education, science and technology in latin america and the caribbean: a statistical compendium of... by IDB

Education, Science and Technology in Latin America and the Caribbean A Statistical Compendium of Indicators

Inter-American Development Bank 2006

Cataloging-in-Publication provided by the Inter-American Development Bank Felipe Herrera Library Education, science and technology in Latin America and the Caribbean : a statistical compendium of indicators. p. cm. Includes bibliographical references. “Prepared by the Education, Science and Technology Subdepartment of SDS under the supervision of Daniel Malkin who originated the idea”—t. p. verso. 1. Knowledge management—Latin America. 2. Education—Latin America—Social aspects—Statistics. 3. Science—Social aspects—Latin America—Statistics. 4. Information technology—Social aspects—Latin America—Statistics. I. Inter-American Development Bank. Sustainable Development Dept. Education, Science and Technology Subdepartment.

658.4038 E338 —dc22 This document was prepared by the Education, Science and Technology Subdepartment of SDS under the supervision of Daniel Malkin, Deputy Manager for Education, Science and Technology, who originated the idea. Juan Carlos Navarro, Aimée Verdisco and Julien Hautier wrote the chapter on education. Marta Cehelsky, Gonzalo Rivas, Soledad Mackinnon and Vania Salas Gar-cia worked on the chapter on science and technology. Danilo Piaggesi, Rafael Anta, Robert Vitro and Luana Ladu prepared the chapter on information and communication technology. Sabrina Passos supported the process. Valuable comments were received from José Antonio Mejía-Guerra (SDS/POV) and Vladimir Lópes-Bassols (OECD). The opinions expressed herein are those of the authors and do not necessarily reﬂect the ofﬁcial position of the Inter-American Development Bank. Permission is granted to reproduce this report in whole or in part for noncommercial purposes only with proper attribution to the Bank and the Sustainable Development Department.

Education, Science and Tecnology Subdepartment Sustainable Development Department Inter-American Development Bank 1300 New York Avenue, N. W. Washington, D.C. 20577 USA Fax:

202-312-4261

Email:

danielma@iadb.org

Website:

www.iadb.org/sds

Foreword

nvestment in knowledge and its efﬁcient diffusion throughout the productive sectors and the society at large have become key drivers of economic growth. They lie at the heart of gains in productivity and international competitiveness, sustainable development, and overall im-

provements in the welfare of nations and their populations. Knowledge remains an elusive concept. But for the practical purposes of policy-making and analysis, investment in knowledge can be subsumed in three main components, the magnitude and evolution of which can be mapped and monitored through a variety of indicators: education, science and technology (S&T) and information and communication technology (ICT). Fostering investment in education from early-on through the post-secondary level, coupled with quality teaching and a range of opportunities for life-long training, is an essential condition for raising the skill level of the labor force and increasing the economic prospects of an ever wider range of the population. Along with a skilled labor force, public and private investment in research and development (R&D) and technological activities (including infrastructure) constitutes a main source of innovation and the efficient integration of nations into the globalized economy. Investment in ICT infrastructure and software dramatically broadens access to, and reduces the cost of, information to economic agents and individuals. Through its impact on the productive processes, as well as on the development of e-activities and services in the public and private sectors, ICT plays a determining role in expanding the scope and enhancing the efficiency of economic and social activities. Recognizing the importance of investment in knowledge, advanced countries have developed information systems to better monitor its magnitude and evolution and assess its impact on economic performance. International organizations such as the OECD have developed comprehensive systems of indicators on education, S&T and ICT that allow international comparisons and benchmarking, facilitate the identification of best practices and inform policy decisions of its member countries regarding incentive structures and regulatory regimes, as well as governance practices adapted to knowledge-based economies. In contrast, efforts to consolidate information systems and disseminate results remain incipient throughout Latin America and the Caribbean, as in other developing regions. Given its longstanding record of strengthening the knowledge infrastructure of the region and the continued emphasis on these activities, the Inter-American Development Bank has taken steps to develop internationally comparable indicators on education, S&T and ICT. Based on available information, this compendium sheds light on the progress made in the countries of Latin America and Caribbean, highlights the challenges ahead, and benchmarks the region against a selection of more advanced countries of the OECD and China.

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Beyond its expected usefulness for the Bank’s operational departments, this compendium represents a ﬁrst step in positioning the IDB as a focal point of information on knowledge-related activities in its borrowing member countries. It should be emphasized that this is a modest step. The IDB does not have the institutional means to develop its own system of internationally comparable indicators. Nor have countries of the region yet paid due attention to the importance of developing information systems on knowledge-related activities, with the notable— albeit partial—exception of those related to education. Thus, many of the indicators presented here are based on a reasoned compilation of data developed and maintained by international institutions such as the OECD, the World Bank, UNESCO, the Red de Indicadores de Ciencia y Tecnología Iberoamericana e Interamericana (RICyT) and the International Telecommunications Union (ITU). The publication is organized in three chapters each focusing on one area of knowledge investment: education, S&T and ICT. Two salient points emerge from all three chapters: (i) the great scarcity of available statistics and indicators for a large number of countries; and (ii) the wide— and often increasing—gap between Latin America and the Caribbean and benchmark countries in knowledge-related investment. Taken together, the messages the indicators send are clear. Throughout the region efforts need to be stepped up to bridge the knowledge gap, upgrade skills, and increase innovation and competitiveness. But policies designed and implemented to sustain such efforts must be informed by a solid information base to monitor the pace of change and the outcomes obtained.

Daniel Malkin Deputy Manager Education, Science and Technology Subdepartment

Contents

A. Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A.1 Educational Attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 A.1.1 Average Years of Schooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 A.1.2 Enrollment Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 A.1.3 Repetition and Survival Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 A.1.4 Length of Quasi-universal Education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 A.2 Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 A.2.1 The Effectiveness Gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 A.2.2 Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 A.3 Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 A.3.1 Learning Inequalities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 A.3.2 Social Exclusion in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 A.3.3 The Distribution of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 A.3.4 Gender Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 A.4 Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 A.4.1 Public Spending on Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 A.4.2 Expenditures Per Student . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 A.4.3 Relationship Between Spending and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 A.5 Public-Private Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 A.6 Connections Between Education, Labor Markets and the Economy . . . . . . . . . . 27 A.6.1 Private Returns to Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 A.6.2 Unemployment by Level of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 A.6.3 Contribution of Education to Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

iII

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B. Science and Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 B.1 Human Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 B.1.1 Researchers per 1000 Labor Force (Full Time Equivalent) . . . . . . . . . . . . . . . . . . . . 34 B.1.2 Researchers by Sector of Employment (Full Time Equivalent) . . . . . . . . . . . . . . . . . 35 B.1.3 Doctoral Degrees in Physical and Social Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . 37 B.2 Level and Structure of R&D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 B.2.1 R&D Expenditures as a Percent of GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 B.2.2 R&D Expenditures by Source of Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 B.2.3 R&D Expenditures by Sector of Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 B.3 Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 B.3.1 Patents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 B.3.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

C. Information and Communication Technology

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 C.1 Digital Divide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 C.2 Fixed Telephone Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 C.3 Mobile Telephony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 C.4 Personal Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 C.5 Internet Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Annex I: Technical Notes

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Annex II: Statistical Annex References

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

A. Education

EDUCATION

Introduction

his material represents a compilation of the most important available indicators of educational performance in Latin America and the Caribbean. It covers the essentials of key aspects generally deemed relevant for education policy analysis and decision-making: ac-

cess, equity, quality, efﬁciency and connections between education and labor markets and the economy.

The sequencing of the graphs is intentional in that, taken together, they suggest a story whose outline reflects the giant strides the countries of the region have made in education over recent decades. They have expanded access to primary education to the point that almost every country is within a stone’s throw of the Millennium Development Goal (MDG) of universal completion of primary education. Most remarkably, and unique around the world, girls of recent cohorts have been incorporated into the region’s education systems on an equal footing with boys which, in practice, means that another education-related MDG (gender parity in education) has been achieved. Preschool education has also expanded and, in some countries, coverage currently rivals levels found in several developed economies. Secondary education has undertaken an extremely accelerated expansion and overall levels of education in the labor force have increased to an average of 6 years. This success story, however, coexists with several fundamental flaws. Across the spectrum of education indicators, and regardless of the progress made, a growing gap in attainment and quality continues to separate Latin America and the Caribbean from Asia and Europe. Exhibiting the largest income inequalities in the world, the countries of Latin America and the Caribbean have not been able to create equal educational opportunities for all or to use educational policy as a means for offsetting existing income and social inequalities. Even if most children enter school, many leave prematurely or learn little; this is particularly the case of the children of low-income families. Social exclusion creates additional obstacles. Children from indigenous populations share less in the general progress made in primary enrollment. Performance, measured by the results attained by Latin American countries participating in comparative learning assessments (such as the OECD Program for International Student Assessment or PISA), remains consistently poor. This not only puts the region at a disadvantage in a globally integrated economy, but also provides a strong indication that its competitiveness is driven less by human and knowledge capital and more by (for example) the comparatively low costs of labor. This is a mixed picture of progress: of inclusion mixed with remaining pockets of exclusion, of schools where everybody reports for first grade but many never learn what they should and need to learn. The scenario it paints leads to the simple conclusion that education represents one of the most important developmental challenges the region faces in the immediate future; more will need to be done to eliminate competitive disadvantages.

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The state of education data and indicators and their use in decision-making has improved dramatically over the course of the last decades in Latin America and the Caribbean. However, reviewing this section of the statistical compendium will reduce any temptation toward complacency regarding the current state of systematic and comparable information on education activities in the region. Almost no systematic information is available on public and private training activities. Higher education remains a well-traveled research area, encompassing multiple country studies and international comparisons, but no uniďŹ ed and standard description of its main dimensions has emerged that could feed a comparative exercise such as the one attempted here. Nor are there sufďŹ cient data on scientists, engineers and other professionals educated and trained to work in strategic sectors of the economy. Comparative quality and learning data are available for a small group of countries, and only for a few and far apart points in time. The equity dimension, even if considerably helped by the information contained in household surveys, lacks, for many countries, the required level of detail and disaggregation. Regarding the resource base, few countries have advanced in properly accounting for private contributions, although all indications are that they are substantial. The different types and degrees of adult literacy, a concept that has already been measured in OECD studies and is key to understanding the link between education and growth, remain beyond the reach of most Latin American and Caribbean decisionmakers. Clearly, it remains essential to sustain and expand recent efforts to improve data collection and organization within the framework of cross-country standards if education policy is going to be based on evidence in years to come.

EDUCATION

A.1. Educational Attainment A.1.1 Average Years of Schooling Graph A.1.1: Average Years of Schooling for the Population 15+ (1960 and 2000)

Source: Thomas, Wang, and Fan (2003), based on Barro and Lee, 1993 and various updates.

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Latin America and the Caribbean has witnessed signiďŹ cant progress in expanding access to education. Average years of schooling across the region have increased from 3.5 years in 1960 to more than 6 years in 2000. More children attend school now than at any other time in the past. More importantly, these children represent all socioeconomic and ethnic backgrounds. They enter school earlier, attend school for longer periods of time and complete ever-higher levels of education. As a result of this expansion, the average years of schooling in some countries has more than doubled since 1960 (for the 15+ cohort). This means that workers entering the labor force today have more years of schooling than those from previous generations.

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Despite the progress made, a gap continues to separate Latin America and the Caribbean from the more developed economies. Average levels of education in the OECD hover well above (more than three-fold in some instances) indices observed in Latin America and the Caribbean. Whereas countries throughout the region are approaching universal coverage and completion of the primary cycle, other nations, including those of the OECD, are moving well toward universal coverage and completion of secondary. The difference is important: by almost all estimates, workers in the knowledge economy require 12 years of formal education to ensure a decent standard of living and keep pace with the demands and changes of an increasingly globalized labor market.

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The case of Korea merits special note. In 1960, average years of schooling stood on a par with or fell below levels found across Latin America and the Caribbean. Over the course of the next 40 years, it surged ahead, increasing levels of education well beyond those observed in Latin America and the Caribbean as well as almost all of the OECD countries. Fueled by sustained and rapid economic growth and the use of public policy to mitigate inequalities, Korea more than doubled average years of schooling for the population aged 15 and above (from 4.2 to 10.8 years). The gains made by Korea thus provide an instructive tale for Latin America and the Caribbean: accelerated educational progress is possible.

EDUCATION

A.1.2 Enrollment Rates Graph A.1.2.a: Gross Enrollment Rate in Pre-primary (1990 and 2002)

Source: World Bank, World Development Indicators Database (http:// devdata.worldbank. org/dataonline/), data from UNESCO Institute for Statistics.

Note: 2001 data are used for Honduras and Guatemala; the 2000 fi gure is used for Chile.

•

Although obligatory in only a few countries in the region, preschool has expanded rapidly over the last decade in both urban and rural areas, and for poor as well as richer populations.

•

Calculating with precision the magnitude of this change is complicated for several reasons. Preschool can last from less than one to three years and targets children aged 4 to 6. Far from being the exclusive domain of governments, it is delivered through a mix of public and private, formal and non-formal modalities, particularly in rural areas. Moreover, it is justiﬁed on a variety of grounds: from childcare for working parents (mostly mothers), to early stimulation, to compensatory interventions for children from lower socioeconomic strata. High enrollment rates capture this diversity and say little about the type, quality or equity of the service delivered.

•

Enrollments in the Caribbean merit note because they are on a par with many of the European members of the OECD. As Graph A.5.1 shows, supply tends to be overwhelmingly private. In Latin America, enrollments are considerably lower. Coverage remains far from complete even in countries where some level of preschool has become obligatory (e.g., El Salvador). Most inputs are either in insufﬁcient supply (e.g., resources, infrastructure) or of deﬁcient quality (e.g., teachers, curricula).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph A.1.2.b: Net Enrollment Rate in Primary (1990 and 2002)

Source: World Bank, World Development Indicators Database (http:// devdata.worldbank. org/dataonline/), data from UNESCO Institute for Statistics. Note: 2001 data used for Honduras.

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The OECD countries have long achieved universal completion of the primary cycle, which is the Millennium Development Goal for education. Throughout Latin America and the Caribbean, net enrollment rates at the primary level are approaching 95 percent. In most cases children entering the education system at age 5 today are expected to receive more than eight years of schooling. Nonetheless, no country in the region has achieved universal net enrollment, let alone completion of the entire primary cycle. Much of the leakage stems from poverty and social exclusion (see Graph A.3.2). Poor children, particularly those living in rural areas and those belonging to racial, ethnic or other minority groups, are likely to begin school late, repeat their grade, drop out, and score poorly on tests. Indeed, countries with higher concentrations of poverty and socially excluded populations, including Guatemala, Haiti and Nicaragua, display comparatively low net enrollment rates and, in some cases (e.g., Honduras), have seen a decline in recent years.

EDUCATION

Graph A.1.2.c: Net Enrollment Rate in Secondary (1990 and 2002)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics.

Note: 1999 data used for Guyana. No data for Haiti, Honduras and China.

•

Completion of secondary education constitutes a key variable discriminating the poor from the non-poor in Latin America and the Caribbean. Throughout the region, net enrollment at the secondary level has more than doubled, increasing from 29 percent in 1990 to 65 percent in 2002. Despite this progress, however, the region continues to face difﬁculties in retaining children in school and turning enrollment into completion. Dropout accounts for 92 percent of this “leakage” in South America and 75 percent of that observed in Central America (more children enroll in secondary education in South America than in Central America, thus generating a high level of dropout-induced leakage).

•

Constraints facing the region in providing universal access and completion of the secondary level are many and varied, running the full gamut of supply and demand. Where sizable portions of the population fail to complete the primary cycle, any demand for higher levels of education likely will be latent. Even when completion of the primary level is a reality, supply at the secondary level may be wanting. Much of the supply remains concentrated in urban areas, thus making it difﬁcult for children in rural areas to make the transition to the secondary level.

•

As with other levels of education, poverty and social exclusion underlie the comparatively low net enrollment rates at the secondary level throughout Latin America and the Caribbean. As children from poor families approach age 15, the opportunity cost of remaining in school increases in spite of the fact that, in some cases, they may not have completed sixth grade. These adolescents often face little choice between school and work. They go to work at an early age, foregoing the reward of greater income in the long run that is linked to longer schooling. Entering the workforce ill prepared, they take low paying, low-skill jobs with little opportunity for advancement. Those who study, or work and study, are likely to

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lag academically and professionally relative to peers that enjoyed a better start and study full time or over longer periods of time. •

Opportunity costs are not the only explanation for low enrollment rates. Perceptions regarding the quality of education received and labor market outcomes also play a role. If the poor or socially excluded perceive little return on their education investment (time and resources), then their resulting low levels of grade attainment may be a rational response to the prevailing conditions. That is, although these populations may terminate their schooling before completing, say, grade 6 because of poverty or the need to work, they also may be leaving because they perceive few concrete beneﬁts to staying in school, either because they are learning little in a low quality environment or see few or discriminatory prospects for gainful employment when they are older.

Graph A.1.2.d: Gross Enrollment Rate in Tertiary (1990 and 2002)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics.

Note: 2000 data used for Barbados; 2001 data used for Honduras and Suriname. No data for The Bahamas and Haiti.

•

Beyond the traditional function of tertiary education as a source of advanced education and training, it also plays an indispensable role in cultivating innovation and harnessing its potential to increase growth and competitiveness. Throughout Latin America and the Caribbean, enrollment at tertiary level institutions has seen a significant increase. This, in part, is due to an expansion in supply led mainly by the private sector (and in some cases, without the necessary institutional mechanisms in place to control the quality of the instruction provided). Despite this progress, gross enrollment rates at the tertiary level throughout the region are about a third of those observed in the OECD (27 percent versus 69 percent, respectively, for 2002). At the heart of the problem is the lack of demand. Deficiencies at the preschool, primary and secondary levels, where internationally competitive standards of enrollment, completion and learning remain lacking, truncate student flows and render a large majority of potential students ineligible to enter the tertiary level (see Graphs A.1.2.a, A.1.2.b, A.1.2.c).

EDUCATION

•

Equity issues stem from these considerations. Completion of secondary education remains stratiﬁed by socioeconomic status, with the upper- and middle-class students more likely to complete the secondary level than poorer students. Many upper- and middle-class students have beneﬁted from higher quality private education and expensive courses preparing them for university exams; yet they attend free public institutions. Poorer students who make the transition to the tertiary level often end up paying for their schooling in private institutions, many of which deliver a lesser quality education than the public universities.

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A.1.3 Repetition and Survival Rates Graph A.1.3.a: Percentage of Repeaters in Primary School (2002/2003)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics. Note: No data for The Bahamas, Barbados, Costa Rica, Haiti, Honduras, Nicaragua and China. Data for Suriname are for 2000/2001. Data for the OECD countries are not presented because the percentage of repeaters at the primary level is marginal and not signifi cantly different from zero.

•

Although repetition has decreased over the last ten years, the region continues to be beset by the considerable drag it places on education systems. Estimated to represent a loss of US$11 billion per year, repetition compromises the ability of governments to make capital investments or invest in other quality enhancing non-salary expenditures. Repetition also lies at the heart of lagging net enrollment and completion rates at (particularly) the primary level, and the failure of large segments of the population to make the transition into secondary.

•

Repetition complicates student flows and compromises the internal efficiency of systems (the efficiency with which systems make graduates of any given cohort of students). There is much evidence to suggest that repetition increases the likelihood that a student will drop out. Existing data suggest that repetition in one grade can increase the likelihood of dropping out by 40 to 50 percent; repeating a grade for a second time can raise this figure to 90 percent.

•

The reasons for high repetition vary. Outside factors including poverty, opportunity costs, and social exclusion play a powerful role. Yet, the inﬂuence of intra-school variables—particularly the quality of teaching, including traditional teaching practices that advocate repetition as a means for improving learning—also remains substantial. Teaching methods have an impact on both the internal and external efﬁciency of education systems. Recent evidence from international tests, including the Third International Math and Science Study (TIMSS) and PISA, suggests that differences in teaching methods explain, at least in part, the differences observed in student performance.

EDUCATION

Graph A.1.3.b: Survival Rate to Grade 5 (2001/2002)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics.

Note: 1999/2000 data used for Belize and Guyana; 2000/2001 data used for Trinidad and Tobago, and 2002/2003 for Chile.

•

Survival to grade five of primary education is commonly considered a pre-requisite for sustainable literacy. In contrast with other regions where many children never enroll in school, dropout explains much of the failure to complete primary (and secondary) school in Latin America and the Caribbean. In Central America, for example, 90 percent of all children enroll in school and just over 65 percent of the relevant cohort completes fifth grade. In South America, nearly all children enroll in school (about 2 percent do not) and 83 percent fi nish fifth grade. Dropout accounts for more than three-quarters (76 percent) of the deficit from universal completion.

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A.1.4 Length of Quasi-universal Education Graph A.1.4: Number of Years at Which over 90 Percent of the Population is Enrolled (2003)

Source: OECD, www.oecd.org/ edu/eag2005.

Note: 2002 data used for Argentina, Brazil, Paraguay, Peru and Uruguay.

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Many of the bottlenecks observed in education systems throughout the region stem from issues related to getting and retaining children in school. Many Latin American and Caribbean countries have managed to enroll and retain students in school through the primary cycle. Yet they continue to face lagging enrollment and completion rates at the secondary and tertiary levels. As a result, no country in the region manages to retain 90 percent of the school-age population in school for more than 12 years, which is the number of years generally considered to be necessary to complete both the primary and secondary cycle, assuming no repetition. Underlying causes include repetition, dropping out, quality and social exclusion (see Graphs A.1.3.a, A.1.3.b, A.3.2). Comparable ďŹ gures for the OECD exceed those found in Latin America and the Caribbean by at least a third and, in some cases, by almost three times. For example, whereas Finland enrolls 90 percent of children for about 13 years, Brazil manages to do so for 8 years, and Jamaica, for only 5 years.

EDUCATION

A.2. Quality A.2.1 The Effectiveness Gap Graph A.2.1.a: The Effectiveness Gap at Age 12, 15 and 18 (2000 or closest year)

Source: IDB: Urquiola and Calder贸n (2005).

Note: No data for The Bahamas, Barbados, Guyana, Suriname, and Trinidad and Tobago. Data taken for nearest survey year to 2000. Also note that the distance from the X-axis to the lowest data observation (point corresponding to age 12) represents the gap to age 12.

Graph A.2.1.b: Maximum Schooling (average years in school and average years of schooling in Chile and Honduras, 2000 or closest year)

Source: IDB: Urquiola and Calder贸n (2005).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

•

The effectiveness gap, derived by comparing the cumulative sum of age-speciﬁc net enrollment rates (years in school) with the number of grades actually completed (years of school), highlights two distinct problems: the lack of universal attendance and the failure of systems to turn years in school into years of schooling, which is largely but not exclusively due to repetition. This gap tends to increase with age, although not uniformly across countries. Whereas most all six to seven year-olds complete one full year of schooling, many systems increasingly fail to turn years in school into years of schooling as age increases. For instance, Guatemala and Honduras, countries that tend to fall toward the bottom of the distribution in terms of enrollment rates and average years of education, place closer to the median. In other words, abstracting from their relative poor performance in children in school, these countries do all right in terms of turning attendance into years of schooling. Chile is at the top of the ranking, outperforming the others in terms of moving toward nearuniversal coverage and completion of secondary education.

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The age-specific effectiveness gaps (Graph A.2.1.a) provide insight into the efficacy of education systems across the region and the heterogeneity between them. Among the fi ndings that emerge are the following. First, and consistent with data presented elsewhere (e.g., on repetition; see Graph A.1.3.a), considerable variation exists within the region in terms of where gaps appear and widen. For example, Jamaica appears comparatively efficient in turning years of schooling into grades completed through the age of 15. Yet it loses considerable ground between the ages of 15 and 18. Inefficiencies in Peru and Guatemala (for example) appear to be concentrated early on, flattening out after the age of 15. Brazil and Belize, to cite two additional examples, lose considerable ground across all age groups; gaps appear by the age of 12 and continue to grow through age 18.

