 46 -07 32 45 Norway 2 9 3 5 0.9 3 5 0 6 2 6 Oman 83 60 79 103 206 60 Pakistan 6.3 46 43 34 73 57 7.7 58 68 50 Panama 03 63 44 149 . 55 PapuaNewGuinea 1 9 93 18 47 19 178 01 59 20 48 Paraguay 2.5 3 1 3 6 1.4 -0 3 19 21 12 3 4 4.1 Peru -0 2 5 3 Philippines 10 2 3 1 0 1 6 -0 9 2 2 0 2 1.8 2 8 2.7 Poland 19 24 -01 -20 -09 37 51 24 Portugal 2.9 0.8 .. Puerto Rico 4.1 3.0 1 8 . 3.6 1.5 4 6 Romania 0 5 -1.4 -0 4 -21 -2 8 Russian Federation 1 9 -9.8 World Development Indicators 1997 131 4.1 prouc Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annial average annual % growth % growth % growth % growth % growth 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 Rwanda 23 -128 07 -108 1 8 -170 26 -1-64 54 -12.3 Saudl Arabla -1 2 17 134 -23 7.5 . -1 2 Senegal 3.1 19 28 1 3 37 20 47 16 3.0 20 Sierra Leone 1 6 -42 4.4 -28 57 -28 3.4 44 -1 1 -59 Singapore 6.4 8.7 -6.2 0 5 54 9 2 6.6 8.3 7.2 8.4 Slovak Republic 20 -2.8 1.6 1 0 20 -104 .. 08 6.2 Slovenia South Africa 13 06 3.0 -03 -1 1 -01 -01 -02 31 09 Spain 32 1 1 -17 SriLanka 42 48 22 24 46 6.5 63 91 47 63 Sudan 06 68 00 28 37 . 15 Sweden 2 3 -0 1 1 5 -1.9 2 8 -0.7 2 6 0 8 2 5 -01 Switzerland 2 2 0 1 Syrian Arab Republic 1.5 7 4 -06 . 6.6 Tajikistan -18.1 Tanzaniac 3.8 3.2 49 41 3.4 84 1.1 36 1.6 1 7 Thailand 76 84 40 3.1 99 108 9.5 L1.6 73 78 Togo 1.8 -3.4 5.6 3.3 1.1 -6.0 1.7 -7.7 -0.3 -8.6 Trinidad and Tobago -2.5 1.0 -5.8 13 -5 5 0 2 -10.1 -0.6 -3 3 -0 1 Tunisia 3.3 39 28 -21 31 40 37 5.3 36 5.6 Turkey 53 32 13 0.9 78 42 7.9 47 44 3.3 Turkmenistan -10 6 Uganda 31 66 23 38 60 110 4.0 122 30 8.2 Ukraine -14 3 -9 7 -21.6 -20 2 United Arab Emirates -20 96 9.3 -42 -18 3.1 13 34 49 United Kingdom 3.2 1.4 United States 3.0 2 6 4.0 3.6 28 1 2 3.1 1.6 3.1 21 Uruguay 04 40 00 45 -02 01 04 -16 09 62 Uzbekistan . -4.4 -0 9 . -6 7 -5.3 . -6 6 Venezuela 1.1 24 3.0 19 16 34 43 18 0.5 17 Vietnam . 83 52 West Bank and Gaza Yemen, Rep Yugoslavia, Fed Rep Zaire 17 25 . 23 .. 2.3 1.6 Zambia 08 -02 36 -05 10 -12 4.0 -10 01 07 Zimbabwe 3.5 1.0 24 16 36 -36 29 -56 2.9 17 Lowincome 60w 68w 3.6w 31w 77w 11 6w 85w 127w 6.9w 6.4w Excl. China & India 27 w 18 w 2.6 w 1 9 w 29w . . . 28w Middleincome 19w Ow 0.9w 2.6w 39w Lower middle income 23 w -1.5 w 0.5 w Uppermiddleincome 13w 26w 24w 1.8w 07w 26w 12w 27w 20w 34w Low & middle income 2.8 w 2.1 w 31w 20w 3.9w 49w 36w 45w EastAsia&Pacific 7.6w 10.3w 48w 3.9w 8.9w 150w 9.7w 151w 90w 84w Europe & Central Asia 2.3 w -6.5w .. LatinAmerica&Carib. 1.7w 3.2w 2.0w 23w 14w 25w 11w 22w 19w 3.8w MiddleEast&N.Africa 0.2w 23w 4.5w 33w :11w 5Ow 16w 12w SouthAsia 57w 46w 3.2w 3.0w 69w 53w 72w 56w 66w 60w Sub-SaharanAfrica 17w 14w 19w 15w 06w 02w 17w 0Ow 25w 15w High income 32w 20w 2.3 w 06w 3.2 w 0.7w 35w 0.5w 3.4 w 2.3 w a. Data prior to 1992 include Entrea b Data prior to 1990 refer to the Federal Republic of Germany before unification c In all tables GDP and GINP data cover mainland Tanzania 132 World Development Indicators 1997 4.11 * 3 __ nience and resource availability Some developing __ countries have not rebased their national accounts Thegrowth of an economyis measuredbythe increase for many years Using an old base year can be mis- Most countries use the definitions of the U N System in value added produced bythe individuals and enter- leading because implicit price and volume weights of National Accounts (SNA), series F, no 2, version prises operating in that economy So, to measure real become progressively less useful and relevant 3, referred to as the 1968 SNA Version 4 of the SNA growth requires estimates of GDP and its components The World Bank collects constant price national was completed in 1993 Until new economic surveys valued in constant prices from one period to the next accounts series in national currencies and the coun- can be implemented, most countries will continue to In principle, real value added can be estimated by try's original base year To obtain comparable series use the 1968 SNA A few low-income countries still measuring the quantity of goods produced in a period, of constant price data, the main sectoral components use concepts from older SNA guidelines, includingval- valuing them at an agreed set of base year prices, of GDP by industrial origin (agriculture, industry, and uations such as factor cost and market prices in and subtracting the cost of inputs, also in constant services) are rescaled to a common base year, cur- describing major economic aggregates * Gross prices This double deflation method, recommended rently 1987, and summed to provide a new estimate domestic product at purchasers' prices is the sum by the U N System of National Accounts, depends of constant price GDP This process gives rise to a dif- of the gross value added by all resident and nonres- on detailed information about prices of inputs and ference between the derived aggregate (based on the ident producers in the economy plus any taxes and the quality of outputs But in some sectors value sum of its components) and directly rescaled GDP It minus any subsidies not included in the value of the added is extrapolated from the base year using volume may also result in differences between the growth rates products. It is calculated without making deductions indexes of output or inputs In other sectors, partic- calculated from the original base year GDP series and for depreciation of fabricated assets or for depletion ularly services, real output is imputed from labor those calculated from the rescaled aggregate Such and degradation of natural resources 0 Agriculture inputs, such as the number of employees or real deviations are unavoidable when aggregating index comprises value added from forestry, hunting, and wages The real output of governments and other numbers To reconcile constant price GDP measured fishing as well as cultivation of crops and livestock unpriced services are calculated in the same way from the expenditure side with the rescaled GDP by production * Industry comprises value added in Without well-defined measures of output, measuring industrial origin, a statistical discrepancy is calculated mining, manufacturing (also reported as a separate the real growth ofthe service sector remains a vexing and added to the private consumption component of subgroup), construction, electricity. water, and gas problem GDP expenditures * Manufacturing refers to industries belonging to divi- Technical progress can lead to improvements in sions 15-37 in the International Standard Industrial both the production process and the quality of goods Measuring growth rates Classification, rev 2 0 Services include value added Either effect, if not properly accounted for, can dis- Country growth rates are calculated using constant in all other branches of economic activity, such as tort measures of value added and thus of growth price data in the local currency Regional and income wholesale and retail trade (including hotels and restau- When inputs are used to estimate output, as in the group growth rates are calculated after converting rants), transport. and government, financial, profes- service sector, unmeasured technical progress leads local currencies to U S dollars usingthe World Bank's sional, and personal services such as education, to underestimates of the quantity and value of output International Economics Department (IEC) conversion health care, and real estate services Also included Unmeasured changes in the quality ofgoods produced factor Growth rates are estimated by fitting a linear are imputed bankservice charges, import duties, and also lead to underestimates of value The result can trend line to the logarithmic annual values of the given any statistical discrepancies noted by national com- be underestimates of real growth and productivity and variable using the least-squares growth rate method pilers as well as discrepancies arising from rescaling overestimates of inflation This produces an average growth rate that corresponds Nonmarket services pose a particular problem, to a model of periodic compound growth The least- ,. especially in developing countries, where much eco- squares growth rate method and the IEC conversion nomic activity may go unrecorded Obtaining a com- factor are described in Statistical methods National accounts data for developing countries are plete picture of the economy requires estimating collected from national statistical organizations and household production, barter exchanges, and illicit central banks by visiting and residentWorld Bank mis- or deliberately unreported activity How consistent sions Data for industrial countries come from OECD and complete such estimates will be depends on data files The World Bank rescales constant price the skill of the analysts and the resources available data to a common base year The complete national to them accounts time series is available on the World Development Indicators CD-ROM For information on Rebasing national accounts the OECD national accounts series see OECD, Countries occasionally "rebase" their national National Accounts, 1960-1994, volumes 1 and 2 accounts by collecting a complete set of observations on the value and volume of production In a new base year Usingthese data, theythen update price Indexes to reflect the relative importance of inputs and out- puts in total output, and volume indexes to reflect relative price levels The new base year should rep- resent normal operation of the economy-a year not characterized by major shocks or distortions. But the choice of base year and the timing of economic sur- veys are also determined by administrative conve- World Development Indicators 1997 133 4.2 Structure of output Gross domestic Agriculture Industry Manufacturing Services product value added value added value addled value added $ millions % of GDP % of GDP % of GDP % of GDP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Albania 2,192 56 21 23 Algeria 42,345 41,435 10 13 54 47 9 9 36 41 Angola 3,722 12 59 3 28 Argentina 76,962 281,060 6 6 41 31 29 20 52 63 Armenia 2,058 44 35 25 20 Australia 160,109 348,782 5 3 36 28 19 15 58 70 Austria 76,882 233,427 4 2 40 34 28 24 56 63 Azerbaijan 3,475 27 . 32 .. .. 41 Bangladesh 12,950 29,110 50 31 16 18 11 10 34 52 Belarus 20,561 13 35 22 52 Belgium 118,022 269,081 2 2 34 24 64 Benin 1,405 1,522 35 34 12 12 8 7 52 53 Bolivia 3,074 6,131 18 35 15 47 Bosnia and Herzegovina Botswana 971 4,318 13 5 44 46 4 4 43 48 Brazil 235,025 688,085 11 14 44 37 33 24 45 49 Bulgaria 20,040 12,366 14 13 54 34 32 53 Burkina Faso 1,709 2,325 33 34 22 27 16 21 45 39 Burundi 920 1,062 62 56 13 18 7 12 25 26 Cambodia 2,771 51 14 6 34 Cameroon 6,741 7,931 29 39 23 23 9 10 48 38 Canada 263,193 568,928 5 40 22 55 Central African Republic 797 1,128 40 44 20 13 7 40 43 Chad 727 1,138 54 44 12 22 16 34 35 Chile 27,572 67,297 7 37 21 55 China 201,688 697,647 30 21 49 48 41 38 21 31 Colombia 33,399 76,112 19 14 32 32 23 18 49 54 Congo 1,706 2,163 12 10 47 38 7 6 42 51 Costa Rica 4,831 9,233 18 17 27 24 19 19 55 58 Cote d'lvoire 10,175 10,069 27 31 20 20 13 18 53 50 Croatia 18,081 12 25 20 62 Cuba Czech Republic 29,123 44,772 7 6 63 39 30 55 Denmark 66,322 172,220 6 4 33 29 22 21 61 67 Dominican Republic 6,631 11,277 20 15 28 22 15 15 52 64 Ecuador 11,733 17,939 12 12 38 36 18 21 50 52 Egypt, Arab Rep 22,913 47,349 18 20 37 21 12 15 45 59 El Salvador 3,574 9,471 38 14 22 22 16 . 40 65 Eritrea 579 11 20 11 69 Estonia 4,007 8 28 17 64 Ethiopia' 5,179 5,287 56 57 12 10 6 3 31 33 Finland 51,306 125,432 12 6 49 37 35 28 39 57 France 664,597 1,536,089 4 2 34 27 24 19 62 71 Gabon 4,285 4,691 7 60 5 33 Gambia, The 233 384 30 28 16 15 7 7 53 58 Georgia 2,325 67 22 18 11 Germany 2,415,764 Ghana 4,445 6,315 58 46 12 16 8 6 30 38 Greece 40,147 90,550 27 21 48 36 30 21 24 43 Guatemala 7,879 14,489 25 19 56 Guinea-Bissau 105 257 44 46 20 24 .. 7 36 30 Guinea 3,686 24 31 5 .. 45 Haiti 1,462 2,043 44 12 9 44 Honduras 2,566 3,937 24 21 24 33 15 18 52 46 Hong Kong 28,495 143,669 1 0 32 17 24 9 67 83 134 World Development Indicators 1997 4.2 Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GDP % of GDP % of GDP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Hungary 22,163 43,712 8 33 24 59 India 172,321 324,082 38 29 26 29 18 19 36 41 Indonesia 78,013 198,079 24 17 42 42 13 24 34 41 Iran, Islamic Rep 92,664 18 25 32 34 9 14 50 40 Iraq 47,562 Ireland 20,080 60,780 Israel 22,579 91,965 Italy 452,648 1,086,932 6 3 39 31 28 21 55 66 Jamaica 2,679 4,406 8 9 38 38 17 18 54 53 Japan 1,059,253 5,108,540 4 2 42 38 29 24 54 60 Jordan 6,105 8 27 14 65 Kazakstan 21,413 12 30 6 57 Kenya 7,265 9,095 33 29 21 17 13 11 47 54 Korea, Dem. Rep Korea, Rep 63,661 455,476 15 7 40 43 29 27 45 50 Kuwait 28,639 26,650 0 0 75 53 6 11 25 46 Kyrgyz Republic .. 3,028 44 24 32 Lao PDR 1,760 52 18 14 30 Latvia 6,034 9 31 18 60 Lebanon 11,143 7 24 10 69 Lesotho 368 1,029 24 10 29 56 7 18 47 34 Libya 35,545 2 76 2 22 Lithuania 7,089 11 36 30 53 Macedonia, FYR 1,975 Madagascar 4,042 3,198 30 34 16 13 13 54 53 Malawi 1,238 1,465 37 42 19 27 12 18 44 31 Malaysia 24,488 85,311 22 13 38 43 21 33 40 44 Mali 1,629 2,431 61 46 10 17 4 6 29 37 Mauritania 709 1,068 30 27 26 30 13 44 43 Mauritius 1,132 3,919 12 9 26 33 15 23 62 58 Mexico 194,914 250,038 8 8 33 26 22 19 59 67 Moldova 3,518 50 28 26 22 Mongolia 861 Morocco 18,821 32,412 18 14 31 33 17 19 51 53 Mozambique 2,028 1,469 37 33 31 12 32 55 Myanmar 47 63 13 9 10 7 41 28 Namibia 2,190 3,033 12 14 53 29 5 9 35 56 Nepal 1,946 4,232 62 42 12 22 4 10 26 36 Netherlands 171,861 395,900 3 3 32 27 18 18 64 70 New Zealand 22,469 57,070 11 .. 31 22 58 Nicaragua 2,144 1,911 23 33 31 20 26 16 45 46 Niger 2,538 1,860 43 39 23 18 4 35 44 Nigeria 93,082 40,477 27 43 40 27 8 9 32 31 Norway 63,283 145,954 4 36 . 15 60 Oman 5,982 12,102 3 69 1 28 Pakistan 23,690 60,649 30 26 25 24 16 17 46 50 Panama 3,592 7,413 .. 11 15 74 Papua New Guinea 2,548 4,901 33 26 27 38 10 8 40 34 Paraguay 4,579 7,743 29 24 27 22 16 16 44 54 Peru 20,661 57,424 10 7 42 38 20 24 48 55 Philippines 32,500 74,180 25 22 39 32 26 23 36 46 Poland 57,068 117,663 6 39 26 54 Portugal 28,526 102,337 Puerto Rico 14,436 35,834 3 1 39 42 37 39 58 57 Romania . 35,533 21 40 39 Russian Federation 344,711 7 38 31 .. 55 World Development Indicators 1997 135 0 .4.2 Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GDP % of GDP % of GDP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Rwanda 1,163 1,128 50 37 23 17 16 3 27 46 Saudi Arabia 156,487 125,501 1 81 5 18 Senegal 3,016 4,867 19 20 25 18 15 12 57 62 Sierra Leone 1,166 824 33 42 21 27 5 6 47 31 Singapore 11,718 83,695 1 0 38 36 29 27 61 64 Slovak Republic 17,414 6 33 61 Slovenia 18,550 5 39 1 57 South Africa 78,744 136,035 7 5 50 31 23 24 43 64 Spain 211,543 558,617 3 Sri Lanka 4,024 12,915 28 23 30 25 18 16 43 52 Sudan 6,760 34 14 7 52 Sweden 125,557 228,679 4 2 37 32 25 23 59 66 Switzerland 101,646 300,508 . Syrian Arab Republic 13,062 16,783 20 23 56 Tajikistan 1,999 Tanzania 5,702 3,602 46 58 18 17 11 8 37 24 Thailand 32,354 167,056 23 11 29 40 22 29 48 49 Togo 1,136 981 27 38 25 21 8 9 48 41 Trinidad and Tobago 6,236 5,327 2 3 60 42 9 9 38 54 Tunisia 8,743 18,035 14 12 31 29 12 19 55 59 Turkey 68,790 164,789 26 16 22 31 14 21 51 53 Turkmenistan . 3,917 Uganda 1,267 5,655 72 50 4 14 4 6 23 36 Ukraine 80,127 18 42 37 41 United Arab Emirates 29,625 39,107 1 2 77 57 4 8 22 40 United Kingdom 537,382 1,105,822 2 2 43 32 27 21 54 66 United States 2,708,150 6,952,020 3 2 34 26 22 I8 64 72 Uruguay 10,132 17,847 14 9 34 26 26 18 53 65 Uzbekistan 21,590 33 34 18 . 34 Venezuela 69,377 75,016 5 5 46 38 16 17 49 56 Vietnam 20,351 28 30 22 42 West Bank and Gaza .. Yemen, Rep 4,790 22 27 14 51 Yugoslavia, Fed. Rep Zaire 14,391 25 33 14 42 Zambia 3,884 4,073 14 22 41 40 18 30 44 37 Zimbabwe 5,355 6,522 14 15 34 36 25 30 52 48 Low income 739,236 t 1,352,256 t 34 w 25w 32 w 38 w 21 w 27 w 32 w 35 w Excl.China&lndia 390,472t 316,889t 25w 33w 25w .. 13w 41w Middle income 2,461,307 t 4,033,376 t 11 w 35w .. 18w 52w Lower middle income . 2,025,853t 13w 36w 49w Upper middle income 989,317 t 1,981,511 t 8 w 9 w 47 w 37w 20 w 18 w 43 w 53w Low & middle income 3,192,729 t 5,393,142 t .. 14 w 36w 20w 48w East Asia & Pacific 464,719 t 1,341,265 t 27 w 18w 39w 44w 27 w 32 w 32 w 38 w Europe & Central Asia . 1,103,330 t Latin America & Carib. 758,570 t 1,688,195 t 10w 10 w 37w 33w 25 w 21 w 51w 55 w Middle East& N Africa 463,031 t .. 9 w .. 57w . 7 w .. 32 w South Asia 219,283 t 439,203 t 39 w 30 w 24 w 27w 15 w 17 w 35 w 41 w Sub-Saharan Africa 292,557 t 296,748 t 24 w 20 w 36 w 30 w 12 w 15 w 38 w 48 w High income 7,758,074t22,485,548t 3w 2w 37w 32w 24w 21w 58w 66w a Data prior to 1992 include Eritrea 136 World Development Indicators 1997 4.2 I *_ ,The output of industry ideally should be measured __ through regular censuses and surveys of firms. But in Aggregate measures of output by industrial origin mostdevelopingcountnessuchsurveystendtobeinfre- * Gross domestic product at purchasers' prices is are obtained bysummingthe value ofthe gross output quent and quicklygo outofdate, so many results must the sum of the gross value added by all resident of producers and subtracting from that sum the value be extrapolated Moreover, much of industnal produc- and nonresident producers in the economy plus any of intermediate goods consumed in production No tion is organized not in firms but in unincorporated or taxes and minus any subsidies not included in the allowance is made in such gross measures for depre- owner-operated ventures that are not captured by sur- value of the products It is calculated without making ciation of fabricated assets or for depletion and degra- veys aimed at the formal sector Even in large indus- deductions for depreciation of fabricated assets or dation of natural resources This concept is known tries, where surveys are more likely to be conducted for depletion and degradation of natural resources as value added The gross domestic product of a regularly, evasion of excise and other taxes lowers the * Value added is the net output of a sector after country represents the sum of value added by all estimates of value added As countries move through adding up all outputs and subtracting intermediate producers in that country Since 1968 the U N System the transition from state control of industry to pnvate inputs It is calculated without making deductions of National Accounts (SNA) has called for estimates enterpnse, such problems become more acute as new for depreciation of fabricated assets or for deple- ofGDP by industrial origin to be valued at either basic firms enter business and growing numbers of estab- tion and degradation of natural resources The pnces (excluding all indirect taxes on factors of pro- lished firms fail to report Following the SNA, output Industrial origin of value added is determined bythe duction) or producer prices (including taxes on fac- should include all such missing values as well as the International Standard Industrial Classification (ISIC). tors of production, but excluding indirect taxes on value of illegal activities and other unrecorded, infor- rev 2 Agriculture corresponds to ISIC divisions 1-5 final output), but some countries report such data at mal, or small-scale operations Data covering these and includes forestry and fishing Industry corre- purchasers' prices-the prices at which final sales areas need to be collected usingtechniques otherthan sponds to ISIC divisions 10-45 and includes man- are made This may affect estimates of the distrib- conventional surveys ufacturing (ISIC divisions 15-37) Services ution of output Total GDP shown here and elsewhere For sectors dominated by large organizations and correspond to ISIC divisions 50-99 in the report is measured at purchasers' prices enterprises, such as public utilities. information on Components are measured at basic prices When output,eemployment,andwagesisusuallyreadilyavail- GDP components are valued at purchasers' prices, able and reasonablyreliable But in the service sector this is noted in Primary data documentahon the many self-employed workers and one-person busi- National accounts data for While GDP by industrial origin is generally consid- nesses are sometimes diff cultto locate and have little developing countries are ered more reliable than estimates compiled from incentive to respond to surveys, let alone report their r,=NA NL collected from national sta- income or expenditure accounts, there are still many full earnings Compounding these problems are the tistical organizations and differences in the definitions, methods, and report- many forms of economic activity that go unrecorded, central banks by visiting ing standards that countries have adopted (see also includingthe work that women and children do for little and resident World Bank box4a) WorldBankstaffreviewthequalityofnational or no pay. For further discussion of the problems of v - | missions Data for indus- accounts data and sometimes make adjustments to using national accounts data, see Srinivasan (1994) -s trial countries come from improve consistency with international guidelines and Heston (1994) O rECD datafiles (see OECD, Nevertheless, significant discrepancies remain National Accounts, 1960-1994, volumes 1 and 2) between international standards and actual practice Dollar conversion The complete national accounts time series is avail- Many statistical offices, especially those in devel- To produce national accounts aggregates that are able on the World Development Indicators CD-ROM oping countries, face severe limits in the resources, internationally comparable, the value of output must time, training, and budgets required to produce reli- be converted to a common currency The World Bank able and comprehensive senes of national accounts conventionallyusesthe U S dollarandappliestheaver- age official exchange rate reported by the International Data problems In measuring output Monetary Fund (IMF) for the year shown When the offi- Among the difficulties faced by compilers of national cial exchange rate is judged to diverge by an excep- accounts is the extent of unreported or informal eco- tionally large margin from the rate effectively applied nomic activity In developing countries a large share of todomestictransactions inforeign currenciesandtraded agncultural output is either not exchanged (because it products, an alternative conversion factor is applied is consumed within the household) or not exchanged Notethatthethree-yearaveragingtechnique (the World for money Financial transactions may also go unrecorded Bank Atlas method) applied to GNP per capita in table Often agricultural production must be estimated 1 1 is not used here indirectly, using a combination of methods This some- times leads to crude approximations that can differ over time and across crops for reasons other than climatic conditions or farming techniques Similarly, the inputs to agriculture, which cannot easily be allo- cated to specific outputs, are frequently "netted out" using equally crude and ad hoc approximations For furtherdiscussion ofthe measurementof agricultural production, see the notes to table 4 3 World Development Indicators 1997 137 0 4.3 Agricultural production Value added Agricultural Food Livestock in agriculture production production production index index Index $ mililons 1989-91 = 100 1989-91 = 100 1989-91 = 100 1980 1995 1980 1995 1980 1995 1980 1995 Albania 1,224 88 126 74 164 Agriculture's importance Algeria 3,466 4,267 70 113 72 114 52 ill Agriculture accounts for about 30 percent of Angola 463 98 103 92 104 87 104 GDP in South Asia, about 20 percent in East Argentina 4,890 16,837 89 110 89 111 104 104 Asia and the Pacific and Sub-Saharan Africa, Armenia 879 and about 10 percent in Europe and Central Australia 8,454 9,710 83 511 84 117 89 107 Asia and Latin Amenca and the Canbbean. Even Austria 3,423 4,330 94 99 94 99 93 102 more striking regional differences are those Azerbaijan 1,281 Bangladesh 6,429 8 989 80 99 79 99 71 123 in the relative size of the rural population. Rural Belarus 2,475 people make; up 70 percent of the total popu- Belgium 2,500 3,612 86a 113' 87' 113' 89g 110. lation in Easi Asia andthe Pacific, South Asia, Benin 498 521 54 120 61 112 66 105 and Sub-Saharan Africa, about 50 percent in Bolivia 564 75 92 74 91 80 59 the Middle East and North Africa, but only 30 Bosnia and Herzegovina percent in Europe and Central Asia and Latin Botswana 126 220 73 110 73 110 71 115 America and the Caribbean. Brazil 23,385 84,725 74 116 74 119 74 127 Increasing agricultural productivity, particu- Bulgaria 2,889 1,592 103 62 101 64 95 50 larly for smallholders, can be a powerful factor Burkina Faso 548 584 56 1L16 58 119 59 111 lfrmliodr,abapwrufco Burkina Faso 548 5084 56 16 78 1196 591 i in achieving poverty reduction, food security, Burundi 530 508 77 95 78 96 91 94 Cambodia 1,421 59 94 60 92 30 132 cameroon 1,933 3,105 83 108 82 111 62 107 Canada 10,005 79 111 79 110 89 108 Central African Republic 300 365 81 108 80 110 50 :15 Food production in the low-income Chad 388 494 82 115 86 122 94 123 economies almost doubled between Chile 1,992 .. 71 124 69 124 74 132 China 60,688 143,612 63b 132b 64b 135b 46' 176b 1980 and 1995 Colombia 6,466 7,485 76 100 73 102 68 101 Congo 199 223 81L 105 79 105 78 110 Congo 199a 223 1,6 05 7 9 112 7 8 114 75 109 gender equity, and sustainable natural resource Costa Rica 860 1,605 72 112 72 114 75 109 C6te d'lvoire 2,633 3,030 71 108 70 114 75 118 management. Nearly three-quarters of the Croatia 1,772 world's poor live in rural areas, where they work Cuba 83 64 85 62 90 73 as farmers, farm laborers, or artisans. Boosting Czech Republic 2,104 2,000 agricultural productivity growth is particularly Denmark 3,161 4,765 82 106 82 106 95 112 important in improving gender equity in coun- Dominican Republic 1,336 1,654 87 99 84 102 69 116 tries with large rural populations, where as Ecuador 1,423 2,138 75 105 76 102 67 121 much as 80-90 percent of the female labor Egypt, Arab Rep 3,993 8,611 74 120 68 121 67 119 force works in agriculture. El Salvador 1,357 1,283 113 113 92 116 92 117 fonger agnculture. Eritrea 58 87 95 87 93 79 105 Stronger agncultural growth Is also crtical Estonia 284 for meeting the food needs of developing coun- Ethiopia 2,695' 2,490 tries. In the next 30 years the world's popula- Finland 4,523 4,628 95 93 95 93 107 93 tion will increase by 2 5 billion. Most of this France 28,168 31,915 95 99 94 99 98 101 increase will take place in developing coun- Gabon 290 387 80 105 80 105 86 104 tries, doubliig the demand for food With land Gambia, The 64 86 70 100 71 95 76 100 and water becoming increasingly scarce, growth Georgia 1,531 in food supplies will have to come primarily Germany 16,791 d 90 87 90 87 99 83 from growth in yields rather than in cultivated Ghana 2,575 2,922 74 151 74 148 79 119 Ghana 2,575 2,922 74 151 74 148 79 119 ~~~~~area or irrigaition. The challenge to more than Greece 6,337 9,840 91 103 94 98 100 99 Guatemala 3,184 89 103 72 106 75 106 double yields is enormous, and it will require Guinea 890 91 123 94 123 87 106 major changes in international and domestic Guinea-Bissau 47 118 67 113 67 114 78 111 agricultural policies, institutional frameworks, Haiti 710 103 92 102 92 104 112 and public expenditure patternstosupportthe Honduras 544 701 87 102 89 100 88 108 development of high-productivity, environmen- Hong Kong 221 207 22 61 7 44 207 32 tally sustainable production systems. 138 World Development Indicators 1997 4.3 Value added Agricultural Food Livestock in agriculture production producton production index index index $ millions 1989-91= 100 1989-91 = 100 1989-91 = 100 1980 1995 1980 1995 1980 1995 1980 1995 Hungary 2,794 96 71 95 71 96 68 India 59,102 86,628 67 114 66 114 62 117 Indonesia 18,701 34,046 67 112 66 112 51 122 Iran, Islamic Rep 16,268 59 134 58 135 67 128 Iraq 79 106 80 107 82 78 Ireland 3,234 88 105 89 106 89 106 Israel 976 89 114 84 115 78 115 Italy 26,044 29,346 103 99 104 99 93 101 Jamaica 220 385 86 113 86 113 74 102 Japan 39,019 99,298 94 95 92 95 85 98 Jordan 412 70 136 70 137 51 166 Kazakstan 2,628 Kenya 2,019 2.267 65 104 66 102 60 97 Korea, Dem Rep Korea, Rep 9,250 26,759 74 116 72 116 54 138 Kuwait 52 114 110 122 108 122 118 128 Kyrgyz Republic 1,224 Lao PDR 684 69 119 69 118 64 145 Latvia .. 489 - . Lebanon 627 64 123 62 122 95 134 Lesotho 75 73 88 89 88 80 87 110 Libya 557 Lithuania 672 Macedonia. FYR Madagascar 1,078 1,006 83 105 83 105 87 104 Malawi 413 547 81 111 90 107 81 106 Malaysia 5,365 11,110 66 116 55 123 41 140 Mali 950 1,070 72 111 75 112 93 110 Mauritania 202 224 85 98 85 98 89 91 Maurtius 119 324 78 106 77 107 63 133 Mexico 16,037 19,328 87 112 84 112 84 117 Moldova 1,636 Mongolia 231 86 77 85 76 91 80 Morocco 3,468 4,648 58 78 59 77 57 96 Mozambique 722 421 106 109 101 109 82 96 Myanmar 91 141 90 142 83 118 Namibia 241 360 112 107 109 106 117 111 Nepal 1,127 1,647 66 105 65 105 75 108 Netherlands 5,957 11,372 86 104 87 104 88 101 New Zealand 2,470 94 111 91 116 95 110 Nicaragua 497 618 105 107 102 110 122 98 Niger 1,080 855 103 117 103 118 110 114 Nigeria 24,673 16,584 61 132 61 132 86 137 Norway 2,221 93 101 93 101 95 101 Oman 152 59 92 60 91 62 90 Pakistan 6.279 14,133 62 121 66 125 59 131 Panama 766 83 106 83 106 71 111 Papua New Guinea 844 1,294 87 106 87 107 84 105 Paraguay 1,311 1,857 57 104 59 113 62 110 Peru 2,113 4,306 77 121 74 124 76 122 Philippines 8,163 16,069 88 115 89 116 75 143 Poland 7,640 84 84 84 85 101 78 Portugal 71 89 71 89 71 103 Puerto Rico 380 410 97 88 97 87 87 92 Romania 7,257 106 98 105 99 104 89 Russian Federation 21,641 World Development Indicators 1997 139 @4.3 Value added Agricultural Food Livestock in agriculture production production production index index Index $ mlhions 1989-91 = 100 1989-91 = 100 1989-91 = 100 1980 1995 1980 1995 1980 1995 1980 1995 Rwanda 539 397 83 81 84 82 82 85 Saudi Arabia 1,675 30 90 30 89 34 110 Senegal 568 970 59 114 59 113 61 128 Sierra Leone 348 318 81 87 85 86 84 102 Singapore 150 146 156 43 156 43 176 42 Slovak Republic 982 Slovenia . 789 South Africa 5,027 5,542 92 85 91 87 89 92 Spain 16,665 86 85 86 85 85 104 Sri Lanka 1,037 2,686 97 113 98 115 94 132 Sudan 2,097 102 124 103 124 91 111 Sweden 4,193 3,912 99 94 99 94 104 101 Switzerland .. 95 93 95 93 100 93 Syrian Arab Republic 2,642 98 127 103 129 72 94 Tajikistan Tanzania 2,329 1,874 75 99 74 99 67 110 Thailand 7,519 18,290 81 110 83 108 69 118 Togo 312 372 68 102 76 101 49 116 Trinidad and Tobago 141 178 103 107 102 107 82 98 Tunisia 1,235 2,136 74 89 74 88 65 126 Turkey 17,215 23,530 76 106 75 106 79 104 Turkmenistan Uganda 893 2,575 68 117 67 114 83 114 Ukraine . 13,475 United Arab Emirates 223 773 49 147 48 148 42 134 United Kingdom 10,068 17,673 93 100 93 100 99 100 United States 68,300 109,100 90 109 90 109 89 112 Uruguay 1,371 1,584 85 108 86 Ill 86 109 Uzbekistan 5,627 Venezuela 3,363 3,998 77 119 78 120 82 125 Vietnam 5,606 62 124 62 122 52 125 West Bank and Gaza 102 141 102 141 50 136 Yemen, Rep. 952 77 113 76 113 71 119 Yugoslavia, Fed Rep Zaire 3,646 . 73 100 72 101 76 108 Zambia 552 910 73 96 75 95 88 118 Zimbabwe 702 752 76 76 76 66 82 77 _ __ ___ Low income 216,892 t 334,058 t 67 w 124 w 68 w 126 w ExcI China& India 95,914t 96,987 t 75w 116w 76w 117 w Middle income .. 401,957t 82 w 88 w 81 w 98 w Lower middle income .. 231,685t 81 w 79 w 80w 93w Upper middle income 74,283t 174,944t 82w 108w 81 w 109w Low & middle income 515,734 t 774,299 t 74 w 106 w 74 w 112 w East Asia & Pacific 116,714 t 247,315 t 66 w 129 w 66 w 132 w Europe & Central Asia 108,275t . . Latin America & Carib. 73,093 t 168,472t 80 w 112 w 79w 114 w Middle East & N. Africa 37,790t 68 w 115 w 67 w 116 w South Asia 75,737 t 116,788 t 69 w 115 w 68 w 116 w Sub-Saharan Africa 64,208 t 49,666 t 78 w 110 w 78 w 111 w High income 265,206t 414,416t 91w 103w 91w 103w a Includes Luxembourg b Includes Taiwan, China c Includes Eritrea d Data refer to the Federal Republic of Germany before unification 140 World Development Indicators 1997 4.3 =-__ method assigns a single price to each commodity so that, for example, one metric ton of wheat has the The agricultural production indexes here, prepared same price regardless of the country in which it was * Value added in agriculture measures the output of by the Food and Agriculture Organization (FAO), show produced The FAD uses purchasing power panty (PPP) the agncultural sector (ISIC divisions 1-5) less the value the aggregate volume of agricultural production rel- exchange rates for comparison of GNP or consump- of intermediate inputs * Agricultural production index ative to the base period 1989-91 The FAD obtains tion expenditures across countries (seethe notes to shows the agricultural production for each year rela- data from official and semiofficial reports of crop table 4 14) The use of international prices eliminates tive to the base period 1989-91 It includes all cmops yields, area under production, and livestock numbers fluctuations in the value of output due to transitory and livestock products except fodder crops Regional When data are not available, the FAD makes its own movements of nominal exchange rates unrelated to and income group aggregates for the FAD production estimates Market data on agricultural commodities the purchasing power ofthe domestic currency Unlike indexes are calculated from the underlying values in are rarely sufficient to measure total production the International Comparison Programme (ICP), the international dollars, normalized to the base period because significant amounts are not marketed FAD calculates international prices only for agricul- 1989-91 Missing observations have not been esti- Estimates of crop yields and areas are subject to tural products Substantial differences may arise mated or imputed 0 Food production index covers various sources of errorthatvary systematically over between the implicit exchange rate derived bythe ICP commodities that are considered edible and that con- time across countries and bytype of crop Estimation and that of the FAD For further discussion of the tain nutrients Coffee and tea are excluded because, practicesvaryfrom countryto countrybutoften involve FAO's methods see FAD (1986) although edible, they have no nutritive value rather coarse estimates based on outdated surveys * Livestock production index includes meat and milk applied to large crop districts Allowances for feed, from all sources, dairy products such as cheese, and seed, and waste are generally based on fixed co- eggs. honey. raw silk, wool, and hides and skins efficients and may not adequately reflect changes in seed varieties or harvesting practices (see Snnivasan I _ 1994, pp 6-9) Direct survey techniques, such as taking cutting samples at harvesttime, generally yield _ FA0 EO Data forvalue added in agri- better estimates But such surveys are more diffi- _ culture are from the World cult to administer, and if not carefully executed, the .Nwd eon Bank's national accounts extrapolation of survey data into estimates of total Prod-tao - files Agricultural production production may be affected by excessive sampling i-d.c dft Indexes are prepared by the error and nonrandom biases Similarly, estimates of . - - FAD and published annually livestock products are often derived from baseline in its Production Yearbook livestock censuses and then extrapolated using a The FAD makes data avail- sequence of assumptions about yields at each stage able to the World Bank in of processing In a recent examination of food pro- electronic files that may contain more recent infor- duction datafor South Asia, Evenson and Pray (1994) mation than the published versions found thatthere has been some improvement in reli- ability but that further progress will require more Figure 4.3a Food production, by region, resources and better measurement techniques 1980 and 1995 The indexes are calculated usingthe Laspeyres for- 1989-91 = 100 mula production quantities of each commodity are 60 80 100 120 140 weighted by the average international commodity EastAsi prices in the base period and summed for each year and the Pacific Because the FAD indexes are based on the concept 198 1995 i980 199S of agriculture as a single enterprise, estimates of the Latin Armerica amounts retained for seed and feed are subtracted and the Caribbean from the production data to avoid double counting O- The resulting aggregate represents production avail- able for any use except as seed and feed The FAD nNddte East and North Africa indexes may differ from other sources because of dif- Q _ ferences in coverage, weights, concepts of produc- tion, time reference of data, the use of international South Asia prices, and methods of calculation p To ease comparison among countnes, the FAD uses international commodity prices to value production These prices, expressed in international dollars (equiv- Sub-Saharar Africa alent in purchasing power to the U S dollar), are denved using a Geary-Khamis formula for the agri- cultural sector (see Inter-Secretariat Working Group Source: Table 4 3 on National Accounts 1993, sections 16-93) This World Development Indicators 1997 141 4.4 Food crops Area under cereal Cereal yield Cereal production Roots and tubers Food aid in cereals Cereal imports production production thousand kilograms thousand thousand thousand thousand hectares per hectare metric tons metric tons metric tons metric tons 1980 1995 1980 1995 1980 1995 1980 1995 1980-81 1994-95 1980 1994 Albania 373 258 2,362 2,547 881 657 99 70 34 44 270 Algeria 3,181 2,502 760 877 2,419 2,194 591 720 '29 23 3,414 7,760 Angola 712 1,122 624 286 444 321 1,355 1,943 25 217 341 475 Argentina 9,924 8,150 1,866 2,878 18,521 23,456 2,092 2,410 8 28 Armenia . 400 356 . 300 Australia 15,587 15,006 1,052 1,770 16,402 26,560 861 1,152 5 66 Austria 1,069 808 4,514 5,540 4,826 4,476 1,264 724 . 131 233 Azerbaijan 606 1,550 939 200 379 275 Bangladesh 10,818 10,699 2,006 2,424 21,698 25,931 1,708 1,864 737 888 2,194 952 Belarus 2,573 2,304 5,927 8,570 57 1,250 Belgium' 430 347 4,747 6,660 2,041 2,311 39 52 . 5,599 5,207 Benin 482 678 718 956 346 648 1,316 2,447 L1 15 61 107 Bolivia 561 701 1,116 1,583 626 1,110 1,062 1,044 55 175 263 434 Bosnia and Herzegovina 252 2,845 717 377 Botswana 191 124 230 371 44 46 7 9 11 7 68 175 Brazil 21,081 19,832 1,576 2,504 33,217 49,653 26,310 29,009 3 33 6,740 8,971 Bulgaria 2,064 1,880 3,705 3,053 7,648 5,739 301 476 156 693 58 Burkina Faso 1,839 3,134 570 795 1,048 2,492 126 75 51 19 77 110 Burundi 204 209 1,064 1,287 217 269 1,030 1,326 L2 48 18 105 Cambodia 1,447 1,359 1,256 1,374 1,818 1,867 202 60 133 64 195 58 Cameroon 1,032 936 864 1,346 892 1,260 1,634 2,080 LO 2 140 226 Canada 19,319 18,373 2,141 2,705 41,365 49,693 2,478 3,774 1 1,383 1,022 Central African Republic 192 145 521 772 100 112 1,102 718 3 1 12 52 Chad 1,048 1,483 547 649 573 963 431 528 L4 14 16 50 Chile 852 618 2,060 4,537 1,755 2,804 910 907 :21 2 1,264 1,277 Chinab 95,054 89,361 2,948 4,664 280,262 416,796 149,508 152,813 17,061 16,331 Colombia 1,336 1,371 2,414 2,559 3,225 3,509 4,046 5,164 5 15 1,068 2,353 Congo 14 29 857 931 12 27 675 710 2 12 88 86 Costa Rica 119 66 2,924 2,424 348 160 45 181 1 2 180 453 C6te d'lvoire 927 1,527 928 1,103 860 1,685 3,294 4,761 1 56 469 466 Croatia 655 4,220 2,764 500 8 82 Cuba 225 130 2,551 1,392 574 181 986 652 2 3 2,316 1,464 Czech Republic . 1,577 . 4,184 6,598 1,330 287 Denmark 1,816 1,464 3,893 6,126 7,070 8,968 842 1,480 355 480 Dominican Republic 151 143 2,934 4,1L9 443 589 210 256 73 2 365 895 Ecuador 415 1,011 1,639 2,010 680 2,032 564 562 9 32 387 486 Egypt, Arab Rep 1,978 2,841 4,095 6,048 8,100 17,182 1,394 1,734 1,865 179 6,028 9,200 El Salvador 428 440 1,706 2,030 730 893 26 107 50 7 144 448 Eritrea 296 517 153 109 140 281 Estonia 350 2,114 740 700 231 82 Ethiopia 5,478 1,505 8,245 2,018 720 928 Finland 1,171 978 2,823 3,408 3,306 3,333 736 798 367 130 France 9,894 8,301 4,854 6,458 48,025 53,606 6,618 5,754 1,570 1,228 Gabon 6 15 1,833 1,867 11 28 380 396 27 64 Gambia, The 62 89 1,274 1,213 79 108 6 6 L6 2 47 97 Georgia 362 1,530 554 250 388 713 Germanyc 7,738 6,589 4,228 6,051 32,713 39,870 17,146 10,382 9,500 3,321 Ghana 939 1,355 718 1,354 674 1,835 3,151 10,493 '34 101 247 311 Greece 1,606 1,270 3,323 3,693 5,336 4,690 1,087 900 1,199 570 Guatemala 747 940 1,448 1,613 1,082 1,516 41 79 L4 144 204 517 Guinea-Bissau 133 143 699 1,406 93 201 50 65 26 2 21 68 Guinea 759 722 955 1,071 725 773 636 801 34 29 171 384 Haiti 413 428 1,017 871 420 373 690 772 84 117 195 311 Honduras 421 500 1,131 1,542 476 771 18 30 36 73 139 278 Hong Kong . 812 652 142 World Development Indicators 1997 4.4 Area under cereal Cereal yield Cereal production Roots and tubers Food aid in cereals Cereal imports production production thousand kilograms thousand thousand thousand thousand hectares per hectare metrc tons metric tons metric tons metnc tons 1980 1995 1980 1995 1980 1995 1980 1995 1980-81 1994-95 1980 1994 Hungary 2,915 2,724 4,806 4,054 14,009 11,042 1,394 1,151 . 155 305 India 104,067 100,680 1,350 2.134 140,491 214,893 15,485 26,300 435 264 424 12 Indonesia 11,740 15,126 2,866 3,840 33,643 58,083 16,233 18,603 382 15 3,534 5,113 Iran, Islamic Rep 8,045 9,796 1,067 1,767 8,583 17,312 1,339 3,200 54 2,779 5,450 Iraq 2,291 3,135 826 907 1,892 2,845 97 420 0 68 2,942 1,099 Ireland 444 272 4,651 6,658 2,065 1,811 880 620 . 553 444 Israel 131 115 2,313 2,200 303 253 173 288 10 1,601 2,311 Italy 5,111 4,225 3,521 4,666 17,995 19,713 2,945 2,076 7,629 6,014 Jamaica 4 3 1,750 1,333 7 4 214 337 37 46 469 335 Japan 2.724 2,342 4,843 5,737 13,192 13,437 5,416 5,157 24,473 29,937 Jordan 185 164 930 774 172 127 13 90 95 Ii1 505 1,347 Kazakstan 18,763 564 10,583 1,950 60 . 100 Kenya 1,795 1,801 1,244 1,885 2,233 3,394 1,191 1,685 173 102 387 622 Korea, Dem Rep 1,611 1,548 3,130 3,386 5,042 5,241 1,920 2,050 510 310 Korea, Rep 1,646 1,181 4,056 5,862 6,676 6,923 1,550 848 678 5,143 11,936 Kuwait 1 2 1 340 455 Kyrgyz Republic 546 1,432 . 782 431 . 19 . 120 Lao PDR 760 560 1,422 2,663 1,081 1,491 182 223 2 10 121 22 Latvia 382 . 1,835 . 701 . 927 390 . 65 Lebanon 26 40 1,731 1,925 45 77 136 222 43 7 678 577 Lesotho 203 61 956 672 194 41 18 62 44 15 107 99 Libya .. 908 1.790 Lithuania 1,141 2,191 2,500 1,594 77 19 Macedonia, FYR . 242 3,000 . 726 154 133 Madagascar 1,331 1,401 1,681 1,984 2,238 2,780 2,302 3,375 27 26 110 140 Malawi 1,054 1,375 1,188 1,293 1,252 1,778 563 576 17 204 36 506 Malaysia 724 705 2,836 3,077 2,053 2,169 455 530 0 1,336 3.509 Mali 1,333 2,996 685 812 913 2,433 124 29 50 17 87 70 Mauritania 112 293 420 840 47 246 6 5 106 22 166 206 Mauritius 1 . 1,000 1 2 12 20 21 2 181 255 Mexico 9,542 10,289 2,189 2,463 20,887 25,344 1,156 1,252 . 44 7,226 8,100 Moldova . 533 3,186 1.698 . 400 . 58 120 Mongolia 555 386 517 676 287 261 38 52 12 70 63 Morocco 4,429 4,021 1,019 453 4,515 1,823 544 783 120 13 1,821 1,678 Mozambique 1,113 1,727 596 653 663 1,127 3,715 4,310 155 320 368 496 Myanmar 5.261 6,985 2,599 2,962 13,673 20,690 190 244 7 5 16 49 Namibia 117 99 632 606 74 60 178 190 26 54 112 Nepal 2,248 3,031 1,687 1,795 3,792 5,440 348 984 45 21 56 62 Netherlands 224 196 5,692 8,112 1,275 1,590 6,267 7,363 . . 5,246 6,676 New Zealand 192 149 3,938 5,738 756 855 229 282 . 63 316 Nicaragua 243 317 1,572 1,868 382 592 18 81 58 33 149 174 Niger 3,880 7,234 457 307 1,775 2,221 179 260 11 32 90 155 Nigeria 7,205 18,634 1,104 1,124 7,957 20,943 18,916 56,006 1 1,828 1,078 Norway 316 363 3,642 3,953 1,151 1,435 571 471 . .. 725 668 Oman 3 3 667 1,667 2 5 1 6 .. 120 460 Pakistan 10,585 12,187 1,613 2,017 17,074 24,586 647 1,497 277 103 613 1,916 Panama 158 185 1,544 1,800 244 333 74 66 2 2 87 273 Papua New Guinea 2 2 2,000 1,500 4 3 1,122 1,267 0 152 275 Paraguay 306 523 1,510 2,166 462 1,133 2,147 2,708 11 1 75 31 Peru 640 813 1,816 2,635 1,162 2,142 2,319 3,369 116 348 1,309 2,289 Philippines 6,708 6,848 1,606 2,214 10,775 15,163 3,129 2,820 85 44 1,053 2,219 Poland 7,847 8,539 2,337 2,940 18,336 25,106 26,391 24,891 417 200 7,811 505 Portugal 1,104 701 1,275 1,863 1,408 1,306 1.268 1,477 255 3.372 2,351 Puerto Rico 1 6,000 . 6 1 40 10 Romania 6,469 6,194 2,994 3,210 19,367 19,885 3,942 3,020 75 2,369 529 Russian Federation 53,047 . 1,165 . 61,795 37,300 10 31.227 World Development Indicators i997 143 4.41 Area under cereal Cereal yield Cereal production Roots and tubers Food aid in cereals Cereal imports production I production thousand kilograms thousand thousand thousand thousand hectares per hectare metric tons metric tons metric tons metric tons 1980 1995 1980 1995 1980 1995 1980 1995 1980-81 1994-95 1980 1994 Rwanda 225 93 1,213 1,624 273 151 1,665 1,534 15 269 16 97 Saudi Arabia 453 815 587 4,196 266 3,420 4 170 3,061 6,182 Senegal 1,235 1,212 547 874 676 1,059 41 68 153 16 452 579 Sierra Leone 441 295 1,249 1,146 551 338 125 262 12 30 83 141 Singapore 2 1,324 776 Slovak Republic 859 4,107 3,528 442 201 Slovenia 113 5,044 570 430 3 512 South Africa 6,532 5,978 2,023 1,245 13,217 7,440 732 1,524 159 913 Spain 7,527 6,641 2,480 1,730 18,666 11,487 5,791 4,219 6,073 5,047 Sri Lanka 868 938 2,500 2,902 2,170 2,722 678 440 226 342 884 927 Sudan 4,261 8,071 670 473 2,857 3,821 293 156 195 132 236 1,022 Sweden 1,509 1,083 3,520 4,450 5,312 4,819 1,084 1,074 124 211 Switzerland 171 215 4,614 5,958 789 1,281 853 680 1,247 380 Syrian Arab Republic 2,702 3,686 1,437 1,665 3,884 6,136 292 553 44 59 726 952 Tajikistan 270 930 251 140 97 450 Tanzania 2,902 3,254 1,020 1,419 2,961 4,617 5,586 6,670 236 118 399 195 Thatland 10,786 10,630 1,911 2,386 20,612 25,358 16,940 18,382 26 3 213 740 Togo 465 674 637 691 296 466 914 865 4 8 41 69 Trinidad and Tobago 4 4 3,250 3,750 13 15 20 12 252 162 Tunisia 1,307 1,017 916 626 1,197 637 120 205 99 22 817 1,592 Turkey 13,163 14,242 1,855 1,977 24,419 28,163 3,002 4,750 9 2 6 878 Turkmenistan 565 2,211 1,249 11 50 940 Uganda 723 1,341 1,491 1,551 1 078 2,080 3,438 5,246 57 62 52 56 Ukraine 12,858 2,522 32,429 14,729 151 1,500 United Arab Emirates 1 1 5,000 7,000 2 7 1 4 426 759 United Kingdom 3,939 3,151 4,944 6,978 19,473 21,987 7,105 6,445 5,498 3,321 United States 71,629 59,614 3,771 4,647 270,122 276,999 14,285 20,764 199 7,363 Uruguay 552 564 1,618 2,645 893 1,492 159 208 45 277 Uzbekistan 1,658 1,691 2,803 500 4,100 Venezuela 808 793 1,915 2,576 1,547 2,043 603 655 2,484 2,015 Vietnam 5,992 7,154 2,016 3,523 12,080 25,205 6,613 5,077 150 64 1,160 387 West Bank and Gaza Yemen, Rep 851 756 1,016 1,091 865 825 136 200 34 91 Yugoslavia, Fed Rep 2,342 3,582 8,388 931 250 Zaire 1,113 2,123 799 799 889 1,697 13,748 18,358 77 83 350 253 Zambia 628 662 1,567 1,331 984 881 334 668 84 11 498 35 Zimbabwe 1,679 1,844 1,185 531 1,989 980 81 162 1E, 4 156 100 Low income 284,237 t 313,899 t 1,896w 2,617 w 538,916 t 821,606 t 245,405 t 321,914 t 4,353 t 6,298 t 30,803 t 33,241t Excl China& India 85,116 t 123,858 t 1,388w 1,533w 118,163 t 189,917 t 80,412 t 142,801 t 3,917 t 6,034 t 13,318 t 16,897t Middle income 145,369 t 248,426 t 2,014w 2,100w 292,764 t 521,848 t 125,190 t 209,044t 3,331 t 1,963 t 100,198t 87,905t Lower middle income 91,171 t 195,110 t 2,002 w 1,949 w 182,567 t 380,344 t 90,363 t 168,219 t 3,277 t 1,881 t 77,173 t 53,749 t Upper middle income 54,198t 53,316 t 2,033w 2,654w 110,197 t 141,504 t 34,827 t 40,825 t 53t 82t 23,025t 34,156t Low & middle income 429,606 t 562,325 t 1,936 w 2,389 w 831,680 t 1,343,454 t 370,595 t 530,958 t 7,683 t 8,260 t 131,001 t 121,146 t East Asia & Pacific 140,655 t 140,673 t 2,711 w 4,069 w 381,364 t 572,346 t 196,776 t 202,431 t 791t 216t 25,523 t 29,233 t Europe & Central Asia 34,439 t 134,804 t 2,613w 1,792 w 90,004 t 241,533 t 36,243t 107,551 t 14t 1,700t 43,606t 6,036t Latin America & Carib 49,108t 48,035t 1,798w 2,517w 88,270t 120,899t 43,845t 49,972t 583t 1,134t 25,648t 31,842t Middle East & N Africa 25,492 t 28,776 t 1,254 w 1,827 w 31,979 t 52,628 t 4,674 t 8,320 t 2,328 t 481t 23,881 t 38,211 t South Asia 131,716t 130,034t 1,438w 2,130w 189,455t 276,985t 19,179t 31,429t 1,797t 1,775t 4,211t 4,114t Sub-Saharan Africa 48,196 t 80,003 t i,050 w 988w 50,608 t 79,063 t 69,878 t 131,255 t 2,171 2,954 t 8,132 t 11,710 t High income 153,747t 132.192t 3,350w 4,183w 515,046t 552,917t 78,708t 76,236t 9491 78,871t 87,329t a Includes Luxembourg b Includes Taiwan, Chena c Dats prior to 1990 refer to the Federal Repubiic of Germany before unification 144 World Development Indicators 1997 4.4 This table is concerned mainly with cereal production 0 Area under cereal production relates to harvested because cereals are widely produced and consumed area, although some countries report sown or culti- as a primary source of nutrition In countries where vated area only 0 Cereals include wheat, rice, maize, cereals are notextensivelycultivated, roots andtubers barley, oats, rye, millet, sorghum, buckwheat, and are the principal alternatives The indicators have mixed grains. Production data on cereals relate to been selected to show both the production of basic crops harvested for dry grain only Cereal crops har- foodstuffs and the availability of grain through imports vested for hay or harvested green for food, feed, or and, when countries cannot finance their import silage and those used for grazing are excluded. requirements, food aid 0 Roots and tubers refer to potatoes, sweet pota- The data on area under cereal production and on toes, cassava, yams, taro, yautia, and arrowroot. cereal yield and production relate to crops harvested Root crops grown principallyforfeed, such as turnips, for dry grain only These data may be affected by a mangels, and swedes, are not included * Food aid variety of reporting and timing differences (See also In cereals covers wheat and flour, bulgur, rice, coarse the discussion in the notes to table 4 3 ) The Food grains, and the cereal component of blended foods. and Agriculture Organization (FAO) allocates produc- The time reference for food aid is the crop year (July tion data to the calendar year in which the bulk of through June) 0 Cereal imports are measured in the harvesttook place But most of a crop harvested grain equivalents and defined as comprising all cere- near the end of the year will be used in the following als in Standard International Trade Classification year In general, cereal crops harvested for hay or (SITC), rev 2, groups 041-046 harvested green for food, feed, or silage and those used forgrazing are excluded But millet and sorghum. |_ which are grown as feed for livestock and poultry in Europe and North America, are used as food in Asia, FAO'. The data here come from the Africa, and countries of the former Soviet Union _- . FAO The most recent pub- Food aid in cereals is based on data for crop years _ . lished source for commodity (July through June) reported by donors and interna- production data is the FAO's tional organizations, including the International Wheat Production Yearbook 1994 Council and the World Food Programme Food aid c- Data on cereal imports come information from donors may not correspond to actual from the FAO's Trade Yearbook receipts bybeneficianesaduringagiven period because __ _ 1994 Data on food aid are of delays in transport and recording or because aid published in the FAO's Food sometimes is not reported to the FAO or other rele- Aid in Figures 1994 The FAD makes data available to vant international organizations Aid receipts may the World Bank in electronic files that may contain also be omitted from customs reports of imports more recent information than the published sources Cereal imports are generally based on calendar year customs data reported by the importing coun- tries to the FAD When official data are missing, the FAD uses estimates based on data from other sources The FAD uses the Standard International Trade Classification (SITC), rev. 2, to categorize imports Cereal imports include wheat flour For fur- ther discussion of the classification of commodity imports see the notes to table 4 9 World Development Indicators 1997 145 4.5 Key agricultural inputs Arable land Irrigated land Fertilizer Farm machinery Share of labor force consumption in agriculture hundreds of grams of plant nutrient per hectare hectares per capita % of arable land of arable land tractors Y 1980 1994 1980 1994 1980/81 1994/95 1980 1994 1980 1990 Albania 0 26 0 22 52 8 49 9 1,335 254 10,105 9,000 57 55 Algeria 0 40 0 29 3 4 6 9 314 153 47,000 913,799 36 26 Angola 0 49 0 34 2 2 2 1 49 29 10,250 10,300 76 75 Argentina 0 97 0 80 5 8 6 3 42 147 166,700 280,000 13 12 Armenia 13,000 16,000 21 17 Australia 3 01 2 65 3 4 4 5 263 352 327,000 315,000 6 5 Austria 0 22 0 19 0 2 0 3 2,491 1,685 320,100 343,000 10 8 Azerbaijan 0 32 0 27 61 4 50 0 195 35,300 32,000 35 31 Bangladesh 0 11 0 08 17 1 33 9 455 1,081 4,200 5,300 74 64 Belarus 0 66 0 61 2 6 1 6 995 117,200 123,000 26 20 Belgium 0 080 0 080 0 1 0 1 5,8710 4,1060 116,603a L12,000a 3 3 Benin 0 52 0 35 0 3 0 5 5 91 105 140 67 62 Bolivia 0 39 0 33 6 8 4 2 14 45 4,000 5,350 53 47 Bosnia and Herzegovina 0 18 0 3 55 29,000 28 II Botswana 0 44 0 29 0 5 0 2 35 24 2,150 6,000 64 46 Brazil 0 41 0 32 3 3 5 9 855 933 545,205 735,000 37 23 Bulgaria 0 47 0 50 28.7 19 0 1,986 449 61,968 37,000 20 14 Burkina Faso 0 40 0 35 0 4 0 7 15 65 115 135 92 92 Burundi 0 29 0 19 0 8 1 2 9 26 90 170 93 92 Cambodia 0 32 0 39 4 8 4 5 39 33 1,350 1,365 76 74 Cameroon 0 80 0 54 0 2 0 3 46 43 572 500 73 70 Canada 1 86 1 56 1 3 1 6 424 490 657,400 740,000 7 3 Central African Republic 0 84 0 63 7 6 155 210 85 80 Chad 0 70 0 52 0 2 0 4 3 21 160 170 88 81 Chile 0 38 0 30 29 6 29 8 314 979 34,380 41,312 21 19 China' 0 10 0 08 45 4 51 5 1,530 3,088 747,893 709,654 76 74 Colombia 0 19 0 15 7 7 13 7 601 1,077 28,423 37,000 39 25 Congo 0 09 0 07 0 7 0 6 35 112 670 700 58 48 Costa Rica 0 22 0 16 12 1 23 8 1,453 2,585 5,950 7,000 35 26 Cote d'lvoire 0 38 0 27 1 4 2 0 172 170 3,050 3,700 65 60 Croatia 0 36 0 26 0 2 . 