42486 DIRECTIONS IN DEVELOPMENT Countries and Regions Social Exclusion and Mobility in Brazil Estanislao Gacitúa Marió and Michael Woolcock, Editors Social Exclusion and Mobility in Brazil Social Exclusion and Mobility in Brazil Edited by Estanislao Gacitúa Marió Michael Woolcock © 2008 The International Bank for Reconstruction and Development / The World Bank 1818 H Street, NW Washington, DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org E-mail: feedback@worldbank.org All rights reserved 1 2 3 4 10 09 08 07 This volume is a product of the staff of the International Bank for Reconstruction and Development / The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street, NW, Washington, DC 20433, USA; fax: 202-522-2422; e-mail: pubrights@worldbank.org. ISBN: 978-0-8213-7219-7 eISBN: 978-0-8213-7220-3 DOI: 10.1596/978-0-8213-7219-7 Library of Congress Cataloging-in-Publication Data Exclusão social e mobilidade no Brasil. English. Social exclusion and mobility in Brazil / edited by Estanislao Gacitúa Marió, Michael Woolcock. p. cm. Includes bibliographical references (p. ) and index. ISBN 978-0-8213-7219-7 -- ISBN 978-0-8213-7220-3 (electronic) 1. Marginality, Social--Brazil. 2. Social mobility--Brazil. 3. Poverty--Brazil. 4. Brazil--Social conditions. I. Gacitúa Marió, Estanislao. II.Woolcock, Michael J.V., 1964- III.World Bank. IV.Title. HN290.Z9M263513 2007 305.5' 690981--dc22 2007025311 Contents Foreword ix Acknowledgments xi Contributors xiii Abbreviations xv Executive Summary xvii Chapter 1 Overview: Assessing Social Exclusion and Mobility 1 Estanislao Gacitúa Marió and Michael Woolcock, with Marisa von Bulow Chapter 2 Social Exclusion and Poverty Dynamics 35 Regis Bonelli, Gustavo Gonzaga, and Alinne Veiga Chapter 3 Legitimization and Acceptance: How Brazilians Survive Inequalities 81 Maria Celi Scalon and Ignacio Cano Chapter 4 Conclusions and Recommendations 97 Estanislao Gacitúa Marió v vi Contents Bibliography 109 Index 129 Figures 1.1 Race of Household Head, by Income Decile, 2001 16 2.1 Size of Selected Cohorts, 1977­2001 38 2.2 Incidence of Poverty among Older Cohorts, 1981­2001 50 2.3 Percentage of Brazilians Living below the R$160 Poverty Level, by Race, Years of Schooling, and Cohort, 1989­2001 55 2.4 Percentage of Brazilians Living below the R$80 Poverty Level, by Race, Years of Schooling, and Cohort, 1989­2001 57 2.5 Percentage of Women Living below the R$160 Poverty Line, by Cohort, Years of Schooling, and Urban or Rural Location, 1981­2001 59 2.6 Income from Main Job, by Gender, Years of Schooling, and Cohort, 1981­2001 60 2A.1 Proportion of People with One to Three Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 72 2A.2 Proportion of People with Five to Seven Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 74 2A.3 Proportion of People with 9­10 Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 76 2A.4 Proportion of People with 12­14 Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 78 3.1 Perceptions that Socioeconomic Rewards Are Reaped from Effort and Intelligence/Skills 90 3.2 Perceptions that Family and Social Networks Are Important for Getting Ahead 90 3.3 People's Perceptions of the Severity of Conflicts between Various Social Groups 93 3.4 Perceptions of the Degree of Racial Conflict, by Whites and Blacks 94 Contents vii Tables 1.1 Selected Poverty Indicators for Brazil, 1977­98 9 1.2 Elasticities of Poverty to Economic Growth 10 1.3 Selected Social Indicators, by Region 11 1.4 Public Social Expenditures, by Sector, 1995­2002 18 2.1 P-Values for Gender-Based Income Differences Tests, by Cohort, 1977­2001 39 2.2 Number of Poor, by Cohort, 1977­2001 40 2.3 Incidence of Poverty, by Cohort, 1977­2001 41 2.4 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with No Schooling, by Cohort, 1981­2001 43 2.5 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians Who Completed Lower-Primary School (Four Years of Schooling), by Cohort, 1981­2001 45 2.6 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians Who Completed Upper-Primary School (Eight Years of Schooling), by Cohort, 1981­2001 46 2.7 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians Who Completed Secondary School (11 Years of Schooling), by Cohort, 1981­2001 47 2.8 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians Who Completed Tertiary Education (15 or More Years of Schooling), by Cohort, 1981­2001 49 2.9 Ratio of Average Household Income in 2001 to Average Household Income in 1981, by Cohort, Years of Schooling, and Gender 51 2.10 Average Household Size, by Cohort and Years of Schooling, 1977­2001 52 2.11 Ratio of Average Income from All Sources in 2001 to Average Income from All Sources in 1981, by Cohort, Years of Schooling, and Gender 53 2.12 Pension and Retirement Income as a Percentage of Income from All Sources, by Cohort, 1981­2001 62 viii Contents 2.13 Pension and Retirement Income as a Percentage of Income from All Sources, by Cohort, Gender, and Years of Schooling, 1981 and 2001 62 2.14 Pearson Correlations of Income Rankings among Cohort- Schooling-Gender Groups, 1981­2001 63 2.15 Pearson Correlations of Income Rankings among Cohort- Schooling-Gender-Race Groups, 1989­2001 64 2.16 Pearson Correlations of Income Rankings among Cohort-Schooling-Gender-Urban/Rural Groups, 1981­2001 65 2.17 Pearson Correlations of Income Rankings among Cohort-Schooling-Gender-Urban/Rural-Regional Groups, 1981­2001 65 2.18 Regression Results on Income Divergence between 1981 and 2001 65 2.19 Determinants of Being Poor among People from Oldest and Youngest Cohorts, 1981 and 2001 67 2A.1 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with One to Three Years of Schooling, by Cohort, 1981­2001 71 2A.2 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with Five to Seven Years of Schooling, by Cohort, 1981­2001 73 2A.3 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with 9­10 Years of Schooling, by Cohort, 1981­2001 75 2A.4 Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with 12­14 Years of Schooling, by Cohort, 1981­2001 77 3.1 Level of Income Dispersion Considered "Just" in Selected Countries 85 3.2 Frequency Distribution of Most Important Policy Options for Reducing Inequality 92 3.3 Logistic Regression Coefficients for Determinants of Racial Conflict, by Variable in the Equation 94 Foreword This book examines issues related to reducing inequality in Brazil.As the volume's editors assert with authority, the current national political cli- mate in Brazil provides an unprecedented space for discussing this topic. Among the several investigations that have looked at exclusion and social mobility in Brazil, very few have presented as much empirical evi- dence as the studies included in this volume. In addition to reviewing the pertinent literature, Social Exclusion and Mobility in Brazil examines the changing income dynamics among homogeneous groups over a 20-year period.The analysis points to factors--such as ethnicity, education, gender, occupation, and location--that affect the probability that a group will remain in the situation of poverty. The volume also examines Brazilians' perceptions of these circumstances and the cultural values that make coex- istence possible given very high levels of inequality and low levels of mobility. It reveals that Brazilians expect the state--and only the state--to create mechanisms capable of transforming this situation. This volume presents a set of recommendations for discussion by cit- izens, academics, and policy makers. These topics include improving labor market equality and increasing access to assets; improving the social security system; supporting the formation of human capital, par- ticularly among youth; reducing discrimination based on characteristics such as race and gender; and strengthening citizenship and participation. ix x Foreword This book is a product of the continuing partnership between the Instituto de Pesquisa Econômica Aplicada (IPEA) and the World Bank in the preparation and dissemination of studies to be used as tools for reflection and debate regarding the design and redesign of public poli- cies. It demonstrates the importance of future partnerships between the World Bank and IPEA that focus on ways to reduce inequality in Brazil. Glauco Arbix President, Instituto de Pesquisa Econômica Aplicada Acknowledgments Valuable peer review comments to previous drafts were provided by World Bank colleagues Francisco Ferreira, Daniel Gross, Anthony Hall, Maria Valeria Pena, Robert Schneider, Emil Daniel Tesliuc, and Joachim von Amsberg. We also wish to thank Shelton Davis for his support and suggestions throughout this project. Kathy Lindert collaborated in the initial drafting of the research proposal that guided this study. For finan- cial support, we are grateful to the World Bank Brazil Country Management Unit and the Environmentally and Socially Sustainable Development Department, as well as the "Getting Beyond Averages" project funded by the World Bank's Research Support Budget (part of the Development Economics Vice Presidency). Finally, Flavia Carbonari was instrumental in supporting production of the book, which was man- aged by the World Bank Office of the Publisher. xi Contributors Regis Bonelli is a research associate at the Instituto de Pesquisa Econômica Aplicada (IPEA), a consultant to Fundação Centro de Estudos do Comércio Exterior (FUNCEX), and a partner at ECOS- TRAT. He has been a visiting research fellow at the Centre for Brazilian Studies and a senior associate member of St.Antony's College, University of Oxford. He holds a bachelor's degree in engineering from the Pontifical Catholic University of Rio de Janeiro and a doctorate in economics from the University of California at Berkeley. Ignacio Cano is a professor of sociology at the Universidade Federal Fluminense. He has published on violence, human rights, and the use of force by police in Brazil. He holds a doctorate in sociology from the Universidad Complutense de Madrid and completed postdoctoral stud- ies at the University of Michigan and the University of Arizona. Estanislao Gacitúa Marió is a lead social development specialist with the Sustainable Development Department in the Latin America and Caribbean Region at the World Bank. He joined the Bank in 1993 and holds a doctorate in philosophy from the Virginia Polytechnic Institute and State University, a master's degree in sociology from Kansas State University, and a doctor of veterinary medicine degree from Universidad de Chile. xiii xiv Contributors Gustavo Gonzaga is an associate professor of economics at the Pontifical Catholic University of Rio de Janeiro, where he received his master's degree in economics. He holds a doctorate in economics from the University of California at Berkeley. Maria Celi Scalon is a professor at the Federal University of Rio de Janeiro. She has been a visiting scholar at Nuffield College, University of Oxford. She holds a master's degree and a doctorate in sociology, both from the Graduate Research Institute of Rio de Janeiro. Alinne Veiga is a researcher at the Instituto de Estudos do Trabalho e Sociedade (the Brazilian Institute of Work and Society). She holds a master's degree in social statistics from the University of Southampton in the United Kingdom. Marisa von Bulow is a professor in the political science department at the University of Brasília. She holds a master's degree in social sciences from the Latin American School of Social Sciences (FLACSO-Mexico) and is currently doing her doctoral studies in political science at Johns Hopkins University. Michael Woolcock is a senior social scientist with the Development Research Group at the World Bank. He is currently on external leave as research director of the Brooks World Poverty Institute at the University of Manchester (United Kingdom). He has taught at the University of Queensland (Australia), Brown University, and Harvard University. He holds a master's degree and doctorate in sociology from Brown University. Abbreviations ECLAC Economic Commission for Latin America and the Caribbean IBGE Instituto Brasileiro de Geografia e Estatística IPEA Instituto de Pesquisa Econômica Aplicada ISSP International Social Survey Programme PNAD Pesquisa Nacional por Amostra de Domicílios (National Household Survey) xv Executive Summary Over the past decade, the concept and definition of poverty have become increasingly complex and encompassing, moving beyond the restricted notion of income-based poverty (Middleton, O'Keefe, and Visser 2001). Nobel laureate Amartya Sen (1999) emphasizes that poverty must be seen as the deprivation of basic capabilities rather than merely the lack of adequate income, or the destitution, vulnerability, exclusion, and lack of power needed to achieve an adequate standard of living and other rights.The 2000/2001 World Development Report (World Bank 2001) emphasizes various dimensions of poverty, including power- lessness, voicelessness, vulnerability, and fear. Closely related to these factors, the notion of inequality refers to the dispersion in the distribution of various dimensions of well-being across the population as a whole and between various groups (men and women, different ethnic groups or age groups) (World Bank 2003). Sen (1999) suggests that the opportunities to transform individual and group capabilities into higher states of well-being depend on multiple per- sonal and social characteristics that affect social mobility. Birdsall and Graham (2000) show that the relation between poverty, inequality, and social mobility is crucial for attaining sustainable development, because lack of socioeconomic mobility exacerbates inequality and discourages investment and economic growth. xvii xviii Executive Summary The degree of inequality and mobility varies widely across countries. These differences are critical, because they profoundly shape citizens' short-term opportunities and long-term prospects. Empirical research has (correctly) argued that high levels of inequality impede growth (by enabling inefficient resource allocation) and undermine citizens' sense that they have a collective stake in the future of their society. Where the rich inhabit a separate "moral universe"--able to purchase their own education, health care, transport, and security, while everyone else makes do with underfunded, substandard public services--it is difficult to build the broad political constituencies (and corresponding tax base) needed to support the provision of higher-quality services for all and to respond to economic (and other) crises. Some have argued that inequality can be tolerated if it is combined with (or accurately reflects) meritocratic social mobility--that is, if the socioeconomic status of the talented, diligent, and honest rises while that of the inept, lazy, or corrupt falls. This is one of the myths of the United States, for example. In the case of Brazil, however, low mobility and high inequality come together, representing the worst-case economic scenario: the race starts with certain groups enjoying a considerable head start, and there is little the others can do to overcome this disadvantage over their lifetime (or the lifetimes of subsequent generations). Even in countries that are otherwise applauded for being relatively "equal" economically, severe social (and other) barriers to mobility--caste, ethnicity, age, religion--can exclude certain groups from accessing public services and the job, housing, and financial markets on which the growth process depends. Most postsocialist countries, for example, entered their transition with unusually low levels of inequality but various forms of social exclusion (of the unskilled, of minority groups such as the Roma); institutional weakness has conspired to erode the sense of confidence among the excluded (in some countries a large proportion of the popula- tion) that a better future awaits them or is even possible. A large body of research has established that the level of economic inequality in Latin America generally, and in Brazil in particular, is high. Few studies, however, have sought to investigate empirically whether and how forms of social exclusion--and the concomitant opportunities for and constraints on mobility--serve to consolidate (or perhaps com- pensate for) inequality. Has the growth process that has unfolded over the past 50 years in Brazil persistently excluded particular groups? Which groups have remained stuck in poverty, and why? Drawing on various data sources (including pseudo-panels and qualitative field Executive Summary xix work), this study explores these issues, providing policy makers with a sense of some options for redressing them. The results indicate that the poor, especially Brazilians of African descent, have been systematically excluded from the growth process, despite laudable improvements in education levels and broad reductions in poverty. This exclusion has persisted (even intensified) for decades. Brazilians recognize the magnitude and salience of inequality, but they believe that responsibility for addressing the problem lies exclusively with the government. The current political climate in Brazil offers a unique opportunity to open a national dialogue on exclusion and mobility--one to which citizens and policy makers alike must contribute if a politically supportable consensus is to be forged on how best to respond to an issue with clear instrumental and intrinsic significance for Brazil's present and future well-being. References Birdsall, Nancy, and Carol Graham, eds. 2000. New Markets, New Opportunities? Economic and Social Mobility in a Changing World. Washington, DC: Brookings Institution Press. Middleton, Neil, Phil O'Keefe, and Rob Visser. 