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The country-specific examples (Graph A.2.1.b) illustrate how individual systems perform. Chile, for example, is comparatively efficient, losing less than a year to inefficiencies by the time children reach the age of 18. In Honduras, the gap between years in school and years of schooling appears early on and, by the time children reach 18, doubles that found in Chile.

EDUCATION

A.2.2 Learning Graph A.2.2.a: PISA Scores on the Reading Scale (2000 and 2003)

Source: OECD/PISA database, www. pisa.oecd.org.

Note: To date, only 6 Latin American countries have participated in PISA.

Graph A.2.2.b: Percentage of Students at Each Level of Proficiency on PISA Reading Scale (2003)

Source: OECD/ PISA database, www.pisa. oecd.org.

Note: The zero-line appearing in Graphs A.2.2.b and A.2.2.c constitutes a threshold, below which performance is so low that even the most routine or obvious tasks are completed with diffi culty.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph A.2.2.c: Percentage of Students at Each Level of Proficiency on PISA Mathematics/Space and Shape Scale (2003)

Source: OECD/ PISA database, www.pisa. oecd.org.

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Education quality is an endemic problem throughout Latin America and the Caribbean. Far from being confined to students from the lower end of socioeconomic scales, low performance afflicts schools and children at all income levels (see Graph A.3.1). Results from PISA show that the best (and most affluent) Latin American performers participating in the 2000 and 2003 tests scored well below the best performers in other regions. For example, in Brazil and Mexico, more than 50 percent of students face difficulties in reading, performing routine or obvious tasks (i.e., they score at or below level 1; see the Technical Notes for more details); this figure increases to about 70 percent in the case of math. Comparable figures for the OECD are 20 percent for reading and 25 percent for math. In Uruguay, the region’s top performer, merely 15 percent of students perform at internationally competitive levels (IV and V) in reading; only 10 percent do so in math (levels IV, V and VI). Percentages for the OECD are about double for reading and almost four times greater for math.

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Much of the explanation for the region’s low performance can be found in the structure of the test and issues discussed elsewhere (e.g., the prevalence of repetition in Latin America and the Caribbean; see Graph A.1.3.a). PISA tests 15-year olds, regardless of whether or not they are on-grade. It thus captures the (under)performance of over-age repeaters (e.g., a 15year old in grade 6), an issue of far more consequence in Latin America than in the OECD.

EDUCATION

A.3. Equity A.3.1 Learning Inequalities Graph A.3.1: Average PISA Scores on the Mathematics Scale by Socioeconomic Quartile (2003)

Source: OECD/PISA database, www.pisa. oecd.org.

•

The effect of poverty on access to education and educational achievement trumps the effect of other variables commonly included in education production functions. Simply stated, poverty breeds poor educational performance. Poor children, regardless of their gender, race, ethnicity or place of residence, tend to begin school late, repeat their grade, drop out, and score poorly on tests. Their probability of ﬁ nishing primary school is low and falls further for entering and ﬁnishing subsequent levels of education.

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Latin America and the Caribbean provide a telling example of the impact of socioeconomic status on education. Although when measured against international standards dismal learning results are not limited to the region’s poor (see Graphs A.2.2.a, A.2.2.b, A.2.2.c), results from PISA found greater income-related inequality in reading scores in the Latin American countries than in any other country, with the exceptions of Portugal and the United States. Mathematics scores tell a slightly different story. The dispersion of scores in Mexico appears less than in the OECD on average and (similar to results found for reading) less than that observed in the United States.

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In contrast to most OECD countries, where greater variation in learning can be observed within schools, variation in learning in Latin America largely reflects differences between schools. In-school differences largely are due to gaps in talent and motivation between students, in the effectiveness of teachers, or the socioeconomic background of the student body within a school. These factors influence learning in much the same way at all schools, smoothing performance across the education system at hand. Much the opposite holds true in Latin America. Variation in learning stems mainly from the differences in inputs, infrastructure and resources (human and financial) between schools. Results produced by education systems across the region thus tend to be stratified by income, locality and other exogenous factors, to a degree larger than most high-income countries.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.3.2 Social Exclusion in Education Graph A.3.2: Current School Enrollment Rates by Group in Bolivia, Brazil, Guatemala and Paraguay (2000 or closest year)

Speaks Spanish Only Speaks Guaraní and Spanish Speaks Guaraní Only Speaks Other

•

Source: IDB: Marshall and Calderón 2006.

Poverty retains much of its explanatory power even in cases where large pockets of social exclusion exist. For example, in Bolivia, the enrollment gap between indigenous and nonindigenous disappears when controlling for gender, socioeconomic status and rural residence; and in Brazil raw differences are reduced by more than half (although they are still signiﬁcant) when these other variables are taken into account.

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That said, group identiﬁcation variables (i.e., those associated with ethnic, racial, or linguistic minorities) could wield a negative impact on education. In some cases, the exclusion of these groups from formal education systems is greater than would otherwise be expected from their socioeconomic status or place of residence (rural areas). Although enrollment tends to decrease with age, regardless of group identiﬁcation, the fall is more dramatic among ethnic, racial and linguistic minorities. For example, in Paraguay, enrollment rates for Guarani-only speakers fall 45 points, from 93 percent for the 6-12 cohort to 47 percent for the 15-18 cohort. Rates for Spanish-only speakers fall as well, although not as dramatically (from 98 percent to 79 percent, respectively).

EDUCATION

A.3.3 The Distribution of Education Graph A.3.3: Education Gini Index for the Population 15+ (1960 and 2000)

Source: Thomas, Wang, and Fan (2003).

Note: No data for The Bahamas, Belize and Suriname.

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An increase in the average years of education implies greater access to education across all levels and for all populations. Thus, the progress that the region has made in terms of increasing average years of schooling is reﬂected in a more equitable distribution of education within its societies. Over the course of the 40 years between 1960 and 2000, the region reduced its education Gini coefﬁcient (see the Technical Notes for details). Younger cohorts, on average, tend to have more years of education than older cohorts and education within these younger cohorts is more equitably distributed than in older generations. This holds true whether data are discriminated by income, gender or urban/rural. Notably, improvements in the equity of education have occurred despite the resilience of structural inequalities: in some countries, education Ginis have fallen while income inequalities have increased.

•

The comparison with Korea again merits note and serves as an example that, under the right conditions, rapid improvement is possible. Korea experienced an extraordinarily fast expansion in coverage and decline in the education Gini, which dropped from 0.55 to below 0.20 in 40 years. These same data indicate that Latin America and the Caribbean reduced its Gini by 0.08 and gained just over 2 years of education, on average (see Graph A.1.1).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.3.4 Gender Equity Graph A.3.4: Average Years of Schooling by Gender

Source: IDB, SDS/EDU, based on Household Surveys.

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A success story of recent vintage can be reported with respect to the educational attainment of girls. Women have made steady progress and, with the cohorts born around 1965-1970, achieved parity (measured by years of schooling) with men. This trend continues and, on average, women across Latin America and the Caribbean have more years of schooling than men. In contrast with Asia, Africa and the Middle East, gender parity in education, and thus the corresponding MDG, have been achieved in Latin America and the Caribbean. Exceptions are found in the case of indigenous children in rural Guatemala and Bolivia (among other countries) where girls receive slightly less schooling than boys. Pending issues with regards to girls and indigenous children are the persistent patterns of discriminatory role models reproduced in textbooks, teaching materials and teachers’ attitudes which tend to inﬂuence learning and career options in a restrictive way.

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Boys have not fared as well. Signs of an alarming trend of male underachievement and marginalization in education, first observed in the Caribbean, is setting in across the region. Whereas enrollment rates through secondary are about equal in terms of gender, graduation and O/A level, passes are not. Girls outperform boys, with the gap in their favor increasing at the tertiary level. Evidence exists to suggest that remedial classes are overwhelmingly comprised of boys, yet those who graduate and/or achieve at-grade-level work in these classes tend to be girls. Complicating matters even further, there appears to be a “clash” between traditional mores and gender expectations reflected in curricula, texts and pedagogy on the one hand, and the “real” world on the other. For example, the value of education is pressed on young girls early on, especially those from lower socioeconomic classes; boys apparently are not sent the same messages. In school and the home, boys have fewer positive role models. Teachers are overwhelmingly female and, increasingly, households are headed by women. As a result, school systems have proved less readily able to diagnose and mitigate issues related to learning difficulties in boys.

EDUCATION

A.4. Financing A.4.1 Public Spending on Education Graph A.4.1: Public Spending on Education as a Percentage of GDP (1990 and 2002)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics.

Note: Data for the United States, Ecuador, Brazil, Japan, China and Honduras are for 2001. Figures for China and Honduras are for 1999 and 1998, respectively.

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Latin America and the Caribbean have steadily increased expenditures on education. Between 1990 and 2002 public expenditure on education as a share of GDP grew from a regional average of 2.8 percent to 4.3 percent. Guatemala, Nicaragua, the Dominican Republic and El Salvador are spending considerably less (about 3 percent of GDP), while others (Chile, Costa Rica, the English-speaking Caribbean) spend up to 8.5 percent of GDP on education. The observed increase in public education expenditure reﬂects rapid growth in enrollments in secondary and higher education, and there is evidence that expenditure per pupil in primary and secondary education is going up. However, current levels of real per student expenditure merely represent a return, approximately, to the levels observed prior to the debt crisis of the 1980s.

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Through 2020, an increasing percentage of the population will be in the labor force, and dependency ratios and the population aged 6 to 18 will decline. This demographic window of opportunity could lead to greater savings and growth and should make it easier for the region to raise education expenditure, increasing coverage and performance.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.4.2 Expenditure Per Student Graph A.4.2: Expenditure Per Student as a Percentage of Per Capita GDP by Level (2002)

Source: World Bank, World Development Indicators Database (http://devdata. worldbank.org/ dataonline/), data from UNESCO Institute for Statistics.

Note: No data for The Bahamas, China at the primary level and Latin America at the secondary level. No data for Belize, Dominican Republic, Ecuador and Guatemala at the tertiary level. 2001 data used for all levels in the USA, Japan, and Brazil, the primary level in Ecuador and LAC, and the secondary and tertiary levels in Trinidad and Tobago. Data for 2000 are used for the secondary and tertiary levels in Ecuador and the tertiary level in Barbados. Data for 1999 are used for the secondary and tertiary levels in LAC, and for all levels in China.

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One element of the coverage and performance equation is the level of spending per student which, depending on the country, currently is in the annual range of US$150 to US$1,700 throughout Latin America and the Caribbean, compared with US$4,100, on average, in the OECD. Seen in this light, the performance of the OECD should come as little surprise: absolute levels of spending matter. Yet this distribution of spending has implications as well. Throughout the region, public spending on education tends to be more skewed toward the upper levels. The region spends, on average, three times more per student at the tertiary level than at either the primary or secondary level. By contrast, in some OECD countries, spending at lower levels of education exceeds that spent at the tertiary level. Equity concerns follow. Countries with expenditures concentrated at lower levels of education tend to have lower education Gini coefďŹ cients (see Graph A.3.3), thus indicating that education throughout their respective societies is more equitably distributed.

EDUCATION

A.4.3 Relationship Between Spending and Learning Graph A.4.3: Relationship Between Expenditure Per Student and Average Combined PISA Score for Reading, Math and Science (2000)

Source: OECD/ UNESCO-UIS, 2003

•

While the trend of per-student expenditure is rising across Latin America and the Caribbean, questions about the efﬁciency and effectiveness of spending remain. Part of the problem lies with grade repetition, estimated to represent a loss of US$11 billion per year (as noted elsewhere). Other issues are associated with the composition of educational spending and the chronic underfunding of capital spending and other quality enhancing non-salary expenditures relative to administrative costs. Learning thus suffers. When cumulative per student spending is plotted against PISA scores, the results indicate that, with the exception of Uruguay, learning in the participating Latin American countries is less than would have been expected given their respective levels of investment. Yet these countries are not alone. A cursory inspection of the graph points to the fact that several nations outside the region, including some OECD countries, exhibit a degree of inefﬁciency in meeting this standard.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.5. Public-Private Supply Graph A.5: Percentage of Private Enrollment by Level (2002/2003)

Source: World Bank, World Development Indicators Database (http:// devdata. worldbank. org/ dataonline/), data from UNESCO Institute for Statistics.

Note: No data for China and OECD. Data for Guyana are for 2001/2002. At the tertiary level, data for The Bahamas, Brazil, and Korea are for 2001/2002 and for 2000/2001 for Chile.

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Private enrollments throughout Latin America and the Caribbean tend to be highest at the preschool level. To some degree, this comes as little surprise: few countries have made preschool obligatory for any age cohort. Those that have, (e.g., Chile and El Salvador) display relatively lower levels of private enrollment in preschool. Provision at the primary and secondary levels remains largely public in Latin America and the Caribbean as well as in the OECD. It merits noting that, much like preschool, the participation of the private sector at the tertiary level in Latin America and the Caribbean is signiďŹ cant and has undergone considerable expansion over the last decade. Enrollment in private institutions currently accounts for about one-third of all enrollments at the tertiary level. Following experiences found elsewhere, including in the OECD, some countries in Latin America and the Caribbean have initiated steps to create and put in place accountability mechanisms, including systems for controlling the growth of tertiary level providers and the quality of the programs they offer.

EDUCATION

A.6. Connections Between Education, Labor Markets and the Economy A.6.1 Private Returns to Education Graph A.6.1: Private Returns to Secondary and Tertiary Education (early and late 1990s)

Source: Inter-American Development Bank (2004).

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One impact of the changing world economy has been an increase in the rate of return to tertiary education relative to all other levels. This trend appears to be worldwide. It reﬂects the impact of skill-based technological change on wages and captures the ever-greater market premium paid for jobs requiring non-routine cognitive tasks, including analytical capabilities or intense interaction between people. As computers or other types of automation increasingly replace more routine tasks, workers with lower levels of education face falling wages. On a more macro level, rising labor productivity (e.g., as generated through more years of education) has been found to account for at least half of the growth in per capita GDP.

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The steady increase observed in the returns to tertiary education demands a new way of thinking about education and its relationship to the labor market, particularly in Latin America and the Caribbean. In the not-too-distant past, investments in primary education to the exclusion of other levels were widely promoted and justiﬁed on the grounds that primary education yielded the highest returns. This is no longer the case. Returns to primary education are decreasing and returns to secondary are declining or are stagnant due to the increase in the relative supply of secondary school graduates. Although investments in primary and secondary will continue, and are clearly justiﬁed on the basis of equity, the importance of tertiary education for competitiveness and growth cannot be overlooked.

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A.6.2 Unemployment by Level of Education Graph A.6.2: Distribution of the Unemployed by Level of Education (2000)

Source: World Bank, World Development Indicators Database (http:// devdata.worldbank. org/dataonline/), data from International Labor Organization.

Note: Data for The Bahamas, El Salvador, Guatemala (primary level only), and Honduras (primary and secondary levels only) are for 1998; data for Barbados, Belize, Brazil, Trinidad and Tobago, Venezuela, Ireland and Japan are for 1999.

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As labor markets around the region place ever-higher premiums on higher levels of education, those with lower levels of education find themselves increasingly excluded from formal employment. The OECD and Latin America and the Caribbean are quite similar in this regard: workers with tertiary education are far more likely to be employed than those with lower levels of education. One key difference, referred to elsewhere (see Graph A.1.1 on average years of education and Graph A.1.4 on the number of years for which 90 percent of students is enrolled), is that highest rates of unemployment tend to affl ict those with secondary education to a larger extent in the OECD than in Latin America and the Caribbean. This likely reflects the fact that completion of secondary is near universal across the OECD (meaning that comparatively few people reach the primary level only) and, consistent with global trends, the respective labor markets reward those with higher levels of education. In those countries in Latin America and the Caribbean where enrollment remains high through the secondary level (e.g., Chile, Trinidad and Tobago) trends in unemployment parallel those observed in the OECD.

EDUCATION

A.6.3 Contribution of Education to Growth Graph A.6.3: Growth Decomposed by Contributing Factors (1972-2000) Source: Own elaboration based on National Accounts Statistics, UN Statistics (19702001), World Development Indicators (WDI), World Bank (19702001), and Barro and Lee (2002), International data on educational attainment (19702000). Data for Asia exclude Japan and China.

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Education is a key element in increasing productivity and improving personal welfare. Estimates of the relative importance assumed by physical capital, labor, human capital, and the combined productivity of these factors (i.e., total factor productivity, TFP; see the Technical Notes for more details) have for economic growth indicate that, in Latin America, labor remains the main source of growth; and total factor productivity produces a limited effect. This scenario diverges from that observed in the East Asian economies, where TFP—which captures the contribution of technology and know-how, among other residuals—drives economic performance. The contribution of human capital to growth is low in Latin America and bears an obvious relationship to total factor productivity. Human capital underlies the abilities of workers throughout a given economy to generate, apply or otherwise assimilate productivity-enhancing means, including technology, technological knowledge, and technical or institutional change.

SCIENCE AND TECHNOLOGY

B. Science and Technology

SCIENCE AND TECHNOLOGY

Introduction

he countries of Latin America and the Caribbean recognize that the development of capacity and infrastructure for science, technology and innovation is essential for their economic development and competitiveness. However, despite increased investments and outputs in some countries, support for the development of capacity for innovation in the region has not been commensurate with the need or the challenge. The region lags substantially behind more technologically advanced countries on key measures of S&T capacity and innovation, and the gap is growing. Furthermore, there are signiﬁcant differences among the countries of Latin America and the Caribbean.

The section on science and technology indicators that follows presents data in three general areas: human resources, R&D expenditures and outcomes. In all three areas there are specific improvements in some countries, but both the region as a whole and individual countries fall well below the performance levels of more advanced countries. The rate at which doctoral degrees are granted and the number of researchers in the workforce—key indicators of capacity—are several orders of magnitude below that of the OECD countries, and R&D expenditures as a fraction of GDP are less than half the OECD average, even for the strongest performers. While the trend in the more technologically advanced countries is to increase R&D expenditures, in the region as a whole, except for Brazil, Chile, and Mexico, the fraction of GDP invested in R&D has declined. There has been an increase in R&D investment by business in some countries (for example, Brazil, Mexico and Uruguay). But while the business sector is the largest supporter of R&D in the more advanced countries—tending toward two thirds or more of the total, the public sector dominates R&D spending in the region. Similarly, industry also plays a lesser role with respect to R&D performance, with governments and universities generally constituting about two thirds of all activity. There are some bright spots with respect to outputs. Some Latin American countries have increased their patenting activity and, particularly, their share of articles published in scientific journals. Yet, on both measures, the region lags more advanced countries. In addition, in the last seven years, the rate of increase in this indicator has fallen short of what would be required to approach the accelerated rates of patenting in countries such as Finland, China and Spain. The availability of reliable data for a broad range of trends related to science, technology and innovation is critical as a tool for evaluation and decision-making. The limited data available in the countries of Latin America and the Caribbean, even in the most advanced, together with gaps in reporting and questions regarding reliability and definitions stand in the way of in-depth analysis of their needs and progress. It also deprives governments of an essential tool for developing strategies and policies related to innovation and competitiveness. Consequently, the improvement of data and analysis is an important priority in the region.

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B.I. Human Resources B.1.1 Researchers per 1000 Labor Force (Full Time Equivalent) Graph B.1.1: Researchers Per 1000 Labor Force (1995 or earliest available, and 2003 or latest available) Other countries

2003

Latin America & Caribbean

1995

1.8

1.6

1.4

12 1.2

Sources: Red de

1.0 Indicadores de

0.8 Ciencia y Tecnología

0.6

0.4

– RICYT (2003) (hereafter, RICYT), and Organization for

0.2 Economic Cooperation

0.0 and Development

liv i Pa a na m a Co lo m bi Ec a ua do r

o ic

ex M

ile

in nt

rg e A

ai n Ch in a

25 Ire la nd

D K

EC O

an nl Fi

– OECD/MSTI database (hereafter, OECD).

Notes: There are two scales, one for each group of countries. The earliest data available for Argentina are 1997, for Bolivia 1998, and for Colombia are 1996. The latest data available for the United States, Argentina, Mexico and Bolivia are 2002.

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The proportion of researchers in the total labor force is signiﬁcantly lower in the countries of Latin America and the Caribbean (0.64 in 2003) than in the more advanced countries. Argentina, which leads the region with 1.6 researchers per 1,000 persons in the labor force, still compares unfavorably with the technologically more advanced countries of the OECD where the corresponding rates can be 10 to 15 times higher. For example, it reaches 14.7 in Finland, 9.7 in Japan and 9.1 in the United States.

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More dramatically, the trends over the period and countries for which data are available show that the gap is increasing. By some estimates (RICYT), the number of researchers per 1,000 economically active individuals in the region increased slightly (by less than 10 percent) between 1995 and 2003. Over the same period, the rate of growth in the number of researchers in the total labor force increased by almost 20 percent in the OECD over a larger base, with some countries, such as Finland, Spain, Ireland and Korea making considerable progress. Notably, the number of researchers in China is growing more than a third faster than the labor force, while there seems to have been a leveling off in Japan and the United States. In contrast, available ﬁgures show that in Argentina the proportion of researchers has decreased.

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Another striking feature is the broad differences among the countries of Latin America and the Caribbean in the period under review. Argentina (1.63), Chile (1.16) and Uruguay (1.0) are in the lead with respect to the number of researchers in the workforce. While Bolivia, Chile, Colombia, and Mexico showed signiﬁcant improvements in this indicator, 3 out of the 10 countries for which data are available—Argentina, Ecuador, and Panama—posted a decline.

SCIENCE AND TECHNOLOGY

B.1.2 Researchers by Sector of Employment (Full Time Equivalent) Graph B.1.2: Researchers by Sector of Employment (1995 or earliest available, and 2003 or latest available) Business Sector

Government

Higher Education

Other countries

Latin America

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Sources:

0% 95

USA

Korea

Japan

OECD

Ireland

Finland

China

EU25

Spain

95 .

Brazil

Mexico

Argent

Colmb

Urug

Panama

RICYT, and OECD.

Notes: In the United States and OECD, there are no data available for Higher Education in 2002. In Panama, the datum for Business Sector is 0 percent.

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In a pattern that has changed little since 1995, few researchers in Latin America and the Caribbean are employed by the business sector. In contrast with more advanced countries, the overwhelming majority of researchers in countries of the region are employed by universities and, to a lesser extent, government research institutes. Such an imbalance may impair innovation performance.

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In contrast to the regional pattern, the share of researchers employed in the business sector increased in Mexico and Brazil. In the case of Brazil, this share almost doubled over the period under review.

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It merits noting that in several countries the percentage of researchers employed by the business sector declined. In the case of Colombia, it fell by almost 50 percent and, in Argentina, by 25 percent.

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From 1995 to 2003 the share of researchers employed by governments in Latin America and the Caribbean increased. Panama experienced the largest increase, from 41 percent to 59 percent. This contrasts with countries like Finland where the share of government-employed researchers was cut in half during the same period.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

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In more technologically advanced countries, trends point to an overall increase in the share of researchers employed by the business sector and a relative decrease in the share employed in public laboratories compared to those employed by universities. Among the OECD countries, the United States and Korea lead in the employment of researchers by the business sector, with around 80 percent and 74 percent, respectively.

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China is experiencing the same structural trends as advanced countries. A growing share of researchers is employed by the business sector.

SCIENCE AND TECHNOLOGY

B.1.3 Doctoral Degrees in Physical and Social Sciences Graph B.1.3: Number of PhDs Per 100,000 Inhabitants (1995 or earliest available, and 2003 or latest available) Other countries

2003

1995

Latin America & Caribbean 3.6

3.2

2.8

2.4

2.0

8 6

1.6

1.2

0.8

0.4

0.0

Spain

USA

Brazil

LAC

Mexico

Chile

T rin&T ob

Uruguay

Colombia

Source: RICYT.

Notes: There are two scales, one for each group of countries. The earliest data available for Colombia are 1998. The latest data available for United States, Trinidad and Tobago and Colombia are 2002, for Spain 2001 and for Uruguay 2000.