1,482 5,438 4,006 24 15 Cuba 0 34 0 31 22 9 27 0 1,590 368 68,300 78,000 24 18 Czech Republic 0 33 0 7 887 60,000 13 11 Denmark 0 52 0 46 14 7 19 2 2,364 1,971 189,426 146,573 7 6 Dominican Republic 0.25 0 19 11.6 16 9 363 642 2,150 2,350 32 25 Ecuador 0 31 0 27 21 1 18 4 295 546 6,198 8,900 40 33 Egypt, Arab Rep 0 06 0 06 100 0 100 0 2,714 2,433 36,000 78,099 61 43 El Salvador 0 16 0 13 15 2 164 832 1,325 3,300 3,430 43 36 Eritrea 0 i5 5 4 850 79 Estonia 0 68 0 76 362 19,418 15,000 15 14 Ethiopia 0 20 1 7 42 3,000 860 80 Finland 0 54 0 51 2 3 2 5 1,908 1,484 212,000 2:30,000 12 8 France 0 35 0 34 4 6 7 6 2,972 2,418 1,473,600 1,440,000 8 5 Gabon 0 66 0 43 0 9 0 9 2 9 1,250 1,500 76 61 Gambia, The 0 25 0 16 0 6 1 2 127 47 45 45 84 82 Georgia 0 19 0 21 41 7 41 6 275 24,900 18,200 32 26 Germanyd 0 16 0 15 3 7 4 0 4,126 2,419 1,613,502 1,300,000 7 4 Ghana 0 33 0 26 0 2 0 1 34 23 3,500 4,100 61 60 Greece 0 41 0 34 24 5 37 9 1,342 1,528 140,305 227,000 31 23 Guatemala 0 25 0 19 5 0 6 5 489 958 4,000 4,300 54 52 Guinea 0 16 0 11 12 8 12 7 4 15 150 290 91 87 Guinea-Bissau 0 35 0 33 6 0 5 0 7 18 16 19 86 85 Haiti 0 17 0 13 7 9 8 2 4 56 175 230 71 68 Honduras 0 48 0 35 4 1 3 6 162 281 3,250 4,900 56 40 Hong Kong 0 00 0 00 42 9 28 6 7 4 1 1 146 World Development Indicators 1997 4.55 Arable land Irrigated land Fertilizer Farm machinery Share of labor force consumption in agriculture hundreds of grams of plant nutrient per hectare hectares per capita % of arable land of arable land tractors % 1980 1994 1980 1994 1980/81 1994/95 1980 1994 1980 1990 Hungary 0 50 0 49 2 5 4 2 2,624 631 55,452 36,200 18 15 India 0 25 0 19 22 9 28 3 329 797 382,869 1,257,630 70 64 Indonesia 0 18 0 16 16 5 15 2 451 848 9,240 55,608 59 57 Iran, Islamic Rep 0 35 0 29 36 1 40 1 447 561 78,000 118,000 46 41 Iraq 0 42 0 28 32 2 44 3 170 654 23,350 32,000 28 16 Ireland 0 33 0 37 5,414 5,718 145,100 167,500 19 14 Israel 0 11 0 08 49 2 44 4 1,919 2,391 26,800 25,630 6 4 Italy 0 22 0 20 19 3 24 3 1,698 1,697 1,072,168 1,470,000 13 9 Jamaica 0 11 0 09 13 8 16 0 729 1,187 2,800 3,080 31 24 Japan 0 04 0 04 62 6 62 9 3,721 4,032 1,471,400 2,050,000 11 7 Jordan 0 16 0 10 11 0 15 8 427 346 4,561 7,634 24 21 Kazakstan 2 41 2 08 5 5 6 1 35 237,368 210,000 24 22 Kenya 0 26 0 17 0 9 1 5 144 305 6,546 14,000 83 80 Korea, Dem Rep 0 10 0 09 58 9 73 0 3.838 3,765 44,300 75,000 45 38 Korea, Rep 0 06 0 05 59 5 65 0 3,657 4,672 2,664 80,000 37 18 Kuwait 0 00 0 00 100 0 100 0 4,400 2,000 25 100 2 1 Kyrgyz Republic 0 40 0 32 65 4 70 4 197 26,300 23,000 34 32 Lao PDR 0 22 0 19 16 7 17 2 58 23 464 890 80 78 Latvia 0 68 0 68 548 32,800 55,600 16 16 Lebanon 0 11 0 08 28 1 28 8 869 915 3,000 3,000 13 5 Lesotho 0 22 0 17 1 0 0 9 154 188 1,400 1,850 41 41 Libya 0 68 0 42 10 8 21 7 256 306 23,200 34,000 25 11 Lithuania 0 93 0 82 125 65.753 28 18 Macedonia, FYR 0 32 10 6 227 47,100 34 22 Madagascar 0 34 0 24 21 5 35 0 29 36 2,650 2,920 85 84 Malawi 0 22 0 18 1 4 1 6 250 214 1,200 1,420 88 95 Malaysia 0 35 0 39 6 7 4 5 944 1,586 7,430 38,926 41 27 Mali 0 31 0 26 2 9 3 2 69 84 830 840 93 93 Mauritania 0 13 0 09 25 1 23 6 67 192 270 330 72 55 Mauritius 0 11 0 10 15 0 17 0 2,492 2,754 325 370 27 17 Mexico 0 37 0 27 20 3 24 7 505 620 115,057 172,000 37 28 Moldova 0 55 0 50 9 9 14 3 528 50,300 53,300 43 33 Mongolia 0 71 0 56 3 0 6 1 69 42 9,700 11,700 40 32 Morocco 0 41 0 35 15 2 13 5 258 312 24.684 42,000 56 45 Mozambique 0 26 0 21 2 1 3 4 90 22 5,750 5,750 84 83 Myanmar 0 30 0 22 10 0 13 3 100 172 9,273 12,000 76 73 Namibia 0 64 0 44 0 6 0 9 2,550 3,150 56 49 Nepal 0 16 0 11 22 4 36 1 97 384 2.514 4,600 95 95 Netherlands 0 06 0 06 58 4 59 6 8,262 5,454 178,000 182,000 6 5 New Zealand 1 12 109 5 2 7 5 1,326 1,608 92,349 76,000 11 10 Nicaragua 0 45 0 31 6 4 6 9 435 244 2,200 2,700 39 28 Niger 0 64 0 41 0 6 1 8 8 3 96 180 93 91 Nigeria 0 43 0 30 0 7 0 7 57 120 8,600 11,900 55 43 Norway 0 20 0 21 9 1 10 8 3,174 2,297 130,700 148,100 8 6 Oman 0 04 0 03 92 7 92 1 259 1,587 95 150 50 48 Pakistan 0 25 0 17 72 3 80 3 532 1,023 97,373 283,300 62 56 Panama 0 28 0 26 5 0 4 8 551 481 5,458 5,000 29 26 Papua New Guinea 0 12 0 10 148 313 1,379 1,140 83 79 Paraguay 0 55 047 3 5 3 0 36 101 7,300 16,500 45 39 Peru 0 20 0 18 33 0 41 1 336 505 11,900 13,000 40 36 Philippines 0 18 0 14 14 0 17 2 383 655 10,533 11,500 52 45 Poland 0 42 0 38 0 7 0 7 2,339 976 619,353 1,310,690 30 27 Portugal 0 32 0 29 20 1 21 7 824 876 85,000 150,000 26 18 Puerto Rico 0 03 0 02 39 0 50 6 3,666 4,181 6 4 Romania 0 47 0 44 21 9 31 3 1.165 389 146,592 161,223 35 24 Russian Federation 0 98 0 89 3 7 4 1 116 1,324,000 1,147,500 16 14 World Development Indicators 1997 147 .54.5 Arable land Irrigated land Fertilizer Farm machinery Share of labor force consumption in agriculture hundreds of grams of plant nutrient per hectare hectares per capita % of arable land of arable land tractors % 1980 1994 1980 1994 1980/81 1994/95 1980 L994 1980 1990 Rwanda 0 20 019 0 4 0 3 1 9 84 90 93 92 Saudi Arabia 0.21 0 21 20 1 114 209 947 1,200 2,100 45 20 Senegal 0.42 0 28 2 6 3 0 83 85 460 550 81 76 Sierra Leone 0.15 0 12 4 0 5 4 36 56 317 550 70 67 Singapore 0 00 0 00 5,500 48,000 44 65 2 0 Slovak Republic 0 30 5 0 685 32,810 14 12 Slovenia 0.16 0 14 0 7 2,860 5C,O0O 15 5 South Africa 0.45 0 33 8 5 9 6 803 631 172,725 12E,885 17 14 Spain 0.55 0 51 14 8 18 2 811 916 523,907 789,747 19 12 Sri Lanka 0.13 0 11 28 0 29 2 882 1,131 24,263 33,000 52 49 Sudan 0.67 0 47 14 5 15 0 65 56 9,600 10,500 72 69 Sweden 0.36 0 32 2 3 4 1 1,624 1,148 181,000 165,000 Switzerland 0 07 0 06 6 1 5 8 4,409 3,364 94,717 114,000 6 6 Syrian Arab Republic 0 65 0 40 9 5 19 6 224 636 27,544 7E,150 39 34 Tajikistan 0 22 0 15 69 3 83 5 814 31,700 30,000 44 41 Tanzania 0 15 0 12 4 2 4 3 125 114 10,000 6,600 86 84 Thailand 0 39 0 36 16 5 23 1 150 615 18,000 12CI,751 71 64 Togo 0.90 0 61 0 3 0 3 11 46 200 370 69 66 Trinidad and Tobago 0 11 0 09 18 1 18 0 688 492 2,350 2,650 11 11 Tunisia 0 74 0 56 5 2 7 8 132 180 25,800 27,500 39 28 Turkey 0 64 0 46 9 5 15 1 511 543 435,283 763,529 60 53 Turkmenistan 1.05 0 34 30 9 87 8 845 37,100 50,000 39 37 Uganda 0 44 0 37 01 01 1 4 2,600 4,700 89 93 Ukraine 0.71 0 66 5 7 7 5 349 408,837 436,713 25 20 United Arab Emirates 0.02 0 02 1,328 9,077 160 166 4 7 United Kingdom 0,12 0 10 2 0 1 8 2,936 3,837 512,494 500,000 3 2 United States 0 84 0 72 10 8 114 1,127 1,027 4,726,000 4,800,000 3 3 Uruguay 0 50 0 41 5 5 10 7 558 828 32,878 33,000 17 14 Uzbekistan 0 27 0 20 81 2 88 9 1,073 157,300 170,000 38 34 Venezuela 0 25 0 19 3 6 4 9 642 613 38,000 49,000 15 12 Vietnam 0 12 0 10 23 5 26 6 236 1,745 24,105 3,700 73 72 West Bank and Gaza 0.26 0 18 4 6 4 3 2,145 4,800 Yemen, Rep 0 17 0 11 19 8 31 1 77 74 4,400 5,480 70 58 Yugoslavia, Fed Rep 0 39 1 8 230 414,889 39 29 Zaire 0.28 019 01 0 1 10 5 1,900 2,430 72 68 Zambia 0 89 0 57 0 4 0 9 154 112 4,640 6,000 76 75 Zimbabwe 0 37 0 27 3 1 4 1 676 593 16,717 19,500 74 69 Low income 0 21 w 0 16 w 24 8 w 27 8 w 522 w 1,022 w 1,553,073 t 2,613,438 t 73w 69 w Excluding China & India 0 30 w 0 22 w 15 8 w 17 1 w 161 w 296 w 422,311 t 646,154 t 72w 67 w Middle income 0 44 w 037w 10 5 w 13 0 w 507 w 5,511,610 t 7,957,446 t 38 w 32 w Lower middle income 0 45 w 0 38 w 10 9 w 13 6 w 424 w 4,206,993 t 6,106,698 t 41 w 36 w Upper middle income 0 43 w 0 35 w 9 2 w 110 w 675 w 746 w 1,304,617 t 1,850,748 t 31 w 21 w Low & middle income 029w 0 23 w 17 0 w 19 7 w 591 w 740 w 7,064,683 t 10,570,884 t 63 w 58 w East Asia & Pacific 0 14 w 0 11 w 32 2 w 34 8 w 1,027 w 1,976 w 888,118 t 1,049,457 t 73w 70 w Europe & Central Asia 0 75 w 0 63 w 8 3w 98w 319 w 3,984,991 t 5,628,963 t 27 w 23 w Latin America & Carib 0 38 w 0 30 w 98w 12 3 w 541 w 647 w 1,101,134 t 1,517,156 t 34 w 25 w Middle East& N Africa 0 31 w 0 24 w 23 2 w 28 6 w 400 w 565 w 301,615 t 532,462 t 48 w 36 w South Asia 0 23 w 0 18 w 27 8 w 34 1 w 346 w 803 w 511,989 t 1,584,670 t 70 w 64 w Sub-Saharan Africa 0 40 w 0 28 w 3 7 w 4 1w 145 w 135 w 276,836 t 258,176 t 72 w 68 w High income 049w 0 43w 95w 10 5w 1,285w 1,169w 14,180,196 t 15,374,253 t 9w 6w a Includes Luxembourg b Includes Taiwan, China c Includes Eritrea d Data prior to 1990 refer to the Federal Republic of Germany before unification 148 World Development Indicators 1997 4.5@ - I-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The Food and Agriculture Organization (FAO) collects * Arable land includes both land defined bythe FAO as data on agricultural inputs through annual question- arable and land underpermanentcrops The FAO defines naires sent to participating governments The FAO arable land as land that is under temporary crops attempts to impose standard definitions and report- (double-cropped areas are counted once), temporary ing methods, but exact consistency across coun- meadows for mowingorfor pasture, land under market tries and over time is not possible or kitchen gardens, and land temporarily fallow (less Comparative measures of the inputs to agricul- than five years) Land abandoned as a result of shifting ture help in assessing differences in productivity and cultivation is not included * Irrigated land is the area degree of modernization in the sector However, levels purposely provided with water, including land irrigated of inputs and rates of application vary from country by controlled flooding * Fertilizer consumption mea- to country and over time depending on the type of sures the quantity of plant nutrients used per unit of crops, the climate and soils, and the production arable land Fertilizerproductscovernitrogenous. potash, process used and phosphate fertilizers (including ground rock phos- The FAO's definition of arable land includes land that phate) The time reference for fertilizer consumption is is not under active cultivation The calculation of arabIe the crop year (July through June) * Farm machinery land per capita is based on World Bank population esti- gives the number of wheel and crawler tractors (exclud- mates (see table 2 1) Available data on irrigated land ing garden tractors) in use in agriculture at the end of do not distinguish the frequency, quantity, or method the calendar year specified or during the first quarter of of irrigation Fertilizer consumption measures the thefollowingyear * Shareoflaborforceinagriculture quantity of plant nutrients available for direct appli- is the proportion of the total labor force recorded as cation and is calculated as production minus exports working in International Standard Industrial Classification, plus imports Traditional nutrients-animal and plant rev 2, major division 1 (agriculture, hunting, forestry, manures-are not included It should be noted that and fishing) FAO measures of world exports and imports do not balance because of differences in reporting dates, l_ time reference periods, and treatment of intermedi- ate products _ FAOE~ - Data on arable land, irriga- Data on the laborforce in agriculture should be used :- - - tion, and farm machinery with caution In many countries much of the agricul- are published in the FAO's tural employment is informal and unrecorded - - Production Yearbook1994 Data on fertilizer consump- _ *et££te tion are published in the - b ' ag4. FAO's Fertilizer Yearbook The FAO makes data avail- C. _ sJllill_ able to the World Bank in electronic files that may contain more recent informa- tion than the published sources Data on the share of the laborforce in agriculture comefrom the International Labour Organisat on's Yearbook of Labour Statistics. World Development Indicators 1997 149 O 4.6 Structure of manufacturing Value added in Food, beverages, Textiles Machinery Chemicals Other manufacturing and tobacco and clothing and transport manufacturing' equipment $ millions % of total % of total % of total % of total % of total 1980 1994 1980 1994 1980 1994 1980 1994 1982 1994 1980 1994 Albania Algeria 3,257 3,442 27 13 18 14 10 15 3 5 43 54 Angola 234 Argentina 22,685 56,500 19 . 13 19 9 41 Armenia 487 Australia 30,722 48,585 17 18 7 6 22 20 7 8 46 48 Austria 21,384 46,629 16 17 10 6 25 28 7 8 42 42 Azerbaijan 2,559 Bang adesh 1,422 2,536 24 43 4 16 14 Belarus Belgium 28,515 17 17 8 8 24 24 11 14 40 37 Benin 112 114 59 14 6 21 Bolivia 449 28 32 11 5 4 1 3 3 54 60 Bosnia and Herzegovina Botswana 36 160 Brazil 71,098 108,886 14 11 25 11 - 40 Bulgaria 20 10 13 5 52 Burkina Faso 261 357 59 19 3 1 17 Burundi 63 115 78 11 0 3 8 Cambodia 126 Cameroon 593 762 56 26 9 12 4 1 3 8 29 54 Canada 47,077 81,478 14 15 7 5 23 29 8 9 48 41 Central African Republic 54 49 22 8 11 10 Chad 182 Chile 5,911 27 30 9 6 6 6 8 11 51 47 China 81,836 203,589 10 13 18 13 22 24 11 10 38 40 Colombia 7,772 9,893 30 29 16 11 9 10 10 15 35 34 Congo 128 112 35 16 5 . 44 Costa Rica 899 1,547 46 46 10 8 8 9 7 10 28 27 C6ted'lvoire 1,304 1,419 35 35 15 11 10 7 40 47 Croatia 2,491 24 12 21 11 33 Cuba 55 7 1 37 Czech Republic Denmark 11,411 24,472 24 23 5 4 25 23 10 12 37 38 Dominican Republic 1,015 1,582 66 6 1 6 21 Ecuador 2,072 3,611 34 24 13 7 7 5 9 7 38 57 Egypt, Arab Rep 2,678 5,782 19 21 30 13 11 13 9 13 31 40 El Salvador 589 37 25 22 16 4 4 11 20 27 34 Eritrea 38 Estonia 604 Ethiopia 275b 145 Finland 13,019 21,526 12 13 8 3 21 27 6 8 53 50 France 160,811 254,933 13 15 8 6 30 29 8 9 41 41 Gabon 195 435 24 4 9 4 58 Gambia, The 15 23 35 2 3 60 Georgia 459 Germany Ghana 347 426 37 36 11 5 2 2 5 10 46 47 Greece 6,968 9,891 18 27 23 16 14 12 8 10 37 35 Guatemala 39 10 5 17 28 Guinea 157 Guinea-Bissau 18 Haiti 152 Honduras 344 501 51 49 9 12 2 2 5 5 34 33 Hong Kong 6,392 11,456 5 10 42 35 18 22 2 2 34 31 150 World Development Indicators 1997 4.6 Value added in Food, beverages, Textiles Machinery Chemicals Other mnanufacturing and tobacco and clothing and transport manufacturlngr equipment $ millions % Of total % of total % of total % of total % of total 1980 1994 1980 1994 1980 1994 1980 1994 1980 1994 1980 1994 Hungary 8,066 11 21 11 8 28 19 11 10 38 42 India 27,422 47,288 9 11 21 13 25 25 14 19 30 32 Indonesia 10,133 41,186 32 26 14 15 13 12 11 10 30 36 Iran, Islamic Rep 8,567 15,363 15 16 19 12 12 19 5 10 49 41 Iraq 13 9 8 I1 60 Ireland 29 27 8 3 17 30 14 21 32 20 Israel 12 12 26 8 42 Italy 125,881 210,392 9 10 12 13 29 33 11 6 39 38 Jamaica 446 733 Japan 309,747 1,146,205 9 10 7 4 33 38 9 10 43 38 Jordan 651 23 27 7 6 1 4 7 16 62 48 Kazakstan 2,455 Kenya 796 645 34 42 12 9 15 10 9 9 30 30 Korea, Dam. Rap. Korea, Rep 18,260 102,049 17 10 19 12 17 34 10 9 36 36 Kuwait 1,581 2,616 7 8 5 7 4 7 7 2 76 76 Kyrgyz Republic Lao PDR 179 Latvia .. 1,030.. . .. . Lebanon 850 Lesotho 21 122 73 7 .4 16 Libya 682 31 10 .. 16 43 Lithuania 1,527 Macedonia, FYR 29 23 . 15 4 29 Madagascar 335 34 45 .3 .6 13 Malawi 129 215 58 12 4 .5 .20 Malaysia 5,054 22,387 24 9 7 6 20 36 5 10 43 39 Mali 70 119 29 51 8 .11 Mauritania .. 106 - .. -.. . Mauritius 147 694 36 32 30 45 6 2 6 4 23 16 Mexico 43,089 74,233 24 -. 4 26 . 16 . 30 Moldova 913. Mongolia .37 . 48 I 1 13 Morocco 3,167 5,343 . Mozambique. .. Myanmar Namibia 90 234. Nepal 78 362 Netherlands 30,866 58,531 . 24 3 24 . 14 36 New Zealand 4,950 7,345 26 27 11 8 17 14 6 6 40 45 Nicaragua 549 304 53 -. 8 1 10 .28 Niger 94 30 25 .2 16 .28 Nigeria 7,229 3,607 21 . 13 13 . 13 39 Norway 9,239 15 23 4 2 27 26 7 8 48 40 Oman 39 495 Pakistan 3,389 8,214 32 . 22 ..9 12 .25 Panama .49 47 10 7 2 2 6 8 34 35 Papua New Guinea 242 421 40 1 16 .3 41 Paraguay 733 1,230 38 12 I 3 46 Peru 4,176 11,603 25 13 13 . 10 40 Philippines 8,354 14,917 30 31 13 11 12 14 14 16 31 28 Poland .12 31 17 8 32 19 8 7 31 35 Portugal 13 22 .16 7 42 Puerto Rico 5,306 14,132 17 5 . 13 52 14 Romania .22 . 13 10 . 8 . 47 Russian Federation . 97,357 . 20 9 . 15 . 10 .. 46 World Development Indicators 1997 151 @44.6 Value added In Food, beverages, Textiles Machinery Chemicals Other manufacturing and tobacco and clothing and transport manufacturing' equipment $ millions % of total % of total % of total % of total % of total 1980 1994 1980 1994 1980 1994 1980 1994 [980 1994 1980 1994 Rwanda 172 18 Saudi Arabia 7,740 .. . Senegal 456 479 50 58 19 2 4 3 8 14 20 23 Sierra Leone 54 48 51 69 5 1 . . . 44 30 Singapore 3,415 18,119 5 4 5 2 44 58 5 9 41 28 Slovak Republic Slovenia 152 18 18 19 11 33 South Africa 16,607 25,298 12 15 9 8 21 19 9 10 48 48 Spain 120,931 16 19 12 7 23 25 9 11 41 38 Sri Lanka 668 1,629 32 48 14 23 6 2 6 8 42 19 Sudan 424 521 Sweden 26,293 38,821 10 10 3 2 33 37 7 11 47 41 Switzerland 10 3 32 55 Syrian Arab Republic 25 31 44 Tajikistan .. .. .. .. . . Tanzania 555 242 23 33 8 6 30 Thailand 6,960 40,791 55 . 8 9 7 . 21 Togo 89 86 47 13 . . 8 32 Trinidad and Tobago 557 444 22 35 4 2 9 4 4 20 61 38 Tunisia 1,030 2,863 18 19 19 22 7 6 15 5 42 46 Turkey 9,333 24,076 18 17 15 13 14 20 lt) 9 42 41 Turkmenistan Uganda 53 242 Ukraine 34,232 Unfted Arab Emirates 1,130 2,967 12 2 . 2 7 77 United Kingdom 125,830 185,594 13 14 6 5 33 31 10 13 38 37 United States 593,000 1,126,200 11 13 6 5 34 32 10 12 40 39 Uruguay 2,627 2,998 28 17 . 10 7 38 Uzbekistan . 3,196 Venezuela 11,104 9,946 19 22 7 2 9 10 8 11 57 54 Vietnam 2,760 West Bank and Gaza Yemen, Rep. 606 Yugoslavia, Fed Rep. Zaire 2,064 Zambia 718 1,026 44 13 9 . 9 . 25 Zimbabwe 1,248 1,477 23 31 17 15 8 7 9 5 42 42 Low income 151,808 t 294,045 t Exci China & India 38,690t Middle income 687,018t Lower middle income Upper middle income 197,307 t 345,007 t Low & middle income . 963,642 t East Asia & Pacific 124,518t 341,881 t , .. .. .. Europe & Central Asia . . . .. Latin America & Carib 186,165 t 312,017 t . . .. Middle East& N Africa 32,557t 52,699t South Asia 33,695 t 61,355 t .. .. Sub-Saharan Africa 36,114 t 40,925t High income 1,890,070 t 4,080,236t a Includes unallocated data b Includes Eritrea 152 World Development Indicators 1997 4.6 Figure 4.6a Five largest developing manufacturing economies, 1994 Data on the distribution of manufacturing value added * Value added in manufacturing is the sum of gross share of value added in manufacturing by industry are provided bythe United Nations Industrial output, less the value of intermediate goods consumed for developing economies DevelopmentOrganization(UNIDO) Theclassification in production, for industries classified in ISIC major China 21% of manufacturing industries is in accordance with the division 3 0 Food, beverages, and tobacco comprise United Nations International Standard Industrial ISIC division 31 0 Textiles and clothing comprise Classification (ISIC). rev 2 Manufacturing comprises division 32 * Machinery and transport equipment Others 44% Brazil 11% all of SIC major division 3 comprise groups 382-84 0 Chemicals comprise K Russian UNIDO obtains data on manufacturing value added groups 351 and 352 0 Other manufacturing includes Federation 10% from a variety of national and international sources, wood and related products (division 33), paper and Argentina 6% Mexico 8% includingthe Statistical Division of the United Nations paper-related products (division 34), petroleum and Source: Table 4 6 Secretariat, the World Bank. the Organization for related products (groups 353-56), basic metals and Economic Cooperation and Development, and the mineral products (divisions 36 and 37), fabricated metal International Monetary Fund To improve comparabil- products and professional goods (groups 381 and 385). Figure 4.6b Shares of manufactured ity of the data over time and across countries, UNI DO and other industries (group 390) When data for tex- goods produced, by income group, 1994 supplements originally reported data with information tiles, machinery, or chemicals are shown as not avail- from industrial censuses, statistics supplied by able, they are included in other manufacturing Low income national and international organizations, unpublished 6% die Income data that it collects in the field, and estimates by the p 14% UNIDO Secretariat Nevertheless, coverage may be lessthancomplete,particularlyfortheinformalsector Data on value added in manufacturing in U S dol- _ _o m ~ To the extent that direct information on inputs and larsarefromtheWorld Bank'snational accountsfiles outputs is not available, estimates may be employed The data used to calculate share of value added by High mcome that may result in errors in industry totals And there industry are provided to the World Bank in electronic 80% remain differences among countries in the refer- files by UNIDO The most recent published source is Source Table 4 6 ence period (calendar or fiscal year) and the valua- UNIDO's Intemational Yearbook of Industna! Statistics tion method (basic. producers', or purchaser prices) 1996 used in estimating value added See also the notes to table 4 2 Data on manufacturing value added in U S dollars are from the World Bank's national accounts files These figures may differ from those used by UNIDO to calculate the shares of value added by tndustry Thus estimates of value added in a particular indus- try group calculated by applying the shares to total value added will not match those found in UNIDO sources World Development Indicators 1997 153 4.7 Growth of merchandise trade Export Import Export Import Net barter Income volume volume value value te-rms of trade terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1987 = 100 1987 = 100 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1680 1995 1980 1995 Albania -1 7 0 5 1.0 13 3 Algeria 2 5 -0 8 -51 -5 7 -4 0 -9 7 -3 0 0.0 171 83 171 105 Angola 11.3 4.2 -3.4 -4.1 6.4 -3.6 -1.2 2.2 149 86 89 115 Argentina 3 1 -1.0 -8 6 45.8 2.1 10 5 -6 5 36 8 142 120 131 177 Armenia Austral(a 5 8 81 4 9 5.1 6 6 5 2 6.4 7 9 123 101 84 161 Austria 6 4 3 9 5 8 1.9 10 2 16 8 7 2 0 93 93 68 149 Azerbaijan . . . Bangladesh 7 5 12 7 1 8 5 3 7 5 14 2 3 7 12 0 121 94 73 184 Belarus Belgiuma 4 4 4 2 4 0 0.3 7 8 2 5 96 101 76 139 Benin 7 7 -03 -6 3 294 9 9 314 -4 9 24.3 10C8 110 55 137 Bolivia 17 -5 4 -2 8 18.9 -19 4 3 -0 3 13 4 173 67 172 108 Bosnia and Herzegovina Botswana 114 -0.8 7 7 -5 6 17 9 2 6 9 4 -19 919 152 32 96 Brazil 61 66 -15 85 51 90 -19 183 111 101 68 134 Bulgaria .. 4 4 -12 6 4 2 -10 4 Burkina Faso 5 4 13 21 83 7 2 -2 3 3.8 -2.3 102 103 69 84 Burundi 7 4 -48 1 4 -14 6 2 6 6 4 2 3 -0 9 133 52 75 62 Cambodia 501 41.0 Cameroon 4 5 -1.7 -14 -112 2.5 1 8 0 6 -5 2 145 79 176 202 Canada 5 7 8 4 6 2 6.3 6 8 8 7 7 9 6 8 113 100 77 167 Central African Republic 2 5 3 5 6 0 -3.3 3 3 8 6 7 6 4 9 116 91 92 86 Chad 5 4 -10 0 10 5 -12.1 9 4 -7 3 12 6 -8 4 87 103 66 104 Chile 5 7 10 5 14 14 5 8 0 12 0 2 7 14 9 120 94 88 153 Chinat 114 14 3 10 0 24 8 12 9 19 1 13 5 20 7 115 105 52 199 Colombia 9 7 4.8 -1 9 22 3 7.8 6 8 0 0 24 3 131 80 85 146 Congo 5 5 9 7 -2 0 2.5 0 4 -0.2 -0 5 3 2 142 93 92 96 Costa Rica 4 9 101 2 8 15.1 4 6 12 2 4 5 12 3 123 92 86 154 C6te d'lvoire 3 3 -7 5 -4 0 5.4 17 3 5 -1 4 3 0 128 81 101 70 Croatia 3 8 .. 12 2 Cuba -1 1 -36 9 -0 4 -34 0 -0 9 -33.2 1 7 -29.0 136 109 102 20 Czech Republic Denmark 4 4 5 4 3 6 3.4 8.5 5 9 6 3 4 5 9L 100 68 151 Dominican Republic -1 0 -10 2 2 6 8 9 -21 0 0 3 3 7 9 143 123 126 64 Ecuador 3 0 8 9 -3 9 10.0 -0 4 9 4 -1 4 16 6 147 71 137 129 Egypt, Arab Rep -0 2 -01 -0 7 -2 9 -3 7 2 7 1 4 5 8 14'2 95 147 94 El Salvador -2 8 13.0 13 16 2 -4 6 12 1 2 4 18 7 158 89 155 108 Eritrea Estonia 57.9 80 2 Ethiopiab 12 -9 4 3 3 -33 -0 8 119 4.3 4 3 145 74 116 49 Finland 2 3 8 7 4 4 -1.9 7 4 81 6 9 0 6 86 95 72 132 France 41 2.3 5 0 0 8 7 6 4 5 6 5 18 9(1 106 74 155 Gabon 0 6 5 7 -2 0 2 0 -3 3 2 0 1 1 -0.9 152 90 172 151 Gambia, The 2 3 26.9 10 9 0 12 -14.8 2 8 -51 123 111 75 113 Georgia Germany' 4 6 2 2 4 9 2.9 9.2 3 8 7.1 3 7 86 96 65 138 Ghana 3 9 91 1 6 12.8 0 3 7 3 2 8 8.6 156 64 126 95 Greece 51 11.9 5 8 128 5 8 2 7 6.6 1.8 98 111 65 155 Guatemala -1 3 8.2 -0 6 19 3 -2 2 11 6 0 6 13 7 142 93 151 120 Guinea -36 -86 -29 -28 40 -4 1 99 -11 15C 91 170 88 Guinea-Bissau -5 1 -183 1.3 -5.4 3.8 9 5 3 6 -1.1 37 92 93 83 Haiti -2 9 -11.2 -4 4 -6.8 -13 -10 6 -2 9 6.6 92 52 99 28 Honduras 1.3 10 7 -1 0 70 1 6 4 2 0.6 5 0 122 77 105 89 Hong Kong 15 4 15.3 11 0 15 8 16 8 15 9 15 0 18 1 116 87 47 211 tData for Taiwan, China 116 5 9 12 8 141 14 8 9 5 12 3 12 7 78 112 32 144 154 World Development Indicators 1997 Export Import Export Import Ntbarter Income volume volume value value terms of trade terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1987 =100 1987 - 100 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980 1995 1980 1995 Hungary 3.0 -1.8 0.7 7.9 1.7 3.6 0.1 10.9 112 97 87 96 India 6.3 7.0 4.5 2.7 7.2 11.5 4.2 8.0 96 150 68 314 Indonesia 5.3 21.3 1.2 9.1 -0.3 11.7 2.6 11.5 146 79 126 187 Iran, Islamic Rep. 7.4 10.2 -4.0 15.7 2.5 -0.6 0.1 -11.0 191 90 130 130 Iraq 0.2 -55.2 -13.2 -28.9 -5.9 -4 5.0 -11.1 -35.1 163 93 301 Ireland 9.3 11.4 4.7 5.6 12.7 12.7 7.0 8.4 93 90 52 211 Israel 5.9 10.0 4.6 12.3 8.3 1 1.0 5.9 11.5 95 109 59 178 Italy 4.3 6.0 5.3 -1.7 8.7 5.4 6.9 0.3 85 107 65 158 Jamaica 1.2 1.3 3.1 7.0 0.7 4.2 2.9 9.9 122 105 120 136 Japan 5.0 0.4 6.5 4.0 9.0 8.7 5.1 6.7 65 127 46 158 Jordan 7.4 7.1 -3.1 13.0 6.1 9.8 -1.9 8.1 133 128 59 159 Kazakstan . .. .. .. .. Kenya 2.6 16.6 1.1 -5.6 -1.1 1.2.5 1.5 6.5 136 98 122 161 Korea, Dem. Rep. . .. .. .. .. Korea. Rep. 13.7 7.4 11.2 7.7 15.0 1 2.8 11.9 12.1 94 102 34 216 Kuwait -2.0 42.3 -6.3 23.0 -7.6 35.3 -4.1 13.3 156 88 246 105 Kyrgyz Republic . .. .. .. .. Lao PDR . ... .. 10.3 38.5 20.3 29.3 . Lebanon -1.2 -7.8 -7.4 23.5 -3.6 1.3.7 -5.4 19.4 125 95 147 93 Lesotho . ... .. 3.4 2 7.5 1.8 4.9 . Li bya 0.2 -11.0 -6.4 7.7 -7.2 -'I6.5 -4.2 -0. 1 189 93 257 74 Lithuania .. . . . .. . 65.0 . Macedonia, FYR .. . . . . 4.0 .. 4.2 Madagascar -0.1 -6.8 -4.6 -5.6 -1.1 4.8 -2.8 -2.2 121 82 124 68 Malawi 0.1 -1.8 1.3 -1.6 2.0 --8.5 3.2 -5.7 96 87 109 100 Malaysia 11.5 17.8 6.0 15.7 8.6 20.0 7.7 20.3 139 92 72 229 Mali 2.6 -3.7 1.2 -3.4 6.0 -0.9 2.9 -0.9 91 103 113 167 Mauritania 7.8 3.5 1.1 4.4 8.2 --3.7 2.8 3.9 114 106 44 82 Mauritius 8.6 2.0 11.0 2.5 14.4 4.8 12.6 4.7 82 103 47 132 Mexico 12.2 14.7 5.7 18.7 8.2 13.7 8.6 12.8 146 92 58 160 Mongolia .. . . . 0.4 -12.5 -3.8 -24.7 . Morocco 4.2 0.8 2.9 1.7 6.1 1.1 3.6 3.5 96 90 81 105 Mozambique -10.5 -0.3 -1.0 2.9 -9.6 3.4 0.1 -0.2 121 124 284 120 Myanmar -7.0 27.2 -7.0 38.7 -7.9 21.1 -4.5 29.9 155 107 231 228 Namibia .. . . . . 3.8 .. 0.9 Nepal 7.8 22.1 4.9 6.8 8.2 10.6 7.0 15.0 107 85 55 214 Netherlands 4.5 5.8 4.6 4.3 6.5 7.1 6.3 5.6 97 103 77 171 New Zealand 3.6 5.4 4.6 5.5 6.2 7.8 5.4 9.0 96 108 73 154 Nicaragua -4.4 -8.7 -4.1 7.3 -5.8 9.6 -3.1 6.9 151 95 157 85 Niger -6.4 -2.0 -4.5 2.5 -5.4 -8.1 -3.5 -8.0 113 101 174 64 Nigeria -2.4 -1.9 -17.5 7.6 -8.4 -5.0 -15.6 2.9 178 86 363 115 Norway 6.6 6.5 4.2 0.7 5.3 2.8 6.2 3.0 123 95 83 169 Oman 13.1 9.8 -1.6 18.5 2.9 2.4 0.7 9.0 210 77 84 151 Pakistan 9.5 8.8 2.1 10.3 8.1 6.1 3.0 7.0 122 114 60 146 Panama 2.6 23.3 -4.1 14.3 -0.4 14.1 -3.6 10.7 129 86 94 137 Papua New Guinea 4.5 19.3 -0.2 2.1 4.8 20.6 1.3 2.0 120 90 87 181 Paraguay 9.9 -1.9 3.2 7.3 11.6 -3.3 4.2 13.6 113 101 84 179 Peru -1.9 11.0 -1.0 12.1 -1.5 11.1 1.3 19.7 131 83 150 112 Philippine-s 2.9 10.2 2.4 15.2 3.9 16.2 2.9 18.0 115 114 94 1 74 Poland 4.8 3.9 1.5 26.4 1.4 9.2 -3.1 23.2 95 109 81 156 Portugal 12.2 0.5 9.8 2.4 15.1 5.0 10.3 3.3 99 92 44 152 Puerto Rico.. ... ......... . Romania -6.8 -4.7 -0.9 -5.3 -3.8 7.3 -3.8 2.4 64 111 99 43 Russian Federation.. ... ......... . WVorld Development Indicators 1997 155 Export Import Export Impcort Net barter Income volume volume value value terms of trade terms of trade average anrual average annual average ann.ual average rannua % groMh % growtn %/ grawth Sc growi- 1987 - 100 1987 - 100 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980-90 1990-95 1980 1990 7980 1995 Rwanda 5.6 -19.6 1.3 -1.9 2.9 -19.9 2.7 0.6 192 75 63 42 Saudi Arabia -8.2 4.0 -8.4 5.9 -13.4 -0.8 -6.1 -0.5 155 92 489 156 Senega 2.6 3.6 1.0 6.1 3.4 -18.0 1.6 --13.1 105 107 72 67 Sierra Leone -2.1 -4.3 -9.9 -1.1 -2.4 -17.8 -8.7 -2.1 113 89 164 78 Songapore 12.2 16.2 8.6 12.1 9.9 17.6 8.0 15.5 1-09 89 60 308 Slovenia .. . . . . 16. 7 . 20.1- South Africa 0.9 2.8 -0.8 5.3 0.8 3.2 -1.3 9.6 109 i11 116 i11 Spain 6.9 11.2 10.1 5.3 10.9 9.0 10.6 3.2 92 114 56 249 Sri Lenka 6.3 1 7.0 2.0 15.0 5.5 14.6 2.7 14.6 107 88 72 215 Sudan -1.3 - 7.4 -6.7 5.1 -0.8 -2.9 -5.3 5.3 132 105 103 65 Sweden 4.6 7.4 4.9 5.0 8.0 5.4 6.7 2.3 91 102 65 146 Switzerland 6.0 3.3 4.9 -6.7 9.5 3.4 8.8 0.9 79 60 66 110 Syrian Areab Repub ic 6.4 -3.2 -9.3 22.3 2.4 -1.1 -8.5 17.2 138 78 150 145 Tajikistan . Tanzania -1.8 10.0 -3.3 12.7 -4.1 10.5 -2.0 6.5 142 83 173 -13 7 Tlailand .14.3 2-1.6 12.1 12.7 14.0 18.6 12.7 15.3 125 100 53 352 Togo 4.9 9.0 1.1 -11.2 1.2 -9.0 2.0 -13.1 159 90 130 49 Trinidad and Tobago -4.3 4.9 -12.1 8.1 -9.9 4.2 -12.0 2.8 146 86 250 99 Tunisia 6.2 7.7 1.3 6.4 3.5 8.4 2.7 7.3 131 97 101 155 Turkey 12.0 8.8 11.3 11.2 14.0 10.3 9.3 8.7 88 109 22 132 Turkmenistan . .. .. .. .. Liganda -1.4 3.9 -0.6 28.7 -4.3 26.4 1.4 43.5 157 58 116 47 Lnined Arab Emiranea 6.1 6.3 -1.3 21.0 -0.8 1.4 0.7 1 7.5 179 93 149 149 Lnited Kingdom 4.4 1.8 6.3 0.9 5.8 4.7 8.4 2.9 105 102 83 143 cniled Statea 3.8 5.6 7.2 7.4 5.7 7.7 8.2 8.9 89 102 91 194 cruguay 2.9 -3.1 -2.0 21.7 4.4 4.8 -1.3 17.1 108 112 85 122 Lzbekistan . Venezuela 1.6 -0. 1 -6.1 19.3 -4.4 1.4 -3.2 5.1 168 82 189 118 Vietnam 18.8 16.9 8.8 24.2 Weat Bank and Gaza . .. .. .. Yemen, Rep. 1.5 7.2 -5.9 11.1 .. 9.0 .. 2.3 141 84 Yurgoslavia, Fed. Rep. .. . . . . -2.0 .. 2.7 Zaire 1.8 -26.6 1.5 -26.8 3.3 -16.5 3.6 -15.7 118 85 166 32 Zambia -3.5 26.9 -5.0 -6.2 1.4 -11.8 -3.5 0.5 118 85 147 85 Zimbabwe 2.2 -6.6 -2.2 -5.1 2.5 2.0 -0.4 2.7 120 84 105 92 Low income 5.3 w 8.3 w 1.6 m 13.0 w 5.1 w 12.7 w 3.9 w 13.4 w 121 m 91 m 105 m 89wm Exc . Cnina & India 1.4 w 2. 7wv. -4.2 w 5.0 w -0.7 w 3.0 w -2.1 w 6.6 w 122wm 91wm 109wm 58w Middle income 2.6 w 6.9 w -0.2 w 11.0 w -0.3 w 7.6w'A 0.8 w 10.8 wV 131 w 94wm Lower middle income . .. .. .. .. Upper midd e income 1.7 w 7.3 w -0.6 w 12.6 w -1.5w 6.5w 0.6 w 12.9 wN 134 w 95w 87 m 132wm Low & middle income 3.0 w 7.2 w 0.2 w 11.4 w 0.7 w 8.8 w 1.5 w 11.4-w 123 m 93wm 103 m 114wm East Asia &Pacific 9.3 w 1 7.8 w 7.1 w 1 7.0 w 8.0 w 17.8 w 9.1w', 17.9~ v, Europe & Central Avia . .. .. .. .. Latin America & Carib. 5.2 w 6.6 w -0.5 w 15.1 w 3.0w' 9.1 w 1.1 w 14.3 w 134 m 94wm 123 0m 120 m M~ddle East & N. Africa -2.0 w 1.1 w -5.8wN 5.9 w -7.6w' -4.0w -3.7 w 0.6 w 142 m 92w 147 w 11907 South Asia 6.6 w 8.6w 3.5w' 5.3 w 6.8 w 10.7 w 3. w' 8.8 w 107w n_94wm 70wm 185wm Sub-Saharan Africa 0.9 w 0.9w -3.6 w 1.9 w -0.7 w' 0.9 w -2.8 w 5.0 w 120 m __91 w -105 m 88wm High income 5.2 w 5.4 w 3.2 w 4.6wv 7.8 w 6.9 w 7.5 w 6.2 w 95wm 97w 72wm 138wm a. Irc u.des Luxemabourg. a. Data aria' to 1992 ire .rde Eratrea. c. Data prier to 1990 refer to toe Ca0era Repub9ic c' Germanv before Ur fication. Aoi .0 rOo':v oomen. nc zEr27s 19O` 4.7 *=_ strucLed as the rat o o the export price index to the , import pr cc ndcx. When the nct bar tor termrs of trade Statistics on nternatona merchandise r at-e are increase, a country's expo-ts are becoming .more o GrowAthratesofexportandimpaprtvouirmaesareca- baseo on transactions recordec by custorns se xices. valuab eo its mports cheaper. cuatea from 1987 constant U.S. dollar orice ser es. By international agreementthese data a's reao teo The ncome terms of trade p'ovde another mea- Tne World Bank uses the prce indexes produced by to the United Nations Statist,ical Off ce, which- main- sure of the relatve purchasing power ofexports. When UNCTAD for loa- and me dd e-income econom es and tains a commodity trace database known as COM- tee p'ices of a country's expor :s r se relative to the those in the MF's International Financral Staristics TRADE. The Uniteo Nations Conference on Trade and oricesofts imoorts. its res dants can purchase more for high-income econor es. o Average annuai growAth Development(UNCTADicompiles a vane;yof mtetmr- goods at a gven evas o' dormest c proouctioa. The rates otexport and importvaluesarecalculatedfrom tional trade statistics, including price ant tolume b.N. Systenm of Nationa Accour is specifies that real current U.S. dol ar pnce series. o W'et barteY teryms indexes, based on the COMTRADE daze Tne Wor d GDP snoilc be adjusieo to ref ect tnis trading gain of trade are the ratio of tne 1987 (base year) export Bank supplements data from UNCTAD wmite d,-ta 'rom or loss caused by changes n the tenrms of trade. (GDP pr ce ndex to the corresponding import pince .ndex. the International Monetary Fund (IMF) and, to ensu'e adjusted fo' terms oftrace ef-ecr is known as gross o Income teryms of trade are the ratio of the 1'987 that its information as the most recent axai able, domnest c income.' The Wor d Bank measures t'ad- (base year) export valse ndex to the correspond ng with data taken directiy from the COMNTRADit data- ing geans or losses as trae income terms o' trade, or mport pr ce index. base. capac ty to import. The capac :y to import is calcu- NMerchandisetrade inciudes allgoodsthtaddioot lated bydeflatingtheexPortvlte indexbythe import subtract from an ecoromy's mateicl reacurces. price ir.dex. Because the terms oftrace icdexes are Currency in circulaton. tities of ownersh p. anni secu- usaa IY calculated us ng item- cr category leve aver The main source of cu-rent rities are excluded, but monetary gold is n. utlec. age unit values rather thar actuai. rices (wh cln may -.'.' .' '-. trade va es s aIhe UNCTAD Trade statistics are collected on tne basis of tre cus- be di'ficu t to collect). thee are subiect to sign ficant .' tr ; ade database. suppe- toms area ofa country. which in mosrcsses Co ncides var-atin oande ror, deperdin,g tc tne composilt nof mented bc dare from the with its geographic area. Goods unded-fore an aid pro- reade fro. one reoortnng periot to the next. MFs IntereafonatFmnancial grams are included, but goods dest ned for ex.aterr- Statis"cs and the Unitec toria agencies (such as embassies) are not. Nat'ons Commodity Trace There are many difficu t es in collecting and iabu at- ,COCMTRADE) database aed ingtrade statistics. In principle, all transactLons shotud by Word Bank estimaLes. be reported twice. once by the exporting country and UNCTAD publishes its trade data in its annuaJ Handbook once by the importing country. But timely and acsaL rate of international Trade and Development Statastics The reports are often iackng, particularly for devrsioing UnJted Nations pLbilshes trade date in its .nrternavona/ countries. As a result. it s often necessany to est mate Tradc Statrstics Yearbook. thetrade of develop ngcountr es hrom the trade reported by their partners. This approach captures trace w th high-income countres, out may miss trace catween developing countries, particu ary in Afr ca. n some cases national authonties may suppress or re srepre- sent data on certain trace flows (such as militarv equip- ment, oil. orthe exports ef a dominant producer) becaise of economic or polit cal concerns. In other cases rnportec trade data may be distorted by delioerate unde orove- ? : invoicing to eftect capi-ta transters or avoic taxes. Ant 1987 100 in some regions smuggling and black maraet trad ng result in unreported trade flows. For these anc other 130 ' Middle-income reasonstradevalues based oncustoms data do 'erfrom economies those ca culated through the baiance of pavnents 120 accounts. \ * Low-m~~com e The growth rates here are calculated from 1987 M* ecoomies base yearvolume indexes. They may differ froe those 110 O derived from national sources becacse nations pr ce icoexes cayuse base years andcweighLing orocedures economies that differ from those used by UNCTAD or tae IMF. 100 Terms of trade 90 The terms of trade measure the re at ve pr ces of a 1980 1983 1986 1989 1992 country's exports and imports. There are a 'umnber of ways to calculareterms oftrade. The rostcDmmon Source: World Bank staff estimates. is the net barter, or commodity, terms of trEace, con- _. _ Wor d Deve opment Inc cators 1997 57 4.8 Structure of merchandise exports Merchandise Fuels, minerals, Other primary Machinery and Other Textile fibers, exports and metals commodities transport manufactures textiles, and equipment clothing' $ rnj lions %of total 8S of total % of tota % of total % cf total 1980 1999 1980 1993 I 1980 1993 1980 1993 1980 1993 1980 1993 Albania 367 205 . .. . . Algeria 13,900 8,594 99 96 1 1 I. 0 2 .. 0 Angola 1,880 3,508 78 100 9 0 . ..13 ..0 Argentina 8,020 20,967 6 11 71 57 6 11 17 21 7 3 Armenia . . 271 .. . . ... . Australia 21.900 52.692 34 30 46 29 8 8 1s 28 10 9 Austria 17,500 45,200 5 4 12 7 28 38 56 52 11 8 Azerbaijan . . 612.. ... ... . Bangladesh 793 3,173 .. 0 31 48 1 0 68 81 75 78 Belaruis . . 4,621 . .. . . . Be giuml 64,500 136,864 . .. . . . Benin 63 163 5 .. 87 ..1 .7 ..25 Bolvia 942 1.101 86 56 11 25 1 2 2 17 0 3 Bosn a and Herzegovina Botswana 502 2,130 . .. . . . Brazil 20,100 46,506 11 12 s0 28 17 21 22 39 5 4 Bulgaria 10,400 5,100 .. . Burkinra Faso 161 274 .. .... ... Burundi 65 106 5 .. 92 70 .. 3 3 28 3 Cambodia .. 855 . ... ... . Cameroon 1,380 2,331 33 54 64 35 1 8 3 6 4 4 Canada 67,700 192,198 28 17 23 17 26 40 24 26 1 1 Contral African Republic 116 187 0 -. 71 ..0 ..29 ..8 Chad 71 456 1 .. 92 .4 ..4 Cnile 4,710 16,039 65 43 25E 38 2 3 8 16 1 2 Chinat 18,100 148,797 20 6 32 13 3 16 45 65 .. 31 Colombia 3,920 9.764 3 26 77 34 2 6 18 34 9 10 Conigo 911 952 90CI 4 .0 ..7 ..0 Costa Rica 1,000 2.611 1 1 65 66 4 4 30 29 5 5 CSte dIlvoire 3,130 3,939 .. 15 .. 68 .. 2 .. 15 Croatia .. 4,633 .. 11 .. 18 .. 14 .. 57 .. 19 Cuba 5,580 1,100 5 .. 90 .. . 6 Czech Republic .. 21,654. ... ... . Denmark 16,700 49,036 5 4 38 29 24 27 32 40 5 5 Dominican Republic 962 765 3 6 73 41 1 2 23 50 Ecuador 2.480 4,307 63 42 34 50 1 2 2 5 1 2 Egypt, Arab Rep. 3.060 3,435 87 55 22 12 0 1 11 32 24 20 El Sa vador 967 998 5 3 59 49 3 3 33 45 25 16 Estoni-a .. 1,847 ., .. ... . Ethiopia' 425 423 8 1 92 95 0 0 0 4 3 3 Finlano 14,200 39,573 8 6 22 11 18 32 52 51 7 2 France 116,000 286,738 8 5 18 17 33 38 41 40 6 5 Gaboni 2,170 2,713 100 85 .. 12 .. 0 .. 3 Gambia,The 31 16 0 .. 90 63 1 ..9 37 3 Georgia .. 347.. ... .. . Germany' 193,000 523,743 7 4 7 6 44 48 42 42 5 5 Ghana 1,260 1,227 17 25 82 52 .. 0 1 23 0 Greece 5,150 9.384 26 22 28 30 3 8 44 40 18 15 Guatemala 1,520 2,156 6 2 70 68 2 2 23 28 17 6 Guinea 401 582. . . . . .. Guinea-Bissau 11 23 .. .. * Honduras 830 1,061 7 3 8.1 84 .. 0 13 13 4 3 Hong Kong 19,800 173,754 2 2 5 3 20 26 73 70 28 39 tData for Taiwan, China 19,800 111,585 2 2 10 5 25 40 63 53 22 15 4.8@1 Merchandise Fuels, minerals, Other primary Machinery and Other Textile fibers, exports and metals commodities transport manufactures textiles, and equipment clothing' $ millions % of total % of total % of total % of total % of total 1980 1995 1980 1993 1980 1993 1980 1993 1980 1993 1980 1993 Hungary 8,670 12,540 9 8 25 24 32 24 34 44 8 12 India 8,590 30,764 8 7 33 18 8 7 51 68 26 30 Indonesia 21,900 45,417 76 32 22 15 1 5 2 48 1 17 Iran, Islamic Rep. 14.700 18,346 93 93 2 3 0 0 5 4 4 Iraq 26,300 380 99 I.1 . 0 ..0 Ireland 8,400 44,191 3 2 39 23 19 29 39 46 9 4 Israel 5,540 19,046 2 2 16 7 13 31 69 60 10 6 Italy 78,100 231,336 7 3 8 8 33 37 52 52 12 12 Jamaica 963 1.414 23 12 14 22 1 0 62 65 1 9 Japan 130.000 443,116 2 2 2 1 58 68 37 29 5 2 Jordan 574 1,769 41 27 26 22 2 4 32 47 5 5 Kazakstan .. 5.197 . .. .. .. Kenya 1,250 1,878 36 16 52 66 1 2 12 17 3 3 Korea, Dem. Rep... . .. * Korea. Rep. 17,500 125,058 1 3 9 4 20 43 70 51 30 19 Kuwait 19.700 12,977 89 5 1 7 3 48 8 40 1 4 Kyrgyz Republic .. 409 . .. .. .. Lao PDR 31 348 23 .. 70 ..1 ..7 Latvia .. 1,305 . .. .. .. Lebanon 868 982 8 .. 27 .. 17 .48 ..8 Lesotho 58 143 . .. .. .. Libya 21,900 7,540 100 95 .. 1 . 0 .. 4 .. 0 Lithuania .. 2,707 .. 11 .. 25 .. 24 .. 40 .. 18 Macedonia, FYR .. 1,244 . .. .. .. Madagascar 401 364 10 8 84 72 2 2 4 18 4 13 Malawi 295 325 . .. 93 94 0 0 7 6 7 Malaysia 13,000 74,037 35 14 46 21 12 41 8 24 3 6 Mali 205 326 0 .. 91 ..5 .4 ..41 Mauritania 194 404 78 52 20 47 1 0 1 1 1 Mauritius 431 1,537 .. 2 73 32 3 2 25 65 19 54 Mexico 15,600 79,543 73 17 15 9 4 49 8 26 3 4 Moldova .. 746 . .. .. .. Mongolia .. 324 . .. .. .. Morocco 2,490 4,802 45 14 31 29 1 6 23 51 10 25 Mozambique 281 169 11 14 87 66 1 3 2 18 Myanmar 472 646 . .. .. .. Namibia .. 1,353 . .. .. .. Nepal 80 348 0 .. 69 16 -. . 31 84 39 Netherlands 74,000 195,912 26 11 23 25 17 24 34 40 5 4 New Zealand 5,420 13,738 6 7 74 66 4 6 17 22 20 7 Nicaragua 451 520 4 3 83 90 1 0 13 7 9 12 Niger 566 225 86 .. 12 ..1 .2 ..1 Nigeria 26,000 11,670 97 94 2 4 0 0 0 2 Norway 18,600 41,746 59 59 9 10 12 13 20 18 1 1 Oman 2,390 6.065 96 9 1 21 3 54 1 17 0 6 Pakistan 2,620 7.992 8 1 44 14 1 0 47 85 57 78 Panama 358 625 24 3 67 81 0 0 9 16 3 5 Papua New Guinea 1,030 2,644 49 52 48 37 .. 10 3 2 .. 0 Paraguay 310 81 7 .. 0 88 83 .. 1 12 16 42 23 Peru 3,900 5,575 63 50 19 33 2 1 16 16 9 11 Philippines 5,740 17,502 21 7 42 17 2 19 35 58 7 9 Poland 14,200 22,892 20 22 9 18 36 19 35 41 7 7 Portugal 4,640 22,621 7 9 21 13 13 17 59 61 27 25 Puerto Rico . .. .. .. .. .. Romania 11,200 7,548 .. 13 .. 10 . 17 . 60 .. 17 Russian Federation ..81,500 . .. .. .. .. World Development Indicators 1997 159 NMeyhudlme Fsam, mimerals Othie psimery Mecllnewy and Other ThiUll Ibe. ePerto id mwtals commoidile tranport I m Inufatne t.itil.., mid equipment detMngr S millions %oftoftal teof total S of total teof total % of total 1980 1995 I1980 1993 i9a0 1993 1980 1993s i 1980 1993 1980 1993 Rwanda 72 45 9 .. 90 ..0 .0 ..0 Saudi Arabia 109.000 46.624 99 90 0 1 0 2 0 7 Senegal 477 340 39 25 46 54 3 2 12 19 3 4 Sierra Leone 224 42 35 45 25 28 0 ..40 27 0 Singapore 19.400 118.268 31 14 18 6 27 55 24 25 4 4 Slovak Republic .. 8,585 . .. .. Siovenia .. 8.286 a8. 6 .. 27 .. 58 .. 15 South Africa 25.500 27.860 33 16 28 11 4 8 36 66 4 3 Spain 20.700 91.716 9 5 20 17 26 41 45 36 5 4 Sri Lanka 1.070 3.7gB 19 1 65 27 0 2 16 71 13 52 Sudan 543 493 2 .. 9 98 .. I 1 1 41 Sweden 30.900 79.908 9 7 12 8 40 44 39 41 3 2 Switzerland 29.600 77.649 5 2 4 4 32 30 59 64 7 4 Syrian Arab Republic 2.110 3.970 78 71 16 20 1 0 5 9 13 13 Tajikistan .. 749 . .. .. .. Tanzania 511 639 10 .. 76 I.1 . 14 ..24 Thailand 6.510 56.459 14 2 58 26 6 28 22 45 10 15 Togo 338 209 66 52 23 42 2 1 9 5 4 25 Trinidad and Tobago 3.960 2.455 94 58 2 8 1 3 4 32 0 1 Tunisia 2.200 5.475 56 13 8 12 2 10 34 66 18 43 Turkey 2,910 21.600 8 4 65 25 3 8 24 64 28 40 Turkmenistan .. 2.008 . .. .. .. Uganda 345 461L 1 . 97 100 3 1 0 ..2 Ukraine .. 13.647 .. ... ... ... United Arab Emirates 20.700 25.650 96 I.1 . 1 ..2 Unite Kingdom 110.000 242.042 18 10 a 9 35 41 39 41 5 5 United States 226,000 584.743 9 4 23 14 39 49 29 33 4 3 Uruguay 1.060 2.106 1 0 61 57 4 8 34 35 36 28 Uzbekistan .. 3.805 . .. .. .. Venezuela 19.221 18.457 98 84 0 3 0 3 1 1.1 . 1. Vietnam 339 5.026 . .. .. .. West Bank and Gaza . . . .. .. .. Yebmen. Rep. .. 1.937 0 .. 49 .. 25 ..26 ..6 YAigosaviWa. Fed. Rep. .. 2.760 9 11 18 12 28 30 45 47 10 10 Zaire 1.630 438 56 69 14 13 1 1 30 17 0 Zambia 1.300 781 . .. .. .. Zimbabwe 1,415 1.885 23 16 39 48 2 3 36 34 1L 11 Low income 84.204 t 245.456 t .. .... ... Exc. China & India 58.817 t 64.769 t .. .... ... Middle Incomne 586,567 t 893.331 t . .. .. .. Lower middle income . . . .. .. .. -Upper mniddle-i-nc-o m-e246-,3-2,9 t -3-7,2,.898 -t . .. .. .. Low 8. middle income -660.833 t 1.152.249 t . .. .. .. East Asia & Pacific 69.623 t 359.102 t . ..__ .. .. Latin America & Carlb. 98,589 t 221.210 t . .. .. .. Middle East & N. Africa 203.379 t 106.441 t . .. .. .. South Asia 13.848 t 46.455 t . .. .. .. Sub-Saharan Africa 77.237 t 72.847 t . .. .. .. Higp income 1.393.926 t 3.997.288 t . .. .. .. a. Textile ribers nr part of other primary commodities: textiles and clothinig mirs part of other manufactures. b. Includes Luxembourg. c. Data prior to 1992 Include Enitree. d. Data prior to 1990 refer to the Federal Republic of Germany before unification. 160 World Development Indicators 1997 4.8 Figure 4.8a Merchandisi exports from dexeloping economies. 1980-94 Data on trade ii goods come from one of two sources: a Merchanb exports show the f.o.b. value of goods percentage of world exports customs reports of goods entering an economy or provided to the rest of the world valued in U.S. dol- 25 reports of the linancial transactions recorded in the lars. They are classified using the Standard balance of payments. Because of differences in timing International Trade Classification (SITC). series M. SubOSw Affix. 20m/ , and definitions. estimates of trade flows are likely to no. 34. revision 2. a Fuels. minerals. and metals 20 oSouthASadiffer among sources. In addition. several interna- comprise the commodities in SITC section 3 (min- South Asia tional agencies process trade data. making estimates eral fuels, lubricants, and related materials), divisions 15 - _ ! to correct for unreported or misreported data, which 27 and 28 Icrude fertilizers and crude minerals, leads to other differences in the available data. excluding coal, petroleum. precious stones, metal- 10 The most detailed source of data on international liferous ores, and metal scrap), and division 68 (non- trade in goods Is the COMTRADE database maintained ferrous metals). a Other primary commodities : 5 by the United Nations Statistical Office iUNSO). The comprise SITC sections 0, 1. 2, and 4 (food and live International Monetary Fund (IMF) collects data on animals, beverages and tobacco. inedible crude mate- O total exports and imports as part of its balance of rials except fuels, and animal and vegetable oils and 1982 1986 1990 1994 payments statistics. It also publishes customs-based fats), excluding divisions 27 and 28. e Mechinery statistics on international trade in its Direction of and transport aquipment comprise tile commodi- e Trade Statisti s. ties in SITC section 7. a Other manufacture com- … -- ~ - The value ol exports is recorded as the cost of the prise SITC sections 5-9, excluding section 7 and goods delivered to the frontier of the exporting coun- division 68. e Textile fibers, textiles. and clothing, try for shipmenit, the f.o.b. (free on board) value. Many representing SITC divisions 26. 65. and 84 (textiles. countries collect and report trade data in U.S. dol- textile fibers, yarn. fabrics. and clothing and acces- lars. When ccuntries report in local currency, the sories), are a subgroup of other primary commodi- UNSO applies the average official exchange rate for ties and of other manufactures. the period shown. Countries may report trade according to the spe- cial or general system of trade Isee Pnmary data doc- umentation). Under the general system exports - --- The principal sources ofmer- comprise outward-moving goods: Ia) national goods . . .chandise trade data are the wholly or partly produced In the country: eb) foreign baUNSO's COMTRADE data- goodsl neothe transformed nor declared fbr domes- base; the Unmted Nations tic consumpticn in the country, that move outward from intemational Trade Statis- customs storage: and (c) nationalized goods that have - tics Yearbook: UNCTAD's been declared from domestic consumption and move Handbook of International outward without having been transformed. Under the K . i Trade and Development special systeri of trade, exports comprise categories S . _ . tatistics: and the IMF's Ia) and Ic). In some compilations categories (b) and International Financial Statistics and Direction ofTrade Ic) are classified as re-exports. Because of differences Statistics. in reporting practices, data on exports may not be fully comparable aLross economies. The data on total merchandise exports here have been taken principally from series reported In the IMF's Intemational Financial Statistics, supplemented by data published in the United Nations Monthly Bulletin of Statistics, the IMF's Direction of Trade Statistics. UNCTAD's Handbook of International Trade and Development Statistics, and, in some cases, by World Bank staff esti mates. Data on the structure of exports by major commodity groups are based on UNCTAD sta- tistics. Because of delays in reporting and process- ing data, the most recent year for which shares of merchandise trade can be calculated is 1993. Wnrid Dlvalonment Indicators 1997 181 4.9 Structure of merchandise imiports Merchandise Food Fuel Other primary Machinery and Other imports commodities transport manufactures equipment $ m.i ons % of total S of total %/ of total 7 of total % of toalI 1909 1995 1980 1993 1980 1993 1990 1993 1990 19S3 1990 1993 Alb6n a 364 679 . .. . . . A geria 10,6C0 9,570 21 29 3 1 5 5 37 31 35 34 Angoila 1,330 1.745 24 ..I . 2 ..38 . 36 Argentino 10.500 20,122 5 5 10 2 7 4 40 50 37 39 Armorha . . 674 . .- ... . Australia 22,400 61,280 5 5 4 6 5 3 36 43 40 43 Austria 24,LOO 55.300 6 5 16 5 9 6 29 37 40 47 Azerbaijani . . 955.. ... .. . Bonrgladesho 2,600 6,496 24 35 IC -14 9 30 25 14 33 28 Belarus . . 5,149.. ... ... . Belgium, 71,900 125,297 .. ... . . Benin 331 493 26 ..9 . 2 .23 ..40 Sciwa 665 1,424 19 9 1 5 3 4 41 48 37 34 Bosniia snd Herzegovina Botswana 692 1,907 .. ... ... . Brazil 25,000 53,783 10 10 43 16 6 7 20 33 21 33 BL garia 9,6505I, 01 ,) 36 7 .. 22 .. 27 Burkina Faso 359 549 .. ... ... . BLorundi 168 234 . . . . . . Cambodia . 1.213 -. . . .. Cameroon 1,600 1,241 9 16 12 3 2 2 34 27 44 33 Canada 621500 168,426 8 6 12 4 7 4 46 50 2 7 35 Central African RepubJic 91 174 21 ..2 ..3 ..34 41 Chad 74 220 .. ... ... . Chile 5,800 15,914 15 6 18 10 4 3 33 43 30 38 China- 19,900 129.113 ., 3 . 6 .. 7 . 42 .. 43 Colomb a 4,740 13,853 12 9 12 4 6 5 38 39 33 014 Congo 590 670 19 .. 14 2.3 . 23 ..42 Costa Rica 1,540 3,253 9 9 15 9 4 3 241 26 48 55 CSte dolvoire 2,970 2,906 13 .. 16 ..3 ..33 35 35 Croatia .. 7,582 .. 9 . 10 .. 5 . 24 .. 52 Cuba 6,510 1,650 .. . . ., , Czech Republic .. 26,523 .. ... . . . Denm-ork 19.300 43,223 12 .13 22 6 9 5 20 209 38 46 Dominicon Republc 1,640 2,976 17 .. 25 ..4 .22 ..32 Ecuador 2,250 4,193 8 5 1 2 4 4 40 49 37 41 Egypt, Arab Rep. 4,860 11,739 32 24 1 2 8 10 27 31 32 34 El Salvador 966 2,653 16 15 16 14 A 5 13 26 47 41 Eston a .. 2,539.. ... ... . Ethiopia' 717 1.033 8 6 25 11 3 1 29 44 36 38 Fniland 15,600 29,114 7 7 29 13 8 9 27 34 30 39 Franca 135,000 275,275 10 11 27 9 9 5 21 34 33 41 Gabon 674 892 :19 ..I . 2 .37 ..41 Gambia,Tire 165 140 23 .. 11 ..2 ..268 36 Georgia .. 687.. ... .., . Germany' 198.000 464,220 12 10 23 8 10 0 10 33 36 44 Ghana 1,130 1,590 10 .. 27 ..3 .30 ..31 Greece 10,500 21,466 9 6 23 25 8 6 36 36 24 25 Guatema a 1,600 3.293 8 11 24 14 4 3 22 32 42 41 Guinea 270 690 .. ... ... . Gu,inea-B ssau 55 70 20 ..6 .3 ..37 ..35 Haiti 375 553 24 .. 13 ..3 .20 ..40 Hcnduras 1,010 1,219 10 i1 16 13 2 3 30 26 42 47 Hong Kong 22.400 192,774 12 6 6 2 6 -3 _ 22 33 _54 56 tData for Taiwan, China 19,700 103.698 8 5 25 8 15 10 28 40 24 36 4.1 Merchandise Food Fuel Other primary Machinery and Other imports commodities transport manufactures equipment $ millions 0/of tota % of total % of total % of total % of total 1980 1995 1980 1993 1980 1993 I1980 1993 1980 1993 1980 1993 Hungary 9,220 15.073 8 6 16 13 13 5 29 37 33 39 India 14,900 34,522 9 4 45 30 8 110 13 14 25 42 lndonesia 1-0,800 40,918 13 7 16 8 6 9 34 42 32 34 Iran, Islamic Rep. 12,200 12,700 -13 ..0 .5 ..44 ..38 Iraq 13.900 490 13 ..0 .3 ..54 ..31 Ireland 11,200 32,568 12 10 15 5 5 3 27 37 41 45 Israel 9,780 29,579 11 7 27 7 6 4 21 33 36 49 Italy 101,000 204.062 13 13 28 10 13 9 21 29 25 39 Jamaica 1,100 2,757 20 14 38 19 3 3 12 23 27 41 Japan 141,000 335,882 12 18 50 21 19 13 6 17 13 32 Jordan 2,400 3,698 18 20 17 13 3 3 28 27 34 37 Kazakstan . . 5,692 . .. .. .. Kenya 2,120 2,949 8 8 34 33 3 5 28 25 28 29 Koroa, Dem. Rep... . .. . Korea, Rep. 22,300 135.119 10 6 30 18 17 13 22 34 21 29 Kuwait 6,530 7,784 15 13 1 1 3 3 36 42 46 41 Kyrgyz Republic .. 610 . .. .. .. Lao PDR 29 587 . .. .. .. Lebanon 3,650 6,721 16 .. 15 ..6 .25 ..38 Lesotho 464 821 . .. .. .. Libya 6,780 5.380 19 24 1 0 2 3 38 34 40 39 Lithuania ..3,083 .. 11 .. 45 .. 9 .. 15 .. 21 Macedonia. FYR .. 1,420 .. . . .... Madagascar 600 499 9 11 15 12 4 2 34 41 39 34 Malawi 439 491 8 .. 15 ..2 .34 ..41 Malaysia 10,800 77,751 12 7 15 4 6 4 39 54 28 30 Mali 439 529 19 .. 35 ..1 .23 ..23 Mauritania 286 700 30 .. 14 ..1 .27 -. 28 Mauritius 609 1,959 26 13 14 9 5 3 16 25 39 50 Mexico 19.500 72,500 16 8 2 2 7 4 43 48 32 38 Moldova .. 841 . .. .. .. Mongolia .. 223 . .. .. .. Morocco 4,160 8.563 20 17 24 14 10 9 21 29 25 31 Mozambique 800 784 . .. .. .. Myanmar 353 1,335 .. .. .. . Namibia .. 1,196 . .. .. .. Nepal 342 1,374 4 .. 1 8 .2 ..32 ..44 Netherlands 76.600 176,420 15 15 24 9 7 5 20 30 35 41 New Zealand 5,470 13,958 6 8 23 7 6 4 30 38 36 44 Nicaragua 887 962 15 23 20 15 2 1 14 26 49 34 Niger 594 309 14 .. 26 ..4 .27 ..29 Nigeria 16,700 7,900 17 ..2 ..3 .39 ..39 Norway 16,900 32.702 8 7 17 3 8 7 29 39 39 45 Omon 1,730 4,248 15 19 11 3 2 2 39 44 33 32 Pakistan 5.350 11,461 13 14 27 17 6 7 25 35 29 27 Panama 1,450 2,511 10 10 31 13 1 2 21 31 37 45 Papua New Guinea 1,180 1,451 21 .. 15 I.1 . 35 ..28 Paraguay 615 2.370 . 11 .. 12 .. 1 . 40 .. 35 Peru 2,500 9,224 20 20 2 8 5 3 41 36 32 34 Philippines 8,300 28,337 8 8 28 12 5 5 24 32 35 43 Poland 16,700 29,050 14 12 18 17 11 6 27 29 30 36 Portugal 9,310 32,339 14 19 24 24 11 8 25 24 27 26 Puerto Rico . .. .. .. .. .. Romania 12,800 9,424 .. 14 .. 286 7 . 22 .. 31 Russian Federation ..58,900 . .. .. .. .. Wor.d Development Indicators 1997 163 4.9 Merchandise Food Fuel Other primary Machinery and Other imports commodities transport manufactures equipment S mWion)s ½ ' xoia Iof olxal Iof Iooai % of rotal n'otai 1980 1995 1990 99 1980 0993 1990 1993 1990 1_993 1980 1993 Rwanda 243 235 12 13 ..11 ..265 39 Saud Arabio 30,200 27.458 14 . ..2 ..39 ..44 Senega 1,050 704 25 29 23 11 1 3 23 23 25 34 Sierra Leone 427 135 24 ..2 .2 ..40 32 Singapore 241000 124,507 9 6 29 11 7 3 29 49 26 31 Slovaa Republ.o . 9,070 . .. .. Slovenia .. 9,452 .. 8 .. 11 .. 7 .. 30 .. 44 South Africa 19,600 30,555 3 6 0 1 5 4 38 44 54 46 Spain 34,100 115,019 13 14 39 11 11 5 18 35 20 35 Sri Lanka 2.040 5,165 20 18 24 9 3 3 23 21 28 53 Sudar 1.580 1,275 26 . 13 .2 ..29 ..31 Sweden 33,400 64,438 7 8 24 9 7 5 27 36 35 42 Switzer ano 36,300 76,985 8 7 11 4 10 5 24 29 47 55 Syr an Arab Repj,bIc 4,120 4,616 14 19 26 4 5 4 21 32 33 42 Taj ksatan .. 799 . .. .. .. Tanzania 1,250 1.619 13 ..21 ..3 .35 ..28 Thiliand 9.210 70,776 5 5 30 8 7 7 25 45 32 36 Togo 551 386 1 7 23 23 10 2 3 23 28 36 3 7 Trinidad ond 7oba9o 3.160 1,714 11 15 38 1 6 3 3 25 33 24 32 Tunisia 3,540 7,903 14 8 21 8 8 6 23 32 34 40 Turkey 7.910 33,710 4 6 48 14 5 10 18 38 25 33 Turkmenisaon .. 1,472 . .. ... . Uganda 293 1,058 8 .30 ..2 ..27 ..34 Lkraine .. 15,945 . .. .. .. Lniteo Arab Fmrte 8,750 21,024 11 ..11 . 3 ..36 ..39 Lnited Kingoom 116,000 263,719 13 11 14 5 11 6 26 39 36 39 Lniteo States 257,000 770,852 8 5 33 1-0 7 4 25 43 27 38 Uruguay 1,580 2,867 8 8 29 9 7 4 30 40 25 39 Uzoekiatan .. 3,598 . .. .. .. Venezuela 11,827 11,988 15 11 2 1 5 5 43 50 36 32 Vietnam 1,310 7.272 . .. .. .. West Bank ano Gaza . . . .. .. .. Yemoen, Rep. .. 1,962 28 ..7 I.1 . 28 ..36 Yugoslavia. Fad. Rep. .. 4,300 8 9 24 19 12 5 28 23 29 43 Zaire 836 397 21 ..8 .4 ..32 ..36 Zamoia 1,340 1,258 5 ..22 ..2 .35 ..36 Zimcaobwe 1,448 2,241 3 18 1 12 4 4 64 35 28 31 golI- i= I:~ Low income 97,748:t 251,806: Fool. China & noia 65.465:t 86,0681t M ddllo income 465,925 t 987,3091 t _ Lower midole income -Uppor mioole income 161,848:t 379,450 t _ __ Low & mioole income 547,417: 1,233,749t Eoast Asai & Pactfic 65,139rt 368,683 t _____ Europa & Contral Asa a . .. Latin America & Carib. 