2001. Negotiating Poverty: New Directions, Renewed Debate. London: Pluto Press. Sen, Amartya. 1999. Development as Freedom. Oxford: Oxford University Press. World Bank. 2001. World Development Report 2000/2001: Attacking Poverty. Washington, DC: World Bank. ------. 2003. "Inequality in Latin America and the Caribbean: Breaking with History?" World Bank, Office of the Chief Economist for Latin America and the Caribbean, Washington, DC. C H A P T E R 1 Overview: Assessing Social Exclusion and Mobility Estanislao Gacitúa Marió and Michael Woolcock, with Marisa von Bulow In 2001 the World Bank updated an earlier poverty assessment of Brazil that analyzed the relation between income variables and household characteristics.The report provided an updated poverty profile, analyzed the impact of public social spending on poverty, and reviewed the effec- tiveness of selected policy interventions in order to provide suggestions for the development of a national poverty reduction strategy. The report identified that further work was needed to assess issues related to inequality, opportunity, and social exclusion. Previous research on poverty and inequality in Brazil has focused on the extent to which various factors (including labor markets, human cap- ital, prejudice, location) contribute to poverty and inequality. Little attention has been given to social exclusion processes to explain why certain groups lack equal access to resources (economic, cultural, and political) or the same opportunities as other groups to improve their living standards. Moreover, very little is known about the perception Brazilians have of inequality or the factors or individual characteristics they perceive as determining income inequalities and social mobility. In response to these concerns, this study was proposed to advance the development community's understanding of social exclusion processes 1 2 Gacitúa Marió and Woolcock, with von Bulow in Brazil. The initial objective was to analyze how processes of social exclusion created barriers to social mobility among the poor in Brazil, in order to identify some policy levers or interventions that could be used to remove those barriers and contribute to more effective poverty allevia- tion and social inclusion. With that objective in mind, an interdisciplinary group of Brazilian scholars (a political sociologist, a cultural anthropologist, and an economist) was asked to prepare position papers to help identify the key research questions through a literature review, propose basic hypotheses to be tested, and design a research proposal to answer the questions identified. Based on these papers, the World Bank drafted a research proposal and discussed it with scholars and representatives of civil society organ- izations in Brazil. As a result of the Bank's internal peer review process and external consultations, a revised research proposal was prepared to shed light on why certain social groups have maintained their relative socioeconomic position compared with others over time and what Brazilians think about income inequality and social mobility.1 The fruit of the research that resulted from the revised proposal is presented in this volume, which explores three main questions: · Are there groups of poor people in Brazil that experience less income mobility than others because of their race, education, age, gender, or location? · Which factors do Brazilians perceive as determinants of income inequality? · Are there differences between social groups regarding their perception of income inequality and social mobility? To answer these questions, the report analyzes the links between eco- nomic processes, political-institutional constraints, and cultural elements that contribute to poverty and affect social mobility. It looks at income dynamics to track changes in the positions of social groups relative to others with similar characteristics, and it analyzes perceptions of inequality to shed light on the processes that contribute to the lack of social mobility of certain social groups. This chapter outlines the approach, hypotheses, and methodology used in the study; describes the context in which exclusion and mobility occur in Brazil; and summarizes the report's main findings. Chapter 2 investigates whether poverty dynamics can be partly explained by social exclusionary processes based on the ethnic, educational, occupational, Overview: Assessing Social Exclusion and Mobility 3 regional, and spatial (location) characteristics of individuals and households. Chapter 3 tries to understand how Brazilian society produced and sustained cultural values that legitimize inequality. Chapter 4 discusses the main conclusions and policy recommendations that emerge from the study. The Nature of Poverty in Brazil Brazil is a country of sharp disparities. The gap between the richest and the poorest is one of the largest in the world: the wealthiest 1 percent earns more than the poorest 50 percent (Paes de Barros, Henriques, and Mendonça 2001). Until 1980 Brazil was one of the fastest-growing economies in the world; even between 1980 and 2000, GDP per capita grew at a respectable average annual rate of 2.5 percent. Despite recurrent crises, growth and economic stabilization over the past decade contributed significantly to poverty reduction and improve- ment in social indicators, surpassing levels that would be expected given Brazil's average income (World Bank 2001a). Changes in public policy have doubtless played a crucial role in these achievements by seizing the opportunities created by growth, mitigating the negative impacts of shocks, and expanding services to the poor. Although the poverty rate has dropped over the past 20 years--from about 40 percent in 1977 to 36 percent in 2000--it is still high compared with other countries with similar income per capita (Paes de Barros, Henriques, and Mendonça 2000). Most conspicuously, income inequality is among the highest in the world, with a Gini coefficient of 0.58­0.60 that has remained fairly constant over time (Paes de Barros, Corseuil, and Leite 2000).2 Paes de Barros, Corseuil, and Leite (2000) show that GDP per capita is about 4.2 times the poverty line and 8.5 times the extreme poverty line. For these authors, the immediate origin of poverty in Brazil lies in the inequality of income distribution, resources, and wealth. Compounding Brazil's inequality, the evidence suggests that some social groups and individuals are more likely to remain stuck in poverty. A significant number of extremely poor people with low education levels remain at the bottom of the income distribution, largely untouched by economic development. Poverty has become increasingly concentrated among populations with specific characteristics: children and youth; indigenous people and nonwhites; nonimmigrants; and the unemployed, people employed in agriculture or rural areas,3 and people employed in the informal sector, which includes a range of workers, from street 4 Gacitúa Marió and Woolcock, with von Bulow vendors to workers in small companies. In other words, there has been a trend toward homogenization and concentration of poverty among some social groups. These poor--the bottom 10­15 percent of Brazil's income distribution--appear to be excluded from the benefits of eco- nomic growth, employment, and education. These groups are the focus of this study. Approach and Focus This study builds on a social exclusion perspective, which holds that the interaction of specific structural mechanisms restricts the accumulation of, or access to, different resources that allow individuals to participate in society and move out of poverty. Social exclusion processes occur through the interaction of many dimensions, including sociodemographic characteristics (gender, age); cultural elements (ethnicity, race, norms, values, and definitions of what is "acceptable"); economic factors; and politico-institutional elements (representation, organizational structure, and so forth). Social exclusion refers to processes that increase the exposure to risks and vulnerability of certain social groups. Social exclusion mechanisms create barriers that prevent vulnerable groups from accessing assets and productive resources and from participating in the market and in social, cultural, and political institutions. Exclusion works through institutional- procedural processes that limit the opportunities of certain groups to exercise their rights to equal access to markets, services, and means of political participation and representation based on built-in features of the functioning of those institutions. Exclusion processes can be based on and operate through prejudices and preconceptions that may result in the refusal by society to grant rights to the poorest or in the granting of rights of lower quality, especially in economic, social, and cultural matters. These prejudices and preconcep- tions are extremely difficult to eliminate, because they are subjective. Sociocultural exclusion refers to the differentiated access of groups to the social and material benefits of society, when the causes of this differ- entiation reside in the existence of conflicts of a nonstructural origin or in the unequal disposition of symbolic goods. Sociocultural exclusion tends to be relied on to justify procedural exclusion.4 Processes of social exclusion can best be explained through a multi- disciplinary approach. This report combines disciplines to establish the link between economic processes, political-institutional constraints, and Overview: Assessing Social Exclusion and Mobility 5 cultural (valued) elements that contribute to social exclusion and affect social mobility.With respect to mobility, it draws inferences based on the income dynamics of homogeneous groups, tracking changes in the positions of social groups relative to others with similar characteristics. Regarding inequality, it looks at Brazilians' perceptions of inequality. Why should scholars, policy makers, and citizens care about social exclusion and mobility? All three groups have long expressed varying degrees of concern about inequality. A focus on exclusion and mobility seeks to provide an additional dimension that explores the factors shaping whether and how particular groups escape from or get trapped in poverty. Some scholars (for example, Hirschman 1973) have suggested that high levels of inequality can or may be tolerated where there is a perception that mobility is desirable and possible (see also Ravallion and Lokshin 2000). That is, the direct consequences of high inequality may be at least partially mediated where there is a clear sense that the talented, diligent, and honest move up and the inept, lazy, or corrupt move down.5 Even in countries that are otherwise applauded for being relatively equal economically, severe social (and other) barriers to mobility-- caste, ethnicity, age, religion--can exclude certain groups from accessing the services (schools, hospitals, courts) and markets (jobs, housing, finance) on which the growth process depends. Most transition economies, for example, began their transition with unusually low levels of inequality, but various forms of social exclusion (of the unskilled, of minority groups such as the Roma) and institutional weakness have eroded the sense of confidence among the excluded--in some countries a large proportion of the population--that a better future awaits them or is even possible.6 Brazil's very high inequality is well known, but is it also a country with low mobility? If so, who is excluded from enjoying the fruits of the growth process, and what might be done to redress the problem? Hypotheses and Methodology This volume analyzes some of the exclusionary factors and processes that contribute to the lack of mobility of the poor in Brazil. Its chapters explore a set of hypotheses to explain some of the possible causes of the presumed lack of mobility of social groups among the poor in Brazil.The authors analyze changes in average income rankings of groups with similar (educational, gender, race, spatial, location) characteristics; explore 6 Gacitúa Marió and Woolcock, with von Bulow the factors explaining those changes; and examine Brazilians' perceptions of inequality, its causes, and social mobility. The volume concludes by proposing some policy recommendations on ways to remove barriers to greater social inclusion. The study adopts a social exclusion perspective to analyze relative changes in the positions of groups with similar characteristics (cohort, educational level, gender, region) to shed light on the factors that affect the probability of being stuck in poverty (a situation similar to the chronic poverty concept) and to identify the perceptions Brazilians have of inequality.7 It starts by using Brazilian National Household Surveys (PNAD) to identify homogeneous groups based on certain personal and household characteristics. It then draws inferences on the extent of income mobility in Brazil among the poor, tracking changes over time in the positions of social groups and individuals relative to others with different characteristics and identifying signs of (eventual) income convergence (or divergence) across groups.8 It then uses data from the International Social Survey Programme (ISSP) survey to look at the per- ceptions Brazilians have of inequality and lack of mobility.9 The analysis of income changes is based on pseudo-panels obtained from PNAD cross-sectional data covering selected years between 1977 and 2001 (1977, 1981, 1985, 1989, 1993, 1997, and 2001).10 Although the PNAD data are not collected on a panel basis, sampling is done using repeated census tracks. This allows researchers to follow groups of individuals over time by classifying them according to a certain set of criteria (cohorts, education, gender, race, migration, household structure, location, and so forth). Individuals and households in each year are not the same. However, repeated cross-sections of groups of individuals can be a good approximation of each cohort if the sample sizes are sufficiently large. Five cohorts were selected: people born in 1940­44, 1945­49, 1950­54, 1955­59, and 1960­64. For each group, average real earnings were computed and the various sources of income studied. Specific tests were conducted to check income differences in individual cohorts, controlling for gender, educational level, race, and region. The extent of income mobility over time was inferred based on income-ranking correlations. A pseudo-panel regression approach was used to test whether there was any tendency toward income convergence (or divergence) across cohort groups between 1977 and 2001. Finally, a probit model to estimate the probability of being poor in each cohort-schooling group in 1981, 1985, 1989, 1993, 1997, and 2001 was developed using a set of variables reflecting personal and household characteristics. Overview: Assessing Social Exclusion and Mobility 7 To address the issue of perceptions of inequality and lack of mobility, logistic regressions were used to explore which dimensions explain these perceptions. Ordinary least squares analysis was used to identify which social, economic, and demographic characteristics best explain the differences in the "accepted" level of income inequality. The main explanatory variables considered included household