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In many Latin American and Caribbean countries, the number of doctoral level (PhD) graduates has expanded, albeit from a low base. Brazil and Mexico tripled the number of doctoral degree recipients in the total population between 1995 and 2003, while in Chile the increase surpassed 100 percent.

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In more advanced countries the trends vary. From 1995 to 2002, there was a slight decrease in the number of PhD graduates in the United States (from 10.7 to 9.7 per 100,000 population). During the same period, Spain posted a 17.9 percent increase in the number of doctoral graduates.

•

In a number of countries in the region, the rapid acceleration in the production of advanced degree holders raises two important issues: maintaining the quality of the degrees vis-à-vis international standards; and maintaining a balance between the growing supply of highly skilled persons and the often weak demand for their skills (e.g., demand from the business sector for researchers).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

B.2. Level and Structure of R&D B.2.1 R&D Expenditure as a Percent of GDP Graph B.2.1: R&D Expenditure as a Percent of GDP (1995 or earliest available, and 2003 or latest available) 2003 3.6

1995

Other countries

Latin America & Caribbean

3.2 2.8 2.4 2.0 1.6 1.2 0.8 0.4

in a ex ic Pa o na Ve ma ne zu el Bo a liv U ia ru g Tr uay in & To Ec b ua do r

LA C A

rg e

az il Ch ile

Fi nl an d Ja pa n U SA K or ea O EC D EU 25 Ch in a Ire la nd Sp ai n

0.0

Sources : RICYT, and OECD.

Notes: The earliest data available for Argentina and Trinidad and Tobago are 1996. The latest data available for Mexico, Bolivia and Uruguay are 2002. Panama, the US Smithsonian Tropical Research Institute represents 29 percent of the total R&D expenditure.

•

R&D intensity, as expressed by percentage of R&D expenditures of GDP, has risen in the more technologically advanced countries over the 1995-2003 period, sometimes rapidly as in the case of Finland. In China this intensity has more than doubled. In contrast, and with the exceptions of Brazil, Trinidad and Tobago and Mexico, the R&D intensity of all other Latin American and Caribbean countries for which data are available has decreased or leveled off, sometimes dramatically, as the cases of Venezuela and Bolivia illustrate. The data show that there has also been a leveling off in Mexico in recent years.

•

R&D intensity in Brazil reaches 1 percent of GDP and is the highest in the region. Brazil was overtaken by China in 2000, which now devotes about 1.4 percent of GDP to R&D.

•

Overall R&D expenditures for those Latin American and Caribbean countries for which data are available increased by 15 percent, from US$9.5 billion in 1995 to almost US$11 billion in 2002. Notably, this total is less than Korea’s investment in R&D (US$12 billion) in 2003.

•

Three countries account for more than 70 percent of all R&D expenditures in the region. Brazil is the front runner, with 42 percent, followed by Argentina and Mexico, with 20 percent and 11 percent, respectively.

SCIENCE AND TECHNOLOGY

B.2.2 R&D Expenditure by Source of Financing Graph B.2.2: R&D Expenditure by Source of Financing (1995 or earliest available, and 2003 or latest available) Business Sector

Other sources

Other countries

Latin America & Caribbean

100% 90% 80% 70% 60% 50% 40% 30% 20% 10%

Sources:

0% 95 03 95 03 95 03 95 03 95 03 96 03 95 03 95 03 Japan

Korea

Finland

USA

OECD

Ireland

EU25

Spain

95 01 95 02 95 03 95 03 95 02 95 02 95 02 95 03 95 03

RICYT, and

Colmb

OECD.

Urug

Brazil

LAC

Mexico

Chile

Bolivia

Venez

Panam

Note: Government, Higher Education, Private Organization and Abroad are grouped under “Other sources”. In the OECD, there are data missing under “Other Sources” on financing from abroad.

•

In the more technologically advanced countries, the business sector is the main and growing source of R&D financing. This sector funds more than two-thirds of total R&D expenditures in the OECD. In Korea, Japan, Finland and the United States business fi nancing of R&D is above 70 percent. In contrast, and with the exceptions of Brazil, Colombia and Uruguay, the importance of this sector in financing R&D remains extremely limited in Latin America and the Caribbean.

•

Moreover, except for Brazil and Uruguay, the share of the business sector in R&D ﬁnancing has declined over time. In Chile, business ﬁnancing of R&D declined by almost 50 percent between 1995 and 2002, and in Venezuela it dropped from 47.4 to 1.0 percent between 1995 and 2003.

•

It merits noting that in advanced countries such as Korea and Ireland, where the relative share of government funding has increased, total R&D expenditures have grown as well. In absolute terms business funding of R&D has not declined.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

B.2.3 R&D Expenditure by Sector of Performance Graph B.2.3: R&D Expenditure by Sector of Performance (1995 or earliest available, and 2003 or latest available) Business Sector

Government

Higher Educ.

Other countries

Privat e Non-Profit

Latin America & Caribbean

100% 90% 80% 70% 60% 50% 40% 30% 20% 10%

Sources:

0% 95 03

95 03

Korea

Japan

Finland

USA

Ireland

China

Spain

95 02

95 03

Uruguay

Chile

95 02 96 03

95 02

95 03 96 03

Mexico

Bolivia

Ecuador Trin&Tob Panama

Argent

95 03

RICYT, and OECD.

Note: In Panama, the datum for Business Sector is 0 percent.

•

In advanced countries, the government plays a limited and declining role in R&D performance (around 10 percent for the OECD as a whole) while the business sector accounts for a major and growing share. In contrast, in Latin America and the Caribbean, the government plays a dominant role followed by higher education. Government-performed R&D is highest in Trinidad and Tobago (70 percent), followed by Argentina, Mexico and Panama (each with more than 40 percent).

•

Except for Bolivia and Colombia, the share of R&D performed by higher education institutions declined in Latin America and the Caribbean between 1995 and 2003.

•

Between 1995 and 2003, only two countries (Chile and Uruguay) showed a signiﬁcant increase in R&D performed by the business sector. In Chile, for example, business R&D performance rose from 6.4 to 37.8 percent of total R&D. Given the relatively low level of business ﬁnancing of research and development expenditures, this translates into an important source of public support for business R&D. In other countries (most noticeably in Colombia and Peru), the participation of the business sector in R&D declined. In Colombia, it fell by half.

•

In China the importance of R&D undertaken by the government fell dramatically between 1995 and 2003 due largely to a drastic privatization and streamlining of public laboratories. China’s R&D performance structure is now quite similar to that of more advanced OECD countries.

SCIENCE AND TECHNOLOGY

B.3. Outcomes B.3.1 Patents Graph B.3.1: Patents Granted by United States Patent and Trademark Office (1995 and 2003) Other Countries

2003 1995

90 000 81 000 72 000 63 000 54 000 45 000 36 000 27 000 18 000 9 000 0

900 750 600 450

Source: United States Patent and Trademark Offi ce,

300 150 0

USA

Japan

Korea

Finland

Spain

China

Ireland

USPTO (2004) http://www.uspto. gov/web/offi ces/ ac/ido/oeip/taf/ reports.htm#by_ geog

Notes: There are two scales, one for each group of countries.

Latin America and the Caribbean 2003 1995

135 120 105 90 75 60 45 30 15 la

ile Co lo m bi Ba a ha m as Co sta Ri ca Pe ru Pa na m a U ru gu ay El Sa lv ad o Ja r m ai ca

ue ez

in a Ve n

ic o A

rg e

ex M

Br az

0 Source: USPTO (2004) - http://www. uspto.gov/web/offi ces/ac/ido/oeip/taf/ reports.htm#by_geog

•

There was an increase in the number of patents granted by the United States Patent and Trademark Ofﬁce (USPTO) to residents of Latin America and the Caribbean, although the growth rate in the number of patents is considerably lower than that of the more technologically advanced countries.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

•

Taken together, Latin America and the Caribbean almost doubled the number of patents granted by the USPTO from 1995 to 2003, from 191 to 350. Brazil was the highest performer in 2003 (130), followed by Mexico (84).

•

The growth rate of patent acquisition in Latin America and the Caribbean, while signiﬁcant, is overshadowed by that of the more technologically advanced countries. In Ireland and Korea, for example, the number of patents granted quadrupled between 1995 and 2003. During the same period China increased its number of patents six fold, from 62 to 404.

SCIENCE AND TECHNOLOGY

B.3.2 Publications Graph B.3.2.a: Scientific and Technical Journal Articles Per 100,000 Inhabitants (1995 and 2001) Other countries

2001

Latin America & Caribbean

1995

9 8

90 7

5 4

45 3

2 1

o LA Co C sta Ri Ve c ne a zu Co ela lo m bi a Pe ru

a in U

ile

rg e

ea Ch in a

n ai

or K

la Ire

pa Ja

EC O

an d nl Fi

Source: World Development Indicators.

Notes: There are two scales, one for each group of countries.

Graph B.3.2.b: Scientific and Engineering Article Output of Emerging and Developing Countries by Region: 1988-2001 50

Thousands of articles

Sources: Institute

for Scientifi c

Information, Science

and Social Science

Citation Indexes;

CHI Research, Inc;

National Science

Foundation, Division

of Science Resources

0 1988

1989

1990

1991

1992

Latin America

1993 Asia

1994

1995

1996

1997

1998

1999

2000

2001

Statistics, special tabulations; and

Eastern Europe and former USSR

World Bank.

Graph B.3.2.c: Portfolio of Scientific and Engineering Articles for Seven Latin American Countries: 1988-2001 25.0

2001

1988

20.0

Sources: Institute for Scientifi c

15.0

Information, Science and Social Science

10.0

Citation Indexes; CHI Research, Inc;

5.0

and National Science Foundation, Division

0.0 Clinical medicine

Biomedical research

Biology

Chemistry

Physics

Earth and Engineering Mathematics Social and behavorial space and sciences technology

of Science Resources Statistics, special tabulations.

Note: The seven countries are Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico and Venezuela.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

•

Overall, the production of scientiﬁc publications credited to Latin American institutional authors increased 69 percent from 1995 to 2001 (Graph B.3.2.a).

•

The growth rate for the region was greater than that of emerging and developing countries in other regions (Graph B.3.2.b).

•

The increase in the number of Latin American articles was concentrated in ﬁve countries: Argentina, Brazil, Chile, Mexico and Uruguay (Graph B.3.2.a).

•

Although Brazil is the largest producer of scientiﬁc publications in the region, and doubled its rate of production compared to 1995, Argentina, Chile and Uruguay perform better when ratios of publication/population are considered. Argentina and Chile each had a ratio of 8.1, followed by Uruguay with a ratio of 4.6.

•

The increase in articles authored by residents of Latin America and the Caribbean was greatest in engineering and technology, biology and the physical sciences (i.e., chemistry, physics and earth and space). In contrast, the number of articles in the social and behavioral sciences and the ﬁelds of clinical medicine and biomedical research increased at a slower than average rate; this resulted in a decline in their share of the portfolio (Hill, 2004).

SCIENCE AND TECHNOLOGY

C. Information and Communication Technology

INFORMATION AND COMMUNICATION TECHNOLOGY

Introduction

his section presents the results of a compilation of statistics and indicators for the period 2000-2004 regarding access, use and barriers to use of information and communication technology (ICT) in Latin America and the Caribbean as well as in other selected countries

or regions. The choice of the period covered reflects the fact that the dissemination of ICT in the region really started changing pace significantly at the turn of the century; mapping data prior to 2000 would have been less relevant for the purposes of this exercise. Reliable internationally comparable statistics could be compiled only for access to four ICT technologies: fi xed-line telephony, mobile telephony, personal computers and Internet users. All data came from statistics compiled by the International Telecommunication Union (ITU) with supplemental Internet indicators from Internet World Stats. The sparse findings from the few indicators of access confirm a limited capacity in Latin America and the Caribbean for producing the statistics on ICT needed to understand its impact on economic and social development, and for use in the design of policies as well as in decision-making. In this context, a number of international organizations are already collaborating in the program Partnership for Measuring ICT for Development to help national statistics offices to produce data on ICT for development through the use of commonly agreed standards and methodologies. The available indicators regarding access to ICT are useful in a comparative sense, but offer only partial information about the impact of ICT on development. To gain a better understanding of this impact, it is necessary to know more about the uses of these technologies. Despite the limitation of the available statistics, it is possible to conclude that while significant progress has been made in increasing ICT access in the region during the period under review, a wide gap remains between the region and developed economies. In general, the data show that large countries like Argentina, Brazil and Mexico, as well as smaller ones such as Barbados, Chile, Costa Rica and Uruguay have been among the leaders in the diffusion of ICT. The figures from Asia and member countries of the Organization for Economic Cooperation and Development (OECD), and the European Union (EU25) demonstrate the rapid progress they have achieved in ICT penetration during this period.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C.1. Digital Divide Graph C.1: ICT Access Per 100 Inhabitants (2001 to 2004)

Sources: ITU and Internet World Statistics

Note: HPAE means the “High Performance Asian Economies” of Hong Kong, Singapore, Korea and Taiwan.

•

The level of access to ICT in Latin America and the Caribbean is well below that attained in developed economies. While the gap has remained almost constant in ﬁ xed and mobile telephony over the period studied, it has widened with respect to personal computers and Internet users.

•

Continuation of the current trends in ICT access would appear to be insufﬁcient for the region to make the quantum leaps needed to provide a foundation for ICT to become a key contributor to sustainable economic growth.

•

In general terms, the two principal barriers to ICT access in Latin America and the Caribbean are limited telecommunications infrastructure and the relatively high cost of access for most people.

•

The relative low cost and mobility of mobile telephony explain why it has shown signs of increased penetration in recent years.

INFORMATION AND COMMUNICATION TECHNOLOGY

C.2. Fixed Telephone Lines Graph C.2. Fixed Telephone Lines Per 100 Inhabitants (2000 and 2004)

Source: ITU

•

With a few exceptions, ﬁ xed telephony penetration in Latin America and the Caribbean remains low and is increasing at a relatively low rate, despite the high priority given to telecommunication infrastructure expansion in most countries.

•

While the countries of Latin America and the Caribbean have increased the rate of expansion of fi xed telephony (18 percent), more developed economies, with a high level of fi xed telephony penetration, have had negative growth rates (16 percent in the OECD and 13 percent in the EU25 for the five-year period) due to a transfer from fi xed to mobile telephony in some sectors of the population.

•

The low penetration of ﬁ xed telephony suggests that deployment of broadband access will continue to be slow in the region, given the magnitude of investment required for wireless or wireline broadband technologies.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C.3. Mobile Telephony Graph C.3. Mobile Telephone Subscribers Per 100 Inhabitants (2000 and 2004)

Source: ITU.

•

Mobile telephony penetration has been very rapid in Latin America and the Caribbean in the past few years. For the five-year period under review, the rate of increase of mobile telephony subscribers (172 percent) was almost nine times the rate of increase in fi xed telephony (18 percent). Despite this significant increase, most countries of the region still lag behind the OCDE and EU25 countries in mobile telephony penetration.

•

The increase in mobile telephony penetration in Latin America and the Caribbean reflects the extension of telephony service to people who previously lacked access. However, the increase in mobile telephony in OECD countries, together with decreases in fi xed-line telephony, reflect a trend of substitution of mobile for fi xed-line service.

•

As image and text capabilities are added to mobile telephony (e.g., SMS, e-mail), incentives should emerge for developing information services giving more added value to mobile telephony.

INFORMATION AND COMMUNICATION TECHNOLOGY

C.4. Personal Computers Graph C.4. Personal Computers Per 100 Inhabitants (2000 and 2004)

Source: ITU

•

Despite advances in the penetration of personal computers (PC), a significant gap remains between the region and developed economies. The level of PC penetration needs to increase significantly for the region to experience the full benefits of the “network effect.”

•

The penetration numbers conﬁrm that the price of PCs remains high and beyond the reach of a family with average levels of income in the region. However, the number of PCs per inhabitant is not a realistic indicator of PC access. A combination of different public, private, NGO and community initiatives are offering public access to PCs and the Internet via telecenters, cyber cafés, chambers of commerce for small and medium enterprises and schools, among others.

•

Another barrier to increased access to PCs may be related to the skills required to effectively use them. However, the efforts being made throughout the region to equip schools with PCs should help expand access to these skills.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C.5. Internet Access Graph C.5.1 Regional Evolution of Internet Users (2000 and 2005) 500% Internet penetration

454%

450%

% Growth 2000-2005

404%

400% 350% 300% 250%

273% 219%

200%

176%

150%

132%

100% 50%

36% 15%

10%

2.5%

10%

Middle East

Africa

Asia

53%

109% 68%

Source: ITU, Internet World

0% LAC

Europe

Oceanía/Australia North America

Stats, 2005

Graph C.5.2 Internet users per 100 inhabitants (2000 and 2005)

Source: ITU and World Internet Statistics

•

Current Internet penetration in Latin America and the Caribbean (15 percent) lags significantly behind the levels of developed economies, despite a significant increase in the rate of growth (272 percent) in the last five years.

•

In addition to the “digital divide” between the region and developed economies, there is concern about a digital divide between and within Latin American and Caribbean countries, caused in part by the concentration of access infrastructure in speciﬁc geographic areas such as urban centres.

INFORMATION AND COMMUNICATION TECHNOLOGY

•

Available indicators of Internet access are of only partial value in understanding internet usage, since they must be combined with speciﬁc indicators of broadband users who really may beneﬁt from the potential of the Internet.

Graph C.5.3 Internet Broadband Users Per 100 Inhabitants (2001 and 2005) 35 2005

Latin America

Other countries

2001

Source: “Promoción y

Masifi cación de los servicios

de banda ancha en ColombiaNovember

2004”; “Acceso a Internet 2005

INDEC Argentina”; “Desarrollo de

Servicios de Banda Ancha en Peru-

0 Korea

•

OECD

China

Pacific Asia

Chile

Argentina

Brazil

Peru

Mexico

Colombia

2004”, and The World FactBook.

The levels of broadband access in the OECD and High Performance Asian Economies reﬂect the fact that they are taking full advantage of the Internet. A large majority of users in Latin America and the Caribbean use dial-up connections with low capacity for accessing on-line contents and services.

•

The countries of the region also recognize that broadband access is crucial. However, they are in earlier stages in attracting private investment, creating competitive environments and other enabling conditions for broadband capacity to take off. Chile, Argentina and Brazil, are among the leaders in broadband access in the region.

•

Fixed-line telephony infrastructure is an enabling factor for the deployment of Internet broadband. Thus, higher levels of investment in telecommunications infrastructure are needed to extend Internet broadband to a wide spectrum of the population.

•

The convergence of broadband capacity and wireless access is a strategic opportunity for the future of broadband connectivity in Latin America and the Caribbean.

•

The case of Korea, which is the world leader in broadband access, demonstrates that rapid progress can be made with vision and commitment for ICT deployment, particularly with respect to the Internet.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph C.5.4 Internet Hosts, Per 10,000 Inhabitants (2001 and 2004) 2500

Other Countries

2004 2001

L atin A m e ric a & C aribbe an

2000

400 350 300 250

1500 200

1000

150 100

500 50

0 Ur ug Ar uay ge nt in a Br a M sil ex ico Ch ile Be Tr in lize .& Do To m b .R ep . L Co AC lo m bi a Co Per sta u R Pa ica n Gu ama ate Ni mal ca a ra Ve gua ne zu Pa ela ra gu Ba ay ha m Bo as liv Gu ia y Ba ana rb ad Ec os El uado Sa r lv a Ho dor nd u Ja ras m Su aica rin am e

Fi nl an d Ja pa n OE CD Ko re a EU 25 Ire lan d Sp ain Ch in a

Sources: ITU

Note: Internet hosts refer to the number of computers directly connected to the worldwide Internet network.

•

The number of Internet hosts is a barometer of a country’s capacity to produce, distribute and use local content for domestic as well as international purposes. The signiﬁcant difference in the order of magnitude in the number of Internet hosts between OECD and Latin American and Caribbean countries is an indicator of the magnitude of the fundamental challenge facing the region. If the Internet is to become a tool of socioeconomic development, the countries of Latin America and the Caribbean need to expand both broadband and the production of content in response to local demand.

•

The growth in the number of Internet hosts, particularly in Argentina, Belize, Brazil, Chile, Costa Rica, Dominican Republic, Mexico and Uruguay, demonstrates that Internet development during the four-year period is progressing in response to demand in these countries.

INFORMATION AND COMMUNICATION TECHNOLOGY

Annex I Technical Notes

ANNEX 1: TECHNICAL NOTES

A. Education A.1. Educational Attainment

Graph A.1.1: Average Years of Education for the Population 15+ (1960 and 2000) Average (mean) years of education for the population aged 15 and above. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available.

Graph A.1.2.a: Gross Enrollment Rate in Pre-primary (1990 and 2002) Gross enrollment ratios at the pre-primary level are the number of children enrolled in pre-primary, regardless of age, expressed as a percentage of the population in the theoretical age group (as deﬁned by the national government) for pre-primary education. Some caveats should be noted. Preschool is delivered through a variety of modalities, some formal, some informal, not all of which explicitly follow any curricula or adhere to speciﬁc learning goals. In some cases, provision of preschool centers around the care of children for working parents; in others, it serves to provide children with nutritional and health-related interventions. Gross enrollment rates thus tend to be high and, like all enrollment rates, give no indication of the type, quality or equity of service being delivered.

Graph A.1.2.b: Net Enrollment Rate in Primary (1990 and 2002) The net enrollment rate at the primary level is the number of students in the theoretical age group for primary education (as deﬁned by the national education system) enrolled in primary education, expressed as a percentage of the total population in that age group.

Graph A.1.2.c: Net Enrollment Rate in Secondary (1990 and 2002) The net enrollment rate at the secondary level is the number of students in the theoretical age group for secondary education (as deﬁ ned by the national education system) enrolled in secondary education expressed as a percentage of the total population in that age group.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph A.1.2.d: Gross Enrollment Rate in Tertiary (1990 and 2002) The gross enrollment ratio at the tertiary level is the number of students enrolled in tertiary, regardless of age, expressed as a percentage of the population of the ﬁve-year age group following on from the secondary school leaving age.

Graph A.1.3.a: Percentage of Repeaters in Primary School (2002/2003) Repetition at the primary is the total number of students enrolled in the same grade as in a previous year, expressed as a percentage of the total enrollment. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available.

Graph A.1.b: Survival Rate to Grade 5 (2001/2002) The survival rate to fifth grade is calculated on the basis of the reconstructed cohort method, which uses data on enrollment and repeaters for two consecutive years. It is defined as the percentage of a cohort of student enrolled in the first grade of a primary cycle in a given school year that are expected to reach grade 5, regardless of repetition. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available.

Graph A.1.4: Number of Years at Which over 90 Percent of the Population is Enrolled (2003) Summary indicator expressing the number of years at which 90 percent of the school-age cohort is enrolled in the education system. It excludes enrollment in the pre-primary level.

A.2. Quality

Graph A.2.1.a: The Effectiveness Gap at Age 12, 15 and 18 (2000 or closest year) An indication of how effectively an educational system turns average years in school (contact with the system) into average years of schooling can be derived by comparing cumulative sum of age-speciﬁc net enrollment rates (years in school) with the number of grades actually completed (years of school). The resulting indicator—the “effectiveness gap”—sheds light on the dynamics of system performance and education policy such as, for example, why all 18 year-olds in country X do not achieve 12 years of schooling.

ANNEX 1: TECHNICAL NOTES

Household Surveys Used for Graphs A.2.1.a, A.2.1.b and A.3.5

1 2 3 4 5

Country

Survey date is

Argentina Belize Bolivia Brazil Chile

Oct-2000 Apr-1999 Nov-Dec-2000 Sept-1999 IV Q-2000

6 7 8 9 10

Colombia Costa Rica Dominican Republic Ecuador El Salvador

III Q-2000 Jul-2000 2000 Nov-2000 2000

11 12 13 14 15

Guatemala Guyana Haiti Honduras Jamaica

Jul-Nov 2000 1999 2001 Sept-1999 2000

16 17 18 19 20

Mexico Nicaragua Panama Paraguay Peru

2000 2001 Aug- 2000 Sept-2000 Aug-2001 IV Q-2000

21 22 23

Trinidad and Tobago Uruguay Venezuela

May-Jun 1992 2000 II Q-2000

Level at which the survey representative Urban area only National National National National National National National National National National Urban area only Urban area only National National National National National National National Urban area only Urban area only National

See also IDB: Urquiola and Calderón, 2005; and IDB: Marshall and Calderón, 2006.