107.971:t 237,576:. Miodle Lao: & N. Afr[ca 103,850 t 110,6411t South Aso a 25,8630t 60,51220 Sub-Sahoran Aftic 00 68,693ot 77,574t H.gn income 1,503,743:t 4,037,671:t a. oInducs Luxembourg. b. Data prior to 1992 noe ude Ertrea. o. Dara prortoo 1990 referor 0019 Federa Repub oaf Gormary nofore unfioot oo. '.01 Deve _-nn- 110010t1_ 1009 4.99 Figure 4.9a Merchandise imports of _ C_ developing economies, 1980-95 Imports are the mirror image of exports, and data * Merchandiseimportsshowthec.i.f.valueofgoods percentage of world imports on imports are derived from the same sources as received from the rest of the world valued in U.S. dol- 25 data on exports. In principle, world exports and lars. Merchandise imports are classified using the imports should be identical. Similarly, exports from Standard International Trade Classification (SITC), 20 Sub-Saharan Africa an economy should equal the sum of imports by the senres M. no. 34, rev. 2. Group totals for merchan- rest of the world from that economy. But differences dise imports are calculated bo s mple aggregation after So/So uth Asia in timing and definitions result in d screpancies in estimatingvalues forcountresforwhich dataare miss- 15 r - reported values at all levels. For further discussion ing. Missing values are imputed for a group only when of indicators of merchandise I rade see the notes to data are available for countries with at least 66 per- 10 Middle East an Nort Africa tables 4.7 and 4.8. cent weight in 1987 for that group. Other indicators nv 9 ;,t, < . . The value of imports is generally recorded as the in the tab e are not aggregated because the coverage 5 cost of the goods when purchased by the importer is generally poor. a Food comprses the commoditues plus the cost of transport anc insurance to the fron- in SITC sections 0, 1. and 4 and d vision 22 (food and East Asia and the Pacific tier of the importing country--the c.i.f. (cost, insur- live an mals, beverages and tobacco, animal and veg- 1980 1985 1990 1995 ance, and freight) value. A few countries, including etable oils and fats. oil seeds. oil nuts, and oi ker- Australia, Canada, and the Jnited States, collect nels). e Fuel comprises the commodities in SITC Source: World Bank staff estimates, import data on an f.o.b. (free on board) basis and section 3 (mineral fuels, lubricants, and related mate- then adjust them for freight and insurance costs. rials). e Other primary commodities comprise SITC Many countries collect and report trade data in U.S. secton 2(inediboecrudematenalsexceptfuels),exclud- dollars. When countries report in local currency. the Ing division 22 (nl seeds, oil nuts, and oil kernels) and Un ted Nations Statistical Off ce applies the average division 68 (nonferrous metals). * Machinery and official exchange rate for the period shown. transport equipment comprise the commodities in Countries may report trade according to the spe- SITC section 7. a Other manufactures, calculated cial or general system of trade (see Primary data doc- resioually from the total value of marufactured imports. umentation). In countries that report trade according represent SITC sections 5-9. exc uding section 7 and to the general system. impo-ts include both goods d vision 68. imported for domestic consurnption and imports into bondec warehouses ann free trade zones. Under the special system imports comprise goods imported for domestic consumption and withdrawals for domes- The princ pal sources of mer tic consumption from bonded warehouses and free . chandise trade data are the tradezones. Goods shipped tirough a countryforthe '.I. , UNSO's COMTRADE data- purpose of transport are excluded. base; the United Nations International Trade Statis- tcs Yearbook; UNCTAD's Handbook of Internationai Trade and Development Statistics; and the IMF's International Financial Statistics and Direction of Trade Statistics. World Development Indicators 1997 165 4.10 Structure of service exports Service exports Transport Travel Communications, i Insurance computer, and financial information, services and other services $ mIlloos ¾~~~~ of totat ¾ of total % of total ¾ of total 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Albania 11 99 42.1 18.1 6.4 65.9 46.8 14.7 4.7 1.3 Algeria 476 628 41.0 ..24.1 ..29.5 0.0 5.4 Angola .. 112 .. 34.2 .. 0.0 .. 57.5 .. 8.3 Argentina 1.876 2,889 42.9 41.4 18.3 36.6 38.4 22.0 0.3 0.0 Armenia .. 29 .. 49.9 .. 4.9 .. 45.2 Australia 3,860 15,556 49.3 31.9 29.5 47.2 19.9 16.6 1.3 4.3 Austria 9,423 23,506 7.3 11.1 68.9 55.7 21.3 22.6 2.6 10.6 Azerbaijan .. 172 . .. .. Bangladesn 163 162 . .. .. Belarus . 123 . .. .. Belglim0 12,925 38,555 32.7 25.1 14.1 14.8 48.5 41.0 4.7 18.4 Benin 62 104 56.8 56.8 14.1 22.5 26.8 20.6 2.3 0.0 Bolivia 88 231 33.5 26.3 41.0 31.3 16.6 34.5 8.9 7.9 Bosnia and Herzegovina ...... Botswana 101 260 41.7 14.7 22.2 62.2 32.0 16.0 4.1 7.1 Brazil 1,737 6,135 46.8 42.4 7.3 15.8 38.1 25.3 7.9 16.5 Bulgaria 1,211 1.420 36.3 34.0 28.7 33.3 31.0 32.7 4.0 Burkina Faso 49 56 17.3 11.8 10.2 32.6 72.5 55.5 0.0 0.0 Burundi .. 17 .. 12.0 .. 8.4 .. 78.5 .. 1.0 Cambodia .. 114 .. 27.7 .. 46.8 .. 25.3 . 0.0 Cam'eroon 374 397 . .. Canada 7,377 21,770 24.3 17.1 40.2 37.0 35.5 45.9 Central African Repub ic 54 33 6.5 0.0 5.1 0.0 86.1 100.0 2.4 0.0 Chad 0 55 0.0 1.9 100.0 21.3 0.0 76.1 0.0 0.7 Chile 1,263 3,153 32.2 42.9 13.9 28.6 51.9 2 7.1 2.1 3.3 China 2,512 19,130 52.3 17.5 28.0 45.6 11.7 27.2 8.0 9.7 Colombia 1,342 3,439 31.1 41.6 35.6 25.0 27.6 21.7 5.6 11.7 Congo 111 7 6 46.0 42.3 6.7 5.2 42.7 52.5 4.6 0.0 Costa Rica 194 1,310 24.9 13.7 43.7 51.2 30.7 35.'. 0.7 0.0 CMe dIlvoire 564 551 50.3 34.2 14.4 13.0 25.7 48.9 9.9 3.8 Croatia .. 2,569 .. 25.5 .. 61.6 .. 12.9 Czech Repubilc .. 6,725 .. 21.8 .. 42.8 .. 34.4 .. 1.0 Denmark 5,853 15,377 44.4 50.0 21.1 24.0 31.8 26.0 2.7 Dominican Repub ic 309 1,950 7.8 1.8 55.8 81.1 35.9 17.1- 0.5 Ecuador 367 854 35.1 38.9 35.6 29.9 13.3 18.6 16.0 12.8 Egypt. Arab Rep. 2,393 6,767 . .. .. El Salvador 139 388 18.3 24.9 9.6 22.0 50.5 46.3 21,6 8.9 Eritrea ..91 . .. .. Estonia .. 877 .. 42.6 .. 40.7 .. 16.3.. 0.4 Ethiopia' 110 330 . .. .. Finland 2,733 7,847 35.1 28.3 25.0 21.5 37.0 48.5 2.9 1.8 France 43,506 97,770 24.2 20.9 19.0 28.2 53.4 33.21 3.4 17.8 Gabon 325 253 21.5 32.0 5.2 2.4 67.7 60. C 5.6 5.4 Gambia, The 18 54 0.0 15.5 100.0 52.4 0.0 31.0 0.0 0.2 Georgia . .. .. .. Germany' 33,058 86,022 26.6 22.5 15.1 18.9 57.4 45.7 0.8 12.9 Ghana 107 142 33.6 57.5 0.4 7.7 64.9 32.0 1.2 2.7 Greece 3,947 9.605 23.6 3.9 43.9 43.1 32.4 52.8 0.1 0.2 Guatemala 211 666 18,9 8.1 29.2 31.9 46.5 56.2 5.4 3.8 Guinea .. 119 .. 10.7 .. 0.7 .. 88.3 .. 0.2 Guinea-Bissau 6 ..8.9 ..12.5 .78.6 ..0.0 Halti 90 100 5.9 5.0 85.0 82.1 7.8 12.3 1.2 0.6 Honduras 82 258 36.9 21.9 30.1 31.1 18.5 45.3 14.5 1.7 Hong Kong 3,686 . .. .. .. !,'Dr Dev Thoxoac-o rC, C.-. 4.10 Service exports Transport Travel Communications, Insurance computer, and financial information, services and other services 1980 $ ilos1995 1980 % fttl1995 1980 % fttl1995 1980 % fttl1995 1980 % fttl1995 Hungary 633 4,271 5.4 10.4 653.5 40.4 30.5 45.4 0.6 3.8 India 2,949 6,893 . .. .. Indonesia 449 5,681 15.1 0.0 50.8 95.9 34.1 4.1 0.0 0.0 Iran, Islamic Rep. 731 438 4.5 20.8 4.0 2.5 91.5 65.5 0.0 11.2 Ireland 1,381 4,802 36.6 22.3 42.0 46.0 21.4 31.7 0.0 0.0 Israel 2.722 7,741 38.1 26.0 36.0 37.2 24.9 36.6 1.0 0.2 Italy 19,192 65,043 23.9 22.9 46.7 42.2 22.9 28.7 6.5 6.2 Jamaica 401 1,388 28.0 18.1 31.2 73.6 6.7 7.4 4.2 0.9 Japan 20,240 65,212 62.9 34.3 3.2 4.9 32.4 59.8 1.6 0.9 Jordan 997 1,719 . .. .. Kazakstan .. 64 . .. .. Kenya 577 1.034 38.0 30.1 41.4 47.0 19.8 21.8 0.8 1.1 Korea, Dem. Rep. . .. .. .. Korea, Rep. 4,710 26,243 33.5 40.3 7.8 19.6 53.1 37.6 5.6 2.5 Kuwait 1,225 1,491 57.7 76.6 30.8 7.2 11.5 16.2 0.0 0.0 Kyrgyz Republic . .. .. .. Lao PDR .. 97 .. 15.6 .. 52.8 .. 31.2 .. 0.4 Latvia .. 712 .. 91.5 .. 2.8 .. 3.3 .. 2.4 Lebanon .. 55 . .. .. Lesotho 32 38 2.0 8.2 37.8 46.0 60.2 45.8 0.0 0.0 Libya 164 .. 64.5 ..6.2 ..29.4 ..0.0 Lithuania .. 485 .. 59.3 .. 15.9 .. 23.9 .. 0.9 Macedonia, FYR .. 200 .. 20.5 -. 15.3 .. 64.2 Madagascar 79 242 49.4 26.9 6.3 23.8 44.0 47.3 0.4 2.0 Malawi 32 16 49.8 58.5 29.5 20.6 19.8 20.6 0.9 0.3 Malaysia 1,135 7,308 41.6 28.7 28.0 51.6 29.8 19.6 0.6 0.1 Mall 58 77 30.9 38.2 25.8 27.0 42.2 33.8 1.0 0.9 Mauritania 56 31 26.3 6.7 11.9 41.6 61.8 51.6 0.0 0.0 Mauritius 140 778 38.4 25.7 30.2 55.3 31.2 19.0 0.2 0.0 Mexico 4,591 10,281 9.7 11.4 69.7 60.0 10.4 21.7 10.2 6.9 Moldova .. 103 .. 1.0 . 55.1 .. 27.7 .. 16.1 Mongolia 37 57 26.5 26.2 8.6 36.0 64.9 33.5 .. 4.4 Morocco 783 1,996 20.3 21.1 57.9 58.3 20.7 19.2 1.1 1.4 Mozambique 118 242 78.5 ..0.0 ..21.5 ..0.0 Myanmar 60 309 . .. .. Namibia .. 297 .. 0.0 . 88.3 .. 9.8 .. 1.9 Nepal 127 709 . .. .. Netherlands 17,150 48.377 51.5 40.5 13.1 13.6 34.3 44.9 1.2 1.1 New Zealand 1,009 4,297 58.2 35.6 21.1 50.3 19.6 14.3 1.1 -0.3 Nicaragua 44 119 36.0 11.9 48.6 45.9 14.9 40.5 0.5 1.7 Niger 41 30 33.5 1.2 15.2 15.3 50.9 83.5 0.4 0.0 Nigeria 1,127 1.953 80.9 16.4 6.0 2.8 6.5 80.2 6.6 0.6 Norway 8,615 13,105 74.5 56.9 8.8 16.7 16,3 19.0 0.4 7.4 Oman 9 13 100.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 Pakistan 617 1.622......... Panama 902 1,319 47.0 51.1 19.0 17.7 25.8 26.9 8.2 4.2 Papua New Guinea 43 309 33.8 12.5 28.3 8.1 36.9 79.4 1.0 0.0 Paraguay 118 1,229......... Peru 715 1,234 30.9 27.4 40.9 41.8 24.9 24.7 3.2 6.0 Philippines 1,447 9,348 14.2 2.9 22.1 12.2 63.6 84.3 0.0 0.7 Polend 2,018 8,617 59.2 35.3 11.9 2.9 24.1 51.6 4.8 10.2 Portugal 2,006 8,173 23.5 17.9 57.3 59.2 18.1 18.5 1.2 4.4 Puerto Rico . .. .. .. .. Romania 1,063 1,494 37.6 31.5 30.5 39.5 27.8 23.6 4.2 5.4 Russian Federation .. 12,400 .. 49.4 .. 34.6 .. 15.3 .. 0.5 World DeveloomenL Indicators 1997 187 4.1 0 Service exports Transport Travel Communicationis. Insurance computer, and financial information, services and other services 5 -r ils of to-a % of tota I/_,otalb of total 1980 150- 98C8 1995 1989 ~ 995 1980 1995 1980 1995 R\o.ensda 32 1 2 42.3 33.9 10.8 18. 0 46.3 1'9 0.6 38.2 Saud], Aralb a 5,191 3.480 15.3 ..25.9 ..58.8 ..0.0 Senegal 337 499 19.1 9.5 29.3 29.5 51.3 60,3 0.3 0.4 Sierra Leane 49 100 31.4 12.3 25.5 69.0 43.1 18.5 0.0 0.2 Singapore 4.856 29,375 26.9 16.8 29.6 28.2 42.5 53. 6 1.1 1.3 Slovek Repusblic 2,378 . 26.9 .. 26.2 .. 43. 0 ..4.9 S ovenia .. 2.018 .. 25.0 .. 53.5 .. 2t1 .1 0.4 Sooth Afloe 2,929 4.516 41.8 29.4 47.1 32.7 2.5 9. 3 8.6 8.6 Spain 11,393 40.027 25.9 14.7 60.0 63.8 11.8 17.8 2.4 3.9 Sr Lanka 231 831 18.8 38.0 42.9 30.5 37.4 27. 7 1.0 2.7 Sudan 216 106 . .. .. Sweden 7.489 15,444 40.3 32.3 12.9 22.4 44.0 44.2 2.6 1.2 Sw>tzerland 6.868 26,095 18.8 9.7 46.0 36.2 30.5 27.3 4.7 26.6 Syrias Arab Rapeb is 365 1.966 17.2 12.1 42.9 67.4 39.9 2C.5 0.0 0.0 Ta ikis.an . .. .. .. Tarzan a 165 566 39.5 0.3 12.6 88.6 46.4 11.1 1.5 0.0 Tha[ans 1.490 14,845 20.1 18.5 58.2 54.1 21 .2 28.7 0.5 0.7 Tags 7z 73 38.3 14.2 35.2 32.6 25.0 52.7 1.4 0.5 Trhridad ens Tobags 411 343 27,7 56.6 37.3 22.6 35.0 12.0 0.0 8.9 Tun sea 1.067 2.509 1'9.4 23.9 84.1 61.0 14.8 13.7 1.6 1.3 Torkey 711 14.606 37,4 11.7 45.9 33.9 16.3 52.8 0.4 1.5 'irkmen stan . .. .. .. UganJa V0 72 . .. .. Ukraine .. 2.846 .. 75.6 .. 6.7 .. 15.0 ..2.7 bnated Aab Estrates 1.090 .. .. .. Laitea Kirgasm 36,452 71.400 38.9 23.8 19.0 26.2 42.1 38.4 0.0 11.4 Lanied Stares 47.5sO 208.550 29.9 22.3 22.3 33.5 44.6 40.6 3.2 3.6 iruguay 468 1,171 18.6 23.1 63.1 52.2 -14.8 24 7 2.9 1.2 J.zsekisTaan. . .. . . Venezuela 693 1,487 41.1 38.6 35.1 45.7 9.5 15 5 14.4 0.2 8' etraa-. 2.074 West Bans ars Gaza .. .. .. Yemen, Rep. .. 173...... Yugcssas a. Fea. Rep. ......... Zaire 57 ..40.4 ..15.8 .42.1 1.8 Zambia 152 lIs5 56.8 ..1. .25.3 4.1 7 msbabwe 169 252 56.9 24.3 14.6 46.7 26.1 28.7 2.4 0.3 Lar in-rneam 9,253 t 37,8731t Eac . China & ndia 6,3041t 11,850 Miadle ascome 46.3841t 175,214t Lower mds e ircome 20.882 t107,2298t Upper mids e ircosme 25,502 t 67,9868t Losw & aids a inrosme 55,637 t 213.087 t baa. Asia & Pasiti 4,409 t 59.717 Eurspe & Certra Asis 9,442 t 72,7801t Latin Amer-ce & Cs'lS. 16.614:t 38,717 V Missle East &.Afrias 12,509 19,9108t Sass~ Asia 4. 176 . 8.622 Sob-Sarnarani Alnas 8,488 t 13,3528t Higa ncome 302,1'16 t 0' 3.005 t .... a. I:' udes Luaerncos 9. b. DatE a, oro 1992 ]nc mde lritrea. c. Dsta or c to 1990 'star to the Feasts Repub a of Germary aefore un Sicat as. annae 0mn .cC o" 4.10 Services go international _ C _ Services are the fastest-growing component of world trade. As the markets for services have See the notes to table 4.11. o Services refer to economic output of intangible expanded, so have foreign direct investment commnodities that may be produced, transferred, and (FDI) flows to services, which now account for consumed at the same time. International transac- close to one-fifth of all trade and three-fifths tions in services are defined by the IMF's Balance of all FDI. Most of the FDI in services has flowed of Payments Manual (1993), but defin tions may nev- between OECD countries, but developing coun- ertheless vary among reporting economies. tries' share of this investment has been o Transport covers all transport services (sea, air, increasing. land, internal waterway, space, and pipeline) per- Although services statistics have many defi- formed by residents of one economy for those of ciencies, it is clear that trade in services has another and involving the carriage of passengers, grown faster than trade in merchandise in the the movement of goods (freight), rental of carriers past decade. During 1980-95 service trade with crew, and related support ans auxiliary services. grew an average 8 percent a year, compared Excluded are freight insurance, which is included in with 6 percent for merchandise trade (in nom- insurance services: goods procured in ports by non- inal terms). The rapid growth boosted com- resident carriers and repairs of transport equipment, mercial services' share in global trade from which are included in goods; repairs of railway facil- 16 percent in 1980 to 18 percent in 1995. The ities, harbors, and airfield facilities. which are most dynamic trade is in such private services included in construction services; and rental of car- as financial, brokerage, and leasing services. riers without crew, which is included in other ser- Growing at an average annual rate of 9.5 per- vices. a Travel covers goods and serv ces acquired cent, trade in these services rose from 37 per- from an economy bytravelers fortheir own use dur ng cent of commercial services trade in 1980 to visits of less than one year in that economy for either 45 percent in 1993. business or personal purposes. . Communications, International trade in services is not a new computer, information, and other services cover phenomenon: transport, travel, tourism, and international te ecommunications and postal and insurance have long been important traded courier services; computer data: news-related ser- activities. What is new is the rapid expansion vice transactions between residents and nonresi- of international service transactions in the past dents; construction services; royalties arc icense decade or so. There are also new modes of fees; miscellaneous business, professionas, and supply, such as transmitting services over elec- technical services; personal, cultural, and recre- tronic networks. Many services considered non- ational services; and government services not tradable only a few years ago are now actively included elsewhere. * Insurance and financial ser- traded. Seamless, around-the-clock, around- vices cover various types of insurance provided to the-globe financial services, for example, have nonresidents by res dent insurance enterprises and become standard. vice versa, and financia intermed ary and auxiliary Rapid advances in telecommunications and services (except those of nsurance enterprises and information technology have been a central - Figure 4.10a World M. pension funds) exchanged between residents and force in the internationalization of services. h'7.V*, 1980-95 nonresidents. Also important have been the deregulation of trillions of U.S. dollars service industries and liberalization of foreign .. - trade and investment regimes. For developing 14 countriestakingadvantage of the more liberal 12 A - Data on exports and trade and investment regimes, the internatio- /i - imports of services come nalization of services offers opportunities for 10 - from the balance of pay- expanding into new exports and attracting more 8 ments data files of the FDI. It should also broaden the range of pro- International Monetary ducer services and technical capabilities that 6 Fund IIMF). The IMF pub- developing countries can provide by importing - = lishes balance of payments the new technologies. data in the International By making more activities in industrial coun- 2 Financial Statistics and in tries contestable and thus more subjectto com- _ the Balance of Payments Statistics Yearbook. The petition and by revealing new complementarities, 0 feature textwas adapted from the World Sank's G/oba/ technological progress has created important 1980 1985 1990 1995 Economic Prospects and the Developing Countries new opportunities for long-distance service Source: World Bank staff est ma:es. 1995. exports from developing countries. World Development Indicators 1997 169 4.11 Structure of service imports Service imports Transport Travel Communications, Insurance computer, and financial information, services and other services St -Socs % of trto S of tota S/ of total S of total 1950( 1995 1990 1995 1980 1999 1990 1995 1999 1995 Albania 16 1 57 43.7 38.5 0.0 4.2 51.4 43.4 4.9 13.9 Algeria 2,697 1,193 39.9 ..12.4 ..40.9 ..6.8 Angola .. 1.734 .. 28. . 4.2 .. 65.6 ..2.1 Argent na 3,788 5,047 33.6 41.3 47.3 41.0 19.1 17.8 0.0 0.0 Armen'a .. 52 .. 82.6 .. 6.2 .. 0.9 .. 10.3 Austral a 6.532 17.611 47.4 36.9 28.1 26.3 23.2 29.4 1.3 5.4 Austria 6,204 21,034 12.7 9.2 50.6 50.2 32.4 25.7 4.2 14.9 Azeroaian .. 312 . .. .. Bangladesh 173 710 . .. .. Belarus .. 174 . .. .. Belgiumnt 12,827 36,620 29.7 21.1 23.7 25.9 39.5 38.6 5.2 14.4 Benin 109 11 56.9 66.8 7.1 5.3 25.9 23.0 10.1 5.8 Bolivia 259 338 53.3 60.9 21.3 21.0 16.2 22.7 9.1 5.4 Bosnia and Herzegovina Botswaria 216 444 42.4 42.2 26.0 32.7 27.8 17.1 3.7 8.0 Brazil 4,671 13.630 50.5 42.6 7.5 24.9 35.0 23 3 1.0 9.2 Bu garia 549 1.191 61.3 37.3 6.6 16.4 34.4 46 4 5.7 0.0 Burkiro Faos 209 138 58.0 46.9 15.3 16.4 21.6 32 6 4.6 4.0 Borednd. 102 .. 30.0 .. 24.9 . 4-1 1 ..3.9 Cambooia .. 166 44.7 .. 4.4 4. 66 8 4.2 Cameroon 377 497 . .. .. Canaoa 10,566 30,141 19.1 19.9 37.5 33.9 43.6 z06.2 0.0 0.0 Contral African Repub ic 142 114 47.3 43.7 24.5 38.0 23.5 10.4 4.7 7.9 Chad 24 199 6.4 48.1 6 7.5 13.0 35.4 37.6 0.7 1.3 Chde 1,683 3.306 52.4 49.2 12.6 21.5 32.3 26.7 2.7 2.6 China 2.024 25,223 61.6 37.8 3.3 14.6 30.7 30.7 4.4 16.9 Colomoia 1.170 3.349 45.3 34.0 20.5 24.7 24.0 26.2 10.2 14.1 Congo 480 775 27.0 27.1 6.1 5.0 63.5 65.9 3.5 2.0 Costa Rica 286 947 58.2 40.1 21.1 34.6 13.9 19.4 6.7 6.6 CSte dIlvors 1.531 1,094 38.6 43.6 15.6 14.5 37.7 39.3 7.9 2.6 Croatia. 2,708 .. 49.7 .. 26.5 .. 21.6 Czech FRspub ic .. 4,682 .. 16.4 .. 33.5 .. 43.6 5. .2 Danmark 4,663 15,111 47.7 46.1 27.4 26.4 22.9 23.5 2.0 Dominican Repuol c 399 647 39.6 55.1 41.6 25.3 14.7 14.0 4.1 5.6 Ecuador 704 983 36.0 47.7 32.4 23.9 19.1 15.5 12.4 13.0 Egypt, Arab Rep. 2.343 3,761 . .. .. E Salvador 273 237 29.3 ..36.6 20.7 ..11.2 Ertrea -. 44 . .. .. Estonia .. 4968. 44.6 .. 18.2 .. 33.3 ..4.0 Emniopia' 90 239 . .. .. Finland 2.555 9,834 39.4 23.4 23.1 23.6 35.1 48.4 2.4 4.6 France 32,148 76,530 26.4 27.1 16.7 20.6 48.1 31.2 4.6 20.9 Gabon 789 894 22.0 26.8 12.2 1 7.6 60.1 49.9 5.7 5.8 Gambia, The 42 69 55.8 40.3 3.5 20.6 33.2 35.2 7.4 3.9 Georgia.. .... .. GermaWn 42,375 132,520 25.1 19,7 41.2 36.3 33.2 34.9 0.6 7.1 Ghana 270 406 39.7 49.7 12.1 5.0 45.8 38.5 2.3 6.8 Greece 1,426 4.365 41.5 27.4 21.6 30.3 31.1 36.1 5.6 4.2 Guatemala 487 695 37.0 40.1 33.6 20.3 26.0 31.2 3.3 6.4 Gu nea .. 371 . 38.9 .. 5.7 .. 50.5 ..4.9 Genoea-Bissau 14 21 47.9 52.6 11.0 0.0 36.0 41.5 5.0 5.6 Ha ti 162 233 49.3 56.5 25.1 14.9 23.0 26.9 2.7 1.7 Honduras 174 334 53.3 56.9 17.8 17.1 16.6 21.0 12.3 2.4 Hong Kong 2,643 . ... IM. 4.11 Service imports Transport Travel Communications, Insurance computer, and financial information, services and other services 1-980$mlin 1995 1980 % fttl1995 1980 %ftta1995 I 1980 % fttl1995 1980 % fttl1995 Hungary 524 3,629 60.3 10.2 26.9 29.5 6.1 53.9 6.7 5.1 India 1,516 6.954...... Indonesia 4,998 13,475 40.1 35.2 11.9 16.1 44.2 45.4 3.8 3.2 Iran, Islamic Rep. 5,223 3,405 43.6 29.1 32.5 4.4 17.4 59.1 6.4 7.4 Ireland 1,593 10,516 43.9 16.7 36.6 19.3 16.1 62.5 3.4 1.6 Israel 2,310 9,257 44.1 33.9 :35.6 38.3 18.5 25.2 1.8 2.5 Italy 16,249 63,332 43.6 36.7 11.6 20.1 31.0 34.0 13.1 8.4 Jamaica 370 1,034 55.4 47.6 8.9 14.3 23.6 28.7 11.8 9.4 Japan 32,360 122.698 52.2 29.3 14.2 29.9 31.3 36.3 2.3 2.4 Jordan 828 1,209...... IKazakstan .. 273...... Kenya 502 871 66.2 55.4 4.6 16.6 18.0 22.5 11.2 5.6 Korea, Dem. Rep.... .. . Korea, Rep. 4,089 27,885 55.8 36.2 8.6 22.7 30.0 36.0 5.6 3.1 Kuwait 3,067 4,936 38.8 32.5 43.7 47.0 16.9 19.1 0.6 1.3 Kyrgyz Republic........ . Lao PDR .. 125 .. 32.5 .. 23.6 .. 43.1 ..0.7 Latvia .. 246 .. 62.3 .. 9.9 .. 21.4 ..6.4 Lebanon .. 403...... Lesotho 50 64 31.6 50.8 15.8 10.6 49.7 33.6 2.8 5.0 Libya 2,303 .. 51.4 ..20.4 ..23.2 ..5.0 Lithuania .. 498 .. 58.7 .. 21.3 .. 18.7 ..1.0 Macedonia, FYR .. 782 .. 49.4 .. 7.5 .. 43.1 Madagascar 311 359 57.3 43.0 9.9 16.3 28.0 36.6 4.8 2.9 Malawi 179 217 81.7 83.0 5.6 6.5 5.3 2.0 7.4 8.5 Malaysia 2,957 10,101 44.3 49.0 24.5 19.6 31.2 31.5 0.0 0.0 Mali 212 394 65.8 51.1 9.6 16.7 16.3 26.7 6.2 5.6 Mauritania 128 249 59.1 51.8 13.6 9.9 24.2 36.4 3.1 1.9 Mauritius 174 641 64.7 39.2 12.9 24.8 15.2 31.5 7.2 4.5 Mexico 8,514 9,407 28.2 34.6 47.0 33.5 16.3 21.0 8.5 10.8 Moldova .. 185 .. 51.8 .. 30.5 .. 7.9 ..9.7 Mongolia 31 95 48.4 63.7 0.3 20.4 51.3 15.8 0.0 0.0 Morocco 1,436 2,063 34.4 36.2 6.8 14.7 55.7 43.2 3.3 6.0 Mozambique 124 390 79.0 ..0.0 ..14.5 ..6.5 Myanmar 85 122 . .. .. Namibia .. 467 .. 26.0 .. 17.5 .. 47.7 ..8.2 Nepal 81 243 . .. .. Netherlands 18,148 46,317 43.9 30.7 26.6 25.2 27.2 41.1 2.4 2.9 New Zealand 1,843 4,600 39.4 39.6 28.3 27.9 31.8 27.7 0.6 5.0 Nicaragua 104 218 50.7 37.5 29.9 18.4 14.2 40.9 5.2 3.2 Niger 279 149 43.0 51.8 6.6 13.1 43.7 33.1 6.7 1.5 Nigeria 5,285 .. 33.7 ..18.7 ..43.8 ..2.8- Norway 6,996 14,392 52.2 38.5 21.1 28.5 23.3 22.9 3.4 10.1 Oman 518 964 34.1 42.7 8.2 4.9 55.9 47.7 3.8 4.7 Pakistan 853 2,379 -.. . .-.. Panama 588 978 65.4 69.5 9.5 12.6 14.9 9.8 10.2 8.1 Papuas New Guinea 302 613 60.4 29.3 5.9 9.5 28.9 61.2 4.8 0.0 Paraguay 260 960......... Peru 880 2,015 55.4 44.9 12.2 15.0 25.2 32.0 7.2 9.0 Philippines 1,439 6,926 52.1 29.6 7.4 6.1 39.8 62.1 0.8 1.6 Poland 2,023 7,158 59.9 24.7 12.9 6.0 25.3 55.3 2.0 13.3 Portugal 1,525 6,536 48.8 25.7 19.1 32.4 27.2 33.2 4.9 8.6 Puerto Rico . .. .. .. .. Romania 1,045 1,727 76.8 29.6 7.0 40.4 7.7 24.0 8.5 5.6 Russian Federation .. 20,500 . .. .. .. World Deve opment Indicators 1997 171 Service imports Transport Travel Communications, Insurance computer, and financial information, services and other services $ mi lions Si at total Si of iota Si nf tonto of tots, 1980 1995 1980 1_995 190 1995 1990 190G 1980 1995 Rwanda 123 115 63.5 50.3 9.3 33.1 27.3 36.6 0.3 0.0 Seaud Arabia 30.231 18,328 17.1 11.7 8.1 0.0 73.3 8 7.8 1.3 1.3 Senega 340 552 46.9 36.7 17.6 9.7 28.9 48.8 6.3 4. 7 Sierra Leone 85 1 08 54.8 14.5 9.8 57.5 23.4 24.8 11.9 3.2 Singapore 2,912 16,634 38.3 29.4 11.4 30.9 46.1 33.9 4.3 8.8 Siosak Republic .. .832 .. 16.4 .. 17.5 .. 61.3 ..4.8 Sovenia .. ,292 .. 33.6 .. 32.0 .. 32.0 ..1.5 Sonth Africa 3,805 5.970 48.4 47.7 20.3 29.5 20.0 14.3 11.3 8.2 Spain 0,732 29.161 38.6 98.8 21.5 20.1 34.6 43.4 5.4 1.6 Sri Lamnka 381 1.245 60.4 56.5 9.5 16.1 23.5 22.7 6.5 5. 7 Sudan 259 115 . .. .. Sweden 7.018 17,206 35.9 28.2 31.6 31.6 28.1 38 8 4.4 1.4 Switzerland 4.885 15,402 30.4 24.6 48.8 50.1 19.3 24.2 1.6 1.1 Syrian, Arab Repub in 521 1,437 26.6 54I.1 33.9 27.7 37.3 18 2 2.2 Taeikistan .. 25 . .. .. Tanozania 295 754 62.1 28.8 6.7 47.8 25.6 20.8 5.5 2.7 Thailand 1,644 18,804 64.4 41.4 14.8 20.1 14.8 33 4 5.9 5.1 Togo 167 78 62.7 48.4 14.1 29. 7 16.7 11 5 6.6 10.4 Trinidd and Tobago 645 242 45.7 38.9 21.6 28.7 23.5 24 9 9.2 7.3 Tunia a 600 1.352 51.1 41.7 17.1 18.5 25.5 33 7 5.7 5.9 Turkey 569 5,024 50.5 26.1 18.3 18.1 27.1 456 0 4.2 7.8 Tunkrneniaran .. 478 . .. .. Uganda 123 281 . .. .. Ukraine .. 1,334 .. 34.0 .. 15.7 .. 42.9 ..7.3 Lnited Aran Emirates 2,890 United Kingdom 27,933 61,717 47.5 27.9 22.9 39.9 29.6 3 0.09 0.0 1.3 United Statea 40,970 140.430 37.5 31.0 25.4 33.4 35.0 31.2 2.1 4.4 Uvuguay 476 813 31.8 43.9 42.6 29.0 18.1 27.1' 7.5 Uzoekistan . .. .. .. Venezuela 4.253 4,887 31.7 28.0 47.0 35.1 16.5 34.8 4.8 2.2 Vietnam .. 1,916...... WAest Bank ano Gaza......... Yemen, Rep. .. 433...... Ytgoslavia, Fed. Rep. ......... Za re 608 .. 41.9 ..8. . 46.1 3.9 Sambia 651 375 53.5 ..8.5 ..33.9 4.0 Zimbabwe 395 712 43.3 50.7 40.3 1 6.9 12.9 29.8 3.5 2.6 Low income 16.720 t 46,945t Excl. Cnina & India 15.204 1 14,769t Mliddle noome 92.963 1196,587 1 Lower middia income 31,960 t108,6791 ___ Upper midnJe income 61,002 t 87.90sf t___ ___ Low & midole income 109,6821t 243,5321t SEalt Aaia & Pacific 6,6081t 77.983 t __ ___ Europe & GentraJ Asia 5,8741t 59.5566 Letin Americe & Carib. 28,7511t 49,287 ___ Minnie East & N. Afric9 47.174 t 30,427t SoucO Asia 3.161 t 9,193 t __ __ Sub-Saharen Afnca 18.1141t 17,076 t ..___ . High income 287.6831t 901,7121 t... a. no ides Lunxembourg. a. Data polor to 1992 nocude E,-rrea. a. Data ctior to 1990reetatnitote zedata RefpubIicc-3ermaqn. latefr u,rfloatlon. Oc dn aev npmerl no1010 car§'97 4.11 . Figure 4.11a Services as a share - C total trade, 1980-95 U Balance of payments statistics, the main source of a Services refer to economic output of intang ble percent information for international trade In services. have commodities that may be produced, transferred, and many weaknesses. Until recently some large consumed at the same time. International transac- HRg-income economies 20 economies-such as the former Soviet Union-did tions in services are defined by the IMF's Balance of acridd~ < not report data on trade in serv ces. The level of dis- Payments Manual (1993), but defnitions may nev- Low-income economies * *.#* aggregation of important components may be lim ted ertheless vary among reporting econom es. 15 -.. , and varies significantly across countries. There are * Transport covers all transport serv ces (sea, air, ,, * . , . S- * , . ......... , , incons stencies in the methods used to report items. land, internal waterway, space, and pipel ne) per- Midieincomoe econonies and the recording of major flows as net items is formed by residents of one economy for those of 10 common pract ce (for example, insurance transac- another and involvingthe carriage of passengers, the tions are often recorded net as premiums less claims). movement of goods (freight), rental of carners with These factors contribute to a cownward bias in the crew, and related support and auxiliary services. 5 value of the services trade reported in the balance Exc uded are freight insurance, which is included in of payments. insurance services; goods procured in ports by non- Efforts are being made to improve the coverage, resident carriers and repairs of transport eouipment, 0 1980 1985 1990 1995 qua ity, and consistency of the data. The Organization which are included in goods; repairs of railway facil- 1980 :1985 . .....1990 1995 for Economic Cooperation and Development and tes. harbors, and airfeld facilities, which are included Source: World Bank staff estimates. Eurostat, forexample, are workirigtogetherto improve in construction services: and rental of carriers with- the collection of statistics on trace in services in out crew, which is included in other services. a Travel member countries. And the Inlernational Monetary covers goods and services acquired from an econ- Fund is implementing the new classification of trade omy bytravelers forthe r own use duringvisits of less in services Introduced in the fifth edition of its Balance than one year in that economy for either business or of Payments Manual (1993;. personal purposes. a Communications, computer, But because of the difficu ties in capturing all the information, and other services cover international dimensions of international trade in services, thle telecommunicat[ons and postal andocourer services; record is I kely to remain incomplete. Cross-border computer data: news-related service transactions intrafirm service transactions. which are usually not between residents and nonresidents: construction captured in the balance of payrnents, are increasing services; royalties and tcense fees: m scellaneous rapidly as foreign direct investment expands and elec- business, professional, and technica services; tronic networks become pervasive. One example of personal. cultural, and recreational services; and such transactions is transnat onal corporations' use government services not included elsewhere. of mainframe computers around the clock for data a Insurance and financial services cover various processing, exploiting time zone differences between types of insurance provided to nonresidents by res- their home country and the host countries of their ident insurance enterprises and vice versa, and finan- affiliates. Another important dimension of services cial intermediary and auxiliary services (exceptthose trade not captured by conventional balance of pay- of insurance enterprises and pension funds) ments statistics is establishmenttrade-sales in the exchanged between residents and nonresidents. host country by foreign affiliates. By contrast. cross- border intrafirm transactions in merchandise may I_ be reported as exports or imports in the balance of payments. Data on exports and imports of services come from the balance of payments data files of the Internationa Monetary Fund (IMF). The IMF publishes balance of payments data in the /nrernational Financial Statistics and in the Balance of Payments Statistics Yearbook. World Development Ind cators T997 173 4.12 Structure of demand Private General government Gross domestic Exports of goods Imports of goods Gross domestic consumption consumption investment and services and services savings % of GDP %of GDP %of GDP of nGDP of GDP of GDP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 A bania 56 93 9 15 35 16 14 38 -8 Ageria 43 56 14 16 39 32 34 27 30) 30 43 29 Argo a 9 4 7 27 ( 4 56 43 Argentina 76 82 A 25 18 5 9 6 8 24 18 Armenia 47 116 16 13 29 9 24 62 -29 Australia 59 60 18 17 25 23 16 20 18 20 24 22 Austria 56 55 18 19 28 27 37 36 39 39 26 26 Azerbaijan 96 A16 27 39 4 Bangladesh 92 78 6 14 1-5 17 6 14 18 22 2 8 Belarus 58 22 25 43 47 20 Belgium 63 62 18 15 22 18 63 74 66 69 19 24 Benin 96 82 9 9 15 20 23 27 43 37 -5 9 Bo]livia 67 79 14 13 15 15 21 20 17 27 19 6 Bosnia and Herzegovina Botswana 53 45 19 32 38 25 53 49 63 52 28 23 Brazil 70 62 9 17 23 22 9 7 11 6 21 21 Bulgaria 55 61 6 15 34 21 36 49 31 45 39 25 Burkina Faso 95 78 10 16 17 22 10 14 33 30 -6 6 Burundi 92 95 9 12 14 11 9 12 24 31 -1 -7 Cambodia 82 11 19 11 24 6 Cameroon 70 71 10 9 21 15 27 26 27 20 20 21 Canada 55 60 19 19 24 19 28 37 27 35 25 21 Central African Repub ic 94 80 15 13 7 15 26 18 43 27 -10 6 Chad 99 93 8 17 4 9 24 13 41 33 -6 -10 Chile 67 62 12 9 25 27 23 29 27 27 20 29 Cnina 51 46 15 12 35 40 6 21 7 19 35 42 Colombia 70 75 10 9 19 20 16 15 16 20 20 16 Congo 47 64 18 12 36 27 60 62 60 66 36 23 Costa Rica 66 60 18 17 27 25 26 41 37 42 16 24 CSte dIlvoire 63 67 17 12 27 13 35 41 41 34 20 20 Croatia 66 33 14 40 53 1 Cuba - Czech Republic 60 20 25 52 56 20 Denmark 56 54 27 25 19 16 33 35 34 29 17 21 Dominican Republic 77 80 8 4 25 20 19 26 29 29 15 16 Ecuador 60 67 15 13 26 19 25 29 25 27 26 21 Egypt, Arab Rep. 69 61 16 13 28 17 31 21 43 32 15 6 El Salvador 72 86 14 8 13 19 34 21 33 34 14 6 Eritrea 95 32 20 30 77 -27 Estonia 58 23 27 75 84 18 Ethiopia' 83 81 14 12 9 17 11 15 17 25 3 7 Finland 54 54 18 21 29 16 33 38 34 30 28 24 France 59 60 18 20 24 18 22 23 23 20 23 20 Gabon 26 42 13 10 28 26 65 61 32 30 61 48 Gaobla,The 79 76 20 19 26 21 47 53 72 72 1 5 Georgia 56 103 13 7 29 3 17 29 -9 Germnary 58 20 21 23 22 23 Ghana 84 77 11 12 6 19 8 25 9 34 5 10 Greece 60 74 16 19 29 19 21 22 26 34 23 7 Guatemala 79 86 8 6 16 17 22 19 25 28 13 B Guinea 81 8 15 21 25 11 Guinea-Bissau 77 98 29 8 30 16 8 13 44 35 -6 -5 Haiti 82 101 10 6 17 2 22 4 31 13 8 -7 Honduras 70 73 13 14 25 23 36 36 44 45 17 14 Hong Kong 60 59 6 9 35 35 90 1_47 91 149 34 33 4.12 I Private General government Gross domestic Exports of goods Imports of goods Gross domestic consumption consumption investmerit and services and services savings % of GDP % of GDP %ofGDP % ofGDP % of GDP % of GOP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Hungary 61 68 10 11 31 23 39 35 41 37 29 21 India 73 68 10 10 21 25 7 12 10 15 17 22 Indonesia 52 56 11 8 24 38 33 25 20 27 37 36 Iran, Islamic Rep. 53 53 21 13 30 29 13 21 16 16 26 34 Ireland 67 57 19 15 27 13 48 75 61 61 14 27 Israel 50 58 39 29 22 24 40 29 51 40 11 13 Italy 61 62 15 16 27 18 22 26 25 23 24 22 Jamaica 64 80 20 9 16 17 51 69 51 76 16 10 Japan 59 60 10 10 32 29 14 9 15 8 31 31 Jordan . 75 .. 22 . 26 .. 49 . 72 ..3 Kazakstan .. 65 .. 15 . 22 .. 34 . 37 .. 19 Kenya 62 72 20 15 29 19 28 33 39 39 18 13 Korea, Dem. Rep... . .... . Korea, Rep. 64 54 12 10 32 37 34 33 41 34 25 36 Kuwait 31 49 11 33 14 12 78 55 34 49 58 18 Kyrgyz Republic .. 67 .. 23 .. 16 .. 26 . 32 . 10 Lao PDR.. . ......... Latvia 60 65 8 20 26 21 .. 43 . 48 .. 16 Lebanon .. 110 .. 12 . 29 . 10 .. 60 .. -22 Lesotho 124 85 36 23 42 87 20 21 122 117 -60 -9 Libya 21 .. 22 . 22 .. 66 . 31 .. 57 Lithuania .. 63 .. 20 . 19 .. 58 . 61 .. 16 Macedonia,FYR .. 82 .. 14 .. 15 .. 37 .. 49 ..4 Madagaacar 89 91 12 7 15 11 13 23 30 31 -1 3 Malawi 70 76 19 20 25 15 25 29 39 40 11 4 Malaysia 51 51 17 12 30 41 58 96 55 99 33 37 Mali 91 79 10 11 17 26 16 22 35 38 -2 10 Mauritania 68 80 25 9 36 15 37 50 67 54 7 11 Mauritius 75 65 14 12 21 25 51 58 61 61 10 22 Mexico 65 71 10 10 27 15 11 25 13 22 25 19 Moldova . 81 .. 20 . 7 .. 35 . 43 .. -1 Mongolia 74 .. a 46 .. 19 .. 39 . 27 Morocco 68 71 18 15 24 21 17 27 28 35 14 13 Mozamnbique 78 75 21 20 22 60 20 23 42 79 1 5 Myanmar 82 89 A 21 12 9 2 13 2 18 11 Namibia 44 52 17 31 29 20 76 53 66 56 39 17 Nepal 82 79 7 8 18 23 12 24 19 35 11 12 Netherlands 61 57 17 14 22 22 51 53 52 46 22 29 New Zealand 62 60 18 15 21 24 30 32 32 30 20 26 Nicaragua 83 95 20 14 17 18 24 24 43 52 -2 -9 Niger 67 82 10 17 37 6 24 13 38 17 23 1 Nigeria 56 .. 12 10 22 .. 29 .. 19 .. 32 Norway 51 50 18 21 25 230 43 38 37 32 31 29 Oman 28 42 25 31 22 17 63 49 38 40 47 27 Pakistan 83 73 10 12 18 19 12 16 24 19 7 16 Panama .. 64 .. 15 . 24 .. 39 . 40 .. 22 Papua New Guinea 61 48 24 12 25 24 43 61 53 45 15 39 Paraguay 76 79 6 7 32 23 15 36 29 46 18 14 Peru 57 83 11 6 29 17 22 12 19 18 32 11 Philippines 67 74 9 11 29 23 24 36 28 44 24 15 Poland 67 63 9 18 26 17 28 28 31 26 23 19 Portugal 65 65 14 17 34 280 24 28 37 38 21 18 Puerto Rico 75 .. 16 14 17 17 65 .. 73 .. 10 Romania 60 66 5 12 40 26 35 28 40 32 35 21 Russian Federation 62 58 15 16 22 25 .. 22 . 22 .. 26 World Deve[opment Indicaiors 1997 175 4.12 Private General government Gross domestic Exports of goods Imports of goads Gross domestic consumption consumption investment and services and services savings % ofGDP `/Sof GDP /S of GDP S,of GDP % of GP S of GDP 190 1995 1980 1_995 1980 1995 1980 1995 1980 '1995 1980 1995 Rwanda 83 93 12 14 16 13 15 6 26 26 5 -7 Saudi Arabia 22 43 16 27 22 20 71 40 30 30 62 30 Senega 78 79 22 11 15 16 28 32 44 37 0 10 Sierra Leore 91 98 6 11 16 6 23 13 39 27 2 -9 Singapore 53 40 10 9 46 33 207 216 38 51 Slovalk Republic .. 50 .. 20 28 63 61 .. 30 S ovenia ,. 8 21 .. 22 .. 56 57 .. 21 South Africa 50 61 13 21 28 1s 36 22 28 22 36 18 Spain 66 62 13 16 23 21 16 24 16 23 21 22 Sri Lanka 80 74 9 12 34 25 32 36 55 47 11 14 Sudan 81 .. 16 15 .. 12 .. 24 3 Sweden 51 55 29 26 21 14 29 41 31 36 19 19 Swltzerlancl 67 59 13 14 24 23 37 36 40 32 20 27 Syrian Arab Repuolic 67 23 .. 28 18 .. 35 10 Tajikistan 71 11 17 114 114 16 Tanzania 69 97 12 10 2.9 31 14 30 24 68 19 -7 Thailand 65 54 12 10 29 43 24 42 30 46 23 36 Togo 53 80 22 11 30 14 51 31 56 35 25 9 Trinidadf and Tobago 46 62 12 13 31 14 50 39 39 29 42 25 Tunisia 62 63 14 16 29 24 40 45 46 48 24 20 Turkey 76 70 10 10 16 25 5 20 12 25 11 20 Turkmnenstan --- . . -- . Uganda 89 63 11 10 6 16 19 12 26 21 0 7 Ukraine - .. .. . United Arab Emirates 17 54 1-1 18 28 27 76 70 34 69 72 27 United Kingdom 59 64 22 21 17 160 27 28 25 29 19 15 United States 63 68 16 16 20 16 10 11 11 13 19 15 Uruguay 76 74 12 13 17 14 15 19 21 20 12 13 Lzbekistan 59 .. 25 . 23 .. 63 .. 2 24 Venezuela 55 73 12 6 26 16 29 27 22 22 33 21 Vietnam .. 77 7 27 36 47 .. 16 West Bank ana Gaza . . . -- . . Yemen,Rep. .. 61 .. 29 12 .. 43 .. 5 10 Yugos avia. Fed. Rap... . . . . Zaire 82 6 . 10 16 16 .. 10 Zamoia 56 68 26 9 23 12 41 31 45 40 19 3 Zimbabwe 64 64 20 19 19 22 30 34 33 40 16 17 Low income 66 w 59 w 12 w 12 w 24 w 32 w 13w% 19 w 15 w 21 w 22 w 30 w Excl. Cnina & India . 80 w . 13 w . 20 w . :24 w . 30 w .. lvw Middae income . 59 w . 14 w . 25 w . :24 w .. 25 w . 25 w Lower middle income .. - -- - Upper middle income 56 w 61w' 12 w 15 w 25 w 21 w 27 w 22 w 20 w 20wv 32 w 23w Low &middle income 57 w 63w 14 w 14 w 26 w 27 w 23 w 22 w 20 w :24w 30 w 22 w East Asia & Pacific 56 w 51 w 12 w 11 w 28 w 39 w 16 w :29 w 15 w 31 w 286w 38w Europe & Central Asia ... .........._ _ _ _ _ _ _ _ Latin America & Carib. 67 w 67 w 11 w 12 w 25 w 20 w 16 w !L7 w 18 w 18 w 23 w 19W' Middle East & N. Africa 39 w . 16 w . 26 w . 47 w .. 30w . 45 w South As~a 75 w 69w 9 w 11 w 20 w 23 w 8 w 14wv 13 w 16 w 15 w 20 w Sub-Saharan Africa 60 w 67 w 14 w 17 w' 23w 19 w 31 w 28 w 27 w 31 w 27 w' 16 w H gh income 60 w 63 w 17 w 15 w 23 w 21w 22 w :22w 22 w :21 w 23 w 21 w a. Ge'reral goverrmrert censwmpt on figures are so-, availaole separate y; t1hey are ircluded ir private consumption. o. Oata prior to 1992 isolude Etritas. c. noc uoes statistical discrepancy. 3cv" C0_ ~' 0 4.12 corporatec enteprises, are usually very unrelable. , Estimates of changes in stocks are rarely ccmplete Government pol cymakers and statistic ans-ftaingthe bunc usually include the most important act v ies or s Privateconsumptionisthemarketvalueofallgoods tasks of mob liz ng resources and strengther ng dif- commod ties. In some countrie, these estimates are and services, includingdurable products (such as cars, ferent secors-l-ave tended to focus on the gro.Nth of derived as a compos te residual along with aggregate vwashing machines, and home computers) purchased output. Perhacs fo this eason, and because produc- private consumption. Adjustments should be made to or received as income n kind by households arc non- tion data are eas er co co lect than expenditcre data, tne value of the stock change for holding gains due to prof t insttutions. It excludes purchases of dwel ings many courtnes cortinue to generete theiF prmary price charges. In h ghly nflationay economies this but Inreudes imputed rent 'orcwrer-occupied dw.ellings. estimate c GDP and underlying national accounts using elemient can be substantial. In practice. it may include any statist cal discrepancy the product on approacn. And manycountr es eitherdo Exports and imports are compiled from customs in the use of resources. s General government con- not estimate the se carate componenans of a' onal data and from ba ance of payments data obtained from sumption includes a I current expenditures for pur expendicue or, if they co, cerive cre main aggregates cerntal banks. While the data on exports and imports chases of goods and services by a I levels of ind rectly us ng GOP (output) as ;he control Local. from the payments side provide reasonably reliable government, exc uding most government enterpr ses. Expenditures from GDP comopnse prvate ccnsump- -ecords of c-oss-border transactions, even these data It also includes capital expend ture or nationai defense tion, general govern-vent consumption, gross comes- may nct adhere strictly tc .he appropr ate valuation and secur ty. s Gross domestic investment consists tic fixed capital -crmation (private and pub ic and t ming defnit ons of the ba ance of payments or, of out ays on additions to the fixed assets o'the econ- nvestmert), canges in invertories, and exports (minus more importart, correspond with the change-of-own- omy p us net changes in the level c' inrentor es. Fixed mports) of goocds and services. Conventions ly, such ersh.p crime, on. With increasing globa.ization of irterna- assets cover land improvements (fences. ditches, expendiLures arer ecdec ir purchasers pni-es and tiena busiress, thi issue has assumed greater drains, and sc on): plant, machinery, and equipment therefore include net nd-ect taxes. signif cance. Neithercustcms nerbaiance of payments purchases; and the construct on of roads, railways, Prvate consumption s the rarket rOce on purchasers' data capture the illegal transactions that take place andthe like, Incud ngcommercial and industrial b.d- pr ce va ue of ali goods and serv ces purchasec or in many countries. Lega but unreported shutt etrade- ings, offices, schools, hospitals, ard private resaden- received as income in kinc ov househo ds and nornofit goocs carried by travelers acrcss borders-may fur tial dwvellings. a Exports and imports of goods and nstitutions arc, sometimes, unincorporated erterprises. ther distort trade stat stics. services represent the value of all goods and other It exc udes purchases of owell ngs bu; includes imputed For further d scussion o' th t problems of bui ding market services provided to the world. Included IS the rent for owneroccupicd dwsilrgs. Privatc corsumotion and maintaining national accounts scc Srinivasan valuc of merchandise. frcight, nsurance, travel, and is often estimated as a res dual. by subtract ng from f1994), Heston (1994), ard Ruggles (1994). And for cther nonfactor services. Factor and property neome GDP all other known expendicures. The resu ting aggre- the c ass c analysis of the rel abi.ity of 'oreign trade (former y called factor services), such as nvestment gate may ncorporate fairy la-ge d screpanrces. Even and national income statisticssae Morgenstern(1963). income, interest, and labor ,ncome, is exciudec. when crvate consump: on s ca.cu ated separately, the Transfer payments are excluded from the calcuiation nouseholc surveys on whici the est-mates are basec of GDP. o Gross domestic savings are ca culated as tend to be one-year studies and imitee ir coverage. the difference between GDP and tota consumpt on. Consequerty. tney apidly become outdated snd must be spplemented by avaeietyofprice-and quantty-based j= statistical procedures. Complicating the ssue is tnat in manydeve opingcournt'estree dstincon betwe!en cash Nationa accounts data for developing countr es are outays for personal WL rsaess and .hose ior household collected from national statistical organizations and se maybe blurred. central banks byvisiting and residentWord Bank mis- Gross domestic investrent corsists of octlays on sions. Dats for industrial countries come from OECD additions no the fixed assets o' tre economry plus l.. . data fles. For information on the OECD rational net changes in ne levee of inventor es. In general. accounts ser es see OECD, NVationas Accounts. expend tures on nationa defense and security are 1960-1994, volumes l and 2. The comp ete nat.onal regarded as part of general government conSump- accounts t;me series IS availab e on the l/orld tion. Under the nevv 1993 U.N. Syscem of National 100 Gross Development Indrcators CD-ROM. Accounts (SNA) guidel nes, horever, cap ta out ays investment on defense estab,lshments used by the general public, Govenment such as schools and hospitals, and on certa n types 60 of private housing for fam.ly use are noludec n gross 40 Prvate domestic investment. -nsumpti-n Investment data may e esti-mated from direct sur- veys of erterprises and adminestmative records or based a cesoar-e on the commocity fJow method us ng daca Frow trade East Latin Soute Sub- Asia America Asia Saharan and construction activities. Wh le the quality of public and the and the Africa I f xed investment daca depends on cre quality of gov- Pacfic Caribbean emnment accoontirg systems (vsh c tend to be weak a. Exports minus imponts. n developing countries), measures of pr vate fixed Source: World Bank staff est maites. investment, particularly capital outlays by smali. un n- World Development Ind cators 1997 177 *E 4.13 Growth of consumption and investment Private consumption Private General Gross consumption government domestic per capita consumption investment average anual average anrual average annua average annal.a $mi eons %growth % growth % gravtn %/ grnrwtn 1980 195 1980-90 1990-95 1980-90 1990-95 1980-90 199C- 95 1980-90 1990-95 Albania .. 2,029 ....... .-0.3 38.4 Algeria 18,293 23.003 1.9 0.5 -1.1 -1.7 4.7 4.8S -2.3 -4.7 Angola .. 2,320 0.3 -14.0 -2.5 -16.6 2.7 -5.1- -6.8 0.1 Argentina ... ....... .-4.7 16.0 Armenia .. 2,395 3.5 -17.6 2.2 -18.6 5.9 -8.09 6.2 -17.7 Ausatralia 94,360 222,170 3.0 3.6 1.5 2.4 3.6 2.6E 2.7 5.6 Austria 42,706 129,400 2.4 2.2 2.3 1.4 1.3 2.6C 2.5 3.6 Azerbaijan .. 4,308...... Bangladesh 11,857 22,663 4.0 0.9 1.3 -0.7 .f 3.4 1.4 8.2 Be arus -. 10.392 .. -10.1 .. -10.3 .. -8.4 .. -17.0 Belgium 74,274 165,441 1.7 1.4 1.6 1.0 0.5 1.1 3.2 -0.9 Benln 1.356 1,249 1.1 3.4 -2.1 0.4 1.3 -1.2 -6.2 12.1 Boliva 2,064 4,353 0.6 3.1 -1.4 0.7 -3.1 5., -9.9 4.2 Bosnia and H-erzagovfna Botswana 514 1, 714...... Brazil 163,832 425.391 1.6 4.4 -0.4 2.9 7.3 -1.3 0.2 3.5 Bulgaria 11,089 6,536 2.5 -6.5 2.6 -5.7 9.1 -6.7 2.4 -7.1 Burkina Face 1.631 1,453 2.6 4.6 0 1.7 6.2 2.5 .6. -5.6 Burundi 843 1,011 3.7 -0.7 0.8 -3.3 6.4 6.2 4.5 -5.0 Cambodia .. 1.976 ...... Cameroon 4,710 5,511 3.9 -4.6 1.1 -7.3 5.3 -8.5 -2.7 -4.1 Canada 145,745 338,288 3.5 1.4 2.2 0.1 2.4 0.2 5.2 2.3 Cenlra. African Republic 753 907 3.1 -2.6 0.7 -4.7 -6.7 -6.2 4.6 -8.7 Cnad 579 1.11 7 3.8 5.9 1.3 3.3 16.6 -9.1 19.0 -2.9 Chile 19,395 41.71L 1.3 8.6 -0.4 7.0 0.4 3.5~ 9.6 11.9 China 103.442 333,360 9.7 10.9 8.1 9.7 7.8 12.3 11.0 15.5 Colombia 23,452 50,767 2.7 5.3 0.8 3.4 4.2 6.7 0.5 19.0 Congo 707 1,394 2.7 3.6 -0.5 0.7 4.0 -12.2 -11.9 -7.9 Cosna Rica 3,167 5,509 2.9 4.6 0 2.2 1.1 2.7 5.3 6.6 Cote dolvoire 6,386 6,780 1.6 -0.2 -2.2 -3.3 -0.1 -0.2 -28.6 138.3 Croatia .. 11.883 Cuba Czech Republic. 26,646 ..2.3 0.0 Denmark 37.050 92,607 1.8 3.2 1.8 2.0 1.1 1.5 4.0 -1.1 Dominican Republic 5.109 8,618 1.7 4.9 -0.5 2.9 1.3 -3.9 3.7 4.9 Ecuador 6,095 11,072 1.0 2.5 -0.7 0.2 -1.4 -1.4 -3.8 5.3 Egypt, Arab Rep. 15,848 34,568 2.4 2.4 -0.1 0.3 4.5 1.2 2.7 -1.5 El Salvador 2,567 7,096 0.8 7.2 -0.2 4.9 0.1 2.3 2.2 14.7 Eritrea .. 541 . .. Estonra .. 2.288 .. -5.9 -4.8 .. 2.5 - -13.4 Ethiopia' 4.282 4,314 ..3.5 21.9 Finland 27,761 69,032 3.8 -1.3 3.4 -1.8 3.4 -1.5 3.0 -6.3 Franca 391,263 919,222 2.6 1.2 2.1 0.7 2.2 2.6 2.8 -2.8 Gabon 1,120 1,066 0.4 -14.1 -2.4 -16.5 0.2 2.3 -4.6 -0.5 Gambia, The 185 277 3.6 7.3 -0.1 3.1 3.8 -10.9 0.6 3.0 Georgia .. 2.383 3.0 -22.4 2.2 -22.2 -0.4 -43.2 0.3 -21.2 Germany ..1,375.151 ....... .- Ghana 3,730 4,892 2.2 3.4 -1.2 0.6 4.2 0.2 4.5 0.9 Greece 25,919 67,172 2.3 1.6 1.8 1.0 2.6 0.6 -0.0 1.9 Guatemala 6,217 11,142 1.1 4.6 -1.7 1.5 2.7 5.2 -1's 10.7 Guinea . 2,981 .. 4.3 .. 1.5 .. 1.0 -. 0.6 Guinea-Bisaua 81 251 4.6 2.0 2.8 -0.1 4.8 3.3 5.8 1.2 Haiti 1,197 1.635 0.9 -2.2 -1.0 -4.1 -4.4 -11.2 -0.6 -45.7 Honduras 1,806 2,864 2.7 2.0 -0.7 -1.0 3.3 4.8C 2.9 10.0 Hong Kong 17,013 84.499 6.7 6.5 5.3 4.8 5.0 6.0 4.0 11.7 413S Private consumption Private General Gross consumption government domestic -per capita consumption investment average ann,al average annual average annua average annual $ m[llions %' grow-t % growth 86 growth % growth 1980 1995 1980-90 1990-95 I )80-90 1990-95 1980-90 1990-95 1980-90 1990-95 Hungary 13,562 29,714 0.8 1.4 1.2 1.7 2.5 -7.3 -0.4 6.6 India 125,809 219,943 5.3 4.5 3.1 2.6 7.7 3.5 6.5 5.3 Indonesia 40.821 110,900 5.5 4.7 3.6 3.0 4.6 3.1 7.0 16.3 Iran, Islamic Rep. 48,854 .. 2.8 2.2 -0.8 -0.6 -5.0 8.6 -2.5 -0.8 Ireland 13,585 32,917 2.2 3.0 1.9 2.5 -0.3 3.0 .. -3.8 Israel 11,397 53,387 5.3 7.7 3.5 4.2 0.5 2.5 2.2 11.5 Italy 276,261 660.193 3.0 0.2 2.9 0.0 2.7 -0. 1 2.1 -3.2 Jamaica 1,710 3,528 4.5 4.8 3.3 3.8 6.2 -0.9 -0. 1 5.8 Japan 623.284 3.083.912 3.7 1.7 3.2 1.5 2.4 2.0 5.3 -0.8 Jordan .. 4,584 -7.2 11.9 -10.5 5.7 -2.4 3.4 7.3 6.5 Kezalkstan .. 13,943 .. -6.8 ... . -17.1 .. -16.7 Kenya 4,506 6.572 4.7 4.5 1.1 1.8 2.6 11.3 0.8 0.0 Korea, Dam. Rep. . . . .. .. Korea, Rep. 40,534 241,030 8.1 7.1 6.9 6.1 5.5 5.1 11.9 7.2 Kuwait 8,836 13.045 -1.4 ..-5.5 ..2.2 ..-4.5 Kyrgyz Republic .. 2,039 . .. .. Lao PDR . . . .. .. Latvia .. 3,914 ......5.0 1.2 3.4 -37.1 Lebanon .. 10,045 . .. .. Lesotho 455 879 1.6 -1.2 -1.0 -3.3 2.8 8.4 6.9 12.1 Libya 7,171.. ....... Lithuania .. 4.476 . .. .. Macedonia. FYR .. 1,625 . .. .. Madagascar 3,611 2,898 -0.6 0.7 -3.5 -2.4 0.5 -2.6 4.9 -4.5 Malawi 866 1,111 1.7 0.3 -1.6 -2.4 6.3 0.3 -2.8 -11.2 Malaysia 12.378 43,222 3.7 7.2 1.0 4.7 2.7 8.6 2.6 16.0 Mali 1,490 1,917 1.1 0.4 -1.3 -2.5 6.5 -2.2 5.4 6.1 Mauritania 481 849 3.1 2.8 0.5 0.2 -6.8 1.6 -4.1 -1.3 Mauritius 854 2,565 6.7 4.8 5.8 3.4 3.3 4.4 10.2 1.7 Mexico 126,745 166.410 1.0 0.5 -1.3 -1.4 1.9 1.4 -3.1 -1.2 Moldova .. 2,852 . .. .. Mongolia . ..........1.7 Morocco 12,788 23,067 3.7 2.4 1.5 0.4 6.5 3.9 2.5 -2.5 Mozambique 1,591 1,105 0.9 2.8 -0.7 0.4 -2.0 9.8 -2.5 8.6 Myanmar ... 0.6 4.8 .... ..-4.1 9.4 Namibia 969 1,510 2.4 0.0 -0.3 -2.7 2.6 3.8 11.9 -2.8 Nepal 1,600 3.361 6.6 10.1 4.0 7.4 4.6 3.2 1.8 6.3 Netherlands 104,571 226,082 1.7 2.1 1.2 1.4 2.1 1.2 3.1 -0.3 New Zealand 13,801 34,913 2.0 2.3 1.3 0.9 1.2 -0.7 1.7 12.4 Nicaragua 1,770 1,751 -3.4 3.5 -6.2 0.3 3.0 -15.9 -4.7 4.1 Niger 1.704 1,822 -0.7 0.5 -3.9 -2.7 1.4 1.9 -5.9 0.3 Nigeria 51.920 .. -3.0 ..-5.9 ..-1.8 2.4 -8.6 Norway 28.955 72,450 2.3 2.9 1.9 2.3 3.1 2.6 0.6 Oman 1,657 4,732 . .. .. Pakistan 19,688 43.977 4.7 5.3 1.6 2.3 10.3 0.6 5.9 4.0 Panama .. 4.257 .6.3 .. 4.6 .. 0.5 .15.3 Papua New Guinea 1,568 2.360 0.4 7.8 -1.7 5.4 -0.1 0.9 -0.9 0.4 Paraguay 3,467 6,199 2.4 4.5 -0.6 1.8 1.5 8.6 -0.8 2.6 Peru 12,006 48,826 1.2 3.3 -1.0 1.2 -1.4 3.2 -4.2 7.4 Philippines 20,910 54,938 2.6 3.2 0.2 1.0 0.6 2.9 -2.1 3.2 Poland 38.182 74,409 1.1 4.5 0.4 4.3 1.2 4.9 0.9 1.1 Portugal 19,035 66,159 2.6 1.8 2.5 1.8 4.8 1.2 Puerto Rico 10,756 22,539 3.5 2.3 2.5 1.5 5.1 0.2 8.9 3.7 Romania .. 23,626 .. -0.7 .. -0.2 .. 2.4 .. -10.0 Russian Federation .. 169,454........ . Wor d Deve opment Inaicators 1997 179 4.13 Private consumption Private General G ross consumption government domestic per capita consumption investment average annu.a average annua average annue average anrna $millions % growrh % growth 0/ growtl- % grow'ah 1980 1995 lOaC 90 1990-95 1980 90 1990-95 198C-90 1990- 95~ 1980 90 1990-95 Rwanda 969 1.046 1.5 -2.1 -1.5 ..4.9 -0. 6 3.7 -6.3 Sau.di Arabia 34.538 52,024 . .. .. Senegal 2,365 3,821 2.5 1.3 -0.5 -1.4 2.8 -37- 3.9 4.7 Sierra Leone 1.057 805 1.5 -2.0 -0.7 -2.9 0.2 2. 5 -6.5 -20.0 Singapore 6,030 33,074 5.6 6.6 4.1 4.5 6.7 6.' 3.7 6.0 Slovak Republic .. 8,494 3.8 -6.0 3.5 -6.3 4.8 -3. 5 1.1 -7.7 Slovenia .. 10,732 . .. .. South Africa 39,543 82.709 2.3 1.0 -0.1 . 3.5 2.3 -4.8 4.7 Spain 139,348 350.559 2.7 0.9 2.4 0.7 5.3 1.0 5.7 -2.6 Sni Lanka 3.230 9,560 3.8 6.0 2.4 4.7 7.3 5. 3 0.6 6.8 Sudan 5,447 .. 0.2 ..2.3 ..0.3 ..-1.1 Sweden 64.624 120,153 1.8 -0.7 1.5 -1.3 1.6 -0. 4.3 -7.2 Switzerland 64,650 178,266 1.7 0.4 1.2 -0.5 3.0 0.; 4.9 0.0 Svrian Arab Repuolic 8,690 .. 3.4 ..0.1 .-2.9 . 7.0 Tanzar.a 3,911 3.487 . .. .. Thailand 21,175 90.267 5.9 6.1 3.9 7.1 4.2 5. C 9.4 10.2 Togo 600 1,011 4.6 1.4 1. 6 -1.5 -2.2 -7.0 2.9 -16.4 Tr nidad and Tobago 2.860 3,071 -1.3 -5.6 -2.6 -6.4 -1.7 1.2 -10.1 1.0 Tunisia 5,360 11,422 2.9 3.6 0.3 1.9 3.8 3.7 -1.8 1.4 Turkey 42,067 115,046 -4.7 3.5 -6.9 1.8 2.9 2.0 5.3 2.0 Turkmenistan . . . .. .. Jganda 1.935 4.696 2.9 6.4 0.2 3.0 1.8 6.8 9.6 7.9 Ukraine . .. .. .. United Arab Emirates 5,116 19,423 4.6 ..-0.5 ..3.9 ..8. 7 Unitea Ki;ngdom 320,290 709,115 4.1 1.3 3.6 1.0 1.1 1.0 6.4 United States 1,708.280 4.692,418 3.4 2.7 2.4 1.7 3.0 -0.5 3.4 4.1 Uriguay 7,680 13,214 0.5 8.2 -0. 1 7.5 1.8 2.6 -7.8 12.9 Uzbekistan . . 12,937 -.7.2 .. -9.2 .. -4.0 ..9.2 Venezuela 38,065 54,992 1.3 2.4 -1.3 0.0 2.0 -1.2 -5.3 3.8 Vietnam .. 15.622 . .. .. West Bank anoJ Gaza . . . .. .. Yemen, Rep. .. 2,750... . Yuges se~a, Fed. Rep.,.. . .. .. Zaire 11,736 .. 2.9 ..-0.4 .0.3 ..-0.4 Zambia 2,145 3,577 3.9 0.9 0.8 -2.0 -3.4 -3.3 -2.7 -10.2 Zimbabwe 3,453 3,608 0.8 6.5 -2.5 3.7 7.2 -3.7 1.3 1.5 ~~VT u ~~M I: Low income 433,839 t 809,268 t 5.1 w 5.9 w 3.0 w 4.0 w 5.9 a'A 5.9 w 6.2 aw 10.4 w Excl. Chins & Indoa 216.008 t 245,968 1.8 w - 1.1 w -0.9 w -1. 4 w 3.1 w ..-1.3 w 3.6wv M ddle income .. 2,298, 093 t Low'er middle nricome .. 1,163,514 t Upper middle income 554,295 t 1,159,357 t 1.7 a' 2.8 a' 0.3 w 1.1 av 4.3 a 0.8w' -1.4 w 5.6 w Low & midale ncome 1,788,055 t 2,966,052t 3.3 w 3.9 w 1.2 w 2.1 w 4.4 v. 1.8 w 6.5wv East Asia & Pacific 221,707:t 703,540 t 7.1 w 8.5 w 5.4 w 7.1 w 5.8 w 9.5 av 8.5 A' 14.4w' Europe & Central Asia .. 623,814 t . .. .. Latin America & Carib. 506.024 t 1,027.909 t 1.5 w 3.4 w 0.6 w 1.6 w 4.2 a' 0.4 w -1.5 w 5.7 w Middle East & N. Africa 178.543 t .. ....... South Asia 165.188 t 305,128: 5.1 w 4.5 w 2.8w' 2.5 w 8.0w', 3.1 w 6.1w' 5.3w' Sub-Saharan Africa 174.871:t 184,372t 1.2 w 0.5 w -1.7 w -2.2 w 2.0 a' 1.3 w -4.0 a' 3.4 w Hign income 4,675,234 t 13,997,835 t 3.3 w 2.1 w 2.7 w 1.4 w 2.6 w 0.7 w 4.1w -0.2w' a. General gevernment censumwpt an figures are nct available saparate y: tlhey are incILded ir pr vate to rsimptan. b. Data prior to 1992 ncJude Ertrera. 