income, schooling, age, social stratum, sex, race, rural/urban location, employment status, and perceived mobility. Development and Exclusionary Processes Brazil's development pattern has benefited the nonpoor more than the poor. Brazil developed through a process of "conservative modern- ization," whose principal feature was the failure to incorporate large segments of the population into modern sectors of the economy, society, and political system.11 In principle, all Brazilians have equal rights; in practice, certain social groups lack these rights and are excluded from the services provided by the government. Until the 1930s, the Brazilian economy was dominated by the agricultural sector, mainly coffee for export and low-productivity agricul- ture and livestock for domestic markets. Reflecting a shift in the power structure from the rural to the nascent urban elites, an embryo welfare state began to emerge in the 1930s. In the 1950s, Brazil embarked on a massive industrialization program to end its dependence on foreign imports and strengthen its economy.As a result, Brazil experienced a rapid change, from a predominantly rural agricultural socioeconomic structure to an urban, industrial-based structure. Economic growth and industrialization in Brazil after 1945 were constant and rapid until the beginning of the 1980s.12 However, it was not until the 1970s that the industrial and service sectors far surpassed the agricultural sector.13 Between 1960 and 1980, more than 30 million people moved from the countryside to the cities. Such a pattern of growth initially tended to favor higher-paid formal sector workers to the detriment of rural people. The transfer of labor from rural to urban sectors led to a deep transformation in the social structure and had as a major consequence the ascending mobility of many workers and their families. This context generated and reinforced expectations of upward individual mobility and status attainment, in which the major target was to gain access to goods and benefits from development. By 1980 Brazil's industrial output was the seventh largest in the Western world. Progressively, the changes that took place in the labor market in Brazil as a result of industrial restructuring processes resulted 8 Gacitúa Marió and Woolcock, with von Bulow in a dramatic increase in the share of workers that were part of the informal sector.This trend had a negative impact on the mobility perspec- tives of the poor. These structural changes were slowed by the recurrent crises over the past two decades. After the East Asian financial crisis, average annual GDP growth declined from 0.6 percent between 1990 and 1997 to ­0.8 percent in 1998­99 (CEPAL 2000), with negative results on income and, conse- quently, social mobility. The macroeconomic stabilization program adopted after the implementation of the Real Plan in 1994 had a lim- ited positive impact on the proportion of the poor in the population, which declined from 41.7 percent in 1993 to 33.9 percent in 1995.14 However, stabilization did not affect inequality, as measured by the Gini coefficient (table 1.1) (Rocha 2000a; Paes de Barros, Corseuil, and Cury 2000; Paes de Barros, Corseuil, and Leite 2000). Poverty is less responsive to growth in Brazil than in other countries (table 1.2). Extremely high inequality is the main reason for the low elasticities of poverty to growth. These results suggest that a key obstacle to overcoming poverty in Brazil is not the lack of resources but their unequal distribution (Paes de Barros, Corseuil, and Leite 2000), and that a more equal income distri- bution would have a greater impact on economic mobility. Paes de Barros, Mendonça, and Duarte (1997) estimate that keeping average income constant, policies that cause the Gini coefficient to decline by 0.10 would have an impact on the proportion of the poor equivalent to about one decade of economic growth of 3 percent a year.15 Brazil is not a poor country but an unequal country with a large poor population (Henriques 2000), a distinction that, as shown below, has widespread implications for the policies proposed. Previous research has also shown how some segments of the population are more likely to be poor than others. As is well known, poverty in Brazil has a distinct regional component, which the decline in overall poverty did not reduce.16 In sum, the literature shows that it has been hard to achieve consistent economic growth and that even when it has been achieved, it has had limited impacts on reducing poverty and social exclusion. Exclusionary Mechanisms Exclusionary processes affect the lives of the poor in Brazil through multiple mechanisms. Recent research explores the relation between income poverty and multiple household characteristics.17 These studies have helped identify the determinants of poverty in Brazil and assess the Table 1.1. Selected Poverty Indicators for Brazil, 1977­98 Average per capita Percentage of GDP as multiple of total income Number of Percentage Percentage Extreme Poverty poor of extreme of poverty line line Gini Poorest Poorest Richest Richest Richest Year (millions) poor poor (R$65) (R$132) coefficient 20% 40% 20% 10% 1% 1977 40.7 16.3 39.6 6.6 3.3 0.62 2.4 7.7 66.6 51.6 18.5 1978 45.2 20.7 42.6 6.8 3.4 0.60 2.1 7.6 64.1 47.7 13.6 1979 42.0 15.9 38.8 7.1 3.5 0.59 2.7 8.4 63.8 47.5 13.6 1981 50.6 18.8 43.1 7.1 3.6 0.59 2.6 8.5 63.2 46.9 12.8 1982 51.9 19.4 43.1 7.0 3.5 0.60 2.5 8.2 63.9 47.4 13.2 1983 62.7 25.0 51.0 6.7 3.3 0.60 2.5 8.1 64.5 47.9 13.6 1984 63.5 23.6 50.4 6.9 3.4 0.59 2.4 7.7 66.6 51.6 13.3 1985 56.9 19.2 43.5 7.3 3.6 0.60 2.1 7.6 64.1 47.7 14.3 1986 37.6 9.8 28.2 7.7 3.8 0.59 2.7 8.4 63.8 47.5 13.9 1987 55.4 18.5 40.8 7.8 3.9 0.60 2.6 8.5 63.2 46.9 14.3 1988 62.5 22.1 45.3 7.6 3.8 0.62 2.5 8.2 63.9 47.4 14.4 1989 60.6 20.7 42.9 7.7 3.9 0.64 2.5 8.1 64.5 47.9 16.7 1990 63.1 21.3 43.8 7.3 3.6 0.62 2.1 7.3 65.8 49.2 14.3 1992 57.3 19.3 40.8 7.1 3.5 0.58 2.3 8.4 62.2 45.8 13.3 1993 59.4 19.5 41.7 7.3 3.7 0.60 2.2 7.9 64.5 48.6 15.1 1995 50.2 14.6 33.9 7.8 3.9 0.60 2.3 8.0 64.2 47.9 13.9 1996 50.1 15.0 33.5 7.9 4.0 0.60 2.1 7.7 64.2 47.6 13.6 1997 51.5 14.8 33.9 8.1 4.1 0.60 2.2 7.8 64.2 47.7 13.8 1998 50.1 13.9 32.7 8.7 4.3 0.60 2.3 8.0 64.2 47.9 13.9 Source: Adapted from Paes de Barros, Corseuil, and Leite 2000. 9 10 Gacitúa Marió and Woolcock, with von Bulow Table 1.2. Elasticities of Poverty to Economic Growth Extreme poverty line Poverty line Item (R$65) (R$132) World (excluding Central and -- ­1.57 Eastern Europe) Latin America ­1.3 ­0.94 Brazil 1996 profile with ­1.15 ­0.80 fixed distribution Brazil actual 1985 ­0.74 ­0.58 1996 ­1.09 ­0.85 Source: World Bank 2001a. -- = Not available. impacts of antipoverty policies. Location contributes to poverty: half of Brazil's poor live in the northeast region (although the region is home to only 30 percent of the total population), and 55 percent live in rural or small urban areas (home to just 35 percent of the population). Social exclusion disproportionately affects rural people, particularly those living in the northeast.18 Most of the poor live in farm households located in remote, isolated, sparsely populated, and low-productivity areas, for whom income from farming and agricultural labor represents about 70 percent of total household income (World Bank 2001b). The most vulnerable are elderly people, widows, and some farm workers in poorly endowed areas.Those "trapped" in poverty face tremendous obsta- cles for social mobility, because they do not benefit from technological innovation, migration, or opportunities in commercial agriculture. The trend toward the informalization of labor markets has been observed in rural as well as urban regions. In the northeast, the share of salaried workers in agriculture fell from 41 percent in 1981 to 32 percent in 1997; during the same period, the percentage of "unpaid" (family) workers rose from 22 percent to 30 percent. Throughout the 1980s and 1990s, only 28 percent of the agricultural labor force was employed in the formal sector and earning a regular wage (World Bank 2001b). The rural poor are also consistently worse off than urban populations in terms of access to public services. Among the poorest 20 percent of households in the rural northeast, 75 percent of household heads and 51 percent of all family members older than 10 have no schooling, about 10 percent of rural school-age children are not enrolled in school, and rural secondary education is virtually nonexistent (World Bank 2001b). In some Overview: Assessing Social Exclusion and Mobility 11 very isolated rural regions (in the Amazon states, for example), access to education is further hampered by a lack of transportation for students and teachers and the dispersion of a relatively small number of children over very broad areas. Although the relation between income inequality and poverty changes across regions, the data suggest that underdevelopment is a key factor in accounting for the high incidence of poverty in the rural north- east, while less inequality is the main factor explaining the relatively low incidence of poverty in the south. Over time, social conditions have improved countrywide. But the differences between the northeast and the rest of the country have not changed much, and the northeast remains well below the country average on most indicators (table 1.3). The data presented in chapter 2 indicate that the distinction between urban and rural areas is essential for analyzing poverty dynamics, particu- larly for understanding the importance of migration from rural to urban regions as determinants of social mobility and the role of retirement and pension income for poor rural workers. The analysis shows that poverty rates evolved differently in urban and rural areas and across social groups within rural areas. Between 1981 and 1989, for example, about 90 percent of women with no education in rural regions were poor, across cohorts; between 1997 and 2001, a significant decrease in poverty rates was observed only for the two oldest cohorts, which include women who Table 1.3. Selected Social Indicators, by Region Formal Personal employment, Poverty rate nominal Illiteracy 1999 (percent)b income per rate, (as percentage Years of Gini capita, 2000 2001 of total schooling, coefficient, Region (R$/month)a (percent) employment) 2001 2001a 1992 1999 Northc 577 7.8 45.3 6.3 0.598 47.5 34.9 Northeast 448 17.7 43.2 5.3 0.617 60.6 50.2 Southeast 945 7.1 68.3 6.2 0.586 26.9 16.8 South 796 5.4 71.2 6.6 0.572 29.4 19.8 Center-west 856 9.2 52.9 6.7 0.622 37.2 23.6 Brazil 769 11.4 61.3 6.2 0.609 38.9 28.4 Source: Adapted from Gomes 2002 (based on IBGE Demographic Census 2000 and PNAD 2001). a. Average income of household units with nonzero nominal income. b. Estimates by Getulio Vargas Foundation, based on IBGE Demographic Census 2000 and PNAD 2001. c. Urban north. 12 Gacitúa Marió and Woolcock, with von Bulow became eligible for pension benefits. The authors of chapter 2 conclude that pensions may have played a role in reducing poverty for the oldest cohorts of less-educated people, especially in rural areas. Other locational differences also contribute to discrimination, poverty, and lack of social mobility. The continued displacement of the rural population has resulted in the growth of urban slums. The Voices of the Poor report for Brazil collected testimonies from poor people living in favelas (slums) in urban areas. One of the most common forms of discrimination they report is based on place of residence. Several of those interviewed reported having been denied a job based on where they lived or having been afraid or ashamed to tell their boss and colleagues where they lived. People who live in the most violent areas are automatically associated with criminal behavior and activities (see the analysis by Lago 2003 and Perlman 2003).19 Another source of discrimination relates to place of origin. This type of discrimination is experienced by nordestinos (people who migrated from the northeast region) in São Paulo. There is broad empirical evidence that education is a key correlate of income inequality in Brazil (see Almeida Reis and Paes de Barros 1990; Paes de Barros and Lam 1993; Verner, Blom, and Holm-Nielsen 2001). The household heads of 63 percent of poor households have no more than four years of schooling (people with this level of education make up 42 percent of the population). Low levels of parental education increase the likelihood of an individual falling below the poverty line, but an additional year of schooling by parents is associated with only 0.3 year of additional schooling for their children (see Paes de Barros and others 2001), suggesting that the level of education generates a certain intercohortal inertia.20 Chapter 2 provides a detailed analysis of the relation between education, poverty, and income dynamics over time for different educational groups, by gender and race. Changes in the average real earnings over time of each group indicate that even though younger cohorts have more years of schooling than older cohorts, the latter group presents more positive changes in real earnings over time. However, poverty reduction has not been uniform across age and educational cohorts. People with no education and older cohorts display more income growth, more poverty reduction, and less income concentration than younger cohorts. Even so, a substan- tial proportion of households headed by a person with no education remain stuck in poverty. The oldest cohorts and those with no education have experienced greater poverty reduction over time than young and Overview: Assessing Social Exclusion and Mobility 13 more-educated cohorts. In fact, among younger cohorts, the average income of Brazilians with little or no education fell over time.This trend is explained by a number of factors, including returns to work experience; proactive gov- ernment social policies (including retirement and pensions); economic-cycle effects; and household size and age composition dynamics. According to Verner, Blom, and Holm-Nielsen (2001), workers with lower levels of education experienced a substantial decrease in returns to education, while workers who completed tertiary education saw an increase in returns to schooling. Returns to tertiary education increased sharply between 1982 and 1998, while returns to primary education fell 26 percent and returns to lower secondary education dropped 35 percent. These figures suggest that returns to schooling are the primary link between education and wage inequality. The impact of a more equitable distribution of schooling influences wage inequality through the supply impact on returns to schooling. The findings of Verner, Blom, and Holm- Nielsen (2001) suggest that as long as the returns to schooling rise sharply with years of completed schooling, a significant improvement in wage inequality will not take place. Wage bonuses associated with education are higher among those entering the market (Arbache 2001). This suggests that education may enhance the chances of entering the labor market at a higher income level at the start of one's career but lose power as the individual moves from one job to another over time. Unemployment is a key factor of exclusion. In 1995 the average annual unemployment rate was 4.65 percent (as measured by the Instituto Brasileiro de Geografia e Estatística [IBGE]) in six metropolitan regions; at the beginning of 2002 it was about 7 percent. The strongest effect was in the industrial sector, where formal jobs and better labor conditions are concentrated: the share of the workforce employed in industrial production declined from 23.7 percent in 1989 to 20.6 percent in 2001 (SDTS 2003).The data presented in chapter 2 indicate that self- employed workers have a lower probability of being poor than informal workers, particularly among older cohorts. As expected, unemployment increases the probability of being poor for the youngest cohort but not for the oldest cohort. Unemployment in Brazil has increased dramatically since the late 1980s. It is highest among formal skilled and low-level white-collar workers, par- ticularly young people (15­24 years old). Elimination of unemployment would reduce poverty rates by only about 20 percent, however (Ferreira and Paes de Barros 1999; Paes de Barros, Corseuil, and Leite 2000). 