Graph A.2.2.a: PISA Scores on the Reading Scale (2000 and 2003) The Program for International Student Assessment (PISA), administered by the OECD, measures how well 15-year olds are prepared to meet the challenges of life’s civic and work demands. The Program administers tests and background questionnaires to samples of 4,500-10,000 students in participating countries. Reading, mathematical, and scientiﬁc achievements are measured to assess the students’ ability to apply knowledge and skills to tasks that are deemed relevant to their future. To date, only six Latin American countries have participated in PISA tests: Argentina, Chile and Peru in 2000; and Brazil, Mexico and Uruguay in 2003. Although Mexico is a member of the OECD, it is presented with the Latin American participants in the graphs. The lowest data point on Graph A.2.2.a corresponds to the lowest 25 percent of scores, the diamond-point to average country score, and the highest point, to the highest 25 percent of scores.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph A.2.2.b: Percentage of Students at Each Level of Proficiency on PISA Reading Scale (2003) Graph A.2.2.c: Percentage of Students at Each Level of Proficiency on PISA Mathematics/Space and Shape Scale, 2003 Interpretation of PISA Scores on the Reading and on the Mathematical Scales Summary Proficiency Level Descriptors for Reading Literacy, 2003 Proficiency at Level 5 Students proficient at Level 5 on the reading literacy scale are capable of completing sophisticated reading tasks, such as managing information that is difficult to find in unfamiliar texts; showing detailed understanding of such texts and inferring which information in the text is relevant to the task; and being able to evaluate critically and build hypotheses, draw on specialized knowledge, and accommodate concepts that may be contrary to expectations. Proficiency at Level 4 Students proficient at Level 4 on the reading literacy scale are capable of difficult reading tasks, such as locating embedded information, construing meaning from nuances of language and critically evaluating a text. Proficiency at Level 3 Students proficient at Level 3 on the reading literacy scale are capable of reading tasks of moderate complexity, such as locating multiple pieces of information, making links between different parts of a text, and relating it to familiar everyday knowledge. Proficiency at Level 2 Students proficient at Level 2 are capable of basic reading tasks, such as locating straightforward information, making low-level inferences of various types, working out what a well-defined part of a text means, and using some outside knowledge to understand it. Proficiency at Level 1 Reading literacy, as defined in PISA, focuses on the knowledge and skills required to apply “reading for learning” rather than on the technical skills acquired in “learning to read”. Since comparatively few young adults in OECD countries have not acquired technical reading skills, PISA does not seek to measure such things as the extent to which 15-year-old students are fluent readers or how well they spell or recognize words, but focuses on measuring the extent to which individuals are able to construct, expand and reflect on the meaning of what they have read in a wide range of texts common both within and beyond school. The simplest reading tasks that can still be associated with this notion of reading literacy are those at Level 1. Students proficient at this level are capable of completing only the least complex reading tasks developed for PISA, such as locating a single piece of information, identifying the main theme of a text or making a simple connection with everyday knowledge.

ANNEX 1: TECHNICAL NOTES

Proficiency below Level 1 Students performing below Level 1 are unlikely to demonstrate success on the most basic type of reading that PISA seeks to measure. This does not mean that they have no literacy skills but such students have serious difficulties in using reading literacy as an effective tool to advance and extend their knowledge and skills in other areas. Students with literacy skills below Level 1 may, therefore, be at risk not only of difficulties in their initial transition from education to work, but also of failure to benefit from further education and learning opportunities throughout life.

Summary Proficiency Level Descriptors for Mathematical Literacy, 2003 Proficiency at Level 6 Students at Level 6 can conceptualize, generalize, and utilize information based on their investigations and modeling of complex problem situations. They can link different information sources and representations and flexibly translate among them. Students at this level are capable of advanced mathematical thinking and reasoning. These students can apply this insight and understandings along with a mastery of symbolic and formal mathematical operations and relationships to develop new approaches and strategies for attacking novel situations. Student at this level can formulate and precisely communicate their actions and reflections regarding their findings, interpretations, arguments, and the appropriateness of these to the original situations. Proficiency at Level 5 Students at Level 5 can develop and work with models for complex situations, identifying constraints and specifying assumptions. They can select, compare, and evaluate appropriate problem-solving strategies for dealing with complex problems related to these models. Students at this level can work strategically using broad, well-developed thinking and reasoning skills, appropriate linked representations, symbolic and formal characterizations, and insight pertaining to these situations. They can reflect on their actions and formulate and communicate their interpretations and reasoning. Proficiency at Level 4 Students at Level 4 can work effectively with explicit models for complex concrete situations that may involve constraints or call for making assumptions. They can select and integrate different representations, including symbolic, linking them directly to aspects of real-world situations. Students at this level can utilize well-developed skills and reason flexibly, with some insight, in these contexts. They can construct and communicate explanations and arguments based on their interpretations, arguments, and actions. Proficiency at Level 3 Students at Level 3 can execute clearly described procedures, including those that require sequential decisions. They can select and apply simple problem solving strategies. Students at this level can interpret and use representations based on different information sources and reason directly from them. They can develop short communications reporting their interpretations, results and reasoning.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Proficiency at Level 2 Students at Level 2 can interpret and recognize situations in contexts that require no more than direct inference. They can extract relevant information from a single source and make use of a single representational mode. Students at this level can employ basic algorithms, formulas, procedures, or conventions. They are capable of direct reasoning and making literal interpretations of the results. Proficiency at Level 1 Students at Level 1 can answer questions involving familiar contexts where all relevant information is present and the questions are clearly deﬁ ned. They are able to identify information and to carry out routine procedures according to direct instructions in explicit situations. They can perform actions that are obvious and follow immediately from the given stimuli. Source: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2003.

A.3. Equity

Graph A.3.1: Average PISA Scores on the Mathematics Scale by Socioeconomic Quartile (2003) Refer to the technical note for graph 2.2.a, 2.2.b and 2.2.c for details regarding PISA scores. The socioeconomic status index is derived from students’ responses on parental occupation. For more information, see Annex A.1. of “Learning for Tomorrow’s World – First Results from PISA 2003”, OECD 2004 (http://www.pisa.oecd.org/dataoecd/58/57/33918098.pdf ) and refer to Ganzeboom, H., P. de Graaf and D. Treiman (1992) “A Standard International Socio-Economic Index of Occupational Status”, Social Science Research 21: 1-56. The lowest value on Graph A.3.1 corresponds to the score of the bottom socioeconomic quartile, the diamond-point to the mean value and the highest value to the score of the top socioeconomic quartile.

Graph A.3.2: Social Exclusion in Education: Current School Enrollment Rates by Group in Bolivia, Brazil, Guatemala and Paraguay (2000 or closest year) Current enrollment is deﬁned as a yes or no response for whether the child is enrolled in school the given survey year (see notes for Graph A.2.1.a). In all cases respondents select the category they feel is most appropriate. To date, only seven countries –(four of which are included here: Bolivia, Brazil, Guatemala and Paraguay; the other three are Panama, Honduras and Mexico) include variables related to racial, ethnic or linguistic minorities in their household surveys. It should be noted that no ofﬁcial category of “social exclusion” exists in any household survey and that the categories that do exist are not strictly comparable across countries. For example, Bolivia uses a category of “group;” Brazil of “skin color;” Guatemala of “predominant ethnic

ANNEX 1: TECHNICAL NOTES

group;” and Paraguay of “language spoken.” For instance, for Brazil, the category “parda” is loosely defined as a mixture of black and white. Country-specific graphs thus are presented. Gaps are calculated by subtracting the average for the specific group from the reference category (Non indigenous/White, Ladino or Spanish only speaker, respectively).

Graph A.3.3: Education Gini Index for the Population 15+ (1960 and 2000) (Taken from Thomas, Wang, and Fan, 2003) Similar to the income Gini coefficient, the education Gini measures the ratio to the mean (average years of schooling) of half of the average schooling deviations between all possible pairs of people. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available. Data on the distribution of schooling differ from income data in two aspects: First, years of schooling is a discrete variable, and as a result, the education Lorenz curve is a kinked line with several kink points. Second, the education Lorenz Curve is truncated along the horizontal axis. In many developing countries a big proportion of population is illiterate (schooling=0). Some may well argue that there is a problem in measuring inequality where a large proportion of the population has no schooling. A discontinuity appears when a few people get some schooling and everyone else has 0 years of schooling. In that case Gini and Theil indices will be very high, and other inequality measures (e.g. the mean logarithmic deviation) are not defined when the basic variable takes the value of zero. It is assumed that the human capital of those with no schooling is not zero. In this second step, we define human capital as an exponential function of school attainment with a rate of return of r, shown in equation (1).

hi = e

r• yi

(1)

hi is the human capital of individual i; yi is the number of years of schooling of individual i; r is the average rate of return of schooling. The Gini coefﬁcient of the human capital, as deﬁned above, then is calculated and is shown in equation (2).

⎛ 1 ⎞n ⎟⎟∑ ∑ pi hi − h j p j Hgini = ⎜⎜ ⎝ µ H ⎠ i =1 j <i

(3)

Hgini is the Gini coefficient of the human capital as defined in (1), and µH is the average human capital of the concerned population. The Gini coefficient varies between 0 and 1, with 0 indicating a perfect distribution of schooling and 1, absolute inequality.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Graph A.3.4: Average Years of Schooling by Cohort (1930-1975) and Gender (based on 14 Latin American countries) The values for Latin America presented in graph A.3.1 are the results of a simple average of the years of schooling for 1930 to 1975 cohorts (5 years interval) corresponding to the 14 countries for which data are available: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Panama, Paraguay, Uruguay and Venezuela. All data come from the household surveys used in A.2.1.a and A.2.1.b.

A.4. Expenditure

Graph A.4.1: Public Spending on Education as a Percentage of GDP (1990 and 2002) Public expenditure on public education including any subsidy to private education at the primary, secondary, and tertiary levels, as a percentage of Gross Domestic Product.

Graph A.4.2: Expenditure Per Student as a Percentage of Per Capita GDP by Level (2002) Public expenditure per student (by level) is the public current spending on education divided by the total number of students by level, as a percentage of GDP per capita.

Graph A.4.3: Relationship Between Expenditure per Student and Average Combined PISA Score for Reading, Math and Science (2000) Average combined PISA score for math and reading regressed on cumulative per student expenditure (PPP) through age 15.

A.5. Public-private supply

Graph A.5: Percentage of Private Enrollment by Level (2002/2003) Enrollment in private educational institutions at a given level of education expressed as a percentage of total enrollment at the same level. “Private” refers to all educational institutions not operated by a public authority, whether or not they receive ﬁ nancial support from such authorities. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available.

ANNEX 1: TECHNICAL NOTES

6. Connection Between Education, Labor Markets and the Economy

Graph A.6.1: Private Returns to Secondary and Tertiary Education (early and late 1990s) Returns to secondary education calculated as the log of the wage difference per year of education with respect to the primary level, assuming completion of the secondary level. Returns to tertiary education calculated as the log of the wage difference per year of education with respect to the secondary level, assuming completion of the tertiary level.

Graph A.6.2: Distribution of the Unemployed by Level of Education (2000) Percentage of total unemployed persons by level of educational attainment. Average for Latin America and the Caribbean calculated as the simple mean of countries for which data are available.

Graph A.6.3: Growth Decomposed by Contributing Factors (1972-2000) Total factor productivity (TFP) is the weighted average productivity of all inputs, where the weights to these inputs are their shares in the total cost of production. The total factor productivity approach decomposes the sources of growth into two general categories, factor accumulation and productivity growth, and allows the contribution of each to be estimated. The method (Uzawa-Lucas model) rests on an econometric estimation of the production function for a given economy. In this production function, the output is determined by a combination of physical and human capital, labor, and technology.

The Uzawa-Lucas Model: The production function of the economy is represented by:

Yt = K tα ( At ht Lt )

1−α

(1− s H )

1−α

The production function depends on, physical capital (Kt ), technology (At ), and human capital (ht ), and labor (Lt ). Where (1– sH ) is the share of the workers’ time devoted to market production. Taking the logs of the production function and each derivative against time, we can estimate the contribution of each factor to growth and deduce TFP by taking the residual of the equation. The residual represents the share of growth not explained by the above-mentioned factors, and thus provides an estimate of productivity increase.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

(

)

(

)

(

)

(

) (

ln Yt = α ln K t + 1− α ln At + 1− α ln ht + 1− α ln Lt + 1− − α ln 1− s H .

)

Yt K A h L = α t + (1− α ) t + (1− α ) t + (1− α ) t Yt Kt At ht Lt

Yt K h L A − α t − (1− α ) t − (1− α ) t = (1− α ) t Yt Kt ht Lt At

The total factor productivity in this model is deﬁ ned as one minus the elasticity of substitution of capital to labor, times the growth rate of human capital plus the growth rate of technology. .

⎛h A TFP = (1− α )⎜⎜ t + t ⎝ ht At

⎞ ⎟⎟ ⎠

Defi nition of Variables:

α→

Elasticity of Substitution: Is the rate at which one factor of production can be exchanged for another one in the production function. We assume that and , meaning that we can exchange 0.7 units of labor for 0.3 units of capital.

→

Gross Capital Formation (used to calculate the variable Capital Stock 1): consists of outlays on additions to the ﬁ xed assets of the economy plus net changes in the level of inventories. The variable is taken from the expenditure GDP and its used to calculate the capital stock. (Source: National Accounts, UN Statistics.)

As an additional measure we used the Gross Fixed Capital Formation (used to calculate the variable Capital Stock 2): consists of additions to ﬁ xed assets excluding inventories. Kt

→ Physical

Capital Stock: capital stock is calculated according to the law of motion of

capital (Kt = It + (1- δ)Kt-1. It is assumed that the capital fully depreciated in 1970 and from then on it is accumulated.

δ→

Depreciation: A loss in the value of property due to physical deterioration and wear or to obsolescence and lack of adaptability. Physical capital is assumed to depreciate at a constant rate of 6 percent per year.

→

Labor Force: The group of people who have a potential for being employed, this is the population 16 years of age and over. This variable has been taken from the World Bank Development Indicators (WDI).

→

Human Capital: We used three different human capital measures. All of these were calculated using data from Barro and Lee, International data on educational attainment. The following variables are constructed for the population 15 years of age and over, for the period 1972-2000.

ANNEX 1: TECHNICAL NOTES

B. Science and Technology Data for trends in this section are drawn primarily from the following sources: -

Red de Indicadores de Ciencia y Tecnología (RICYT)

Organization for Economic Cooperation and Development (OECD)

United States Patent and Trademark Ofﬁce (USPTO)

The World Bank World Development Indicators (WDI)

The US National Science Foundation (NSF)

The availability of data for Latin America and the Caribbean is limited both in scope and in depth. The larger, more economically developed countries, Brazil, Chile, Mexico and Argentina, tend to have both a more developed S&T capacity and better data, but even for these countries, there are gaps with respect to speciﬁc areas of reporting and for particular years.

B.1. Human Resources

Graph B.1.1. Researchers per 1000 Labor Force For Latin American and Caribbean, the data are taken from RICYT and refer to researchers per 1000 economically active population (EAP). For OECD and China, the data come from the OECD database and refer to researchers per 1000 labor force, which comprises all persons who fulﬁll the requirements for inclusion among the employed or the unemployed during a speciﬁed brief reference period. In the case of Argentina, Bolivia, Brazil, Chile, Colombia, Nicaragua, Paraguay, Trinidad and Tobago and Uruguay the researchers include R&D fellows. In the case of Brazil, the EAP reported for 2000 is based on data from 1999. For Finland, from 1998 to 2003, the data reported are for university graduates rather than researchers. And in the case of Korea, from 1995 to 2003, the data exclude R&D in the social sciences and humanities.

Graph B.1.2: Researchers by Sector of Employment, 1995 (or earliest available) and 2003 (or latest available) These data indicate the number of researchers as a percentage of the national total.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

The sectors of employment for OECD data are: government, business sector, higher education, and private nonproﬁt institutions. The data for Finland, refer to university graduates instead of researchers; in Korea, the ﬁgures exclude R&D in the social sciences and humanities; and in the USA, the government sector is based refers to civilian employment only and excludes defense.

Graph B.1.3: Number of PhDs per 100,000 inhabitants, 1995 (or earliest available) and 2003 (or latest available) The data cover graduates of PhD programs in the physical and social sciences. Arts and Humanities are not included.

B.2. Level and Stucture of R&D

Graph B.2.1: R&D Expenditure as percent of GDP, 1995 (or earliest available) and 2003 (or latest available) Based on the definition used by Frascati Manual, R&D expenditures refer to Gross Domestic Expenditure on R&D, a measure of R&D performed within a country’s borders, including that of foreign firms but excluding affiliates abroad. Data on Panama include the expenditure of the Smithsonian Tropical Research Institute, which represents 29 percent of the national total. The data for Venezuela for 1994, 1995 and 1996, incude expenditures of the manufacturing industry. Korea excludes R&D in the social sciences and humanities, and the United States excludes most or all capital expenditure.

B.3. Outcomes

Graph B.3.1: Patents Granted by USPTO, 1995 and 2003 The USPTO is the most frequent locus of patenting outside the region by citizens of Latin American and the Caribbean. These data show the utility patents granted by the US Patent and Trade Ofﬁce; country of origin of the ﬁrst-named inventor at the time of grant.

ANNEX 1: TECHNICAL NOTES

Graph B.3.2.a: Scientific and Technical Journal Articles per 100,000 inhabitants, 1995 and 2001 Based on the definition of the National Science Foundation, the scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.

Graph B.3.2.b: S&E Article Output of Emerging and Developing Countries by Region. 1988-2001 Developing and emerging countries are those classiﬁed as low or middle income by the World Bank. Article counts are assigned to the country on the basis of the institutional addresses listed in the article. For articles with multiple –country authors, counts are apportioned to each country on the basis of the proportion of authors from each country. Articles with institutional authors in Hong Kong are included in China (Hill, 2004).

Graph B.3.2.c: Portfolio of S&E Articles for Seven LAC Countries-1988-2001 The seven countries are Argentina, Brazil, Chile, Colombia, Costa Rica Mexico, and Venezuela. Article counts are assigned to the country on the basis of the institutional addresses listed on the article. For articles with multiple-country authors, counts are apportioned to each country on the basis of the proportion of authors from each country. Fields are defined and classified by CHI Research, Inc. Computer science is included in engineering and technology. Social and behavioral sciences consist of psychology, social sciences, health sciences, and professional fields (Hill, 2004).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C. Information and Communication Technology General Note Data compiled by the International Telecommunication Union (ITU) cover the public telecommunication sector. Communication data come from an annual questionnaire sent to telecommunication authorities and operating companies. These data are supplemented by annual reports and statistical yearbooks of telecommunication ministries, regulators, operators and industry associations. In some cases, estimates are derived from ITU background documents or other references. Other data are provided by the relevant international and national organizations.

C.1. Digital Divide

Graph C.1: ICT Access per 100 Inhabitants, 2001 and 2004 Data for ﬁ xed-telephone lines, mobile subscribers, internet users and PCs, are presented on the basis of 100 inhabitants, not households.

C.2. Fixed telephone lines

Graph C.2: Fixed telephone lines per 100 inhabitants, 2000 and 2004 Fixed-telephone lines refer to telephone lines connecting a customer’s equipment (e.g., telephone set, facsimile machine) to the Public Switched Telephone Network (PSTN) and which have a dedicated port on a telephone exchange. Note that for most countries, “fi xed lines” also includes public payphones and many countries also include ISDN channels in fi xed lines. Fixed telephone lines per 100 inhabitants are calculated by ITU by dividing the number of fi xed lines by the population and multiplying by 100.

C.3. Mobile telephone subscribers

Graph C.3: Mobile telephone subscribers per 100 inhabitants, 2000 and 2004 Cellular mobile telephone subscribers refer to users of portable telephones subscribing to an automatic public mobile telephone service using cellular technology that provides access to the PSTN. The per 100 inhabitants is obtained by dividing the number of cellular subscribers by the population and multiplying by 100.

ANNEX 1: TECHNICAL NOTES

C.4. Personal Computers

Graph C.4: Personal Computers per 100 inhabitants, 2000 and 2004 The estimated number of Personal Computers (PC), is presented in terms of PCs per 100 inhabitants. The ﬁgures for PCs come from the ITU annual questionnaire supplemented by other sources. There is no data for Bahamas, Dominican Republic and Haiti.

C.5. Internet Access

Graph C.5.2: Internet users per 100 inhabitants, 2000 and 2005 The graph for “Internet Users” is based on nationally reported data to ITU. In some cases, ITU surveys were carried out to obtain more precise estimates. The reported ﬁgure for Internet users is divided by the total population to obtain users per 100 inhabitants.

Graph C.5.3: Internet broadband users per 100 inhabitants, 2001 and 2005 Data on broadband users for a period of four or more years were only available for six LAC countries. The number of broadband users represents an approximation of results obtained from various national reports (see list in the bibliography) and, as such, is not precisely comparable, however, it suggests relative orders of magnitude.

Graph C.5.4: Internet Hosts, per 10,000 inhabitants, 2001 to 2004 Internet hosts refer to the number of computers directly connected to the worldwide Internet network. Note that Internet host computers are identified by a two-digit country code or a threedigit code generally reflecting the nature of the organization using the Internet computer. The number of hosts is assigned to economies based on the country code although this does not necessarily indicate that the host is actually physically located in the economy. In addition, all other hosts for which there are no country code identification are assigned to the United States. Therefore the number of Internet hosts shown for each country can only be considered an approximation. Data on Internet host computers are from Internet Software Consortium and RIPE (Réseaux IP Européens).

EDUCATION

Annex II Statistical Annex

ANNEX II: STATISTICAL ANNEX

1. Educational Attainment 1. Educational Attainment

Table A.1.1: Average Years of Schooling for the Population of Age 15 and Over Country

1960

1965

1970

1975

1980

1985

1990

Latin America & Caribbean

3.5

3.6

Argentina

5.2

5.4

Bahamas

n.a.

Barbados

5.9

5.8

Belice

n.a.

Bolivia

5.6

5.2

Brazil

3.0

3.1

Chile

5.2

5.1

5.7

Colombia

3.1

2.9

3.0

Costa Rica

4.0

4.1

3.9

Dominican Republic

2.7

2.6

Ecuador

3.2

3.3

El Salvador

2.0

Guatemala

1.5

Guyana

1995

2000

4.1

4.4

4.9

5.2

5.5

5.9

6.1

6.2

7.0

8.0

8.3

8.7

n.a.

9.1

6.8

7.4

7.8

8.2

8.5

n.a.

5.1

5.0

5.2

5.3

5.5

5.7

3.5

3.2

3.4

3.6

4.0

4.3

4.6

5.7

6.4

6.6

6.8

7.0

7.3

4.2

4.3

4.4

4.6

4.8

5.1

5.2

5.4

5.6

5.8

6.1

3.5

3.7

3.9

4.2

4.6

4.8

5.1

3.5

4.5

6.1

5.9

6.1

6.4

2.2

2.7

2.9

3.2

3.4

3.9

4.2

4.5

1.6

1.7

1.9

2.7

2.8

3.0

3.2

3.5

4.5

4.4

4.5

4.9

5.3

5.6

5.8

6.1

6.4

Haiti

0.8

1.2

1.9

2.8

2.9

2.8

2.7

Honduras

1.9

2.0

2.2

2.6

2.8

4.1

4.2

4.5

4.8

Jamaica

3.1

3.5

3.9

4.4

4.8

5.2

5.5

5.8

6.0

Mexico

2.8

2.9

3.7

4.0

4.8

5.2

6.8

7.0

7.3

Nicaragua

2.3

2.5

2.9

3.0

3.2

3.5

3.7

4.1

4.6

Panama

4.6

4.5

4.8

5.0

6.6

6.5

7.9

8.1

8.3

Paraguay

3.6

4.2

4.3

5.1

6.1

6.0

6.1

Peru

3.3

4.5

6.0

5.9

6.1

7.2

7.5

Suriname

n.a.