4.13 .4 -4.I The data here on pr sate consumption i current a Private consumption is the market value of a I 1987 U.S. dollars U.S. doilars and growth rates of pnvat aend general goods and servsces, includingdurable products (sucn 1,400 government consumption atd gross domestic invest- as cars, wash ng machines, and home computers) 1'200 A an the .r ment are derived from the nations accounts and con- purchased or received as income in kind by nouse- verted using official exchange -ates, except as noted holds and nonprofit institutions. It excluces purchases 1,000 Ii Primary data documentatio[r. The estimate of con- of dwellings but inc udes imputed rertfor owner occ- 800 sumption per capita here differs from that in tabie poed dwei ings. In practice, it may include any sta- 600 4.14, where ourchasing powei parity conversion fac- tistical discrepancy in the use of resources. e Private Sub-Saharan Africa tors have been used to give a better est mate of consumption per capita is calcu ated using private 400 Soth * doomestic purchasing power. Consumption and i nvest- consumption in constant pr ces and Wor.d Bank pop- ment as shares of current GBlP are shown in table u ation estimates. o General government con- East Asia and the Pacific 0 O 4.12, sumption includes all current expenditures for 1980 1985 1990 1995 Measures of consumrption and investment growth purchases of goods and services by al evels o' gov Source: World sank staff estimates. are subject to two kinds of inaccuracy. The first stems ernment, excl ud ing most government enterprises, _______________________________________ _______ from the difficulty in measuring current price levels, measured in constant prices. It also inc:udes capi- as described n the notes to tnble 4.12. The second tal exsoenditure on national defense and security arises in deflating current price data to measure grovwth * Gross domestic investment consists of outiays on in real terms, where resu ts era directly dependent on additions to the tIxed assets of the economy plus the relevance and reliability of the price indexes used. net changes in the level of mvenmories, measured in Measunog price changes is more difficult for invest- constan prices. Fixed assets cover lanc improve- ment goods than for consumption gooos because of ments (fences. ditches. drains. ard so on); plant, the one-time nature of many investments and because machinery. and equipmert purchases, and the con- the rate of technologica progress in capita' goods struction of roads, ra Iways, and the ike, Including makes capturing quality change difficult. Many coun- commercial and irdustria bu Idings. offices, schools, tries est;mate investotent frorn the supply side. iden- hospitaes. and priate residential dwe.l ngs. tfying capital goods entering an economy directly from detailed production and international trade statist cs. . - . This means that the price indexes used in deflating production and international trade w 11 determine the Narional accounts data for def ator for nvestment experditures on the demand '4- develop ng countries are side. Q NATIONAL collected from national sta- To obta n government consumption in constant A CUGNTcS tistee organizarens and prices, countries may adjust .urrent values by apply- central banks by vsietng ing deflators that use a weighted index of govern- N T4ONAX and resident World Bank mentwagesandsaoaries,orsmplytakeagovernment l=: ON - missions. Data for indus- employment index as a measure of output. Neither to 4 tral countries come from technique captures improveinents in productivity or OECD data files. For infor- changes in the quality of government services. Many mation on the OECD national accounts seroes see countries estimate private consumption as a resid- OECDO Natonal Accounrs, 1960-1994, vo umes 1 ual ihat includes statistical discrepancies accumu- and 2. The complete nat onal accounts time series lated from orher domestic sources. Deflators for is available or the World Development Indrcators pnvate consumption are usu ally calculated from con- CD-ROM. surmer price series. In rescaling constant price nationa accounts data to a common base year for tie purpose of compiling international aggregates. the World Bank assigns dis- crepancies between the output and expenditure esti- mates of GCP to private consumption. This may ead to differences between the growth rate of private con- sumption measured on the basis of the country's original base year and the g-owtn rates shown here. Because the methods used to def ate consumption and investment can vary wide y among countries, com- parisons across countries, perhaps even more than those over time, should be t reated with caution. World Development Indicators 1997 1.81 '½, 4.14 Structure of consumption in PPP terms Private Household consumption, consumption per capita Gross All Bread Clothing rent, fue , Fuel Hea th Transport and Other Consumer 0ood and cereals and footwear and power and power care Education commnunication consumption durables $ 04% 0 % % 04 % 0/ % % % 1993 i 1985 93 1985-93 1985-93 1985-93 1985-93 1985 93 1985-93 1985-93 1985-93 1985 93 Australia' 12,307 14.3 2.3 3.5 15.5 2.6 12.2 8.6 12.8 33.1 6.6 Austria0 12,960 13.0 2.6 7.0 17.5 3.5 12.6 10.6 12.7 26.6 6.7 Bangladesh 1,049 46.8 33.5 7.1 24.5 5.4 3.4 3.8 5.1 9.3 2.1 Belarus' 3,669 17.5 6.3 8.6 12.2 3.6 13.3 20.0 2,0 26.4 3.7 Belgium' 13,632 14.5 2.6 5.7 12.7 3.5 14.2 11.3 11.1 30.5 9.2 Benin 1,326 18.1 3.6 16.9 30.2 1.7 6.0 8.4 6.3 14.1 4.7 Botswana 2,888 11.6 5.1 11.6 15.3 1.2 8.7 27.5 3.3 21.9 6.8 Bulgaria' 3,452 16.0 2.8 5.8 18.3 4.8 10.0 16.9 7.1 26.0 2.7 Cameroon 1,427 11.4 1.2 9.4 25.5 1.2 13.4 16.0 7.2 17.1 2.0 Canada' 14,169 9.0 1.7 4.6 19.9 4.2 10.9 9.3 11.4 34.9 7.3 Congo 1,132 19.5 5.4 6.9 16.9 2.1 8.3 25.6 10.8 12.0 0.4 Cote d'voire 894 25.1 3.9 9.2 9.0 0.8 15.5 10.5 5.2 25.6 6.7 Croatia' 3,825 18.9 5.4 3.3 21.6 3.1 10.9 10.7 13.9 20.7 3.2 Czech Republic0 5,802 16.2 4.2 4.9 20.8 5.5 10.3 12.4 5.7 29.7 4.7 Denmark' 14,002 10.1 1.3 3.9 19.8 3.1 8.9 12.6 9.2 35.6 6.1 Egypt, Arab Rep. 2,509 25.5 7.0 10.1 28.8 1.7 4.6 14.0 3.3 13.7 3.7 Ethiopia 372 27.6 5.5 13.8 23.6 3.6 4.0 8.0 8.0 14.9 4.7 Finland0 11,431 10.5 2.0 3.3 20.4 4.8 11.9 11.1 9.5 33.2 4.2 Franceb 13,811 12.4 2.0 4.1 16.8 2.7 21.2 8.1 11.8 25.6 5.8 Germany0 14,704 10.7 2.4 5.6 16.3 3.1 15.5 5.9 12.1 33.9 8.5 Greece' 8,544 28.1 3.0 5.0 13.1 1.7 7.3 5.6 15.5 25.5 3.1 Hong Kong 13,954 11.6 1.9 8.7 9.8 1.2 9.6 4.0 6.7 49.6 13.3 Hungary' 4,397 14.4 2.8 4.1 19.8 5.7 9.4 13.8 8.1 30.4 4.3 India 849 43.4 16.2 8.2 19.4 2.7 7.2 7.0 5.7 9.0 1.5 Iran, Is amic Rep. 3,624 28.5 8.5 7.7 28.2 4.9 11.8 4.4 6.8 12.6 3.5 Ireland' 8,500 13.8 3.4 5.8 16.0 3.3 11.4 13.2 7.7 32.0 4.7 Italy0 12,903 14.0 2.2 6.5 19.2 2.8 13.8 7.0 9.4 30.0 6.6 Japan' 13,322 10.9 3.5 5.3 16.6 1.8 17.5 8.3 9.5 31.9 4.4 Kenya 893 20.2 7.2 10.2 25.0 1.7 3.0 19.5 5.3 16.8 5.5 Korea, Rep. 5,878 26.9 13.9 6.7 12.0 3.0 8.5 9.9 7.6 28.4 6.4 Madagascar 568 32.0 9.7 10.1 23.8 5.2 3.5 15.0 3.7 12.0 1.9 Malawi 592 21.2 4.1 13.8 11.5 2.2 5.8 17.1 4.3 26.3 6.7 Mali 432 34.0 10.9 10.9 12.6 4.1 6.8 15.6 7.1 12.9 3.4 Mauritius 8,834 8.1 2.0 4.3 47.0 1.4 3.3 10.9 5.9 20.5 5.5 Moldova0 1,509 28.5 11,5 6.3 12.1 7.1 9.8 22.0 3.0 18.3 3.2 Morocco 2,454 23.6 6.9 16.0 18.5 0,9 5.0 15.7 5.8 15.5 4.8 Netherlands' 12,142 10.7 2.5 5.9 15.6 2.6 16.2 8.2 9.4 34.0 7.4 New Zealand' 10,571 11.8 2.7 4.1 19.9 3.4 10.5 9.3 13.4 30.9 6.8 Nigera 984 28.2 4.3 6.5 14.0 1.5 4.3 7.6 3.6 35.8 7.6 Norway' 12,473 13.3 1.8 5.4 20.9 10.3 14.4 10.7 7.6 27.8 7.0 Pakistan 1,604 33.0 12.6 8.0 17.0 5.4 5.8 2.5 9.9 23.7 3.8 Philippines 1,970 47.0 24.4 3.9 16.6 4.4 3.9 9.4 1.5 17.7 2.7 Poland' 3,559 19.0 4.2 3.1 24.3 4,9 11.9 14.8 6.7 20.0 3.2 Portugal0 8,801 19.8 4.7 5.7 10.1 1.6 7.3 15.8 10.9 30.3 5.9 Romania' 2,547 27.6 8.7 8.9 16.4 4.9 5.7 11.9 5.2 24.3 4.2 Russian Federationb 2,648 18.2 6.6 7.7 20.1 9.6 12.9 18.2 5.5 16.4 3.4 Rwanda 760 18.8 1.5 13.2 21.0 4.3 5.9 13.7 4.6 22.8 12.4 Senegal 1,346 28.3 4.4 16.2 23.1 3.5 3.5 11.4 4.9 12.6 3.7 Sierra Leone 470 29.8 4.0 6.7 45.5 4.2 2.8 7.0 4.2 4.0 0.5 Slovak Republic' 4,309 16.6 4.9 4.9 24.5 6.6 14.3 16.5 :3.5 19.7 2.9 Slovenia' 7,544 14.1 2.8 4.2 18.2 4.2 11.2 10.3 14.3 27.7 3.4 Spainb 9,326 16.8 2.5 7.0 14.4 2.3 11.0 7.7 12.2 30.9 4.7 Sri Lanka 2,373 37.1 18.2 8.5 6.2 2.4 9.0 8.9 12.5 18.0 4.2 Swedenb 12,502 10.4 2.2 5.1 23.2 4.6 11.2 8.6 10.6 31.0 5.9 Switzerland0 16,582 11.9 2.1 6.1 20.5 4.3 13.2 8.4 11.2 28.7 7.7 Tanzania 520 30.1 15.5 10.4 31.0 2,5 9.6 11.0 1.4 6.5 0.8 c :. t ' 30g z^i1^ E:- 4.14 Private Household consumptlon' consumption per capita Gross All Bread Clothing rent, fuel, Fuel Health Transport andi Other Consumer food and cereals and footwear and power and power care Education communication consumption durables $ I % %t % % % % %t %9% 1993 1985-93 1985-93 1985-93 1985-93 1985-93 1985-93 1985-93 1985-93 1985-93 1985-93 Thailand 4,102 29.1 11.9 9.4 9.7 2.6 12.2 10.0 10.2 19.4 4.8 Tunisia 2,494 24.3 2.8 9.6 19.5 2.0 8.5 16.9 4.7 16.5 7.5 Turkeyb 3,878 23.3 6.2 7.4 23.2 3.7 4.9 9.5 7.3 24.4 6.6 Ukraineb 2,010 19.0 7.9 5.1 21.9 11.0 13.0 21.6 1.8 United Kingdom' 13,275 10.6 2.4 6.0 17.6 2.7 9.7 7.9 11.4 36.8 6.6 United States5 18.507 8.4 1.6 6.5 16.6 3.0 12.0 7.0 14.3 35.2 8.7 Zambia 659 13.9 1.7 12.2 23.1 3.3 11.0 27.6 4.4 7.8 0 Zimbabwe 1,275 21.4 5.3 12.0 29.8 3.6 2.6 10.0 3.4 20.8 6.3 Note: Except where otherwise indicated, data are based on results of the 1985 round of the Interrational Comparison Programme (ICP). a. For the components constituting the shares see Definitions in the notes to this table. b. Data are based on results of the 1993 round of the ICP. World Deuelopment Indicators 1997 183 4.14 International comparisons of consumption tion of more than 1 million using the most recent For lower-income countries services gener- Cross-country comparisons of consumption PPPs from the International Comparison allyaccountfora highershare of consumption expenditures are difficult to interpret. When the Programme (ICP). The summary tables 4.14a in PPP terms than in nominal terms. The con- expenditures are denominated in national cur and 4.14b divide the countries into five income ventional view is that the share of income spent rencies or in a single currency using an exchange groups based on their GDP per capita in PPP on services increases with per capita income. rate conversion, the comparisons do not account terms, and give the average shares for selected This is true only in nominal terms, however. forthe sometimes substantial differences in rel- components of consumption based on both PPP In real terms the share of services remains ative prices. As a result, they also do not reflect and local currency values. more or less constant regardless of income differences in the real relative quantities of dif- Comparison of the averages based on local level. But the re ative prices of services are ferent types of consumption embodied in each currency and PPP values reveals some interest- generally lower n poorer countries. So the country's expenditure patterns. ing differences. The most significant relates to implication is that people in higher-income This problem has led to the use of purchas the share of food in total consumption, used as countries do not buy proportionately more ser- ing power parities (PPPs) to convert reported a basic indicator in many poverty studies. While vices-theyjust pay more forthe services they vaiues to a common unit of account. PPPs mea the share of food varies inversely with income in buy. sure the relative purchasing power of different both summary tables, the size of the shares is Although PPPs are more useful than official currencies over equivalent goods and services. quite different. For example, the average share exchange rates in comparing consumption pat- PPP-based expenditures therefore correctfordif- of food for the lowest-income group is 48 per- terns. caution should be used in interpreting ferences in relative prices and so allow mean- cent of consumption in nominal terms, but only PPP results. PPP estimates are based on price ingful comparisons of consumption across 29 percent in PPPterms. Butthe differencetends comparisons of comparable items, but not all countries. PPP-based figures also reveal the to disappear as income rises, because the rel- items can be matched perfectly in quality across underlying relationships between the structure ative price of food (measured by the ratio of the countries and over time. Services are particu- of consumption and income, because the quan PPP for food to the PPP for GDP) is higher for larly difficult to compare, in part because of tities of differenttypes of consumption arevalued lower-income economies. Because this tendency differences in productivity. Many services are at average international prices. Thus PPPs pro- holds both for countries and for income groups not sold on the open market in all countries- vide a consistent and meaningful approach to within a country, one conclusion is that the inci- forexample, governmentservices-so they are analyzing how the structure of consumption denceofpovertyislikelytobehigherifmeasured compared using input prices (mostly wages). changes with the level of development. in PPP terms than if measured in nominal terms. Because this approach ignores productivity dif- Table 4.14 presents the structure of private Another interesting difference relates to the ferences, it may irflate estimates of real quan- consumption for 65 countries with a popula- relative shares of services in consumption. tities in lower-income countries. Table 4.14a Structure of consumption in PPP terms, by income group, 1993 percent Income group Transport Other (GDP per capita Clothing and Health and commu- consump- in PPP terms) Food footwear Rent care Education nication tion $s,0DD or less 29 10 23 6 ' 3 5 is $1.001-4,000 25 n0 i8 8 14 6 19 $4,001 io,000 19 6 23 1 13 6 23 $10,0C1 20,000 15 5 16 ii § 11 33 $20 000- 5 15 14 9 11 31 Source: Wo ld Bark staff esria-es. Table 4.14b Structure of consumption in nominal local currency terms, by income group, 1993 percent Income group Transport Other (GDP per capita Clothing and Health and commu- consump- in PPP terms) Food footwear Rent care Education nication tion $1,000 or ess 48 8 11 3 6 7 18 55,001 400C 38 9 10 6 7 9 21 $4,001 10.000 27 a 14 7 7 9 28 $.0,00'-20,000 15 7 15 9 7 13 34 $20,000+ 11 5 18 12 8 12 33 Source: Wor d Bank staff est Tates. Di~ vO- evr 'onmenL nd caLo s 109- 4.14 _a [ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~a The data here are based on the rnost recent estimates a Private consumption inc!udes the consumption t 6 i E6 * t _ 11 6 _ _ d _ from the International Comparison Programme lCP. expenditures of individuals, households, and non-profit, 70 The ICP database is compi ed in tvo steps. First, regional nongovernmental organizations It also includes gov- *pPpte-rn comparisons are carrieo out, ard aggregate PPPs are ernment expenditures for education and health ser- to60 Nominal "mrns computed for developing countnes by region and for vices. e Household consumption showsthe percentage a 50caad theOECDcountries.Secono,theresultsarelinkedacross shares of selected components of consumption coin- o 40 _ | regions to establisr globai cons stency. The figures for puted from detaiis of GDP converted using purchasing O 30 the OECD countries and for the countries of the former power parities. e Bread and cereals comprise the main 20 a sGreece SovieL Union anc Eastern Eurcee are from the 1993 staple products-rice, flour, bread, al other cereals, 20 inea round of the ICP. The rest are from the 1985 round. and cereal preparations. e Gross rent consists of both Ct10 Tannana Consumption refers to private (that is, household) actual rent and imputed rent (the hypothet cal cost of 0 and nonorofit (nongovernmertal) consumpt on as renting ahe same property in the ooen market) and 0 20 40 60 80 100 cefined in tre U.N. System cf National Accounts. repaik and maintenance charges. e Fuel and power GNP per capita (U.s. = lOCi Estimates of private consumption of educat on and exclude energy used for transport (rarely reported to Note: The trend lines are based on data from 68 heatth services nc ude government as wel as private be more than 1 percent of total consumption n ow- countries. Toe upper trend line is in PPP terms, the lewer trend line in nominal terms. Data are fto 1985 ass lass, outlays. The ICP concept of er hanced consumptIon, and middle-income economies). . Health care and Source: Werld Bark staff estimnates. < ortotal consumption of the Oopulation, Focuses on who education may rnc ude government as well as private ___________ _- consumes goods and services ratherthan on who pays expeoditure. * Transport and communication cover for them. That is, it emphas;zes consumption ratner all personal costs of transport, telepnones, and the than expenditure. This approach improves nternational like. e Other consumption covers beverages and 1 iE E S 1p p pi i ;jI comparabilty because aggregate measures based on tobacco, nondurabla housenold goods, household ser- cons,mp.ion are less sensLive tD differences in national vces, recreational services, and serv ces (inc uding oractices in financing health arcd education services. mealsi supolied by hotels and restaurants; the pur 70 + PPP terrns Beca..se nointries tend to rconcentrate on produc- chase ofcarryoutfood s also recorded here. Th sgron,p 60 4>qlTanla Nominalterms tion numbers, however, estimeting the aetailed struc- also covers consumer durables, comprising household , 50 ture of consumption is one of the weaker aspects of appliances, furniture, floorcoverings, recreational equip- e 40 \ | national accounting in low and middle-income men., ard watches and jewelry. 40 a jIl \ | economies. The composition of consumption is esti- o30 GCreere mated through household expenditure surveys asd - _ v 20 related survey informaticn and thereeore shares any 10 Itale st bias nherent in the orig nal samnple frames. Forexam- = The source of purchasing 0 Candaa ple, m some countries surveys are limited lo urban wha-i====w, power parity cata as the 0 20 40 60 80 100 areas or even more narrowly ':o capital cibes and so International Comparison GNP per capita (U.S. =100) are not representative of national expenditure patterns. Programme (ICP), coordi- Urban survevs show ower than average shares forfood nated by the United Nations Note: The trend lines are based on data from 68 countr es. The upper trend line is in eominal terms. the atde higherthan average srhares for gross rent, fuel and Statistical Division. The lower trend line in PPP terms. Data are for 1985 and 1993. W power, transport and communication, and other con- World Bank collects detailed Source: World Bank staff estimates. sumption. Contro led food prices and Incomplete ICP benchmark data from national accountingforsubs;stence activities may also regional sources, estab- contribute to low shares for fcod. Adjustments based lishes global consistency across the regiona data sets, en other available indicators rnay have to be made to and computes regression-based estimates 'U' non- mprove the cross-country coinparability of consump- benchmark countries. For detailed information on the t on patter ns. See Ahmad (19941 for an extensive dis- regional sources and compilation of benchmark data cuss on of the ICP and its methods. see the World Bank's Purchasing Povver of Currencies: Comoaring National Incomes Using ICP Data (1993b). For information on how regress on-based PPP estimates are derved see Ahmad (1992). World Deve opment Indicators 1997 185 23R7 4.15 Macroeconomic indicators Current G ross Gross Overall External GDP Seiginor- Money Real Real Gross account domestic domestic fiscal debt deflator age and quasi effective interest inter- balance savings investment deficit (present money ea(change rate national value) rate reserves a-aeac average on~ a0000 -Unal of m,ort o~ DP Iof 3DP 5 of GDP X of GDP %of GDP 9 gcoth of GDP %growtr- 10?90 - 101 0 % coverag '993 95 1093-95 1903395 1993-95 1993 95 1993 99 1-993 99 1993095 1993-99 1993-95 1993 95 Aban ia -2.8 -21.0 14.3 .. 41.3 22.4 .. 23.68. -14.7 3.2 A geria -2.8 27.9 31.0 .. 56.2 28.2 1.6 12.5 ... .5 ,Ango a .. 36.2 18.7 .. 218.2 11,534.4 . Argentina -2.7 17.5 18.8 .. 26.7 3.1 0).4 7.0 .. 5.7 6.6 Armenia -7.5 -20.8 9.8 .. 10.2 943.8 14.9 Aj at ra.I a -4.8 20.8 21.8 -2.0 .. 1.0 0.3 0.2 35.2 .. 2.2 Austria -1.2 25.7 25.9 -3.6 .. 2.8 0.3 5.1 94.9 -0.3 3.1 Azerbaijan -3.8 1.2 16.1 ..3.8 935.1 14.2 288.1 Bangladesh -2.6 8.3 15.4 .. 32.5 6.8 1.2 15.7 ..2.5 8.1 Belarus -1.8 19.2 30.8 ..4.5 1,142.1 . 158.4 .. 83.4 Be glum .. 23.0 17.7 -2.3 .. 2.4 0. 1 3.8 104.1 2.3 Bentn 0.0 5.6 15.9 .. 41.7 34.5 0.5 20.35.. 4.4 BoIvia -7.8 5.8 15.2 -2.6 62.4 8.9 1.8 15.6 94.8 10.5 5.5 Boania ann Herzegov 09 Botaswana 9.1 23.5 25.3 .. 13.1 13.0 3.6 12.2 .. -0.2 24.0 Brazil -0.9 21.9 21.0 .. 27.1 542.4 7.0 316.0 .. 55.0 8.2 Bu~gana -2.6 18.8 20.3 -7.3 103.3 65.7 Bursoina Easo -0.8 7.6 22.2 .. 26.4 1 7.5 1.5 25.9 ... 6.1 Burmndi -1.8 -6.7 12.6 .. 49.4 13.7 0. 7 33.3 92.4 .. 8.0 Carnbodna -6.0 6. 7 14.5 .. 69.2 8.6 .. 39.5 ... 0.7 Cameroor -3.9 18.7 15.c . 72.2 13.4 -0.2 9.0C 7 1.2 -1.9 0.1 Canada -3.0 19.7 16.8 ... 1.8 0.0 7.1 62.8 4.3 0.9 Cantr'al African Repqbo1c -3.4 5.0 12.5 .. 48.1 20.1 4.1 36.4 69.9 -5.2 7.7 Chad -6.8 ... . 37.7 23.6 1.3 39.8 .. -6.2 2.7 Chiea -1.6 26.0 27.6 1.9 39.4 13.1 4.8 18.4 I114.6 2.6 9.2 China -0.4 41.5 41.2 -2.0 16.4 16.0 8.7 32.2 ..3.7 5.1 Colonmba -4.7 16.2 19.9 .. 27.6 21.7 1.7 27.9 128.5 5.2 5.8 Congo -30.9 21.8 33.3 .. 222.8 22.1 0.9 13.2 .. -5.1 0.3 Costa Rica -4.2 23.1 27.2 -2.9 42.0 20.3 3.0 13.1 100.7 2.1 3.0 CSte a socre -3.5 16.8 11.2 .. 177.5 27.8 2.0 31.7 76.7 .. 0.8 Groatia -2.6 8.0 14.0 0.0 16.5 .. 3.3 36.0 ... 2.0 Ciba Czech Repub in 0.4 20.2 21.2 1.3 31.0 10.0 .. 24.8 ..4.5 4.6 Denmark 2.1 21.0 14.9 -2.2 .. 1.6 1.2 -2.2 103.3 3.3 1.7 Gernin can Repub in -2.1 16.8 20.8 .. 38.4 8.2 1.4 13.3 109.9 .. 0.9 Ecuaador -4.5 21.5 19.6 0.2 80.7 25.3 1.8 44.0 128.5 o.5 3.9 Egypt, Arab Rep. -0.9 5.8 17.1 5. 4.7 8.3 3.9 10.6 ..2.4 11.0 El SaJvaner -0.9 4.7 18.9 -0. 6 21.5 10.4 2.9 17.1 ..2.7 3.4 Eritreaa. -28.8 15.1 ...... Estena -2.9 19.4 27.3 0.2 5.2 37.2 4.1 30.6 ... 2.8 Etiompia -1.4 3.0 15.5 -7.6 60.0 9.3 2.1 16.8 ..0.7 5.8 Finland 15 22.1 15.5 ... 2.0 1.3 3.7 66.0 1.6 3.1 France 0.8 19.8 17.6 -4.9 .. 1.6 0.1 8.8 99.9 2.6 1.6 Gabon 5.1 43.7 24.4 .. 76.3 32.3 1.0 23.0 6'.7 -9.2 0.6 Gambia, Tra -0.5 4.3 21.0 .. 59.4 6.5 0.9 4.8 99.4 6.3 Georgia .. -20.2 3.3 .. 37.4 1,477.9 . Germany -0.9 22.5 21.8 .... 0 0 3.3 114.5 .. 2 4 Ghana -6.6 4.5 16.4 .. 53.7 33.1 2.1 43.0 .. -4.0 3.6 Greec;e -1.5 6.0 18.4 ... 9.6 2.4 24.8 96.1 6.1 6.8 Gua:emala -5.0 8.9 1 7.5 -0.5 19.9 11.0 0.7 13.9 ..1.7 3.1 Guinea -4.8 10.2 14.9 . 57.8 3.2 0.3 3.6 .. 11.6 1.2 Guinea-Bisaua -21.4 -1.2 20.6 .. 220.5 32.7 2.6 45.6 ..2.1 2.2 Haiti -1.4 -8.4 3.1 .. 24.4 30.4 5.2 29.3 Hondnaraa -8.0 15.4 26.3 -0.1 95.5 27.0 1.6 29.8 .. -8.5 1.1 Hong Ktong .. 33.6 31.5 ... 6.0 . 4.15 Current Gross Gross Overall External GOP Seignor- Money Real Real Gross account domestic domestic fiscal debt deflator age and quasi effective interest inter- balance savings investment deficit (present money exchange rate national value) rate reserves average average months annual annual of import % of GDP % of GDP % of GOP % of GDP % ot GDP % gro,,tn -A of GDP I% g,otnh 1990 = 100 'A overage 1993-95 1993-95 1993-99 1993-95 1993-95 1993-95 19939 1993-95 1993-95 1993-99 1993-95 Hungary -8.9 16.0 21.6 .. 65.7 21.8 3.4 13.4 129.7 -0.8 5.6 India -1.2 21.3 23.0 -6.5 24.9 9.5 2.5 15.6 ...5.7 Indonesia -2.2 35.6 35.1 .. 50.7 7.9 .... ..3.2 Iran, Islamic Rep. .. 30.3 27.6 -0.2 .. 37.6 4.3 31.7 Ireland 3.0 27.6 13.8 ... 1.2 1.1 9.9 60.3 -1.1 1.9 Israel -4.5 13.2 23.2 -3.1 .. 10.6 -0. 1 23.1 .. 2.0 2.5 Italy 1.6 21.1 17.4 -10.6 .. 4.2 ... 77.5 2.5 2.6 Jamaica -3.2 14.0 19.9 .. 86.8 27.2 4.8 36.1 .. 0.3 2.1 Japan 2.7 31.1 29.1 ... -0. 1 0.6 2.9 145.6 1.4 3.6 Jordan -16.0 2.7 30.9 2.7 102.5 4.3 5.9 4.5 .. -1.2 5.1 Kazaakstan -3.3 17.7 24.1 .. 10.8 575.8 .... ..2.5 Kenya -0.6 16.9 16.5 -2.3 74.7 14.3 3.8 23.7 ...1.9 Korea, Dam. Rep. . .. .. .. Korea, Rep. -0.8 35.6 36.1 0.2 .. 5.5 1.2 17.1 .. 3.1 2.5 Kuwait 11.3 21.1 15.2 ... 2.7 -0.3 6.8 .. 9.2 4.5 Kyrgyz Republic -7.6 9.3 15.8 .. 11.3 97.3 . Lao PDR -13.7 ... . 42.7 13.4 1.5 23.9 .. 2.0 1.6 Latvia 3.5 20.7 16.4 .. 5.5 31.3 ..8.7 .. -10.9 4.3 Lebanon -44.7 -31.1 24.1 .. 21.3 .. 6.7 20.68. . 15.2 Lesotho 7.0 -15.2 65.3 .. 37.8 6.0 1.3 9.5 110.9 1.0 4.5 Libya ... . . .. . 12.0 Lithuania -3.8 14.3 20.4 .. 6.6 46.9 .. 29.8 .. -37.0 2.4 Macedonia, FYR .. 6.5 17.0 .. 48.9... Madagascar -8.5 2.7 11.1 .. 93.8 44.2 1.8 33.0 Malawi -19.5 0.3 14.3 .. 60.6 59.2 4.0 46.1 75.2 -10.0 1.1 Malaysia -4.7 36.7 38.1 2.1 34.5 5.1 3.9 16.3 103.3 ..4.6 Mali -8.6 7.8 24.8 .. 71.6 22.4 -0.1 22.7 ...4.1 Mauritania -9.3 9.4 18.0 .. 158.5 5.5 1.8 -2.9 ...1.2 Mauritius -3.4 23.4 29.5 -0.5 33.9 5.7 -0.1 15.5 .. 3.9 4.1 Mexico -4.8 16.6 20.6 .. 42.2 20.7 0.8 27.4 .. 4.4 2.0 Moldova -. -2.2 7.3 .. 1.5 .. 5.2 68.8 Mongolia 5.4 ... -3.3 40.4 47.1 4.3 53.7 .. 0.3 2.6 Morocco -3.0 14.9 21.1 .. 64.8 3.5 1.2 8.6 1-09.1 ..4.5 Mozambique .. 4.4 57.7 -. 280.5 47.5... Myanmar .. 11.8 12.6 -2.9 .. 16.4 .... ..3.4 Namibia 4.1 12.1 18.6 -.. 10.1 0.6 25.9 .. 0.7 1.2 Nepal -7.7 13.5 23.0 .. 27.8 7.0 2.3 18.1 ... 6.5 Netherlands 4.2 26.2 20.3 -2.1 .. 2.2 0.0 3.1 102.1 1.9 2.8 New Zealand -4.6 24.6 22.4 0.3 .. 0.0 0.1 4.1 94.5 6.8 2.5 Nicaragua -37.4 -10.9 17.9 -2.1 496.4 10.8 2.7 52.1 88.3 -2.2 1.0 Niger -6.5 ... . 52.2 17.3 -0.7 5.4 ...3.8 Nigeria -3.7 ... . 105.5 76.9 3.0 36.5 114.0 -22.9 1.6 Norway 2.4 ... ... 1.3 0.3 4.4 100.0 3.4 Oman -9.2 26.4 1 7.7 -12.2 23.9 0.7 0.1 7.2 .. 7.4 2.4 Pakistan -4.5 15.7 19.6 -6.9 40.2 13.3 2.5 15.6 ...2.4 Panama .. 23.1 24.5 .. 98.5 2.1 .. 11.6 .. 3.7 0.9 Papua New Guinea 12.2 33.8 19.4 -5.0 45.9 10.8 0.5 -1.3 98.3 2.4 0.9 Paraguay -14.9 -12.8 23.1 .. 23.4 15.5 2.2 22.9 112.1 4.7 Peru -5.9 14.6 19.0 0.5 45.9 15.5 2.0 32.9 .. 1.7 8.2 Philippines -4.3 16.0 23.5 .. 56.1 8.7 1.4 24.3 116.0 1.3 3.0 Poland -4.4 17.3 16.1 .. 37.8 28.3 1.8 36.6 184.0 1.8 3.2 Portugal -0.6 ... ... 4.5 -3.6 9.7 113.0 3.6 7.4 Puerto Rico . .. .. .. .. Romania -3.3 23.4 27.2 -1.0 16.5 79.8 4.1 101.2 ...3.6 Russian Federation 2.1 29.9 27.7 .. 26.0 249.6 .. 160.3 ...1.9 Wor dl Development Ind cators 1997 187 A 4.15 Current Gross Gross Overall External GDP Seiginor- Money Real Real Gross account domestic domestic fiscal debt deflator age and quasi effective interest inter- balance savings investment deficit (present money exchange rate national value) rate reserves ave-oge ~ verage rmantis aor..o ar-a 0f .0,port of CDP of GD0 of ..~DP / of GDP o f GDP a grc,n of GDP gooir 10 100 cN. o-rage 19~93- 05 1005-00 1905 05 1993-90 1993 95 1993 99 10993 05 1993095 19% 395 1903-95 0993-99 Rwanda .. 19.7 1 1.4 .. 41.5 34.2 1.6 29.0 ...2.3 Saudi Arabia 9.9 28.5 22.1 ... 2-9 ..2.9 94.4 ..2.5 Senegal 2.8 7.5 14.2 .. 53.1 16. 7 -0.1 22.0 .. 1.2 Sierra Leone -6.1 0.2 8.4 -4.6 -116.8 22.6 0). 1 14.1 108.4 -6.7 2.5 Singapore 13.9 .. 34.9 5.3 .. 3.1 1.1 11.4 .. -C.6 5.8 S oves Rapub c 1.4 26.9 26.3 .. 28.6 11.6 .. 17.9 ..3.4 2.9 Slovenia 1.7 21.6 21.0 .. 14.8 .. 1.2 36.4 ...1.9 Souts A'rca -0.6 19.0 17.1 68.6 .. 10.3 0.5 17.2 101.8 1.4 1.3 Spain -0.6 20.4 20.65.. 4.3 0.6E 6.9 90.2 3.6 4.3 Sri Lanke -4.3 16.1 25.9 -5.0 41.9 9.1 2.0C 19.3 ..6.6 4.3 S.darn.. . .. 90.6 7. 7 51.2 0.. .3 Sweden 0.1 17.9 13.9 -11.6 . 3.3 1.1 . 74.2 2.3 3.5 Switzerano 7.3 27.4 22.9 ... 0.8 -0.3 4.4 13D7.9 1.5 Syrian Areab Repuolic -2.4 ... -0.7 115.1 11.5... Tala,fstan -4.9 2.4 20.9 .. 22,4 236.7- Tanzesia -19.4 -0.2 31.6 .. 49.4 26.3 3.9 35.4 ...1.6 Thailand -8.3 36.2 41.6 2.3 36.0 5.6 1.4 14.9 .. .6 5.3 loge -9.3 4.3 9.7 .. 66.7 34.9 -1.6 32.8 f5.7 ..3.8 Trirfdad and Tobago 4.1- 23.4 1... 44.4 6.2 1.2 10.1 67.2 -1.6 1.9 7-nsi a -6.3 21.2 25.8 . 50.8 6.1 0.6 7.3 ...1.9 Turkey -1.0 21.3 24.4 -3.5 40.0 93.6 3.2 123.5 ..4.8 3.2 Turomenisaor 11.3 ... .7.5 1,230.5... Uganda -9.1 4.2 15.5 .. 46.7 7.5 1.1 23.9 8 7.2 -2.7 3.4 Ukraine .... ..6.1 616.6 0.0 278.9 .. -76.2 snintee Arab Emnirates . 32.6 25.3 0.3 ... 1.2 9.1 United Kisgdorm -0.8 ... 2.4 0.2 .. 9.5 1.2 1.4 Onited States -2.0 ... -3.1 .. 2.3 0.4 2.9 96.1 ..2.1 Uruguay -2.2 13.7 14.4 -0.9 30.8 41.2 3.7 41.6 151.9 -3.7 5.1 Jzbelkacan -1.2 24.7 31.3 .. .5 639.1... Venezuela 1.3 20.2 16.0 -3.5 54.~5 56.5 1.6 52.7 12,3.0 -6.0 7.9 vietna-' -8.6 1 6.7- 24.4 .. 137.0 17.0 ..0.0 Weat Bans arc Gaza..... ....... Yemen, Rep. -6.8 -4.0 14.2 .. 127.6 .. 12.1 41.3 ...1.4 Yugeslavia, Fed. Rep. ..... ....... Zaire ... . . .. . 6,968.9 100.0 Zambia .. 5.0 9.7 .. 137.5 51.3 .. 62.0 110.8 7 mbabcwe -7.2 ..- 62.0 -. -. ...0 K,,c ~'v -Crer 4.15 S Increasing macroeconomic stability U, 111 For markets to function well, governments, busi- nesses, and financial institutions need timely, The table shows three-year averages for key macro- e Current account balance is the sum of net trade accurate information on the macroeconomic economicvariables in the most recent period forwhich (exports minus imports) in goods, services, and and financial condition of a country. dataareavailabie. Mostofthe ndicatorsappearelse- income pus retcurrenttransfers. e Grossdomestic Improvements in the current account and in winere in this book. Readers may wish to consult the savings are the doference between GDP and tota fiscal deficits are often viewed as signs of a notes to tables 4.12, 4.16, 4.17. 4.20-4.22, 4.24. consumotion. a Gross domestic investment is the strengthening economy. Lenders monitor a and 5.5forfurtherdiscussion ot the sources and reh- sum of all out ays on addit ons to capital assets, plus country's creditworthiness bywatchingtrends ability of the data. changes in inventories. e Overall fiscal deficit IS in the level of reserves and in external debt Seignorage. which does not appear elsewhere n the overa I budget deficit of the centra government, ratios. Investors look at investment levels and this book, is the net revenue derived by the govern- calculated as current and capital revenue and offi- money supply and exchange rate trends to ment or monetary authorit es th ough the issuance of cial grants received, less total expenditure and lend- assess the prospects for growth and financial money. In the past seignorage was measured as the ing mirus repayments. a External debt (present stability. Portfolio managers, who frequently difference between the face va ue of money and the value) is the discounted present value of future debt move large volumes of funds from one coun- value of the metals itwas made 'rom. Nowthat money service payments, ncluding private, public, and pub- try to another in a search for high rates of is printeo, the cost of its production can be ignored. tc y guaranteed short- and long-term debt. The dis- return, look very closely at changes in key Seignorage is therefore measured by the change in count rate reflects market lending rates for the macroeconomic indicators in makingdecisions. holdingsofreserveorbasemoney.,wh chin mostcoun- currency In which the loan is denominated. o GDP And because the size and volatility of these tries is equal to the non-interest-bearing liabilities of deflator is the price inex that measures the cnange movements in foreign capital can be destabi- the central bank. in the price level of GDP relative to real output. lizing, governments have increasingly used e Seignorage is the annual change in holdings of macroeconomic indicators as a tool of eco- reserve money (IFS line 14). a Money and quasi nomic management. money referto the M2 defin tion of moneysupp y (IFS lines 34 and 35). e Real effective exchange rate is - a trade-weighted index of a country's real exchange rates. An Increase represents an appreciation of the About 30 developing economies currency. a Real interest rate is the deposit Inter- contained inflation to single dig;itis est rate ( FS Iirie 60 ) adjusted for the rate of nfla- tion as measured by the GDP deflator. e Gross during 1993-95 0 international reserves compr se hold ngs of mone- tary gold, special drawing r:ghts (S[bRs), the reserve In the short to medium term there are five pos tion of meribers n the Internat.onal Monetary key elements in macroeconomic stability: low Fund (IMF), and holdirgs of foreign exchange under and predictable inflation, appropriate real inter- the control of monetary author t es. expressed in est rates, stable and sustainable fiscal policy, terms of the number of months of mports of goods an exchange rate that is not perceived to be and services they could pay for. over- or undervalued, and a viable balance of payments (see Fischer and Easterly 1990; and Fischer 1993). The two most susceptible to policy control are the inflation rate and the gov- The end cators in the table ernment's fiscal deficit. Uncertainty about come from the World either of these indicators tends to slow private Bank's natiora accounts investment and thus productivity growth. And - data files and the IMF's inflation distorts price signals, leading to poor Internationai Financial decisionmaking and costly efforts to hedge Statistcs ano Government against the loss of monetary assets. F Finance Statistics. No single indicator by itself or in compari- son to the value for another country is fully diagnostic. For example, a high level of debt may reflect the confidence of investors in the economy's future prospects. A large current account deficit may stem from high levels of private investment. And high inflation or a large fiscal deficit can occur despite good policies- the result of a supply-side shock caused by a poor harvest or deteriorating terms of trade. World Developmen: Indicators 1997 189 4.16 Central government finances Current Total Overall Financing Domestic Debt revenue'a expenditure budget deficit from abroad financing and interest (including grants) payments Interest Total % of %of GDP %of GDP %of GDP % of GOP % of GDP % of GDP revenu~e 1980 1994 1980 1994 1-980 1994 1980 1994 1980 1994 [ 1994 1994 Argentina 15.6 12.3 18.2 12.0 -2.6 .. 0.0 .. 2.6 Australia 21.7 23.3 22.7 27.2 -1.5 -2.8 0.2 0.7 1.3 2.1 20.7 6.2 Austria 34.6 36.0 37.4 40.5 -3.4 -5.6 0.9 2.1 2.5 3.5 54.6 10. 7 Azerbaijan.. . ......... Bangladesh 11.3 .. 10.0 .. 2.5 .. 2.5 .. 0.7 Belarus .. 31.4 -. 37.8 .. -5.2 .. 2.7 .. 2.4 25.9 2.1 Belgium 43.7 45.3 51.0 50.4 -8.2 -0.3 2.4 -0. 1 5.8 0.4 8.0 2.7 Bolivia .. 17.1 .. 25.2 .. -3.6 .. 4.9 .. -1.3 .. 14.0 Bosnia and Herzegovina . .. .. .. Botswana 34.0 57.3 34.0 46.2 -0.2 11.5 1.4 1.0 -1.2 -12.5 4.0 Brazil 22.6 25.6 20.2 37.4 -2.2 -3.9 0.0 .. 2.2 ... 59.3 Bulgaria .. 37.4 .. 43.1 .. -4.5 .. 1.5 .. 2.9 161.9 37.3 Burkina Faso 11.8 11. 7 12.2 1 7.1 0.2 .. 0.4 .. 0.0 ... 12.1 Burundi 13.9 .. 21.5 .. 3.9 .. 2.0 .. 1.9 Camoodia . .. .. .. .. Cameroon 16.4 14.2 15.7 15.9 0.5 -1. 7 0.7 1.9 -1.2 0.4 70.6 12.9 Canada 18.7 20.8 21.3 25.5 -3.5 -4.4 0.6 .. 2.9 Central African Republc 16.5 .. 22.0 .. -3.5 .. 2.1 .. 1.5 Chio 32.0 21.9 28.0 20.4 5.4 1.6 -0.8 .. -4.7 ... 4.4 China .. 6.4 .. 9.4 . -1.9 .. 0.2 .. 1.7 Colombia 12.0 16.8 13.4 14.5 -1.8 -0.5 .. . . . . 10.5 Congo 35.3 .. 49.4 .. -5.2 .. 3.8 .. 1.4 Costa Rica 17.8 24.9 25.1 30.6 -7.4 -5.7 1.1 -0. 1 6.3 5.8 .. 16.2 Met dIlvoire 22.9 .. 31.7 .. -10.8 .. 6.5 .. 4.4 Croatia .. 42.8 .. 41.1 .. 1.7 .. 0.0 .. -1.6 .. 3.1 Czech Republic .. 41.0 .. 42.5 .. 0.9 .. -0.1 . -0.8 18.7 3.5 Denmark 35.5 41.3 39.3 44.4 -2.7 -2.3 .. . . . . 15.4 Dominican Republic 14.2 1 7.1 16.9 17.1 -2.6 0.0 1.4 -1.2 1.2 1.5 .. 7.9 Ecuador 12.8 15.7 14.2 15.7 -1.4 0.0 0.5 .. 0.9 Egypt, Arab Rep. 45.5 41.2 45.6 42.8 -6.3 2.0 2.1 -1.0 4.2 -1.0 .. 24.8 El Salvador 11.4 12.0 17.1 14.5 -5.7 -0.8 0.3 1.0 5.5 -0.2 Estoni'a .. 35.0 .. 31.9 .. 1.4 .. 01. -1.3 Ethiopia' 16.3 14.1 19.6 27.4 -3.1 -8.5 1.2 7.6 1.9 0.9 64.3 14.1 Finland 27.2 33.0 28.2 43.8 -2.2 -13.4 0.8 10.7 1.4 4.1 36.1 11.2 France 39.6 40.2 39.5 46.9 -0.1 -5.5 0.0 0.0 0.1 5.5 .. 7.3 Gabon 35.5 .. 36.5 .. 6.1 .. 0.0 .. -6.1 Gambia, The 23.4 23.2 32.2 20.4 -4.5 3.5 1.2 2.9 3.3 -6.4 Germany .. 32.4 .. 33.7 .. -2.5 .. .1 . 1.2 30.2 6.3 Ghana 6.9 16.7 10.9 20.6 -4.2 -2.5 0.7 1.3 3.5 1.2 Greece 30.7 27.8 35.5 43.1 -5.0 -15.7 1.9 6.8 3.1 8.8 127.6 52.0 Guatemala 9.4 7.6 12.1 8.9 -3.4 -1.2 1.4 0.0 3.0 ..0.5 Guinea .. 13.3 .. 20.9 .. -3.1 . 4. 1 .. -10 . 9.8 Guinea-Bissau.. ... .......... Hait: 10.6 .. 17.4 .. -4.7 . . . . . Honduras 14.6 ... ... -0.1 .. 0.1 .. 0.0 1.5 Hong Kong.. ... ....... 4.16 I. Current Total Overall Financing Domestic Debt revenue' expenditure budget deflicit from abroad financing and interest (including grants) payments Interest Total % Of debt current Nof GDP % of GDP % of GDP % of GDP % of GOP % of GOP revenue 1980 1994 1980 1994 1980 1.994 1980 1994 1980 1994 1994 1994 Hungary 53.4 .. 56.2 .. 2.8 .. 2.1 .. 0.7 India 11.7 12.6 13.2 16.5 -6.5 -5.5 0.5 0.4 6.0 6.1 54.5 35.5 Indonesia 21.3 18.3 22.1 16.3 -2.3 0.6 2.1 -0.1 0.2 -0.5 37.5 11.3 Iran, Islamic Rep. 21.6 24.9 35.7 25.2 -13.8 -0.1 -0.6 0.0 14.4 0.1 .. 0.1 Ireland 34.7 37.5 45.0 42.6 -12.5 -2.2 .. . . . . 18.6 Israel 50.4 37.5 70.2 43.2 -15.6 -2.9 7.9 2.6 7.8 0.3 127.8 16.5 Italy 31.2 39.6 41.0 49.9 -10.7 -10.5 0.2 .. 10.5 .. 83.8 28.0 Jamaica 29.0 .. 41.5 .. -15.5 ... ... Japan 11.6 20.9 18.4 23.8 -7.0 -1.5 .. . . 1.5 44.7 Jordan .. 27.2 .. 30.7 .. 1.1 .. -0.7 .. -0.4 120.0 Kazakstan . .. .. .. .. Kenya 21.9 21.7 25.2 27.5 -4.5 -3.2 2.4 .. 2.1 Korea. Dem. Rep. . .. .. .. .. Korea. Rep. 17.4 19.5 17.0 17.6 -2.2 0.3 0.8 -0.1 1.4 -0.2 7.9 3.1 Kuwait 89.3 .. 27.8 56.2 58.7 ... ... Kyrgyz Republic . .. .. .. .. Lao PDR . .. .. .. .. Latvia .. 25.5 .. 27.6 .. -4.2 .. 1.7 .. 2.5 .. 1.7 Lebanon .. 14.6 .. 35.1 . .. .. Lesotho 34.2 51.3 .. 47.3 . ... .. 4.8 Lithuania .. 25.3 .. 27.2 .. . . . . . . 0.5 Macedonia, FYR . .. .. .. .. Madagascar 13.2 8.3 .. 18.9 .. 4.8 .. 2.9 .. 1.9 51.2 41.1 Malawi 19.1 .. 34.6 .. -15.9 .. 8.3 .. 7.7 Malaysia 26.3 28.8 28.5 24.7 -6.0 3.9 0.6 .. 5.4 .. 59.3 13.0 Mali 10.9 .. 21.3 .. -4.6 .. 4.3 .. 0.4 Mauritania . .. .. .. .. Mauritius 20.8 22.4 27.3 22.8 -10.3 --0.3 2.5 -0.2 7.8 0.4 31.7 9.4 Mexico 15.1 16.7 16.8 16.8 -3.0 .. -0.1 .. 3.1 Mongolia .. 22.2 .. 21.6 .. --1.9 .. 1.5 .. 0.4 5.3 2.7 Morocco 23.3 28.6 33.1 29.9 -9.7 --1.4 5.3 -0.2 4.4 1.6 83.7 18.4 Mozambique . .. .. .. .. Myanmar 16.0 7.2 15.9 11.0 1.2 --3.6 1.2 0.0 -2.4 3.6 Namibia .. 35.3 .. 40.7 --4.8 .. 0.1 .. 4.7 .. 2.4 Nepal 7.8 9.5 14.3 14.7 -3.0 .. 1.9 .. 1.2 Netherlands 49.4 48.1 53.0 52.4 -4.6 --0.5 0.0 -4.0 4.6 4.5 61.6 9.9 New Zealand 34.1 34.9 38.1 34.4 -6.7 0.8 3.6 .. 3.1 .59.3 Nicaragua 23.3 22.9 30.5 32.0 -7.2 --4.3 4.6 5.5 2.4 -1.2 .. 20.3 Niger 14.4 .. 16.4 .. -4.7 .. 4.0 .. 0.7 Norway 37.4 40.1 34.6 41.1 -1.7 --6.5 -0.7 3.0 2.4 3.5 23.2 5.7 Oman 38.2 31.7 38.5 43.9 0.4 -:4.2 -3.6 7.9 3.1 3.3 33.6 Pakistan 16.2 18.7 17.5 24.4 -5.7 --6.9 2.3 2.2 3.4 4.7 .. 34.2 Panama 26.9 28.2 32.3 28.2 -5.5 4.3 5.7 -0.9 -0.2 -3.4 .. 6.4 Papua New Guinea 23.0 22.0 34.4 29.4 -1.9 --4.1 2.5 -0.2 -0.5 4.3 43.0 Paraguay 10.7 14.1 9.8 13.0 0.3 1.2 2.2 -0.8 -2.5 -0.4 12.8 5.6 Peru 17.1 15.0 19.4 17.0 -2.4 3.0 0.6 1.1 1.6 -4.2 .. 14.0 Philippines 14.0 18.0 13.4 18.4 -1.4 -1.5 0.9 -0.7 0.5 0.6 56.4 Poland .. 41.7 .. 44.1 . --2.3 .. -0.6 .. 2.9 69.5 10.0 Portugal 26.2 34.2 33.3 42.5 --8.5 -2.3 1.9 -0. 1 6.6 2.3 .. 22.1 Puerto Rico . .. .. .. .. .. Romania 45.3 29.9 44.7 31.9 0.5 --2.5 .. 0.0 .. 2.5 .. 4.4 Russian Federation .. 20.2 .. 27.0 .. -:O.5 .. 0.9 .. 9.6 .. 8.6 World Deve opment Indicators 1997 :191. 4.16 Current Total Overall Financing Domestic Debt revenuea expenditure budget deficit from abroad financing and interest (including grants) payments Interest Tota % of debt current S/ ot GDP % of GDP /S of GDP S of GDP S of GIDP S of GDP revenue 1980 1994 1980 1994 1980 1994 1980 1994 1980 1994 1994 1994 Rwanda 12.8 10.6 14.4 24.2 -1.7 -6.9 2.6 3.3 -0.9 2.3 48.4 16. 7 Saudi Arabia . .. .. .. .. Senegal 24.1 .. 23.1 .. 0.9 0.1 -2.7 ..1.8 Sierra Leone 15.5 13.0 27.3 20.2 -12.1 -5.0 3.6 5.3 8.5 -0.2 89.4 Singapore 25.4 26.5 20.0 1 7. 7 2.1 0.1 -0.2 0.0 -2.0 --15.8 76.9 4.9 SIovak Repuol c . .. .. .. .. South Africa 23.5 26.9 22.2 33.3 -2.3 -6.2 -0.2 1.2 2.5 5.1 55.8 21.0 Spain 24.2 32.0 26.7 39.4 -4.2 -7.0 o.o 8-0 4.2 -1.0 51.2 11.1 Sri Lanka 20.2 19.0 41.3 27.1 -18.3 -8.5 4.5 2.0 13.8 6.5 94.8 34.6 Sudan 13.8 .. 19.6 .. -3.3 .. 2.8 ..0.5 Sweden 36.0 36.8 39.4 48.7 -8. 1 -12.8 3.2 13.4 4.9 -0.6 66.7 16.9 Switzerland 19.7 22.4 20.3 27.1 -0.2 -28 .. 0.0 .. 2.8 21.5 Syrian Arab Republic 26.8 22.5 48.2 26.6 -9.7 -3.8 -0. 2 ..9.8 Tajikistan . .. .. .. .. Tanzania 1 7.9 .. 27.9 .. -7.0 .. 1.3 ..5. 7 Thailand 14.3 18.5 18.9 16.4 -4.9 1.8 1.1 0.2 3.7 -2.0 6.1 3.3 Togo 30.3 .. 30.8 .. -2.0 .. 1.6 ..0.4 Trinidad and Tobago 42.5 .. 30.3 .. 7.2 ... ... Tunisia 31.3 28&8 31.6 32.0 -2.8 -2.5 2.3 ..0.6 . 51.8 11.4 Turkey 18.1 19.3 21.4 23.3 -3.1 -3.9 0.4 -1.8 2.6 5.7 44.0 23.8 Turkmenistar,. . . . . .. .. Uganda 3.1 .. 6.1 .. -3.1 .. 0.0 ..3.1 krairoie.. ............. Lnited Arab Emirates . .. 12.1 11.8 2.1 0.2 0.0 0.0 -2.1 -0.2 United Kingoom 35.2 35.3 38.3 41.7 -4.6 -6.6 0.3 -0.2 4.3 0.0 4.7 2.6 LJnited States 20.2 20.0 22.0 23.0 -2.8 -3.0 0.0 0.4 2.8 2.6 51.9 16.0 Uruguay 22.3 32.2 21.8 35.1 0.0 -2.8 0.9 1.1 -0.9 1.7 26.4 6.1 Uzbekistan.. ... .......... Venezuela 22.3 18.9 18.7 18.8 0.0 -4.1 1.8 .. -1.9 ... 20.6 West Bank and Gaza.. ... ... ...... . Yemen, Rep. .. 21.4 .. 38.4 .. -1 7.3 .. 0.3 . 1 7.0 .. 19.9 Yugoslavia, Fee. Rep. . .. .. .. .. .. Zambia 25.0 12.0 37.2 18.2 -18.5 -6.5 8.8 ..9.7 ... 19.5 Zimbabwe 24.1 .. 34.9 .. -10.9 .. 2.3 ..8.6 a. Eve u.dirg grarts. b. Gate prior to 1992 include tritrea. 0 5>127. a._s.4 4.23 . Data on the external debt of developing countries are a Total external debt is the sum of public, publicly gathered by the World Bank through its Debtor guaranteed, and private nonguaranteed long-term debt, Reporting System. World Bank staff calculate the use of IMF credit. and short-term debt. * Long-term total external indebtedness of developing countries debt is debt that has an original or extended matu- using loan-by-loan reports submitted by these coun- rity of more than one year and that is owed to non- tries on public and publicly guaranteed borrowing, residents and repayable in foreign currency, goods, alongwith information obtained from creditorsthrough or services. It has three components: public, publicly the debt data collection systems of such agencies guaranteed, and private nonguaranteed loans. as the Bank for International Settlements and the . Public and publicly guaranteed debt comprises Organization for Economic Cooperation and long-term external obligations of public debtors, includ- Development. The data are a so supplemented by ing the national government, political subdivisions information on loansand credits of major multilateral loran agencyofeither), and autonomous public bodies, banks and loan statements of official lending agen- and external obligations of private debtors that are cies in major creditor countries and by estimates by guaranteed for repayment by a public entity. * IBRD country economists of the World Bank and desk offi- loans and IDA credits are the market-rate loans (from cers of the International Monetary Fund (IMF). the :nternational Bank for Reconstruction and Despite an ongoing effortto standardize the report- Development) and the concessional loans (from the ing of external debt (see, for example, International International Development Association) owed to the Working Group of External Debt Compilers 1987), the World Bank. , Private nonguaranteed external debt coverage, quality, and timeliness of debt data vary comprises long-term external obligations of private across countries. Coverage variDs for both debt instru- debtors that are not guaranteed for repayment by a ments and borrowers. With a widening spectrum of public entity. * Use of IMF credit denotes repurchase oebt instruments and investors and the expansion obigations to the IMF for all uses of IMF resources of private nonguaranteed borrowing. comprehensive (excludingthose resulting from drawings on the reserve coverage of long-term external debt becomes more tranche). It is shown forthe end of the year specified. complex. Reporting countries differ in their ability to It comprises purchases outstanding under the credit monitor debt, especially private nonguaranteed debt. tranches, including enlarged access resources, and Last year more than 30 countries reported their pri- all special facilities (the buffer stock, compensatory vate nonguaranteed debt to :he World Bank; esti- financing, extended fund, and oil facilities), trustfund mates were made for approx mately 30 additional loans, and operations underthe structural adjustment countries known to have significant private debt. Even and enhanced structural adjustment facilities. pub ic and publicly guaranteed debt is affected by coverage and accuracy in repcrting-again because =_ of monitoring capacity and, soimetimes. willingness to provide information. A key partthat is often under- The principal sources of reported is military debt. external debt information Variations in reporting rescheduled debt also affect are reports to the World cross-country comparability. For example, when Bank through its Debtor rescheduling under the auspices of the Paris Club, Reporting System from some countries calculate the effects of reschedul- membercountriesthathave ing according to the date of tie general agreement r eceived IBRD loans or IDA with the Paris Club. while others use the completion credits. Additional informa- dates for the individual bilateral (post-Paris Club) tion has been drawn from agreements. To ensure consistency, the World Bank the files of the World Bank and the IMF. Summary estimates the effect of the Pa-is Club agreement for tables of the external debt of developing countries the second group until the authorities reflect it in are published annually in the World Bank's Globai the reported data. Other areas of inconsistency Development Finance (formerly World Debt Tables). include country differences in treatment of arrears. reporting of debt owed to Russ a, and treatment of nonresident national deposits denominated in for- eign currency. World Development Indicators 1997 221 4.24 External debt management Present value of debt Total debt service Public and publicly guaranteed debt service % of central % of exports of % of exports of government % of GNP goods and services tOof GNP goods ano servoces current revenue 1993 1995 1993 1995 1980 1995 1980 1995 1980 1995 Albania 46.5 32.1 146.0 94.7 ..0.3 .1.0 ..0.4 Algeria 51.7 63.9 217,9 221.9 9.9 11.2 27.4 38.7 Angola 314.6 260.0 312.3 310.5 .. 11.0 ..12.5 Argentina 26.5 30.6 379.9 296.0 5.5 3.5 37.3 34.7 6.1 11.6 Armenia 6.9 13.7 .. 92.8 ..0.3 ..2.9 Australia . .. .. .. Azerbaijan 0.7 8.0 ... .0.3 . Bangladesh 33.5 31.5 205.1 166.7 2.1 2.5 23.7 13.3 Belarus 2.7 6.4 ... .0.9 . Benin 36.6 45.7 136.7 159.5 1.4 2.4 6.3 8.4 Bolivia 62.2 67.1 354.8 303.7 12.6 6.4 35.0 28.9 ..27.1 Bosnia and Herzegovina . .. .. .. .- Botswana 13.4 13.1 .. 1.7 2.2 2.1 3.2 Brazil 35.2 22.9 324.1 257.4 6.5 3.3 63.3 37.9 15.3 4.7 Bulgaria 130.9 87.3 278.6 278.6 0.2 10.7 0.5 18.8 ..14.4 Burkina Faso 21.1 28.0 137.4 148.3 1.3 2.1 5.9 11.1 8.4 8.3 Burundi 50.4 49.8 471.4 375.0 1,0 3.7 ..27.7 4.8 Cambodia 77.5 52.0 485.3 145.2 ..0.2 ..0.5 Cameroon 54.8 96.6 272.3 354.8 4.8 5.8 15.3 20.1 17.0 19.3 Canada . .. .. .. Central African Republic 40.2 52.2 253.9 253.9 1.3 1.4 4.9 6.8 3.2 Chad 32.3 39.6 187,4 196.6 0.8 1.4 8.3 5.8 ..8.0 Chile 43.8 41.3 156.9 138.5 10.2 7.8 43.1 25.7 15.6 20.2 Chins 17.9 16.4 84.2 70.6 0.5 2.2 8.4 9.9 Colombia 30.5 27.0 150.2 132.8 2.9 5.1 16.0 25.2 13.2 29.4 Congo 201.8 324.9 374.1 374.1 7.1 10.1 10.6 14.4 11.7 Costa Rica 48.1 39.6 116.9 89.6 7.7 7.1 29.1 16.4 23.9 23.8 C6te d'lvoire 188.1 184.9 510.5 366.6 14.5 11.6 38.7 23.1 37.4 Croatia 16.5 18.1 ... .2.3 .5.7 ..2.2 Czech Republic 29.0 36.0 .. 54.8 1.6 5.7 ..8.7 ..10.6 Denmark . .. .. .. Dominican Republic 47.5 33.1 94.1 70.0 5.9 3.7 25.3 7.5 16.3 16.9 Ecuador 97.1 75.8 356.3 237.3 9.0 8.5 33.9 26. 7 Egypt, Arab Rep. 58.9 55.6 142.5 157.7 5.8 5.1 13.4 14.6 8.6 11.5 El Salvador 22.3 22.0 78.9 66.5 2.7 3.0 7.5 8.9 Estonia 3.8 6.5 .. 10.2 ..0.5 ..0.4 .. 2.4 Ethiopia' 50.4 65.7 397.3 301.5 1.1 3.0 7.3 13.6 France . .. .. .. Gabon 71.7 89.1 127.9 127.9 11.2 12.0 17.7 15.8 26.1 14.4 Gambia, The 64.9 59.3 94.9 96.2 1.9 6.7 6.3 14.0 Georgia 19.8 44.4 ... .0.9... Germany............ Ghana 51.4 61.1 249.3 236.1 3.6 6.0 13.1 23.1 Greece............ Guatemala 21.6 19.0 106.1 86.7 1.8 2.3 7.9 10.6 . Guinea 60.7 59.4 .. 294.3 ..5.1 ..25.3 ..20.2 Guirea-Bissau 219.1 230.2 3,251.9 3,251.9 4.5 6.3 31.6 66.9 Haiti 29.3 19.5 384.4 627.1 1.8 4.7 6.2 45.2 13.2 Honduras 101.0 101.2 252.3 207.4 8.5 15.1 21.4 31.0 26.0 Hong Kong . .. .. .. 4.24 I Presenvt value of debyt Total debrt service Public and publicly guaranteed debt service % of central 8 of exports of % of exports of government % of GNP goods and services % of GNP gooos and services current revenue 1993 1995 1993 1990 1980 1995 1980 1995 1980 1995 Hungary 64.7 72.4 211.3 173.1 6.8 16.7 24.9 39.1 15.2 India 27.9 22.6 229.2 159.6 0.8 4.1 9.3 27.9 5.6 21.8 Indonesia 54.0 54.5 195.7 194.2 4.1 8.7 14.0 30.9 10.6 29.0 Iran, Islamic Rep. 14.6 14.6 119.7 103.2 1.0 ..6.8 .. 4.7 20.2 Jamaica 90.4 123.1 133.8 103.3 11.4 21.3 19.0 17.9 26.6 Japan . .. .. .. Jordan 116.0 108.4 152.0 140.7 ..9.7 8.4 12.6 Kazakstan 6.1 21.8 ... .1.1 ..4.6 Kenya 106.3 72.3 232.8 183.8 6.2 8.7 21.0 25.7 Korea, Dem. Rep. . .. .. .. Korea, Rep. ............ 25.1 5.7 Kyrgyz Republic 6.8 14.9 ... .2.0 ..4.8 Lao PDR 46.4 42.9 183.1 154.5 ..1.5 ..5.8 Latvia 4.0 7.0 ... .0.6 .1.6 ..1.7 Lebanon 17.1 24.8 100.3 154.8 ..2.1 ..13.1 ..9.1 Lesotho 22.5 26.0 45.0 63.5 0.9 2.7 1.5 6.0 7.9 8.3 Lithuania 3.4 8.8 11.8 21.7 ..0.6 .1.4 ..1.1 Macedonia, FYR 51.9 56.6 ... .1.6 ..11.8 Madagascar 86.0 105.0 517.0 416.8 2.6 2.3 20.3 9.1 13.7 19.1 Malawi 42.1 79.3 236.0 237.4 7.7 7.6 27.7 25.9 Malaysia 37.7 38.6 42.1 33.6 4.0 8.1 6.3 7.8 5.9 12.2 Mali 58.2 74.7 266.9 282.4 1.0 3.3 5.1 12.6 5.3 Mauritania 177.3 166.2 362.9 311.6 7.1 11.4 17.3 21.4 Mauritius ... 43.3 43.3 4.7 5.5 9.1 9.0 14.7 15.1 Mexico 33.0 67.2 174.2 163.8 5.8 9.9 44.4 24.2 26.9 18.8 Moldova 5.2 16.1 ... .1.8 ..8.0 Mongolia 44.3 39.5 70.0 70.0 ..5.6 .9.1 ..14.8 Morocco 73.9 61.7 206.8 180.7 7.9 11.3 33.4 32.1 27.5 44.8 Mozambique 316.2 339.9 1,045.9 904.0 .. 13.3 ..35.3 Myanmar 7.7 6.5 469.1 432.5 ... 25.4 14.5 11.9 3.3 Nepal 25.4 26.2 125.9 97.4 0.4 2.2 3.2 7.8 2.6 18.5 Netherlands . .. .. .. New Zealand . .. .. .. Nicaragua 687.8 520.3 2,423.5 1,122.9 5.7 17.9 22.3 38.6 26.3 53.7 Niger 46.3 56.7 273.7 312.0 5.7 3.1 21.7 19.8 10.6 Nigeria 109.9 132.3 240.6 88.8 1.3 6.3 4.1 12.3 Norway . .. .. .. Oman 26.5 28.0 43.4 45.8 4.7 4.6 6.4 7.5 Pakistan 39.3 38.4 205.9 223.8 3.7 5.1 18.3 35.3 15.4 25.2 Panama 110.1 98.2 84.6 80.3 13.4 5.3 6.2 3.9 48.3 15.4 Papua New Guinea 58.2 45.1 93.7 68.2 6.0 13.7 13.8 20.8 Paraguay 20.4 27.2 ... 3.1 3.5 18.6 .. 16.0 27.9 Peru 47.1 52.2 376.6 385.4 10.9 2.1 44.5 15.3 42.7 9.5 Philippines 61.7 49.4 178.1 113.5 6.7 7.0 26.6 16.0 Poland 47.8 30.5 220.1 107.6 5.3 3.5 17.9 12.2 ..4.4 Puerto Rico . .. .. .. .. Romania 15.3 18.3 55.2 68.7 ..2.7 12.6 10.6 7.5 2.8 Russian Federation 20.4 34.9 ... .2.0 .6.6 ..9.1 World Development Indicators 1997 223 4.24 Present value of debt Total debt: service Public and publicly guaranteed debt service 8 of central Aof exports of % of exports of government A ofG0NP goons and servenes Aof 0NP goons and serv nov cucnent revenue 1993 199: .993 1995 1990 1995 1980 199: 1980 1995 Rwanda 21.9 42.4 397.6 397.6 0.7 1.8 4.2 ..2.9 8.5 Saudi Arabia . .. .. .. Senegal 47.2 53.7 189.1 158.6 8.9 6.3 28.7 18.7 30.1 Sierra Leone 156.4 100.0 595.3 471.4 5.6 10.8 23.2 60.3 Singapore....... Slovak Republic 27.6 31.3 .. 48.6 ..6.2 ..9.7 Slovenia 1 1.4 18.0 16.9 31.2 ..3.9 ..6. 7 Soutn Africa .. .... .. Sri Lance 42.4 43.6 104.1 99.2 4.5 3.2 12.0 7. 3 10.3 19.1 Sudan 244.1 244.1 2.985.3 2.418.1 3.9 0.5 25.5 0. 5 Sweden . .. Swinzeriand . .. .. .. Syrian Arab Repub ic 132.1 118.3 320.9 295.4 2.4 1.9 11.4 4.6C 8.6 23 Tajikistan 12.3 33.0 ... .0.0 .. 0.0 Tanzania ... 660.6 430.0 3.1 6.3 21.1 17.A 8.1 Thailand 38.5 35.3 94.8 77.6 5.0 4.6 18.9 10.21 9.5 7.7 Togo 05.5 74.8 226.0 172.5 4.6 2.5 9.0 5.7 11.0 Trinidad and Tobago 47.6 52.2 108.1 85.7 3.9 9.1 6.8 14.8 Tunisia 34.4 51.8 120.2 101.7 6.4 6.7 14.6 17.0 15.6 27.2 Turkey 36.2 42.6 212.9 162.3 2.3 6.9 26.0 27.7 8.5 30.1 Turkmenistan 4.5 9.4 ... .2.5 ..4.1 Uganda 37.1 33.4 746.8 291.0 4.5 2.4 17.3 21.3 Ukraine 2.8 10.0 ... .1.2 .. .3 Lnited Arab Emirates . .. .. .. United Kingdom . .. .. .. United States . .. .. .. Lruguay 35.8 31.2 137.7 139.1 3.1 4.9 18.8 23.5 8.8 14.4 Uzbekistan 4.1 6.9 ... .1.0 ..6.0 Venezue a 62.6 46.8 205.8 205.8 8.7 6.7 27.2 21.7 19.1 19.4 Vietnam 161.0 114.06.... 1.9 ..5.2 West Bank end Gaza . .. .. .. Yemen, Rep. 150.0 127.7 194.5 158.0 ..2.6 .3.2 ..3.3 Yugoalavia, Fed. Rep.' . .. .. .. Zaire 173.9 226.0 ... 3.9 0.3 13.2 .. 29.2 Zambia 164.3 138.6 ... 11.4 68.4 25.3 174.4 29.6 53.5 Zimnbabwe 68.0 64.9 177.8 147.4 1.2 10.5 3.8 25.6 Lowv income .. 1.5 w 3.2 w 9.6 w 1o./Jwv Exci. China & Inoia Middle income .. 3.6 w 4.9 w 13,6w 174 w Lower midole income . .. .. Upper middle income . .. .. Low & middle income . .. .. East Asia & Pacific ... 2.2 w 4.3 w 11.5 w 12.8 w Europe & Central Asita ... 1.6 w 4.2 w 7.4 w 13.8 w Latin America & Cani.b .. . 6.5 w 5.1 w 36.3 w 26.2 w Middle East & N. Africa ... . 2.5 w 4.2 w 5.7 w 14.9 w South Asia .... 1.3 w 3.4 w 11.7 w 24.6 w Sub-Saharan Africa .... 3.3 w 4.9 w 9.8 w 14.5 w High income... a. InnIidues E,trea. b. Oats refer to tfe former Yugoslanva. 4.24 Debt sustainability I=._ _ -I. When is the burden of debt on a country so great that national solvency is threatened? Data on debt are in U.S. dol ars converted atof'c,al 0 Presentvaleoeidebtisathes.mofs-ort-termexter- Debt sustainability analysis looks at the future exchange rates. The data ncl:ude pr vate nonguar- ral debo plus the discounted sufn of total debt ser- path of the economy and the expected evolu- anteed debt reported by more than 30 developing vice payrments due on pub ic, pubic y guararteed, tion of the country's current obligations to countries and complete or part al estimates fo- an and private longuaranteed long-term external cebt determine when and if debt service problems additional 30 that do not repor' this type of debt but over 'ae ife of exist ng oars. a Total debt service is are likely to arise. for which it IS Known to be sigo,ificant. Government the sum o' pr ncipa reosayments and nterest paid n The method typically used involves choosing debt denominated in loce cuniency s no: reported foreign currency. goods, n, services on long-term debt a time horizon (often 10-20 years) and pro- here because data availabi ty s poor. and interest payments orly onl short-term debt. jecting the change in the main macroeconomic The present value of externa: debt onovides a mea- o Public and publioDy guatanreed debt service is the variables to that horizon. These projections, sure of current and future debl obligations that can sum of princ pa 'epayments and intlerest paid on ong- together with estimates of future inflows of pri- be compared with the cunrent va ue of such indica- term coligations of pub ic debtors. vate and official capital, are then used to con- tors as GNP and exports of gocds and services. It s struct the balance of payments accounts and calculated by discounting the debt service (interest the estimated financing requirement for the plus amort:zation) due on long-term externa deb: country. This requires much explicit or implicit over the life of existing loans. Short -erm debt (cebt y,p,y-iyut « The princ pal sources of economic modeling based on assumptions with a matunty of one year or luss) is ,nciuded at its nforma on0on externa debt about the indebted country's future economic face value. The discount rate applied to long-term ' are reports to tne Word policy. debt is determined by tne currency of repayment of Bank through its Debto, For external debt to be judged sustainable, the loan and is based ot the OECD's commercial Reporting System from the projected scenario must satisfy two con- interest reference rates. IBRD loans and IDA credits member countries :hat have ditions. First, during the projection period bal- are discounted using the latent IBRD ending rates, received IBRD loans or IDA ance of payments equilibrium must be achieved and obl gations to the IMF ase discounted at tne credits Additonal infornea- without resortingto exceptional financing (such SDR lending rate. When the discount rate IS greater .ion has been drawn from as debt restructuring or emergency borrowing than the interest rate of the loan. the present vclue din files or the World Bank anc lhe IriLe ria uoria from official sources). Second, indebtedness is less than the nomina sum o. future debt service. Monetary Fund. Summasytables of the external debc at the end of the period must be low enough Data or the present value uf debt and debt ser- of developing countries are oublsned arnaily in to make future debt service problems unlikely. vice are from the Wor d Bank's Debtor Reporting the World Bank's Global Development Finance (for- The second condition is typically evaluated by System. The ratios shown here may differ from those merly Vlorid Debt Tables'. computing indebtedness indicators such as published elsewhere, however, because estimates the ratio of debt to GDP or of debt service to of exports of goods and services and gross natioral exports (possibly on a present value basis) product have been rev sec to incorporate data ave I for the last years of the projection period. able as of February 1, 1997. There are no absolute rules on what values are too high for these ratios. But empirical analysis of the experience of developing coun- tries and their debt service performance has shown that debt service difficulties become increasingly likely when the ratio of the present value of debt to exports reaches 200-250 per- cent and the debt service ratio exceeds 20-25 percent. What constitutes a sustainable debt burden nevertheless varies from one country to another. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels than countries with inefficient tax systems, distorted prices, and high current expenditure rates. World Development Ind cators 1997 225 - *-- - - - --r- -i-i - - - t is increasingly recognized that "governments need to do less in those areas where markets work or can be made to work reasonably well" (World Bank 1991b) and more in those areas-such as education, health, nutrition, and regulation-where markets alone cannot be relied utpon. By unleashing competitive forces and enhancing interna- tional competitiveness, a healthy private sector can provide both growth and jobs. Many developing country governments are shifting their priorities from preserving jobs in a stagnant public sector to creating jobs in a vibrant private sector. This shift implies a fundamental change in the role of government-from owner and operator to policymaker and regulator, working closely with the private sector to develop a com- petitive, outward-looking economy (World Bank 1995e). This section provides indica- tors that reflect these shifting roles. The new strategy requires that developing countries: * Establish a more inviting business environment. Sound macroeconomic management has to supplant stop-go policies that undermine the confidence of the private sector. But governments also have to promote competition and reduce risk-and especially to cut the high costs of doing business. This means pressing ahead on an array of policy, legal. regulatory, and institutional reforms in partnership with business and labor. * Acceleratefinancial rebfrm. Governments also have to restructure and, when appro- priate, privatize banks, strengthen regulation and supervision, and develop the basic financial infrastructure to service a broad segment of the population, especially small businesses. a Go faster and farther wJith public entepnrise reform. Governments have to privatize utili- ties and large enterprises-and, where appropriate, liquidate major loss-makers. Employing only a small fraction of the labor force, these enterprises absorb a large part of government expenditures and account for a large part of the losses of the banking system. Failure to deal with these losses threatens reform programs and diverts resources from pressing social needs. Two major objectives of the new strategy are to stop the hemorrhaging of the bank- ing system and to imprcve infrastructure services essential for competing in a dynamic global economy. Many countries havc implemented parts of this new strategy for private sector devel- opment, and the response has been impressive. But even in countries with well-estab- lished institutions and legal systems-and the human resources to translate commitment into action-reform is a long process that may take more than a decade and is subject to reversal and fragility. The poorest countries lack many of the prerequisites for such a sustained effort- and have little latitude for error. The challenges are particularly daunting in Africa, where the business environment for entrepreneurs is shaky, markets are small, skills are shallow and narrowv, the supporting infrastructure is weak, and laws and regulations are very restrictive. Tracking progress How to track countries' progress in developing the private sector? By following three sets of inclicators (World Bank 1991a). A changing public-private balance is reflected in an expanding private sector and a dwindling government role in the economy. Private sector growth shows up in higher private sector credit and investment, in flows of pri- vate capital, and in expanding capital markets. As the private sector grows, the govern- World Developmenrt Indicators 1997 227 ment shifts out of providing services and into building human resources-and its intervention in the economy subsides. This shift shows up in the amount and composition of central gov- ernment expenditure, in the amounts of public investment, pub- . -n ... . -.. *;.--.- *n* - * * . ... licly guaranteed debt, and domestic borrowing, and in the shares * , . - .* - * * . *.. - -. a. of government and state-owned enterprises in economic activity. *- - - - - . 666 To capture the potential of the economic environment to pro- mote private sector development, incentives for investment are 6 9. measured by integration with the global economy, trade policies, - key prices in the economy, trade competitiveness, tax policies, 6 * - - . - - . - . - and the legal and regulatory framework. And because support sys- . - - *e - - 6 . - -. -. - . . tems are essential for increasing the potential for private sector ** .-*.-s-. - . - - . - *6 development, we look at the financial sector's depth and effi- ciency, the level of people's skills, the dependability of infra-. . . . ..- 6-- . structure, and scientific and technological capacity- The private sector's share in economic activity has increased - dramatically in many countries and in the developing world as . a whole, but in far too many countries excessive fiscal deficits still crowd out private investment and raise the cost of domes- tic borrowing. Even so, some countries have begun to attract sizable amounts of private capital flows in recent years (tables 5.1 and 5.2). For countries, this reflects their greater receptivity to for- eign capital, and for investors, their search for higher returns and for better diversification of risk (box 5a). Developing country stock markets are also beginning to attr-act significant inflows of foreign portfolio equity invest- 6 6 6 6 . i ment-as well as domestic funds. In 1990-95 the stock market capitalization in developing economies rose from $390 billion to - 6. 6 6 6 .. . n*l $1.5 trillion, up from 4 percent to 8 percent of global stock - - * *:e - .. - . 6 . -. market capitalization (table 5.3). Stock market development is closely related to economic . .. - development. In the initial stages of economic development, commercial banks tend to dominate the financial system. As economies grow, specialized financial intermediaries and equity miiarkets develop. The rieason? Many profitable investmenits require a long-term commitment of capital, but investors are tvp- ically reluctant to relinquish control of their savings. By allowing savers to acquire liquid assets, equity markets make investments less risky and more attractive-and allow companies to tap cap- 6 - 6 - 6 S -. . .. a ital for their longer-term investments. The development of stock - - markets also makes it easier for governments to sell off state- owned enterprises. But despite more than a decade of divestiture efforts, state 6 6 6 enterprises remain as ubiquitous in developing economies as they were 20 years ago. Indeed, their presence has shrunk sig- nificantly only in the former socialist economies and a few middle-income countries. In most developing countries, partic- -.- -.. - ularly the poorest, bureaucrats run as large a share of the econ- omy as ever (table 5.4). . - 6 State enterprises often are less efficient than private firms, and their deficits are typically financed in ways that undermine macroeconomic stability. In addition, subsidies to state enter- prises often divert scarce funds from public spending on educa- ticn and health. And because state enterprises tend to loom large in low-income countries, they are likely to be most costly in countries that can least afford them. Privatizations of state enterprises in developing countries generally have so far had more qualitative effects than quantita- tihe effects-increased efficiency, more new domestic firms, and a proven government commitinent to private sector develop- ment. And even though sales have yet to generate much rev- ernue, public enterprises are accruing fewer losses than they once did. Getting the incentives rfght If the private sector is to lead economic growth, incentives must be in place to increase private investment, boost the productiv- ity of private firms, and spur competition. Perhaps even more important is removing constraints to private sector develop- ment. Distorted incentive policies call for reforms that address -~ _ product and factor prices, special tax incentives, trade protec- Figure Sa _ _ tion, state subsidies, and preferential access to foreign exchange stock market G and other scarce resources. in l,wrmi; _ Real exchange rates, real wages, real interest rates, and rela- tive commodity prices convey vital information about the inter- 300 Q, action of the agents in an economy-and that economy's o 250 - interaction with the rest of the world (table 5.5). Some relative o: price movements are immutable. For a small, open economy the g 200 South Africa I real exchange rate in the long run is determined by the coun- 'wN 150 ~ try's endowments, tastes, and technologies. But policymakers ,NR * Chile can influence relative prices only in the short run-and whether N 100 a their policy initiatives stick depends on the behavior of real ailland,' N vvages and the accompanying monetary and fiscal policies. ca 50 > > * * f- Relative prices also reflect an economy's openness by show- 0 5!! * b , + ing how far domestic prices of traded goods are from interna- o 2 4 ,6 ;,u tional prices. Trade restrictions account for most of the gap GNP per capita ($ ii between domestic and international prices (table 5.6). They push investmento trahe wrong esohad tin and they force con- samers to pay higher than world prices. ; ~~~~~~~~Openness to trade goes hanid in hand with faster economic growth. Many developing economies have been lowering their tariffs and reducing the coverage of nontariff barriers, with fuir- ther progress expected now that the Uruguay Round is coming irto force. But tariffs are still high in many countries, in part tbecause the countries need the revenues tariffs generate. For example, tariffs in South Asia averaged 30 percent after the U.ruguay Round, substantially lower than in the 1980s, but still rnuch higher than those in East Asia (around 12 percent). Industrial country tariffs now average around 3 percent (see table 6.4). As countries develop, they usually build up their capacity to tax residents direcdy, and indirect taxes become less important as a source of revenue. Thus the share of direct taxes in total revenues is one measure of the development of the tax system. Openness to foreign competition, foreign knowledge, and loreign resources energizes the development process in many ways, and lowering trade taxes enhances openness. The fastest- Wond Development Inalcators 1997 229 Figure 5b Govonrment Implementing the private f- 4 sector Ei. development agenda sFnciaite Consumers i~~~~~~~~~A 4 t2teiii! !(1W sector Private Utilities sector Remaining ! : ncial Ut| ities enterp system Manufacturing Services Agriculture * Savings 20-30% GDP 'I 1111l _~~~~~~~~~~~~~~~~~~~~ growing economies over the past 15 years have not relied on cial sectors are proving a threat to middle-income countries tax revenues from exports, and, seeing this pattern, many trying to attract large private capital flows. other countries have pulled down their export taxes. High Financial reforms are now at the top of the agenda for eco- export taxes-typically levied on primary products, particu- nomic reform in many countries. Measures include stopping the larly agriculture-are inadvisable because they reduce the hemorrhaging of public enterprises, privatizing banks when incentive to export and encourage a shift to other crops. appropriate, improving bank management, and strengthening Similarly, high marginal income taxes tend to penalize work bank supervision. and savings (table 5.8). The progressivity of a tax system-as Infrastructure is a second key support system for private measured roughly by the highest marginal tax rate on indi- sector growth. The quality and adequacy of infrastructure ser- vidual and corporate income-can show how the tax system vices are important determinants of how successful firms are in builds or reduces the incentives for succeeding on the job or delivering products and services of high quality at low prices in in business. International investors, for example, use such the shortest possible time (tables 5.11 and 5.12). Poor infra- data as an indicator of the hospitality of governments to their structure increases private costs by increasing investment and interests. Of course, considerations of equity and incentives transactions costs and restricting access to domestic and interna- need to be balanced in a tax policy geared to socially sustain- tional markets. able development. Low-income countries have improved their infrastructure The overall incentive framework determines how private but are far behind middle-income countries. In Sub-Saharan investors see risk, perceptions that usually are highly subjec- Africa telecommunications coverage is among the lowest in the tive. To get a handle on the reality a country faces in trying to world, averaging 11 lines per 1,000 people compared with 34 in attract private capital, country risk ratings take account of East Asia and the Pacific and 91 in Latin America. Indeed, there objective indicators as well as policies and prevailing preju- are more teleplhones in Tokyo than in the whole of Sub-Saharan dices (table 5.9). Africa. National transport systems also fail to deliver the logisti- cal support needed by private firms, and poorly maintained Putting support systems in place roads add to excessive freight costs. An efficient and vibrant financial system is an important pre- Private sector growth is also enhanced by expediting access condition for private sector development. It mobilizes savings to technology (table 5.13). Technology is the knowledge that and allocates them to investments by private entrepreneurs leads to improved machinery products and processes. It is (table 5.10). It links savers and borrowers, manages risk, and embodied in imported inputs and capital goods, sold directly operates the payment and settlement systems. And it helps shift through licensing agreements, and transmitted through foreign resources from declining to dynamic sectors. direct investment. The ability to assimilate technology is a func- Yet in many developing economies, particularly the poorest, tion of the pool of trained manpower and investments in inappropriate policies have hobbled fledgling financial systems. research and development, but public spending on research and Large budget deficits were monetized, and inflation flowered. To development has often been wasteful and misdirected. keep nominal rates from rising, governments controlled interest Information technology is now at the vanguard of technological rates. The resulting reduction in real rates reduced incentives for change, with countries rapidly expanding their use of comput- the formal banking system to intermediate savings, encouraging ers and tapping the 'World Wide WNeb (table 5.14). capital flight and overborrowing. This neutralized commercial The shift in emphasis from states to markets constitutes a tall banks, and credit was allocated by government decree. Banks lost agenda requiring simultaneous and difficult actions in many their ability to screen and assess credit risks, and central banks areas over long periods (figure 5b). Future editions of the World allowed their oversight functions to wither. Development Indicators should enrich our capacity to monitor The unhappy outcomes? Bad loans accumulated, and the these trends further, as data coverage and quality improve, par- losses were periodically covered by printing money. Weak finan- ticularly in the area of institutional development. VWorlo Development Vdicazors 1997 2 1 . ~5.1 Credit, investment, and expenditure Private Foreign direct investment Credit to Private nion- ICentral government investment private sector guaranteed debt expenditure %of gross domestic /S of gross % of exter na fixed investmnent domestc investment % of GDP Sof GDP debt % of GDP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Albania .. . . 19.4 .. 3.2 .. 4.1 .. 0.0 .. 34.1 Algeria 2.1 0.0 0.8 0.0 42.2 5.2 0.0 0.0 Angola .. . . 39.4 .. 10.7 . .0.0 0.0 Argentina . .. 3.5 .1. 1 0.9 0.3 25.4 18.3 24.3 1-2.6 18.2 Armenia .. . 4.3 .. 0.4 . .3 .. 0.0 Australia . ... . . . 51.9 75.5 . . 22.7 27.6 Austria . ... ... .. 75.8 94.8 . .. 37.4 40.5 Azorbaijan .. . . 19.9 .. 3.2 . . 1.0 .. 0.0 Bangladesh 58.9 55.0 0.0 0.0 0.0 0.0 8.1 21.0 0.0 0.0 10.0 Belarus .. . . 0.4 .. 0.1 . . 8.2 .. 0.0 Belgium . ... ... .. 29.3 65.5 . .. 51.0 50.4 Bern:n. 33.6 1.9 1.7 0.3 0.3 28.6 8.9 0.0 0.0 Botvia 51.3 40.2 10.3 2.5 1.5 2.4 15.5 52.8 3.4 4.5 .. 23.1 Bosnia and Herzegovina .. . . 0.0 Botswana . .. 30.6 6.5 11.5 1.8 12.1 13.2 0.0 0.0 34.0 46.2 Brazil 72.0 76.2 3.5 3.2 0.8 0.7 42.5 34.8 23.2 20.0 20.2 37.4 Bulgaria . .. 0.0 5.2 0.0 1.1 .. . .0 0.0 .. 43.1 Burkina Paso 0.0 0.2 0.0 0.0 16.7 6.9 0.0 0.0 12.2 Burundi 0.0 1.7 0.0 0.2 9.8 19.8 0.0 0.0 21.5 Cambodia .. . . 15.0 .. 5.4 .. 4.1 0.0 0.0 Cameroon . .. 9.2 8.9 1.9 1.3 29.5 0.0 5.9 2.1 15.7 15.9 Canada . ... ... .. 73.6 81.4 . .. 21.3 Centra African Republic . .. 9.0 3.3 0.6 0.3 13.9 4.1 0.0 0.0 22.0 Chad . .. 0.0 13.3 0.0 0.5 24.4 4.9 0.0 0.0 Chile 67.6 80.4 3.1 10.4 0.8 2.5 46.8 52.7 38.8 44.7 28.0 19.2 China . .. 0.0 12.7 0.0 5.1 53.4 88.6 0.0 0.2 .. 9.4 Colombia 58.3 59.7 2.5 16.4 0.5 3.3 30.5 40.6 7.4 12.1 13.4 14.5 Congo . .. 6.6 0.2 2.3 0.0 15.5 7.9 0.0 0.0 49.4 Costa Rica 61.3 79.4 4.1 16.8 1.1 4.3 27.9 13.4 15.0 5.6 25.1 28.4 Cbte d'lvolre 53.2 64.1 3.5 1.4 0.9 0.2 40.8 20.2 27.0 14.0 31.7 Croat[a .. 3.2 .. 0.4 .. 33.8 .. 34.3 .. 46.5 Czech Repub ic . .. 0.0 23.2 0.0 5.7 .. 67.5 0.0 11.4 .. 42.0 Denmark . ... ... .. 42.1 32.6 . .. 39.3 43.5 Dominican Republic 68.4 54.1 5.6 12.2 1.4 2.4 30.8 26.7 12.7 0. 5 16.9 17.1 Ecuador 59.7 60.9 2.3 14.0 0.6 2.5 22.8 34.3 18.7 3.2 14.2 15.7 Egypt, Arab Rep. 30.1 58.7 8.7 7.5 2.4 1.3 15.2 47.1 1.4 0.9 45.6 42.8 El Salvador 47.5 79.9 1.3 2.1 0.2 0.4 33.6 35.5 17.5 0.2 17.1 14.5 Estonia .. . . 17.9 .. 5.0 .. 14.6 .. L.8 .. 31.9 Ethiopia . .. 0.0 0.8 0.0 0.1 20.1 15.0 0.0 0.0 19.6 27.4 Finland . . 48.5 64.5 . .. 28.2 43.8 France . .. 104.8 85.5 . . 39.5 46.4 Gabon 2.7 -4.1 0.7 -1.1 15.8 8,4 0.0 0.0 36.5 Gamoia, The 0.0 13.1 0.0 2.6 24.2 9.8 0.0 0.0 32.2 20.4 Georgia . ... 0.0 .. 0.0 C. . . ). Germany .. . . . . . . 99.7 .. . . 33.8 Ghana .. 27.4 6.4 19.6 0.4 3.6 2.2 5.2 0.7 0.5 10.9 20.6 Greece . .. 5.9 6.2 1.7 1.2 53.2 43.7 . .. 35.5 43.1 Guatemala 53.8 84.1 8.9 3.0 1.4 0.5 16.2 19.5 24.2 4.3 12.1 8.9 Guinea 55 S.4 . . 6.4 . . 0.9 .. .0 0.0 0.0 Guinea-Bissau . .. 0.0 2.4 0.0 0.4 .. 7.3 0.0 0.0 Haiti . .. 5.3 7.3 0.9 0. 19.6 15.5 0.0 0.0 17.4 Honduras 0.9 5.6 0.2 1.3 28.8 24.9 13.0 2.5 Hong Kong . ... .. 22.4 Private Foreign direct investment Credit to Private non- Centrai government Investment private sector guaranteed debrt expenditure % of gross domestic % of gross % of external fixed investment domestic investment % of GDP % of GDP debt % of GOP 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Hungary . .. 0.0 44.0 0.0 10 3 48.3 26.2 0.0 13.1 56.2 India 55.5 61.6 0.2 1.6 0.0 0 4 25.4 25.0 1.6 7.1 13.2 16.1 Indonesia 56.5 76.0 0.9 5.8 0.2 2 2 8.8 .. 15.0 18.7 22.1 16.3 Iran, Islamic Rep. 52.3 61.0 0.0 .. 0.0 .. 43.8 23.2 0.0 1.4 35.7 25.2 Ireland . ... .. . .. 44.0 54.8 . .. 45.0 42.6 Israel . ... ... .. 68.3 66.8 . .. 70.2 44.8 Italy . ... .. . .. 36.4 ... . 41.0 49.9 Jamaica . .. 6.6 21.8 1.0 3.8 21.9 31.6 3.9 3.0 41.5 Japan . ... .. . .. 132.7 210.1 . .. 18.4 23.8 Jordan .. . . 0.2 .. 0.0 .. 71.6 0.0 0.0 .. 30.7 Kazakstan .. . . 8.1 . 1.3 . 7.1 .. 1.8 Kenya 54.8 49.6 3.7 1.8 1.1 0.4 29.5 33.8 12.9 6.0 25.2 27.5 Korea, Dem. Rep. . .. . . .. .. Korea, Rep. 75.7 73.8 . .. . .. 50.9 69.9 . .. 17.0 17.7 Kuwait . ... .. . .. 41.6 22.4 . .. 27.8 51.4 Kyrgyz Republic .. . . 3.2 .. 0.5 .. .. 0.0 Lao PDR .. . . . . 5.0 .. 9.1 0.0 0.0 Latvia .. . . 14.3 .. 3.0 .. 7.4 .. 0.0 .. 30.3 Lebanon .. . . 0.3 .. 0.3 .. 57.7 0.0 1.7 .. 35.1 Lesotho . .. 3.2 2.6 1.4 2.2 9.8 19.8 0.0 0.0 .. 47.3 Libya . .. -13.9 .. -3.1 .. 11.2 ... Lithuania .. . . 4.8 .. 1.0 .. 7.2 .. 0.0 . 27.4 Macedonia, FYR .. . . 0.0 .. 0.0 .. . 23.8 Madagascar .. 53.3 -0.2 2.7 0.0 0.3 19.2 11.5 0.0 0.0 .. 17.2 Malawi 21.4 14.1 3.3 0.5 0.8 0.1 20.7 7.8 0.0 0.0 34.6 Malaysia 62.6 65.1 12.5 16.8 3.8 6.8 49.9 129.5 18.9 32.7 28.5 23.0 Mali .. 51.7 0.7 0.2 0.1 0.0 23.8 10.7 0.0 0.0 21.3 Mauritania . .. 10.5 1.9 3.8 0.3 31.0 22.7 0.0 0.0 Mauritius 61.4 62.0 0.4 1.5 0.1 0.4 21.6 48.3 5.1 14.8 27.3 23.3 Mexico 56.1 80.7 4.1 18.2 1.1 2.8 19.7 41.0 12.7 11.2 16.8 16.8 Moldova .. . . 27.1 .. .8 .. 5.0 .. 0. Mongolia . .. 0.0 .. 0.0 1.2 .. 13.3 .. 0.0 .. 23.6 Morocco 53.1 37.3 2.0 4.3 0.5 0.9 27.0 48.9 1.6 1.5 33.1 Mozambique . . 0.0 3.7 0.0 21.5 . ..0.0 0.8 Myanmar . ... .. . ..5.5 ..0.0 0.0 15.9 11.0 Namibia 40.7 61.0 0.0 8.9 0.0 1.5 .. 56.9 .. . . 40.7 Nepal .. 70.6 0.0 0.8 0.0 0.2 8.6 18.6 0.0 0.0 14.3 17.5 Netherlands . ... .. . .. 93.6 98.6 ... 53.0 50.8 New Zealand . ... .. . .. 18.3 89.8 ... 38.1 36.1 Nicaragua . .. 0.0 11.7 0.0 3. 7 48.3 36.0 0.0 0.0 30.5 32.8 Niger . .. 5.3 0.8 1.9 0.1 16.9 4.5 35.3 8.2 18.4 Nigeria 30.8 .. -3.6 .. -0.8 2.4 12.1 7.8 12.3 0.9 Norway . ... .. . .. 51.5 72.1 . .. 34.6 41.1 Oman . .. 7.3 6.8 1.6 1.2 13.7 29.2 0.0 0.1 38.5 42.3 Pakistan 42.7 52.8 1.4 3.5 0.3 0.7 24.0 27.1 0.2 5.3 17.5 23.2 Panama 59.3 84.0 .. 12.6 -1.3 3.0 61.6 82.3 0.0 0.0 32.3 28.2 Papua New Guinea 58.6 79.6 11.8 38.3 3.0 9.2 17.6 19.6 19.3 28.4 34.4 29.4 Paraguay 82.1 76.9 2.2 9.8 0.7 2.6 18.4 21.8 15.8 0.7 9.8 13.0 Peru 70.6 81.3 0.5 19.6 0.1 3.3 12.9 15.3 6.5 4.1 19.4 19.0 Philippines 68.9 80.1 -1.1 8.9 -0.3 2.0 42.2 45.0 14.1 9.0 13.4 18.4 Poland . .. 0.1 17.0 0.0 3.1 6.4 12.8 0.0 2.4 .. 43.4 Portugal . ... .. . .. 54.4 58.3 . .. 33.3 42.5 Puerto Rico . .. .. .. .. .. Romania .. . . 4.6 .. 1.2 . ..0.0 6.3 44.7 31.9 Russian Federation .. . . 2.3 0.(.6 .. 7.6 0.0 0.0 .. 22.2 WAorld Developnent Indica-Lors 1997 223 Private Foreign direct investment Credit to Private lion- Central government investment private sector guaranteed debt expenditure % of gross domestic % of gross % of exterral fAec investment domestic Invsteomnt % of G DP S of GDP nebt % of GDP 1980 195 1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Rwanda . .. 8.7 0.7 1.4 0).1 5.7 6.8 0.0 0.0 14.4 24r2 Saudi Arabia -9.4 1.5 -2.0 -1,5 22.8 63.7 Senegal 3.3 0.1 0.5 0.0 42.3 14.7 0.6 .1 23.1 Sierra Leone -9.2 2.1 -1.6 0.1 7.4 2.8 0.0 0.0 27.3 20.2 Singapore . . . . 81.0 103.7 . ..20.0 1 7. 7 Slovak RepuoDlic . 3.7 .. 1.1 .. 277 0.0 1.5 Slovenia .. . 4.2 .. 0.9 .. 27.3 .. 42.3 South Africa 50.8 72.8 -0.1 0.0 0.0 0.0 60,3 130.6 . ..22.2 33.3 Spain . ... .. . .. 78.2 74.0 . ..26.7 39.4 Sr Lanka 41.2 0.2 3.2 1.9 1.1 0.5 17.2 26.7 0.2 1.1 41.3 28.3 Sudan . .. 0.0 .. 0.0 .. 14.9 5.3 6.3 2.8 19.6 Sweden . ... ... .. 78.0 107.9 . ..39.4 45.0 Switzerland . . . . . . 114.9 172.2 . ..20.3 2 7.1 Syrian Arab Republic 0.0 .. 0.0 0.4 5.7 10.9 0.0 0.0 48.2 28.6 Tajikistanr. 4.3 .. 0.8 .. . . 0.0 Tanzania 1.1 13.1 0.3 4.2 2.6 8.9 3.4 0.6 27.9 Tnailand 68.1 77.1 2.0 2.9 0.6 1.2 41.7 139.9 20.5 3 7.4 18.9 15.8 Togo . .. 12.3 0.0 3.7 0.0 27.6 20.8 0.0 0.0 30.8 Trinidad and Tobago 9.7 39.5 3.0 5.6 28.7 43.6 0.0 3.5 30.3 Tunisia 46.9 52.7 9.0 6.1 2.7 1.5 46.4 68.4 6.1 1.0 31.6 Turkey 45.6 80.0 0,1 2.2 0.0C 0.5 13.6 19.0 2.6 9.6 21.4 22.8 Turkmenistan . . . . . 0.0 .. . . 0.0 UJganda . .. 0.0 13.1 0.0 2.1 3.9 4. 1 0.0 0.0 6.1 Ukraine .. . . . . 0.3 . . l).0 .. 1.0 United Arab Emnirates . ... ... .. 22.9 50.0 . ..12.1 11.8 United Kingdom . ... ..27.6 118.2 36.3 41.8 United States . ... ... .. 80.0 109.0 . ..22.0 22.9 Uruguay 68.2 70.1 16.5 7.6 2.9 0.7 37.2 28.8 12.7 2.4 21.8 31.2 Uzbekistan .. . . 1.1 . 0.5 .. . . 0.0 Venezuela .. 40.3 0.3 7.6 0.1 1.2 48.2 12.2 10.6 5.6 16.7 18.8 Vietnam .. . . 16.4 .. 6.9 . ..0.0 0.0 West Bank and Gaza . .. . . .. .. Yemen, Rep. .. 3.1 . 0.0 .. 550.0 0.0 .. 38.4 Yugoslavia, Fed. Rep.'. . . ... .. Zaire 0.0 .. 0.0 ..0.0 ..0.0 0.0 0.0 Zamnbia 6.9 13.8 1.6 1.6 19.9 7.2 2.7 0.2 37.2 16.8 Zimbabwe 0.2 2.2 0.0 0.6 18.6 35.3 0.0 7.8 34.9 Low income .. ... . 4.8 w 2.7wv Excl. China & India Middle income . ... .. 12.4 w 10.7 w' Lower middle incomne.. . Upper middle incomne Low & middle incomne East Asia & Pacific . .. 13.5wa 14.3 w Europe & Central Asia . .. 13.1wa 4.8 w Latin America & Cari.b. . 16.5 w 12.6 w Miodle East & N. Afr:ca 0.7 w 1.5 w South Asia 0.9 w 5.3 w Suo-Saharan Africa . ..5.4 w 3.1 W High income a. Oats for 1980 refer to the former Yugoslavia. 5.1 How big should a government be? ,,, . And what should its role be in nurturing, regu- lating, or monitoring the functioning of mar- Because data on subnationa unmIs of government- a Private investment covers outlays by the private kets? The model state for many classical state, provincial, and municioal--are not readily aval- sector nclucc ng prvate norprofit agencies) on aodi- economists and philosophers in the 19th cen- able, the size of the publ c sector IS measured here tions to iLs F xed assets. Gross domestic fxed Invest- tury was a minimal one that left most aCtivi- by the s ze of the centra government. WrIle the cen- mert inc udes sim lar out ays bo the pobl c sector. ties to markets. In the 1870s governments tral government is usuallythe agest single economic o Fcreign direct:investment is net inflows of Inest were generally small: in what are today's major agent in a country and typica ly accounts for most of ment to acquP'e a lasz ng rmanagement interest (10 industrial countries government spending the revenues, expendilures. and deficits of tne pub ic percent or mo-e of vot ng stock) ir an enterprise oper- amounted to just over 8 percent of GDP. In sector, in some countries state. provinc al, and loca ating in an economy other then hrat of the nvestor. It 1994 government spending for the same governments are important participants .n the ecor- 5sthe sum o'equiy capita , re nvestmentofearr ngs. countries averaged 47 percent of GDP, reflect- omy. In additon, the activties covered under "centrai other long-ter'n capital.. art snort-term captal as ing large increases in defense spending and government" can vary depending on the account ng shown n the ba ance o' payments. Gross domestic the provision of goods and services, such as concept used (consolidated or budgeta'y). For most investment is gross domest - fixed Investment pus infrastructure, education, health care, and countries central government finarce clata have been changes n stocks. o Credit to private sector refers social safety nets (Tanzi and Schuknecht consolidatec in.o one overall a:cou_nt, but for otners to financial resources provioed to the orivate secto- 1995). Developing countries generally have onlybudgetarycentra governmentaccountsareava:l- such as through loans. purchases of noneouity secu- much smaller governments, largely reflecting able. which often omit tie operations of state-owned rities. and trace cred ts and othe accounts a much smaller commitment to a welfare enterprises (see Pnmary cata documentatton). rece vable-that establ sh a claim for epaynment. For state. When d rect estimates of pr vate gross domest c some countr es these cla rs include crecit to public Governments have two principal responsi- f xed investment are not avaihlo e, such investrent enterp'dses. o Private norguarenaeed debt cons sts bilities: providing rules to make markets work is estimated residual v as the dofference betmveen of external ob iga. ons o' prvate debtors nhat are not efficiently and taking corrective actions when total gross domestic investmren: and conso idated guaranteed 'or repayment by a nubl c entity. Total markets fail. These responsibilities encom- pubi c investment. Total investment' may be esti- external debt is the sum of public and pub c y guar- pass many of the traditional functions of the mated directly from surveys of enterprises anc anteed long terr debt, private nonguaranteed ong state: establishing law and order, ensuring admin,strative records or indii'ectly usng .ne cow- term debt.lsIMIcred t. ard short-term debt. o Ceartral property rights, and providing goods and ser- modity f ow method. Conso idated measures oF government espenditure comprises the experditures vices that the private sector cannot or will not publ c investment may om t mportant subnationa of a governmert off ces, depa't-ents, establ sh- provide, including universal education, public units of government. In addition, pub ic nvestment merns. and other bodies tha. are agenc es o' nstYu- health services, and basic infrastructure. data may include financia as eel as ohys ca capital ments o' tne certral authority of a country. It inc udes The indicators in the table measure the rel- investment. As the d.fference between two esti- bothn current and cap tal (development) excend tures. ative size of the state and markets in the matec quantities, prvate investment may be under- national economy. There is no ideal size for va ued or overvalued and subject o large errors over j the state, and size alone does not capture its time. full effect on markets. Large states may sup- Statistics on fore gn direct investment are based or Private nvesLment data port prosperous and effective markets: small baeance of payfiients dats reported by the a'e frorn the nternational states may be predatory toward markets. The Internationa Monetary Fund IsIF), supp emenued by Finance Corporation's resources of a large state may be used to cor- data on net foreign direct investment reported by se Trends in Private Invest- rect genuine market failures in key areas-or OECD and officia national soLrces. The cafa sufTer momer rn Developing Coun- merely to subsidize state enterprises making from def ciencies relat.ngto oef nitions, coverage, and toes 1996. Data on goods or providing services that the private cross-country comparabil ty (see the notes to table fore gn direct nvest"vent sector might have produced more efficiently. 5.2 for a detai.ed discuss on ol data on 'ore gn direct are based on est.mnates A large share of private domestic investment nves.ment). E- compiled by the IsF n the in total investment may reflect a highly com- Data on domaest c ctra t to ahe orivate sector are Balance of Payrments Stabstics Yearbook, supple- petitive and efficient private sector-or one taken from the banking sirvey o' the IMF's rnen:ed by Wored Bank staff esLimates. Data on that it is subsidized and protected. Thus, like International Financiai Statistics or, when [he domestic credt are from the IMF's Inte'nationai other indicators in this volume, the indicators broader aggregate is not available. f'om its more- Financial Sratstics. and government expendture here provide an important but far from com- tary survey. The moretary survey inc udes monetary data are f'om tne ItF's Government F'nance plete picture of what they measure-in this authorities (tre centra bank, and depost money Staisricsa Yorbocf. Exmerna debt figL'es are rom the case the roles of states and markets. banks. In add tion to these, the banking survey World Bank's Deb.orPeportingSystem as reported ir includes other barking nstitutions such as savings Lhe World Bank's Giobal Deveropment Fnance 1997 and oan inst tut ons. f:nance comparies, ard devel- fforrerly Worid Deot Tabiest. opmane banks. In some cases credoi to the private sector may include crecit to state-owned or partial y state owred enterpnses. Word DeveJopment nd cators 1997 235 rn 5.2 Private capital flows Net private Foreign direct Portfolio investment Bank and capital flows investment trade-related lending Bonds Equity $mil, ens $ millions $ rrilons $ millions $ mlilhions 1990 1995 1990 1995 1990 1995 1990 1995 1990 1995 Albania 31 70 0 70 0 0 0 0 31 0 Algeria -493 129 0 5 -15 -278 0 -477 401 Angola 195 523 -335 400 0 0 0 C) 30 123 Argentina -203 7,204 1,536 1.319 -857 4,906 13 211 -1,196 768 Armenia 0 8 08 0 0 0 00 0 Australia..-.... . Austria... ..... Azerbaijan 0 110 0 110 0 0 0 C)0 0 Bangladesh 70 10 3 2 0 0 0 33 67 -25 Belarus 0 103 0 20 0 00 0 83 Belgium...... .. Benin 1 1 1 1 0 0 0 0) 0 0 Bolivia -13 191 11 150 0 0 0 0) -24 41 Bosnia and Herzegovina ... .... Botswana 77 64 95 70 0 0 0 0) -19 -6 Brazil 505 19,097 989 4,859 129 2,636 0 4,411- -613 7,190 Bulgaria -42 489 4 135 65 -6 0 40(1 -111 -41 Burkina Faso 0 0 0 0 0 0 0 0) 0 0 Burundi -5 1 1 2 0 0 0 C0 -6 -1 Cambodia 0 164 0 151 0 0 0 C0 0 13 Cameroon -124 49 -113 102 0 0 0 C0 -12 -53 Canada . .. .. .. Central African Republic 0 3 1 3 0 0 0 C) -1 0 Chad -1 7 0 7 0 0 0 0) -1 0 Chile 2,098 4,230 590 1,695 -7 489 320 274 1,194 1,772 China 8,107 44,339 3,487 35,849 -48 317 0 2,807 4,668 7,303 Colombia 363 3,741 500 2,501 -4 855 0 131 -133 254 Congo -100 -49 0 1 0 0 0 Cl -100 -50 Costa Rica 23 384 163 396 -42 -4 0 1, -99 -9 Cote dolvoire 57 36 48 19 -1 0 0 110 14 Croatia 0 346 0 81 0 0 0 C 0 265 Cuba . 1 7 ...0 C..- Czech Republic 843 5,596 207 2,568 0 38 0 82 636 2,907 Denmnark . .. .. .. Dominican Republic 130 237 133 271 0 0 0 C -3 -34 Ecuador 183 561 126 470 0 1 0 1 57 90 Egypt, Arab Rep. 698 294 734 598 -1 0 0 2 -35 -306 El Salvador 8 8 2 38 0 0 0 C 6 -30 Estonia 0 207 0 201 0 0 0 7 0 -1 Ethiopia' -44 -42 12 7 0 0 0 C -56 -49 France ..... - Gabon 103 -125 74 -50 0 0 0 0 29 -75 Gambia, The -7 10 0 10 0 0 0 C -7 0 Georgia 0 0 0 0 0 0 0 0 0 0 Germany . .. .- .. .- Ghana -5 525 15 230 0 0 0 267 -20 29 Greece .. 1,005 1,053 ...0 43 - Guatemala 42 85 48 75 -11 46 0 0 5 -35 Guinea -1 20 18 35 0 0 0 0 -19 -15 Guinea-Bissau 2 1 2 1 0 0 0 C 0 0 Haiti 8 2 8 2 0 0 0 0 0 0 Honduras 75 65 44 510 0 -13 0 C 31 28 Hong Kong Net private Foreign direct Portfolio investment Bank and capital flows investment trade-related lending Bonds Equity 1990 $mlos1995 1990 $ ilos1995 1990 milos1995 1990 $ (los1995 1990 $mlns1995 Hungary -308 7,841 0 4,519 921 2,094 150 483 -1,379 745 India 1,851 3,592 162 1,300 147 210 105 1.517 1,438 566 Indonesia 3,235 11,648 1,093 4,348 26 2,248 312 4,873 1,804 180 Iran, Islamic Rep. -392 .. -362 17 0 0 0 0 -30 Iraq ...0 0 ...0 0 Jamaica 92 188 138 167 0 13 0 0 -46 8 Japan . .. .. .. Jordan 254 -143 38 43 0 0 0 11 216 -197 Kazakstan 0 500 0 284 0 0 0 0 0 216 Kenya 124 -42 57 32 0 0 0 0 67 -74 Korea, Dem. Rep. ...0 1 ...0 0 Korea, Rep. . .. .. .. Kyrgyz Republic 0 15 0 15 0 0 0 0 0 0 Lao PDR 6 88 6 88 0 0 0 0 -1 0 Latvia 0 224 0 180 0 43 0 0 0 1 Lebanon 12 1,153 6 35 0 750 0 34 6 333 Lesotho 17 32 17 23 0 0 0 0 0 9 Libya ...159 90 ...0 0 Lithuania 0 194 0 73 0 60 0 4 0 57 Macedonia, FYR 0 0 0 0 0 0 0 0 0 0 Madagascar 6 4 22 10 0 0 0 0 -16 -6 Malawi -3 -14 0 1 0 0 0 0 -3 -15 Malaysia 1,799 11,924 2,333 5,800 -212 2,240 293 2,299 -614 1,585 Mali -8 1 -7 1 0 0 0 0 -1 0 Mauritania 6 3 7 3 0 0 0 0 -1 0 Mauritius 85 304 41 15 0 150 0 4 44 135 Mexico 8,155 13.068 2.549 6,963 661 4,321 563 520 4,382 1,265 Moldova 0 79 0 64 0 0 0 0 0 15 Mongolia 16 -4 0 10 0 0 0 0 16 -14 Morocco 350 572 165 290 0 0 0 150 185 132 Mozambique 30 67 9 36 0 0 0 0 26 28 Myanmnar -3 61 5 10 0 0 0 16 -8 36 Namibia ...29 47 . .0 0 Nepal -9 -2 6 8 0 0 0 0 -15 -10 Netherlands ........... New Zealand............ Nicaragua 21 -7 0 70 0 0 0 0 21 -77 Niger 9 -23 -1 1 0 0 0 0 10 -24 Nigeria 469 453 588 650 0 0 0 6 -119 -203 Norway............ Oman -259 126 141 150 0 0 0 5 -400 -28 Pakistan 182 1,443 244 409 0 0 0 729 -63 305 Panama 127 962 132 220 -2 0 0 20 -3 -12 Papua New Guinea 204 578 155 453 0 -32 0 450 49 -293 Paraguay 67 174 76 200 0 0 0 0 -9 -26 Peru 59 3,532 41 1,895 0 0 0 1,611 18 26 Philippines 639 4,605 530 1,478 395 1,060 0 1,961 -286 449 Poland 71 5.058 89 3,659 0 250 0 921 -18 228 Puerto Rico . .. .- .. .. Romania 4 687 0 419 0 0 0 1 4 267 Russian Federation 5,604 1,116 0 2.017 310 -810 0 141 5,294 -232 Word Developmen: [nd cators 1997 237 . ~5.2 Net private Foreign direct Portfolio investment Bank and capital flows investment trade-related lending $ millens $ m cns $ m ions $ncmilio11ns $ millions 1990 1995 1990 1995 1990 1995 1990 1995 1990 1995 Rwanda 6 1 8 1 0 0 0 0 -2 0 Saudi Arabia ..1,864 -1,877 ..00 Senega. 42 -24 57 1 0 0 0 C) -15 -25 Sierra Leone 36 -28 32 1 0 0 0 j 4 -29 Singapore . .. Siovek Republic 278 653 0 183 0 0 0 60 278 410 Slovenia 0 838 0 176 0 0 0 C)0 662 South Africa . -5 3 0 4,5 71 Spain . .. .. Sri Lanka 54 140 43 53 0 0 0 6' 11 15 Sudan 0 0 0 0 0 0 0 0, 0 0 Swedenr-- .. .. Syrian Arab Republic 18 43 71 65 0 0 0 0) -53 -22 Tajik star 0 15 0 13 0 0 0 0 0 Tanzania 5 137 0 150 0 0 0 0) 5 -13 Tha ann 4,498 9,143 2,444 2,068 -87 2,023 449 2,154, 1,692 2,698 Togo 0 0 0 0 0 0 0 00 0 Tr nidad and Tobago -69 271 109 299 -52 97 0 C) -126 -125 Tunisia -122 751 76 264 -60 666 0 C) -136 -102 Turkey 1,736 2,000 664 885 597 616 35 630 420 -131 Turkmenistan 0 20 0 0 0 0 0 C)0 20 1ganda 16 112 0 121 0 0 0 0 16 -10 Ukraine 0 247 0 267 0 0 0 C0 0 -20 United Arab Emirates . -. UJnited Kingdom . .. .. Ulnited Stateas.... Uruguay -192 217 0 124 -16 63 0 z. -176 26 Uzoekistan 0 235 0 115 0 0 0 C) 0 120 Venezue a -133 848 451 900 345 -328 0 7-929 -110 Vietnam 18 1,487 16 1,400 0 0 0 155 2 -67 West Bank and Gaza . ., ,, Yemen, Rep. 30 -2 -131 0a 0 0 C' 161 -2 Yugos avia, Fed. Rep. . .. .. 0 0l Zaire -24 1 -12 10 00 Cs -120 Zambia 194 30 203 66 0 0 0 C' -9 -36 Zimoabwe 85 99 -12 40 -30 -30 0 16 128 71 Low income 11,415 t _53,446 t 4,509 t 41,570 t675t 483 t los t 5,611 t 6,734 t 5,782t Exc). China & India . .. ,. . Middle income 31,9625t 130,742:t 20,040 t 63,919:t 2,265S5 28,025 t 2,134 t 26,476:t 7,533 t 22,322t Lower middle ncome .., ... .. Upper middle income . .. ,. .. Low & middle income 43,377 t 184,188 t 24,549 t 95,489 5 2,3225t 28,508 t 2.239 t 32,087 t 14,267 t 28,1045t East Asia & Pacific 19,3235t 84,1375t 10,1795t 51,7765t 755t 7,8565t 1,7505t 14,714 t 7,3195t 9,791t Europe & Centra, Asia 9,5145t 30,0595t 2,1025t 17,21S t 3,0895t 5,2905t 2355t 2,772 t 4,0885t 4,7825t Letsn America & Carib. 12,483 t 54,2615t 8,1215t 22,8975t 1015t 13,1145t 1,0995t 7,19C t 3,1625t 11,0605t Middle East & N. Africa 6095t 1,4145t 2,7575t -347:t -1485t 1,0605 t Oa 2025t -2,0005t 4995t Soctth Asia 2,152:t 5,1'915 4645t 1,7915t 1475t 21 05t 105:t 2,3405t 1,4365t 8505t Sub-Saharan Africa 2475t 9,1285t 9265t 2,1575t -941 t 9785t 05t 4,868 5 262 C 1,125 High income _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ Note: Totass for low,- and miid: a-income ecorrom as may' n05 add u,5 5o rag seal sassa sdue tc) rsallocated amou,nts. a. Includes Erstrea. b. Data for 1990 reler to the formrer Yugoslasia. 5.2@ =_ transactions by nonresidents in local equity markets - IS gathered from national authorit es. Investtment The data on foreign direct investment (F)l) are positions of mutual funds, and market sources. a Net private capital flows consist of prnvate debt and based on balance of payments data reportecl by the The volume of portfo io investment reported by nondebt flows. Private debt flows include commercial International Monetary Fund (IMF). supplemented by the World Bank generally differs from that reported bank lending, bonds, and other private credits: nondebt data on net fore gn direct investment reported by the by other sources because of d fferences in the clas- private flows are foreign direct investment and portfo- OECD and official nationa sources. The data suffer sif cation of economies, in the sources, and n the lio equity nvestment. e Foreign direct investment is from deficiencies relating to definitions, coverage, method used to adjust and disaggregate reported net inflows of investment to acquire a lasting manage- and cross-country comparability. information (there are differerces in particular with ment interest (10 percent or more of voting stock) in an The internationally accepted definitior, of FDI is the balance of payments data reported by tne IMF: enterprise operating in an economy other than that of that provided in the fifth edition of the IMF's Baeance see table 4.22). Differences in reporting arise par- the investor. It isthe sum ofequitycapita, reinvestment of Payments Manual (1993). To ensure a common ticularly for foreign investmentis In local equity mar of earn ngs, other long-term capital, and short-term cap- definition of FDI, the OECD has also published a def- kets. where there is a ack of clarity. adequate ital as shown in the balance of payments. * Portfolio nition, in consultation with the IMF, Eurostat, and the d isaggregation. and compretiensive and periodic investment flows are net and include non-debt-creating United Nations. Both definitions describe FDI as reporting in many developing economies. By con- portfolroequityflows(thesumofcountryfunds,depos- having three components: equity investmenl.s, rein- trast, capital f ows through iriternational debt and itory receiots, and d rect purchases of shares byforeign vested earnings, and short and long-term intercom- equity instruments are wel' recorded, and the dif onvestors) and portfolio debt flows (bond issues pur- pany loans between parent firms and foreign ferences in reporting lie primarly in differences in chased by foreign investors).a Bank and trade-related affiliates. But many economies report data that the classification of economies, in the exchange lending covers commercial bank ending and other pri- exclude at least one of these components-often rates used, in whether particu ar tranches of the vate credits. reinvested earnings-and that can lead to serious transactions are included, or in the treatment of cer- underest mation. In addition, the def nrtion of "long- tair offshore ssuances. term" differs among economies. And the balance of payments data on FDI do not include capital raised The principal source of in the host economnies, which has become an impor- , , information for the table is tant source of financing for FDI projects. Because of , 0 reports to the World the widely differing definitions and collection meth- 4 Bank's DRS from memoer ods used, re ying on a variety of sources for FDI data ' economies that have can lead to very different results for a sinr.!e econ- i , treceived IBRD loans or omy. There is also increasing awareness that FDI -s IDA credits. These data data are limited because they capture only cross- J i nI are compiled and pub- border Investmentf ows involving equity partic pation lished in the World Bank's and omit ronequity cross-border transactiois such annual Global Development Finance (formerly VVorld as intrafirm flows of goods and serv ces. Debt Tables). Additional information has been drawn Despite the drawbacks, the data on FDI are invalu- from the data files of the World Bank and the MF. able for ana ytical purposes. For a detai ed discus- sion of FDI data issues see the World Bans's World F Debt Tables 1993-94 (volume 1, chapter 3 . Figure 5.2a Net """'z, *!'f Portfolio flow data are compiled from several offi- ' 1980-95 cia and market sources, including Euromoney data- bases and publications, Micropal Inc . Lipper billions of U.S. dollars Analyt cal Services, published reports of private 200 investment houses, central banks, national securi- *EastAsiaandthePacific t!es and exchange commissions, national stock QrLatinAmercaandthecaribbean exchanges, and the World Bank's Debtor Reporting 150 QeuropepanceneaiAsia * South Asia System (DRS). E'Sub-Saran Afrca Gross statistics on international bond and equity rMiddi East nd Nornh Afnca ssues are produced by aggregating individual trans- 100 actions reported by market sources. The net values of pubic and publicly guaranteed bonds are ieported through the DRS by member economies :hat have 5 rece ved either IBRD loans or IDA credits. lntormation or, private nonguaranteed bonds is collected from O market sources, since officiai national sources 1980 1985 1990 1995 reportng to the DRS are not asked to report the Source: World sank, Debtor RepDrting System, and breakdown between private nonguaranteed bonds World Bank staff estimates. and private nonguaranteed loans. Information on r World Development Indicators 1997 239 5.3 Stock markets Market capitalization Value traded Tarnover ratio Listed domrestic IFC Global Index companlies price-earnings ratio va .1e of shares traded as 5 of $ m Ors % f GDP 5 of GDP oapital[zait on 1990 1995 1990 1995 1990 1995 1990 1995~ 1990 1995 199C 1-999 Argentina 3.268 37,783 2.3 13.5 0.6 1.6 22.7 12.3 179 149 -3.1 16.0 Armena ..3 . 0.1 .. 0.0 .. 6627 ..I Australia 107.611 245.218 36.4 70.3 13.3 28.1 31.6 42.2 1,089 1,178 Austria 11,476 32,513 7.2 13.9 11.7 11.0 110.3 62.1 97 109 Azerbaijan... ...... Banglaoesh 321 1,323 1.4 4.5 0.0 0.5 0.0 13.3 138 163 Belarus... ...... Belgium 66,449 104,960 34.1 39.0 3.3 5.7 9.2 16.1 182 143 Bo iva .. 97 .. 1.6 .. 0.0 .. 1.7 ..7 Bosnia and Herzegovina ... ... .. . Botswana 261 397 7.1 9.2 0.2 0.9 6.1 9.6 9 12 Brazi! 16,354 147.636 3.4 21.8 1.2 11.7 16.4 47.0 561 543 4.7 36.3 Bulgaria .. 62 C. .5 . 0.0 . 7.7 .. 26 Burkina Faso.. . ... .. . Burindi... ...... Cambodia.. . ... .. . Cameroon.. . ... .. . Canada 241,920 366,344 42.6 64.4 12.5 32.3 26.7 53.9 1,144 1L,196 Central African Repablic Chile 13,645 73,860 44.9 120.8 2.6 18.1 6.7 15.6 215 264 7.9 17.2 China 2.028 42,055 0.5 6.0 0.2 7.1 80.9 116.3 14 323 .. 16.7 Colombia 1.416 17,893 3.5 23.5 0.2 1.6 5.6 7.9 80 190 6.4 11.3 Congo . . . . . .. Costa Rica 311 434 5.5 1.7 0.1 0.0 5.8 4.6 82 118 Cote dcIvoire 549 667 5.1 8.6 0.2 0.1 3.3 2.2 23 31 Croatia .. 581 . 3.2 .. 0.3 8. .6 . 1 Czech Republic .. 15,664 .. 35.0 .. 8.1 .. 33.6 .. 1,635 .. 11.2 Denmark 39.063 56,223 30.3 32.6 8.6 15.1 28.0 46.9 258 213 Dominican Republic ... ... 0.0 0.4 . Ecuador 69 2,627 0.5 14.6 0.0 0.4 0.0 2.1 65 65 Egypt, Arao Rep. 1,765 8,086 5.0 17.1 0.4 1.4 7.3 11.0 573 746 El Salvador . . . . . .. Eritreea.. .... .. Finland 22,721 44.138 16.9 35.2 2.9 15.2 14.7 46.1 73 73 France 314,384 522,053 26.3 34.0 9.8 47.5 34.4 149.8 578 450 Gabon . . . . . .. Gambia, Toe . . . . . .. Georgia . . . .. .. Germany 355,073 577,365 22.9 23.9 22.1 47.5 139.3 218.9 413 676 Ghana 76 1,680 1.1 26.6 0.0 0.3 0.0 1.2 13 19 Greece 15,228 17,060 22.9 18.8 5.9 6.7 36.3 38.1 145 212 16.7 10.5 Guatemala . , . . . .. Guinea-Bissau.. . ... .. .... Honduras 40 338 1.3 8.6 .. 3.3 0.0 67.2 26 99 Hong Kong 83.397 303.705 111.5 211.4 46.3 74.4 43.1 37.3 284 518 .cc - zd .ev apme>i lica'mc _7 Market capitalization Value traded Turnover ratio Litddomestic IFC Global Index companies price-earnings ratio vlue of shares trade d as % of $ mnill ons % of GDP % of GD? capital ization 1990 1995 1990 1995 1990 1995 1990o 1995 1990 1995 1990 1995 Hungary 505 2.399 1.5 5.4 0.4 0.8 46.3 17.7 21 42 .. 12.0 India 38,567 127,199 12.9 39.2 7.3 4.2 66.5 10.8 6,200 7,985 17.8 14.2 Indonesia 8,081 66,585 7.1 33.6 3.5 7.3 77.3 25.3 125 238 20.3 21.4 Iran, Islamnic Rep. 34 .282 6,561 28.0 .. 4.3 .. 30.4 15.9 97 169 Ireland .. 25,817 .. 42.5 .. 21.8 .. 77.4 .. 80 Israel 3,324 36,399 6.0 39.6 10.1 10.0 95.8 26.5 216 654 Italy 148,766 209,522 13.6 19.3 3.9 8.0 26.8 44.6 220 250 Jamaica 911 1,391 21.4 31.6 0.8 7.7 3.4 21.7 44 51 Japan 2.917,679 3,667,292 98.2 71.8 54.0 24.1 43.8 33.3 2.071 2,263 Jordan 2,001 4,670 49.8 75.2 10.1 10.3 19.6 11.2 105 97 7.8 18.2 Kazakstan... ......... Kenya 453 1,889 5.3 20.8 0.1 0.7 2.1 2.6 54 56 Korea, Dem. Rep... . ... ...... Korea, Rep. 110,594 181.955 43.6 39.9 29.9 40.7 60.4 99.1 669 721 16.5 19.8 Kuwait .. 13.623 .. 51.1 .. 24.0 0.0 52.9 .. 52 Kyrgyz Republic . . . . . .. . . Lao PDR . . . .. .. .. Lebanon . . . . . .. . . Lithuania .. 158 . 2.0 .. 0.5 . 37.2 .. 351 Macedonia, FYR . . . . . .. . . Madagascar . . . . . .. . . Malaysia 48,611 222,729 113.6 261.1 25.4 90.0 24.6 36.4 282 529 23.6 25.1 Mauritania . . . .. .. .. Mauritius 268 1,381 10.1 35.2 0.2 1.8 4.5 4.8 13 28 Mexico 32,725 90,694 13.2 36.3 4.9 13.7 44.2 31.1 199 185 10.3 28.4 Morocco 966 4.376 3.7 14.4 0.2 2.6 0.0 0.0 71 5-1 Mozambique . . . . . .. . . Myanmar . . . . . .. . . Namibia 21 189 0.8 6.2 0.0 0.1 0.0 1.7 3 10 Nepal .. 244 .. 5.5 . 0.4 . 6.7 .. 83 Netherlands 119,825 356,481 42.2 90.0 14.2 62.8 29.0 77.7 260 387 New Zealand 8,835 31,950 20.3 56.0 4.4 14.7 17.3 28.4 171 205 Nicaragua . . . . . .. . . Nigeria 1,372 2,033 4.2 7.6 0.0 0.1 0.9 0.6 131 181 6.0 12.5 Norway 26,130 44,587 22.7 30.5 12.1 16.7 54.4 60.3 112 151 Oman 945 1,980 9.0 16.4 1.1 1.8 12.3 11.5 55 80 Pakistan 2,850 9,286 7.1 15.3 0.6 5.3 8.7 29.8 487 764 7.0 15.0 Panama 226 831 3.8 11.2 0.0 0.1 0.9 1.2 13 16 Papua New Guinea.. . ... ...... ... Paraguay... ............ Peru 812 11,795 2.5 20.5 0.3 6.9 11.4 39.4 294 246 25.9 14.5 Philippines 5,927 58,859 13.4 79.3 2.7 19.9 13.6 25.8 153 205 11.3 19.0 Poland 144 4,564 0.2 3.9 0.0 2.4 38.9 72.7 9 65 .. 7.0 Portugal 9,201 18.362 13.7 17.9 2.5 4.1 17.0 24.5 181 169 11.9 14.8 Puerto Rico . . . .. .. .. .. Russian Federation .. 15,863 .. 4.6 .. 0.1 .. 2.0 43 170 . World Deve[opment Indicators 1997 241 Market capitalization Value traded Turnover ratio Listed domestic IFC Global Index companies price-earnings ratio ,a ue of stares t'r [lncl-~cr I0997 5.5 Purchasing power parity (PPF') conversion factors C_ are based on surveys of the comparative purchasing Prices measured relative to the overall price leve or power of currencies by the United Nations Inter- a Official exchange rate is an annual average based in relation to other prices in the economy provide national Comparison Programrnie hICP). The conver- on exchange rates (local currency units to U.S. doi- vital information to the three main economic agents: sion factors can be treated as an exchange rate lars) determined bycountry authorities, orrates deter households, producers, and the government. In a relative to the "international dc lar," a common cur- mined largely by market forces in the legally market-based economy the decisions of these rency or unit of account that equalizes price levels in sanctioned exchange market. * Ratio of official to agents about the allocation of resources are influ- all economies. It has the sanre purchasing power parallel exchange rate measures the premium enced by relative prices. and relative prices reflect, overtotal GNP as the U.S. dollar in a given year. (For people must pay to exchange the comest c currency to a large extent, the choices of these agents. Thus further discussion of the PPP conversion factor see for dollars in the black market relative to the off cial re ative prices-the real exchange rate, real wages, the notes to table 4.14.) exchange rate. a Real effective exchange rate is the real interest rates, and relative commodity prices- Many interest rates exist in an economy, ref ect- nominal effective exchange rate (a measure of the convey vital information aboutthe interaction of eco- ing differences in creditors, deDtors, the terms gov- value of a currency against a weighted average of sev- nomic agents n an economy and in relation to the erning loans and deposits. and competitive era foreign currencies) divided by a price deflator or rest of the world, at a given point in time as well as conditions. In some economies interest rates may index of costs. a Purchasing power parity conver- over time. be set by administrative actiDn or regulation. In sion factor aS the number of units of a country's cur- The exchange rate is the relative price of a cur- economies with imperfect markets or where rency required to buy the same amounts ofgoocds and rency in terms of another currency. Official exchange reported nominal rates are rot indicative of the services in the domestic market as one dollar would rates are established by governments. Parallel, or effectve rates, it may be difficult to obtain data on buy in the United States. * Deposit interest rate is 'black market," exchange rates reflect rates negoti- interest rates that reflect actual market transac- the rate paid by commercial or simi ar banks for ated by traders and are by their nature difficu t to tions. The deposit and lending ates n the table are demand, time, or savings deposits. * Lending inter- measure reliably. Parallel exchange rate markets collected by the International vtonetary Fund (IMF) est rate is the rate charged by banks on oans to often account for only a small share of transactions as representative interest rates offered by banks to prime customers. a Real interest rate is the deposit and may therefore be both thin and volatile. The par- resident customers; however, :he terms and condi- interest rate adjusted for inf ation as measured bythe allel rates reported here are collected by Currency tions attached to these rates differ from country to GDP deflator. a Key agricultural producer prices are Data & Intelligence, Inc. from a variety of sources, country. Real interest rates are calculated by adjust the domestic producer prices per metric ton for wheat some within the country and some outside but doing ing nominal rates by an estimate of the rate of infla- and maize converted to U.S. dol ars using the officia business with entities based in the country. The tion in the economy. A negative rea interest rate exchange rate. sources nclude import-export firms, banknote col- indicates a loss in the purchasing power of the prin- lectors trading with local partners. and other busi- cipal. The real interest rates in the table are calcu- - - - ness travelers. For currencies that are heavily lated as (i - P) /J1 + P), where i is the nominal restricted from free trade and transferability. the interest rate and P is the rate of inflation. Official and real effective exchange rates are from "black market" rate s used. For currencies with little Domestic prices for two key agricu tural commodi- the IMF's International F/nancial Statistics. or no exchange restrictions but which trade at rates ties, wheat and maize, show that prices often are not Estimates of parallel market exchange rates are from that differ slightly from the rates fixed bygove-nment equalized across international markets (even after Currency Data & Intelligence, nc.'s Global Currency banking institutions or rates observed in official adjusting for freight. transport, insurance, and dif- Report. PPP convers on factors are from ICP and interbank channels, "free market" rates are usec. ferences in quality). Marnet imperfections, such as Worlo Bank staff estimates. Deposit and lending For currencies for which multiple rates exist, the taxes, subsidies. and trade bzrriers, drive a wedge interest rates are from the IMF's International rates are averaged to derive an estimate applicable between domestic and iriternational prices. Financial Statistics. Real interest rates are calcu- to unofficial transfers. Commodity prices in local currency are converted lated using World Bank data on the GDP deflator. Real effective exchange rates are derived by into U.S. dollars using official. period average Agricultural price data are compiled by the Food and deflating a trade-weighted average of the nominal exchange rates. Agriculture Organization (FAO) and published in its exchange rates that apply between trading part- Production Yearbook. The IMF and the FAO provide ners. For most industrial countries the weig_ts are the World Bank with electron c data files that are usu- based on trade in manufactured goods with other a ly more up-to-date than the print publ cations cited industrial countries and an index of relative, nor- here. malized unit labor costs is used as the deflator. For other countries the weights take into accourt trade in manufactured and primary products curing 1980-82 and an index of reiative changes in con- sumer prices is used as the defJator. An increase in the real effective exchange rate represents an appreciation of the local currency. Because of con- ceptual and data limitations, movements. in real effective exchange rates should be interpreted with considerable caution. World Development Indicators 1997 251 5.6 Trade policies All products Primary products Manufactured products Covered by Covereo by Co,eree by Standard ncrrariff Stanoard rnecTar ff Starndard rentr-t Mean ter 'f ceo anion of barr - ~ Melan traiff aeviat en of ba,re,s Mlean tariff ode aton a' -naiers tariff rates yr % ta,iff rates r rf nta f-' roles %/ 1990-93 1990-93 1990-93 O99093 90-9>4 19~O~O3 1990 913 1 990-9 3 1990-92 Albaria . .. A geria 24.8 1 9.6 9.5 2 1. 