14 Gacitúa Marió and Woolcock, with von Bulow Several studies suggest two other major forms of labor market exclusion: underemployment and precarious employment.21 Forty percent of the population as a whole live in households headed by someone who works in the informal sector, but 58 percent of the population below the poverty line do so.22 The data indicate growing pressure in the labor market to move from formal to informal labor relations. Together with outsourcing, this trend is making employment less stable. The growth of the informal sector became more pronounced in the early 1990s. The percentage of informal workers in the active economic popula- tion increased from 39.3 percent in 1990 to 48 percent in 1996, reaching 50 percent in 2001 (Presidência da República 1997; Ramos 2002). These workers tend to earn less than the minimum wage and receive fewer benefits than those in the formal sector (for example, they do not receive paid vacations, unemployment insurance, or social insurance).23 The analysis in chapter 2 concludes that among workers with no schooling, those in the formal sector have a lower probability of being poor than those employed in the informal sector.According to IBGE, the number of workers holding a job without a contract rose 11.5 percent between May 1999 and May 2000. While the formal market created 62,000 new job positions over this period, the informal sector created 491,000 new jobs--an annual rate of growth of 5.2 percent. Another important change in the labor market is the increase in the demand for skilled labor, which has been strongest in industries that experienced increased competition as a result of trade liberalization.24 This increase occurred together with a drastic reduction in the number of less-skilled jobs and a decline in wages. Most of the jobs created between 1989 and 2001 consisted of nonwage jobs (domestic and autonomous labor) or jobs that offered no more than twice the minimum wage. Workers with less than four years of education have been most affected by these labor market trends. Particularly affected have been young people: among 10- to 24-year-olds, the unemployment rate tripled, from about 5 percent in 1989 to more than 14 percent in 1997. In 1999 unem- ployment reached about 20 percent for 10- to 24-year-olds, 17.2 percent for 20- to 24-year-olds, and 27.8 percent for 15- to 19-year-olds.25 The unemployment rate among 18- to 24-year-olds reportedly rose 68 percent between 1995 and 2005, reaching 17.8 percent in 2005 (IBGE 2006). About 20 percent of Brazil's population is between 15 and 24. According to IBGE, 35 percent of those under 14--about 21 million children--live in poverty, and at least 8 million youth between 15 and 24 live on less than a dollar a day.26 Brazil's infant mortality rate is 36 Overview: Assessing Social Exclusion and Mobility 15 deaths per 1,000 live births (UNICEF 2001), and more than 60 percent of children live in homes with inadequate sanitation. Almost one-third of Brazil's children and youth are illiterate, a condition that reinforces their marginalization. Brazilian statistics show that 2.8 million children between the ages of 7 and 14 do not attend school, and of those who are registered, only 10 percent complete primary school.27 The most vulnerable groups to the changes in the labor market also include women and nonwhites.According to data from the 1996 PNAD, 64.6 percent of white women, 34.5 percent of nonwhite men, and 24.2 percent of white men earn less than the minimum wage. In addition, women and nonwhites participate more in the "informal" sector than do whites (do Valle Silva 2000; Soares 2000;World Bank 2002). In 2004 the average wage for women was two-thirds that of men, and the average for nonwhites was about half that of whites (IBGE 2006). The participation of women in the labor market has grown continuously over the past two decades (reaching 49 percent in 1999 and 52 percent in 2005), although it remains lower than that of men in all regions (IBGE 2005). Another positive change has been the steady (albeit slow) decrease in the wage gap between men and women since 1988. In 1977 men earned about 70 percent more than women; by 1997 this differential had dropped to about 25 percent, falling at an average rate of 1.4 percent a year (Leme and Wajnman 2000). However, in 2005 the average wage for women was still 71.2 percent that of men (IBGE 2005). Despite this narrowing of the wage gap, gender discrimination still needs to be taken into account in explaining earnings differentials. The gap prevails even when education and hours worked are taken into account; indeed, it increases with years of education, reinforcing the hypothesis that gender-based discrimination is practiced where there is competition for better jobs or positions (Leme and Wajnman 2000). Other factors outside the labor market, such as marriage, children, or work interruptions, may also explain part of this gap. The Brazil Gender Review (World Bank 2002) indicates that Brazil continues to have one of the widest gender wage gaps in Latin America, despite higher levels of education of women.Trade liberalization has also affected the gender division of labor. While female participation in the labor market has increased, female unemployment and participation in the informal sector have also risen. According to Cunningham (2000), women are more likely to work in the informal sector, as men move to the self-employment sector. In fact, 70 percent of economically active women work in the tertiary sector. While women's participation in the 16 Gacitúa Marió and Woolcock, with von Bulow labor market rose during economic expansions, their mobility rates remain lower than those of men. Women are more likely than men to work in less-privileged positions, particularly in the traditional produc- tive sector, although they also occupy nonmanual positions, especially in the public sector. Such differences result from unequal opportunities in the employment structure caused by gender segregation.28 Chapter 2 analyzes income changes over time for different cohorts by educational level and gender. The data presented indicate that while the average household per capita income of women is not statistically different from that of men in a given cohort-education group, gender discrimination exists in the Brazilian labor market (with women earning less than equally qualified men on average). People of African descent constitute about 45 percent of Brazil's pop- ulation. They earn half the average income of the white population, a proportion that remained more or less stable between 1995 and 2001.29 Prejudice and racial discrimination are key factors in the exclusionary processes that characterize Brazilian society: 62 percent of the poor live in households headed by nonwhites. While the variation in incomes attributed to discrimination in the labor market may be lower than in other countries, a substantial portion of these interracial differences in Brazil seem to be caused by discriminatory practices (figure 1.1). Social exclusion of nonwhites is not only a social or an economic prob- lem, it is also a racial (and cultural) issue--and must be recognized as such. Figure 1.1. Race of Household Head, by Income Decile, 2001 100 90 80 70 60 cent 50 per 40 30 20 10 0 lowest third fourth fifth sixth venth eighth ninth second se highest income decile nonwhite white Source: IBGE (PNAD) 2001. Overview: Assessing Social Exclusion and Mobility 17 Awareness of racial discrimination has increased across social groups, particularly during the past two decades. The national discourse of Brazil as a "racial democracy," so popular in the past, has given way to expressions such as "Brazilian-style racism," which acknowledges discrimination while trying to grasp the specific ways in which racism leads to social exclusion.30 In addition to educational barriers, the nonwhite population also suffers from discriminatory barriers that undermine their access to the labor market and leave them with unequal wages. Racial discrimination explains almost one-third of the white/nonwhite difference in obtaining a job in the formal sector, 24 percent of the wage gap for the self-employed, and 11 percent of the wage gap for workers in the formal sector (Soares 2000; Jaccoud and Beghin 2002). White males are the most privileged, and nonwhite females face the greatest social inclusion.These data confirm the testimonies of poor urban women in the Voices of the Poor project, who describe the racial discrimination they suffer when seeking a job. Poverty rates for nonwhites are much higher than those of whites with the same levels of schooling. Among people with no education, 50­70 percent of whites and 70­85 of nonwhites were poor in 2001. Furthermore, reductions in the incidence of poverty among nonwhites were smaller and more circumscribed than for whites (declining only between 1997 and 2001). In sum, nonwhite households experienced higher poverty rate levels and less poverty reduction over time. These results suggest that racial prejudice is a significant mechanism that contributes to persistent poverty and the marginalization of Brazil's nonwhite population. Attention must be given to institutional mechanisms that contribute to poverty by diverting public expenditures from reaching the poorest sectors. About two-thirds of the federal budget is allocated to "social expenditures" (health, education, labor, and social assistance), which cor- responds to about 20 percent of Brazil's gross internal product. About two-thirds of total social expenditures are directed to social security, a percentage that has steadily increased in recent years (table 1.4). However, only about 13 percent of total social public expenditures ben- efit the poorest quintile of the population (von Amsberg, Lanjouw, and Nead 2000). In fact, the most important criticism of public expenditure in relation to poverty is that it does not focus on the poorest families. Expenditures on social security, for example, have been strongly criticized because they disproportionately benefit the wealthiest Brazilians. In 1997 the poorest quintile of the population received only about 8 percent of spending on pensions and benefits to public servants (World Bank 2001a).31 18 Table 1.4. Public Social Expenditures, by Sector, 1995­2002 (R$ million) Year Sector 1995 1996 1997 1998 1999 2000 2001 Budgeted 2002 Health 9.92 (18.6) 10.29 (16.5) 12.84 (18.4) 12.81 (15.8) 15.52 (17.0) 17.57 (18.1) 20.89 (19.1) 18.31 (15.8) Education 5.14 (9.6) 5.32 (8.5) 5.62 (8.0) 9.72 (12.0) 10.60 (11.6) 5.99 (6.2) 6.91 (6.3) 7.87 (6.8) Assistance and 37.35 (69.7) 45.09 (72.2) 49.18 (70.4) 56.53 (69.8) 63.49 (69.5) 70.62 (73.0) 79.90 (73.0) 88.05 (75.9) social security Agrarian reform 0.860 (1.6) 0.908 (1.4) 1.17 (1.7) 1.09 (1.3) 0.892 (1.0) 0.911 (0.9) 1.15 (1.0) 1.27 (1.1) Sanitation and 0.279 (0.5) 0.859 (1.4) 1.06 (1.5) 0.855 (1.1) 0.864 (0.9) 1.77 (1.8) 0.590 (0.6) 0.473 (0.4) housing Total 53.56 (100) 62.4 8 (100) 69.88 (100) 81.01 (100) 91.37 (100) 96.87 (100) 109.44 (100) 115.98 (100) Percentage of 22.0 21.6 17.9 16.3 15.5 15.7 18.1 17.8 total public expenditure Source: Based on data from the Ministério do Planejamento 2003 (www.planejamento.gov.br/orcamento). Note: Parenthetical figures show percentage of total spending. Overview: Assessing Social Exclusion and Mobility 19 The reduction in poverty rates between 1981 and 2001 was concen- trated among the oldest cohorts, especially the less educated, as a result of the significant increase in the ratio of pension and retirement earnings to income from all sources. For the oldest cohort, the share of pension and retirement earnings in income from all sources rose from 2.3 percent in 1981 to 34.6 percent in 2001, with the largest increases among women and people in rural areas. As chapter 2 shows, this ratio significantly reduced the probability of a person with no schooling being poor in 2001. The 1988 Constitution introduced significant changes to social security in Brazil by extending the right to receive retirement benefits to rural workers. As a result, there was a substantial increase in social security coverage during the 1990s. The effect of this policy change shows up in the results of this research. Two key factors behind the observed reduc- tion in poverty are thus the extension of social security coverage to rural areas in the early 1990s and the increase in the real value of the minimum wage following adoption of the Real Plan. Despite the bias in social expenditures, the expansion of pension and retirement benefits to the poor, particularly in rural areas, had a positive impact on poverty alleviation. Social Mobility and Perceptions of Inequality Social mobility by the poorest segments of the population is very limited in Brazil. Brazilians acknowledge as much and somehow have adapted to this reality. Social mobility. Relative social mobility by the poorest segment of the population has changed far less than absolute mobility rates, which rose in Brazil as a result of industrialization.32 Social mobility between cohorts is greater in the more developed regions of the country, compounding regional difference and reducing mobility among the poorest.33 But throughout the country, mobility is limited: even when people move up the socioeconomic ladder, they do so only modestly. Brazil is organized into well-demarcated classes, with a certain degree of fluidity within class divisions but significant rigidity limiting the crossing of class boundaries. Such characteristics have tended to become dominant with the slowdown in structural changes marking the period from the 1950s through the 1970s.The evidence suggests that whites are much more efficient at converting their experiences and educational investments into monetary returns, while nonwhites suffer increasing disadvantages in climbing the social ladder.34 20 Gacitúa Marió and Woolcock, with von Bulow This study looks at income dynamics over a 20-year period to approach the issue of social mobility and inequality. The results presented in chap- ter 2 indicate that average household per capita incomes increased for all groups in the three oldest cohorts (1940­44, 1945­49, and 1950­54), particularly those with less schooling. At the same time, the income ratios between the last and first years for all cohorts decreased when moving from older to younger cohorts. The analysis of income-rank correlations over time indicates that groups of households saw little change in their relative position.The data show very high income-ranking correlations, indicating that the positions of each cohort-gender-schooling group changed very little across years, regardless of year of birth, years of schooling, gender, or ethnicity. Only when regional and rural dimensions are added to the analysis do some differences begin to emerge. Income inequality, as measured by the Gini index, decreased for all cohorts from 1981 to 2001, despite some fluctuations. Income inequality was smaller and fell more for the oldest cohorts, which experienced more income growth and less income concentration than younger cohorts. However, the proportion of the poor decreased only slightly, to 62 percent for the 1940­44 cohort, 68 percent for the 1945­49 cohort, 72 percent for the 1950­54 cohort, 76 percent for the 1955­59 cohort, and 79 percent for the 1960­64 cohort in 2001. These shares are still sizable, indicating that at the end of the period analyzed, the vast major- ity of people in all five groups considered still lived below the poverty line. The analysis of income dynamics suggests that there is little social mobility and that income growth is negatively correlated with initial income levels. Perceptions of inequality. What do Brazilians think about these facts? What is their perception of inequality? Which factors are perceived as determinants of income inequalities? Are these factors perceived as fair or unfair? What are the consequences of these perceptions? These questions, explored in chapter 3, can be answered only by understanding the cultural values that allow inequality and the population to come to terms with one of the highest levels of income disparity in the world. Brazilians see their society as a very unequal one: there are no illusions about the extreme disparities that exist.About 96 percent of people agree or strongly agree that income differences in Brazil are too large. All social groups share this view, irrespective of educational level, gender, income, mobility experience or expectation, and rural or urban residence. Overview: Assessing Social Exclusion and Mobility 21 A normative and legitimating discourse helps explain why inequality is tolerated