Trinidad & Tobago

4.8

5.0

5.4

5.7

7.2

6.8

6.9

7.1

7.4

Uruguay

5.2

5.0

5.5

6.0

6.6

6.8

7.0

7.3

Venezuela

2.9

3.0

3.2

3.6

5.4

5.6

5.0

6.6

China

n.a.

4.4

4.8

4.9

5.8

6.1

6.4

Finland

5.4

5.7

6.1

6.7

7.2

7.8

9.4

9.7

10.0

Ireland

6.4

6.5

6.8

7.1

7.4

7.8

8.8

9.1

9.3

Japan

7.8

7.6

7.4

7.8

8.5

8.7

9.0

9.2

9.5

Korea

4.2

5.4

4.9

6.6

7.9

8.7

9.9

10.6

10.8

OECD

n.a.

Spain

3.7

3.8

4.8

6.1

5.9

6.6

7.0

7.5

United States

8.5

9.1

9.5

9.7

11.9

11.6

11.7

11.9

12.0

Source: Thomas, Wang, and Fan (2003).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.1.2.a: Pre-primary Gross Enrollment Rate (%) Country Latin America & Caribbean

1990

...

1998

1999

2000

2001

2002

...

Argentina

n.a.

...

Bahamas

n.a.

...

n.a.

Barbados

n.a.

...

Belize

...

Bolivia

...

Brazil

...

Chile

...

n.a.

Colombia

...

Costa Rica

...

n.a.

...

El Salvador

n.a.

...

Guatemala

Dominican Republic Ecuador

n.a.

...

n.a.

Guyana

...

120

118

113

120

Haiti

...

n.a.

...

n.a.

Jamaica

...

Mexico

...

Nicaragua

...

Panama

...

Paraguay

...

Peru

...

Suriname

...

n.a.

...

Uruguay

...

Venezuela

...

China

...

Finland

...

Ireland

101

...

n.a.

Japan

...

Korea

...

OECD

...

Spain

...

100

102

106

111

United States

...

Honduras

Trinidad & Tobago

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

ANNEX II: STATISTICAL ANNEX

Table A.1.2.b.: Primary Net Enrollment Rate (%) Country

1990

...

1998

1999

2000

2001

2002

Latin America & Caribbean

...

n.a.

Argentina

...

n.a.

Bahamas

...

n.a.

Barbados

...

100

Belize

...

Bolivia

...

Brazil

...

n.a.

Chile

...

n.a.

Colombia

...

Costa Rica

...

Dominican Republic

...

Ecuador

...

100

El Salvador

...

n.a.

Guatemala

...

Guyana

...

Haiti

...

n.a.

Honduras

...

n.a.

Jamaica

...

Mexico

...

100

Nicaragua

...

Panama

...

100

Paraguay

...

Peru

...

100

Suriname

...

n.a.

Trinidad & Tobago

...

Uruguay

...

Venezuela

...

China

...

n.a.

Finland

...

100

Ireland

...

Japan

100

...

100

Korea

100

...

100

OECD

...

100

...

100

...

Spain United States

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.1.2.c.: Secondary Net Enrollment Rate (%) Country Latin America & Caribbean

1990

...

1998

1999

2000

2001

2002

...

n.a.

Argentina

n.a.

...

Bahamas, The

n.a.

...

n.a.

Barbados

n.a.

...

Belize

...

Bolivia

...

n.a.

Brazil

...

n.a.

Chile

...

n.a.

Colombia

n.a.

...

Costa Rica

...

n.a.

Dominican Republic

n.a.

...

Ecuador

n.a.

...

El Salvador

n.a.

...

n.a.

Guatemala

n.a.

...

n.a.

Haiti

n.a.

...

n.a.

Honduras

n.a.

...

n.a.

Jamaica

...

Mexico

...

n.a.

...

n.a.

Guyana

Nicaragua Panama

...

Paraguay

...

Peru

n.a.

...

n.a.

Suriname

n.a.

...

n.a.

Trinidad & Tobago

n.a.

...

Uruguay

n.a.

...

n.a.

...

n.a.

China

n.a.

...

n.a.

Finland

...

Ireland

...

Venezuela

Japan

...

n.a.

100

Korea, Rep.

...

OECD

...

n.a.

...

n.a.

...

Spain United States

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

ANNEX II: STATISTICAL ANNEX

Table A.1.2.d.: Tertiary Gross Enrollment Rate (%) Country Latin America & Caribbean

1990

...

1998

1999

2000

2001

2002

...

Argentina

n.a.

...

Bahamas

n.a.

...

n.a.

Barbados

...

n.a.

Belize

n.a.

...

n.a.

Bolivia

...

Brazil

...

Chile

n.a.

...

n.a.

Colombia

...

Costa Rica

...

n.a.

...

n.a.

Ecuador

...

n.a.

El Salvador

...

Guatemala

n.a.

...

n.a.

Guyana

n.a.

...

n.a.

Haiti

n.a.

...

n.a.

Honduras

...

n.a.

Jamaica

...

n.a.

Mexico

...

n.a.

Dominican Republic

Nicaragua Panama

...

Paraguay

...

n.a.

...

n.a.

...

n.a.

...

Uruguay

...

Venezuela

...

n.a.

China

...

Finland

...

Ireland

...

Japan

...

Korea

...

OECD

...

Spain

...

United States

...

Peru Suriname Trinidad & Tobago

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.1.3.a: Percentage of Repeaters in Primary School Country

1998/1999

1999/2000

2000/2001

2001/2002

2002/2003

Latin America & Caribbean

Argentina

Bahamas

n.a.

Barbados

n.a.

Belize

Bolivia

Brazil

n.a.

Chile

n.a.

Colombia

Costa Rica

n.a.

Dominican Republic

Ecuador

El Salvador

Guatemala

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

Jamaica

n.a.

Mexico

n.a.

Panama

Paraguay

Peru

Suriname

n.a.

Trinidad and Tobago

Uruguay

Venezuela

Nicaragua

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

ANNEX II: STATISTICAL ANNEX

Table A.1.3.b.: Survival Rate at Grade 5 in Primary School Country

1998/1999

1999/2000

2000/2001

2001/2002

Latin America & Caribbean

Argentina

Bahamas

n.a.

Barbados

100

Belize

n.a.

Bolivia

Brazil (Survival at grade 4)

n.a.

Chile

100

n.a.

Colombia

Costa Rica

n.a.

Dominican Republic

Ecuador

El Salvador

Guatemala

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

Jamaica

n.a.

Mexico

n.a.

Panama

Paraguay

Peru

Suriname

n.a.

Trinidad and Tobago

100

n.a.

Uruguay

n.a.

Venezuela

n.a.

China

100

Finland

100

Ireland

Japan

n.a.

Korea

100

Nicaragua

OECD

n.a.

Spain

n.a.

United States

n.a.

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.1.4: Enrollment Rates Over 90 Percent (2003)

Country

Ending age Number of years at of compulsory which over 90% of the education population is enrolled

Age range at which over 90% of the population is enrolled

Brazil*

7 - 14

Chile

7 - 15

Jamaica

7 - 13

Mexico

6 - 12

Paraguay*

6 - 12

Peru*

6 - 14

Uruguay*

6 - 15

China

6 - 13

Finland

6 - 18

Korea

6 - 17

United States

6 - 16

Source: OECD. www.oecd.org/edu/eag2005, Annex 3 for notes. Note: Ending age of compulsory education is the age at which compulsory schooling ends. For example, an ending age of 18 indicates that all students under 18 are legally obliged to participate in education. Mismatches between the coverage of the population data and the student/graduate data mean that the participation/graduation rates for those countries that are net exporters of students may be underestimated (for instance, Luxembourg) and those that are net importers may be overestimated. * Year of reference 2002

ANNEX II: STATISTICAL ANNEX

A.2. Quality

Table A.2.1: The Effectiveness Gap at Age 12, 15 and 18 Ages Country

Latin America & Caribbean

0.0

0.3

0.5

0.6

0.8

0.9

1.1

1.2

1.4

1.5

1.6

1.7

2.0

Argentina

0.0

0.3

0.4

0.5

0.6

0.8

1.2

1.3

1.4

Bahamas

n.a.

Barbados

n.a.

Belize

0.0

0.3

0.4

0.5

0.7

0.8

1.2

1.8

2.2

2.6

2.8

3.0

3.7

Bolivia

0.0

0.4

0.6

0.9

0.7

1.4

1.0

1.3

1.4

1.3

1.2

1.9

Brazil

0.0

0.3

0.6

0.8

1.0

1.3

1.6

1.9

2.2

2.4

2.7

3.1

3.7

Chile

0.0

0.3

0.4

0.5

0.6

0.7

0.8

0.9

C. Rica

0.0

0.7

0.8

1.1

1.2

1.3

1.4

1.6

1.7

1.8

Dom.Rep.

0.0

0.3

0.5

0.8

1.1

1.5

1.6

2.0

2.2

2.4

2.5

3.0

Ecuador

0.0

0.2

0.3

0.4

0.7

0.9

1.0

1.1

1.2

1.4

1.7

El Salv.

0.0

0.1

0.3

0.4

0.7

0.9

1.0

1.2

1.1

1.3

1.6

1.7

Guatemala

n.a.

0.0

0.4

0.7

0.9

1.2

1.4

1.8

1.7

1.8

1.9

1.8

Guyana

n.a.

Haiti

0.0

0.2

0.4

0.6

0.9

1.3

1.5

1.9

2.1

2.3

2.6

2.7

3.1

Honduras

0.0

0.6

0.8

1.0

1.1

1.2

1.4

1.5

1.4

1.6

1.8

Jamaica

0.0

0.4

0.5

0.7

0.6

0.8

0.9

1.0

1.5

1.7

3.0

Mexico

0.0

0.1

0.2

0.3

0.4

0.6

0.5

0.6

1.1

Nicaragua

0.0

0.3

0.6

0.9

1.2

1.5

1.6

1.8

1.9

2.0

2.4

2.5

3.2

Panama

0.0

0.3

0.5

0.6

0.9

0.8

0.9

1.1

Peru

0.0

0.3

0.5

0.6

0.7

0.8

1.0

1.3

1.4

1.3

Suriname

n.a.

Trinidad & Tobago

n.a.

Uruguay

0.0

0.1

0.2

0.4

0.5

0.7

1.0

0.9

1.2

1.5

1.9

Venezuela

0.0

0.1

0.3

0.4

0.5

0.6

0.7

0.9

1.2

1.4

1.5

1.6

1.8

Source: IDB: Urquiola and Calder贸n (2005), based on Household Surveys, Mecovi, CEPAL and IDB.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.2.2.a: Mean PISA Scores in Reading and Scores of the Top Income Quartile and Bottom Quartile

Country Uruguay (2003)

Mean Score

Lowest 5%

Lowest 25%

Highest 25%

Highest 5%

Difference between extremes (5%)

434

224

355

518

628

404

Argentina (2000)

418

232

344

495

589

347

Chile (2000)

410

257

350

472

555

298

Brazil (2003)

403

214

328

479

581

367

Mexico (2003)

400

238

335

467

552

314

Peru (2000)

327

175

259

392

489

314

Finland (2003)

543

400

494

599

666

266

Korea (2003)

534

393

484

590

660

267

Ireland (2003)

515

364

460

577

647

Japan (2003)

498

310

431

574

652

342

USA (2003)

495

319

429

568

651

332

OECD (2003)

494

318

430

565

646

328

Spain (2003)

481

313

421

548

625

312

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

Table A.2.2.b: Percentage of Students at Each Level of Proficiency on the Reading Scale (2003) Below Level 1 Level 1 Level 2 Level 3 Level 4 Level 5 (below 335 (from 335 to (from 408 to (from 481 to (from 553 to (above 625 Country points) 407 points) 480 points) 552 points) 625 points) points) Brazil

Mexico

Uruguay

Finland

Ireland

Japan

Korea

OECD

Spain

USA

Source: OECD PISA Database, www.pisa.oecd.org.

ANNEX II: STATISTICAL ANNEX

Table A.2.2.c: Percentage of Students at Each Level of Proficiency on PISA Mathematics/Space and Shape Scale (2003) Proficiency levels Below Level 1 Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 (below 358 (from 358 to (from 421 to (from 483 to (from 545 to (from 607 to (above 668 score 420 score 482 score 544 score 606 score 668 score score Country points) points) points) points) points) points) points) Brazil

54.8

22.7

13.6

6.2

2.0

0.6

0.1

Mexico

39.1

27.8

20.6

9.4

2.5

0.5

0.0

Uruguay

29.3

23.3

22.9

15.2

6.7

2.2

0.4

Finland

2.5

7.3

17.0

25.5

24.6

15.2

7.9

Ireland

10.7

16.9

25.4

23.0

15.4

6.8

1.8

Japan

4.2

7.4

13.9

20.0

21.9

18.2

14.3

Korea

4.8

8.4

14.7

19.7

19.9

16.5

16.0

OECD

10.6

14.2

20.4

21.5

17.2

10.4

5.8

Spain

10.1

16.7

25.5

24.7

15.3

6.0

1.6

USA

12.1

18.2

24.7

22.0

14.2

6.5

2.3

Source: OECD PISA Database, www.pisa.oecd.org.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.3. Equity

Table A.3.1: Average PISA Scores on the Mathematics Scale by Socioeconomic Quartile (2003) Country

Average Score Bottom quarter Second quarter Third quarter

Top quarter

Uruguay

422

388

415

430

478

Mexico

385

357

374

394

424

Brazil

356

317

346

372

410

Finland

544

515

536

552

576

Ireland

503

471

496

513

541

Japan

534

505

534

543

568

OECD

500

455

493

516

548

Spain

485

454

475

496

519

USA

483

448

477

497

530

Source: OECD/PISA Database, www.pisa.oecd.org.

Table A.3.2: Current School Enrollment Rates by Group in Bolivia, Brazil, Guatemala and Paraguay (2000) Age 6-11

Age 12-14

Country

Community

Rate

“Gap”

Rate

“Gap”

Rate

“Gap”

Bolivia

Indigenous

Non Indigenous

----

Indigenous

-5

Black

Parda

White

----

Indigenous

Ladino

----

Speaks Guaraní Only

Speaks Guaraní and Spanish

Speaks Spanish Only

----

Speaks Other

Brazil

Guatemala

Paraguay

Source: IDB: Marshall and Calderón (2005), based on Household Surveys, Mecovi, CEPAL and IDB.

Age 15-19

ANNEX II: STATISTICAL ANNEX

Table A.3.3: Education Gini Index for the Population of Age 15 and Over Country

1960

1965

1970

1975

1980

1985

1990

1995

2000

Latin America & Caribbean

0.51

0.49

0.47

0.44

0.43

0.42

Argentina

0.34

0.35

0.31

0.33

0.29

0.32

0.27

Bahamas

n.a.

Barbados

0.25

0.28

0.18

0.19

0.30

n.a.

0.29

n.a.

0.33

n.a.

Bolivia

0.49

0.52

0.53

0.52

0.51

0.49

0.48

0.47

Brazil

0.63

0.59

0.54

0.47

0.48

0.44

0.43

Chile

0.41

0.42

0.38

0.39

0.37

Colombia

0.53

0.49

0.51

0.46

0.47

0.49

0.48

Costa Rica

0.39

0.40

0.38

0.40

0.41

0.42

Dominican Rep.

0.49

0.51

0.53

0.55

0.57

0.58

0.57

0.55

Ecuador

0.51

0.52

0.51

0.47

0.39

0.44

0.45

0.44

0.43

El Salvador

0.68

0.66

0.62

0.60

0.49

0.51

0.53

Belize

Guatemala

0.75

0.74

0.73

0.63

0.62

0.61

0.59

Guyana

0.32

0.35

0.33

0.35

0.33

0.34

0.33

Haiti

0.93

0.92

0.85

0.78

0.64

0.65

0.68

0.70

Honduras

0.67

0.65

0.62

0.59

0.57

0.48

0.47

0.45

0.43

Jamaica

0.36

0.40

0.30

0.33

0.34

0.35

0.34

Mexico

0.56

0.57

0.51

0.50

0.47

0.38

0.37

0.36

Nicaragua

0.69

0.67

0.65

0.63

0.62

0.60

0.58

0.56

0.52

Panama

0.44

0.46

0.47

0.46

0.38

0.40

0.34

0.33

Paraguay

0.41

0.42

0.40

0.39

0.38

0.36

0.37

0.36

Peru

0.56

0.57

0.49

0.41

0.42

0.36

n.a.

Suriname Trinidad & Tobago

0.33

0.34

0.31

0.21

0.27

0.29

0.28

Uruguay

0.39

0.38

0.39

0.35

0.36

0.33

0.34

0.35

Venezuela

0.58

0.55

0.60

0.58

0.43

0.46

0.35

0.38

n.a.

0.55

0.51

0.49

0.42

0.40

0.38

China Finland

0.24

0.26

0.27

0.29

0.31

0.29

0.23

0.22

Ireland

0.30

0.29

0.28

0.24

Japan

0.29

0.26

0.28

0.26

0.24

Korea

0.55

0.44

0.51

0.39

0.33

0.28

0.21

0.20

0.19

OECD

n.a.

Spain

0.38

0.39

0.28

0.35

0.39

0.37

0.36

0.35

United States

0.27

0.23

0.22

0.24

0.12

0.15

0.17

0.16

Source: Thomas, Wang, and Fan (2003).

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.3.4: Average Years of Schooling by Cohort (1930-1975) and Gender (based on 14 Latin American countries) Cohort Country

Survey 1930 1935 1940 1945 1950 Year Male Fem. Mean Male Fem. Mean Male Fem. Mean Male Fem. Mean Male Fem. Mean

Latin America Argentina

3.5

3.8

4.6 7.8

4.0

4.4

5.0

7.4

7.6

4.4

4.8

5.9 8.9

5.3

5.7

6.7

8.6

8.8

9.2

6.0

6.4

9.5

9.3

Oct-00

7.5

6.9

7.1

8.1

7.5

7.8

Bolivia

Nov-Dec 2000

4.1

1.7

n.a. 4.0

2.5 n.a. 4.7

2.1

n.a. 5.5

2.6 n.a. 6.0

5.1 n.a.

Brazil

Sep-99

2.1

2.6

3.0

2.9

4.2

3.9

4.1

4.6

4.8

4.7

Chile

IV Q-2000

5.8

5.5

5.7

6.2

5.7

5.9

6.8

6.6

6.7

8.0

7.3

7.6

8.9

8.5

8.7

Colombia

III Q-2000

4.0

3.3

3.6

4.5

4.1

4.3

4.9

4.5

4.6

5.9

5.5

5.7

6.6

6.3

6.4

Costa Rica

Jul-00

3.5

3.3

3.4

4.1

3.3

3.7

5.3

4.3

4.8

5.4

4.6

5.0

6.9

6.4

6.7

Ecuador

Nov-00

4.3

3.5

3.9

4.4

3.7

4.1

5.1

4.6

4.8

5.7

5.4

5.6

7.1

6.4

6.8

El Salvador

2000

1.8

1.3

1.5

3.1

2.1

2.6

3.1

2.2

2.6

4.2

3.1

3.6

5.6

3.5

4.5

Guatemala

Jul-Nov 2000

1.8

0.9

1.4

2.4

2.0

2.2

1.9

1.2

1.5

2.8

1.6

2.1

3.7

2.0

2.8

Honduras Panama Paraguay Uruguay Venezuela

4.1

1999

1.8

2.5

2.3

2.4

3.5

3.3

3.4

4.2

3.6

3.9

5.7

4.1

4.8

Aug-00

5.0

4.8

4.9

5.6

5.9

5.7

6.3

5.7

6.0

7.6

7.1

7.3

6.9

7.1

Sept 2000 4.5 Aug 2001

2.8

3.7

5.6

3.9

4.7

4.4

3.7

4.0

5.5

5.8

5.6

5.9

5.0

5.2

2000

6.7

7.0

6.9

7.6

7.3

7.4

6.9

7.9

7.4

8.1

8.8

8.5

8.9

9.0

II Q-2000

4.6

3.4

3.8

4.1

3.9

4.0

6.3

5.3

5.8

6.5

6.1

6.3

7.2

7.1

ANNEX II: STATISTICAL ANNEX

Table A.3.4: Continued Cohort Country

Survey 1955 1960 1965 1970 1975 Year Male Fem. Mean Male Fem. Mean Male Fem. Mean Male Fem. Mean Male Fem. Mean

Latin America Argentina

7.5 Oct-00

6.9

9.5 10.2

7.3

7.6

7.5

7.6

8.1

8.2

8.3

8.5

8.9 8.7

9.8 10.1 10.4 10.2 10.5 10.9 10.7 10.4 10.9 10.6 10.6 11.2 10.9

Bolivia

Nov-Dec 2000

7.2

4.9 n.a.

8.2

5.5 n.a.

8.7

6.5 n.a.

8.8

7.1 n.a.

9.6

8.0 n.a.

Brazil

Sep-99

5.3

5.6

5.5

6.4

6.0

5.9

7.0

6.1

6.9

6.1

7.2 6.7

Chile

IV Q-2000

9.7

9.5

9.6

9.5

9.9

9.7 10.0 10.4 10.2 10.4 10.5 10.5 11.1 11.2 11.1

Colombia

III Q-2000

7.6

7.3

7.4

7.6

7.7

8.3

8.7

8.5

8.3

8.9

8.6

8.9

9.4 9.2

Costa Rica

Jul-00

7.6

7.5

7.6

7.5

7.7

7.6

8.2

7.8

8.0

7.2

7.7

7.5

7.2

7.8

7.5

Nov-00

7.9

7.0

7.4

8.3

7.8

8.1

9.0

8.9

9.0

9.3 10.1

9.7

El Salvador

2000

5.5

4.7

5.1

5.9

4.9

5.4

7.1

6.3

6.7

7.1

6.8

7.0

7.4

7.7

7.6

Guatemala

Jul-Nov 2000

4.8

3.0

3.9

4.5

3.6

4.1

5.1

3.9

4.4

5.9

4.2

5.0

6.3

4.9 5.5

1999

6.9

5.7

6.2

5.6

5.9

5.8

6.5

7.0

6.8

6.9

6.7

7.3

Aug-00

9.0

8.3

8.7

8.8

9.4

9.1

8.9 10.2

9.7

9.1 10.0

9.6

9.8 10.3 10.1

Sept 2000 7.0 Aug 2001

5.8

6.4

6.9

7.0

6.9

7.7

7.9

7.8

7.7

7.8

8.0

Ecuador

Honduras Panama Paraguay Uruguay Venezuela

6.5

7.9

6.5

7.1

8.4 8.2

2000

9.5

9.6

9.8

9.7

9.8

9.7 10.3 10.0

9.9 11.2 10.5

II Q-2000

7.8

8.0

7.9

7.8

8.3

8.0

8.3

8.8

8.6

8.5

8.6

9.6

9.1

9.7 9.2

Source: IADB, SDS/EST, based on Household Surveys.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.4. Financing

Table A.4.1: Public Spending on Education as a Percentage of GDP Country

1990

...

1998

1999

2000

2001

2002

Latin America & Caribbean

2.8

...

3.9

4.4

4.3

Argentina

1.1

...

4.0

4.5

4.6

4.8

4.0

Bahamas

4.0

...

n.a.

Barbados

7.8

...

5.2

5.6

7.1

7.0

7.6

Belice

4.6

...

5.4

5.0

5.8

5.6

5.2

Bolivia

2.3

...

5.5

5.7

5.5

6.0

6.3

Brazil

n.a.

...

5.2

n.a.

4.3

4.2

n.a.

Chile

2.5

...

3.7

3.8

3.9

n.a.

4.2

Colombia

2.4

...

3.9

4.4

4.3

4.5

5.2

Costa Rica

4.4

...

n.a.

4.9

4.4

4.7

5.1

Dominican Rep.

n.a.

...

2.5

n.a.