6 2 0.5 2 6. 8 2 6,2 19.4 2.8 Angola' 1 1.6 ..0.7 1 0.6 ..0.0 1 1.9 ..0.9 Argentina 9.9 6.9 0.2 8.3 5.4 0.1 10.2 7.1 0.3 Armenia...........- Australia 9.9 11.9 8.1 2.2 4.6 0.0 11.7 12.4 9.4 Austria... 3.5 ...1 2. 1. . 1.4 Azerbaijan . .. .. Banglaoesh 84.1 26.1 ..79.6 37.4 ..85.6 22.3 Belarus . .. .. Belgium 6.7 5.6 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Benina 37.4 - 17.0 35.0 ..24.3 36.3 ..14.2 Boliv a 9.7 1.2 2.0 10.0 0.1 1.6 9.6 1.2 1.8 Bosnia and Herzegovina Botswana..-.... Brazil 11.1 6.3 1.5 6.2 5.5 4.1 1 1.7 6.3 0.4 BuJgaria..-.... Burkina Faso..-.... Burundi... 0.3 ...0.2 ..0.4 Cambooia . .. .. Cameroon 18.7 12.0 .2 1.3 9.6 ..18.0 12.6 Canaoa 10.5 26.6 4.0 19.3 60.6 2.3 8.6 6.6 4.4 Centra African Repuolic' 32.0 . 5.1 28.9 . 9.3 33.0 . 3.1 Ched . .. .. Chils 11.0 0.7 0.1 1100.0 0.3 10.9 0.6 0.0 Chima 36.3 26.0 11.3 34.4 25.1 11.5 37.6 29.0 1 1.3 Colomb a 13.3 4.9 1.7 12.6 6.0 1.0 13.5 4.6 1.6 Congo 16.6 9.5 ..21.6 9.3 ..17.7 9.4 Costa Rica' 21.1 . 0.6 20.4 . 0.0 21.5 . 1.0 CSte dIlvoire 4.9 1.5 . 4.6 1.0 . 4.9 1.6 Croatia...... Cuba...... Czech Reoublc 6.7 6.4 8.0 10.5 . 6.4 3.9 Denmark 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Dominican. Rapab ic . .. .. Ecuador 12.3 5.5 63.6 12.5 6.4 67.5 12.4 5.5 61.6 Egypt, Arab Rep. 28.3 28.9 45.2 26.6 45.0 43.6 29.5 24.2 45.6 EJ Salvador' 21.1 .. 19.2 19.9 ..17.7 21.5 ..19.7 Eritrea .. . .. Esoonia..-.... Ethiopia 28.6 23.9 22.5 32.6 21.6 42.9 28.2 24.3 14.7 F:nlanord. 3.7 ...25.2 ...0.1 France 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Gabon..-.... Gambia, The... Georgia.. . .. Germany 6.7 5.6 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Ghana 16.0 8.2 . 18.5 8.1 ..14.1 8.0 Greece 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Guatema an 22.68. 7.4 20.9 . 12.5 23.5 . 5.0 Guinea a 8.9 . 36.2 9.2 . 46.9 8.8 . 35.1 Guinea-Bissau.. .. .. Haitil 11.6 - 30.8 14.5 ..34.5 10.5 ..29.7 Hroojrdrs 10.1 6.S . 12.2 6.2 ..9.7 6.5 Hong K(ong 0.0 0.0 0.5 0.0 0.0 0.8 0.0 0.0 0.3 26u. .01r -_ leve o0100, nc ccavors _ 907 5.60 All products 1Primary products Manufactured products Covered by Covered by Covered by Standard nontariff Standard nontariff Standard nontariff Mean tariff deviation of berriers Mean tariff deviation of barrters Mean tarlff devietior of terriers % teriff rates % tariff rates % tariff retes S/ 1990-93 1990-93 1990-93 1990-93 1990-93 1990-93 1990-93 1990-93 1990-93 Hungary 11.0 9.7 -. 15.8 17.1 ..10.1 6.8 India 56.3 23.6 62.6 43.5 39.2 71.7 59.4 16.6 58.9 Indonesia 19.4 16.1 2.7 17.4 12.5 4.6 20.3 17.1 2.0 Iran, Islamic Rep.a 20.7 .. 99.3 16.8 ..99.0 22.2 ..99.4 Ireland 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Israel 8.3 10.8 ..5.7 8.8 ..9.0 11.2 Italy 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Jamaica' 17.3 ..6.6 11.6 ..10.3 19.3 ..4.8 Japan 6.3 8.4 3.9 10.2 11.4 9.2 5.1 6.8 2.4 Jordana 13.8 .. 12.9 7.2 ..37.0 16.2 -. 3.6 Kazakstan . .. .. Kenya 35.1 13.3 37.8 38.0 14.1 37.0 34.6 13.5 38.3 Korea, Dem. Rep. . .. .. Korea. Rep. 9.0 6.6 ..13.9 13.1 ..7.8 1.3 Kuwait ...3.5 ...6.8 ...1.8 Kyrgyz Republic . .. .. Lao PDR . .. .. Latvia . .. .. Lebanon . .. .. Lesotho . .. .. Libyaa 18.3 .. 10.3 14.2 ..15.0 19.7 ..8.4 Lithuania . .. .. Macedonia, FYR . .. .. Madagascar ...1.7 ...0.8 ...1.6 Malawi ... 91.3 ...84.8 ...93.8 Malaysia 14.3 14.0 2.1 11.9 13.2 1.2 15.2 14.3 2.4 Mali 3.0 2.4 ..3.9 2.1 ..2.8 2.5 Mauritania . .. .. Mauritius ... 35.2 ...30.8 ...36.9 Mexico 12.6 5.4 3.9 12.4 6.3 8.5 12.6 5.3 1.8 Moldova . .. .. Mongolia . .. .. Morocco 24.5 13.2 ..23.7 15.4 ..25.3 12.4 Mozambique 5.0 0.0 ..5.0 0.0 ..5.0 0.0 Myanmar . .. .. Namibia . .. .. Nepal 16.7 15.9 0.7 8.5 13.9 1.0 19.0 16.0 0.5 Netherlands 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 New Zealand 8.5 10.4 0.0 4.3 5.9 0.1 9.7 11.1 0.0 Nicaragua 10.7 17.8 ..15.6 37.3 ..9.5 6.0 Niger . .. .. Nigeria 34.3 25.0 8.8 32.2 22.5 22.7 35.1 25.6 3.1 Norway 5.7 6.6 5.4 0.8 3.9 7.9 6.7 6.7 5.0 Oman 5.7 9.2 ..8.1 19.5 ..5.1 3.3 Pakistan 51.0 21.9 14.5 44.4 23.1 6.8 53.0 21.2 17.3 Panama......... Papua New Guinea0 7.0 ..2.6 4.5 ..9.4 7.7 ..0.0 Paraguay 9.3 6.9 1.8 8.2 5.4 6.4 9.5 7.2 0.0 Peru 17.6 4.4 ..17.6 4.4 ..17.7 4.4 Philippines 20.0 11.0 ..21.8 13.1 ..19.5 10.3 Poland 12.0 7.8 ..12.9 9.9 ..11.7 6.9 Portugal 6.7 5.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 Puerto Rico . .. .. .. Romaniaa 16.7 ..0.0 13.8 ..0.0 18.0 ..0.0 Russian Federation . .. .. .. World Dave opmenrt Indicators 1997 253 5.6 All products Primary products Manufactured products Coverd o, Covered Lv Covered by Storoarc nontariff Standard nontariff Sterdarc nontar ff Mean, tent, f dcoet on of haorte Moon toOt' den alien o' bno-ir Mooeoa ' enao of boo ow' % tar ft eten 30 3I ofr'lf roves 30 3 tariff -oven 30I 1-990-939 3 19903 19093 1990-93 1990-93 09~31990-93 1990-93 1990-93 Rwanda 34.8 33.1 47.1 41.0 ..32.1 30.5 Saudi Araoia 12.1 3.3 3.9 12.0 3.6 4.4 12.2 3.2 3.4 SenegaF 34.2 ..7.2 38.9 ..8.4 32.3 ..6.1 S erra Leone' 25.8 .. 100.0 19.4 ..100.0 28.0 .. 100.0 SIngapore 0.5 2.7 0.3 0.2 2.5 1.2 0.6 2.8 0.0 Sovak Republ.o.i.c. .. Slovernla . -. . South Afr ca 19.7 21.9 9.0 12.0 ..21.2 22.7 Spa n 6.7 5.8 13.4 9.2 10.2 22.0 8.1 4.0 11.5 Sri Lanka 24.1 18.1 3.8 28.5 22.8 2.8 23.6 16.9 4.0 Sudan0 56.6 .. 10.0 56.6 ..12.0 56.4 ..9.4 Sweden ...5.7 ...23.7 ...1.6 Switzerland ...8.8 ...25.3 ...4.8 Syrian Arab Repub jc0 14.8 .. 36.6 13.1 ..30.7 15.5 ..38.7 Tajikistan......... Tanzania 19.6 12.3 79.7 25.3 12.7 64.3 18.1 11.9 85.9 Thailand 23.1 16.9 6.5 32.2 23.2 6.8 21. 6 14.9 4.2 Togo......... Trindaad and Tobago 18.7 15.3 23.4 24.6 18.0 30.8 17.4 14.1 20.5 Tunisia 30.0 11.7 32.7 30.3 13.0 37.3 30.2 11.2 30.6 Tujrkay 9.3 5.7 96.4 9.9 9.1 93.9 9.5 4.4 97.3 Tu,rkmeniatan......... Uganda 17.1 9.1 ..20.9 10.5 ..16.3 8.5 Ukraine......... Lnited Arao Emnratesol 4.65. 1.0 3.2 ..2.9 4.9 ..0.3 Lnined Kingdom 6.7 3.8 13.4 9.2 10.2 22.0 6.1 4.0 11.5 United States 5.9 7.1 4.3 5.9 11.7 4.0 6.0 6.0 4.3 Uruguay 9.3 7.1 ..8.2 5.4 ..9.5 7.4 Uzbekistan......... Verezue a 13.4 4.8 2.4 12.9 5.8 30 13.5 4.5 1.7 Vetrcam 12.0 16.5 ..13.6 18.2 12.0 14.9 Wean Bank and Gaza......... Yemen, Repo.' 16.2 .. 28.7 17.9 ..25.2 16.6 ..30.2 Yugoslavia, Fed. Rep.......... Zatore . 100.0 ... 100.0 ... 100.0 Zambia 25.9 10.6 ..30.2 10.3 ..25.1 10.S Zimbabwe 93.6 -99.7 ____ . 91.2 a. Data arc for the mic-1980n. 5.6 0 Trade and tariffs F M Economies regulate their imports through a combination of tariff and nontariff measures. The oata show the average appl ed tariff rates before a Primary products are commodities classified in The most common form of tariff is an ad val- the conc usion of the Uruguay FRound of the General SITC sections C, 1. 2, 3, and 4 plus div sion 68 (non- orem duty, but tariffs may also be levied on a Agreement on Tariffs and Trade 'GATT) in 1994. Tariff ferrous metals). a Manufactured products are com- specific, or per unit, basis. Tariffs may be and nontariff measures may be applied generally. modities classi'iec ir SITC sections 5, 6, 7, 8, and 9. used to raise fiscal revenues or to protect against imports from all sources, or selectivey, excluding division 68. a Mean tariff is the simple domestic industries from foreign competi- against imports from specific trading partners. average of the appled rates for a I prooucts subject tion-or both. Nontariff barriers, which limit Countres typically maintain a h erarchy of trade pref- to tariffs. o Standard deviation of tariff rates mea- the quantity of imports of a particular good, erences applicable to different trading partners. The sures the average dispers:on oftariff rates aroundthe take many forms. Some common ones are applied rates In the table are as valorerm equivalents mean; it is also calculatec using unwe.ghted tariff licensing schemes, quotas, prohibitions, and of the most-favored-nat on duties charged on imports data. a Products covered by nontariff barriers Is the export restraint arrangements. not covered by preferential trade arrangements such percentage of tariff lines to wrhich nontariff barrers Some countries set fairly uniform tariff as the North American Free Trade Agreement or the are applied. No attempt is made to estimate their rates across all imports. Others are more European Union. The mean tarifl is the simple average tariff equ valent. selective, setting high tariffs to protect across all tariff lines. Simple averages are a better favored domestic industries and setting low indicator oftariff protection than averages weightedby bv _ tariffs on goods that have few domestic sup- import values, which are b ased downward. especial y pliers or that are necessary inputs for domes- when tariffs are set so high as to discourage trade. rrr .o-,-:w .rrn Mean tariff rates and their tic industry. The tariffs bound under the IJruguay Round w I be standard deviat ons were The standard deviation of tariffs is a mea- phased in over five years beginning in 1995. So, the calculated by World Bank sure of the dispersion of tariff rates around rates shown here are generally representative of cur- . staff us ng the Trade their mean value. Highly dispersed rates are rent levels of protection. Nontariff barriers are be ng Analysis and Irformation evidence of discriminatory tariffs that may phased out slowly; in some cases their conversion to =. System (TRAINS) database distort production and consumption deci- tariffs may result in higher rates of tariff protect on. maintaired by UNCTAD. sions. But this tells only part of the story. The See table 6.4 for estimates of the tariff concessions M`j. i.=. Estimates from TRAINS effective rate of protection-the degree to made during the Uruguay Rourd. were supplemented by which the value added in an industry is pro- For some developing countries data on mean tar ffs data from UNCTAD's Directory of Import Regimes. tected-may exceed the nominal rate if the and nontariff barriers are foryetrs before 1987: many Part t and A Stabfst.car Analysis of Trade Contro/- tariff system systematically differentiates of these countries maintain their tariff oata in the old Measures of Developing Countnes. Data on nontariff among imports of raw materials, intermediate Customs Cooperation Council Nomenciature (CCCN) barriers of develop ng countres come from the products, and finished goods. classification system, wnich is no longer compatible UNCTAD Database on Trade Control Measures, pub- Nontariff barriers are generally considered with the six-digit Harmon zed System codes used to lished in these same UNCTAD sources. Data on non- more detrimental to economic efficiency than maintain the UNCTAD Database on Trade Control tarff barriers of high- ncome countries come from tariffs because efficient foreign producers Measures. The CCCN had fewer tariff lines, and the special compi ations by World Bant staff using data cannot undercut the barrier by reducing their definitions of these lineswere notunIform across coun- prov ded by UNCTAD. costs and thus their prices. A high percentage tries. For other countries data on tariffs and nontartf of products subject to nontariff barriers indi- barriers are for 1990-93. Data on nontarff barriers for cates a protectionist trade regime, but the fre- high-income OECD members ae for 1993. quency of nontariff barriers does not measure The commodity groupings are based on the their restrictiveness. Moreover, a wide range Standard International Trade Ctlassification (SITC). To of domestic policies and regulations (such as construct aggregates based on tariff lines, the SITC health regulations) may act as nontariff barri- classification wvas mapped into equ valent tariff I nes ers but are not measured by this indicator. A of the Harmonized System. full evaluation would require careful analysis of the individual measures. Table 5.6a OECD imports covered by nontariff barriers before and after the Uruguay Round One of the goals of the Uruguay Round nego- tiations was the "tariffication" of agricultural From developing countries From other OECD countries nontariff barriers. Many others, such as the Pre-Uruguay Round Post-Uruguay Round Pre-Uruguay Rourd Post-Uruguay Round Sector % % ° % Multifibre Arrangement, will be phased out over Agnculture 13.3 1.7 22.0 1.8 a period of years. Although many nontariff bar- Other primary 1'.6 3.6 6.2 1.2 riers were replaced by ad valorem tariffs, the Manufacturing 17.0 1.5 8.3 3.5 reduction of nontariff barriers on the imports Total imports s5.0 5.0 8.3 3.4 of high-income countries should offer new opportunities for competitive exporters in Note: Post-Uruguay Round nontariff barriers are based on Wond sank staff estimates. Source: Los and Yeats 1994. developing countries (table 5.6a). _ Wor d Deve opment Indicators 1997 255 S 5.7 Export competitiveness Nominal export growth Nominal export growth 1983-84 to 1988-89 1988-89 to 1993-94 From From FroT From export From From expoot Anonual world market divers fi- Anrua worlo market diversf - average oemand shore cation average demono share cation 511 °/F % % % % Fr F % Albania 6.4 6.5 -3.0 3.0 -4.5 5.8 -18.3 10.4 Export dynamics Algeria -6.5 -1.8 -5.2 0.5 2.2 5.5 -2.8 -0.2 Thirty years ago the research staff of the Angola 8.7 -4.1 11.3 1.8 3.3 5.6 -1.3 -0.8 General Agreement on Tariffs and Trade (GATT), Argentina 8.8 9.1 -4.6 4.6 8.6 5.6 0.9 1.9 the predecessor to the World Trade Armenia .. Organization. examined broad patterns of Australia 10.4 -0.4 9.7 1.1 4.6 4.5 -2.3 2.6 export growth among developing countries. Austra 17.2 15.9 1.5 -0.4 5.7 8.0 -1.7 -0.5 Their work addressed three issues: To what Azerbaijan .. .. .. .. Bangbadean 14.5 8.0 8.2 -0.2 16.4 7.0 8.8 0.0 extent are differences in the patterns of mer- Bang adesih 14.5 8.0 6.2 -0.2 16.4 7.0 8.8 O.o hnieepr rwhars onre Belarus ~~~~~~~~~~~~~~~~~~chandise export: growth across countries Belarus-. Belgium 14.8 14.3 0.7 -0.2 3.0 7.4 -4.0 -0.1 explained by growth in the markets for their tra- Benir -4.8 -3.4 -4.9 3.6 10.6 4.9 7.3 -1.7 ditional exports? Had some countries suc- Bolivia -6.5 -4.8 -4.0 2.3 3.8 4.3 -8.5 8.9 ceeded in expanding their market share of their Bosnia and Herzegovina t.. raditional exporls? And how important was Botswana .. .. .. . .. - trade diversification in determining the export Brazi 9.4 11.7 -3.2 1.2 3.1 5.8 -3.8 1.3 performance ofcountries? (See GATT 1966, pp. Bulgaria 1.7 13.1 -12.7 3.0 13.2 7.9 0.6 4.3 23-32.) Burkina Faso 6.8 10.4 -4.0 0.7 -2.5 5.4 -7.6 0.1 The GATT analysis decomposed the growth of Burundi 3.5 13.4 -9.4 0.7 6.3 5.5 -12.5 1.5 Cambodia 28.7 17.1 -12.5 25.6 82.0 8.2 84.9 -9.1 nominal exports over the perio 1959-65 into Cameroon -3.4 -3.1 -1.0 0.7 -0.4 4.2 -4.2 -0.2 three multiplicative factors. The first. f, mea- Canada 8.6 8.5 -0.5 0.6 6.2 7.1 -1.1 0.3 sured the growth due to expansion of the world Central African Republic -0.3 8.4 -8.8 0.8 -1.7 3.0 -4.1 -0.4 marketforthecountry'straditional exports. The Chad -13.0 5.2 -18.6 1.7 -2.3 5.5 -11.3 4.3 second, C, measured the growth due to expan- Chile 15.7 12.8 2.0 0.6 7.7 3.1 1.6 2.8 sion of its market share for its traditional China 22.8 4.3 14.1 3.2 21.9 6.3 12.4 2.0 exports. The third, f, measured as a residuai, Colombia 10.9 6.5 -1.9 6.1 7.4 5.5 -1.1 3.0 captured the growth in exports due to diversifi- Congo -2.8 -3.2 0.2 0.2 4.4 4.1 0.2 0.1 cation into nontraditional exports. By construc- Costa Rica 9.9 14.0 -2.9 -0.7 12.2 9.0 1.5 1.3 Cote olvoire 0.6 -4.0 3.0 1.7 2.6 4.7 -1.2 -0.8 tion, nomina export growth is equal to the Croatia product of the three factors: f * 2 . f3. Cuba 9.5 -3.6 14.5 -0.8 -5.6 4.5 -10.2 0.6 The ndicators in the table update the GATT Czech Repub ic .. .. .. . . , . results for 1983-84 through 1988-89 and Denmark 12.2 10.4 1.2 0.4 3.7 7.9 -3.1 -0.8 1988-89 through 1993-94. (Two-year aver- Dominican Repub ic 12.6 14.5 -3.6 2.0 12.4 8.5 3.1 0.5 ages were used as starting and ending Ecuador -0.5 3.6 2.7 0.5 9.2 4.5 3.0 1.5 points to reduce the influence of a single- Egypt, Arab Rep. -3.6 -0.7 -4.6 1.8 7.5 5.2 -1.7 4.0 year outlier.) The growth in total nominal El Salvador -4.6 14.5 -17.9 1.4 11.7 9.9 0.4 1.2 exports sod the three growth factors are Eritrea . . . . Estonia shown as compound annual growth rates Ethiopia 0.8 3.6 3.9 1.3 6.1 4.2 9.8 -0.1 over the two periods. Finland 14.4 13.3 0.6 0.3 4.9 7.2 -2.9 0.8 In 1966 the GAlT concluded that during the France .. . .. .. . period 1959-61 to 1964-65 "above average Gabon -5.8 -3.5 -2.6 0.3 11.2 4.1 6.9 -0.2 total export performances were, in the majority Gamoia, The 24.4 13.6 9.1 0.4 2.4 8.8 -8.0 2.3 of cases, associated with gains in market Georgia . . . .. .. .. .. shares" (p. 30). Irving Kravis, in his well-known Germany 15.4 14.3 1.3 -0.4 2.7 7.5 -4.6 0.1 article "Trade as a Handmaiden to Growth." Ghana 13.7 6.7 6.0 0.5 5.5 3.1 1.6 1.0 reviewed the same data and concluded that Greece 9.4 0.6 7.1 1.5 -2.2 5.3 -8.7 1.7 Greece 9.4 0.6 7.1 1.5 -2.2 5.3 -8.7 1.7 ~~~~"the successful performers among [developing Guatemala 1.3 0.3 -0.6 2.3 12.7 6.4 -0.5 6.6 Gauinea-Bissau 5.1 11.3 -6.1 0.6 3.6 4.5 -2.0 1.1 countries] were differentiated from the less suc- Guinea 3.1 13.0 -9.5 0.8 31.2 6.0 24.8 -0.8 cessful primarily by increases in their shares in Haitl 0.0 16.1 -13.7 -0.2 -21.4 11.2 -29.9 0.8 world markets fortheirtraditional exports rather Honduras 4.4 13.0 -7.2 -0.4 10.6 7.0 2.6 0.8 than by good fortune in world demand for their Hong Kong 19.2 17.7 -1.5 2.8 2.2 10.8 -7.6 -0.2 particular exports" (1970, p. 868). The data in the table encompass a wide range of export per- <2or i X 1 - .De cl linci nc - 99i 7 5.7 Nominal export growth Nominal export growth 1983-84 to 1988-89 1988-89 to 1993-94 From From From From export From From export Annual world market diversifi- Annual world market diversif.- average demand share cation average demand share cation %i % % N N % % % % formance-from Iraq's loss of 47 percent a year Hungary 10.8 13.1 -2.8 0.7 8.5 7.1 0.1 1.3 during the second period to Yemen's increase India 9.8 1.5 6.4 1.6 12.8 5.6 4.0 2.6 of 85 percent a year during the first. Some of Indonesia 1.1 -4.7 1.0 5.0 12.1 4.3 0.3 7.1 theseextremechangeswerecausedbyunusual Iran, lslamic Rep. -10.9 -2.7 -8.8 0.4 10.0 4.6 4.3 0.8 events, such as wars, or by the fact that the Iraq 5.5 -2.7 8.2 0.2 -47.3 5.4 -49.9 -0.1 economy started from a very low level. Ireland 17.2 14.2 2.6 0.0 9.0 8.3 2.1 -1.4 Among large exporters and particularly among Israel 15.9 13.3 1.6 0.7 7.5 8.2 -1.0 0.3 Italy 14.9 14.1 1.0 -0.2 4.4 7.7 -3.2 0.1 high-income countries, the data reveal a pattern Iama0ca Jamaica 7 9 6.4 1.7 -0.2 7.4 5.8 1.7 -0. 1 of declining market shares for traditional Japan 14 L 15.7 -0.7 -0.7 7.5 82 -0.7 0.1 exports and relatively small growth through Jordan 7.8 3.4 5.5 -1.2 -2.8 5.3 -8.4 0.8 export diversification. However, these data do Kazakstan .. .. .. .. not reflect complementary data on the expan- Kenya 2.3 1.4 -0.4 1.3 4.2 4.0 -1.3 1.5 sion of service exports, which have become a Korea, Dem. Rep. 22.0 14.3 2.6 4.1 11.1 7.5 -7.4 11.6 leading factor in the growth of world trade. Thus Korea, Rep. 22.1 13.4 7.0 0.6 6.5 8.8 -3.0 0.9 high-income economies may be losing shares in Kuwait -1.0 -3.5 2.3 0.3 1.7 5.5 3.5 -0.1 their traditional markets for manufactured Kyrgyz Republic .. .. .. .. Lao PDR 31. 8 13.6 8.1L 7.3 21.7 8.9 8.6 2.9 goods by diversifying their export regimes into La .3 .. Latvia.. . .. . Lebanon 1.2 15.0 -13.5 1.8 1.2 6.6 -6.6 1.7 - -fmmmommomommmism Lesotho .. .. .. .. Libya -8.2 0.5 -8.9 0.2 2.9 5.4 -2.6 0.2 For many economies the laest Lithuania .. .. .. .. factor in expert gro'wth 3aas bleen Macedonia, FYR .. .. .. .. Madagascar 0.5 10.1 -9.6 1.0 7.2 2.3 -2.5 7.5 the general expansion of wfror9d Malawi .9 11.1 -7.3 0.8 4.0 6.4 -2.0 -0.3 trade ~~~~~~~~ ~ ~~~~~~~Malaysia 1(1.2 0.8 5.4 3.7 17.0 8.7 1.9 5.6 l:rade @ Mali 7.2 11.7 -5.6 1.6 7.6 4.3 1.5 1.7 Mauritania 10.4 -1.6 12.0 0.2 -2.0 6.1 -7.6 0.0 services. A similar pattern can be seen among Mauritius 21.8 16.5 3.7 0.8 4.8 8.4 -3.1 -0.3 the rapidly growing economies of Asia-Hong Mexico 4. 6.0 -7.6 6.2 10.6 7.9 2.3 0.2 Kong, the Republic of Korea, and Singapore. In Moldova .. .. .. . contrast, the less mature economies of Mongolia 22.9 14.5 4.3 2.9 19.9 7.5 7.4 3.9 Indonesia, Malaysia, and Thailand still show evi- Morocco 13.2 14.1 -1.1 0.3 7.1 6.4 0.0 0.6 dence of rapid diversification of their trade in Mozambique 14.8 5.1 2.7 6.4 -13.0 4.0 -14.4 -2.3 goods. Myanmar 0.5 5.2 -6.8 2.5 16.5 3.6 12.6 -0.1 Table 5.7a summarizes the average growth Namibia Nepal 17.1 6.1 11.7 -1.2 14.9 6.8 8.5 -0.8 of total exports and each of the three growth Netherlands 11.4 13.5 -2.5 0.7 2.8 7.7 -5.3 0.8 factors for two groups of countries: those that New Zealand L. 3 14.2 -3.1 0.6 4.8 4.0 -1.9 2.8 suffered a loss in nominal exports in both peri- Nicaragua -8.3 10.8 -19.5 2.8 3.3 4.7 -4.0 2.7 ods and those that increased their exports in Niger 8.6 -2.2 8.9 2.0 -17.2 3.1 -20.4 0.9 both periods. The data suggest that most Nigeria -6.2 -5.1 -1.4 0.3 5.1 4.2 0.6 0.3 economies that gain in exports do so by Norway 3.1 2.3 3.6 2.0 5.2 5.2 1.4 -1.4 expanding their traditional markets (world Oman -3.1 3.5 -6.9 0.7 6.3 8.0 -2.4 0.9 demand), while those who lose do so by losing Pakistan 13.3 7.5 11.5 -1.3 8.4 6.4 1.2 0.7 their share of traditional markets. This holds Panama 7.5 8.9 -1.8 0.5 2.9 6.7 -7.3 4.1 Papua New Guinea 9.3 8.6 0.6 0.1 11.2 2.8 0.1 8.1 true In both periods, but In the second period Paguy1. 66 67 0, -21 38 91 38 even gainers show losses in market share, Peru 1.9 -3.5 4.8 0.8 3.4 3.6 -1.6 1.4 while losers make a greater effort to diversify Philippines 6.9 9.5 -5.5 3.4 10.4 8.0 -0.8 3.0 their exports. Poland 10.1 12.5 -5.2 3.3 14.2 6.3 6.6 0.8 The correlation between export growth and Portugal 20.9 13.6 5.9 0.5 6.0 7.9 -2.9 1.1 growth in each of the three factors is shown in Puerto Rico .. .. .. .. .. .. table5.7b.Thecorrelationcoefficientssuggest Romania 3.9 12.2 -7.4 0.1 -5.3 6.3 -12.7 2.1 a different story than the simple averages. Russian Federation .. .. .. .. .. .. WVorlo Development nd cators 1997 257 .* 5700: 5.7 Nominal export growth Nominal export growth 1983-84 to 1988-89 1988-89 to 1993-94 Table 5.7a Average annual growth of exports and export growth factors, Fromn From 1983-94 From From export From Frr eor, Annual word morket diversif- Annual world market d versif- average demard share cation aveoage dermand share cation 1983-84 to 1988-89 % F Fr Fr Fr % F Fr1 Joto For d Market Diversif- Country group exports demand shae cation Rwnanda 4.3 13.9 -10.5 2.4 -14.1 10.2 -21.7 0.4 PosotiNe g'owth 3.2.8 1C.( 1.9 C.6 South Africa 8.3 11.6 -5.5 0.8 3.4 1.0 -1.8 4.3 Negatve growth -6.7 1.9 -9.4 1.O Saudi Arabsa -8.4 4.0 -13.0 1.2 8.9 6.3 2.5 0.0 All econom es 11.2 8.3 1.6 1.0 Senegal 9.1 9.4 -1.4 1.1 -8.3 3.4 -11.5 0.2 1988-89 to 1993-94 Sierra Leone 8.2 3.1 4.2 0.7 0.1 3.1 -4.1 1.2 Posrvo growth 6.9 7.1 -0.8 0.6 Sngapore 12.1 13.2 -2.5 1.6 15.2 9.4 6.7 -1.3 Regative growth 9.9 6.4 -17.3 2.5 SJovak Repub ic .. .. .. A econom es 6 7 7 0 -C.9 C.6 Slovenia Spain 14.7 13.5 0.0 1.0 9.4 7.4 2.4 -0.5 Source: 0cric Baok staof es-rmates. Sri Lanka 9.4 7.5 0.8 1.0 13.7 6.1 5.6 1.5 Sudan -2.5 6.7 -9.6 1.1 -5.4 2.9 -9.6 1.8 Sweden 13.0 13.7 -0.5 -0.1 2.2 7.5 -4.8 -0.1 In the first period the diversification factor Switzer and 16.5 16.4 0.6 -0.3 3.3 8.2 -3.0 0.3 has the strongest correlation with export Syrian Arab Republic -2.6 -0.4 -3.2 1.0 23.0 6.2 16.2 -0.4 growth. Thus countries that diversified tended Taj kistar .. . . . .. .. .. .. to be the most successful in expanding total Tanzania 1.7 1.1 -4.0 4.7 0.3 3.2 3.3 0.5 exports. Growth i l world demand for traditional Th'ailand 21.9 14.0 :1.1 5.8 17.4 8.7 1.0 6.8 T-ailancl 21.9 14.0 1.1 5.8 17.4 8.7 1.0 6.8 exports is also positively correlated with export Togo 7.5 9.6 -3.9 2.1 -9.4 4.6 -13.3 -0. 1 Trinidad and Tobago -7.8 -0.4 -8.9 1.6 3.6 5.3 -0.6 0.9 go out there is virtually no correlation for Tunisia 10.9 -0.7 9.3 2.1 9.8 5.8 3.4 0.3 countries with a oss in exports. The negative Turkev 19.9 -0.1 15.6 3.8 7.4 5.9 -1.2 2.7 sign on the diversification factor suggests that Tarkmenistan . . . .he greater the loss in trade, the more Uganda -4.3 9.4 -12.6 0.1 -2.6 3.8 -7.9 1.9 economies tried to diversify. In the second Ukraine . .. .. .. .. . .. .. period. growth in rnarket share is strong y asso- Unitea Arab Emirates -4.4 -1.7 -4.0 1.3 8.5 5.4 1.4 1.5 ciated with total export growth, but diversifica- United Kingdom 9.8 10.1 -1.2 0.9 5.7 7.6 -1.2 -0.6 tion is relatively unimportant. United States 10.0 13.3 -3.6 0.8 8.6 7.6 0.9 0.1 Care must be used in drawing conclusions Lruguay 1 3.2 13.9 -1.5 0.9 8.4 6.9 -4.1 5.7 about development strategies from these data. Lzbekistan Venezue a -4.3 1.2 -6.8 1.5 7.2 4.9 0.4 1.7 While successful exporters have diversified Vietnam 26.2 9.0 3.0 12.4 39.4 4.2 14.9 16.5 their commodity mix, diversification does not West sank and Gaza .. . .. .. . . guarantee growtr-. For many economies the Yemen, Rep. 85.4 7.1 3.0 67.9 9.7 7.7 26.8 -19.7 largest factor in export growth has been the Yugoslavia, Fed. Rep. .. . .. .. .. .. .. .. genera expansion of world trade, which has Zaire 5.0 -0.9 6.0 -0.1 -10.7 5.2 -15.1 1.3 allowed their export markets to grow. Worid Zambia 8.2 12.9 4.5 0.3 -3.1 2.9 -6.1 0.3 trade patterns continue to change. In seeking Zimbabwe 12.6 8.6 1.6 2.0 -1.4 2.1 5.0 1.7 new opportunities, some economies will enter new markets while other will seek to increase their share of existing marAets. And for some the new frontier les in services, giving up their hold on traditional markets to the newly indus- trializing economies. 5.7 li Table 5.7b Correlation of export growth C_ factors with export growth, 1983-94 1 Dala on commod ty exports were taken from the o Total export growth s the compound annua rate 1983-84 to 1988-89 Unted Nations COMTRADE catabase. using partner of growth in the value o- merchandise exports. World Market Drsersifi- Countrygroup demand share cation countryreports of imoorts atrhe three-digitStandard o Export growth from world demand measures the Positive growth 0.232 0.250 0.703 International Trace ClassV'icat on (SITC) level. TIe compojnd an iua growth mn exports due to growln of Negative growth 0.009 0.677 -0.366 usa oF partner trade mmim zes the e';ects of nore- the world marketfor the country's traitional exoorts. All economies 0.408 0.511 0.644 port ng among developing tountries. Because most Trad'tional exports are def ned as the 10 largest large importeis report trade on a time>y bas s, the tnree-digit tommodity groups, or the g'oups that 1988-89 to 1993-94 trade irc uded is estima.ed 'o cover 95 percent of made up a, least 75 percent of the country's trade in Positive grovwth 0.116 0.784 0.145 wornc trade n a given year. -.vo-year averages are the base year.,nninchever s greater. Y Export growth Negatise growth -0.043 0.854 0.169 compuiec to recluce bae effect of a sing e unusual frorm market share measLres the rompound annual All economies 0.219 0.943 -0.012 year. No trace data were reporied for Chma in 1983. growth n exports cue Lo growth in the country's share Source: World Bank staff estimates. so 1 984 cata are used for the oase year. The results of .he wor d market in ts trad tiona exports. for Germany for 1'983-84 .o 1.988-89 refer on y tD o Export growth from export diversification mea- the former Federal Republic of Germany. sores tne corpound annual growth in exports due to Tradit onal exports 'or a couritry are def ned as the groa.th of nontrEdci onal exports. lnree-digit commodity groups :hat made up at least 75 percent of the valie of rhoe country's exoors in 1983-8k ano Inrcuded at least the 10 arges com- modi.y grouos. TIe same export bundle IS used to Raw da a come from tme Un tec \ations COMTRADE ca culate toe ndexes in 988-89 to 1993-94. databoase. Corputations were carned out by staff of Trade gfowth due to world cemanc for tractiona the World Bank's IrternaLonal Economics Depart- exports is computed as ment. Development Data Group. = Xmr/3m0 where Xint s the va ue of tota cor d trade n tns coun- try's traditiona exports at the etd of the per od and X.nt, is foe corresponc ng va Lie a: tha beg nning of the period. The growth due to an nc-ease (or decrease} n maeket share is computed as t2 = (Xmt/vmw/( Pmt/Am> where xmr ard xm5n are the co.ntry's exports of trad- cona goods at the end anc Vie beg nning of the oerioc. thusfactort sthe rat o of the contry's snare of wor d trade in trad tioral exports a' tne end o; the per od to its snare at the n)eginr ng of the pe tod. The unird factor. irade cive'sificatio , is determined as the residua export growth over the period t can be shown that f- (xm0,x)/xmt/Xt1 winere xt ard x, represent tre country's totE trac- cona and nontradit onE exports. ThLus the .rade bive sification factor represents the reciproca of the change in shares oftrac tiona expo'ts fom the begin- nirgto the eno ofthe perod. In other words, it shows the roon made available n the count'vs exoort bundle for nortrad:tional expors. Wh le GATT (1966) staxed the 'actors n ndex 'orm relative to the r base year leve the tab e shows then as compound anni.al growth rates. Woric Development Indicators 1997 259 5.8 Tax policies Tax Taxes on income, Domestic taxes Export duties Import duties Highest marginal revenue profits, and on goods and tax rate capital gains services 8 of Inodividual Corporate value addeo of rote on income rar,e 8Y c' GDP Of of tore taxes industry and services Of of exports Ofof rnpcrts of1 exceeding $ Or 1995 0980 1995 1980 1999 1980 1995~ 1990 1995 199 1996 1996 Albania 18.3 .. 10.7 .. 21.1 . .. Argentina .. .0 .. . .. .. .30 120,060 30 Armenia... ...... ... Austra ia 22.3 67.6 70.4 5.2 4.0 0.5 0.0 8.3 4.7 47 38.841 36 Au.stria 32.9 22.8 21.1 9.3 9.3 0.2 0.0 1.6 7.7 50 63.091 34 Azerbaijan . - Banigladeso . 14.9 .. 5.7 .. 3.5 .. 16.5 Belgium 43.7 40.3 36.0 10.8 ... ...55 76,011 30 Benie .. . .. 2.2... Bolivia 11.8 .. 3.68. ... 0.0 .. 5.2 13 .. 25 Bosnia a00 Herzegovina... ... ... ... Botawana 28.1 45.6 59.4 0.3 1.6 C 10.1 0.0 21.2 23.6 30 22,080 25 Brazi[ 18.6 13.6 21.0 ......20 . . 25 Bulgaria 29.0 . . 21.7 .. 12.0 .,.50 4.847 40 Burk'na Faso .. 20.1 .. 2.9... ... Burunodi. 20.4 .. 10.1... ... Cambodia.. . .. .... ... Cameroon 9.6 23.7 29.7 4.1 3.0 6.5 10 19.0 29.3 60 14,020 39 Canada .. 60.8 . 3.9 .. 1.0 ..4.5 ..29 43,387 36 Contra. African Repub ic .. 17.7 .. 6.0 .. 9.3 .. 23.9 Chile 17.6 22.1 21.0 12.3 -... . 7.0 ..45 6,523 15 Chins 5.7 9. .1 .. 5.8 . ... ..45 11,840 30 Colombia 14.0 28.9 40.9 3.4 6.5 7.2 0.0 1-2.3 6.7 35 46,360 35 Congo .. 63.7 .. 3.1 .. 0.1 .. 14.0 ..50 14.964 49 Costa Rica 22.0 _14.6 12.9 6.6 10.1 6.6 1.3 6.9 8.9 25 27.661 30 OCte o'lvoire .. 14.1 .. 8.0 .. 8.0 .. 28.8 ..10 4.489 33 Croatia 43.0 .. 11.1 .. 27.1 . Cuba... ...... Czecn Republic 37.5 .. 16.1 1. 3.9 ...40 20,106 39 Denmark 35.4 40.7 46.3 23.2 20.7 .. ..0. 0.1 65 . 38 Dominican Ropuol c 14.9 24.6 15.5 3.9 6.2 6.2 0.0 1'4.9 12.1 26 11,462 25 Ecuador 13.9 46.5 56.5 2.5 4.6 3.0 0.0 16.3 8.3 25 64.519 25 Egypt, Arab Rep. 26.3 29.5 32.2 5.5 7.4 5.3 0.0 26.0 18.1 48 . . 40 El Salvador 12.1 23.6 27.7 5.4 7.4 10.3 0.0 4.4 6.1 30 22,857 25 Eritrea... ...... ... Estona 33.2 .. 22.3 . 1.1. ... ..256. 28 Ethiopia 11.0 25.3 28.8 9.1 .. 33.7 2.6 17.2 12.6 FinJand 20.3 30.8 31.6 21.0 19.2 . ..1.9 1.3 39 61.140 28 France 38.1 19.1 18.7 12.9 11.5 . ..0.1 0.0 ... 33 Gabon .. 60.1 .. 1.6 . 1.7 .. 38.3 ..55 .. 40 Gambia, The 21.8 18.1 15.5 1.3 72.0 12.7 0.1 22.1 17.0 Georgia... ...... ... Germany 30.0 19.4 17.0 .... ..0.0 0.0 53 77.506 30 Gnana 12.9 22.0 21.6 15.9 10.6 50.0 53.2 14.2 11.9 35 16,200 35 Greece 26.0 19.4 31.4 23.3 3&.1 . ..6.3 0.1 45 62,474 40 Guatemala 6.8 14.4 16.8 .. 4.9 9.9 0.0 7.6 8.2 30 31,86 7 30 Guinea-Bissau .. . . . . . . . Hait.. 15.9 .. . . 10.2 .. 12.3 . Honduras .. 32.9 .. 5.1 .. 7.4 ..7.9 ..40 106,382 35 Honig Kong ... .20 10,339 17 .0~ e cot-1 21~ n 2121 10 5.8 Tax Taxes an income, Domestic taxes Export duties Import duties Highest marginal revenue profits, and on goods and tax rate capital gains services % of Inclividja Corporate value added o' rate or incomec rate % of GDP % of tota, taxes industry and services % of exports % of imports % exceeding $ ft 1995 1980 1999 1980 1995 1980 1995 1980 1995 1996 .1996 1996 Hungary .. 22.1 .. . .. .. . 48 8.131 18 India 9.6 21.9 28.8 8.9 6.6 1.8 ..29.9 .. 40 3,824 40 Indonesia 16.4 82.0 52.8 2.4 7.5 0.9 0.2 5.1 5.9 30 22.727 30 Iran, Islamic Rep. 8.2 12.2 26.5 1.0 ... .20.8 13.6 54 173,851 10 Ireland 35.1 38.4 41.9 6. . . . .0 4.2 48 14,246 40 Israel 33.4 47.3 45.0 .. . . . . 0.6 50 57.256 36 Italy 38.4 32.1 37.7 . ... ... .. 51 184,078 37 Jamaica .. 5.0 .. 15.4 ... .2.3 .. 25 1.270 33 Japan 1 7.6 74.6 43.1 2.5 3.1 . ..2.3 4.1 50 300,782 38 Jordan 20.4 17.0 15.7 -. 8.4 . ..15.8 15.4 Kazakstan ... . . .. .. . 40 .. 30 Kenya 19.6 33.4 30.7 14.8 1 7.2 1.3 0.0 11.8 8.2 35 348 35 Korea, Dem. Rep. .. . . .. .. Korea, Rep. 17.7 25.4 35.9 9.3 7.0 . ..7.7 4.7 40 103,133 28 Kuwait 1.2 63.6 24.7 0.2 0.0 . ..2.7 3.0 0 .. 55 Kyrgyz Republic . . . . . . . . . Lao PDR . . . . . . . . . Latvia 23.1 -. 8.4 -. 11.4 . ... .. 35 109,489 25 Lebanon 10.8 -. 15.1 -. 1.3.. ... . Lesotho 44.4 15.5 14.9 5.3 10.2 3.4 0.1.. . Lithuania 24.4 .. 13.0 .. 12.9 . ... ..... 29 Macedonia, FYR -.. . . . . . . . Madagascar 8.2 17.1 15.0 8.4 3.6.. ... . Malawi -. 38.9 .. 10.6 ... .. . 38 2,745 38 Malaysia 20.6 42.1 45.6 5.6 7.5 9.0 0.9 8.9 3.9 30 58,594 30 Mail. 20.5 .. 10.7 . . . . . Mauritania . . . . . . . . . Mauritius 18.2 17.3 14.3 4.8 6.9 . ... .. 30 3.079 35 Mexico 14.8 38.8 39.3 2.7 8.6 17.6 0.0 4.6 4.7 35 22,283 34 Mongolia 20.3 .. 43.5.. ... ... . Morocco .. 22.0 .. 10.0 ... .. . 44 6,697 35 Mozambique . . . . . . . . . Myanmar 4.6 4.9 33.5 . ... ..17.6 . Namibia 31.4 .. 32.2 .. 13.2 . ... .. 35 22,577 35 Nepal 9.1 6.6 13.6 8.0 7.9 5.1 1.9 13.8 9.7 Netherlands 42.9 33.1 27.0 10.6 10.9 . ... .. 60 53,468 37 New Zealand 34.4 75.1 64.5 7.1 .. 0.1 0.0 4.5 3.9 33 19,837 33 Nicaragua 23.6 8.9 11.8 .. 16.2 .. 0.0 .. 11.8 30 25,310 30 Niger .. 28.1 .. 4.5 . . . . . Nigeria ... . . .. .. . 30 2,728 30 Norway 31.6 30.2 20.7 15.4 .. 0.0 0.0 0.9 1.0 28 6,891 28 Oman 8.5 92.8 77.7 0.2 ... .1.6 2.7 0 .. 50 Pakistan 15.3 16.8 20.3 8.6 10.6 1.8 0.0 22.4 23.9 35 9.740 46 Panama 20.1 29.0 25.0 .. 5.7 0.5 0.2 3.0 3.0 30 200.000 34 Papua New Guinea 18.9 67.5 58.2 4.0 3.2 . ... .. 35 16,969 25 Paraguay 9.1 16.6 16.0 2.7 6. 7 0.8 0.0 13.2 4.4 0 .. 30 Peru 14.4 28.1 18.6 .. 8.2 .. 0.0 . 13.0 30 54,495 30 Philippines 16.0 23.6 33.9 7.8 6.3 1.0 0.0 13.4 13.6 35 20.477 35 Poland 36.7 .. 30.9 .. 12.5 . ... .. 45 13,442 40 Portugal 30.9 20.9 26.7 . .. 0.0 0.0 4.4 0.0 40 37,714 36 Puerto Rico ... . . .. .. . 33 .. 20 Romania 26.3 0.0 34.0 .. 9.0 . ... .. 60 6,875 38 Russian Federation 16.1 .. 15.5 . 5.8 . ... .. 35 13,521 35 World Development .nd cators 1997 281. Tax Taxes on income. Domestic taxes Export duties Import duties Highest marginal revenue profits, and on goods and tax fate capital gains services '/ c' Ind~~~~~~~~~~~~~~oiv:ojea Corporate va ue addec of rate on ncorme rate % of GDP C of r'otal taxes .ndustr'v and serv ces % of exports 'A of Iraoorts % exceeo ng $ % 1995 1980 1995 1980 1995 1980 1999 1980 1995 1996 1996 1996 Rwanda .. 20.6 .. 5.3 . . . . . Saudi Arabia ... . . .. .. .0 . 45 Sortega. . 21.4 .. 7.7 . . . . Sierra Leone 12.5 24.9 23.0 4.3 6.2.. ... . Singapore 1 7.2 47.0 45,6 4.2 4.7 . ..0.9 0.3 30 273,841 27 S over Repubiic . . . .. .- . South Africa 25.2 63.6 52.8 6.2 11.7 0.1 0.0 3.0 1.1 45 22.577 35 Spain 28.7 25.2 33.7 .... . 6.0 0.1 56 77,593 35 Sri Lanka 16.0 16.5 14.5 6.0 15.6 22.0 0.0 9.6 11.2 35 2,101 35 Sweden 32.8 21.1 16.9 12.8 15.5 . .1.5 1.2 30 28,024 28 Sw;tzerland 21.5 .15.0 16.0 . .. .,4.0 5.0 13 424,247 46 Syrian Arab Repub ix 1 7.8 24.6 27.5 1.6 . 1.7 6.7 11.6 23.9 Tanzania .. 35.3 .. 19.1 ... .. .30 2.808 35 Tha:lend 17.1 1'9.3 34.1 6.6 8.3 4.4 0.1 11.1 9.4 37 159,426 30 Togo .. 36.6 .. 6.4 . . Trin[dac and Tobago 8. 6.7 .. 1.6 ..9.68 . 35 6.742 35 T..nisia .. 19.1 .. 68 . . . . . Turkey 14.3 61.9 40.4 5.1 9.6 8. . .9 3.2 55 247,895 25 TLurkmenistan . . . . . . . . . Uganda .. 11.6 . 46.4 ... .. .30 5,309 30 Lkr-aine . . . .. .. Unitec Arac Emirates 0.6 .. 0.0 0.0 0.5 .. United Kfingdom 33.5 43.4 36.9 12.7 11.0 0.0 0.0 0.1 0.1 40 39,644 33 United States 19.0 61.7 56.6 0.9 0.8 . ..3.0 2.6 40 263.750 35 Uruguay 27.6 11.5 10.3 .. 10.6 .. 0.2 .. 7. 7 0 -. 30 Lzbekistan . . . .. . . .. Venezuela 14.8 79.4 47.5 1.0 4.2 . ..9.6 9.9 34 .. 34 V etnam ... . . .. .50 6,335 25 West Bank end Geza . . . . . .. Yemen, Rep. 13.0 .. 37.6 . 11.3 ..30.1 Yagos~avia, Fed. Rsp, .. . .. . .. Zamboia 13.4 41.2 37.4 12.4 9.1 ...35 1,764 35 Zimbeowe .. 57.9 .. 9.0 ... .. .40 6,451 38 Low income 6.1 w .. 18.1 w .. 6.6 w ExcL Chinea & Ind a -.. Mliocle incomne... Lower middle income 17.7 w .. 27.0 w Upper mniodle income 20.1 a 25.9 w 31.3 w Low & midd a income ... East Asia & Pacific 10.0 w .. 22.3 w . 5.5w Europe &Central Asia 22.8 w . 21.1 w Latin America & Carib. 16.5 w 26.0 w 31.1 w Middle East & N. Afr ca... Soutn Asia 10.5 w 20.6 w 27.3 a .7 w 7.1 w Sub-anra Africa .. 42.3 w . 8.2 wa High income 25.3 w 51.5 w' 45.6 w 4.3 w Aor D-r>c jpcr 12C1001C- lIt. 5.8 As countres develoo, they typicaly expand tneir C_ capacst to tax res dents dc!rectl . and ind rect taxes Taxes are compulsory, unrequ,ted payments made to become less important as a revenue source Thus the o Tax revenue comprises compulsory unrequited, governments oy ndrvicuals, bus nesses, or nstitu- share of taxes on ncome, profit:s, and capital ga ns nonrepayablereceipts forpublicpurposescoilectedby tions. They are described as unrequited becauxe gov- is ore measure of a tax systern's level of deve op- centra governmerts. It includes Interest collected on ernments provide nothing specifitally in retuirn for ment. In the early stages of dlevelopmnent govern- tax ar-rears and penasies collected on nonpaymient or them, aithougn they may use the funds received to merts tend to rely or inc rect taxes because the ate oeymentoftaxes and sshown ret of refunds end provide goods or servces to inervidua s or cormuni- admin straeve costs of collecton are relativein low other corrective transactions. a Taxes on income, ties. The sources of the revenue received by govern- Tr-ere are two principa sou.rces of irdirect taxes: cus- profits, and capital gains include taxes levied by cen- ments and the re ative contnbutions of these sources toms revenues and domest c taxes on goods ano ser- tra governmerts on the actual or presumptve neo are determined by policy choices scout whe e and vices. Tne table shows these tomestc taxes as a ncore of indiv dua s and profts of enterprises. Also how to :mpose taxes and by changes n the structure percentage of value added n lndustry and serv ces. Included are taxes leviex on capital gairs, whether of the economy. Tax policy may reflect concerns about Agriculture and rining are excluded From tne denom- rea zed on sales of and, securit es, or ottrer assets. diestbutional e-rfects. economic efficiency ocn(ludeng rnator because ind:rect taxatioii of these sectors is Social securcty con;rbutio-es based on gross oay, pay corrections for exterralities) and the practice prob usually neg igib e. What s nissing here is a measure rol. or number of emp oyees are not nc udea, but ems of administering a tax system. There is nc single ofthe uniformity ofthese taxes across industries and socia security conurbutioes eased on personal correct model for distrbuting tax revenues among along the value adced chain o' production. Wthout income efter deeuc: ons end pewsonal exempt ons are sources, nor s ary d stribution kely to reina n con sucn data no c ear nferences can be drawn about included. a Domestic taxes on goods and services stant over time. how neutra a tax system is between suosectors with incduce eFI taxes and duties levied by cenrial govern- The defhotions useo here are those used by tne resoect to incertives. Revenues raised by some gov- menus on the production, extraction. sale, transer, International Monetary Fund (I1MFi in its vfanuai on ernment.s by cha'ging higner prices 'or goods pro- easing, or delivery of goods and rendering of services. Government Finance Statistics. Taxes traditionally duced by s-;te-ownec erterpr ses are not counted as or 1 respect of the use of goocs or perm ss on to use have been class fed either as d rect-taxes levied taxrevenues. goods oto perforn activit es Such taxesinclde gen- directly on tr,e income, profits, or property of inciv d- Export ard import duties are shcwn separately eral sales taxes, turnover or value added taxes, uals ano corporations-or ndirect-sales arc excise because tcnir burder on growth is I kely to be hign-. exc ses, and motor vehicle taxes. o Export duties taxes acd duties. Indirect taxes have been coristrued Export duties, typ cally evied on pr mary (partcularly nclude all levies collected on goods at the po nt of as those that cculd .e passed on by ircreasing the agncltural) products, reduce Le incentive to export export from the country. Reoates on exported goods pr ces of goods or serv ces sold to in-ermediate or and encourage a sh ft to other tops. Hignr import tar- cormprising repayments of previous y pa d gene-al con- fina purchasers. But it is extremely d fficu t tD deter- iffs penal ze consumers, promDte inefficient pwoduc- sumpt on taxes, excises. or imoort du' es should be mine the incidence of taxes, so the dist notion has tion, and impicitly tax expots. By contrast, lowering deducted from tne gross receipts of tne appropriate been dropped both from the United Nat ons System trade taxes enhances openness-to foreign competi- taxes, not from export dity rece pts. a Import duties of Natonal Accounts and by the IMF. althougn it t on, 'oreign knowledge, and fore gn resources-eaer- comprise a leves col ecred on goocs at the ooin of remains useful for genera discussion. g.zing the development process n many ways. The entry into the country. Tn-ey nclude levies for revenue The level of taxation IS typ'cal.y meas.reo by tax economies growing fastest ocer tne east 15 years purooses or import protection, wr-ether on a specirc revenue as a sn-are of GDP. Corpar ng lexe a of tax- have not relied on tax revenues from exports anc, or ac valorem basis, as long as they are restrictec by ation across countries prov des a ouick overview of seeing tnis pattern, many other countries have aw to imoorted products. a Highest marginal tax the structure of fiscal incentives 'ac ng the private reduced the r export duties. For some countries, such rate is the h ghest rate shown on the schedu e of tax sector. In this tab e tax data measured in local cur- as members of to e European Union. most customs rates apD ied to the taxable ircome of inocvicuals ane rencies are norma ized by scale vadables in :ne dutes are collected by the supranational authority: corporations. For some countries the n,ighest marg nal same un ts to ease cross-country comloarisons. these revenues are nor reported n the incividual tax rate Is also the basic or flat ate, and other sue Data for i980 are Included to give a qpick impres- countries' accourts. taxes, decuctons, and tre ike may apply. Also pre- sion of cnanges overtime. The tab e relies on centra The revenues colected oygcvernments are the oit- sentec are the icome levels above which the nighest governmert Cata, whicr may consideraoly under comes of tax systems that are often complex. con- marg nal tax rates app y for ind viduals. state to-e total tax bu,den. particu arly n countr es taining many except ons, exeniptions. penalties, and where provinc al and municipa governments are other nducements that affect tax incidence and rhus mportant. inf jence the decis ons of workers, managers, and Ratios of tax to GDP may re'lect weae admimstra- entrepreneurs. A potent. aly imnoortant influence on Data on lax reverues are tion are iarge-scale tax avoidarce or tax avasion. both oomestic and interrational nvestors is the pro- from print anc electronic They a so may reflect the presence oF a sublstantial gressivity of a tax system. as nieasured ronghly bythe 00 tions of the IMF's para lel economy with unrecorded anc undisclosed highest margina tax ra.e on inrdivdua and corporate Government Finance Statis- ncomes. These ratos tend to rse witin level of ircome. aics Yearbook. Data on inc - income. with more developed countries relying on vidua a*rd corporate tax taxes to finance a much broader range of social ser- rates are from Price Water- t ces and socia secraiy than less developac coun- a house, Indiduai Taxes: A tries are able to prov de. Mane of toe poorest Wordwiede Sumrnary(1996G countries have low tax revenues relaeive TO GDP, and anc Corporate Taxes: A bWordavice Summary(1996). so must rely heavily on external assistance. World Development Inaicators T997 263 5.9 Portfolio investment regulation and risk Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICRG risk Investor country credit- sovereign sovereign long-term rating credit rating worthiness long-term debt rating rating debt rating Fore gr [Donescic Foreign Dom-eso Repaoriation Repatration currency ou'renoy cu.rrerncy currency Frtry of Income of 2ao11a Decemiee' Sepoemoer September Novemoer November Noveember Novembe' 1999 1995 1995~ 1996 1996 1996 1996 1998 1996 1999 Aobania .. . .64.5 14.1 34.2 Algeria .. . .59.0 22.8 37.7 Angolea. .. . 48.5 12.4 17.7 ArgertIna Free Free Free 73.5 38.9 57.3 81 ..B8- 888- Armen a ....28.5 Australla ..85.5 71.7 91.4 Aa2 Aaa AA AAA MAusar..89.5 55.0 95.2 Aae . AAA AAA Azerbaijarn .. . . . .1.9 Bangladesh Free Free Free 65.0 26.9 4C.3 Selarus . .. ...14.5 27.7 Beig .um .. . .87.5 79.6 93.4 Aal ..AA- AAA Benin. . 17.2 30.2 Bolivia 6. 5.5 25.4 40.7 Bosnie and Herzegovina Botswana Free Free Free 79.0 49.8 51.1- Brazt! Free Free Free 67.0 38.3 56.8 81 .. 5 8B Bulgar a ..64.5 23.5 40.4 83 Burkine Fuseo. 61.0 17.5 34.2 Burundi Camoodia ... .35.3 Cameroon ..57.0 18.8 32.1 Canada ..85.6 79.4 91.8 Aa2 Aal AA+ AAA Ceentra African Repuo!ic ... .33.0 Chad .. . .. .30.0 CFhle Rel. freea Free Delayed' 82.0 61.2 77.4 A- AA Chbna Speclal Free Free 74.5 57.2 71.3 43 .. 88 Colomoia Auth. errlyc Free Free 62.0 45.7 62.4 Baa3 .. 88- A± Congo 6. . . 0.5 14.7 22.7 Costa Rica Free Free Free 73.0 33.9 41.2 CSte dicvoire Free Free Free 64.0 18.5 39.8 Croatia Free Free Free ..26.0 47.2 Cuba .. . .62.0 10.6 11.5 Czech Repoblc Free Free Free 83.5 52.0 73.7 Baal ..A Denmark .. . .89.5 80.7 94.8 Aol Aaa AA-, AMA Dominican RepLublc .. . .69.0 23.1 35.4 Ecuador Free Free Free 59.5 26.4 45.0 Egypt, Arab Rep. Free F,ee Free 67.5 35.1 45.7 6a2 E Salvador ..66.5 21.6 40.7 BB..8 886- Eritreea.. .. Estonla ...31.1 48.0 Ethiopia 6. 3.5 -15.9 28.1 Fitmanod. 85.0 73.1 91.5 Ae2 AA4- AAA France ..82.5 87.1 95.7 Aaa Aaa AAA AAA Gabon ..64.5 25.7 37.7, Gambia. Tee 6. 4.5 ..35.1 Georgia ...9.4 24.1 Germanyv. 85.0 90.9 95.7 ..Aaa 4AA AAA Ghnaa Free Free Free 62.0 29.6 44.6 Greece Free Free Free 77.5 50.3 72.8 Baa3 .. BBS- Guatemala ..65.5 22.7 35.4 Guinea-Bissau ..44.0 ..21.3 Guinea ..53.0 14.5 30.0 Haiti ..49.0 10.4 27.3 Honduras ..55.5 16.7 33.1. Hong Kong ..85.0 65.3 82.4 A A- 27~'1 1 5.9 Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICR0 risk Investor country credit- sovereign sovereign long-term rating credit rating worthiness long-term debt rating rating debt rating Foreigo Domest c Foreign Domestic Repatriation Repatriation currency currency currency currency Entry of rocoms of cap tal December September September November November Novewoer November 1009 195 1 000 1000 1096 1 090 199 0 1996 1009160 Hungary Free Free Free 77.5 44.7 67.2 Eel .. 88- A- India Auth. only' Free Free 69.0 46.3 63.7 BB+.88 888-- Indonesia Rel. free' Restricted Restricted 70.0 52.2 70.8 8aa3 .. 88 A+ Iran, Islamic Rep. .. . .72.0 24.7 35.6 Iraq .. . .35.0 9.1 9.4 Ireland .. .. ..6.5 74.5 92.3 Aa2 Ass AA AAA Israel .. . .68.5 52.2 75.5 ...A- AA- Italy 8. . . 2.5 72.4 67.6 Aa3 Aa3 AA AAA Jamnaica Rel. free' Free Free 71.0 27.5 36.8 Japan 8. . . 9.5 91.1 94.0 ..Ass AAA MAA Jordan Free Free Free 73.0 33.1 47.6 8a3 ..BB- 888- Kazakstan . .. ...19.6 30.6 BB..8- BB+ Kenya Rel. free' Free Free 67.5 27.9 42.3 Korea. Dem. Rep. .. . .45.5 ..5.4 Korea. Rep. Rel. free' Free Free 85.0 72.1 84.3 A.. A- Kuwait .. . .80.5 54.7 74.8 Kyrgyz Republic .. . .. .23.6 Lao PDR .. . .. .30.5 Latvia . .. ...25.7 47.1 Lebanon .. . .62.5 27.2 44.3 Lesotho .. . .. .31.6 Li bya . .. 63.5 27.9 17.1 Lithuanis Rel. free' Free Free ..25.3 55.2 8a2 Macedonia, FYR .. . .. .36.3 Madagascar .. . .54.5 ..30.8 Malawi .. . .59.5 19.7 33.7 Malaysia Free Free Free 81.5 67.7 80.2 Al .A-- AA+ Mall. .. . 56.0 16.7 33.6 Mauritania .. . .. .32.3 Mauritius Auth. only' Free Free ..50.8 51.3 Baa2 Mexico Free Free Free 70.0 41.6 60.3 8a2 8aa3 BB BEEt Moldova .. . .. .31.5 Mongolia .. . .68.0 ..26.4 Morocco .. . .71.5 39.3 . Mozambique .. . .49.0 14.0 21.7 Myonmar .. . .56.0 21.4 43.8 Namibia Free Free Free 79.0 ..30.8 Nepal . .. ...25.7 40.3 Netherlands .. . .89.5 89.2 97.9 ..AAA MAA New Zealand .. . .85.0 71.6 92.0 Aal Aaa AA+ AAA Nicaragua .. . .56.5 11.4 24.7 Niger .. . .52.5 ..32.0 Nigeria Ciosed Restricted Restrictedi 50.5 15.2 31.1 Norway .. . .92.5 83.1 95.0 Aol Ass MAA AAA Oman Free Free Free 77.5 52.7 64.4 ....88- Pakistan Free Free Free 62.0 29.2 49.8 81 .. + Panama Free Free Free 68.0 28.5 49.0 . Papuas New Guinea .. . .68.5 32.9 45.3 . Paraguay .. . .74.0 32.1 49.0 BB..8- 888- Peru Free Free Free 66.0 30.0 47.6 - Philippines Special' Free Free 71.5 40.5 61.5 8a2 BB 8 868+ Poland Free Free Free 60.0 44.0 57.1 Eaa3 .. 88- A- Portugal Free Free Free 8S.S 69.2 80.2 Al A. A- AAA Puerto Rico.. .......... Romania .. . .65.0 31.0 53.1 8a3 BB 8- 888- Russian Federation .. . .62.5 21.4 42.6 8a2 .. 8- Worlo Development IndiCat ors 1997 265 *5*59 Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICR0 risk Investor country credit- sovereign sovereign long-term rating credit rating worthiness long-term debt rating rating debt rating Fore[gn Domestic ForeIgn Domestr Repatriat on Pepatr ation currency currencv currenoy currency 7ntry , '0. 2.4 Benin 22.4 11.5 26.7 30.0 5.9 7.8 9.0C. 7.7 Bo iva 33.3 57.3 26.8 48.8 19.5 33.3 16.0 32.z_ 33.5 45.0 Bosnia and Herzegovina Botswana -53.0 -40.2 25.2 27.0 15.7 20.0 1.8 4.2 -0.4 8.2 Brool 87.2 39.5 25.6 31.2 17.9 28.1 B8lga.0 95.0 ..109.2 6. 1.5 . 3.4 -2.2 Burkino Paso 13.7 5.7 21.3 24.5 7.5 6.1 9.0 ..7.7 BLorndi 24.5 23.8 18.2 22.7 8.5 6.3 ...4.0 9.3 Carmboca .. 6.2 .. 9.0 .. 5.2 . (. 10. . . 12.7 Cameroon 31.2 19.2 22.8 15.7 10.1 8.0 11.0 10.5 10.2 10.0 Canada 88.5 95.5 74.8 78.3 80.3 81.1 1.3 1.5 5.8 2.5 Centra African Ropub ac 14.71 11.2 17.5 21.3 2.1 1.8 11.0 10.5 10.2 10.0 Chad 14.5 12.6 20.9 18.4 0.8 1.4 11.0 10.0; 19.2 10.0 Chile 72.9 58.4 40.8 38.9 32.6 30.3 8.5 4.5~ 40.5 12.2 06 na 90.0 90.9 79.2 104.3 41.4 60.3 0.8 1.1 1.1 8.1 Colomoia 38.3 4 5.0 29.8 38.8 19.3 27.6 8.8 10.Z 38.9 38.7 Congo 29.1 1 6.9 22.0 14.7 6.1 2.3 11.0 10.5, 10.2 10.0 Costs RBco 29.9 20.3 42.6 32.9 29.9 23.8 11.4 1-2.5 24.3 20.7 CSte dvcirer 44.5 30.8 28.8 28.4 10.9 9.8 9.0 .7.7 Croatia .. 2.1 .. 25.8 17.1 499.3 14.7 1,153.9 14.2 Czech Repub ic 93.4 .. 91.4 .. 55.1 .. 5.6 . 6.8 Denmark 85.1 56.5 80.9 59.8 30.3 29.3 8.2 8.9 5.8 4.3 Dominican Repuol c 29.1 28.8 23.9 28.8 12.2 18.1- Ecuador 17.2 32.3 23.3 33.3 12.8 24.2 -8.0 1 2.6~ 29.2 40.7 Egypt. Arao Rep. 130.1 103.9 107.1 105.3 73.9 79.4 7.0 5610.7 10.5 E Salvador 32.0 40.6 30.6 42.4 19.8 32.2 3.2 4.7 12.9 12.1 Estonia 65.0 12.8 136.2 25.2 93.5 5.4 .. 7.2 26.6 10.0 Etropsa 67.3 4d3.1I 42.0 43.9 12.5 16.0 3.6 3,6 -2.3 9.1 F nlandi 84.3 58.2 55.3 58.6 46.7 26.5 4.1 4.5 3.3 1-.7 France 107.0 102.4 64.6 67.7 38.7 43.9 6.0 3.6 2.2 2.1 Gabon 21.9 19.4 19.4 15.3 7.2 6.0 110 10.5 10.2 10.0 Gambia. Toe 3.2 7.6 19.7 24.6 8.4 11.9 15.2 12.5 18.2 19.0 Georgia.. ....... Germane 110.0 129.9 64.5 68.2 44.3 45.5 4.5 7.0 3.3 4.9 Ghana 1'2.4 24.4 13.4 17.8 3.2 5.3 Greece 129.3 113.5 90.0 80.0 72.5 82.1 8.1 7.3 19.3 17.1 G.atems a 17.4 19.3 21.2 25.2 11.8 16.0 5.1 13.3 16.0 15.2 Guinea 6.5 7. 7 9.3 9.3 1.2 1.8 0.2 4.0 12.9 1-5.5 Guinea-Bissau 43.5 8.0 17.0 18.3 4.5 6.2 13.1 6.4 37.5 26.9 Flaili 32.9 33.7 31.4 48.0 159 28.2 Honduras 40.9 28.3 33.6 30.8 18.8 18.3 8.3 15.0 8.8 21.0 Hong Khong (0neL 9 5.100 Domestic credit Liquid liabilities Quasi-liquid Interest rate Spread over provided by liabilities spread LIBOR banking sector ~~~~~~~~~~~~~Lencing minus Lending rate depoi re 'a minus LIBOR percentage percentage % of GDOP % of GDP % of GDP points points 1990 1995 1990 1995 1990 1995 1990 1995 1990 1995 Hungary 82.6 64.1 43.8 45.7 20.8 25.2 4.1 6.5 20.5 26.6 India 54.7 48.5 45.7 48.4 29.8 31.1 ...8.2 13.0 Indonesia 45.5 ..40.4 ..29.1 ..3.3 ..12.3 12.4 Iran, Islamic Rep. 71.0 45.8 57.8 39.1 31.0 20.4 Ireland 58.8 61.3 45.2 57.5 32.9 43.3 5.0 6.2 3.0 0.6 Israel 101.3 79.4 67.0 73.9 60.6 67.8 1-2.0 6.1 18.1 14.2 Italy 75.8 ..74.4 64.7 37.6 27.4 7.3 6.1 5.8 6.5 Jamaica 34.8 30.7 51.1 53.4 38.0 36.4 6.6 20.4 22.2 37.6 Japan 266.8 295.9 187.4 203.3 1-59.6 167.6 3.4 2.7 -1.3 -2.6 Jordan 117.9 93.9 131.2 105.9 77.8 65.1 3.2 5.7 1.7 3.0 Kazakstan .. 