and why Brazilian society allows growing inequalities. There is a tension between the cognitive dimension, which clearly recognizes inequality and the lack of social mobility for the poorest segments of the population, on the one hand, and the normative discourse, which partly explains why such differences exist and which mechanisms impede breaking them down, on the other. This difference between the norma- tive and cognitive levels explains why people identify the gaps in equality but assume that the rules are fair and are willing to play by them. Brazilians also believe that responsibility for improving equality lies with the state, which they expect to ensure that the rules in place to facilitate mobility are enforced. They do not question whether the rules themselves are sufficient to bring about the changes required. The results also reveal another facet of social exclusion: political exclusion (the lack of appropriate channels of representation and partic- ipation) for the most vulnerable segments of the population. Although many people recognize that good governance and the fight against social exclusion would benefit from more participatory forms of public policy design and implementation, the public discourse tends to place all responsibility on the state rather than civil society. Responding to this challenge requires a change in attitude and priorities, as well as concrete policy responses from both government and civil society. The shift in political culture initiated with the process of democrati- zation during the 1980s culminated in the design of the new political system and the constitution of 1988. However, during the 1990s, the cohesion of poor communities (particularly those in urban areas) shrunk as a result of the increase in violence and conflicts (Scheper-Hughes 1992; World Bank 2000; Zaluar 2000). Political exclusion has had deep effects on the relations among residents in poor communities and between those communities and the public arena.35 Much evidence indi- cates that poor people have suffered from unequal access to fair judicial processes and unequal treatment on the part of public bureaucracies, which has affected their capacity for collective action. Conclusions and Recommendations Inequality, exclusion, and lack of mobility are characteristics of life for Brazil's poor and nonwhite citizens. They begin their economic lives far behind their more privileged counterparts, and despite "rising tides" of potential opportunity generated by economic growth and improved 22 Gacitúa Marió and Woolcock, with von Bulow education, they remain mired in poverty, unable to catch up or to envision a future in which at least their children's lives might be better. While recognizing how pervasive and debilitating the problem is, Brazilians continue to perceive the huge inequalities in Brazil as the result of fair rules that are poorly administered. As long as they do so, there will be little change in the conditions that generate and sustain inequality and poverty. Both individuals and civil society organizations need to assume that they (in cooperation with their governments) have direct responsibility for creating the conditions for change. Policy changes are needed to ensure fair opportunities for everyone, but in the end all individuals are responsible for helping create the conditions that make policy changes both desirable and possible. This study seeks not just to provide a detailed diagnosis of the problems of inequality, exclusion, and restricted mobility but also to draw on the best evidence to offer concrete policy recommendations for change and to present data that can be the basis for informed conversation among Brazilian citizens, scholars, and policy makers. There is an emerging con- sensus that the fight against poverty in Brazil cannot be dissociated from the fight against inequality; indeed, income inequality is a main impediment to poverty reduction in Brazil. For this reason, redistributive policies (in addition to growth policies) are essential to enhancing social inclusion.This implies focusing on developing institutions (market, political, social, and cultural) and delivery mechanisms that will sustain progress toward a more inclusive, accountable, and cohesive society. Several areas require attention: · Ensuring fair access to labor markets. Structural factors, such as race- based discrimination, are at the root of labor market exclusion. Some combination of affirmative action­type policies and mechanisms to minimize the risks and vulnerability associated with living and working in the informal sector may be warranted.The demands for greater flex- ibility in labor markets must be balanced with the need for more jobs with greater security. An issue on which there is considerable consen- sus is the need to provide greater incentives for employers to hire young people. Several government programs provide incentives for employers to give young people their first jobs, but much more is needed. · Facilitating access to assets. In terms of social mobility for poor rural people, the land reform program is probably the most important public policy being implemented in Brazil. It is essential to expand Overview: Assessing Social Exclusion and Mobility 23 and consolidate land redistribution through the existing scheme, to use other mechanisms that will allow the rural landless to be settled, and to enhance the ability of small farmers to either buy or rent land to increase the size of their production units. · Expanding and improving social security. One of the most important public ways of providing a safety net, especially for the most vulnerable part of rural poor populations, is by increasing social security coverage. This study shows the significant positive impact that pension and retire- ment earnings have had on older and less-educated cohorts. Although social security has benefited primarily the wealthiest Brazilians, the impact of pensions on poverty alleviation, particularly among the most vulnerable, has been significant. Policy makers should therefore consider strengthening this instrument and improving its targeting to ensure that the poorest benefit. · Building human capital. The data presented in this volume confirm the importance of education in fighting poverty, a fact clearly accepted and valued by all social groups. However, the data indicate that returns to secondary education have fallen while returns to tertiary education have increased. There is a need to continue expanding coverage of basic and secondary education and to strengthen vocational and tertiary education, as the job market increasingly demands an ever more qualified labor force. At the same time, the quality of the education provided across social groups, including marginalized groups, needs to be improved in order to provide equal opportunities and facilitate social mobility. · Focusing on youth. Most poor young people view their opportunities as worse than or not much different from those their parents had. In Brazilian society as a whole, there is a lack of recognition of the roles and rights of poor young people, reinforced by the predominance of negative stereotypes of marginal youth in the media. There is a need to develop young people's entrepreneurial capacity and skills by facilitating access to productive resources and assets and developing their managerial skills. It is also important to reduce social risks, specif- ically dropping out of school and becoming pregnant during the teenage years. It is essential to strengthen the social capital and civic participation (effective representation) of poor and marginal youth in policy decision making and civil society organizations more generally. · Reducing racial inequality and discrimination. Without specific policies targeted at reducing gender and racial inequality, improvements occur too slowly, if at all. Affirmative action policies and targeted programs 24 Gacitúa Marió and Woolcock, with von Bulow are ways of fighting social exclusion based on race. Changes in policies that widen the scope of labor rights and strengthen citizenship are also needed. · Strengthening participation and citizenship. Exclusionary processes are directly related to lack of political representation and voice. The most vulnerable groups, particularly the rural poor, poor women, nonwhites, and poor and marginal youth, lack appropriate channels of representa- tion and participation. Few policies and programs have sought to systematically elicit continuous participation and develop ownership. Experience indicates the importance of supporting new forms of association and noninstitutionalized forms of participation directly linked to the exercise of public authority, such as participatory budgeting. Establishing partnerships between the state and civil society is particularly important to modify the predominant perception of the state as the only agent responsible for reducing poverty and inequality. The current political climate offers a unique opportunity to open a new and more informed national dialogue on the dynamics of exclusion and mobility. All Brazilians must contribute to this dialogue if a politi- cally supportable consensus is to be forged on how best to respond to an issue with clear significance for Brazil's present and future well-being. Notes 1. Two groups of Brazilian scholars--one focusing on tracking changes in income over time by groups with different characteristics, the other focusing on Brazilians' perceptions of income disparity--worked in collaboration with the Bank to answer these questions. Draft versions of their reports were presented at an international conference on inequality and social exclusion, held in Fortaleza, Brazil, May 15­16, 2003. This volume incorporates the feedback received during the seminar and the valuable comments subsequently provided by peer reviewers at the World Bank. 2. Among 92 countries studied by Paes de Barros, Corseuil, and Leite (2000), Brazil had the highest Gini coefficient (just below South Africa and Malawi). On income inequality in Brazil, see also Fishlow (1972), Bonelli and Sedlacek (1991), Bonelli (1996), Bonelli and Ramos (1993), Ferreira and Litchfield (1996), Ferreira (2000), Henriques (2000), and World Bank (2002), among others. 3. See World Bank (2003) for the Bank's strategy for rural development in Brazil. Overview: Assessing Social Exclusion and Mobility 25 4. A vast literature discusses these issues at the conceptual and methodological levels, and applies the notion of social exclusion to the analysis of poverty in Latin America. See, for example, Gaudier (1993); Figueroa, Altamirano, and Sulmont (1996); ILLS/UNDP (1997); Gore and Figueiredo (1997); Gacitúa Marió, Sojo, and Davis (2001); Wodon (2001); Gacitúa Marió and Wodon (2002); Sen (2002); and Aggleton, Parker, and Maluwa (2003). 5. This is one of the myths of the United States, for example (see Alesina and Ferrara 2001). 6. Ravallion (2001) argues that greater global economic integration itself might encourage a "regression to the mean" in terms of relative inequality levels (that is, a Gini of about 0.4), with inequality rising in those countries in which it was previously "artificially low" as a result of government decree (for example, the ex-Soviet bloc countries) and falling in those countries in which it was previ- ously "artificially high" because of historical factors (Brazil, South Africa). 7. To approach the issue of mobility, the study looks at changes over time in the income rankings of homogeneous social groups. For a discussion of the methodological issues involved in measuring mobility among the extreme poor using income, see Birdsall and Graham (2000); the August 2000 volume of the Journal of Development Economics (devoted to the issues of economic mobility and poverty dynamics); Andersen (2000); and Dunn (2003). 8. The PNADs use a sample of about 125,000 households that is representative at the national, urban and rural, regional, and state levels. 9. The ISSP is a continuing annual program of cross-national collaboration, in which more than 38 member countries are currently participating. In 2001 the survey, which included a module on perceptions of inequality, was applied to a nationally representative sample of 2,000 respondents. 10. For a detailed discussion of the methodology, see chapters 2 and 3. 11. For further analysis, see Bacha (1978); Reis (1979, 1999); DaMatta (1995); Schwartzman (2000); and Reis and Schwartzman (2002). 12. GDP grew at an average annual rate of 4.3 percent between 1945 and 1980, establishing Brazil as one of the largest economies in the world. 13. For a history of economic policy in Brazil, see Abreu (1990). Some observers claim that the transition to an industrial society is still in progress and that as a result Brazil is a disarticulated society characterized by high indices of income and wealth inequality; on this point, see Fernandes (1977) and Bacha (1978). 14. The Real Plan was a successful economic stabilization program developed pri- marily to end inflation. Led by former president Fernando Henrique Cardoso, then Minister of Finance, the plan introduced a new currency (the real) on July 1, 1994, as part of a broader plan to stabilize the Brazilian economy. The 26 Gacitúa Marió and Woolcock, with von Bulow real initially appreciated against the U.S. dollar, as a result of large capital inflows. It then began a gradual depreciation process, culminating in the Brazilian currency crisis in January 1999, when the real suffered a maxi- devaluation and fluctuated wildly. 15. World Bank (2001a) estimates indicate that without redistributive policies, an average annual GDP growth rate of almost 8 percent would be required to halve the poverty rate by 2015.The Economic Commission for Latin America and the Caribbean (ECLAC) estimates that doing so without altering the dis- tribution of income would require per capita GDP growth of 4.0 percent a year, a substantially higher rate than the economy has achieved at any time in the past 20 years (UNDP/ECLAC/IPEA 2002). 16. Data for 1993­97 show an increase in the gap between poverty levels in the north and northeast regions and those in the center and south (Rocha 2000). Poverty reduction between 1981 and 2001 is consistently clear only for older cohorts and people in the lowest educational categories who have retirement or pension earnings. 17. See, among others, DaMatta (1995); von Amsberg, Lanjouw, and Nead (2000); Ferreira, Lanjouw, and Neri (2000); Henriques (2000); Schwartzman (2000); von Amsberg, Lanjouw, and Nead (2000); Paes de Barros, Henriques, and Mendonça (2001); Paes de Barros and others (2000); and World Bank (2001a). 18. On the crucial role of the central government in making decentralization (and other development) initiatives viable in this region of Brazil, see Tendler (1997). 19. Returning to communities she had studied in the early 1970s, Perlman (1976, 2003) found that 96 percent of interviewees believed there is discrimination based on living in a favela. Her study also suggests other mechanisms of labor market exclusion, based on subjective value-loaded and cultural factors, such as physical appearance. People living in favelas indicate that the main barrier to mobility is lack of access to jobs (formal or informal) and that they experience discrimination based on place of residence, clothing, weight, and general appearance. These hidden and subtle sources of labor market exclusion reflect unwritten hiring practices that affect both men and women seeking jobs in the formal and informal sectors, particularly in the services industry. Evidence from health studies suggests that place of residence affects health status and infant mortality through processes of exclusion based on the physical, cultural, and psychosocial characteristics of communities segregated from the society at large by extreme poverty (Szwarcwald, Andrade, and Bastos 2002). 20. Birdsall and Graham (2000) make a similar argument in their analysis of economic and social mobility in Latin America.They conclude that education is an outcome of past inequality, and that as such it should be treated as both a contributor to and an outcome of inequality. Overview: Assessing Social Exclusion and Mobility 27 21. See, for example, Machado da Silva (1971), Ramalho (1993), Sorj (1993), DaMatta (1995), and Santos (2000). 22. According to the Departamento Intersindical de Estatística e Estudos Sócio- econômicos (DIEESE 2001), the transformation of the labor market is central to Brazil's social inequalities and poverty. 