2.3

Ecuador

4.3

...

2.6

1.8

1.3

1.0

n.a.

El Salvador

1.9

...

2.3

2.5

2.9

Guatemala

1.4

...

n.a.

Guyana

3.4

...

8.6

8.5

8.4

Haiti

1.5

...

n.a.

Honduras

n.a.

4.0

n.a.

Jamaica

4.5

...

n.a.

5.7

6.1

4.9

Mexico

3.6

...

4.2

4.4

n.a.

5.2

5.3

Nicaragua

3.4

...

2.9

3.8

3.9

3.7

3.1

Panama

4.7

...

5.0

4.8

5.0

4.3

4.5

Paraguay

1.1

...

4.5

4.8

4.9

4.8

4.4

Peru

2.8

...

3.2

3.3

n.a.

2.9

3.0

Suriname

6.4

...

n.a.

Trinidad & Tobago

3.7

...

3.3

3.7

3.8

4.2

4.3

Uruguay

2.7

...

2.5

2.8

3.2

2.6

Venezuela

3.0

...

n.a.

China

2.3

...

2.0

2.1

n.a.

Finland

5.5

...

n.a.

6.2

6.0

6.2

6.4

Ireland

4.8

...

4.4

4.3

4.4

4.3

Japan

n.a.

...

3.5

3.6

n.a.

Korea

3.3

...

3.7

3.8

3.4

4.3

4.2

OECD

5.0

...

4.9

5.1

5.2

5.4

5.7

Spain

4.2

...

4.5

4.4

4.5

United States

5.1

â&#x20AC;Ś

5.4

n.a.

5.7

n.a.

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

ANNEX II: STATISTICAL ANNEX

Table A.4.2: Expenditure Per Student as a Percentage as of Per Capita GDP by Level Country

Primary Secondary Tertiary 1990 ... 1998 1999 2000 2001 2002 1990 ... 1998 1999 2000 2001 2002 1990 ... 1998 1999 2000 2001 2002

LAC

n.a. ... n.a. 13 n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. 45 n.a. n.a. n.a.

Argentina

...

12 12 14 11 n.a. ...

14 16

17 15 n.a. ...

20 18

18 16

Bahamas

n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

Barbados

n.a. ...

17 23 21 24 n.a. ...

21 25 30 31 34 n.a. ...

Belize

...

16 14 n.a. 14 n.a. ...

18 16

Bolivia

n.a. ...

15 n.a. 12 15 n.a. ...

14 13 n.a. 10 13 n.a. ...

Brazil

n.a. ... n.a. n.a. 11 11 n.a. n.a. ... n.a. n.a. 11

Chile

...

64 51

70 n.a. n.a.

20 n.a. 12 n.a. ... n.a. n.a. n.a. n.a. n.a. 51 43 n.a. 45 44

11 n.a. n.a. ...

85 n.a. 59 51 n.a.

13 n.a. 14 n.a. 16 n.a. ...

15 n.a. 15 n.a. 16 n.a. ...

22 n.a. 19 n.a. 18

15 17

38 40 41 39 30

Colombia

n.a. ...

16 15 17 16 n.a. ...

Costa Rica

n.a. ... n.a. 16 15 15 16 n.a. ... n.a. 23 19 n.a. 23 n.a. ... n.a. 55 54 46 51

Dom. Rep. n.a. ... n.a. n.a. n.a. 6

n.a. ... n.a. n.a. n.a.

19 18 n.a. ...

n.a. ... n.a. n.a. n.a. n.a. n.a.

Ecuador

n.a. ...

3 n.a. n.a. n.a. ...

12 n.a.

n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

El Salvador

n.a. ...

9 n.a. 10 n.a. ...

n.a. 9

Guatemala

... n.a. n.a. 7

n.a. ... n.a. n.a.

n.a. ...

10 11

9 n.a. 11

n.a. ... n.a. n.a. n.a. n.a. n.a.

Guyana

n.a. ... n.a. n.a. n.a. n.a. 25 n.a. ... n.a. n.a. n.a. n.a. 22 n.a. ... n.a. n.a. n.a. n.a. 64

Haiti

Honduras

n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

Jamaica

... n.a. n.a. 15 15 14 n.a. ... n.a. n.a. 24

Mexico

...

... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

12 n.a. 14 14 n.a. ...

17 14 n.a. 18 15 n.a. ...

Nicaragua

n.a. ...

12 n.a. 17 n.a. 9

Panama

...

... n.a. n.a. 13 13 12 n.a. ... n.a. n.a. 17

Paraguay

n.a. ... n.a. n.a. n.a. n.a. 5

14 14 11 10 n.a. ...

n.a. ...

n.a. ... n.a. n.a. n.a. n.a. 62

20 19 22 14 16 n.a. ...

37 34 32 29 33

15 14 n.a. ... n.a. n.a. 55 47 28

n.a. ...

Suriname

... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

Trin. & Tob.

...

Uruguay

... n.a. n.a. 8

12 n.a. 16 16 n.a. ... 11

11 n.a. n.a.

44 45 n.a. 35 47

Peru

n.a. n.a. 7

24 23 n.a. ... n.a. n.a. 79 67 42

n.a. ... n.a. n.a. n.a. 21

12 13 n.a. 18 n.a. n.a. ... 113 145 n.a. 71 n.a.

n.a. ... n.a. n.a. 11

n.a. ... n.a. n.a. 19 23 19

Venezuela

n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ... n.a. n.a. n.a. n.a. n.a.

China

n.a. ... n.a. n.a. n.a. n.a. n.a. n.a. ...

Finland

... n.a. 18 17 18 18 n.a. ... n.a. 26 24 26 27 n.a. ... n.a. 40 38 38 37

Ireland

...

11 12 12 12 n.a. ...

18 17

Japan

n.a. ...

21 22 22 n.a. n.a. ...

20 21

21 22 n.a. n.a. ...

15 17

17 17 n.a.

Korea

...

18 17 n.a. 16 n.a. ...

15 16

7 n.a.

Spain

...

19 19 19 19 n.a. ...

26 26 25 24 25 n.a. ...

20 20

USA

n.a. ...

20 21 22 n.a. n.a. ...

24 24

29 n.a. 32 26 n.a.

10 12 n.a. n.a. n.a. n.a. ...

18 18 n.a. ...

21 24 n.a. ...

24 25 n.a. n.a. ...

62 86 n.a. n.a. n.a.

28 27 30 27 26

21 22 23

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

A.5. Public-Private Supply

Table A.5: Percentage of Private Enrollment by Level (2002/2003)

Country

Preprimary

Primary

Lower secondary. General Secondary programs

Lower Upper secondary. Upper secondary. Technical/ secondary. Technical/ vocational General vocational programs programs programs

Latin America & Caribbean

Argentina

n.a.

Bahamas

n.a.

Barbados

n.a.

Belize

100

n.a.

Bolivia

Brazil

n.a.

Chile

n.a.

Colombia

n.a.

Costa Rica

Dominican Rep.

n.a.

Ecuador

n.a.

El Salvador

n.a.

Guatemala

n.a.

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

Jamaica

n.a.

Mexico

Nicaragua

Panama

Paraguay

Peru

n.a.

100

n.a.

100

Suriname Trin. & Tob. Uruguay

n.a.

Venezuela

n.a.

China Finland

n.a.

Ireland

n.a.

Japan

n.a.

Korea

n.a.

OECD

n.a.

Spain

USA

n.a.

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from UNESCO Institute for Statistics.

ANNEX II: STATISTICAL ANNEX

A.6. Connections Between Education, Labor Markets and the Economy

Table A.6.1: Private Returns to Secondary and Tertiary Education

Coefficient of Schooling Secondary Tertiary

Late 90s

Evolution (%) of the coefficient of schooling over the 1990 decade

Country

Early 90s

Secondary Tertiary Secondary Tertiary

Argentina

1992

9.7

12.7

2001

9.1

–6%

42%

Bolivia

1990

6.3

n.a.

1999

6.4

16.1

n.a.

Brazil

1990

18.9

22.9

2001

13.8

25.7

–27%

12%

Chile

1990

19.8

2000

10.9

23.5

–16%

19%

Colombia

1990

8.1

21.1

1990

8.1

20.9

–1%

Costa Rica

1991

10.7

15.9

2000

15.6

–7%

–2%

Dominican Rep.

n.a.

1996

6.2

19.5

n.a.

Ecuador

1995

7.8

14.9

1998

12.5

n.a.

60%

n.a.

El Salvador

1995

8.3

18.3

1999

8.6

21.6

18%

Guatemala

n.a.

1998

10.7

14.6

n.a.

Honduras

1992

14.1

1999

n.a.

–31%

Mexico

1990

8.1

2001

6.6

17.9

–19%

28%

Nicaragua

1993

8.1

14.6

2001

11.9

18.5

47%

27%

Panama

1991

15.8

2000

7.7

–30%

Paraguay

1995

10.8

14.9

1998

12.5

n.a.

16%

n.a.

Peru

1991

8.3

2000

6.9

17.2

–17%

56%

Uruguay

1992

7.1

2000

8.1

13.5

14%

35%

Venezuela

1993

8.5

16.3

1999

7.4

17.6

–13%

Source: Inter-American Development Bank (2004)

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table A.6.2: Distribution of the Unemployed by Level of Education (1990 and 2000) Unemployment with primary education (% of total unemployment) Country

1990

2000 41

Unemployment with secondary education (% of total unemployment) 1990

2000

Unemployment with tertiary education (% of total unemployment) 1990 7

2000

LAC

Argentina

n.a.

Bahamas

38*

28***

50*

59***

8***

Barbados

n.a.

17**

n.a.

75**

n.a.

7**

Belize

n.a.

58**

n.a.

15**

n.a.

5**

Bolivia

n.a.

Brazil

n.a.

26**

n.a.

20**

n.a.

3**

Chile

n.a.

Colombia

26*

58*

15*

Costa Rica

Dominican Rep.

28*

n.a.

23*

n.a.

Ecuador

n.a.

El Salvador

n.a.

57***

n.a.

23***

n.a.

8***

Guatemala

n.a.

37***

n.a.

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

63***

n.a.

22***

n.a.

Jamaica

n.a.

Mexico

63*

n.a. 52

4 24*

n.a.

Nicaragua

n.a.

Panama

n.a.

Paraguay

n.a.

Suriname

n.a.

Trin. & Tob. Uruguay Venezuela

46* 70

China

n.a.

Finland

n.a.

Ireland

38**

n.a.

n.a. 58** n.a. 37

53* n.a. 17 n.a. n.a.

1**

n.a.

24** n.a.

8 n.a.

14** n.a.

n.a.

21**

16**

61**

Japan

23**

51**

26**

Korea, Rep.

OECD Spain USA

n.a.

61 n.a.

58 22

61**

n.a. 16 n.a.

n.a.

Source: World Bank, World Development Indicators Database (http://devdata.worldbank.org/dataonline/), data from International Labor Organization. * 1991 figure

** 1999 figures

*** 1998 figures

ANNEX II: STATISTICAL ANNEX

Table A.6.3: Growth Decomposition by Contributing Factors (1972-2000) GDP%

Capital Stock1 %

Labor force %

Human ktal 1 %

a/a

TFP

LAC

3.4%

0.9%

2.0%

1.0%

–0.5%

0.5%

East Asia

4.4%

1.3%

1.4%

1.2%

0.6%

1.7%

South Asia

5.0%

1.5%

1.7%

0.2%

1.9%

Eastern Europe

0.8%

3.9%

0.5%

–0.2%

–3.5%

–3.7%

Sub–Saharan Africa

2.7%

0.3%

1.9%

1.5%

–0.9%

0.5%

Labor force %

Human ktal 2 %

a/a

TFP

GDP%

Capital Stock1 %

LAC

3.4%

0.9%

2.0%

1.1%

–0.6%

0.5%

East Asia

4.4%

1.3%

1.4%

1.3%

0.4%

1.7%

South Asia

5.0%

1.5%

2.1%

–0.2%

1.9%

Eastern Europe

0.8%

3.9%

0.5%

–0.5%

–3.2%

–3.7%

Sub–Saharan Africa

2.7%

0.3%

1.9%

2.3%

–1.8%

0.5%

GDP%

Capital Stock1 %

Labor force %

Human ktal 3 %

a/a

TFP

LAC

3.4%

0.9%

2.0%

1.1%

–0.7%

0.5%

East Asia

4.4%

1.3%

1.4%

2.1%

–0.3%

1.7%

South Asia

5.0%

1.5%

2.7%

–0.8%

1.9%

Eastern Europe

0.8%

3.9%

0.5%

–0.7%

–3.0%

–3.7%

Sub–Saharan Africa

2.7%

0.3%

1.9%

2.4%

–1.9%

0.5%

GDP%

Capital Stock2 %

Labor force %

Human ktal 1 %

a/a

TFP

LAC

3.4%

0.9%

2.0%

1.0%

–0.5%

0.5%

East Asia

4.4%

1.3%

1.4%

1.2%

0.5%

1.7%

South Asia

5.0%

1.7%

1.5%

1.7%

0.0%

1.8%

Eastern Europe

0.8%

0.0%

0.5%

–0.2%

0.4%

0.2%

Sub–Saharan Africa

2.7%

0.5%

1.9%

1.5%

–1.1%

0.4%

Labor force %

Human ktal 2 %

a/a

TFP

GDP%

Capital Stock2 %

LAC

3.4%

0.9%

2.0%

1.1%

–0.6%

0.5%

East Asia

4.4%

1.3%

1.4%

1.3%

0.3%

1.7%

South Asia

5.0%

1.7%

1.5%

2.1%

–0.4%

1.8%

Eastern Europe

0.8%

0.0%

0.5%

–0.5%

0.8%

0.2%

Sub–Saharan Africa

2.7%

0.5%

1.9%

2.3%

–2.0%

0.4%

GDP%

Capital Stock2 %

Labor force %

Human ktal 3 %

a/a

TFP

LAC

3.4%

0.9%

2.0%

1.1%

–0.7%

0.5%

East Asia

4.4%

1.3%

1.4%

2.1%

–0.4%

1.7%

South Asia

5.0%

1.7%

1.5%

2.7%

–0.9%

1.8%

Eastern Europe

0.8%

0.0%

0.5%

–0.7%

0.9%

0.2%

Sub–Saharan Africa

2.7%

0.5%

1.9%

2.4%

–2.0%

0.4%

Source: Elaboration on the basis of National Accounts Statistics, UN Statistics (1970-2001), World Development Indicators (WDI), World Bank (1970-2001), and Barro and Lee (2002), International data on educational attainment (19702000). Data for Asia exclude Japan and China.

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

B. Science and Technology B.1. Human Resources

Table B.1.1: Researchers Per 1000 Labor Force (Full Time Equivalent)

Country

% Growth 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 rate

Latin America & Caribbean Argentina Bahamas Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Suriname Trinidad and Tobago Uruguay Venezuela

0.58 n.a. n.a. n.a. n.a. n.a. 0.78 0.99 n.a. n.a. n.a. 0.15 0.04 n.a. n.a. n.a. n.a. n.a. 0.54 n.a. 0.31 n.a. n.a. n.a. n.a. n.a. n.a.

0.60 n.a. n.a. n.a. n.a. n.a. n.a. 1.02 0.19 n.a. n.a. 0.23 0.04 n.a. n.a. n.a. n.a. n.a. 0.54 n.a. 0.31 n.a. n.a. n.a. n.a. n.a. n.a.

0.60 1.66 n.a. n.a. n.a. n.a. n.a. 1.03 0.20 n.a. n.a. 0.21 0.04 n.a. n.a. n.a. n.a. n.a. 0.56 0.22 0.31 n.a. n.a. n.a. n.a. n.a. n.a.

0.60 1.67 n.a. n.a. n.a. 0.20 n.a. 1.06 0.22 n.a. n.a. 0.22 0.08 n.a. n.a. n.a. n.a. n.a. 0.56 n.a. 0.43 n.a. n.a. n.a. n.a. n.a. n.a.

0.60 1.67 n.a. n.a. n.a. 0.20 n.a. 1.05 0.23 n.a. n.a. n.a. 0.08 n.a. n.a. n.a. n.a. n.a. 0.55 n.a. 0.27 n.a. n.a. n.a. n.a. 0.59 n.a.

0.61 1.67 n.a. n.a. n.a. 0.19 0.71 1.08 0.23 n.a. n.a. n.a. 0.12 n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.26 n.a. n.a. n.a. n.a. 0.61 n.a.

0.62 1.68 n.a. n.a. n.a. 0.32 n.a. 1.09 0.16 n.a. n.a. 0.12 n.a. n.a. n.a. n.a. n.a. n.a. 0.64 n.a. 0.24 0.18 n.a. n.a. n.a. n.a. n.a.

0.63 1.63 n.a. n.a. n.a. 0.30 n.a. 1.16 0.18 n.a. n.a. 0.14 n.a. n.a. n.a. n.a. n.a. n.a. 0.67 n.a. 0.24 0.18 n.a. n.a. n.a. 1.00 n.a.

0.64 n.a. n.a. n.a. n.a. n.a. n.a. 1.17 0.23 n.a. n.a. 0.12 n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.25 n.a. n.a. n.a. n.a. n.a. n.a.

n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

9.3 –2.1 n.a. n.a. n.a. 47.5 n.a. 18.7 21.2 n.a. n.a. -21.6 n.a. n.a. n.a. n.a. n.a. n.a. 25.2 n.a. –21.1 n.a. n.a. n.a. n.a. n.a. n.a.

China EU25 Finland Ireland Japan Korea OECD Spain United States

0.8 4.5 6.7 4.0 10.1 4.8 6.7 2.9 7.7

0.8 4.6 n.a. 4.3 9.2 4.7 7.0 3.1 n.a.

0.8 4.7 10.6 4.6 9.2 4.7 7.1 3.2 8.4

0.7 4.8 12.0 4.8 9.6 4.3 7.3 3.5 n.a.

0.7 5.0 12.7 4.7 9.7 4.6 7.5 3.5 9.0

1.0 5.2 13.4 4.9 9.6 4.9 7.6 4.2 9.0

1.0 5.3 14.0 5.0 10.0 6.1 7.9 4.3 9.1

1.1 5.5 14.7 5.1 9.7 6.2 7.9 4.4 9.1

1.1 5.5 15.9 5.4 10.1 6.6 n.a. 4.7 n.a.

1.2 n.a. n.a. 5.7 n.a. n.a. n.a. n.a. n.a.

37.5 22.2 137.3 35.0 0.0 37.5 17.9 62.1 18.2

Sources: RICYT, and OECD. Note: The growth rate is from 1995 (or earliest available) to 2003 (or latest available).

ANNEX II: STATISTICAL ANNEX

Table B.1.2: Researchers by Sector of Employment (Full Time Equivalent) Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Latin America & Caribbean Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

35.7

37.2

36.1

36.8

37.6

37.3

38.8

Argentina Government Business Sector

n.a.

16.3

14.5

13.9

12.2

11.9

11.3

12.4

Higher Education

n.a.

46.9

47.2

47.6

50.0

49.5

49.3

46.3

Private Nonprofit

n.a.

1.0

1.1

1.3

1.7

1.9

1.8

n.a.

Bahamas Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Barbados Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

17.5

15.0

n.a.

Business Sector

n.a.

11.3

5.0

n.a.

Higher Education

n.a.

67.0

70.0

n.a.

Private Nonprofit

n.a.

4.1

10.0

n.a.

Government

17.0

n.a.

7.9

n.a.

Business Sector

7.7

n.a.

26.7

n.a.

Higher Education

74.6

n.a.

64.7

n.a.

Private Nonprofit

0.7

n.a.

0.7

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Belize

Bolivia

Brazil

Chile

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.1.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Colombia Government

n.a.

8.3

9.1

8.9

8.4

8.0

7.7

7.9

n.a.

Business Sector

n.a.

11.1

9.8

9.0

8.2

7.5

6.9

6.7

n.a.

Higher Education

n.a.

75.7

76.4

77.5

79.0

80.8

81.7

82.0

n.a.

Private Nonprofit

n.a.

4.9

4.7

4.6

4.3

3.8

3.6

3.4

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Costa Rica

Dominican Republic

Ecuador Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

43.0

41.7

n.a.

El Salvador Government Business Sector

n.a.

Higher Education

n.a.

42.0

43.7

n.a.

Private Nonprofit

n.a.

15.0

14.6

n.a.

Guatemala Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Guyana

ANNEX II: STATISTICAL ANNEX

Table B.1.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Haiti Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

31.0

30.6

29.4

31.6

34.5

n.a.

Business Sector

10.3

11.4

11.3

16.0

16.2

n.a.

Higher Education

57.8

57.3

58.6

51.7

48.7

n.a.

Private Nonprofit

0.9

n.a.

Honduras

Jamaica

Mexico

Nicaragua Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

41.0

36.4

34.6

40.3

70.5

64.3

67.8

59.3

n.a.

Panama Government Business Sector

0.0

2.6

0.0

n.a.

Higher Education

45.8

47.9

52.9

50.5

25.6

19.9

18.1

23.6

n.a.

Private Nonprofit

13.1

13.2

12.5

9.1

3.9

15.7

14.1

17.2

n.a.

Government

n.a.

28.5

30.7

n.a.

Business Sector

n.a.

0.0

n.a.

Higher Education

n.a.

46.2

45.9

n.a.

Private Nonprofit

n.a.

25.3

23.4

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Paraguay

Peru

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.1.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Suriname Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

5.5

5.0

n.a.

13.4

n.a.

Business Sector

n.a.

4.6

5.0

n.a.

1.0

n.a.

Higher Education

n.a.

89.9

90.0

n.a.

85.7

n.a.

Private Nonprofit

n.a.

0.0

n.a.

0.0

n.a.

Trinidad and Tobago

Uruguay

Venezuela Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

35.4

32.8

33.4

31.4

27.8

25.1

23.3

22.3

n.a.

Business Sector

37.0

40.8

38.3

30.7

32.4

50.9

52.3

54.7

56.2

n.a.

Higher Education

25.3

24.0

26.7

33.2

31.7

21.3

22.6

22.0

21.6

n.a.

China Government

EU25 Government

16.7

16.6

15.8

15.7

15.2

14.7

13.5

13.2

13.4

n.a.

Business Sector

44.9

44.4

46.3

46.4

47.3

47.1

48.0

48.4

49.4

n.a.

Higher Education

37.1

37.7

36.6

36.7

36.3

37.0

37.2

37.1

n.a.

20.7

n.a.

15.0

15.3

13.7

12.9

12.3

11.9

11.3

n.a.

Business Sector

39.6

n.a.

51.9

53.0

54.6

56.9

55.1

56.6

n.a.

Higher Education

38.4

n.a.

32.4

31.9

32.3

31.6

29.8

32.1

31.2

n.a.

Finland Government

Ireland Government

4.9

4.3

4.0

3.8

8.7

5.6

6.3

5.5

5.1

Business Sector

58.7

60.1

61.3

62.2

67.2

66.1

66.7

63.9

59.9

56.8

Higher Education

33.3

32.2

31.9

31.4

29.0

25.2

27.6

29.8

34.6

38.0

4.5

4.9

4.8

4.7

4.8

5.0

5.2

5.0

n.a.

Business Sector

57.0

64.8

64.6

65.7

65.8

65.1

63.7

66.7

67.9

n.a.

Higher Education

36.1

27.5

27.8

27.1

27.7

29.6

26.4

25.5

n.a.

Japan Government

100

ANNEX II: STATISTICAL ANNEX

Table B.1.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Government

12.7

12.4

12.0

10.9

11.7

10.7

8.8

8.0

7.9

n.a.

Business Sector

66.9

66.6

68.1

64.9

65.3

66.3

73.5

73.4

73.6

n.a.

Higher Education

19.3

19.6

19.1

23.3

21.7

21.8

16.9

17.6

17.5

n.a.

Korea

OECD Government

9.7

9.2

8.7

8.4

8.3

8.2

7.7

n.a.

Business Sector

61.8

61.5

62.8

63.5

64.0

63.8

64.0

64.3

n.a.

Higher Education

26.9

n.a.

27.0

n.a.

26.4

n.a.