9.5.. ... . Kenya 52.7 51.3 43.2 40.4 29.2 25.6 5.1 15.2 10.5 22.8 Korea, Dem. Rep... ... ... . Korea, Rep. 65.4 69.9 54.4 79.1 45.5 68.1 0.0 0.2 1.7 3.0 Kuwait 216.6 58.7 174.5 80.8 _135.2 66.1 0.4 1.9 4.1 2.4 Kyrgyz Republic.. ... ... . Lao PDR 5.1 11.1 7.2 13.6 3.1 8.9 2.5 11.7 20.0 19.7 Latvi a . . 13.7 . . 22.0 .. 7.8 . . 19.8 . . 28.6 Lebanon 132.6 87.0 193.7 126.6 170.9 118.0 23.0 8.4 31.6 18.7 Lesotho 30.7 -8.6 39.9 30.1 23.0 16.1 7.4 3.1 12.1 10.4 Libya . ... ... ..1.5 1.5 -1.3 3.7 Lithuania .. 17.1 .. 25.5 .. 9.8 .. 18.7 .. 21.1 Macedonia, FYR . .. .. .. Madagascar 26.2 1-8.4 17.8 21.2 5.3 7.6 . Malawi 19.9 14.0 21.3 20.2 11.8 10.4 8.9 10.0 12.7 41.3 Malaysia 77.9 131.9 66.3 121.7 44.3 91.9 1.3 1.7 -1.1 1.6 Mali 13.4 11.1 20.0 20.8 5.4 4.6 9.0 ..7.7 Mauritania 54.7 23.7 28.5 18.8 7.0 5.6 5.0 ..1.7 Mauritius 45.1 68.1 63.4 80.1 49.2 66.1 5.4 8.6 9.7 14.8 Mexico 43.9 53.1 25.0 37.9 18.2 28.5 . Moldova 66.7 17.8 74.6 14.4 37.6 4.6 . Mongolia 68.5 11.0 54.3 29.1 13.8 15.2 .. 54.8 .. 108.9 Morocco 60.1 81.0 61.1 77.7 18.5 28.6 0.5 ..0.7 5.3 Mozambique 37.5 ..38.9 ... ... Myanmar 32.7 ..27.9 ..78 ..2.1 ..-0.3 Namibia 20.7 64.1 24.8 43.2 14.5 26.6 10.6 7.7 1 7.4 12.5 Nepal 28.9 29.9 32.2 36.5 18.5 21.2 ...6.1 Netherlands 107.5 117.8 84.1 84.3 60.1 57.1 8.5 2.8 3.5 1.2 New Zealand 73.5 90.1 65.4 79.5 32.2 41.3 4.3 3.7 7.7 6.2 Nicaragua 206.6 189.4 56.9 34.1 23.2 24.9 12.5 8.8 13.7 13.9 Niger 16.2 8.8 19.8 14.5 8.3 3.6 9.0 ..7.7 Nigeria 23.7 18.7 23.6 28.9 10.3 9.9 5.5 6.7 17.0 14.2 Norway 89.6 77.0 59.9 56.6 27.0 17.8 4.6 2.8 6.0 1.8 Oman 16.6 29.2 28.9 32.5 19.3 22.4 1.4 2.9 1.4 3.4 Pakistan 50.9 50.5 39.7 44.2 10.0 17.9... Panama 55.9 71.8 43.6 70.5 35.0 59.5 3.6 3.9 3.7 5.1 Papua New Guinea 35.8 28.8 35.2 30.0 24.0 19.0 6.8 4.1 7.2 4.5 Paraguay 14.9 22.7 21.5 28.9 12,8 18.7 8.1 9.8 22.7 25.0 Peru 16.2 11.4 19.9 19.2 9.4 13.4 2,334.9 20.6 4,766.2 30.6 Philippines 26.9 62.9 36.8 55.1 28.1 44.9 4.6 6.3 15.8 8.7 Poland 19.5 34.6 34.0 36.5 17.2 23.4 462.5 6.7 495.9 27.5 Portugal 73.8 92.9 65.1 81.7 39.7 52.4 7.8 5.4 13.5 7.8 Puerto Rico . .. .. .. .. Romania 79.7 23.6 60.4 25.1 32.7 15.7 . .. Russian Federation .. 20.7 .. 15.8 .. 7.2 .. 217.5 .. 313.5 World Development Indicators 1997 269 5. 10 Domestic credit Liquid liabilities Quasi-liquid Interest rate Spread over provided by liabilities spread LIBOR banking sector Lenc~ng minis Lending rate deposit rate moanu LB0R percentage percentage % GOp of GO? % of GOP points points 1999 1995 1990 1995 1990 1995 1990 1995 1990 1995 Rwanda 17.0 13.6 14.8 19.7 7.0 7.2 6.3 10.0 4.9 11. 7 Saun~ Araoia 58.7 37.9 47.9 51.1 21.9 24.6 . Senega 33.7 22.1 22.9 20.6 9.7 7.6 9.0 ..7.7 Sierra Leone 11.1 68.5 14.5 10.7 4.0 2.6 12.0 21.6 44.2 22.8 Singapore 76.0 76.2 123.9 115.6 100.9 94.1 2.7 2.9 -0.9 0.4 S over Reoub.lni. 52.3 .. 68.2 .. 39.3 .. 6.6 .. 9.6 Siovensa 36.8 36.8 34.2 36.7 25.8 28.6 1 79.9 2.5 847.5 16.8 South Africa 102.7 153.5 47.1 44.4 28.6 21.7 2.1 4.4 12.7 11.9 Spain 109.0 105.7 76.6 80.5 45.3 53.0 5.3 2.3 7.7 4.0 Sri Lanke 43.0 36.7 35.1 43.4 22.8 32.0 -6.4 -1.4 4.7 8.7 Sunan 29.9 laO9 29.4 24.0 4.2 9.4 . Sweden 145.5) 123.9 46.6 45.5 . ..6.8 4.9 8.4 5.1 Switzerland 180.9 187.6 146.8 147.2 120.0 118.9 -0.9 4.2 -0.9 -0.5 Svrian Arab Ropeb in 56.8 64.2 54.7 69.1 10.5 13.9 . Tajkisitan . .. .. .. Tanzania 42.8 30.9 24.6 35.3 7.8 14.7 .. 18.2 .. 36.8 Thailand 90.7 136.6 74.8 79.5 65.9 70.2 4.2 5.9 8.2 9.7 Togo 21.2 26.9 35.9 31.8 19.0 10.9 9.0 ..7.7 TrndndSansd Tobago 58.5 54.1 54.8 50.4 42.8 38.0 6.9 9.1 4.6 9.2 Tni.sIa 82.5 71.2 51.5 48.4 26.7 27.1 . T-.rkey 25.9 29.6 24.1 32.3 16.4 27.2 . Turkmnteistan . .. .. . Uganda 1 7.8 3.9 7.6 10.8 1.4 2.8 7.4 12.6 30.4 14.2 Uxraine 0).0 0.0 0.0 0.0 0.0 0.0 .. 52.4 .. 116.7 Lnited Arao Emirates 35.2 48.6 47.0 56.7 38.2 42.2 . Lnited Kingdom 123.0 125.7 ..2.3 2.6 6.5 0.7 United Staten 115.6 132.1 68.7 61.1 61.8 45.6 ...1.7 2.8 Uruguay 60.8 38.9 61.2 39.2 53.3 32.5 76.7 60.9 166.2 93.1 U.zbekistan , .. .. .. Verezue a 37.4 37.0 42.3 30.8 29.4 19.9 0.4 7.5 19.9 26.2 Wes' Sank ace Gaza.. ... ... .... Yemen, Reo. 79.6 50.0 72.3 56.5 13.7 18.7... Yugonaava, Fan. Rep. .. ... ... .... Zambia 68.4 50.3 21.7 15.3 10.5 8.9 9.4 ..26.8 110.0 Zimbabwe 53.8 54.8 54.2 48.7 39.3 28.9 2.9 88 8 15.100 The financial system-intermediating =_ ._ Households and institutions save and invest in isolation. The role of the financial system There are several reasons for caution in using the * Domestic credit provided by banking sector is to intermediate between them. Savers (ndicators in the table. These indicators are quanti- nciudes all credit to various sectors on a gross basis, accumulate claims on the financial institu- tative assessments, but qualitat ve assessments of with the exception of credit to the central government, tions, which pass the funds they obtain this policy actions, laws, and regu ations are needed in which is net. The banking sector includes monetary way to the final users. Gradually, as an econ- analyzing overa I financiai conditions. In addition, authorities and deposit money banks, as wellas other omy develops, this indirect lending by savers the accuracy of financial data is dependent on the banking institutions where data are available (includ- to investors results in greater financial assets quality of account ng system>. which are weak in ing nstitutions that do not accept transferab e relative to GDP. This wealth allows increased some developing economies. Some of these indica- deposits but do ncur such liabilities as time and sav saving and investment, enhancing the econ- tors are highly corre ated, paticularly the ratios of ings deposits). Examp es of other banking institutions omy's rate of growth. iquid liabilities, quasi-liquid iabilities, and bank are savings and mortgage loan institutions and bui d- The financial system develops with the credit to GDP, because changes in liquid and quasi- ing andoloan associations. e Liquid liabilities are also economy. As more specialized savings and liquid liabilities flow directly from changes in bank krown as broad money, or M3. They are the sum of financial institutions emerge, a greater diver- credit. The precise definition of the financial aggre- currency and deposits in the central bank (IMO), plus sity of instruments becomes available, gates on which data are presented varies from one transferable deposits and electronic currency (Ml), helping to reduce risk for liability holders. And economy to another. Monetary data are end-of-year plus time and savings deposts. foreign currency as securities markets mature, savers are able leve s. transferable deposits. certficates of deposit, and to invest their resources directly in financial The ratio of domestic credit provided by the bank- securties repurchase agreements (M2) plus travel- assets issued by firms. ing sectorto GDP is used as a measure of the growth ers checks, foreign currency tiwe deposits, commer- No less important than the size and struc- of the banking system because it reflects the extent cia paper, and shares of mutual funds or market ture of the financial sector is its efficiency, to which savings are financial. Liquid liabi ities funds held by residents. * Quasi-liquid liabilities are indicated by the margin between the cost of nclude bank deposits of geneially less than one year the M3 money supply less Ml. a Interest rate spread plus currency. The rat o of these assets to GDP indi- is the interest rate charged by banks on loans to cates the ease with which their owners can use them prime customers minus the interest rate paid by com- to buy goods and services without incurring any cost. mercial or similar banks for demand, time, or savings Borrowers in developing economies Quasi-liquid liabilities are long-term deposits and deposits. * Spread over LIBOR (London Interbank have to pay 1.6 to 313.5 percentage assets, such as certificates cf deposit, commercial Offer Rate) IS the interest rate charged by banks on paper, and bonds, that can be converted into cur- loans to prime customers minus LIBOR. LIBOR is the points over LIBOR for their lOctIl rency or demand deposits bu: at a cost. most common y recognized international nterest rate currency loans The nterest rate spread is a summary measure of and is quoted in several currencies. The average the efficiency of the banking system, known as the three-month LIBOR on L.S. dollar deposits is used mobilizing liabilities and the earnings on ntermediation margin. This measure may not be reli- here. assets. Small margins are crucial for eco- able to the extent that information about interest nomic growth, since they reduce interest rates is inaccurate. banks do not monitor firm man- rates and thus the overall cost of investment. agers, or the government intervenes to fix deposit Interest rates reflect the responsiveness of and lending rates. The sprecd over LIBOR reflects Data on money and inter financial institutions to competition and price the interest rate differential between a country s est rates are collected incentives. ending rate and the Londor Interbank Offer Rate _ from central banks and Selective credit controls and controls on (ignoring expected changes n the exchange rate). finance ministries and are deposit and lending rates distort financial Interest rates are annua averages. reported in the print and markets in some countries. In addition, inter- The indicators here do not capture the activities of electronic versions of the est rates may reflect the diversion of the informal sector, which rernains Important, espe- _ International Monetary resources to finance the public sector deficit cially in developing economies. Because financial Fund's International Finan- through direct borrowing from the banking arrangements based on personal contacts inspire - cial Statistics. system and statutory reserve requirements. more conf dence among owners and users of funds, Moreover, in economies where the financial personal credit or credit extended through commu- sector is dominated by state-owned banks, nity-based pooling of assets may be the only source credit allocation decisions may be excessively of credit available to sma I farmers, small busi- influenced by noncommercial considerations. nesses, or home-based producers. In economies characterized by financial reDression. the rationing of forma credit forces many borrowers and lenders to turn to the informal market. World Development Indicators 1997 271 5.11 Power and communications Electric power Telephione mainlines International telecommunications Transmissior. end distr but.on Qentgoing traffic Averege price Production losses per 1.000 % of tote, Wei:ing time mroLues per per cell million knwh % of output people n argest c ty Years subscribe, $ per three minutes 1994 1994 1995 1995 1995 1995 1995 Albania 3,903 13 12 29 ..548 9.51 Algeria 19,883 16 42 18 9.5 67 9.45 Angola 956 28 6 66 ..300 1.45 Argertina 65,962 18 160 49 0.2 27 7.37 Armenia 5,658 40 156 44 ..92 Australia 167,155 6 510 20 0.0 103 3.00 Austria 53,259 4 465 27 0).1 240 3.77 Azerbaijan 17,600 20 85 45 ..109 Bangladesh 9.891 32 2 96 82 5.97 Belarus 31,397 12 190 80 9.1 67 8.09 Belgium 72,236 5 457 .18 0.0 239 3.05 Banin 6 17 5 68 1.7 212 8.65 Bolivia 2,824 13 35 38 ..71 6.10 Bosnia and Herzegovina 1,921 21 71 ..26 Botswana ...40 46 1.5 499 Brazil 260,682 16 75 17 0.7 24 4.99 Bulgaria 38,133 12 335 18 ..34 5.98 Burkina Faso ..3 67 ..203 12.22 Burunch. 3 89 3.5 163 16.39 Cambodia ..1 82 20.9 959 Cameroon 2.740 4 4 50 25.3 399 12.02 Canada 554.227 6 590 ...154 1.16 Central African Republic ...2 91 1.0 303 24.04 Chad ...1 82 9.2 307 14.42 Chile 25,276 11 132 56 0.3 72 2.79 Chine 928,083 6 34 5 0.2 33 Colombia 43,354 21 100 38 2.2 33 4.12 Congo 435 0 8 60 [2. 6 190 Costa Rica 4,772 8 164 64 0D.8 95 5.06 Cote dIlvoire 2.305 4 8 75 5.6 250 Croatia 8,275 19 269 24 0D.5 164 7.34 Cuba 10,982 13 32 4 5 .32 Czech Republic 58,705 8 236 26 3.3 75 5.65 Denmark 40,097 4 613 .. .0 165 3.35 Domninican Republic 6,182 25 79 60 ..105 Ecuador 8,256 20 61 52 'L.1 53 8.21 Egypt. Arab Rep. 51,947 0 46 34 5.7 37 6.19 El Salvador 3,211 13 53 73 5.0 233 6.46 Eritreea.. 5 77 28.7 70 10.27 Estonia 9.151 17 277 41 4.1 129 5.23 Ethiopia 1.293 3 2 66 34.8 74 Finland 65.546 5 550 25 0.0 112 3.88 France 476.200 6 558 ..0.0 87 3.03 Gabon 933 9 30 83 ..504 Gambia, The ...18 38 10,1 244 6.34 Georgia 6,803 25 96 49 3 Germany 525,221 4 493 5 ..130 4.17 Ghana 6,115 4 4 65 7.0 279 4.70 Greece 40,623 8 493 42 0.6 90 3.82 Guatemala 3,161 13 27 79 4.0 126 Guineas . 2 57 95.0 278 8.88 Guinea-Bissau -.. 9 96 1.4 240 21.72 Haiti 362 32 8 41 8.0 422 6.62 Honduras 2,672 28 29 53 12.3 211 8.57 Hong Kong 26.743 15 530 100 0.0 516 2.65 \O?Dye. c-0n-0n nd -EoYors 1 S 5.110 Electric power Telephone mainlines International Transmission and ~~~~~~~~~~~telecommunications distribution outgoing traffic Aveirage price Production asses per 1,000 % of total Waiting time min utes per per call million krwh % of output people in, largest city years subscriber $ per three minutes 1994 1994 1995 1995 1995 1995 1995 Hungary 33,486 13 185 39 2.1 131 4.77 India 386,500 17 13 13 1.3 29 6.94 Indonesia 53,414 12 17 36 0.2 63 6.07 Iran, Islamic Rep. 79,128 14 79 29 1.4 41 6.02 Iraq 27,060 0 33 . Ireland 17,105 9 365 41 0.0 311 3.32 Israel 32,781 3 418 34 0.1 108 3.43 Italy 231,804 7 434 ..0.1 77 3.36 Jamaica 2.336 19 116 51 4.1 189 Japan 964,328 4 487 0 0.0 27 5.10 Jordan 5,076 9 73 73 9.9 226 9.44 Kazakstan 66,397 14 118 76 4.2 8 Kenya 3,539 16 9 59 6.6 87 11.17 Korea, Dem. Rep. 38,000 84 47 18 ..3 Korea, Rep. 164,993 5 415 34 0.0 30 4.88 Kuwait 22.798 0 230 12 0.2 329 5.53 Kyrgyz Republic 12,932 17 73 53 ..4 Lao PDR ...4 68 ..107 Latvia 4,440 29 280 40 6.3 62 11.40 Lebanon 5,184 16 82 66 ..103 Lesotho ...9 62 3.9 945 Libya 17,800 -.59 29 8.5 147 Lithuania 10,055 20 254 21 4.4 59 7.88 Macedonia, FYR 5,511 12 165 36 1.5 129 Madagascar ...2 57 ..134 30.83 Malawi . 4 57 18.4 230 9.81 Malaysia 39,093 10 166 9 0.3 111 5.99 Mali ...2 70 ..413 18.03 Mauritania ...4 82 1.8 529 5.21 Mauritius ...131 20 2.0 135 6.04 Mexico 147.926 14 96 36 0.3 107 3.01 Moldova 8,228 19 131 33 11.1 117 6.54 Mongolia ...32 55 13.8 25 15.05 Morocco 11.100 4 43 30 0.6 112 8.36 Mozambique 490 0 3 61 5.0 274 19.38 Myanmar 3,500 35 3 46 ..75 25.82 Namibia ...51 47 1.3 622 6.76 Nepal 927 25 4 66 .1 73 8.75 Netherlands 79,647 4 525 ..0.0 180 3.18 New Zealand 35.135 13 479 27 0.0 179 4.78 Nicaragua 1,688 27 23 62 -. 304 8.46 Niger ...1 68 1.4 265 9.73 Nigeria 15,530 28 4 35 3.5 233 3.41 Norway 113,488 8 556 15 0.1 .177 2.36 Oman 6,187 0 77 49 0.2 318 7.80 Pakistan 58,529 19 16 28 0.7 31 5.86 Panama 3,360 24 114 67 0.9 130 Papua New Guinea ...10 86 ..521 10.38 Paraguay 36,415 1 31 75 ..106 8.34 Peru 15,563 18 47 71 0.8 56 5.76 Philippines 27,062 16 21 71 3.6 122 6.22 Poland 135,347 13 148 11 3.8 67 4.58 Portugal 31,380 11 361 32 0.0 84 4.60 Puerto Rico ...332 16 2.8 689 Romania 55,136 9 131 22 9.6 30 5.31 Russian Federation 875.914 9 170 16 15.0 9 9.67 World Development Indicators 1997 273 Electric power Telephone mainlines International telecommunications Transmissioo and distribut on Outgoing tratff Average price Productrionr losses per 1.333 of tore. Waitieg rime moinues per per call million of autorout peecle n 'argest erry years subsoriber $ per tree manues 1994 1994 1995 1995 1999~ 1999 1995 Rwanda ...2 66 ..89 Saud' Arabia 919019 19 96 22 ..312 6.41 Senegal 1,002 13 10 69 1.6 247 7.93 Sierra Leoune ...4 87 7.0 138 8.06 Singapore 20,046 5 478 100 0.0 541 4.02 Siovse RepubIc 24,740 6 208 21 1.7 53 5.45 Slovenia 12,630 4 309 33 1.4 164 6.35 South Africa 189,316 7 95 29 1.0 78 5.04 Spain 161,654 9 385 1 5 0.0 70 3.49 Sri Lanka 4,387 18 11i 68 8.1- 132 7.35 Sudan 1.333 19 3 16 20.5 102 15.78 Sweden 142,895 6 651 22 0.0 159 2.65 Svwtzerand 65,724 5 613 7 0.0 403 3.25 Syrian Arao Repuolic 15.182 0) 63 27 17.2 6 5 14.10 Ta.jkiatan 17,000 13 45 44 ..I Tanzania 1,913 12 3 44 39.5 63 18.39 Thaiiand 71.177 10 59 55 L.9 64 7.30 Togo ...5 76 2.5 391 13.52 TiLnidad and Tobago 4,069 11 160 18 0.8 280 3.48 Turnsia 6,714 11 58 26 2.6 150 6.66 Turkey 78,322 16 212 23 0.6 29 3.93 Tu,rkm"enisatn 10,496 9 76 85 .1 7 L,ganoJa ...2 7 1 ill11 9.29 Uraerine 202,995 10 157 1 0 Un ted Arab Emirates 18,870 0 283 3 7 0.0 749 4.45 United Kingdom 326,383 8 502 ..0.0 139 1.86 .,nited States 3,473,520 7 627 ..0.0 95 Uruguay 7,617 16 196 64 1.8 83 6.17 Uzbekistan 47.000 10 76 29 3.3 78 Venezuela 73,116 15 111 32 3.1 52 6.62 Vietnam 12,270 22 11 36 0.7 54 Went Sane and Gaza...... Yemen, Rep. 2,159 23 12 37 6.2 123 13.44 Yugoa.ava, Fed. Rap. 33,171 10 191 25 4.3 105 2.10 Zamr 6,546 4 i54 ..36 Zambia 7,786 11 8 40 116.0 157 5.12 Zimbabwe 7,334 7 14 51 11.1 359 8.20 I .1. ~~~~~~~~~~f -00 I Low income 1,602,4121 14 w 21 w 23 w 3.3 w 67 w Exc.. Cnina & India -252,1639t 20w v. 54w .. 139 w 9.66wv Middle income 3,321.1469t 13 w 98 w 33 w :3.8 vs 69 vs 6.19 w Lower middle income 2,276,1209 13 w 88 w 35 ws 4.7 w62 w 6.79wv Upoer middle ncome 1,042,469:t 13wv 119 w 28w 1.8 w 85w'A 4.84 w Low & middle income 4,914,0259t 13 w 47 w 27 w 3.5 w 67 w East Asia & Pacific 1,194,3229t 13 w 34 w 16 w 0.6 vs 51 wv Europe & Centre. Asia 1.858,3869t 11 vs 177 vs 25 vs 7.9 w 45 w 6.91 lv Latin Amnerica & Cario. 752,7259t 16 w 91 w 37 w 1.3 ws 74 w 5.02 vs Middle East & N. Africa 342,9899 10w 51wv 28 w 8.5 w 92wvv 7.79 vs Soute Asia 468,843: 18 vs 13 vs 28 vs 1.3 w 385w 6.77 vs Sub-Saharan Africa 289,949 t 12 v, 11 vs 61 ws 15.2 vs 181 w 9.74 vs High income 8,030,9519t 6w 524w 7 w 0.0 sw 104 w 3.69wv a: r Dev~~c.-nn. c~~- - 5.11 S lists may overstate demand as a result of applicants C_ placing their names on the ist several times to An adequate and reliable supply of electrical power improve their chances. e Electric power production refers to gross produc- is an essential ingredient for modern economic devel- Internat onal telecommunications is a fundamen tion in kilowatt-hours by private companies, coopera- opment. Expanding the suppy of electricity t.o meet tal link to the global economy. As telecommunica- tive organizations, loca or regional authorties, the growing demand of increasingly urbanized and tions tariffs have declined with new technical government o'gan zations, and self-producers. It industrialized economies Is one of the great chal- advances, an oversupp y of internationa I nes, and includes consumption by station auxi iaries, any lenges facing developing countries. To meet this chal- *ncreased competition. international telephone losses in the transformers that are considered inte- lenge without incurring unacceptable social, traffic has rapidly expanded. Worldwide, telephone gral parts ofthe station, and electric energy produced economic, and environmental costs often requires traffic has grown 16 percent a year for the past 10 by pump.ng installations. It covers e ectricity gener- institutional, regulatory, and financial reForms to years-about four times faster than global GDP. ated from all primary sources of energy-coal, oil, improve the power sector's performance. gas, nuc ear, hydro, geotherma , vind, tide and wave, An economy's production of electricity is a basic and combustib e renewabies-where data are avai - indicator of its size and level of development. ab e. a Electric power transmission and distribution Although some countries export electricali power, losses include losses in transmission between most production is for domestic consumptior. Power sources of supply and points of distribution and in the production data do not reflect power distribution d stnbution to consumers, including pilferage. losses through faulty equipment and poorly Production less transrission and distribution osses, designed systems and illegal diversions of power by own-use, and transformation losses is equal to end- consumers. Nor do they capture the relability of sup- use electricity consumption. * Telephone mainlines p ies. including frequency of outages, breaxdowns, refer to telephone rines connecting a customer's and load factors. equipment (such as a te ephone or facsimile Data on electrical power production are collected machine) to the public switched telephone network. A from national energy agencies by the nternational ma nline IS normally identiled by a unique number Energy Agency (IEA) and adjusted by the lEA to meet that s the one bi led. Data are presented here as international definitions. Adjustments are nade, for main ines per 1,0CC people; this is a nmeasure of tele- example, to account for electricity production by phone density or pene ration. e Waiting time shows self-producers (establishments that, in add ton to toe approximate number of years app.icants must their main activities. generate electricity wholly or vvait for a te ephone line. It is calculated by dividing partly for their own use). Self-generation by small the number of appicants on the wailing I st by the entrepreneurs and households can be substantial average number of mainlines added per year over the in some countries because of unreliable pubic pastthreeyears.e Outgoingtrafficreferstotnetele- power sources or remoteness. however, and in phone traff c, measured in minutes per subscriber, these cases may not be adequately re'-lscted in that originated in the country with a destinat on out- these adjustments. side the country. a Average price per call is the cost Telecommunications is at the center o, an infor- of a three-monuoe peak rate ca I from any country to mation economy. Governments that realize this have the United States. integrated telecommunications policies with their Figure 5.11a GNP , :1. macroeconomic strategies. National performance telephone density in -! 3Em i_ indicators for the telecommunications sector can economies, 1995 include measures of supply and demand, quality of telephone mainlines per 1,0O people Data on electricity production service, productivity, economic and financiul perfor- - and losses are from the IEA's 50C mance, cap ta investment, and tariffs. *Greece = Energy Statistics and Until a decade ago the number of mainires accu- ' Balances of Non-OECD 400 St. Ktots rately reflected the full capacity of the telephone so00 seals Barbados Countnes 1993-94. the system. But the advent-and rap d spresd-of cel- Slovenia Iy lEA's Energy Statistics of lular telephones has changed the picture. 'See table 300 d L OECD Countries 1993-94, 5.14 for estimates of cellular, mobile phone sub- * U +-rLiguay *Bahrain and the United Nations scribers.) Demand in the telecommunications sector 200 ** * Seychetles - - Energy Statistics Yearbook. is measured by the sum of mainlines and the number Argentina Te ecommunicat ons data come from the nternational of registered applicants for new connections. But in 1CC * *Ornan Saudi Arabia Telecommunication Union's World Telecommunication some countries the list of registered appl icE nts does Development Report and Direction of Traffic database. not indicate the real current pending demarid. There 0 C0 2 4 6 8 10 is often hidden or suppressed demand, reflecting a situation of acute short supply in which many poten- tial applicants have been discouraged from applying Source: Internationa Telecomriunication Union data. for telephone service. And in some cases wating World Development Indicators :1997 275 5.12 Transport infrastructure Roads Railways Air Goods Rail traffic Goods Aircraft Passengers Paved roads Norma zed transported km per ml oin transported departures carried Air fre,gh! 5 roacindex millon ton-km $ GDP ml. on ton-km thousands thou.sands ton-km 1995 1995 1995 1995 1995 1995 1995 i995 AJbania 30.0 28 3 11 1 1 13 0 Algeria 68.9 16 1 ..11 ..47 3,478 21 Argola 25.0 .. ...8 545 58 Argentina 28.5 117 ...112 6,532 178 Armenia 97.1 89 . Australia 38.8 107 ...407 28,690 1,771 Austria 100.0 123 64.368 10 68,474 113 4,369 173 Azerbaijar 93.8 ......20 1,158 34 Bangladesh 9.3 29 ..13 1,281 185 Belarus 98.6 11-3 350 185 47 35 808 2 Belgwinn 109 428 5 63 141 5,001I 595 Benin 31.4 55 ... .1 74 15 Bolivia 4.8 40 ..18 1 17 1,224 49 Bosnia and Herzegovina Botswana 14.2 256 ... .4 100 0 Brazil 9.2 120 ..8 ..435 19,510 1,577 Bulgaria 91.9 98 45 30 15 863 30 Burklna Faso ..74 -.3 137 15 Burundi 7.1 121 I. 9 0 Cambodis 7.5..- Cameroon 12.5 56 14 4 345 39 Canada 35.1 76 30 ..283 20,291 1,637 Central African Republic 1.8 31 . Chad 0.8 21 ...2 92 15 Chile 1-3.8 58 ..5 79 3.197 775 China 89.7 .. 438,000 231 1,310,000 398 47,565 1,501 Colombia 11.9 486.... 149 6,227 491 Congo 9.7 58 26 5 245 16 Costa Rica 16.7 211 ...27 870 44 CSte dolvoire 9.6 90 ..5 4 175 15 Croatia 81.5 ..4 16 3 13 644 2 CLba 55.8 ......13 824 33 Czechr Repub ic ...586 68 55 26 1,285 26 Denmark 100.0 103 ..4 ..101 5,652 134 Dominican Republic 49.3 63 ... .3 316 3 Ecuador 18.4 73 51,900 ..33 1,166 38 Egypt, Arab Rep. 78.0 111ll 118 39 3,897 164 El Salvador 13.9 75 ... .20 1,698 15 Eritrea . .. .. Estonia 54.0 61 10 100 41 7 169 0 Ethiopia 15.0 60 ...26 750 115 Finland 63.0 77 ..10 97 5,212 212 France .. 122 1,275 7 447 34,057 4,578 Gaoon 8.2 35 ..11 12 505 32 Gambia, The 35.3 239 ... .19 Georgia ...7 ..3 1 177 2 Germany 99.0 .. 211,600 5 69,800 513 33,960 5,834 Ghana 24.9 96 ... .3 186 24 Greece 91.7 154 ..2 ..89 6,006 117 Guatemala 27.5 59 ...5 300 25 Guinea 16.4 45 .... 1 15 1 Guinea-Bissau 10.2 I.. .. 21 0 Haiti 24.2 . .. .. Honduras 20.2 115 ... .12 474 2 Hong Kong 100.0 ..14 1 -- -'oVr c evn'.ncw_mnd rc tnjrs 2c17 5.12S Roads Railways Air Goods Rail traffic Goods Aircraft Passengers Paved roads Normalized transported km per million transported departures carried Air freight road indes million ton-km $ GDP million ton-km thousands thousands ton-km 1995 1995 1995 1995 1995 1995 1995 1995 Hungary 44.1 171 35 32 18 22 1,311 29 India 50.1 462 ..190 ..150 13,214 650 Indonesia 45.5 38 ..10 8,851 241 15,194 778 Iran, Islamic Rep. 59.1 ......49 6,291 108 Iraq 86.0 ... .. .31 Ireland 94.0 279 ..3 ..84 6,587 107 Israel 100.0 98 ..2 9 48 3,453 1,071 Italy 100.0 57 ..7 ..271 23.482 1,470 Jamaica 70.6 283 ... .17 1,126 22 Japan 74.0 72 ..6 ..536 91,797 6,538 Jordan 100.0 113 ..11 ..17 1,274 266 Kazakatan 68.4 170 ..714 ..13 709 16 Kenya 13.8 117 ..23 ..13 740 53 Korea, Dem. Rep. .........254 Korea, Rep. 6.3 118 410 12 57 ..29,345 Kuwait 80.8 .. .. 18 1.951 330 Kyrgyz Republic 91.0 ..74 ..40 12 439 1 Lao PDR 13.8 277 ... .4 125 1 Latvia 38.3 78 21 1.84 29 5 183 1 Lebanon 95.0 ......11 770 129 Lesotho 17.9 144 ... .3 28 0 Libya 57.1 ......6 623 0 Lithuania 86.4 271 138 1-18 26 7 210 2 Macedonia, FYR ..71 3 ..2 Madagascar 11.5 168 ... .18 497 32 Malawi 18.4 177 ..8 ..4 149 4 Malaysia 75.0 ...4 ..178 15.418 1,199 Mali 12.0 154 ... .1 74 15 Mauritania 11.2 117 ... .5 228 16 Mauritius 93.0 11ill. . 9 676 116 Mexico 37.3 107 ..11 ..225 14,969 156 Moldova ..105 41 ..13 ..170 Mongolia 11.0 ..147 566 1,850 10 662 3 Morocco 50.3 23 12 21 27 33 2,147 58 Mozambique 18.6 143 ... .3 168 6 Myanmar ... .. .15 334 1 Namibia 13.1 199 ..37 ..7 225 27 Nepal 41.4 69 ... .27 717 17 Netherlands 90.0 60 ..5 ..192 14,463 3.672 New Zealand 58.0 106 ..6 ..128 7.677 580 Nicaragua 10.0 66 ... .1 49 9 Niger 7.9 127 ... .1 74 15 Nigeria 82.6 126 ... .7 548 2 Norway 73.5 103 250 ..4 266 .11,659 140 Oman ..211 ... .16 1,453 133 Pakistan 54.0 242 ..43 ..69 5,343 446 Panama 33.5 123 ... .12 661 7 Papua New Guinea 3.4 34... . Paraguay 9.4 ......1 105 7 Peru 10.9 45 ..I ..38 2,584 27 Philippines ..47 ... .64 7,180 374 Poland 65.3 150 1,630 76 226 32 1,657 66 Portugal ..99 280 7 ..72 4,590 194 Puerto Rico 100.0......... Romania 51.0 108 616,044 121 105,131 22 1.245 18 Russian Federation 78.8 .. 31,000 420 1,213,000 586 26,525 890 World Development Indicators 1997 277 5.12 Roads Railways Air Gocds Rail craft c Goons Aircraft Passengers Pavee roads Normalized transported am per million trarosported departures carried Air freight "Ioad irdes mill on toe-kmn $ GDP million ton-km thsousands tnousancs too-km 1_995 1995 1995 1995 1_995 1995 1995 1995 Rwanda 9.9 97 ......9 Saudi Arabia 42.7 143 I.1. 98 11,525 895 Senega 23.9 116 15 ..4 150 15 Sierra Leona 11.0 105 ...0 15 0 Singapore 97.3 --. .52 19,779 3,687 S ovek Repub ic 32.043 103 60,776 2 41 0 Slovenia ..85 4 20 2 8 371 4 South Africa 32.6 115 ..71 --74 6,396 263 Spa n 99.0 102 562 4 10 279 23.298 690 Sri Lanka ..116 29 ..9 1,156 156 Sudan 36.2 ... .9 497 53 Sweden 76.0 64 11 ..170 9,572 191 Sawinzerland ..72 405,150 46 191 9,859 1,510 Syrian Areab Repualic 71.0 . -12 ...563 Tajikiatan 82.7 ... .3 783 3 Tanzan a 4.2 52 11 ..6 236 3 Thailand 97.4 140 ..14 87 12,771 1,303 Togo 31.6 15ISO. . 1 74 15 Trindaod and Toaaga 51.0 114 ......1,727 Tuniaia 76.8 183 ..21 ..15 1,417 18 Turkey 23.0 192 112,515 9 8,632 79 7,749 215 Turkmenisiana 81.2 -.....12 748 2 Uganda 7.7 55 ..6 ..1 95 1 Ukraine 94.6 92 ..324 ..20 364 19 Inited Arab Emiratea 100.0 44 ... .34 3,551 567 Lnited Kingoom 100.0 63 1.689 4 97 713 59,129 6,831 United Staese 59.9 1099. 27 ..7,469 527,414 19,615 Urug.ay 13.7 148 ..I ..9 477 4 Lzbekistan 67.2 ......16 2,217 9 Venezuela 39.3 726 ... . 0 4,446 120 Vietnam 25.9 -..16 ..27 2,290 2 Wean Sank asd Gaza . .. .. .- Yemen. Rep. 7.9 ... .5 375 4 Yugoalavia, Fed. Rep......- Zaire ... .. .178 Zambla 18.3 191 -.16 ..235 - Zimbabwe 19.0 263 .. 10 626 144 271 "-'cd b.cv ol~rneI 1-d~caz:rse __7 5.12 Transport infrastructure-highways, railways, ports e Paved roads are roads that have been sealed and waterways, airports and air traffic control with asohalt or similar road-building materals. svstems-and the services that feow from it are cr. e Normalized road index as the total length of roads c al to the activities of households, producers, and in a country compared with the expected length of governments. Taken together, the services associ- roads, where the expectation is conditioned on pop- ated with the use of infrastruc-:ure (measured in ulation. population dens ty. per capita income, terms of value added) account for 7-11 percent of urbanization, and regioral-specific dummy var- GDP, with transport being the largest sector. ables. A value of 100 aS "normal." If the index is Transport alone commonly absorbs 5-8 percent of more than 100, toe coumry's stock of roads total paid employment. Providing the infrastructure exceeds the average. e Goods transported by road for transportation to meet the cemands of a modern are the volume of goods transported by road vehi- economy is one of the major challenges of economic cles, measured in millons of metric tons times kilo- development. meters traveled. a Rail traffic is the number of rail Internationally comparable data are not availanle for traffic un ts (the sum of passenger-kilometers and nosttransportsubsectors, Unlikefordemographicsta- ton-kilometers) per million U.S. collars of GDP. tistics, national income accounts, and international * Goods transported by rail are the tonnage of trade, the collection of infrastructure data has not been goods transported times kilometers traveled. Internationalized." Even when data are available, they a Aircraft departures are the number of domestic are often ofhmited value because ofdefinitional incom- and international takeoffs o' aircraft. a Passengers patibilities, inappropriate geographical units of obser- carried include both comestic and international air- vation, or lack of timeliness. Serious efforts need to be craft passengers. a Air freight is the sum of the made to create internatdonally comparable databases tons of freight. express, and d.plomatic bags cErred whose comparability and accuracy can be gradually on each flight stage lthe operation of an aircraft improved. Because performance characteristics vary from takeoff to its next landing) multipled by the significantly by mode of transport and according to stage distance. whether the focus IS measuring the Infrastructure or measunng the services flowing f-em the infrastructure, _ highly specialized and carefully Epecified indicators are required. Data on roads are from the Internationa Road The table includes selected indicators of the size Federation's World Rose Statistics. The normalized and extent of roads, railways, and air transoort road index is based on World Bank staff estimates. systems and the volume of freight and passengers Railway data are from a database mantained by the carried. In addition to quantity, 'he quality of transport World Bank's Transportatior, Water. and Urban service is important in assessing an economy's Development Department, Transport D'vision. Air transport infrastructure. The shipping sector (includ- transport data are from the nterrational C.vI ing port operationsa is importantfor many economies, Aviation Orgarzation. but internationally comparable data in this area are available for only a few countries and so are not pre- sented here. To measure the relative se7e of an indicator over time or across countries, some form of normaliza- tion s required. The table presents two normalized indicators: rail traffic per mil ion dollars of GDP ard the normalized road index. While the rail traffic Irdi- cator is normalized by a single Indicator-the size of the economy-the normalized road index uses a multidimensional regressiorn function to estimate the "normal," or expected, stock of roads in a coun- try (Armington and Dlkhanov 1996). The "normaliz- ing" varables include population, population density, per capita income, urbanization, and regional differences. The value of the normaized road index shows wnether the stock of roads in a country exceeds or falls short of the average for a country of similar characteristics. World Development IndicaLors 1997 279 5.13 Science and technology Scientists and ITechnicians in Expenditures for High-technology Royalty and engineers In research and research and exports license fees research and development development development Sof manufactured P3eceipts Payments oer rmillon peop e per millior people of GNP $ mi. ons exports f; m4[ions $ millions 1981-gm2 198i-921 1981-92' 1995 1995 1950 1995 1990 1995 Albania ... .. .0 0 0 0 Aigeria ... .29 12 0 .I Angola ... .. .0 14 0 0 Argentina 350 197 0.3 1,126 16 4 2 409 206 Armenia . .. .. Australia 2.477 943 1.4 5,802 41 162 240 827 1,010 Austria 1,146 1.101 1.4 11,407 25 91 132 287 521 Bangladesh ... .4 0 0 0 0 0 Belarus 3,300 515 0.9... Belgium 1,856 2,041 1.7... Benin 177 54 0.7 ...0 0 0 0 Bolivia 250 154 1.7 30 15 0 0 3 4 Bosnia and Herzegovina Botswana . .- .0 08 6 Brazil 391 ..0.4 4,021 1s 12 32 54 529 Bulgaria 4.240 1,205 1.7 0 0 0 0 Burkina Faso ... .0 0 0 0 Burundi 32 31 0.3 0 0 0 0 Cambocl;a . .. . Cam-eroon ... .. .1 0 02 Canada 2,322 978 1.6 27,648 23 Central African Repub,ic 55 31 0.2 0 ..0 Cnad ... .. .0 0 0 0 Cnile 364 231 0.7 339 16 0 1 37 50 China ..24,393 19 0 0 0 0 Co!ormba 39 37 0.1 815 21 21 44 13 32 Congo 461 788 0.0 2 12 0 0 0 0 Costa Rica 539 ..0.3 94 14 1 3 9 12 CSne dIvoire ... .. .0 0 0 0 Croatia 1,977 845 ..723 21 Cuba 1.369 878 0.9... Czech Republ~c 3,248 1,298 1.8 2,241 13 .. 13 .. 53 Denmnark 2,341 2.663 1.8 6,912 24 0 0 0 C Dominican Repubic. .. 295 19 0 ..0 4 Ecuador 169 215 0.1 24 8 0 0 37 53 Egypt, Arao Rep. 458 340 1.0 69 6 0 47 0 97 El Sa)vador 19 299 0.0 59 16 0 0 1 3 Eritrea .. . .. Ethiopia ... .. .0 0 a 0 Enland 2.282 2,093 2.1 7,151 21 50 58 317 369 France 2,267 2,972 2.4 67,152 31 1,295 :1,850 1,629 2,320 Gabon 189 17 0.0 11 42 0 0 0 0 Gambia, The. .... . 0 0 0 Georgia......... Germany ... .. .1.987 2,778 3,797 5,439 Gnana ... .. .0 0 0 I Greece 53 49 0.3 497 10 0 0 15 58 Guatemnala 99 107 0.2 93 17 0 0 0 0 Gunea 264 126 . .. . . Guinea-Bissau ... .. .0 0 0 0 Haiti ... .. .0 0 0 0 Honduras ... .3 5 0 0 3 9 Hong Kong ... .8,112 29.. . VoD-o Dc'relo-er'nr ndcal.Os _1007 5.13S Scientists and Technicians in Expenditures for High-technology Royalty and engineers in research and research and exports license fees research and development development development manufactured Receipts Payments per million people per million people %/ of GNP $ mTillions exports $ millions $ millions 1981-92O 1981-921 1981-921 1995 1995 1990 1995 1990 1995 Hungary 1,200 697 1.1 ...49 32 36 70 India I.. .. .1 . 72 Indonesia ....3r615 16 0 0 0 0 Iran, Islamic Rep. ... .. .0 0 0 0 Ireland 1,801 366 0.9 19,811 63 38 114 591 2,554 Israel ... .4,722 28 63 124 73 152 Italy 1,366 742 1.3 32,496 16 1,040 462 1,969 1,166 Jamaica 8 6 0.0 ...3 4 7 19 Japan ... .165,972 39 2,490 6,010 6,040 9,363 Jordan ... .183 26 0 a 0 0 Kazakstan......... Kenya ... .20 5 9 6 6 0 Korea, Dam. Rep.......... Korea, Rep. ... .4-7805 42 37 299 136 2,385 Kuwait ... .77 13 0 0 0 0 Kyrgyz Republic......... Lao PDR ... .. .0 0 0 0 Latvia 3,387 ..0.3 126 17 0 0 0 0 Lebanon......... Lesotho ... .. .0 0 1 0 Libya 361 493 0.2 ...0 ..0 Lithuania 1,278 ...246 23 .. 0 .. 1 Macedonia, FYR 1,258 334...... Madagascar 22 79 0.5 2 3 0 1 0 9 Malawi ... .. .0 0 0 0 Malaysia ... . 7,072 67 0 0 0 0 Mali ... .. .0 0 0 0 Mauritania a.. .... 0 0 Mauritius 361 158 0.0 24 2 0 0 0 0 Mexico 226 399 0.2 21,438 35 73 114 380 484 Moldova ... .15 9 . Mongolia ... .. .0 ..0 Morocco ... .619 25 4 3 60 125 Mozambique ... .1 5 0 ..0 Myanmar ... .. .0 ..0 Namibia I.. . .. 0 3 3 Nepal ... .0 0 0 0 0 0 Netherlands 2,656 1,774 1.9 44,729 40 1,086 2,350 1,751 3,050 New Zealand 1.555 785 0.9 405 11 0 0 0 0 Nicaragua 214 89 ..39 38 0 0 00 Nigeria 15 69 0.1 ...0 0 0 0 Norway 3,159 1,594 1.9 2,525 23 133 287 148 231 Oman ... .65 8 0 0 0 0 Pakistan ... .. .0 2 0 12 Panama ... .. .. .9 11 Papua New Guinea ... .29 35 0 0 0 0 Paraguay ... .3 3 0 ..0 Peru 273 ..0.2 69 9 .. 1 5 60 Philippines ... .2,986 42 1 2 38 99 Poland 1,083 1.380 0.9 1,688 10 0 4 0 44 Portugal 599 381 0.6 2,581 14 14 20 117 217 Puerto Ricoa.. .... . . Romania 1,220 492 0.7 355 8 0 3 0 8 Russian Federation 5,930 1,354 1.8..... . World Development Indicators 1997 281 47 5.13~~~J_%. Scientists and Technicians in Expenditures for High-technology Royalty and engineers in research and research and exports license fees research and development development development ManLrfactu.res Receipts Paymrents per mn o n peep e per m' 'on cenple % of GNP $ mr ions experts $; millions $ milleors 1 981-92' 1981-92' 1981-92? 1995 1995 1990 1995 1990 1995 PRsanda 12 11 0.5 ...0 0 0 0 Saudi A'abisa.... 935 34 0 a 0 0 Senegal 342 467 ..79 39 1 1 0 0 Sierra Leone ... .. .0 0 0 0 Singapore 1,284 583 0.9 69,249 70 0 0 0 0 Slovak ReoubJic ... .908 17 .. 11 .. 79 Sloven's 2.998 2,390 1.5 1,123 15 4 4 5 23 South Africa 319 132 1.0 1,879 15 54 66 139 102 Spain 956 299 0.9 11,834 1_7 90 196 1,022 1.269 Sri Lanes 173 43 0.2 59 3 0 0 0 0 Swveden 3,081 3,148 2.9 17,731 26 563 876 743 999 Switzerland 2,409 1341.8 19.755 26 Syrian Arso lRepubolc ... ...0 ..0 Tajikistan......... Tanzanis --. . .. 0 0 0 Thai and 173 51 0,2 14,826 36 0 1 170 630 Togo ... .. .0 0 0 0 Tinieed and Tobago 240 222 0,6 304 28 0 9 7 0 Tuesia 388 71 0.3 429 10 1 1 1 2 T..rkev/ 209 23 0.8 1,289 6 . Turkmenistar . .- Ugarnda .. . . 0 0 0 Ukraine 6,781 . .. .. United Arac Emirates ... .13 3 . United ,Kingdom ...2.1 79,256 41 2,640 4,566 2,992 2,855 Unrited Statesa 3.873 ..2.9 181,233 43 16,635 26.960 3,138 6,300 Uruguay ... .82 .10 0 1 0 5 Lobelatan ~~~~1,780 313...... VenezueJa ... .377 14 .. 0 ..0 Vietnamo 334 ..0.4 . .. West Bank and lOans. . .. Yem.en, Rep.......... Yugoslavia, Fed. Rep. 1,476 400 Zambia ... ...0 ..0 Zimbabwe ... .27 . 1 Low incomne ... ...14 t 23t 99 t - Encd. Chins & Ine' . . .a. 14 P 24 t 23 t 48st Middle coo......345 t 389 Lower middle Income......... Jpper middle ncome . .. .. 193 t 258 t 1,069 t 1,534t Low & minnie income ... .. .3411 5820t 2,097 t 4,038t Ease As's & Pacific. .. ,. 4 t 1S t 2191t 764 Europe & Central Asia......... Latin America & Caneb. .., .130 t 220 t 1,090 t 1,653t Middle Eastn& N.Africa .. . . 6 t _58t 72 t 261nt South Asia .,,,,,,1t ..73 Sub-Sahoran Africa .,. ., .70 t 95 t 154 t 118t High income .... _...34,159 t 57,155 t 30,422 t 47,926t a. See Primary oIats documennatio,' for survey year. 5.135 ________________i ____________i_______l__________ To translate Davis's industry classification into a C_ definition of high-technology trade, Braga and Yeats Rapid progress in science and technology is changing (1992) used the concordan-e between the SIC e Scientists and engineers in research and the global economy and increasing the importance of grouping and the SITC (Standard Internat onal Trade development are oeople tra ned to work in those capac- knowledge as a factor of production. It is alsc driving Classification), revision 1 class fication proposed by ities (usual y requ res como etion of tertiary education) the rapid shifts in comparative advantage between Hatter (1985). Given the mperfect match between in anyfeld of science who are engaged in professional countries. The table shows a few key indicetors that SIC and SITC codes, Hatter estimated high-technol- work in R&D activities (including administrators). provide a very partial picture of the 'technologica ogy weights (the proportion oi U.S. high-techno ogy a Technicians in research and development are base" in countries: the availability of skilled human imports and exports in each SITC group, based on peop e engaged in R&D activities who have received resources (the scientists. engineers, and technicians 1975-77 U.S. trade data) as a way to highlight the vocational or technical training in asy branch of know- employed in research and development, or R&D), the relative importance of h gh-technology products in edge or technology of a specified standard (usually competitive edge countries enjoy in high-technology any given SITC grouping. In creparing the data on three years beyond the frst stage of secondary educa- exports, and their purchases of technology through high-technology trade, Braga and Yeats considered tion). a Expenditures for research and development royalties and licenses. only those SITC groups (at a four-digit leve ) that pre- are expenditures on any creative, systemat c activty An indication of a country's skilled human sented a high-technology weight greater or equal to undertaken to increase the stock of knowledge (includ- resources Is obtained either from the total stock of 50 percent. Examples of high-technology exports ing knowledge of people, culture, and society) and the scientists, engineers, and technicians or the number include aircraft, office machiriery, scientific instru- use of this knowledge to devise new applicatons. of economically active persons with the necessary ments, and pharmaceutical gDods (see Braga and Included are fundamenta research, app ied research, qualifications to be scientists, engineers, or techni- Yeats 1992). and expenmenta development work leading to new cians. Missing data on potential scientists and engi- Note thatthe appropriateness of this methodology devices, products, or processes. Tota. expenditure for neers have been estimated by the United Nlations relies on the somewhat unrea istic assumption that R&D comprises current expendture. including over- Educational, Scientific, and Cultural Orgasization the use of the U.S. input-output relations and trade head. and capital expenditure. e High-technology (UNESCO) using the number of people who have com- patterns for high-technology product on does not exports are goods produced by ndustries lbased on peted education at ISCED (International S:andard introducea bias in the classification. U.S. industries)thatrankintnetopoeaccordingtoR&D C assification of Education) leve s 6 and 7; for tech- expenditures. Manufactured exports are the commodi- nicians, missing data are estimated using the number ties in the SITC, revision 1, sect ons 5-9 (chemicals of people who have completed education at ISCED and re ated products, bas c manufactures. manufac level 5. These data are normally calculated in terms tured articles, macinery and transoortequipment, and of full-time equivalent staff. Such data cannot take other manufactured articles and goods not elsewhere account of variation in the quality of the tra ning or classified), excluding divis on 68 (non'errous metals). education received, which is considerable. a Royalty and license fees are payments and receipts R&D expenditures may reflect different tax treat- between residents and nonres dents for the authorized went of such expenditures. And in some ccuntries use of intangibe, nonproduced, nonf nanc a assets they may reflect a large and possibly unproductive and proprietary rights (such as patents, copyrights. outlay by governments or state-owned research trademarks, industrial processes, and franchises) and estab ishments. Figure 5.13a for the use through icensing agreements, of produced High-technology exports are those produced by the from the top 10 origin a s`of rototypes lsuch as manuscripts and films). top 10 industries (based on U.S. industries) accord- exporters among. V .' ing to R&D Intensity. The rankings used in preparing 1995 the data on high-technology exports in the table are billions of U.S. dollars based on a methodology developed by Davis (1982). Data on technica personnel Using input-output techniques, Davis estimated the Singapore ! and R&D expend tures are technology intensity for any given industry in the Korea, Rep. collected by UNESCO from United States in terms of the R&D expenditures Malaysia 3 i i member states, mainly required to produce a certain manufactured good. China from offociai rep ies to This methodology takes into account not on y the Mexico UNESCO questionnaires direct R&D investments made by fina producers. but Thailand and specia sumveys, as we I also the indirect R&D expenditures made by suppliers Hong Kong as from off cia reports and of intermed ate goods used in producing tIhe final Israel publications, supplemented good. Industries classified on the basis of the U.S. Brazil 3 by informat on from other national and nternationaJ Standard Industrial Classification (SIC) were ranked Indonesia sources. These data are published in UNESCO's according to their R&D intensity. with the top 10 SIC 0 10 20 30 40 50 60 70 80 Statrstical Yearbook. Information on hgh-techno ogy groups (three-digit classification) designated as high- exports are from the United Nations COMTRADE data- Note: The economies here are drawn from those for technology industries. The industry ranked tenth had which data are shown ntable 5.13. High-technology base, and data on royalty payments are from the an R&D index 30 percent greater than the industry in exports are those produced in the top 10 ndustries Internationa. Monetary Fund's Balance of Payments rankxed by R&o expenditures. eleventh place and more than 100 percent. greater Source: United Nations COMTRAD)E database Statistics Yearbook. than the average for the manufacturing sector. l_d World Deveiopment Indicators 1997 283 5.14 The information age Daily Radios Television Mobile Fax machines Flersonal Internet newspapers sets phones computers hosts er1per 1 er1,000 per 1,000 per 1, 00pr100pr100per 10,000 people people people people neople people people 1992 1995 1995 1995 1995 1995 July 1996 Albania 49 ..89 0.0 ...0.24 A geria 38 . 71 0.2 0.2 3.0 0.01 Angola 12 59 51 0.2 ...0.00 Argentina 143 ..347 9.9 1.4 24.6 2.72 Armenia 24 .,241 0.0 0.1 ..0.28 Australia 265 ..641 127.7 26.3 275.8 220.15 Austria 398 348 497 47.6 31.0 124.2 88.27 Azerbaijan 59 ..214 0.1 0.3 ..0.02 Bangladesh 6 48 7 0.0 0.0 Belarus 186 320 265 0.6 0.9 .. .10 Belgium 310 ..464 23.2 16.4 138.3 42.78 Benin 2 1,127 57 0.2 0.1 ..0.01 Bolivia 57 ..202 1.0 ...0.21 Bosnia and Herzegovina 131 ..94 0.0 Botswana 29 821 23 0.0 2.1 ..0.00 Brazil 55 ..278 9.0 1.3 13.0 2.90 Bulgaria 164 ..260 ..1.1 21.4 2.68 Burkina Faso 0 30 6 0.0 .,0.0 0.00 Burundi 3 65 7 0.1 0.0 Camnbodia ..124 8 1.5 0.1 ..0.00 Cameroon 4 325 75 0.2 ...0.00 Canada 215 ..647 86.5 18.1 192.5 143.33 Central African Republic 1 94 5 0.0 0.1 ..0.02 Chad 0 620 2 0.0 0.0 0.0 Chile 147 ..280 13.6 1.1 37.8 9.27 China 43 163 250 3.0 0.2 2.2 0.09 Colombia 63 ..186 7.1 2.6 16.2 1.43 Congo 6 312 17 0.0 Costa Rica 101 ..220 5.5 0.7 7.58 OSte dIlvoire 7 ..59 0.0 ...0.00 Croatia ...230 7.1 8.0 20.9 5.19 Cuba 122 ..200 0.1 ...0.00 Czech Republic 583 ..406 4.7 7.1 53.2 31.17 Denmark 332 5. 36 157.3 47.8 270.5 147.20 Dominican Repuiblic 36 ..87 ... .0.16 Ecuador 64 ..148 4.6 2.7 3.9 0.53 Egypt, Arab Rep. 41 ..126 0.1 0.4 3.4 0.14 El Salvador 90 ..241 2.5 ...0.07 Eritrea .. 6 0.0 0.2 Estonia ...410 20.5 8.7 6.7 44.42 Ethiopia 1 200 4 0.0 0.0 0.00 Finland 512 ..519 199.2 25.8 162.1 542.69 France ...579 23.8 32.7 134.3 32.69 Gabon 16 ..49 2.5 ..4.5 Gambia, The 2 158 ..1.3 0.6 Georgia ..62 220 0.0 0.1 ..0.22 Germany 0 ..550 42.6 1 7.8 164.9 66.96 Ghana 16 . 16 0.4 0.3 1.2 0.00 Greece 135 ..442 26.1 1.5 33.4 12.12 Guatemala 18 ..54 2.6 1.0 2.6 0.15 Guinea ..76 76 0.1 0.0 0.2 0.00 Guinea-Bissau 6 40 0.0 0.5 . Haiti 7 60 5 0.0 . Honduras 31 102 80 0.0 ..0.15 Hong Kong 822 ..359 129.0 46.0 116.3 38.99 23 I,Vc- di Ceve opme-.inozcaLc-s 1997 5.14 Sp Daily Radios Television Mobile Fax machines Personal Internet newspapers sets phones computers hosts per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 10,000 people people people people people people people 1992 1995 1995 1995 1995 1995 Joly 1996 Hungary 282 ..444 25.9 2.5 39.2 24.58 India 31 120 61 0.1 0.1 1.3 0.02 Indonesia 24 ..147 1.1 0.4 3.7 0.27 Iran, Islamic Rep. 20 250 140 0.1 0.5 ..0.05 Iraq 35 ..74 0.0 Ireland 186 ..382 44.1 22.4 145.0 59.86 Israel 246 ..295 53.5 25.0 99.8 71.75 Italy 106 102 436 67.4 3.5 83.7 19.97 Jamaica 67 750 285 17.9 ...0.77 Japan 577 ..619 81.5 48.1 152.5 39.65 Jordan 53 340 1 75 2.6 7.3 6.0 0.18 Kazakstan ...266 0.3 0.2 ..0.33 Kenya 14 ..18 0.1 0.1 0.7 0.05 Korea. Dam. Rep. 221 163 323 0.0 0.1 Korea. Rep. 412 ..115 36.6 8.4 120.8 10.70 Kuwait 248 ..379 70.7 21.0 57.1 11.80 Kyrgyz Republic ...238 0.0 Lao PDR 3 126 7 0.1 0.1 ..0.00 Latvia 98 ..470 6.0 0.3 7.9 11.65 Lebanon 185 ..268 30.0 ..12.5 0.90 Lesotho 7 79 7 0.0 0.3 ..0.00 Libya 15 ..115 0.0 Lithuania 225 404 364 4.0 1.0 6.5 3.60 Macedonia, FYR 1.. 69 0.0 0.8 ..0.44 Madagascar 4 2-11 24 0.0 ...0.02 Malawi 2 250 ..0.0 0.1 Malaysia 117 469 231 43.4 3.0 39.7 4.24 Mali 4 168 12 0.0 ...0.00 Mauritania 1 187 58 0.0 0.1 ..0.00 Mauritius 74 ..192 10.4 17.7 31.9 0.42 Mexico 116 ..192 7.0 2.1 26.1 2.21 Moldova ..209 300 0.0 0.1 2.1 0.02 Mongolia 92 74 59 0.0 0.9 0.2 0.04 Morocco 13 ..145 1.1 0.3 1.7 0.13 Mozambique 5 49 3 0.0 0.4 ..0.01 Myanmar 7 89 76 0.0 0.0 Namibia 147 ..29 2.3 ...0.54 Nepal 7 36 3 0.0 0.0 ..0.03 Netherlands 303 ..495 33.2 32.3 200.5 138.88 New Zealand 305 ..508 108.0 18.1 222.7 216.81 Nicaragua 23 ..170 1.1 ...0.69 Niger 1 62 23 0.0 0.0 ..0.00 Nigeria 18 ..38 0.1 ..4.1 0.00 Norway 607 ..561 224.4 30.1 273.0 277.46 Oman 41 ..654 3.7 ...0.00 Pakistan 6 ..22 0.3 1.2 1.2 0.03 Panama 90 ..169 0.0 D.. .78 Papua New Guinea 16 ..166 0.0 0.2 .. .00 Paraguay 37 ..73 3.2 ...0.17 Peru 71 ..100 3.1 0.6 5.9 0.96 Philippines 50 166 121 7.3 0.5 11.4 0.46 Poland 159 ..408 1.9 0.8 28.5 9.95 Portugal 47 ..332 34.3 3.4 60.4 17.70 Puerto Rico ..787 322 47.6 150.2 ..0.20 Romania 324 ..201 0.4 0.9 5.3 1.20 Russian Federation 386 341 379 0.6 0.2 17.7 2.17 Word Development Indicators 1997 285 5.14 Daily Radios Television Mobile Fax machines Personal Internet newspapers sets phones computers hosts per 1,0CC per 1.000 per 1,000 per 1,000 per 1.000 Pe, 1,000 per 10.000 peep e peorle people peep a peenla oezple peop~e 1992 1995 1995 1995 1995 1995 -J p 1996 Rwenda 0 95 1 0.0 Saudi Arabia 43 ..255 0.9 4.4 25.1 0.16 Senegal 6 ..37 0.0 ..7.2 0.05 Sierra Leone 2 72 16 0.0 0.2 Singapore 336 ..362 97.7 19.9 172.4 128.60 Slovakr Repub ic 317 170 216 2.3 8.3 41.0 10.24 Slovenia 160 ..374 13.6 7.6 47.7 49.97 South Africa 32 ..101 12.9 1.8 26.5 20.10 Spain 104 1,022 490 24.1 5.5 81.6 15.93 Sri Lanka 27 207 66 2.6 0.6 1.1 0.13 Sudan 24 328 80 0.0 0.2 Sweden 611 ..476 229.4 37.3 192.5 211.02 Switzerland 377 ..461 63.6 26.0 346.0 145.87 Syrian Arab Republic 22 ..89 0.0 0.3 0.1 0.00 Tajkicatan 21 171 258 0.0 0.2 Tanzania 6 398 16 0.1 ...0.00 Thnai and 85 206 221 18.5 1.0 16.3 1.08 Togo 3 367 12 0.0 2.4 0.0 Trindaod and Tcobago 138 ..318 4.3 1.6 19.2 0.51 Tan sia 49 1 75 156 0.4 2.8 6.7 0.04 Turkey 71 128 240 7.0 1.6 12.5 1.26 Turkmen[stan ...217 0.2 Uganda 4 124 26 0.1 0.1 0.5 0.03 Ukraine 116 . 233 0.3 ..5.6 0.87 United Arab Erm ratec 189 ..263 54.2 10.6 48.4 1.97 Un ted Ktingdom 383 ..612 96.0 30.6 186.2 99.01 United Statec 236 ..776 126.4 53.9 328.0 313.16 Lrugu.ay 240 ..306 12.6 3.5 22.0 2.76 Uzbekistan 21 ..183 ,.0.1 ..0.03 Venezuela 205 ..180 13.0 0.8 16.7 Vietnam 8 ..110 0.2 0.1 Wect Bank and Gaza . .. Yemen, Rep. 19 44 254 0.6 0.1 Yugoclavia, Fed. Rep. 0 .1 70 0.0 1.4 11.8 Zaire 3 102 41 0.2 0.1- Zambia 8 118 64 0.2 0.1 Zimbabwe 19 ..27 0.0 0.9 3.0 Low income 29 w ..127 w 1.2 w 0.2 w 1.8 w Excol. Oboea & India lO w .43 w 0.2 w 0.3 w Maicd a income 112 w ..216 w 5.4 w 1.5 w 14.4 w Lsower miodle income 116 w ..201 w 3,2 w 0.6 w 10.3 w Upper miodle income 101 w ..256 w 11.0 w 3.4 w 23.3 w Low & mediae ncome 57 w ..157 w 2.6 w 0.6 w 6.2 w Eact Ac a & Pacific 43 w ..217 w 3.7 w 0.3 w 3.6wv Europe & Central Acia 227 w. 303w . 