23. Earnings differentials between formal and informal sector workers reached 83 percent in 2002 (Neri and Soares 2002). In recent studies, 45.5 percent of formal workers received food, and 50.7 percent received transportation aid; these figures were only 17.5 percent and 15.3 percent, respectively, for informal workers (Tannuri-Pianto and Pianto 2002). 24. Verner, Blom, and Holm-Nielsen (2001) report that the supply of highly skilled labor does not meet demand. Furthermore, the data suggest that there was a deceleration of enrollment and a declining efficiency of the Brazilian tertiary education system during the 1990s. 25. For details, see Rocha (2000); Pereira, Bolzon, and Porto (2001); Pavcnik and others (2003); and SDTS (2003). 26. In 2005, about 70 percent of the poor lived in households with a dependency ratio greater than 1.5. Santos (2002) and chapter 2 of this volume show that the dependency ratio is a powerful variable explaining poverty incidence. 27. A related phenomenon is that more than half of girls 15­19 without school- ing are mothers. Between 1993 and 1998, the birthrate for girls 15­19 rose 19 percent (IBGE 2002). Among girls 10­14, the birthrate leaped 31 percent during the same period. Overall, the number of births among girls 10­19 rose from 565,000 in 1993 to 698,000 in 1998, the latest year for which statistics are available.These statistics, combined with the fact that about 40 percent of pregnant teenagers leave school, indicate the seriousness of the problem. 28. For details, see Scalon (1999), who analyzes gender mobility in Brazil. Oliveira and Machado (2000) show that discrimination by race and gender in the labor market determines different paths of professional development and consequently of well-being. 29. The probability of being poor is about 22 percent for whites and 48 percent for nonwhites, numbers that have remained stable during the past several years (World Bank 2001a). For further analysis, see, among many others, Hasenbalg and do Valle Silva (1988), do Valle Silva (2000), and Jaccoud and Beghin (2002). 30. DaMatta (1997) and Skidmore (1993) discuss the myth of racial democracy in Brazil in detail. 31. The final report of the commission created by the Brazilian National Congress in 1999 to study the causes of social inequality concluded that political- institutional barriers to the promotion of upward social mobility need to be removed, by redirecting social expenditures based on their targeting efficacy, among other measures (Henriques 2000). 28 Gacitúa Marió and Woolcock, with von Bulow 32. Contrary to common belief, economic expansion and educational reforms have not created a meritocratic society in Brazil; unequal opportunities and transmission of positions within the elite persist. For detailed analyses of social mobility in Brazil, see Pastore and Castro (1983), Ferreira and Paes de Barros (1999), Scalon (1999), Pastore and do Valle Silva (2000), and Costa Ribeiro and Scalon (2001). 33. Andrade (1997) shows that social mobility between generations and regions is low in Brazil's six major metropolitan regions, with Rio de Janeiro and Recife registering the worst performance (that is, the least upward mobility and the most downward mobility). 34. Do Valle Silva (2000) shows that the social evolution of whites and nonwhites is significantly different. The principal factors explaining the difference are, in order of importance, the level of education, the first job, and labor market experience. He shows that an individual's path is strongly determined by family income at birth but not by parents' occupation. 35. Perlman (2003) indicates that "in 1969, 36 percent of those interviewed said the Brazilian people do not have the capacity to choose their candidates; twenty years later that number had risen to 51 percent." References Abreu, Marcelo, ed. 1990. A ordem do progresso: Cem anos de política econômica republicana, 1889­1989. Rio de Janeiro: Campus. 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Libros de la CEPAL 70, Comisión Económica para América Latina y el Caribe, Santiago, Chile. UNICEF (United Nations Children's Fund). 2001. Relatório: Situação da infância brasileira 2001. Brasília. Verner, Dorte, Andreas Blom, and Lauritz Holm-Nielsen. 2001. "Education, Earnings, and Inequality in Brazil, 1982­1998." Paper presented at the annual meetings of the Latin American and Caribbean Economic Association, Montevideo. Von Amsberg, Joachim, Peter Lanjouw, and Kimberly Nead. 2000. "A focalização do gasto social sobre a pobreza no Brasil." In Desigualdade e pobreza no Brasil, ed. Ricardo Henriques, 685­718. Rio de Janeiro: Instituto de Pesquisa Econômica Aplicada. Wodon, Quentin, ed. 2001. Attacking Extreme Poverty: Learning from the Experience of the International Movement ADT Fourth World. Washington, DC: World Bank. World Bank. 2000. "Voices of the Poor. Brazil: National Report." World Bank, Washington, DC. ------. 2001a."Attacking Brazil's Poverty:A Poverty Report with a Focus on Urban Poverty Reduction Policies." Report 20475-BR, World Bank, Washington, DC. ------. 2001b. "Rural Poverty Reduction in Brazil: Towards an Integrated Strategy." Report 21790-BR, Washington, DC. ------. 2002. Brazil Gender Review: Issues and Recommendations. Report 23442- BR, World Bank, Washington, DC. ------. 2003. Rural Poverty Alleviation in Brazil: Toward an Integrated Strategy. Report 26760, Washington, DC. Zaluar, Alba. 2000. "Exclusion and Public Policies: Theoretical Dilemmas and Political Alternatives." Brazilian Review of Social Sciences Special Issue 1 (October): 25­42. C H A P T E R 2 Social Exclusion and Poverty Dynamics Regis Bonelli, Gustavo Gonzaga, and Alinne Veiga This chapter seeks to analyze the long-term income dynamics of Brazilian households by · drawing inferences on income dynamics in Brazil using data extracted from the Brazilian National Household Survey (Pesquisa Nacional por Amostra de Domicilios [PNAD]); · tracking changes over time in the income rankings of groups and indi- viduals relative to others with different characteristics and looking for signs of (eventual) income convergence among groups; and · developing a quantitative analysis that explains poverty among certain social groups in Brazil. In particular, it studies whether poverty dynamics are explained in part by social exclusionary processes based on racial, educational, occu- pational, and locational characteristics of individuals and households. The authors would like to thank the participants of the presentation in the 3° Encontro Internacional sobre Desigualdades e Exclusão Social for comments and suggestions. Thanks are also due to Estanislao Gacitúa Marió, Francisco Ferreira, Maria-Valeria Pena, and Michael Walton for many useful insights and corrections to a previous version, as well as to Eduardo Zylberman for competent research assistance. 35 36 Bonelli, Gonzaga, and Veiga The analysis explains the probability of being poor using a set of variables (personal and household characteristics) and identifies changes over time. It examines changes in the positions of groups of poor people relative to other groups with similar characteristics (fixed cohort, gender, and schooling level), identifying groups that have maintained their relative position and those that have fallen (or risen) significantly in their relative position over time. The use of averages of household variables in repeated cross-sections is not without limitations, for several reasons. First, using averages does not allow income mobility of individuals within a group to be captured. For this reason, the analysis is complemented with data on household inequality within groups. Second, household per capita average income trajectories over time may be related to many aspects of household deci- sion making, which include the whole process of family (household) creation and dissolution as well as choices related to the number of chil- dren, labor market participation, and other variables. To bring more insights to the task of correctly interpreting the factors behind the evolution of per capita household incomes, the study there- fore analyzes information on other household variables, such as family size. More research is certainly needed to form a more complete picture. Nonetheless, since the household is the appropriate locus for studying poverty dynamics, analyzing what happened to average household earn- ings over time is an important part of the process. Methodology Longitudinal data are ideal for studying income dynamics over time. However, as in most countries, long-term longitudinal data are not available in Brazil, making it impossible to follow individuals through time. This chapter is therefore based on the analysis of pseudo-panels obtained from cross-sectional data from PNADs covering selected years between 1977 and 2001 (1977, 1981, 1985, 1989, 1993, 1997, and 2001).Although the PNAD data are not collected on a panel basis, sampling is done using repeated census tracks.This allows groups of individuals to be followed over time by classifying them according to certain criteria (cohort, education, gender, race, location, household structure, and so forth). The problem with this methodology is that individuals and house- holds in each year are not the same. This would not be a serious problem if sample sizes were sufficiently large and "the sample for each group considered is drawn from a population in which group membership is Social Exclusion and Poverty Dynamics 37 constant or varies in a completely random fashion in terms of the variable under study" (Attanasio and Székely 2003). In this case, following aver- ages of variables of each group would be a good approximation of the trajectories of each cohort over time. The choice of the dimensions that define a fixed membership group and the number of categories in each dimension will depend on the size of the cells and on the need to form homogeneous groups. This study follows groups of individuals initially defined by year of birth (cohort), gender, and schooling.1 Five cohorts were selected, for individuals born in 1940­44, 1945­49, 1950­54, 1955­59, and 1960­64.2 For each cohort, a second selection criterion was educational status. Nine educational groups were selected: (a) people with no formal schooling; (b) people with incomplete lower- primary education (1­3 years of formal schooling); (c) people who completed lower-primary school (4 years of formal schooling); (d) people with incomplete upper-primary education (5­7 years of formal school- ing); (e) people who completed upper-primary school (8 years of formal schooling); (f) people with incomplete secondary education (9­10 years of formal schooling); (g) people who completed secondary school (11 years of formal schooling); (h) people with incomplete tertiary education (12­14 years of formal schooling); and (i) people who completed terti- ary education (15 or more years of formal schooling). A third selection criterion was gender.3 The relative size of each group was then followed through time, yielding a picture of a fixed (constant) group of people. Average real earnings were calculated for each group.The main earnings variable is average household income per capita, which is also compared with poverty lines. In order to help explain the results, labor earnings from the main job, earnings from all sources, and earnings from pension and retirement income were also studied. For each measure, average earnings for a cohort c (1­5) with educational level e (1­9) and gender g (1­2) in year t (1977­2001) are defined by where ncegt is the number of observations (individuals/households) in a given cohort c with educational level e and gender g in year t. Measures of poverty (the proportion of individuals or households with per capita earnings below a given poverty line, for instance) and income inequality can also be obtained for each group. Averages of household and individual 38 Bonelli, Gonzaga, and Veiga characteristics for each group, which could help explain poverty dynamics and trajectories, can also be calculated. Characteristics of Subgroups This section examines the characteristics of interest of homogeneous sub- groups in the population, especially the poorest ones. The main variables of interest are average earnings and measures of poverty and inequality. In order to identify groups that have been excluded from the benefits of growth, a profile of various groups was developed and a poverty and income distribution analysis performed. Cohort Sizes One would expect the size for each cohort to fall over time, because of deaths and, possibly, some net emigration (figure 2.1). In fact, the size of Figure 2.1. Size of Selected Cohorts, 1977­2001 13 12 11 10 9 8 7 6 5 1977198519932001 1977198519932001 1977198519932001 1977198519932001 1977198519932001 year Source: Authors. Social Exclusion and Poverty Dynamics 39 the first three cohorts actually increased between 1977 and 1985, and the size of the 1960­64 cohort rose between 1993 and 2001. One possible explanation for the 1977­85 increase is changing sampling procedures between the 1970s and later years. This implies that the 1977 data should be used (and interpreted) with caution. It also suggests that more years need to be included in the analysis in order to have a clearer picture of long-term trends and to add robustness to the results. For this purpose, PNAD data for 1997, 1981, and 1989 were added to the database.The analysis is thus based on data from 1981, 1985, 1989, 1993, 1997, and 2001. Gender-Related Income Differences Most of the analysis is based on the income measure derived from house- hold earnings from all sources, or household per capita average income.4 The main measure of the poverty line is a standard one: R$160 at the end of 2001, which corresponds to about US$60 a month in the last four months of 2001. All other nominal figures in the remaining years were converted to this standard.5 The poverty line of R$80 is used as a meas- ure of extreme poverty. Tests were conducted to measure income differences by gender in individual cohorts, controlling for educational level (table 2.1). Two- sample t-tests with equal variances were conducted for each cohort and for the total number of observations. The results show that the hypothesis that there are no mean income differences between genders cannot be rejected, either for all observa- tions (45 for each gender) or for each cohort separately (9 observations for each gender). There is gender discrimination in the Brazilian labor market (on average, women earn less than equally qualified men). However, the results show that the average household per capita income Table 2.1. P-Values for Gender-Based Income DifferencesTests, by Cohort, 1977­2001 Cohort born 1977 1985 1993 2001 1940­44 0.8339 0.9008 0.9054 0.6095 1945­49 0.9490 0.9459 0.7812 0.9994 1950­54 0.8285 0.8457 0.9393 0.9183 1955­59 0.9133 0.8145 0.8802 0.8639 1960­64 0.7105 0.7527 0.8378 0.8400 All cohorts 0.9898 0.8624 0.9997 0.9550 Source: Authors. Note: Number of observations for each cohort = 18. Total number of observations = 90. 