Government

17.7

19.5

18.3

19.4

16.6

16.7

15.2

16.7

n.a.

Business Sector

22.8

21.5

22.3

23.1

24.7

27.2

23.7

29.6

29.8

n.a.

Higher Education

58.4

59.8

56.9

57.3

55.0

54.9

58.6

54.9

53.2

n.a.

Spain

United States Government

5.2

n.a.

4.3

n.a.

3.8

3.7

3.6

n.a.

Business Sector

76.2

n.a.

79.2

n.a.

80.6

80.5

80.3

79.9

n.a.

Higher Education

17.5

n.a.

15.4

n.a.

14.8

n.a.

Sources: RICYT, and OECD.

101

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.1.3: Number of PhDs Per 100,000 Inhabitants

Country

1995

1996 1997

1998

1999 2000 2001 2002

Latin America & Caribbean

0.7

0.8

0.9

1.1

1.2

1.3

1.4

1.6

177.9

Argentina

n.a.

1.1

n.a.

Bahamas

n.a.

Barbados

3.1

1.5

n.a.

Belize

n.a.

Bolivia

n.a.

0.2

0.1

n.a.

Brazil

1.3

1.5

1.6

1.9

2.3

2.4

2.7

3.0

3.6

216.8

Chile

0.4

0.5

0.6

0.9

0.8

111.7

Colombia

n.a.

0.0

0.1

n.a.

466.7

Costa Rica

n.a.

Dominican Republic

n.a.

Ecuador

n.a.

El Salvador

n.a.

Guatemala

n.a.

0.0

n.a.

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

0.2

0.1

n.a.

0.1

0.0

n.a.

Jamaica

n.a.

Mexico

0.4

0.6

0.8

0.6

0.7

0.8

0.9

1.1

1.3

228.9

Nicaragua

n.a.

Panama

n.a.

0.1

0.2

n.a.

Paraguay

n.a.

0.2

0.0

n.a.

Peru

n.a.

Suriname

n.a.

Trinidad and Tobago

0.8

1.1

0.3

1.4

0.6

0.7

0.8

n.a.

0.0

Uruguay

1.7

1.5

1.9

2.5

1.3

0.6

n.a.

–58.3

Venezuela

n.a.

China

n.a.

Finland

n.a.

Ireland

n.a.

Japan

n.a.

Korea

n.a.

OECD

n.a.

Spain

11.8

12.3

13.1

12.8

13.2

13.5

13.6

n.a.

17.9

United States

10.7

10.8

10.9

0.0

10.0

9.7

n.a.

–0.8

Source: RICYT. Note: The growth rate is from 1995 (or earliest available) to 2003 (or latest available).

102

% Growth 2003 rate

ANNEX II: STATISTICAL ANNEX

B.2. Level and Structure of R&D

Table B.2.1: R&D Expenditure as Percent of GDP

Country

1995

1996

1997

1998

% Growth 1999 2000 2001 2002 2003 2004 rate

Latin America & Caribbean

0.59

0.54

0.52

0.62

0.56

0.55

0.53

0.57

n.a.

–3.4

Argentina

n.a.

0.42

0.41

0.45

0.44

0.42

0.39

0.41

n.a.

–1.8

Bahamas

n.a.

Barbados

n.a.

Belize

n.a.

Bolivia

0.36

0.33

0.32

0.29

0.28

0.27

0.26

n.a.

–28.2

Brazil

0.87

0.77

n.a.

0.99

1.02

0.98

0.95

n.a.

9.2

Chile

0.62

0.53

0.49

0.50

0.51

0.53

0.70

0.60

n.a.

–3.2

Colombia

0.29

0.34

0.30

0.21

0.20

0.18

0.17

n.a.

Costa Rica

n.a.

0.30

0.29

0.26

0.33

0.39

n.a.

Dominican Republic

n.a.

Ecuador

0.08

0.10

0.09

n.a.

0.06

0.07

n.a.

–12.5

El Salvador

n.a.

0.08

n.a.

Guatemala

n.a.

Guyana

n.a.

Haiti

n.a.

Honduras

n.a.

0.06

0.05

0.06

n.a.

Jamaica

n.a.

0.06

0.08

n.a.

Mexico

0.31

0.34

0.38

0.43

0.37

0.39

0.40

n.a.

28.1

Nicaragua

n.a.

0.14

n.a.

0.07

n.a.

Panama

0.38

0.37

0.34

0.35

0.40

0.36

0.34

n.a.

–9.4

Paraguay

n.a.

0.08

0.10

n.a.

Peru

n.a.

0.08

0.10

0.11

0.10

0.11

n.a.

Suriname

n.a.

Trinidad and Tobago

n.a.

0.10

0.11

0.13

0.12

0.11

0.10

0.13

0.12

n.a.

15.9

Uruguay

0.28

0.42

0.23

0.26

0.24

n.a.

0.22

n.a.

–22.6

Venezuela

0.61

0.45

0.42

0.37

0.36

0.48

0.39

0.28

n.a.

–54.1

China

0.60

0.68

0.70

0.83

1.00

1.07

1.22

1.31

1.44

118.3

EU25

1.70

1.71

1.76

1.78

1.81

1.82

n.a.

7.1

Finland

2.26

2.52

2.69

2.86

3.21

3.38

3.43

3.48

n.a.

54.0

Ireland

1.28

1.32

1.29

1.25

1.19

1.14

1.11

1.12

1.19

1.21

–7.0

Japan

2.90

2.78

2.84

2.95

2.96

2.99

3.07

3.12

3.15

n.a.

8.6

Korea

2.37

2.42

2.48

2.34

2.25

2.39

2.59

2.53

2.63

n.a.

11.0

OECD

2.08

2.10

2.13

2.15

2.19

2.23

2.28

2.24

2.26

n.a.

8.7

Spain

0.79

0.80

0.79

0.87

0.86

0.91

0.92

0.99

1.05

n.a.

32.9

United States

2.51

2.55

2.58

2.62

2.66

2.74

2.76

2.65

2.68

6.8

Sources: RICYT, and OECD. Note: The growth rate is from 1995 (or earliest available) to 2003 (or latest available).

103

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.2: R&D Expenditure by Source of Financing (percent) Country

1995

1996 1997

Government

55.4

55.5

53.9

Business Sector

34.8

35.3

Higher Education

7.6

7.1

Private Nonprofit

0.9

Abroad

1998

1999 2000 2001 2002

2003 2004

49.4

46.9

43.2

44.6

44.0

42.6

34.4

35.8

34.4

33.8

32.8

34.5

35.2

n.a.

9.6

12.0

16.4

21.5

21.0

20.0

20.4

n.a.

1.0

0.7

0.6

0.5

0.6

0.3

n.a.

1.4

1.2

1.1

2.0

1.8

1.0

1.1

1.3

1.5

n.a.

41.8

44.2

n.a.

Latin America & Caribbean n.a.

Argentina Government Business Sector

n.a.

22.5

26.1

n.a.

Higher Education

n.a.

32.2

25.9

n.a.

Private Nonprofit

n.a.

2.2

2.3

n.a.

Abroad

n.a.

1.2

1.4

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

37.8

30.6

25.0

24.2

24.0

22.0

21.0

20.0

n.a.

Business Sector

17.3

24.5

20.0

20.2

20.0

22.0

18.0

16.0

n.a.

Higher Education

12.2

26.0

27.3

30.0

32.0

33.0

31.0

n.a.

Private Nonprofit

22.4

19.0

18.2

16.0

15.0

17.0

19.0

n.a.

Abroad

10.2

10.0

10.1

10.0

9.0

11.0

14.0

n.a.

Bahamas

Barbados

Belize

Bolivia Government

104

ANNEX II: STATISTICAL ANNEX

Table B.2.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Brazil Government

59.1

57.2

n.a.

Business Sector

38.2

40.0

n.a.

Higher Education

2.7

2.8

n.a.

Private Nonprofit

0.0

n.a.

Abroad

0.0

n.a.

Government

49.2

55.1

Business Sector

44.1

Higher Education

5.9

Private Nonprofit Abroad

n.a.

33.7

35.3

31.5

30.4

n.a.

38.2

37.3

39.5

41.0

n.a.

28.2

27.4

29.1

28.6

n.a.

0.0

n.a.

0.0

n.a.

51.3

40.1

49.6

48.9

39.2

52.0

n.a.

34.8

37.0

43.9

34.5

34.7

39.1

28.8

n.a.

6.6

9.5

14.4

14.8

15.7

20.8

19.0

n.a.

0.8

3.5

2.3

1.7

1.1

0.7

0.9

0.2

n.a.

0.0

n.a.

Government

35.0

55.1

33.0

22.0

24.0

16.6

13.2

22.2

n.a.

Business Sector

52.8

34.8

45.0

50.0

45.0

48.4

46.9

n.a.

Higher Education

10.9

6.6

18.0

25.0

29.0

33.6

38.3

37.2

n.a.

Chile

Colombia

Private Nonprofit

1.4

3.5

4.0

3.0

2.0

1.4

1.7

n.a.

Abroad

0.0

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

39.8

64.2

63.8

72.7

n.a.

Business Sector

32.5

n.a.

Higher Education

0.0

n.a.

Private Nonprofit

4.8

0.3

0.4

n.a.

Abroad

22.8

16.0

15.5

7.1

n.a.

Costa Rica

Dominican Republic

Ecuador

105

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

El Salvador Government

47.0

19.5

n.a.

Business Sector

35.0

Higher Education

7.0

35.0

0.5

n.a.

7.0

68.5

n.a.

Private Nonprofit

0.0

2.5

n.a.

Abroad

11.0

9.0

n.a.

Guatemala Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Guyana Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Haiti

Honduras

Jamaica Government

106

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

ANNEX II: STATISTICAL ANNEX

Table B.2.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Government

66.2

66.8

71.1

60.8

61.3

63.0

59.1

61.0

n.a.

Business Sector

17.6

19.4

16.9

23.6

29.5

29.8

30.6

n.a.

Higher Education

8.4

8.1

8.6

8.0

9.7

6.0

9.1

7.1

n.a.

Mexico

Private Nonprofit

1.1

2.2

0.9

0.1

0.6

0.8

0.3

n.a.

Abroad

6.7

3.5

2.5

7.5

5.3

0.9

1.3

1.0

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

45.5

42.2

44.6

40.2

46.0

34.3

32.8

26.2

25.5

n.a.

Business Sector

0.5

2.2

0.7

0.0

1.4

0.6

10.2

0.6

n.a.

Higher Education

0.9

1.0

2.4

6.1

0.4

0.6

2.1

1.8

n.a.

Private Nonprofit

1.1

1.8

1.3

0.9

0.7

1.2

0.2

1.0

n.a.

52.0

52.8

52.4

56.1

45.6

64.0

55.2

70.9

71.1

n.a.

51.3

60.9

n.a.

Nicaragua

Panama

Abroad

Paraguay Government Business Sector

n.a.

3.9

n.a.

Higher Education

n.a.

3.9

12.1

n.a.

Private Nonprofit

n.a.

0.7

2.1

n.a.

Abroad

n.a.

40.2

21.0

n.a.

Peru Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Suriname

107

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Trinidad and Tobago Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Abroad

n.a.

Government

6.1

18.4

38.8

10.7

9.4

20.3

n.a.

17.1

n.a.

Business Sector

31.1

30.4

32.5

37.8

35.6

39.3

n.a.

46.7

n.a.

Higher Education

50.4

40.9

26.4

48.5

47.1

35.7

n.a.

31.4

n.a.

Private Nonprofit

0.0

n.a.

0.1

n.a.

Abroad

12.4

10.3

2.4

2.9

7.9

4.8

n.a.

4.7

n.a.

34.8

29.9

46.1

41.7

44.1

52.7

55.5

59.1

71.6

n.a.

Business Sector

47.4

55.0

35.3

44.1

40.7

32.6

27.3

22.9

1.0

n.a.

Higher Education

17.8

15.2

18.6

14.2

15.2

14.8

17.2

18.0

27.4

n.a.

Private Nonprofit

0.0

n.a.

Abroad

0.0

n.a.

Business Sector

n.a.

57.6

n.a.

60.1

n.a.

Government

n.a.

33.4

n.a.

29.9

n.a.

Uruguay

Venezuela Government

China

Other sources

n.a.

Abroad

n.a.

2.7

n.a.

1.9

n.a.

EU25 Business Sector

51.9

52.2

53.3

54.0

55.2

55.5

55.4

54.4

53.7

n.a.

Government

39.5

38.8

37.5

36.7

35.5

35.2

34.8

34.9

35.5

n.a.

Other sources

1.8

1.9

2.0

2.1

2.2

n.a.

Abroad

6.7

7.1

7.3

7.2

7.6

8.5

n.a.

Finland Business Sector

59.5

n.a.

62.9

63.9

66.9

70.2

70.8

69.5

70.0

n.a.

Government

35.1

n.a.

30.9

30.0

29.2

26.2

25.5

26.1

25.7

n.a.

Other sources

1.0

n.a.

0.9

1.0

0.9

1.2

1.1

n.a.

Abroad

4.5

n.a.

5.3

5.1

3.0

2.7

2.5

3.1

n.a.

Business Sector

72.3

66.8

67.3

65.4

64.4

65.8

66.8

63.4

59.5

57.3

Government

22.5

24.2

24.3

23.1

21.9

23.4

25.5

28.0

30.4

32.0

Other sources

1.9

1.4

1.7

1.6

1.8

1.9

1.7

1.5

1.6

1.7

Abroad

8.5

7.5

6.7

9.8

12.0

8.9

6.0

7.2

8.5

8.9

Ireland

108

ANNEX II: STATISTICAL ANNEX

Table B.2.2: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Japan Business Sector

67.1

73.4

74.0

72.6

72.2

72.4

73.0

73.9

74.5

n.a.

Government

22.8

18.7

18.2

19.3

19.6

18.6

18.2

17.7

n.a.

Other sources

9.9

7.8

7.5

7.8

7.6

8.0

7.6

7.5

n.a.

Abroad

0.1

0.3

0.4

0.3

n.a.

Korea Business Sector

76.3

77.8

72.5

69.1

70.0

72.4

72.5

72.2

74.0

n.a.

Government

19.0

20.3

22.9

25.9

24.9

23.9

25.0

25.4

23.9

n.a.

Other sources

4.7

1.9

4.5

4.9

5.1

3.6

2.1

2.0

1.7

n.a.

Abroad

n.a.

0.1

0.5

0.4

n.a.

Business Sector

59.4

60.8

61.9

62.2

63.1

64.4

63.8

62.5

61.8

n.a.

Government

34.0

32.3

31.2

30.6

29.6

28.3

28.7

29.6

30.4

n.a.

Other sources

4.0

4.2

4.3

4.4

4.5

4.6

4.7

4.8

n.a.

Abroad

n.a.

Business Sector

44.5

45.5

44.7

49.8

48.9

49.7

47.2

48.9

48.4

n.a.

Government

43.6

43.9

43.6

38.7

40.8

38.6

39.9

39.1

40.1

n.a.

Other sources

5.2

5.0

4.9

4.8

4.7

6.8

5.3

5.2

5.8

n.a.

Abroad

6.7

5.6

6.7

5.6

4.9

7.7

6.8

5.7

n.a.

Business Sector

60.2

62.4

64.0

65.4

67.1

69.5

67.8

65.4

63.8

63.7

Government

35.4

33.2

31.5

30.2

28.4

25.8

27.3

29.2

30.8

31.0

Other sources

4.4

4.5

4.6

4.9

5.4

Abroad

n.a.

OECD

Spain

United States

Sources: RICYT, and OECD.

109

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.3: R&D Expenditure by Sector of Performance (percent) Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Government

20.6

19.5

21.6

22.4

25.8

26.6

26.0

25.8

27.3

n.a.

Business Sector

36.0

37.7

35.6

36.3

33.8

32.9

34.6

35.3

n.a.

Higher Education

42.2

40.8

41.2

39.8

38.7

39.1

38.6

38.0

36.6

n.a.

Private Nonprofit

1.3

2.1

1.6

1.7

1.4

0.9

0.8

0.9

n.a.

Government

n.a.

40.9

39.6

39.5

39.0

38.3

39.9

37.2

41.2

39.7

Business Sector

n.a.

25.9

29.1

30.2

28.3

25.9

22.8

26.1

29.0

33.0

Higher Education

n.a.

31.5

29.8

28.5

30.4

33.5

35.0

33.9

27.4

25.0

Private Nonprofit

n.a.

1.7

1.5

1.8

2.3

2.4

2.3

2.8

2.5

2.3

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Latin America & Caribbean

Argentina

Bahamas

Barbados Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Belize Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

25.0

24.0

23.0

22.0

21.0

22.0

21.0

n.a.

Business Sector

25.0

26.0

27.0

26.0

25.0

n.a.

Higher Education

30.0

35.0

38.0

42.0

45.0

46.0

42.0

41.0

n.a.

Private Nonprofit

20.0

15.0

12.0

9.0

7.0

6.0

12.0

13.0

n.a.

Government

12.4

11.0

n.a.

Business Sector

42.6

45.5

n.a.

Higher Education

45.1

43.5

n.a.

Private Nonprofit

0.0

n.a.

Government

51.7

45.8

39.6

39.7

37.8

40.4

10.6

12.7

n.a.

Business Sector

6.4

8.8

10.8

10.6

10.9

14.9

36.8

37.8

n.a.

Higher Education

40.9

44.5

48.7

48.9

50.4

43.8

38.7

33.8

n.a.

Private Nonprofit

1.0

0.9

0.8

0.9

13.9

15.8

n.a.

Bolivia Government

Brazil

Chile

110

ANNEX II: STATISTICAL ANNEX

Table B.2.3: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Colombia Government

5.0

13.0

5.0

6.0

Business Sector

36.0

Higher Education

41.0

30.0

21.0

35.0

42.0

Private Nonprofit

18.0

30.0

n.a.

8.0

n.a.

45.0

35.0

38.0

18.0

n.a.

57.0

60.0

n.a.

24.0

15.0

22.0

19.0

14.0

n.a.

12.3

12.4

17.0

22.3

19.5

n.a.

Costa Rica Government Business Sector

n.a.

21.7

26.0

24.8

15.8

23.3

n.a.

Higher Education

n.a.

36.6

35.4

36.1

38.5

36.2

n.a.

Private Nonprofit

n.a.

29.3

26.2

22.2

23.4

21.0

n.a.

Dominican Republic Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

45.1

68.5

60.6

62.0

n.a.

28.6

33.5

34.9

n.a.

Business Sector

9.0

4.0

4.4

4.7

n.a.

13.5

11.4

12.9

n.a.

Higher Education

38.2

15.5

19.5

16.1

n.a.

11.1

11.4

10.8

n.a.

Private Nonprofit

7.8

12.0

15.4

17.2

n.a.

46.8

43.7

41.4

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Ecuador

El Salvador

Guatemala

Guyana

Haiti

111

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.3: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Honduras Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Jamaica Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

33.0

36.4

38.7

36.8

45.0

41.7

39.1

41.4

n.a.

Business Sector

20.8

22.4

19.7

28.2

25.5

29.8

30.3

29.8

n.a.

Higher Education

45.8

37.9

39.9

31.6

26.3

28.3

30.4

28.6

n.a.

Private Nonprofit

0.4

3.3

1.6

3.5

3.1

0.3

0.2

0.3

n.a.

Government

n.a.

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

43.0

41.5

43.7

27.9

29.9

62.3

67.1

49.3

51.8

n.a.

Business Sector

0.0

1.6

0.0

n.a.

Higher Education

8.2

8.6

9.0

22.8

28.3

7.1

9.2

7.2

5.8

n.a.

Private Nonprofit

48.8

48.3

47.3

49.3

41.9

30.6

23.7

43.6

42.5

n.a.

Mexico Government

Nicaragua

Panama

Paraguay Government

n.a.

36.4

36.0

n.a.

Business Sector

n.a.

0.0

n.a.

Higher Education

n.a.

19.2

40.8

n.a.

Private Nonprofit

n.a.

44.4

23.2

n.a.

32.7

39.2

35.9

37.0

35.8

31.2

35.4

n.a.

Peru Government Business Sector

n.a.

14.3

11.9

11.6

10.0

10.2

10.6

9.8

n.a.

Higher Education

n.a.

44.7

39.8

40.1

41.9

42.6

47.0

44.7

n.a.

Private Nonprofit

n.a.

8.4

9.1

12.4

11.1

11.4

11.2

10.1

n.a.

Suriname Government

112

Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

ANNEX II: STATISTICAL ANNEX

Table B.2.3: Continued Country

1995

1996 1997

1998

56.2

69.6

1999 2000 2001 2002

2003 2004

Trinidad and Tobago Government

n.a.

55.0

71.5

63.9

64.4

71.4

70.8

n.a.

Business Sector

n.a.

23.7

22.4

6.1

7.0

13.0

13.2

10.5

10.1

n.a.

Higher Education

n.a.

20.2

22.7

24.3

21.5

23.1

22.4

18.2

19.1

n.a.

Private Nonprofit

n.a.

0.0

n.a.

18.5

28.7

40.7

13.6

16.3

25.0

n.a.

19.4

n.a.

Uruguay Government Business Sector

31.2

30.4

33.0

37.9

36.7

39.3

n.a.

49.0

n.a.

Higher Education

50.4

40.9

26.3

48.5

47.1

35.7

n.a.

31.6

n.a.

Private Nonprofit

0.0

n.a.

0.0

n.a.

Venezuela Government Business Sector

n.a.

Higher Education

n.a.

Private Nonprofit

n.a.

Government

42.1

42.8

40.6

42.6

38.5

31.5

29.7

28.7

27.1

n.a.

Business Sector

43.7

43.2

46.1

44.8

49.6

60.0

60.4

61.2

62.4

n.a.

Higher Education

12.1

11.8

11.3

10.4

9.4

8.6

9.8

10.1

10.5

n.a.

Private Nonprofit

n.a.

0.0

n.a.

16.8

16.4

15.5

14.7

14.2

13.5

13.4

n.a.

China

EU25 Government Business Sector

61.6

61.8

62.3

62.4

63.6

63.8

64.0

63.4

63.3

n.a.

Higher Education

20.7

20.9

21.3

21.2

20.8

21.1

21.5

22.1

n.a.

Private Nonprofit

0.9

0.8

0.9

1.0

1.1

1.2

n.a.

Government

16.6

15.8

13.6

12.6

11.4

10.6

10.2

10.4

9.7

n.a.

Business Sector

63.2

66.2

66.0

67.2

68.2

70.9

71.1

69.9

70.5

n.a.

Higher Education

19.5

18.1

20.0

19.6

19.7

17.8

18.1

19.2

n.a.

Private Nonprofit

0.6

0.0

0.5

0.6

0.7

0.6

n.a.

Government

9.0

8.4

7.6

7.2

6.0

8.1

8.7

7.9

7.8

Business Sector

70.0

70.8

71.0

71.8

73.3

71.6

70.1

68.8

66.9

64.8

Higher Education

20.4

20.0

20.7

21.0

20.7

20.2

21.8

22.4

25.2

27.4

Private Nonprofit

0.8

0.7

0.0

Government

9.6

9.4

8.8

9.2

9.9

9.5

9.3

n.a.

Business Sector

65.2

71.1

72.0

71.2

70.7

71.0

73.7

74.4

75.0

n.a.

Higher Education

20.7

14.8

14.3

14.8

14.5

13.9

13.7

n.a.

Private Nonprofit

4.4

4.8

4.7

4.6

2.3

2.1

n.a.

Finland

Ireland

Japan

113

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.2.3: Continued Country

1995

1996 1997

1998

1999 2000 2001 2002

2003 2004

Korea Government

17.0

16.2

15.8

17.5

14.5

13.3

12.4

13.4

12.6

Business Sector

73.7

73.2

72.6

Higher Education

8.2

9.4

10.4

70.3

71.4

74.0

11.2

12.0

11.3

Private Nonprofit

1.1

1.2

1.1

2.1

Government

12.6

11.9

11.2

11.1

10.8

Business Sector Higher Education

66.7

67.6

68.4

68.6

69.0

16.3

16.2

16.1

16.0

Private Nonprofit

2.5

2.6

Government

18.6

18.3

17.4

16.3

Business Sector

48.2

48.3

48.8

Higher Education

32.0

32.3

32.7

Private Nonprofit

1.1

Government

14.0

12.6

Business Sector

70.5

Higher Education Private Nonprofit

n.a.