3.0w 1.1w 17.7 w Latin America & Carib, 86 w ..223 w 7.7 w 2.9wv 17.4 w Midd e Eact & \. Africa 33 w ..145 w 0.9 w 0.9 w Scorn Acia 25 w SOw 0.1 w 0.2w 1.3 w Sub-Saharan Africa 11 w ..36 w 1.0 w ______ Higr ncomne 284 w ..597 w 84.2 w 33.7 w 201.0 w 5.14 S industr es. The proposed evisions are being coordo- nated with .S. and Mexican staetst[cians to enhance The global economy s undergo ng an information eev- comparability vitch n the North Ame, can Free Trade a Daily newspapers Is the number of newspapers oSution thatvii I be ass gnifcant in effectasthe indus- Amea. puolished at Least four times a week, per 1,000 tnal revolut on of the nmneteenth century. A sigri of the In the absence of economic indicators, the table peop e a Radios s the estimated number of rad o rrportance of this revolution s the size of tf-e globa uses a number of proxy in0( cators to rmeasure receivers n use for broadcasts to the general pubtlc, information sector, estimated at $1,425 b. I on n progress n the information age DaLt covering radios oer 1,000 people. o Television sets represent the 1994. are estima'es of receivers n tise and are obtained estimated numberoftelevision sets in use. perl,OOD This estimate, drawn 'rom a number cf oata from stac,stical survevs carried out bv L NESCO. They people. o Mobile phones refers to users of portable sources, covers: vary wide y n reliab ity from councry to courtry and telephones subscrib,ng to an automatic publ c mobile * Te ecommunicat ons services and equ pment. should be used with caution. Estimates of telev sion te ephone service using ce lular techno ogy that pro- * Computer software, services, and equipment. sets also vary in reliability. Scme countries require vides access to the public switched telephone net * Sound and television broadcasting and equipment. that televisior sets be registered. To che extent that work. per 1.000 people. a Fax machines is the * Audiovisual entertainment households do not register the r television sets or do estimated number of facsimi e machines connected The global nformaTion sector thus defined is giow not reg ster a.l o' their televis on sets, the number of to the pub iC switched telephone netvvork, per 1.000 ing faster than the global economy. It a so appears to licensed sets may understate tle true number. people. a Personal computers s of the estmated be immune to economic downswings. While the g obal number of self-contained computers des gned to be economy contracted by 3.3 percent in 1991, the infor used by a single ndvoioual. per 1.000 people. mat on industry grew by 6 percent. Tne sector s con- o Internet hosts IS tne number of computers directly tr bu on to g oba output is growing apace ato stood ure 5.14a A connected to the worldwide network of interconnected at 5.9 percent in 1994. N,noriniT computer systems. per i,000 people. All -osts with- taPita' 1995 Trese estimates notwithstandmg. the economc out a country dentification are assumed to be located impact of the nforman.on sector is difficult lo mea- TV sets per 1,000 people n the United States sure. In statistLca reporting the major industries involved n processing and distrouting informatior- Latvia teecommunicatons, broadcasting, and comcutng- Hungary rave traditonally been categorzed under ctifferent Greece Data covering newsoapers subsectors. These classificaLion differences can be Estonea - and rad os are from overcome, but a far greater proolem s that most Poland UNESCO. wmicn compiles nae ona and intemat onal statistical agencies fail to Czech Republic i _ data ma nly from officia Russian adequate y cover these subsectors. Statistica year- Federation replies by member states to books have profuse data on agricu ture, indLstry. and Lthuania - UNESCO questionnaires anc trade, but few data on services. ;et alone the infor- Argentina special Surveys. but a so mat on industry. Trinidad and from off cial reports and Tobago A further complication s that there are no agreeo 0 100 20C0 300 400 500 publications. supplemented def ni-cons or what constitutes the nformation indus- I licatin Urion data, by information fPom nationa' and international try. Shou d it include both services and eqjipmert? sources. Shou d it include industries that create ano d stribute Data 'or the other indicators are from the annual nonelectronic information-suto as oublishing and ques-.onna re sent to member countries by toe postal serv tes? lotsrnatonal Telecommunication Un-on iITU)i. To-ese Some countries nave taken steps toward mreasuring. data are reported n the WVortot Telecommun'cation the impact of information industies more effectvely., Pll _ _ f people Development Report or the Te ecommun cations Industry Canada, Statistics Canada, and Canadiar Indicalors database. The text also draws on ITL Heritage are urdertaking a major revis on o' the clas- Czech Repubiic sources. Data on Interne. hosts are from Netwo'k sifcation system for industries A new Information Slovenia W zards http://ww.nw.con-). technologies and telecommunications c assif -at on is Slovak Republic proposed that combines te ecommunications, broad- Malaysia casting, and computer services as wel as Lhe con- Hungary sumer e ectonic, telecommunications equipmrent. and Chile computer harcware industries. The reason for the revi- Greece s on s toat, under cu.rrent classifications, it is difficult Mauritus to anaJyze the Information techno og es ancl telecom- innnications industry orto urderstand it clearlv enough South Africa to develop programs and policies. Furthermore, nhe 0 10 20 30 40 50 60 current classificaton svstem does not reflect the letc- nological and regu atory changes that have taeen place Source: International Telecomrr unication union data. in information technologies and telecommurnications Worlo Development Indicators 1997 287 - 7 - : 9: :--i V n S t A : : - - - - i D F_zi-- 7 r _ : \: : - -: - - - - - : :Xt S G At:: iE t V f :: S R e . ;,, x , gn = = w: W 9 ; ; > _ _ w- S==M l_ = m= t r9A _=i_ m S___ _ __mm_W 45 . i 0 . . . . z2.S i 0 ; . 7 . 0 ) : .5 2 z 7 7 )57 g i; i; 0 :; Q 3 _ z _ T 4 i; 7 . e:e = =_, = __ 1 R--S T--B- 1-__ ____ __= ''' a __ _S_.k'.;:ff i ::: _ _ _ __ e e_ _ .E:S:\A=i.9:. \;.zE | i | R R '.: f;'.z0S;tyEdX i _ i ! = e = f !Atm0Ct0 0 . _* _ _ ; 11 i _ t!l i S 1 tf; ; yf ::: 5 _ _ _ e _ _ _ s s | _ 1|1= 1: _ : _ e \: DE . S ( . i -D, _ _ D D | | | s s | f | - o- '7f:gi-'D0t' _-0:.;- ':d ': _ 0 __S W | | _ * _ _w Si iD ,\5D, an t : :: _ _ .f, . . \ S? f . _ |. tEV .............. 0. 7 ' ........ __ _ =-A= f L-f- , 0 _s _t ; ff s{++ + = = rA=2] _- :1=::. -: -:.:=:::==::::: ):L:E:::z: n::SE:: L:L: E:: __ / _ E i. ,- Di ,4 f:S,,: .,,, f b:,,,:: __y _ t +__ z =' = _t:_t- @ _ |_ t1 e ,-=: - it ::: ! i . X , B =e:17:'? " 9 :'VI= - -f': _ ^'t t 0: ;;SS :: 2gs 0 9: w r _ s _ _ _ _ _ e3 _ The network of econornic links binding nations has become stronger in the past four decades. World trade has grown faster than GDP. Foreign investment has increased rapidly. International financial markets have expanded enormously in their scale and in the diversity of their instrturments. And new technologies have revolutionized international communications and altered long-standing patterns of production and employment. All a striking contrast to the first half of the century, when *wars, autarky, and depression impeded the growth of trade and international finance. In some ways the pasi- four decades can be viewed as a return to the pre-1913 era- when goods, labor, and capital moved around the world relatively freely. But there also are striking differences. Compared with the reign of commodities before 1913, trade now has a higher share of manufactures and services, in part a reflection of the declining price of commodities relative to rnanufactures. Other differences between the current period of financial integration and that of the late 19th century are the greater global scope and depth of integration and the speed with which the market can now react. Global economic integration-the wvidening and intensifying of links between the economies of industrial and developing countries-has accelerated rapidly. Underpinning the intensification of these links-which include trade, finance, investment, technology, and migration-are several structural factors. The progressive liberalization of trade poli- cies negotiated during consecutive rounds of trade talks-culminating in the Uruguay Round-has lowered tariffs and stimulated trade. The integration of the world economy through trade has been reinforced by increases in private capital flows, particularly in the 1990s. And technological advances in transport and communications have lowered the cost of operating globally and provided developing countries with new opportunities to benefit from the growing world economy. Despite a brief reversal in the mid-1970s and early 1980s, the global economic enNi- ronment has become increasingly favorable, with expanding opportunities for developing countries that have adopted an outward orientation. This environment has been charac- terized by buoyant growth in industrial cotntries, low world inflation and energy prices, and modest real interest rates (table 6a). The principal beneficiaries of economic integration have been a few larger and more rapidly growving developing economies, while the poorer and more slowly developing economies remain heavily dependent on official aid flows from high-income countries. These poorer economies typically are also less diversified and more dependent on exports of primary commodities to high-income economies-and vulnerable to price fluctuations in global markets. This section provides information on the links between the high-income, industrial economies of the Organization for Economic Cooperation and Development (OECD) and low- and middle-income economies-focusing on trade, private and official capital flows, and labor migration. Disparities in global integration Over the past decade the ratio of trade to GDP, a common measure of integration, has risen-strongly in such regions as East Asia and Europe and Central Asia (table 6.1). The amount of foreign direct investment flowing to developing and transition economies has increased fourfold, raising the ratio of FDI to GDP in some regions. But there are wide disparities: the ratio of trade to GDP fell in many economies, espe- ciallv in Africa, and rose only slightly in South Asia. The distribution of FDI across Wor d Deve opmenrt Ind cators 1997 289 developing economnies is also highl1v skewed: eight de elopinig The relative declin-e of primaryv exports from the developing coUlntries accounted for two-thirds of FDI don s during 1990-93. world is a striking trenid-real commod ity prices f ell by mor-e thain half'ini 1980-93 (table 6.5 . Production ar1(1 trade offprimary com- iiIoclities have 'not grown as fast as xod-ld incomile because of the Tradel has been the mnain engine driving global integration in the low elasticitn of demancd fotr miost commilodioties, espec ially f'ood, secondchalf of'the 20th centurx. Since 1950 trade has grown fastc- and( the clecliniing intensit of nieLals anf( agrictlttiral raw mateni- than oittput. anti the trade finks between high-income OECDI) meI- als in indtistrial economies. Atchough priices are expected to be bers and other cottiitries lhave str-engthenied significantlv particu- flat in the longer teirm. this tirend will be better thain the largc larl fobi mantufactured goods (tables 6.2 anid 6.3). The iimportan-ce price dec lines suffered cduring the 1l8()s and( earlc l990s-and of trade for all econoinies derives friom its impact on production xxill proxide a mor-e stable enxironment for primuary goods exporters efficiencies-through economies of sctale and scope as procitiction to puirstie long-termi ecoIIomiiic restirtictUrinig. expands beyond the size limits of' the clortestic miarket anid through The terms of trade i-einaini a major issut foor SublSalharan Afnrica. inicreases in competitiveness as expostr e to global learning spreads howerexe. erwhicih is still Ithe least diversified region in primarv exports. technolo gical inInovation. Fail re to obtain higlher- Cxport prices hamiper-s the abilin of Sub- Sprurrecl Iy tthe sticcess of the iiexly industrializing economlies Saharan countries to reduhce their debt boLrdc'iis antI to channel in Asia antI a gron%ing body of evidence soipportiiig the grossth- othier resoorces int:o prioritN sectors. enhancing cCfects of integration, moire dteveloping countries are seeking to tise trade, particolalslv mantufacnured exporLts, as a ve hi- ... : ^ :: cle for growtlh and diversification. Ilhe share of' manrifactuiredc The progressive liberalization of trade policies agreed to durilng expotrts, an important inclicator of integration, has risen most sig- consecutive rotuncls of negotiations ofithe General Agreemenit Oil nificantly in drvnamic, fast-ror iou regions. Tariffs ancl ITrade (IGATT) has culilllhatetd in the recluction ol'tar- u\klile OECD import growt)h will continuie to be healthy. dcxvel- iffs from abouit 40 percent in the inmuneliatc posts ar cia to about opineg coLtiltl-v import demand(l will he cx Cii morie buoyant-par- 6 percent today for )EI) C ountries. From the mid l 9770s onwaid, ticularlt in East and( South Vsia. as capital an d interm ecdiate goods stipport for trade libe ralizationi weaken cld as indiistrial anti dlevel- are imnpoi-teti to stippOIrt their large infriastrimctture nee(ls and their opimug coittrtries began to establish new nontariff harriers to trade, fast-growing export sectors. Intertiational trade is expected to stcth asvolutnarvexport restraints amid qciotas. But the latest romlitd (Onilltinle tO grOw rapicllv, sptired bx redlictions in trade barri- of negotiations-the Ur t rgtiav Roumnd--restiltecl in an agreement ei's agreed to in the Uiruguat Rortiid (table 6.4). It is bolstered bt the 01ECD coiiutr-ies to lower tariffs Cven further-to 3.8 per- by regional trade arrangeinerits-sumclh as the North Anierican cent on maritufactured goods bt the setond lhalf'oft the 1990s. Free Trade Agreement. the European tUnion-Mediterrarican The ftIll imnpact of tlue Uri Ugumay Roti oii the stir(ngth of' Initiative, the Asia-Pacific Economic (ooperationm Forttui, and the global tratling sxsiemi is difliCtilt to gatcgc'. Several sttidies have the SontthlcI'n (aone coitiii oin in arket, MIercoscur-aci bx tin ilatt- estiminatedl the global incomem gaiius at lip to S2(00 hillion a sear. ce al ttrade liberalization in I nany developing coitintries. Oxver the Betxseen a qutarter aIid a half' oft il'ese 4ains-swhich colile irotm iext decade world trade is projected to growit in volume hr aii the reduction LLnd bintdinug of tariffs anicd the elimtuination o)f mlon average of G.1 percent a vear-almnost tcwxice the pate ot Nxoil-i tariff harmiucs aid ivoltintarv exporit restraints-are 'xpectedl to oultplt giowstIll. go to fc'xeloping countric's. I igh milenu ile 'cononsit's redUted ctrl- Table Ba Global environment for developing economies, 1974-2006 average annual percentage change (except for LIBOR) 1974-80 1981-90 1991-93 1994 1995 1996' 1997-2006' Real GDP n G 7 cont-resb 3.0 31 'L 2.9 1.9 2.2 2.7 G- 7 ;nflatior (corSUnmcr prices, weigmed by GDP)r ' sLO 43 3.2 2.2 2.3 2 1 2.7 Wo d trade volumer 4.8 4.2 ' 9.6 8.1 e 6.4 Ncminal LIBOR (six morrt rate. $ 9.5 l. 4.6 5.1 6.1 5.6 6 1 Rea sx morth IBOR 0.2 5.2 ' 2.4 3.2 2.5 3.1 Price irndexes ($) Ga5 export in t aJue of manufactures (MUV)f 11 6 3.3 2.1 3.6 8.3 2.5 2 3 C presx 2367 -.3 1L.5 .7 8.2 .L8 s.4 Word 3ank ...nfue comrodity price irdex 15 5 4 4.8 22.2 9,5 6.0 1.3 Note: Data are es of October 1996. a. Es-ir a'es and arojea ors. b -ne G67 countries are Canada, France, Ge rary, Italy. Japan. tre Jn ted Kingdom ar d -he Urated States. c. T'ade data reaer to exports 'L-hoga 1993 and to the average of exports aid impor's from 1994 onward d. Loncor nterbank Of'er Rate. e. Noriral - BOR, adjus-ed for irf ation. f. -he GSr coun tres are France, Ge'mary. Japan. tne Utited K ngd.r, ad ithe Uaited Sta.si g. Oil aries r'r ax -.h aeragfOPEC a rude price haugl 1995 anr tthe averaag of aret, Dua, aqd West Texas Intermediate, equaiy weigr:ed. ftom 1996 onwaru Source: Wor d Bank data ard staff est mates. :.^''. c - 1981 83 to 1990 92 1981-83 to 1991 93° Rwanda 12.1 0.45 .. .. 0.5 0.04 Saud Arabia 49.5 1.31 71.5 1.40 6.2 0.62 0.8 0.01 Senega 28.8 -0.46 16.5 0.43 0.1 0.01 14.1 0.79 Sierra Leone 30.8 3.53 8.2 0.09 0.2 0.03 62.9 -3.06 Singapore 158.0 11.11 78.3 0.32 8.8 0.37 55.6 1.93 Slovak Reoublic .. .. .. .. .. Slovenia .. . .. .. Scuth Africa 28.3 0.16 56.9 1.65 0.1 -0.01 75.1 -2.79 Spa n 15.7 1 54 63.3 .l14 0.7 0.10 71 3 0.52 Sri Larka 32.0 0.33 27.8 0.07 0.2 0.00 22.1 4.27 Sudan 9.4 1.01 7.9 -0.lo 0.0 0.00 0.8 0.01 Sweden 28.0 1.07 77.3 -0.26 0.3 0.17 79.4 0.54 Switzerland 31.8 0.94 95.2 -0.29 0.2 0.08 91.9 0.17 Syrian Arab Republic 21.5 -1.05 19.0 0.44 0.0 0.00 8.9 2.36 Tajikistan .. .. .. Tanzania 26.2 -0.65 9.0 0.51 0.0 0.01 13.5 0.22 Thailand 24.6 3.25 51.9 0.97 0.3 0.05 27.7 3.83 Togo 42.3 1.28 .. .. 0.4 -0.04 10.9 -0.15 Trinidad and Tobago 22.2 3.12 50.9 1.97 2.5 0.01 8.0 2.05 Tunis a 42.3 0.27 45.1 -0.35 1.5 -0.08 36.9 3.34 Turkey 10.0 1.65 27.8 1.60 0.1 0.03 35.7 3.27 Turkmenistan .. .. .. .. Uganda 9.4 0.24 4.6 0.55 0.0 0.00 0.3 0.00 Ukraine .. .. .. .. United Arab E nirates 41.0 1.97 60.7 0.13 0.0 0.00 3.7 0.00 Lnited Kingdom 23.5 1.02 88.7 0.26 1.1 0.05 70.0 1.17 Lnited States 8.1 0.78 95.9 0.63 0.5 0.02 69.1 1.04 Uruguay 19.0 1.06 31.2 0.48 0.9 0.12 33.5 0.67 Uzbekistan .. .. .. .. Venezuela 22.4 0.26 44.0 -0.76 0.2 0.04 2.1 0.82 Vietnam .. .. .. .. West Bank and Gaza .. .. .. .. Yemen, Rep. .. .. . .. 0.3 -0.01 0.0 0.00 Yugoslavia, Fed. Rep. .. .. .. .. Zaire 14.9 1.82 6.0 0.15 0.0 0.00 5.9 0.04 Zamb a 46.1 2.29 10.0 0.31 0.2 0.01 0.7 0.00 Zimbabwe 29.3 0.31 20.5 0.83 0.0 0.01 34.4 -0.29 S L i : 6 j z j ' jil S j; j, u S , SI S J j J j", j. W ; je x ; Low income 25 8 m -0.21 m 16.2 m -0.14 m -0.14 m 0.00 m 10.0 m 0.10 m Excl. China & Ind a 26.2 m 0.27 m 16.2 m -0.14 m p0.14 m 0.00 m 7.1 m 0.10 m Middle income 28.1 m 0.39 m 35 1 m 0.05 m -0.05 m 0.01 m 21.6 m 0.59 m Lower middle income 27.0 m 0.15 m 27.7 m 0.04 m 0.04 m 0.01 m 20.4 m 0.59 m Upper middle income 30.1 m 1.06 r 41.5 m 0.18 m -0.18 m 0.01 m 22.4 m 0.59 m Low & middle income 26.6 m 0.03 m 26.9 m 0.14 m 0.14 m 0.00 m 12.3 m 0.30 n East Asia & Pacific 24.6 m 1.40 m 67.9 m -0.22 m 0.22 m 0.02 m 35.7 m 0 63 rm Europe and Central Asia 34.8 m 0.63 m 30.8 m 0.57 m 0.57 m 0.05 m 70.5 m 0.24 m Latin America & Carib. 22.4 m 0.45 m 26.7 m 0.21 m 0.21 m 0.01 m 15.8 m 0.32 m Middle East & N. Africa 34.3 m -0.12 m 36.5 m 0.45 m 0.45 m 0.00 m 9.2 m 0.25 m South Asia 14.0 m 0.05 m 24.1 m 0.08 m 0.08 m 0.00 m 53.5 m 2.56 m Sub-Saharan Africa 28.8 m 0.35 m 17.1 m 0.14 m 0.14 m 0.00 m 5.9 m 0.04 m High income 29.5 m 1.37 m 75.5 m 0.04 m 0.04 m 0.02 m 72.6 m 0.54 in a. Computed as the doiference betweer tre encpo nts of the perod shown, averaged over lo years. b. Includes L-xembourg. '2-. %oi c 9^vE e c-e c c E r 'cc-,7 6.1 Growth and integration = i = C_ The pace of global economic integration con- tinues to accelerate dramatically. Between I rdicators of the speed of integration were developed o Real trade Is the sum of exports and mports of 1985 and 1994 the ratio of world trade to for the Wor d 3ank's Globae EcDnomic Prosoects and goods and serv ces meas-rec In constan arices. The GDP rose three times faster than during the the Deve oping Counrnes 1996. The concert under- cata here c ffer from trose n the World Bank s Giobal previous decade. During the same 10-year lying tne md cators is hat thc change n variables EconomicPospecLs1996. wneretheywere acus.ed period foreign direct investment (FDI) doubled associazed wlth ntegrat on gives an inoica ion of how for popuiat or size. o nreitutional Investor Leadit as a share of global GDP. Developing countries rapid y an e-onomy s integra. rig wv th tle global econ- ratinag ranks the charces of a country's defau t from have participated extensively in the accelera- omy. Tie changes in the integramon variables are zero to 100. B ri 100 representingthe east chance tion of global integration. Over the past decade comouLred between tohree-year averages to reduce tne of defau t. For fuether discuss on of these ratings see their ratio of trade to GDP has increased, and effect of a s.ngle year. But the rese ts must be n-er- Shapi'o 1996). o Bet foralgr direct investment is theirshare of global FDI has risen to more than ore:ed with ca-e. For exarrp e, a cec ine n irvestmenttocctuirea astngmanagementinterest one-third. Switzer ard's ct'edit rat ng. asleacy one o7 the h.gh- (at least 10 percent of voting stoct) in an en:erpr se A closer look, however, reveals sharp differ- Eas: n the sorld, probEblv shnic not be given the operating in a ceuntry other tian that of the inveslor. ences among countries. Although developing same weight as a sriii a, decline for a cevelop ng It Includes ep jty capital. reirvestment o' earrings, countries as a group kept pace with the growth country. (Se ected country ris; indicators are shown othe long-term capital, and short-ten1 cap ta . The of world trade, the ratio of trade to GDP fell in in tah e 5.9.) nd cateo is computec as a rae a to GDP converted To 44 of 93 developing countries over the past 10 neernational oo lars using purchasing powrer peri- years. There were similar disparities in the dis- es iPPPs). o Fanucfactuved exports are cc -r-od- tribution of FDI: two-thirds of FDI to developing ties n the Standard Interrabtona Trade C assification countries went to just eight countries, and half (SITC), revis on 1, sections 5-9 nchemical and related the countries received little or none. These dis- prodicts, basic manufactures, ranu'actu ec arJ- parities are likely to continue. c es. mach nery and transport equ pment. and o:ner nranufactured articles and gooas not elsewhere c as- sif eat, exc ud ne d v sian 6B Inorferrous metals). i These data first appeared in the Wocrld Bank's Globai Integration matters because there is an asso- Economic Pros,oects and ciation between integration and growth. Fast the Developing Coasmntes growth tends to promote a more open economy, 1996. Data on real trade, and policies that promote an open economy f net fo'e gn cirec nves'- also promote faster growth. Thus lagging inte- " . ment, total exports, and gration is a sign of underlying policy deficiencies. gross donrestic procuct In addition, integration can leadto highergrowth come from .he Word through better resource allocation, greater com- Bank's national accounts and ba ance of oayments petition, transfer of technology, and access to f les. Data on -ranufact_rec exports come f'om the foreign savings. Many of the countries lagging Ur[ted Nationis CONETRADE catabase. Cecit ratings in global integration are amongtheworld's poor- prepared by,nste'uconal Investorappear nthe monthly est. pjbl cation Inst',tiornai !vescor. For many developing countries successful integration depends on fundamental economic reform, requiring difficult policy decisions that often lead to real short-term dislocation. These costs must be acknowledged from the outset, and the effects carefully taken into account in the design of reform programs. But the costs are manageable. In fact, openness to exter- nal trade and investment is often the neces- sary first step to solid, sustainable economic development. World Deve opment irdicators 1997 29E U;\ -- 6.2 Direction of OECD trade High-income European Union United States Japan OECD countries XI-- Hl,;-S :i ;i t1985 1995 1985 995 1985 1995 1985 1995 $ billions Strengthening trade links Low- and middle International trade links between OECD and devel- income economies 245.8 680.5 119.0 316.0 58.8 176.6 50.2 145.3 opingcountries have strengthened considerably East Asia & Pacific 57.5 237.1 13.4 56.6 12.6 52.6 25.7 106.4 during the past tnree decades. The share of Europe & Central Assa 29.3 146.9 23.5 128.4 2.8 10.0 1.5 3.7 exports from high-income OECD countries going Latin America & Carib. 55.0 162.4 14.9 45.3 28.8 91.4 7.6 18.4 Middle East & N. Africa 62.- 68.7 41.0 45.9 8.2 12.5 9.3 6.1 South Asa 14.0 25.1 6.7 13.0 2.7 4.7 3.1 4.7 hastheshareofdevelopingcountryexportsgoing Sub-Saharan Africa 27.9 40.3 19.5 26.8 3.7 5.3 3.0 6.0 to OECD countries. The increase in trade is most High- ncome economies 996.7 2,416.7 585.9 1,434.3 146.4 364.9 125.7 297.3 pronounced in the rapid y integrating regions of Non-OECD 74.5 298.3 21.6 80.4 20.4 78.7 26.1 115.3 East Asia and the Pacific and Latin America. The OECD 922.2 2,118.4 564.3 1,353.9 126.0 286.2 99.6 182.0 freeing of trade regimes in the transition World 1,242.6 3,097.2 704.9 1,750.2 205.2 541.4 175.9 442.6 economies of Europe and Central Asia has begun to have an impact on their trade, especially with the European Union. South Asia, starting from °/ of total exports a much smaller base, has made large relative Low- and miodle- income economies 19.8 22.0 16.9 18.1 28.6 32.6 28.5 32.8 gains. But the Middle East and North Africa and East Asia & Pacific 4.6 7.7 1.9 3.2 6.1 9.7 14.6 24.0 Sub-Saharan Africa continueto lose share intrade Europe & Centra As a 2.4 4.7 3.3 7.3 1.4 1.9 0.8 0.8 with the OECD. Latin America & Carib. 4.4 5.2 2.1 2.6 14.0 16.9 4.3 4.2 Particu ary importantfordeve opingcountries, Middle East & N. Africa 5.0 2.2 5.8 2.6 4.0 2.3 5.3 1.4 because of employment-creating effects, has South Asia 1.1 0.8 0.9 0.7 1.3 0.9 1.8 1.1 been the strong growth of the OECD's market Sub-Saharan Afr ca 2.2 1.3 2.8 1.5 1.8 1.0 1.7 1.3 for their manufac-ured exports. In 1964 only High-income economies 80.2 78.0 83.1 81.9 71.3 67.4 71.5 67.2 Non-OECD 6.0 9.6 3.1 4.6 9.9 14.5 14.8 26.1 about 7 percent of OECD imports of manufac- OECD 74.2 68.4 80.1 77.4 61.4 52.9 56.6 41.1 tures originated in non-OECD countries (based World 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 on 1995 OECD menbership); by 1995this share had risen to about 25 percent. Just as there are differences in the export performance of developing countries, there are those in the import performance of OECD coun- tries. Links between North America and non- OECD countries have expanded at an above-average pace-U.S. and Canadian man- ufactured imports originating in non-OECD coun- tries grew fourfo d between L964 and 1995, from 9 percent to 37 percent. Partly as a result of European integration initiatives, trade in manu- factures between the European Union (EU) and deve oping countries grew at a below-average pace. n 1995 on y about 18 percent of EU imports of manufactures originated in non-OECD countries. Labor-intensive products have been among the most clynamic categories of manu- factured trade for developing countries, which roughly doubled their market share in high-income economies for these products during the past two decades. 6.2 W Fligh-income European Union United States Japan OECD countries " " A ,j tS1985 1995 1985 1995 1985 1995 1985 1995 About the data $ billions Low- and middle- Data on merchandise trade are compiled from cus- income economies 302.9 744.7 133.9 299.1 103.3 277.4 55.5 132.6 toms reports bythe United Nations Statistica Office East Asia & Pacific 78.5 306.3 14.0 69.7 33.3 124.4 27.2 92.2 in the Commnodity Trade ICOMTRADE) dataoase. Europe & Central Asia 23.2 125.8 19.0 107.2 3.2 10.5 0.4 5.7 Latin America & Carib. 84.0 163.9 26.2 37.3 47.4 106.5 6.1 11.3 COMTRADE contains data on the imports and exports Middle East & N. Africa 67.3 75.3 43.8 46.4 6.1 12.3 17.6 15.7 of more than 150 countres or econom c areas clas South Asia 9.5 28.8 3.9 12.9 3.4 10.2 1.7 3.9 sified by product and by country of origin and desti- Sub-Saharan Africa 40.4 44.5 27.0 25.5 9.9 13.4 2.5 3.7 nation. For many countries records tabulated High-income economies 1,047.2 2,301.3 588.6 1,322.4 255.4 491.3 72.0 200.3 according to revision 1 of the Standard Internationa Non-OECD 100.2 227.4 22.3 55.5 46.9 94.6 24.3 59.1 Trade Classification (SITC) system extend back to OECD 947.0 2,073.9 566.3 1,267.0 208.5 396.7 47.6 141.1 1962. In the mid-1970s COMTRADE also began World 1,350.1 3,046.0 722.4 1,621.5 358.7 768.7 127.5 332.8 reporting more detailed SITC revision 2 data, and in the ate L980s it began compiling records in the still % of total imports more detailed revision 3 system. At its lowest leve Low- and middle- the revision 3 system differentiates among more than income economies 2:2.4 24.4 18.5 18.4 28.8 36.1 43.5 39.8 2.000 items. East Asia & Pacific 5.8 10.1 1.9 4.3 9.3 16.2 21.3 27.7 Varous statistical problems may affect the qua - Europe & Central Asia 1.7 4.1 2.6 6.6 0.9 1.4 0.3 1.7 ty of COMTRADE statistics. Because COMTRADE Latin America & Carib. i3.2 5.4 3.6 2.3 13.2 13.9 4.8 3.4 expresses all trade values in U.S. dollars, exchange Middle East & N. Africa 5.0 2.5 6.1 2.9 1.7 1.6 13.8 4.7 South Asia D.7 0.9 0.5 0.8 0.9 1L.3 1L.4 1L.2 rates are used to convert data originally expressed Sub-Saharan Africa 3.0 1.5 3.7 1.6 2.8 1.7 2.0 1.1 in local currencies. In some countries, particularly High-income economies 77.6 75.6 81.5 81.6 71.2 63.9 56.4 60.2 those in which there are black market rates that differ Non-OECD 7.4 7.5 3.1 3.4 13.1 12.3 19.1 17.8 from official rates, the selection of an inappropriate OECD 70.1 68.1 78.4 78.1 58.1 51.6 37.3 42.4 conversion factor may produce important statistical World 10D.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 biases. At more detailed levels of product c assifi- cation countr es may Inadvertently c assify goods in different SITC groups, resulting in discrepancies in , . __ matched partner country statistics. An even more serious problem may be traders' incentives to fa sify * Exports are all merchandise exports by high-income COMTRADE statistics are customs nvoices (to reduce tariff charges orto effect OECD countriesto low- and m ddle-income economies ",,.> y ,,,¢ .. . ,E<,4.n avai able in machine-read- capital flight). Smuggling, which is not reflected in as recorded in the Un ted Nations COMTRADE data- t able form from the Un ted COMTRADE, also may affect data quality, particu- base. O Imports are all merchandise imports by high- Nations Statistical Office. larly fortrade between countries with shared borders. income OECD countries from low- and middle-incomre Athough not as comprehen- And information on trade is missing or unre iable for economies as recorded in the United Nations save as the underlying many African countries that have not yet developed COMTRADE database. S High-income OECD coun- COMTRADE records, deta,leo the capacity to compile accurate statistics. tries in 1995 were Australia, Austria, Belgium. statistics on international Canada, Denmark, Finland, France, Germany, Iceland, trace are pub' shed annually Ireland. Italy, Japan, Luxembourg. the Netherlands, in the LUnited Nations Conference on Trade ano New Zealand. Norway, Portugal, Spain, Sweden, Development's Handbook of Intemational Trade and Switzer and, the United Kingdom, and the United Development Statistics and the united Nations States. a High-income, non-OECD economies in Internahonal Trade Statistics Yearbook. 1995 included Hong Kong, Israel, the Repub ic of Korea, Kuwait, Singapore, UnitEd Arab Emirates, and Taiwan, China. 0 European Union comprises Austria. Belgium, Denmark, Finiand, France, Germany, Greece. Italy, Ireland, Luxembourg, the Netherlands. Portugal, Spain, Sweden, and the United Kingdom. Word Development Indicators 1997 297 X OECD trade with low- and middle-income 6.3 economies High-income European Union United States Japan OECD countries -Qu 2c>ff35G?e y few ad valorem equivalents, so caution should be used in interpreting the statistics. In addit on, for some countries average tariffs and reductions may oe based on a few product categor es with a relative y sma I weight in trade. The first two columns of the table show tne per- centage of mports for which tariffs were bound unoer the GATT before and after the Lruguay Rouined. The next three columns separate mports into tInee cat- egories accordingto whetherthe onst-Uruguay Round bhndings were above, at. or below tine rates applied before the Round. The share of impo'ts for which the bound rate is below the ore-Uruguay Round applied rate is the percentage of imports fcr which there were tariff cuts. Word Development ndicators 1997 301 U- 6.5 Commodity prices 1980 195 1991 1992 1993 1994 1995 Commodity price index Falling commodity prices (1990 = 100) Commodity prices were subject to severe down- Nonfuel commodities 174 133 93 86 86 101 102 ward pressure during 1980-93 (figure 6.5a). Agricu ture 191 145 96 88 93 112 110 The terms of trace for many developing coun- Beverages 253 239 91 73 79 135 127 tries deciined as pr mary commodity prices fell Fertilizers 179 130 100 90 79 85 87 relative to the pd ces of manufactures (see Food 191 124 97 94 93 97 98 tne notes to table 4.7). More than three- Metals and minerals 132 102 87 81 70 77 85 Raw mater als 145 103 97 92 104 114 113 quarters of the decine in nonfuel commodity Petroleum 224 173 83 78 69 63 63 prices was due to the fall in the prices of agri- Steel products' 110 88 96 83 86 84 89 cultura commodities. Part of the explanation MUV G-5 index 72 69 102 107 106 110 119 for falling commodity prices lies with sluggish demand in industrial countries, especia v during the 1980s. But even more important Commodity prices was the sharp increase in the supp y of com- (1990 $) modities on world markets, which grew four Agricultural raw materials times faster than in the 1970s. Most of this Cotton (cents/kg) 284.3 192.1 164.1 119.9 120.4 160.0 178.2 Logs, Cameroon ($/cu. cr7 349.4 253.5 309.2 310.8 291.9 299.7 284.3 supply icrease came from upper-mddle- Logs, Malaysian ($/cu. m) 271.5 177.5 187.4 196.5 366.7 279,1 214.1 income ace hign-income countries. Metals Rubber (cents/kg) 197.9 110.6 80.8 80.8 78.2 102.2 132.3 prices, for example, were depressed by arge Sawnwood. Malaysian exports from countries of the former Soviet ($/cu. m) 550.3 447.7 540.6 569.6 713.3 745.0 619.7 Union and Eastemn Europe following the col- Tobacco r$/mt) 3,160.9 3,807.3 3,424.7 3,226.6 2,535.6 2,399.0 2,210.6 lapse of the Soviet bloc. There is stid debate about whetcer the rela- Beverages (cents/kg) Beverages (cents/kg) ~~~~~~~~~~~~~tive dec ine in cornmodity prices is permanent Cocoa 361.6 328.6 116.9 103.2 105.1 126.7 120.0 Coffee, robustas 450.4 386.1 104.9 88.2 108.9 237.8 232.1 or transitory-or mnere y the result of mismea- Coffee, other nilds 481.4 471.0 183.3 132.4 146.8 300.2 279.1 surKng quality shifts in manufactured goods. Tea, auct ons, avg. 250.3 263.6 167.9 159.8 157.8 143.1 128.1 Still, countries that depend on primary com- Tea, London, al 309.9 289.1 180.3 187.6 175.3 166.2 137.6 modityexports suffered bad yduringthe 1980s. The declining terms of trade reduced the Energy resources availalble for investment, s owing Coal, Australian ($/mtl 55.9 49.2 38.8 36.2 29.5 29.3 33.0 Coal, U.S. ($/mt) .. 68.0 40.6 38.1 35.7 33.1 32.8 \atural gas. Europe ($/mmbtu) . 3.0 2.4 2.5 2.2 2.3 \atural gas. U.S. ($/cmrbtu) .. .. 1.5 1.7 2.0 1.7 1.4 Petro eum ($/bb) 51.2 39.6 19.0 17.8 15.8 14.4 14.4 irdex (1990 = 100) Fertilizers ($/mt) Phosphate rock 64.9 49.4 41.6 39.2 31.0 29.9 29.3 30C Ag-culture TSP 250.3 176.9 130.3 113.3 105.3 119.9 125.3 2 Nonftel commodites 250 / Food / S% Petroleum Fats asd oils ($/mt) 200 Coconut oil 935.8 860.1 423.7 541.8 423.6 551.3 561.1 a 150 Groundnut oi 1,193.1 1,319.2 875.5 572.1 695.3 928.1 830.0 Pan oil 810.4 730.3 331.7 369.1 355.4 479.5 526.0 1' Soybeans 411.4 326.5 234.4 220.9 240.0 228.5 216.9 . Soybean meae 363.9 228.9 192.9 191.7 195.8 174.6 165.0 50 Metals anc minerals Soybean oi 830.0 833.8 444.4 402.4 451.9 558.6 523.5 Gra ns ($/mrt; 0 Grain sorghum 179.0 150.1 102.8 96.4 93.2 94.3 99.7 1970 1975 1980 1985 1990 1995 Maize 174.0 163.6 105.1 97.8 96.0 97.6 103.4 Source: weored Baek. OCommoity Markets acO the Rice 570.5 287.1 287.0 251.6 221.5 242.8 268.7 De,eogn Wcencroeen various ssaes. Wheat 239.9 198.0 125.9 141.8 131.9 135.9 148.2 6.5 0 1980 1985 1991 1992 1993 1994 1995 growth in real output and per capita income and Other food in many cases complicating already severe Bananas ($/mt) 526.4 554.4 547.5 443.8 416.8 398.8 372.8 adjustment problems. But some developing Beef (cents/kg) :383.3 314.0 260.6 230.3 246.3 211.5 159.7 countries have substantially increased their pro- Oranges ($/mt) !542.5 580.8 509.8 458.9 406.9 373.1 445.1 duction and exports of manufactures in the past Sugar, EU domestic 30 years, and for these countries the terms of (cents/kg) 48.7 35.0 61.2 62.8 61.9 62.2 68.8 Sugar, U.S. domestic trade have deteriorated less rapidly. Other coun- (cents/kg) 66.2 44.9 47.5 47.0 47.6 48.6 50.8 tries have increased their exports of high-value Sugar, world (cents/kg) 87.7 13.1 19.3 18.7 20.7 24.2 24.5 commodities-cut flowers, shrimp, fruits and vegetables-commodities that have not expe- Metals and minerals rienced price declines relative to manufactures Aluminum ($/mt) 2,022.2 1,517.5 1,274.2 1,176.6 1,071.5 1,340.1 1,512.3 over the past 15 years. Copper ($/nit 3,030.6 2,066.2 2.288.5 2,139.9 1,799.7 2,093.8 2,458.6 Iron ore (cents/DMTU) 39.0 38.7 32.5 29.6 26.5 23.1 22.6 Lead (cents/kg) 125.8 57.0 54.6 50.8 38.2 49.7 52.8 Nickel ($/mt) 9,053.8 7,141.5 7,980.0 6,567.8 4,979.7 5,753.0 6,891.2 Tin (cents/kg) 2,329.8 1.682.1 547.5 572.3 485.5 495.8 520.4 Data on primary commodity prices are collected from Zinc (cents/kg) 105.7 114.1 109.3 116.3 90.5 90.5 86.4 a variety of sources, including international stuay groups. trade journals, newspaper and wire service a. Series not included in the norfuel index. reports, government market surveys, and ccmmodity exchange spot and near-term forward prices. The most E = l _ reliable and frequently updated price reports are used. When export prices are unavailable, imporc prices * Nonfuel commodities price irdex covers the 31 non- Commodity price data are are used. Annual price series are generally simple aver- fuel commodities that make ur) the agriculture, fertil- compiled bythe Commodity ages based on higher-frequency data. The constant izer, and metals and minerals indexes; agriculture, in Policy and Analys s Unit of price series in the table are deflated using the MUV addition to food, beverages, anJ agricultural raw mate- the World Bank's Inter- G-5 index (see below). rials, includes sugar, bananas, Deef. and oranges; bev- national Economics Depart- The commodity price indexes are calculated as eragesincludecocoa.coffee,ardtea:fertilizersinclude m-. went. More information Laspeyres index numbers in which the fixed weights phosphate rock and triple superphosphate (TSP): food .3 can be obtained from the are the 1987-89 export values for low- and middle- includes rice. wheat, maize. sorghum, soybeans. soy- Mr' unit's quarterly publcation income economies. rebased to 1990. Each index rep- bean oil, soybean meal, palm oil, coconut oi, and A C Commodity Markets and resents a fixed basket of commodity exports. The groundnut oil; metals and minerals include aluminum, the Developing Countnes. The MUV index is con- nonfuel commodity price indexcontains 37 price series copper, iron ore, ead, nickel, tin, and zinc; agricul- structed by the International Economic Analysis and for31 nonfuel commodities. Indexes are compiled sep- tural raw materials include timber (logs and sawn- Prospects Division of the International Economics arately for petro eum ard steel products, snhich are wood), cotton, natural rLubber, and tobacco. Department. Monthly updates of commodty prices not included in the nonfuel commodity price index. * Petroleum price index refers to the average spot are available on the Internet at http://www. The manufactures unit value (MUV) index is a coin- price of Brent, Dubai, and West Texas Intermediate worldbank.org /html/ieccp/.eccp.html. posite index of prices for manufactured exports from crude oil. equally weighted. Steel products price index the five major (G-5) industrial countries (France, is the composite pnce index For eight selected steel Germany, Japan, the United Kingdom, and the United products based on quotations f.o.b. (free on board) States) to low- and middle-income economies, valued Japan excluding shipments to China and the United in U.S. dollars. The index covers products in Standard States, weighted by product shares of apparent com- International Trade Classification (SITC) groups 5-8. oinec consumption (volume ot deliveries) at Germany, To constructthe MUV G-5 index, unitvalue indexes for Japan, and the Unted StateE. MUV G-5 index is the each country are combined using weights cletermined manufactures unit value index for the G-5 country by the export share of each country. exports to developingcountries.. e Commodityprices- for definit ons and sources see the World Bank's Commodity Markets and the Developing Countries or its World Wide Web sits on commodities at http://www.worldbank.org/titml/ieccp/ieccp.html. World Development Indicators 1997 303 Net financial flows from Development 6.6 Assistance Committee countries Official development assistance Other Private flows Total net official flows flows Cor.ribitions Fore gn 3ilatera Multilateral Pr vate 7r,, -,( i.'< :9 itt;, g :040 Bilateral Bilateral to rultilatera darec. sortfolit tootfol.o export Net grants a- :, l c>,] 40-S 0 0 0 Total granta oano ettt n TCal irvestr et investmer- a vest-nent credits cy N0Os S millions, 1994 Australia 1.091 824 0 267 170 800 1.283 -484 0 0 75 2,136 Austria 655 354 '182 120 65 273 66 C 0 206 36 1.029 Belgium 726 431 4 291 334 1,117 -204 1,329 0 -76 52 2,230 Canada 2,250 1,431 9 827 740 2,373 2,720 -137 0 -209 273 5,637 Denmark 1.446 881 -78 643 -74 92 -4 C 0 -46 39 1,319 Fin and 290 213 0 77 67 192 49 24 0 119 3 552 FPance 8,466 5,991 620 L,8D5 .'34 3,837 1,677 1,231 63 712 280 12,717 Germany 6,818 3,549 595 2,674 3,40 12,602 2.944 6,50C 182 2,977 981 23,941 Ire and 109 56 0 53 C 37 0 C 0 37 52 198 Italy 2,705 665 1,169 870 690 31 143 279 0 -905 57 3.42 1 Japar 13,239 5,299 4.259 3,681 3,229 11.807 7,368 5,644 2,570 [,675 213 28.487 Luxembourg 60 40 0 19 0 0 0 0 0 0 5 64 Netherlands 2,517 1,232 232 816 49 1.823 1.872 386 -340 93 266 4.654 New Zealand 110 85 0 25 0 0 0 0 0 0 16 126 Norway 1,137 822 6 309 -1 217 62 0 0 155 127 1,479 Portuga 308 147 68 93 428 -531 -32 0 0 -499 0 205 Spain 1,305 257 597 450 214 2,315 2.315 0 0 0 127 3,532 Sweden 1,819 1,372 1 446 0 420 6 0 -1 497 130 2,369 Switzerland 982 729 -4 258 0 -1,006 538 0 0 -1,012 167 143 United Kingdom 3.197 1.809 -46 1.435 34 8.199 6,258 1,900 0 -156 535 11.965 Un ted States 9,927 8,301 -1,017 2,643 867 46,330 21.407 19.838 606 4.479 2,614 59.738 Total 59,156 35,190 6,115 17,851 10,057 90,682 48,457 36,507 -2,486 7,859 6,046 165,941 Official aid Other Private flows Total net official flows flows Cont a.b.t ons Fcrergn B atera Mu -ilatera PFVra-e C~, ,n.qicac a -S 6.12 World Bank IMF African Developrrment Asian Development Inter-American Others Bank Bank Development Bank | Nonconces- Conces- Nonconces- Connes- Nonconces- Conces- Nonconces- Conces- Nonconces- Conces- $ millions, 1995 IBRD IDA sional sional sional sional sional sional sional sional sional sional Hungary -63 -793 -342 76 India -354 503 -1,719 0 233 17 -9 -11 Indonesia 90 -20 0 212 189 3 13 Iran, Islamic Rep. 79 0 -11 0 Iraq 0 Ireland 0 Israel -99.6 Italy 0 Jamaica -24 -85 13 -5 -2 10 Japan 0 Jordan 81 -2 107 32 40 Kazakstan 107 141 24 40 0 0 Kenya -100 150 0 -39 7 12 -3 4 Korea, Dem. Rep. Korea, Rep. -317 -3 0 Kuwait 0 Kyrgyz Republic 0 81 0 46 0 34 -21 0 Lao PDR 27 0 16 0 56 0 5 Latvia 9 -3 7 5 Lebanon 47 0 5 18 Lesotho 11 7 0 -3 -2 7 0 3 Libya 0 Lithuania 12 63 29 0 Macedonia, FYR 1 42 37 0 16 0 Madagascar -4 69 -1 -14 0 -1 -2 -2 Malawi -12 66 0 2 -5 17 -1 -1 Malaysia -106 0 -15 0 -2 -1 Mali 80 -1 39 -1 46 -1 -3 Mauritania -2 29 0 13 1 5 -4 24 Mauritius -13 -1 0 -2 1 0 -1 Mexico 321 12,144 642 -8 0 0 Moldova 50 64 -30 26 Mongolia 8 -9 0 0 50 0 0 Morocco 78 -1 -100 -4 144 -13 118 Mozambique 160 0 -14 -3 42 0 7 Myanmar -9 0 0 -1 -10 0 -4 Namibia 0 0 0 0 Nepal 74 0 -8 0 46 0 4 Netherlands 0 New Zealand 0 Nicaragua -15 17 -13 0 24 71 15 3 Niger 21 0 -10 -1 -3 0 2 Nigeria -202 85 0 0 68 12 0 0 Norway 0 Oman -9 0 -5 27 Pakistan 52 218 109 -82 -6 321 -9 17 Panama -40 -26 53 -4 0 0 Papua New Guinea 7 -2 34 -7 11 0 -1 Paraguay -15 -1 0 42 19 -2 10 Peru 116 0 191 -8 107 23 Philippines -21 8 -363 -26 7 0 -2 Poland 191 -1,394 -139 0 Portugal -18 0 Puerto Rico Romania 128 -316 185 0 Russian Federation 824 5,453 -244 -151 World Development Indicators 1-997 323 6.12 World Bank IMF African Development Asian Development Inter-American Others Bank Bank Development Bank Nonconces- Ccnces Nocoances- Conces Nonconces- Conces- Nonconces- Conces- Nonconces- Conces- $ millions, 1995 BRD DA sional sioral sional S ona siena swonal sioral sional sionai siona Rwanda 29 14 0 0 14 0 0 Saudi Arabia 0 Senegal -12 101 -41 42 -7 2 -7 -14 Sierra Leone -1 42 0 16 0 28 0 17 Singapore 0 Slovak Republic 8 -201 66 80 Slovenia 14 -3 -4 1 South Africa O 0 Spain 0 Sri Lanka -7 98 0 -34 0 79 0 0 Sudan 0 -35 -5 18 18 0 0 Sweden 0 Switzerland 0 Syr an Arab Reoub c -13 0 -9 103 Taikistan 0 0 0 Tanzania -34 148 0 -19 -13 47 1 1 Thai and -56 -2 0 14 -1 22 20 logo 17 -3 25 0 5 0 -3 Trinidad and Tobago 9 -44 92 0 -4 16 TUnisia -65 -2 -15 132 0 7 32 Turkey 460 -6 341 -225 -66 Turkmenistan 1 0 0 0 Uganda -12 152 0 27 -4 29 -6 2 dkra ne 401 1.196 -3 0 Unted Arab Emirates 0 United Kingdom 0 United States 0 Uruguay -46 -10 12 14 6 0 Uzbekistar 162 161 84 0 Venezuela -69 -462 143 2 -11 0 Vietnam 46 0 92 0 46 0 -1 West Bank and Gaza Yemen, Reo. 34 0 0 16 5 Yugoslavia, Fed. Rep. Zaire 0 -1 0 0 0 0 0 Zambia -50 207 -826 1,254 10 13 -31 2 Z mbabwe -16 15 29 51 3 1 6 43 Low income -21 t 4,719 t -2,423 t 1,604 t 108 t 481 t 723 t 937 t 30 t 157 t -41 t 148 t ExcL China & andia -774t 3,418t -704 t 1,604 t 105 t 487 t -6 t 905 t 30. 157 t -28 t 153t Middle atome 1,455 t 213 t 17,604 t -4 t 240 t 249 t 201 t 263 t 1,277 t 91 t -712 t 571 t Lower midd e income 1,748 t 215 t 4,683 t -4 t 121 t 172 t 216 t 263 t 778 t 98 t -263 t 422 t Upper middle income -293 t -2t 12.921 t t 119 t 77 t -15 t O t 499 t -7 t -449 t 149 t Low & midd.e income 1.434 t 4,932 t 15,181 t 1,600 t 348 t 730 t 924 t 1,200 t 1.307 t 248 t -752 t 719 t East Asia & Pacific 1,016 t 884 t -338 t 150 t O t O t 672 t 411 t t O t 13 t 32 t Europe & Central Asia 1,519 t 364 t 5,047 t 57 t O t O t 24t 74t O t Ot -717 t 7 t Latin America & Carib. -342 t 257 t 12,844 t 34 a 0Ot 0t 0 t 1,307 t 248t 79 t 61 t Middle East & N. Africa 337 t 98 t 199 t O t 128 i50 t O 0 O t O t -21 470 t South Asia -314 t 1,054 t -1SO t -164: 0 t 0 t 228 t 715 t= Ct O t -18t 12 t Sub-Saharan Africa -782 t 2.275 -962 t 1,543 t 218 t 580 t 0 t Ot O t 0 t -88 t 137 t High income__ -335 t -3 t -100 t O t a. .ncludes E trea. S2£ V,'cld Deve opme lrdica:c-s __S97 6.12 S Figure 6.12a Net IBRD and IDA M==. 1970-95 The table shows concessional and nonconcessional * World Bank consists of the IBRD and IDA. 0 IMF billions of U.S. dollars lending by the major multilateral financial institu- nonconcessional lending is the credit provided by the 6 tions-the World Bank, the International Monetary IMF to its members, principally to meet their balance 5 IBRD s"/\ j Fund (IMF), and the regional ievelopment banks- of payments needs. 0 IMF concessional assistance 4 lr , for the calendar year 1995. Ulnlike the data in the is provided through the Enhanced Structural Adjustment ; > . preceding tables, the data here come from the World Facility. * African Development Bank, based in Abidjan, 3 t }7IDA Bank's Debtor Reporting Systsm (DRS) and, except Cote d'lvoire, lends to all of Africa, including North 2 *' * for the data for the World Bank, the IMF, the Asian Africa.o Asian Development Bank, based in Manila, 1 ;5/55 ~ 9 9 Development Bank, and the African Development Philippines, serves countries in South Asia and East | O > Bank, are based on debtor reports. Asia and the Pacific. 0 Inter-American Development 1970 1975 1980 1985 1990 1995 The multilateral development banks fund their non- Bank, based in Washington, D.C., is the principal devel- concessional lending operations primarily by selling opment bank of the Americas. * Others is a residual ISource: World east data. low-interest, highly rated bonds (the World flank, for category in the World Bank's Debtor Reporting System. example, has a MA rating) backed by prudent lend- It includes such institutions as the Caribbean ing and financial policies antI the strong financial Development Bank, European Investment Bank, and backing of their members. These funds are then on- European Development Fund. * Concessional includes lent at slightly higher interest rates, and with rela- all grants and loans with a grant element of at least tively long maturities (15-20 years), to developing 25 percent according to DAC criteria. countries. Lending terms vary with market conditions * Nonconcessional covers all other disbursements. and the policies of the banks. Concessional lending by the World Bank Group is p=_ carried out primarily through the International DevelopmentAssociation(IDA),althoughsomeloans _ Unlike tables 6.6-6.11, by the International Bank for Reconstruction and - which are based on OECD Development (IBRD) are made on terms that qualify DAC data, this table draws as concessional. Eligibility for IDA lending is based on data from the World on estimates of average GNP per capita. which are Bank's Debtor Reporting revised annually. In 1995 coujntries with GNP per . System. These data are capita of $865 or less were eligible for IDA lending. t' published annually in the The IMF makes concessional funds available through World Bank's Global its Enhanced Structural Adjus:ment Facility (ESAF), i Development Finance (for- the successor to the Structural Adjustment Facility, merly World Debt Tables). and through the IMF Trust Fund. Low-income coun- tries that face protracted balarce of payments prob- lems are eligible for ESAF funcs. The regional development banks also maintain con- cessional, or soft loan, windows for funds. But the identity of these funds is not consistently recorded in the DRS. The tabulation off ows from these insti- tutions as concessional and nonconcessional is there- fore based on the Development Assistance Committee (DAC) definition. Under the DAC definition, conces- sional flows contain a grant element of at least 25 percent. The grant element of loans is evaluated assuming a nominal, market interest rate of 10 per- cent. The grant element of a loan carrying a 10 per- cent interest rate is nil, and for a grant, which requires no repayment, it is 100 percent. (See the notes to table 6.8 for further discussion of lending terms and the calculation of the grant elernent.) In some cases nonconcessional loans by these institutions may be on terms that meet the DAC iefinition of conces- sional; thus the figures here n ay not match tabula- tions based on other definitions. World Development Indicators 1997 325 6.13 Foreign labor and population in OECD countries Foreign Foreign labor populationa force c* of tcta c/c of tota Participation rate tnousands pcpulat or labor force % N 1990 1994 1990 1994 1990 1994 1990 1994 Austria 456 b 714 L 5.9 8.9 .. 9.6 Migration's benefits-and costs Belgium 905 922 9.1 9.1 7.5 8.1 49.7 55.1 Today at east 125 million people live outside Denmark 161 197 3.1 3.8 2.0 1.7 69.9 64.9 their country of origin. Each year 2-3 million Fin and 26 62 0.5 1.2 .. .. .. .. new migrants-legal and illega eave devel- France 3,597 .. 6.3 .. 6.4 6.4 62.8 62.2 oping countries. About half go to industrial Germany 5,343 0 6.991 8.4 8.6 8.4 9.0 67.1 67.9 countries. The foreign populaton in OECD coun- Ireland 80 c 2.3 d 2.6 2.9 56.4 60.9 tr es has reachec more than 60 million (legal), Italy 781'e 923<5 1.4 1.6 . Italy ''1c 923P 1.4 1.6 and the share of immigrants originating in devel- Japan 1,075 r 1354 f 0.g r 1.11 Luxembourg 110 1300 28.6 32.00 33.4 41.8 66.1 70.2 opng countres IS icreasing. Netherlands 692 774 4.6 5.0 3.7 4.0 56.4 54.0 In Australia, Canada, and the United States Norway 143h 164< 3.4< 3.8 . 4.5 inflows from developing countries have risen Portugal 108 157 1.1 1.6 .. .. ,. ., slowly, reaching aoout 900,000 a year by 1993. Spair 279 J 46i 0.7 1 1.2 .. 0.6 .. .. In Western Europs a period of large-scale labor Sweden 484 537 5.6 6.1 5.6 5.1 74.5 61.6 k migration due to abor shortages in such coun- Switzerland 1,1001 1,3001 16.3 18.6 .. 22.5 tres as Germany and Switzerland was followed United Kingdom 1.7230 1,946d 3.2 d 3.4 3.5 3.6 70.6 66.4 by a period of restricted migration after the oil shock of 1976, as the fear of recession induced Foreign-born Foreign-born return migration. A dip in the growth of the for- population' labor forcer eign population in the late 1970s and early 1980s was soon followed by a rise-to about 180,000 a year .Zimmermann 1995). N ot -ota N r' -rota Pa-t c pation rate thousands tofu atlcn abor force « % Today population growth in OECD countries i990 1994 1990 1994 1990 1994 1990 1994 is being driven by an increase in net migration and the natural increase of the population Austra is 4,125° .. 22.70 .. 25.8 25.3- 55.4 53.4< Austranaa 4,1425 . 226< 2518.40 29503 55.4< 73.2 (excess of births over deaths), fostered by uaniteeStatesd19,767 243 57 7.956 9 139.4 19.5 773. 7higher fertility rates among immigrants. Since united States 19,767 24,55 7 7.9 9.3q 9.4 .. 73.7 . 1987 more than 60 percent of the population a. Except for France, Japae, Portuga., and the Un>ted Kingoom, data a-e from population registers and -eaer to the population increase in Western Europe has been due to or December 31 of treyears indicated. b. Annua average. c. Data refer to-he Federa Pepulic of Germany before unif cat on. m igraton. In North America migration d. Estimated from the annual labor force survey e. Data are adjusted to take account or the -egu arizafions in 1987 88 and 1990. f. Data reer io registered foreign naJtonals. who include foreigners staying in Japan fo- more tran 90 cays. g. Provis cen accounted for about a quarter of the increase data. h. nc uoes asylLnm seeke-s whose requests are teing processed. . Includes a foreigners who ho d a valii residence between 1982 and 1991. perm t. j. Data re'er to foreigners with a res dence permit. k. Data refer to 1993. . Data refe< to fo-eigners w sh an annua res- dence permit orwith a settlement permt (permanent permit). m. Data are ft E l ICt n ,mfnand t Carlba M E-s MI L S-1,i Ab r S~bbSh AD- The world by region at Asi.5 tha -eto Latin Anft.yt and it. Sab-Saflata Atyle ObEC Orrerirer Samoa Caditbwan Aola rst'iraAt Chi. Arge,oaa d B-saa E Ig- umm T,leph-oe: 202 477-1234 Fiji eareades Borrrrrra Faso CaDa-k Facsimie: 202 477 6391 Kiribal BaIlmC FBilard Telex: MCI 64145 WORLDBANK or Korea. De. Rep BraI Ca o MCI 248423WORLDBANK Oso FOR ~~~~Cir"l Cape Verde, G--ac MCI2 43WRLBN eLaoPGF Ceilerer. C Ape Ir ee r Ve de1G d myEele address: INTBAFRAD WASHINGTONDC Marsm: rh'oar IraW Coma , rca Chad IReIlld W.orld Wide Web: http.//-w.worladb, - org9 Mlcrodesia. Fed. Srt Cob Cmoms Italy 'Eie: book ste-erldbonk-grE M eierra Dom-riri C-nga JepaC u: - =_ - _ Myanmar DujineK.e ReCflIr Gtrboot, Korea. Rep. Papao New Gui-e Ec-,ado E.uarerral GumsC a Leemeeorg Phir'pIres El Sairad.r E,trr- Uhredarrs Solomon Isleans Greeada Ethiopia Ne Zeraan- enarraca bboadereupe Gabo N-or.,- To-ga G -a-arr =Smba, ehe Prtogr tg Vadu,a, G`,srra Ghana Sp-r . -lst-s Hatj Gulne Sw-de : meester Samos Honduras Gulnee-Bissav Soitaersoond=_ g JHmairw h~Koena Lorned Uloedem' - _:S Eu-An. abd C,,Otal At M ,-o -sRombn Unrted states _ Altania N-rsrog,a Li-eria Ormerrre Fendmd Madagascar Otitr le_, : Ooerlarus arruaea Mealir Orrdmra=_a BesnrdaarrdFreegoiFa PuerroRilco Maurrtarria B llrarr.Tb OS r floBaria st Orrts ade boo,5 M500rOt i u b ermudas I _____=__ = Croatrie St LUC5a Merotrs brirFervQ ::::: Coerr Republ c St virceGt ean r Ire Mas mue is - T. Estonis Grenadines ham~~~~~N ibi. Channe lsla-ds ,= Georgia Surinsmc Niger Crprus Gree-e TCr,-daU snd Tebago NigFrie Freererts ,on,ero Urug-ay eearroa Frsrc Goia j-- Ilre or Marl wmFerFrd 5w0 Tor t ano Prinorpe Freec orr si Fot 0rre= is Keadgoran SF0000. G l erresnad n_m,s Krrgoe Rep,,blcr iddle EAAt and Neth Se -ntrerree Gram Lmd. Atca Si,ab L_mee Hong Kong Lirlrraia Argorra Somalia lrer- : Mtrcd E9rpr. Frab Rep Srd-r F, I Mordowa I ran. rsramrc Rep. Swo'iaanr M acso Polanrd Ira edoceanni Rarcooce_ ep a Ro=nis - orac. Tego Monco. Ro do Foderetior Lce-dor tgarro Nprtr arn 0 ards a,dL Slooeo Rsu Mc rorro bya am NeM_ C-edoraE Ta,ikcra Orrao Z--bao Q-Oars T,r e- Sacei Arab a 00un on Oor[rrenisrdr Syr err rApt OFpb c s ogaporK -~ Uzobre kisanesst Bsnhasdaa Gs5oirgrn IsOsOd !05IS:R*6¢ ^ YogosrIaia. FR Y eme. FT' ISerbrMoont,IE.j g-* .# A4ghanistardrr - !5. BdFgraOooh ~w . eS,Z Bcar _ .. eb_- M.Ial t : % d- haI-tn P. ak :tj