40 Bonelli, Gonzaga, and Veiga of women in a given cohort-education group is not statistically different from that of men in the same group. Number and Proportion of Poor People As expected, younger cohorts have larger numbers of poor in any given year (table 2.2). In 1977, for instance, the cohort of people born in the closed interval 1960­64 had 7.82 million poor people. The same year, the cohort of people born in 1940­44 had 3.18 million poor. In 2001 the corresponding numbers were 5.54 million and 2.03 million, suggesting a substantial reduction in poverty over this 24-year period. The number of poor people fell from nearly 23.6 million in 1977 to about 18 million in 2001, a decrease of almost 5.6 million people. In addition, most of the decrease occurred after 1993. Indeed, between 1993 and 2001, about 4.2 million people escaped poverty. Poverty reduction was not homogeneous over time across cohorts. For the sample as a whole, the number of poor people fell 23.6 percent between 1977 and 2001. Poverty fell 36.1 percent among the oldest cohort, 29.2 percent among the youngest cohort, and 22.2 percent among the cohort born in 1945­49. The drop in poverty for the youngest cohort can be partially explained by the fact that this cohort was very young in 1977 (13­17 years old), with most of its members not participating in the labor market or participating with very low or no remuneration. The results change considerably when 1977 is excluded from the comparison. When the analysis is based on the interval 1985­2001, for Table 2.2. Number of Poor, by Cohort, 1977­2001 million Cohort born Year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts 1977 3.18 3.40 3.95 5.22 7.82 23.57 1985 3.27 3.90 4.46 5.01 5.94 22.59 1993 2.79 3.46 4.52 5.33 6.13 22.23 2001 2.03 2.64 3.41 4.38 5.54 18.00 Absolute change 1977­2001 ­1.15 ­0.75 ­0.54 ­0.84 ­2.28 ­5.57 Relative change 1977­2001 ­0.361 ­0.222 ­0.137 ­0.161 ­0.292 ­0.236 Relative change 1985­2001 ­0.377 ­0.322 ­0.237 ­0.126 ­0.069 ­0.203 Source: Authors. Social Exclusion and Poverty Dynamics 41 instance, different results and ordering of poverty decreases across cohorts appear. For the sample as a whole, poverty declined 20.3 percent. The number of poor decreased 37.7 percent within the 1940­44 cohort, 32.2 percent within the 1945­49 cohort, 23.7 percent within the 1950­54 cohort, 12.6 percent within the 1955­59 cohort, and 6.9 percent within the 1960­64 cohort. Although the changes in absolute numbers are important--they suggest that more than 4 million people moved out of poverty in less than a decade--they say little about the incidence of poverty by cohort. Examination of these data reveals that in almost all cases the incidence of poverty fell substantially after 1993 (table 2.3). In 2001 the proportion of poor reached 42 percent, an absolute change of 9.6 percentage points relative to 1977 and 7.5 percentage points relative to 1985. In 1977, 21.3 percent of Brazil's population was poor; by 2001 this fig- ure had fallen to 10.4 percent. Poverty reduction was not uniform across cohorts, with larger declines enjoyed by the older cohorts. Poverty fell 36.1 percent among the oldest cohort, 29.2 percent among the youngest cohort, and 22.2 percent among the cohort born in 1945­49. Here, again, it seems fruitful to analyze cohort changes in the 1985­2001 period rather than the longer period.6 Years of Schooling Results for selected cohort-schooling groups, using data for 1981, 1985, 1989, 1993, 1997, and 2001, are presented in this section. (Gender was Table 2.3. Incidence of Poverty, by Cohort, 1977­2001 percent Poor people Cohort born All as percentage Year 1940­44 1945­49 1950­54 1955­59 1960­64 cohorts of population 1977 53.7 48.4 44.3 47.7 60.6 51.6 21.3 1985 51.8 51.8 49.6 47.7 48.5 49.5 17.3 1993 48.7 49.7 51.3 52 52.6 51.2 14.7 2001 37.8 39 39.7 42.7 46.5 42 10.4 Absolute change 1977­2001 ­15.9 ­9.4 ­4.6 ­4.9 ­14.1 ­9.6 ­10.9 Absolute change 1985­2001 ­14 ­12.8 ­9.9 ­5 ­2 ­7.5 ­6.8 Source: Authors. Note: Measure of poverty is household per capita average income. 42 Bonelli, Gonzaga, and Veiga dropped, because it appeared to have little effect on differences in average per capita household income.) Results and analyses for the remaining groups are presented in the annex. People with no schooling. The number of people with no schooling changed very little over time for each cohort, ranging from about 1.3 million in the youngest cohort to 1.7 million in the oldest cohort (table 2.4). Despite some fluctuations, average income tended to increase over time for the older cohorts and to decrease for the younger ones. This is reflected in a significant decrease in the incidence of poverty within the older cohorts (especially for the 1940­44 and 1945­49 cohorts) but not for the two youngest cohorts. The proportion of the extreme poor, meas- ured by the R$80 poverty line, increased between 1981 and 2001 within the 1960­64 cohort. The drop in poverty was greatest for the oldest cohort. Several factors may account for this trend, including returns to work experience; government social policies; pension and retirement income; economic- cycle effects; household size and age composition dynamics; and com- position bias, reflecting higher mortality rates for the poorest in each cohort-schooling group.7 Income inequality, as measured by the Gini index, decreased for all cohorts between 1981 and 2001, despite some fluctuations. It was lower and declined more for the oldest cohorts. An increase in income inequality in 1989 was observed for most cohorts, as documented in other studies. Older cohorts tended to display more income growth, more poverty reduction, and less income concentration than younger cohorts. Nonetheless, in 2001 the percentage of people living below the R$160 poverty line still remained high, at 62 percent for the 1940­44 cohort, 68 percent for the 1945­49 cohort, 72 percent for the 1950­54 cohort, 76 percent for the 1955­59 cohort, and 79 percent for the 1960­64 cohort. These shares are sizable, indicating that at the end of the period analyzed, the vast majority of Brazilians with no schooling who were born between 1940 and 1964 were poor. People with four years of schooling. The data indicate that the number of people who completed lower-primary school (four years of schooling) between 1981 and 1985 rose for the four oldest cohorts, suggesting either that PNAD sampling procedures changed between the two years or that people in these cohorts completed lower-primary school during this period Social Exclusion and Poverty Dynamics 43 Table 2.4. Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with No Schooling, by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts Number of people (million) 1981 1.607 1.500 1.338 1.253 1.414 7.111 1985 1.649 1.642 1.454 1.335 1.336 7.416 1989 1.686 1.658 1.459 1.248 1.269 7.321 1993 1.596 1.516 1.474 1.347 1.290 7.223 1997 1.644 1.545 1.498 1.326 1.297 7.310 2001 1.520 1.571 1.464 1.305 1.321 7.181 Average per capita household income (R$) 1981 104 99 121 132 133 118 1985 120 96 97 127 119 112 1989 138 123 101 99 111 114 1993 115 108 92 97 86 100 1997 142 138 119 115 113 125 2001 164 146 133 118 110 134 Percentage of people below R$160 poverty line 1981 82.6 85.3 82.5 79.8 81.3 82.3 1985 83.2 86.1 86.2 85.0 83.6 84.8 1989 76.1 81.2 84.8 85.3 84.6 82.4 1993 5.8 78.7 83.7 83.5 85.9 81.5 1997 73.0 73.8 76.6 78.4 81.3 76.6 2001 61.7 67.8 71.7 76.3 78.9 71.3 Percentage of people below R$80 poverty line 1981 56.1 58.7 56.0 50.1 51.5 54.5 1985 53.1 60.5 62.4 60.1 56.0 58.4 1989 49.6 56.5 62.0 65.8 60.8 58.9 1993 46.9 53.6 59.7 61.2 64.1 57.1 1997 43.2 45.2 51.0 52.1 55.9 49.5 2001 26.7 37.0 42.3 48.4 53.2 41.5 Gini coefficient 1981 0.48 0.49 0.55 0.53 0.54 0.52 1985 0.52 0.48 0.49 0.59 0.52 0.52 1989 0.55 0.56 0.55 0.56 0.57 0.56 1993 0.49 0.52 0.50 0.55 0.52 0.52 1997 0.49 0.51 0.49 0.51 0.54 0.51 2001 0.42 0.46 0.48 0.48 0.49 0.47 Source: Authors. 44 Bonelli, Gonzaga, and Veiga (table 2.5). The number of people who completed lower-primary school is higher for each successive cohort except the youngest one, suggesting increased coverage in the educational system. Despite some cyclical fluctuations, there was a substantial increase in average incomes from 1981 onward for the older cohorts, particularly for people born in the 1940s and, to a lesser extent, between 1950 and 1954. This group, especially the older cohorts, enjoyed a significant decrease in poverty after 1993. In contrast, the poverty rates of people with four years of schooling in the 1960­64 cohort fell only modestly, from 61 percent in 1981 to an alarming rate of 57 percent in 2001. Income inequality within each cohort also remained fairly constant, with the largest decrease in the oldest cohort. Indeed, the Gini coefficients changed significantly only in 1989. People with eight years of schooling. The numbers of people who com- pleted upper-primary school display a pattern similar to that of people with four years of schooling (table 2.6). The significant differences between younger and older cohorts suggest substantial improvements in the level of primary education completion. Average household income decreased for all cohorts between 1981 and 1993 and increased from 1993 to 2001. This increase was higher for people born in 1940­54. A peak in 1989 was also observed in almost all cohorts. Poverty increased up to 1993 and decreased between 1993 and 2001. Poverty rates in 2001 were similar to those observed in 1981 for all cohorts, with a very high incidence of poverty (more than 35 percent) in the youngest cohorts.The Gini coefficients show larger changes than those observed in people with just four years of education, especially for the old- est cohorts. However, no pattern was found, with income inequality increasing for some cohorts and decreasing for others. People with 11 years of schooling. The trajectory of earnings for Brazilians who completed secondary school is different from those with fewer years of schooling (table 2.7). Over time, the number of people with 11 years of schooling increased in every cohort. This may indicate that some adults completed secondary school later in life (the finding also argues for caution in analyzing pseudo-panels with education cells). Social Exclusion and Poverty Dynamics 45 Table 2.5. Number of, Average per Capita Household Income of, and Incidence of Poverty among BraziliansWho Completed Lower-Primary School (FourYears of Schooling), by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts Number of people (million) 1981 1.606 1.821 2.196 2.328 2.241 10.192 1985 1.617 1.954 2.241 2.447 2.155 10.414 1989 1.583 1.981 2.355 2.327 2.005 10.251 1993 1.456 1.713 2.200 2.244 1.914 9.507 1997 1.323 1.715 1.965 2.145 1.809 8.957 2001 1.296 1.629 1.890 2.079 1.796 8.689 Average per capita household income (R$) 1981 265 240 232 237 185 232 1985 278 252 221 211 197 232 1989 362 297 238 211 214 264 1993 324 252 203 177 164 224 1997 363 326 278 232 183 277 2001 381 349 275 232 189 286 Percentage of people below R$160 poverty line 1981 42.6 46.8 47.2 48.8 61.2 49.3 1985 40.4 46.4 51.5 55.2 60.1 50.7 1989 36.1 42.8 52.4 57.5 60.8 49.9 1993 36.3 44.1 53.5 61.7 67.6 52.6 1997 28.3 31.9 38.6 48.7 58.1 41.1 2001 23.3 28.9 36.4 46.3 56.7 38.3 Percentage of people below R$80 poverty line 1981 16.1 18.4 17.9 17.4 27.0 19.4 1985 14.9 18.0 21.3 24.1 26.4 21.0 1989 13.7 17.2 23.3 26.6 30.9 22.4 1993 12.9 17.2 22.4 29.0 34.3 23.2 1997 11.1 11.5 15.0 21.2 26.9 17.1 2001 7.8 10.3 14.1 18.4 25.6 15.2 Gini coefficient 1981 0.47 0.46 0.45 0.47 0.47 0.46 1985 0.46 0.48 0.46 0.47 0.48 0.47 1989 0.52 0.51 0.51 0.51 0.55 0.52 1993 0.50 0.47 0.45 0.47 0.49 0.48 1997 0.46 0.45 0.46 0.47 0.45 0.46 2001 0.44 0.47 0.43 0.45 0.46 0.45 Source: Authors. 46 Bonelli, Gonzaga, and Veiga Table 2.6. Number of, Average per Capita Household Income of, and Incidence of Poverty among BraziliansWho Completed Upper-Primary School (EightYears of Schooling), by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts Number of people 1981 303,467 422,351 634,584 920,349 1,258,482 3,539,233 1985 350,569 473,640 684,925 966,936 1,323,623 3,799,693 1989 368,716 453,639 689,932 1,018,126 1,204,118 3,734,531 1993 291,796 466,057 667,564 927,428 1,194,950 3,547,795 1997 320,556 478,347 702,710 1,024,947 1,204,808 3,731,368 2001 300,784 470,179 780,563 1,051,216 1,310,704 3,913,446 Average per capita household income (R$) 1981 479 432 384 351 302 389 1985 519 403 374 327 297 384 1989 644 500 392 334 340 442 1993 494 404 313 280 255 349 1997 649 510 433 353 298 449 2001 643 464 446 335 291 436 Percentage of people below R$160 poverty line 1981 15.9 21.3 23.3 25.7 31.5 23.5 1985 17.1 21.7 27.5 31.2 34.7 26.4 1989 14.9 23.2 30.3 35.8 35.0 27.9 1993 21.3 26.5 33.9 40.4 44.6 33.3 1997 13.5 16.6 22.5 28.3 34.2 23.0 2001 14.4 18.0 23.6 29.5 35.8 24.2 Percentage of people below R$80 poverty line 1981 5.0 6.3 6.5 6.9 9.0 6.8 1985 4.8 5.1 7.5 9.4 9.9 7.3 1989 4.8 8.5 11.6 10.8 11.8 9.5 1993 6.5 9.1 11.8 15.5 14.9 11.6 1997 3.9 5.2 6.8 8.7 11.5 7.2 2001 3.7 5.5 7.9 8.9 12.0 7.6 Gini coefficient 1981 0.44 0.47 0.43 0.43 0.42 0.44 1985 0.47 0.43 0.46 0.44 0.43 0.45 1989 0.49 0.51 0.52 0.49 0.50 0.50 1993 0.51 0.49 0.47 0.48 0.46 0.48 1997 0.47 0.45 0.45 0.45 0.44 0.45 2001 0.50 0.43 0.49 0.44 0.44 0.46 Source: Authors. Social Exclusion and Poverty Dynamics 47 Table 2.7. Number of, Average per Capita Household Income of, and Incidence of Poverty among BraziliansWho Completed Secondary School (11Years of Schooling), by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts Number of people 1981 357,523 555,838 858,117 1,188,180 894,064 3,853,722 1985 394,595 598,257 946,462 1,359,149 1,696,903 4,995,366 1989 410,997 596,037 976,097 1,428,188 1,900,939 5,312,258 1993 415,392 636,989 1,020,578 1,413,822 1,917,657 5,404,438 1997 391,848 652,859 1,039,325 1,523,215 1,952,785 5,560,032 2001 440,354 728,103 1,134,544 658,637 2,196,003 6,157,641 Average per capita household income (R$) 1981 726 660 598 554 576 623 1985 756 671 583 538 497 609 1989 836 739 648 572 571 673 1993 732 572 511 443 431 538 1997 981 814 705 535 520 711 2001 921 778 649 546 465 672 Percentage of people below R$160 poverty line 1981 8.3 9.1 10.9 11.8 12.4 10.5 1985 8.2 10.4 11.7 13.9 16.2 12.1 1989 9.0 13.2 16.3 18.6 18.3 15.1 1993 12.6 17.7 19.9 24.0 24.8 19.8 1997 6.0 8.5 11.3 14.3 17.6 11.6 2001 5.8 9.0 10.5 16.3 20.0 12.3 Percentage of people below R$80 poverty line 1981 1.6 2.1 2.3 2.5 2.9 2.3 1985 1.6 2.6 2.8 3.4 3.9 2.9 1989 2.2 3.2 4.5 5.6 5.2 4.1 1993 3.8 4.8 5.8 7.6 7.6 5.9 1997 2.4 2.3 3.2 4.3 5.0 3.4 2001 1.5 2.9 2.7 4.8 5.3 3.4 Gini coefficient 1981 0.45 0.43 0.43 0.43 0.44 0.44 1985 0.46 0.46 0.44 0.44 0.44 0.45 1989 0.48 0.53 0.51 0.51 0.51 0.51 1993 0.51 0.50 0.50 0.49 0.49 0.50 1997 0.50 0.47 0.49 0.45 0.47 0.48 2001 0.47 0.47 0.46 0.48 0.47 0.47 Source: Authors. 48 Bonelli, Gonzaga, and Veiga The evolution of household average income and poverty followed the same pattern as that observed for groups with fewer years of schooling. Poverty increased between 1981 and 1993 and decreased between 1993 and 2001. Changes in the incidence of poverty were much larger for the youngest cohorts than for other cohorts. The Gini coefficients show little variance and no noticeable trend, suggesting that inequality may not have changed much for people in this category. People with 15 or more years of schooling. The increase in the number of Brazilians with 15 or more years of schooling reflects the substantial expansion of tertiary education (table 2.8).The increases are smaller than those for people with 12­14 years of schooling (see annex), however, suggesting that a substantial number of students start but do not complete tertiary education. Average incomes display the same pattern as that for people who start but do not complete incomplete tertiary education: incomes fell until 1993 before rising significantly through 2001. For any given year, average incomes increased with age, as expected for people with high levels of human capital. The incidence of poverty shows a substantial increase between 1981 and 1993, followed by a smaller decrease between 1993 and 2001. The incidence of poverty was similar across cohorts. Poverty was negligible in 1981­1993 but not in 1993­2001 for the youngest cohorts. The Gini coefficients, which increased in all cohorts until 1989, do not show much variance across cohorts. Incidence of Poverty in the Older Cohorts (1940­44, 1945­49, and 1950­54) Poverty reduction between 1981 and 2001 is consistently clear only for the older cohorts (figure 2.2) and for people with no more than four years of schooling. The younger the group, the less pronounced the escape from poverty. In all cohorts considered and for all educational groups, poverty decreased between 1993 and 2001. Poverty increased among more-educated groups, however, especially after 1985. Income Differentials by Cohort and Years of Schooling Average per capita incomes increased in all three of the oldest cohorts (people born in 1940­44, 1945­49, and 1950­54) between 1981 and 2001 (table 2.9). For the most part, the increase in income was greater for Social Exclusion and Poverty Dynamics 49 Table 2.8. Number of, Average per Capita Household Income of, and Incidence of Poverty among BraziliansWho CompletedTertiary Education (15 or MoreYears of Schooling), by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 All cohorts Number of people 1981 251,575 407,168 456,443 313,963 5,644 1,434,793 1985 297,148 463,022 641,580 573,435 271,902 2,247,087 1989 319,813 519,962 716,118 780,914 676,684 3,013,491 1993 313,461 485,766 658,456 744,476 774,643 2,976,802 1997 295,786 495,133 734,664 894,357 931,868 3,351,808 2001 325,437 502,810 726,876 886,685 984,191 3,425,999 Average per capita household income (R$) 1981 1,384 1,358 1,356 1,267 698 1,212 1985 1,389 1,408 1,321 1,284 1,088 1,298 1989 1,779 1,483 1,401 1,420 1,396 1,496 1993 1,376 1,248 1,187 1,174 1,108 1,219 1997 1,945 1,586 1,473 1,375 1,403 1,556 2001 2,023 1,714 1,450 1,290 1,309 1,557 Percentage of people below R$160 poverty line 1981 1.2 1.5 1.4 2.0 0.0 1.2 1985 1.1 0.4 1.5 1.4 2.5 1.4 1989 1.3 1.5 3.0 2.5 2.4 2.1 1993 1.3 3.9 3.8 4.0 4.5 3.5 1997 1.6 1.7 2.3 2.8 1.9 2.0 2001 0.5 1.1 2.2 3.1 2.0 1.8 Percentage of people below R$80 poverty line 1981 0.3 0.5 0.3 0.5 0.0 0.3 1985 0.2 0.0 0.4 0.1 0.4 0.2 1989 0.3 0.4 0.6 0.4 0.5 0.5 1993 0.5 1.0 1.2 0.9 1.0 0.9 1997 0.5 0.1 1.0 1.1 0.9 0.7 2001 0.3 0.0 0.8 1.2 0.6 0.6 Gini coefficient 1981 0.40 0.41 0.39 0.39 0.36 0.39 1985 0.43 0.42 0.40 0.40 0.42 0.41 1989 0.50 0.49 0.48 0.46 0.45 0.48 1993 0.48 0.49 0.50 0.48 0.46 0.48 1997 0.47 0.43 0.46 0.45 0.46 0.45 2001 0.46 0.45 0.45 0.47 0.46 0.46 Source: Authors. 