76.2

74.9

76.1

n.a.

10.4

10.1

n.a.

1.4

1.0

1.3

1.2

n.a.

10.3

10.4

10.9

n.a.

69.5

69.2

67.8

67.7

n.a.

16.0

16.5

17.3

17.4

n.a.

2.7

2.5

2.6

n.a.

16.9

15.8

15.9

15.4

n.a.

52.1

52.0

53.7

52.4

54.6

54.1

n.a.

30.5

30.1

29.6

30.9

29.8

30.3

n.a.

1.1

1.0

0.9

0.8

0.2

n.a.

12.1

11.5

11.0

10.3

11.3

12.2

12.4

12.2

72.0

73.1

73.8

74.2

74.7

72.7

70.2

69.8

70.1

12.3

12.0

11.7

11.5

12.1

13.5

13.7

13.6

3.2

3.1

3.2

3.3

3.5

3.9

4.2

4.1

OECD

Spain

United States

Sources: RICYT, and OECD.

114

ANNEX II: STATISTICAL ANNEX

B.3. Outcomes

Table B.3.1: Patents Granted by United States Patent and Trademark Office

Country

% Growth 1999 2000 2001 2002 2003 2004 rate

1995

1996

1997

1998

Latin America & Caribbean

n.a.

Argentina

Bahamas

Barbados

n.a.

Bolivia

n.a.

Brazil

n.a.

103.2

125.0

n.a.

110

130

106

106.3

Chile

57.1

Colombia

233.3

Costa Rica

n.a.

100.0

Dominican Republic Ecuador El Salvador

n.a.

0.0

Guatemala

n.a.

Haiti

n.a.

Honduras

n.a.

Jamaica

n.a.

–50.0

Mexico

110.0

Nicaragua

n.a.

Panama

n.a.

100.0

Paraguay

n.a.

Peru

33.3

Suriname

n.a.

Trinidad and Tobago

n.a.

Uruguay

n.a.

0.0

Venezuela

–34.5

China

119

195

289

297

404

379.0

Finland

358

444

452

595

649

618

732

809

865

918

141.6

Ireland

121

141

127

163

186

246.8

Japan

21 764 23 053 23 179 30 840 31 104 31 295 33 224 34 858 35 516 35 350 63.2

Korea

1 161

1 493

1 891

3 259

3 562

3 314

3 538

3 786

3 944

OECD

n.a.

Spain

148

157

177

248

222

270

269

303

309

264

108.8

United States

4 428 239.7

55 739 61 104 61 708 80 289 83 906 85 068 87 601 86 972 87 901 84 271

57.7

Sources: USPTO (2004) - http://www.uspto.gov/web/offices/ac/ido/oeip/taf/reports.htm#by_geog Note: The growth rate is from 1995 (or earliest available) to 2003 (or latest available).

115

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table B.3.2: Scientific and Technical Journal Articles per 100,000 Inhabitants

Country Latin America & Caribbean

1995

1996

1997

1998

1999

2000

2001

% Growth rate

2.4

2.6

2.8

3.1

68.6

2.0

2.2

Argentina

5.8

6.4

7.0

7.2

7.6

7.8

8.1

39.7

Barbados

8.1

3.8

7.3

5.4

5.8

n.a.

Bolivia

0.3

0.2

0.3

0.4

n.a.

Brazil

2.2

2.4

2.7

3.1

3.5

3.6

4.1

89.9

Chile

6.5

6.6

6.9

7.3

7.5

8.1

24.5

Colombia

0.4

0.5

0.6

0.8

73.6

Costa Rica

2.1

2.3

2.1

2.2

2.4

11.2

Dominican Republic

0.1

n.a.

Ecuador

0.2

0.3

0.2

n.a.

El Salvador

0.0

4.0

2.0

n.a.

Guatemala

0.2

0.1

n.a.

Guyana

0.8

0.7

0.5

0.7

0.5

n.a.

Haiti

0.0

n.a.

Honduras

0.1

0.2

0.1

0.2

n.a.

Jamaica

2.8

2.2

1.9

1.5

1.7

n.a.

Mexico

2.1

2.3

2.4

2.7

3.0

3.2

55.8

Nicaragua

0.1

0.2

0.1

0.2

n.a.

Panama

1.2

1.0

1.4

1.1

1.3

n.a.

Paraguay

0.1

0.2

0.1

0.2

0.1

n.a.

Peru

0.3

0.2

0.3

0.4

23.7

Trinidad and Tobago

3.9

3.2

3.3

3.4

2.9

n.a.

Uruguay

3.4

3.6

4.1

4.5

4.8

4.6

35.5

Venezuela

2.0

1.9

2.1

2.3

2.2

2.1

2.2

10.8

China

0.8

1.0

1.1

1.3

1.4

1.6

114.6

Finland

80.9

85.0

88.0

88.6

94.3

98.3

21.4

Ireland

33.6

34.9

35.9

41.1

40.6

41.9

43.1

28.2

Japan

37.9

40.1

39.8

43.2

44.3

43.7

45.2

19.0

Korea

8.4

10.4

12.3

15.3

18.0

20.0

23.3

176.2

OECD

56.4

57.2

56.6

58.2

58.8

58.0

58.8

4.4

Spain

28.9

31.2

33.1

34.9

37.5

37.0

38.7

33.7

United States

77.2

76.1

73.8

73.2

72.8

69.5

70.5

â&#x20AC;&#x201C;8.7

Source: World Development Indicators. Note: The growth rate is from 1995 (or earliest available) to 2001 (or latest available).

116

ANNEX II: STATISTICAL ANNEX

C. Information and Communication Technology C.1. Digital Divide

Table C.1. ICT Access Per 100 Inhabitants Fixed Lines

2001

2002

2003

2004

Growth Rate (%) 2001-2004

HPAE

54.2

53.8

53.5

53.14

–1.9

OECD

52.7

52.1

51.4

49.5

–6.0

LAC

15.7

15.9

16.1

17.3

10.2

Mobile Subscribers

2001

2002

2003

2004

Growth Rate (%) 2001-2004

HPAE

79.2

87.7

93.8

96.2

21.5

OECD

67.9

30.6

LAC

14.7

24.6

32.9

123.8

Internet Users

2001

2002

2003

2004

Growth Rate (%) 2001-2004

HPAE

41.6

49.1

52.7

56.5

35.8

OECD

31.7

36.2

39.2

48.3

52.4

5.7

8.9

11.3

14.4

152.6

2001

2002

2003

2004

Growth Rate (%) 2001-2004

HPA

43.4

50.5

53.9

57.5

32.5

OECD

33.6

36.6

37.7

5.8

6.4

6.5

8.1

LAC

Personal Computers

LAC

Sources: ITU and Internet World Statistics

117

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C.2. Fixed Telephone Lines

Table C.2. Fixed Telephone Lines Per 100 Inhabitants Country

2000

2001

2002

2003

2004

Growth Rate (%) 2000-2004

Latin America & Caribbean

14.6

15.7

15.9

16.1

17.3

18.1

Argentina

22.0

21.8

21.9

22.73

22.8

3.4

Bahamas

37.5

40.2

40.6

41.5

44.1

17.7

Barbados

46.3

48.1

49.4

49.7

50.1

8.2

Belize

14.9

13.7

11.4

12.8

12.9

–13.2

Bolivia

6.2

6.3

7.0

7.2

7.0

12.0

Brazil

18.2

21.8

22.3

23.5

28.8

Chile

21.7

22.6

23.0

21.3

21.5

–0.8

Colombia

17.0

17.2

17.9

19.5

14.9

Costa Rica

22.3

23.0

25.1

27.8

31.6

41.5

Dominican Republic

10.5

11.0

10.4

10.7

1.9

9.7

10.4

11.5

12.2

26.2

El Salvador

10.0

10.2

10.3

11.3

13.4

34.7

Guatemala

5.9

6.5

7.1

8.9

50.4

Guyana

7.9

9.2

13.4

68.6

Haiti

0.9

1.0

1.6

1.7

86.5

Honduras

4.8

4.7

4.8

4.9

5.3

11.5

Jamaica

19.8

19.2

16.9

17.3

14.60

–26.3

Mexico

12.5

13.9

14.8

16.0

17.2

38.1

3.2

3.0

3.3

3.7

3.8

18.1

Panama

15.1

13.9

12.3

12.2

11.8

–21.6

Paraguay

5.2

5.1

4.7

4.6

4.7

–8.1

Ecuador

Nicaragua

Peru

6.7

6.0

6.2

6.7

7.4

11.2

Suriname

17,3

16,4

16,3

16,6

18,5

6,73

Trinidad & Tobago

24.5

24.0

25.0

24.6

0.4

Uruguay

27.8

28.3

28.0

30.9

10.8

Venezuela

10.5

10.9

11.3

11.1

12.8

21.8

China

11.2

13.7

16.7

20.3

24.0

114.1

EU 25

53.7

49.8

48.9

48.0

46.5

–13.4

Finland

55.0

54.0

52.4

49.2

45.4

–17.5

Ireland

48.4

48.5

50.2

49.1

49.9

3.2

Japan

58.6

48.2

47.7

47.2

46.0

–21.5

Korea

47.7

54.5

54.0

53.8

55.3

16.0

OECD

59.4

52.7

52.1

51.5

49.5

–16.6

Spain

42.6

43.4

42.9

41.5

–2.6

United States

66.5

67.2

65.1

62.4

59.9

–9.8

Source: ITU

118

ANNEX II: STATISTICAL ANNEX

C.3. Mobile Telephone Subscribers

Table C.3. Mobile Telephone Subscribers Per 100 Inhabitants 2000

2001

2002

2003

2004

Growth Rate (%) 2000-2004

12.1

14.7

20.0

24.6

32.9

171.5

Argentina

16.9

19.3

17.8

35.4

109.5

Bahamas

10.3

19.7

39.0

36.7

58.7

467.6

Barbados

10.6

19.8

36.1

51.9

73.9

594.0

Belize

7.0

15.2

18.8

20.5

35.1

401.8

Bolivia

7.1

9.4

12.3

15.2

20.1

182.8

Brazil

13.7

16.7

20.1

26.4

36.3

166.0

Chile

22.4

34.2

42.8

51.1

62.1

177.6

5.3

7.6

10.6

14.1

23.2

334.3

Country Latin America & Caribbean

Colombia Costa Rica

5.1

7.6

11.1

18.1

21.7

325.8

Dominican Republic

8.3

14.7

20.7

27.2

28.8

249.5

Ecuador

3.8

6.7

12.6

18.9

26.9

605.0

El Salvador

11.9

13.4

13.8

17.3

27.7

133.9

Guatemala

7.5

9.8

13.2

25.0

232.5

Guyana

4.6

8.7

9.9

13.6

194.9

Haiti

0.7

1.1

1.7

3.8

4.7

602.2

Honduras

2.5

3.6

4.9

5.5

10.1

307.9

Jamaica

14.2

24.4

53.3

68.1

82.2

478.3

Mexico

14.2

21.9

25.8

29.5

36.6

157.4

1.8

3.2

4.6

8.5

13.2

641.6

Nicaragua Panama

14.5

16.4

17.5

26.8

27.0

86.7

Paraguay

14.9

20.4

28.8

29.9

29.4

96.7

5.0

6.9

8.6

10.6

14.9

199.2

9.5

19.8

22.5

32.0

48.5

412.6

12.5

19.7

27.8

39.9

49.6

296.4

Peru Suriname Trinidad & Tobago Uruguay

12.3

15.5

19.3

18.5

50.4

Venezuela

22.5

26.2

25.6

27.3

32.2

42.7

China

6.6

11.0

16.0

20.1

25.8

290.3

EU 25

61.2

65.0

73.9

82.2

91.2

49.1

Finland

72.0

80.4

86.7

91.0

95.6

32.8

Ireland

65.0

77.4

76.3

88.0

93.5

43.9

Japan

52.6

58.8

63.7

67.9

71.6

36.0

Korea

58.3

61.4

67.9

70.1

76.1

30.5

OECD

52.2

62.0

67.9

74.0

81.0

55.1

Spain

60.5

73.4

82.4

91.6

89.5

47.9

United States

38.9

45.0

48.9

54.6

61.0

56.7

Source: ITU

119

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

C.4. Personal Computers

Table C.4. Personal Computers Per 100 Inhabitants Country Latin America & Caribbean

2001

2002

2003

2004

4.9

5.8

6.4

6.5

8.1

65.3

Argentina

7.1

8.0

8.2

8.0

12.0

Bahamas

n.a.

Barbados

8.2

9.3

10.4

12.6

52.6

Belize

12.5

12.8

12.7

13.5

8.2

Bolivia

1.7

2.1

2.3

33.7

Brazil

5.0

6.3

7.5

10.7

113.9

Chile

9.3

10.7

11.9

13.9

48.6

Colombia

3.5

4.2

4.9

6.7

88.2

Costa Rica

14.9

17.0

19.7

21.8

21.9

46.8

Dominican Republic

n.a.

Ecuador

2.2

2.3

3.2

5.5

153.0

El Salvador

1.9

2.2

2.5

3.3

4.5

134.8

Guatemala

1.1

1.3

1.4

1.8

59.4

Guyana

2.6

2.7

3.5

37.8

Haiti

n.a.

Honduras

1.1

1.2

1.4

1.5

1.6

40.7

Jamaica

4.7

5.0

5.4

6.2

33.4

Mexico

5.8

7.0

8.3

10.7

85.3

Nicaragua

2.4

2.5

2.9

3.6

51.0

Panama

3.7

3.8

4.1

10.9

Paraguay

1.3

2.7

3.5

5.9

364.7

Peru

4.1

4.8

4.3

9.8

138.3

Suriname

7.1

8.0

8.2

8.0

12.0

Trinidad & Tobago

6.2

6.9

8.0

10.5

69.6

Uruguay

10.5

11.0

13.3

26.5

Venezuela

4.6

5.3

6.1

8.2

80.0

China

1.7

1.9

2.8

3.9

4.1

140.0

EU 25

25.7

26.8

30.0

31.9

38.3

49.4

Finland

39.6

42.4

44.2

48.2

21.8

Ireland

35.9

39.1

42.1

49.7

38.5

Japan

31.5

35.8

38.2

54.2

71.8

Korea

40.5

47.5

49.3

55.8

54.5

34.6

OECD

30.2

33.6

36.6

37.7

45.0

49.0

Spain

14.5

16.8

19.6

25.4

75.4

United States

57.2

62.4

66.0

74.1

29.5

Source: ITU

120

2000

Growth Rate (%) 2000-2004

ANNEX II: STATISTICAL ANNEX

C.5. Internet Access

Table C.5.1 Regional Evolution of Internet Users

World Region

Population as a % of World Population

Latin America and the Caribbean Middle East

Internet Users

Internet penetration 2005

Users as a % of Total World Users

Growth Rate (%) 2000-2005

8.5

79,033,597

15.2

10.3

272.8

2.9

18,203,500

9.6

1.8

454.2

Africa

14.1

22,737,597

2.5

2.2

403.7

Asia

56.4

364,270,713

9.9

35.7

218.7

Europe

12.4

290,121,957

35.9

28.5

176.1

Oceania/Australia

0.5

17,690,762

52.9

1.8

132.2

North America

5.1

225,801,428

68.1

22.2

108.9

Source: Internet World Stats.

121

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table C.5.2 Internet Users Per 100 Inhabitants 2000

2001

2002

2003

2004

2005

Growth Rate (%) 2000-2005

Latin America & Caribbean

4.1

5.7

8.9

11.3

14.4

15.2

272.8

Argentina

7.3

10.1

11.2

16.1

20.0

175.8

Bahamas

4.3

5.5

19.2

26.5

29.3

28.5

561.9

Barbados

3.7

5.6

11.2

37.1

55.4

56.4

1408.8

Belize

6.3

7.0

10.9

13.4

12.0

92.2

Bolivia

1.5

2.2

3.2

3.9

166.8

Brazil

3.0

4.7

8.2

12.2

12.3

317.7

Chile

Country

16.7

20.1

23.8

27.2

27.9

36.1

116.4

Colombia

2.1

2.7

4.6

5.3

8.0

7.8

275.9

Costa Rica

5.7

9.3

19.3

28.8

23.5

23.2

309.4

Dominican Republic

1.9

4.6

6.1

10.2

9.1

8.9

378.8

Ecuador

1.4

2.6

4.3

4.6

4.7

5.2

266.2

El Salvador

1.1

2.3

4.7

8.3

8.9

9.1

716.1

Guatemala

0.7

1.7

3.3

6.0

6.1

767.7

Guyana

5.8

11.5

14.2

18.9

16.5

184.1

Haiti

0.3

0.4

1.0

1.8

5.9

6.1

2379.7

Honduras

0.9

1.4

2.5

4.0

3.2

3.4

287.7

Jamaica

3.1

3.8

22.9

39.9

1187.1

Mexico

2.7

7.5

10.0

12.0

13.4

16.4

497.9

Nicaragua

1.0

1.4

1.7

2.2

123.4

Panama

3.2

5.8

6.2

9.5

9.8

209.2

Paraguay

0.7

1.1

1.7

2.0

2.5

2.7

270.9

Peru

3.1

7.7

9.0

10.4

11.7

16.3

422.9

Suriname

2.7

3.3

4.2

4.4

6.8

6.5

140.9

Trinidad & Tobago

7.7

9.2

10.6

12.2

57.9

11.1

11.9

21.0

20.9

88.5

Venezuela

3.4

4.7

5.1

6.0

8.8

12.2

259.6

China

1.6

2.6

4.6

6.2

7.2

7.9

393.1

EU 25

26.7

31.7

45.7

99.8

Finland

37.2

43.0

51.0

53.4

63.0

62.6

68.1

Ireland

17.9

23.3

28.0

31.7

29.6

51.2

185.6

Japan

29.9

38.4

44.9

48.3

50.2

60.9

103.4

Korea

41.4

51.5

55.1

61.0

65.7

65.2

57.5

OECD

28.4

31.7

36.2

39.2

48.3

50.9

79.2

Spain

13.7

18.3

19.3

23.9

33.2

37.1

171.3

United States

44.1

50.1

55.2

55.6

62.3

68.7

55.9

Uruguay

Source: ITU and World Internet Statistics

122

ANNEX II: STATISTICAL ANNEX

Table C.5.3 Internet Broadband Users Per 100 Inhabitants Country

2001

2002

2003

2004

2005

Growth Rate (%) 2000-2005

Argentina

n.a.

0.3

0.6

1.1

1.8

528.6

Brazil

0.2

0,39

0.7

1.1

1.5

668.4

Chile

0.4

1.3

2.2

3.1

3.8

759.1

Colombia

0.1

0.2

0.3

560.0

MĂŠxico

0.1

0.2

0.4

0.6

0.7

469.2

Peru

n.a.

0.1

0.3

0.8

1.2

807.7

China

0.6

3.3

5.0

755.2

Korea

17.2

23.8

27.6

29.8

31.4

83.3

OECD

2.9

4.9

7.3

10.3

11.8

306.9

Pacific Asia

1.0

1.2

1.5

56.3

United States

4.3

6.4

8.7

12.1

13.2

204.8

Source: Own calculations based on available national data, and The World FactBook.

123

EDUCATION, SCIENCE AND TECHNOLOGY IN L ATIN AMERICA AND THE CARIBBE AN

Table C.5.4: Internet Access Hosts Country

2001

2002

2003

2004

Latin America & Caribbean

32.3

39.2

52.1

62.5

Argentina

125.2

132.2

196.0

242.4

Bahamas

0.9

1.0

9.5

10.2

109.0

Barbados

4.9

6.0

7.6

7.8

162.3

Belize

12.9

57.8

100.5

141.6

109.1

Bolivia

1.8

1.7

8.4

9.3

119.8

Brazil

95.7

128.7

179.3

193.0

149.6

Chile

79.7

89.8

132.9

142.3

156.0

Colombia

13.4

12.9

26.3

42.5

131.5

151.7 151.66

Costa Rica

21.5

19.2

26.0

26.4

181.5

Dominican Republic

51.6

65.0

74.0

75.0

168.8

Ecuador

2.6

2.0

2.4

6.7

139.4

El Salvador

0.8

0.4

6.2

6.6

112.1

Guatemala

5.7

8.2

16.5

18.8

130.2

Guyana

0.2

0.7

6.9

8.4

102.7

Haiti

n.a.

Honduras

0.5

0.2

2.9

5.7

108.6

Jamaica

5.5

4.9

5.6

5.4

201.1

Mexico

92.6

110.1

130.6

145.2

163.8

4.2

6.5

12.8

17.8

123.7

Panama

27.0

24.6

22.9

21.9

223.3

Paraguay

4.8

7.5

15.6

14.0

134.3

PerĂş

5.2

7.3

24.0

39.7

113.1

Nicaragua

Suriname Trinidad & Tobago Uruguay Venezuela

1.3

24.0

0.4

3.0

142.2

52.9

55.4

61.4

93.4

156.6

220.7

244.2

271.2

333.8

166.1

9.2

9.6

13.7

14.5

163.0

China

9.2

9.6

13.7

14.5

163.0

EU 25

385.4

473.1

569.0

740,08

152.1

Finland

1707.3

2343.1

2436.6

2215.2

177.1

Ireland

333.7

347.2

399.2

421.0

179.3

Japan

559.2

726.7

1015.7

1286.8

143.5

Korea

146.6

85.5

798.9

1130.1

113.0

OECD

655.8

755.2

996.4

1284.1

151.1

Spain

131.0

143.5

213.3

217.5

160.2

3724.9

4004.3

5577.8

6645.2

156.1

United States Sources: ITU

124

Growth Rate (%) 2001-2004

INFORMATION AND COMMUNICATION TECHNOLOGY

References

125

REFERENCES

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Statistical Sources: Organization for Economic Cooperation and Development (OECD). 2005. Education Indicators at a Glance. www.oecd.org/edu/eag2005. /Program for International Student Assessment, PISA. No date. OECD/PISA Database. www.pisa.oecd.org. Programa para el Mejoramiento de Encuestas y la Medición de las Condiciones de Vida en América Latina y el Caribe (MECOVI). No date. Household Surveys Databank. Limited accessibility. Thomas, Vinod, Yan Wang, and Xibo Fan. 2003. Measuring Education Inequality: Gini Coefﬁcients of Education for 140 countries, 1960-2000. Journal of Education Planning and Administration, 17:1. January, pp. 5-33. World Bank. World Development Indicators Database. Available via subscription on-line at: http://devdata.worldbank.org/dataonline.

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B. Science and Technology Hill, S. 2005. 2004 Doctorate Awards Increase in Science and Engineering Fields for the Second Year in a Row. NSF 06-301, Arlington, VA: National Science Foundation, Division for Science Resources Studies. http://www.nsf.gov/statistics/inbrief/nsf04336/. Hill, D. 2004. Latin America Shows Rapid Rise in S&E Articles. NSF 04-336, Arlington, VA: National Science Foundation, Division of Science Resources Studies. http://www.nsf.gov/ statistics/infbrief/nsf04336/. Hill, D. 2002. Latin America: High Tech Manufacturing on the Rise, but Outpaced by East Asia. NSF 02-331, Arlington, VA: National Science Foundation, Division of Science Resources Studies. http://www.nsf.gov/statistics/infbrief/nsf02331/. Inter-American Development Bank. 2001. IDB Science and Technology Strategy for Development, Washington D.C.: Inter-American Development Bank. Lall, S., M. Albaladejo, and M. Mesquita. 2004. Latin American Industrial Competitiveness and the Challenge of Globalization. Washington, D.C.: Inter-American Development Bank. Velho, L. 2004. Science and Technology in Latin America and the Caribbean: An Overview. Discussion Paper Series. United Nations University INTECH, Maastricht, Netherlands. http:// www.intech.unu.edu/publications/discussion-papers/2004-4.pdf.

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