50 Bonelli, Gonzaga, and Veiga Figure 2.2. Incidence of Poverty among Older Cohorts, 1981­2001 Source: Authors. Social Exclusion and Poverty Dynamics 51 Table 2.9. Ratio of Average Household Income in 2001 to Average Household Income in 1981, by Cohort,Years of Schooling, and Gender Cohort born Years of 1940­44 1945­49 1950­54 1955­59 1960­64 schooling Men Women Men Women Men Women Men Women Men Women 0 1.50 1.61 1.46 1.49 1.13 1.07 0.97 0.83 0.92 0.75 1­3 1.56 1.45 1.50 1.49 1.20 1.31 0.94 0.89 1.00 0.82 4 1.51 1.37 1.45 1.46 1.17 1.21 0.98 0.98 1.19 0.90 5­7 1.20 1.25 1.13 1.12 0.99 1.17 0.94 0.90 0.97 0.87 8 1.52 1.21 1.03 1.11 1.09 1.22 0.93 0.97 0.98 0.95 9­10 1.18 1.02 1.03 0.99 1.05 1.13 1.01 1.14 0.72 0.78 11 1.30 1.24 1.19 1.17 1.01 1.15 0.97 1.00 0.76 0.84 12­14 1.70 1.24 1.02 1.54 0.99 1.08 1.00 0.99 0.95 0.84 15+ 1.41 1.56 1.30 1.24 1.00 1.14 1.12 0.92 -- -- Source: Authors. -- = Not available. older cohorts than for younger ones, with people in the two oldest cohorts with less schooling enjoying increases in income of about 50 percent. Between 1981 and 2001, average household income rose for only a few cohort-education groups. Among people with little education, the inci- dence of poverty decreased over time, albeit still leaving large proportions below the poverty line. For the remaining groups, poverty increased and average incomes decreased over time. Given that inequality did not decrease in most cases, substantial numbers and proportions of people appear to have become stuck in poverty. Although there are some noticeable gender differences for some cohort-schooling groups, it is hard to identify a consistent pattern. In general, the largest increases in average income were observed among people in the oldest cohorts with few years of schooling. The use of changes in household average per capita income may dis- tort the picture if there are changes in household composition over time. Thus, for instance, if the household size of older cohorts consistently decreases (as a result of deaths, children leaving to form their own households, and other demographic and economic causes), it may well be that individuals in the household may be worse off (have less income) even as average per capita income increases (table 2.10). This problem suggests that individual income may be a better measure of poverty than household income when evaluating long-term income trends. Using individual income, however, fails to capture the fact that what matters is household income performance and the consumption 52 Bonelli, Gonzaga, and Veiga Table 2.10. Average Household Size, by Cohort andYears of Schooling, 1977­2001 Level of Cohort born schooling/year 1940­44 1945­49 1950­54 1955­59 1960­64 No schooling 1977 6.38 5.70 5.32 6.21 7.63 1981 6.34 5.94 5.30 5.27 6.44 1985 6.18 6.06 5.59 5.10 5.24 1989 5.77 5.93 5.74 5.39 5.18 1993 4.45 4.72 4.76 4.54 4.25 1997 3.99 4.29 4.41 4.35 4.35 2001 3.95 4.41 4.71 4.95 4.93 Four years of schooling 1977 5.48 4.96 5.11 6.31 7.31 1981 5.43 5.07 4.77 5.09 6.42 1985 5.18 5.09 4.82 4.64 5.10 1989 4.73 4.92 4.88 4.78 4.74 1993 4.04 4.21 4.28 4.20 4.17 1997 3.75 3.90 4.17 4.15 4.04 2001 3.47 3.80 4.15 4.35 4.42 Eight years of schooling 1977 4.69 4.39 4.95 6.15 6.21 1981 4.77 4.65 4.51 4.88 5.99 1985 4.72 4.51 4.45 4.37 4.85 1989 4.35 4.55 4.52 4.41 4.38 1993 3.86 4.02 4.06 4.06 3.82 1997 3.46 3.77 3.93 4.00 3.86 2001 3.37 3.80 4.10 4.09 4.09 Eleven years of schooling 1977 4.57 4.28 4.72 5.33 5.86 1981 4.64 4.41 4.37 4.79 5.35 1985 4.48 4.37 4.24 4.16 4.64 1989 4.25 4.35 4.27 4.24 4.22 1993 3.63 3.93 3.87 3.78 3.60 1997 3.49 3.76 3.71 3.79 3.73 2001 3.23 3.62 3.75 3.92 3.96 Fifteen years of schooling 1977 4.04 3.61 3.81 4.35 -- 1981 4.29 3.98 3.72 3.94 4.65 1985 4.11 3.99 3.80 3.49 3.99 1989 4.03 3.95 3.99 3.60 3.55 1993 3.59 3.81 3.74 3.56 3.32 1997 3.23 3.55 3.58 3.58 3.39 2001 2.91 3.37 3.59 3.64 3.47 Source: Authors. -- = Not available. Social Exclusion and Poverty Dynamics 53 equalization that inevitably characterizes families (here loosely associated with households). Data on individual income thus need to be interpreted with caution. Considering individuals' average incomes yields different results from those generated by average per capita household income. Average incomes for the older cohorts (people born in 1940­44 and 1945­49) remained virtually the same in 1981 and 2001 for some educational groups (people with no schooling, people with 1­3 years of schooling, and people with at least 12 years of schooling), but they decreased for others (particularly the three educational groups in the middle of the distribution) (table 2.11). People in the older cohorts were no better off in 2001 than they were in 1981. For people born in 1960­64, the very high income ratios between 2001 and 1981 partly reflect the fact that at least some people in this cohort were not earning income (or their earnings were low) in 1981, when they were 17­21 years old (some were still in school, others were just starting their careers). Income ratios rose continuously as education levels increased (except for the highest educational group) in this cohort, emphasizing the huge income differences associated with increased educational levels. Income gains occurred even for the cohorts born in 1950­54 and 1955­59, however. These gains were more pronounced for the younger cohort, reflecting the typical pattern of earnings curves. What effect did gender have on individuals' average income growth? Women enjoyed larger gains than men for nearly all educational levels and in many cohorts, especially the oldest ones. These findings contrast Table 2.11. Ratio of Average Income from All Sources in 2001 to Average Income from All Sources in 1981, by Cohort,Years of Schooling, and Gender Cohort born Years of 1940­44 1945­49 1950­54 1955­59 1960­64 schooling Men Women Men Women Men Women Men Women Men Women 0 0.79 2.67 0.86 2.18 0.91 2.30 1.17 2.75 1.64 2.24 1­3 0.76 2.09 0.91 2.02 0.96 2.18 1.11 2.33 1.81 2.48 4 0.78 1.57 0.95 1.88 0.99 1.95 1.18 2.12 2.26 2.04 5­7 0.71 1.11 0.74 1.16 0.93 1.75 1.22 1.72 2.50 2.09 8 0.82 1.01 0.79 1.43 1.10 1.47 1.23 1.50 2.58 2.10 9­10 0.65 1.10 0.82 0.95 1.07 1.63 1.59 1.56 3.27 2.47 11 0.75 1.23 0.87 1.28 1.05 1.42 1.50 1.44 2.85 1.98 12­14 1.12 0.86 0.76 1.57 1.20 1.38 2.24 1.77 5.52 3.31 15+ 0.93 1.34 1.06 1.44 1.17 1.55 2.00 1.84 3.39 3.10 Source: Authors. 54 Bonelli, Gonzaga, and Veiga with the results based on average per capita household income. The differences reflect the fact that women tend to form households with men in the same income bracket. Among older cohorts, income gains were much larger for women than for men for all educational groups, espe- cially the least educated. In contrast, for the two youngest cohorts, women enjoyed larger gains than men only among less-educated groups; for groups with more years of schooling, the incomes of men in the youngest cohorts tended to increase more than those of women.8 Part of these gender effects is the result of pension earnings, especially for the older cohorts, since women are entitled to public pensions five years earlier than men in Brazil. Part may also be a result of other sources of income, such as Bolsa Escola (a federal program aimed at enrolling all children in school), which is paid directly to women.9 (This issue is addressed below.) Race-Based Differences The two main limitations for analyzing race are the lack of data before 1989 and likely changes over time regarding racial identity in Brazil, as a result of the increasing sense of ethnic consciousness. Because of the lack of data before 1989, the data are disaggregated by race based only for the PNADs after 1989. With regard to the problem of changes in attitudes regarding one's declared ethnicity in household surveys, there is evidence that those changes were more pronounced within the nonwhite group (that is, changes in the proportions of "negros" and "pardos"); the propor- tion of nonwhites in the population was relatively unaffected. Therefore, this study examines race along the white/nonwhite dimension. Much larger poverty rates are observed for nonwhites relative to whites with the same levels of schooling (figure 2.3). Among whites with no schooling, 50­70 percent were poor in 2001, depending on the cohort, while the rates for nonwhites were 70­85 percent. Poverty rates for all cohorts of nonwhites were 80­90 percent in 1989, 1993, and 1997. A significant reduction in poverty incidence for nonwhites was observed only between 1997 and 2001 for the two oldest cohorts, especially people born in 1940­44. The incidence of poverty among nonwhites with just four years of schooling was also much higher than that observed for whites for all cohorts in all years. The same results are also observed when the R$80 poverty line is used (figure 2.4). Throughout the 1989­2001 period, more than 55 percent of nonwhites with no schooling in the youngest cohorts were poor. Significant reductions of poverty rates are observed only for the oldest cohorts and after 1997. Social Exclusion and Poverty Dynamics 55 These results suggest that race is a crucial dimension of social exclu- sion in Brazil. While poverty rates for whites with low levels of schooling are very high, especially for younger cohorts, the incidence of poverty among nonwhites is even more dramatic, and it did not decline signifi- cantly between 1989 and 2001. Urban/Rural Differences The extension of retirement and pension earnings to rural workers, after the changes implemented by the 1988 Constitution and regulated by the Lei Orgânica de Assistência Social, should have had a significant impact on the earnings of the oldest cohorts. The changes included providing universal coverage and reducing the minimum age of eligibility for rural Figure 2.3. Percentage of Brazilians Living below the R$160 Poverty Level, by Race, Years of Schooling, and Cohort, 1989­2001 (continued) 56 Bonelli, Gonzaga, and Veiga Figure 2.3. (continued) Source: Authors. workers to 60 years for men and 55 years for women. In this subsection, cohort-schooling-gender-urban/rural cells are followed from 1981 to 2001 at four-year intervals. Since the minimum age for retirement was differen- tiated by gender, the cases of men and women are analyzed separately. Between 1981 and 1989, about 90 percent of rural women with no schooling were poor, across cohorts (figure 2.5). Significant poverty reduction was observed only between 1997 and 2001 for the two oldest cohorts, especially people born in 1940­44, which includes women who became eligible to receive pension benefits. Some reduction was observed for urban women, but the decline was greater in rural regions. A similar pattern was observed for women with four years of school- ing, although there was more dispersion across cohorts. This evidence suggests that the extension of pension and retirement benefits to rural workers after the changes implemented by the 1988 Constitution and Social Exclusion and Poverty Dynamics 57 regulated by the Lei Orga^nica de Assiste^ncia Social may have played a role in reducing poverty among the oldest cohorts of less-educated people, especially in rural areas.10 Analysis of the Data The reduction in poverty rates between 1981 and 2001 was concentrated mainly among the oldest cohorts, especially those with little education.This section examines these changes. Income from Main Job and Pension/Retirement Income Two factors--job experience and pension and retirement earnings--may account for the fact that poverty reduction in Brazil between 1981 and 2001 was concentrated among the oldest cohorts, especially those with Figure 2.4. Percentage of Brazilians Living below the R$80 Poverty Level, by Race, Years of Schooling, and Cohort, 1989­2001 (continued) 58 Bonelli, Gonzaga, and Veiga Figure 2.4. (continued) Source: Authors. the fewest years of schooling. To test these hypotheses, a different income concept, income from main job, is used, and gender differences are examined. Main job income. Categories at the lower end of the educational spec- trum (that is, women with less than four years of schooling) and at the very top (that is, women who completed tertiary education) enjoyed substantial gains in income. The picture for men in this cohort indicates typical life-cycle behavior, with income peaking about age 40 and falling thereafter. (Of course, there is also a period effect, with the recession years of the early 1990s negatively affecting the earnings curve between 1989 and 1993.) Evidence on income from main job for people with the highest and lowest levels of schooling is also helpful in understanding income Social Exclusion and Poverty Dynamics 59 Figure 2.5. Percentage ofWomen Living below the R$160 Poverty Line, by Cohort, Years of Schooling, and Urban or Rural Location, 1981­2001 Source: Authors. patterns of men and women (figure 2.6). Although there are some dif- ferences across gender, a similar picture arises for men and women. This picture is consistent with life-cycle earnings in which income increases with experience and on-the-job training up to a certain point, after which it falls. In particular, earnings by the oldest cohorts tend to decline, with lower returns after peak years, while earnings by the youngest cohorts tend to rise over time. In all cases, the earnings curves seem to reflect the effects of economic cycles. Pension and retirement income. Household per capita average income increased, especially in the oldest cohorts, between 1993 and 2001.At the end of the period, the average income of older cohorts was consistently higher than that of younger cohorts. A less clear picture characterizes changes before 1993. One possible cause for the consistent increase in the average incomes of older cohorts with low levels of education is the increase in pension and 60 Bonelli, Gonzaga, and Veiga retirement earnings. The share of these earnings in earnings from all sources rose for all cohorts between 1981 and 2001 (tables 2.12 and 2.13). For the oldest cohort (1940­44), pension and retirement earnings increased from 2.3 percent of total earnings in 1981, when this cohort was Figure 2.6. Income from Main Job, by Gender, Years of Schooling, and Cohort, 1981­2001 (continued) Social Exclusion and Poverty Dynamics 61 Figure 2.6. (continued) Source: Authors. 62 Bonelli, Gonzaga, and Veiga Table 2.12. Pension and Retirement Income as a Percentage of Income from All Sources, by Cohort, 1981­2001 Cohort born Year 1940­44 1945­49 1950­54 1955­59 1960­64 1981 2.3 1.3 0.8 0.7 0.1 1985 2.7 2.2 1.0 0.4 0.6 1989 3.1 2.7 1.2 1.1 0.4 1993 10.4 4.1 2.5 2.2 1.5 1997 16.4 8.6 5.4 2.5 2.0 2001 34.6 16.1 8.3 5.7 3.8 Source: Authors. Table 2.13. Pension and Retirement Income as a Percentage of Income from All Sources, by Cohort, Gender, andYears of Schooling, 1981 and 2001 Year/ Cohort born level of 1940­44 1945­49 1950­54 1955­59 1960­64 schooling Men Women Men Women Men Women Men Women Men Women 1981 0 4.4 5.6 4.8 6.8 4.5 5.8 4.8 6.0 6.7 4.8 1­3 3.4 4.9 2.9 4.8 3.1 4.0 3.9 4.4 5.1 6.3 4 2.8 6.4 2.6 4.5 3.0 4.2 3.9 3.8 4.2 3.9 5­7 2.3 5.5 2.4 3.6 4.0 3.5 4.6 4.3 6.2 6.1 8 3.5 6.1 2.7 2.9 2.7 3.4 4.7 3.8 6.6 5.4 9­10 4.8 5.0 3.1 3.4 2.3 4.5 6.6 5.4 7.1 7.3 11 2.6 4.2 2.4 3.1 3.4 3.7 5.9 4.9 7.5 7.3 12­14 0.7 5.6 2.3 4.2 2.7 4.3 8.3 7.0 8.5 8.4 15+ 0.7 2.6 1.3 3.4 3.8 3.9 7.6 6.8 1.1 6.1 2001 0 26.4 30.5 15.0 23.1 11.9 15.5 10.4 10.9 11.7 14.1 1­3 25.7 28.3 14.0 18.5 9.0 13.3 6.9 10.3 8.4 7.9 4 31.1 29.1 18.0 22.5 13.2 14.9 7.6 8.9 4.9 9.3 5­7 26.8 27.6 23.9 17.8 11.7 15.8 5.8 9.7 4.0 4.8 8 26.4 30.1 24.1 20.1 11.2 15.5 8.6 10.0 4.7 6.0 9­10 35.0 44.3 27.9 25.2 11.3 12.9 5.7 11.4 4.1 4.9 11 37.3 35.1 21.9 24.8 12.1 16.2 5.5 8.3 3.8 5.5 12­14 30.0 43.5 25.9 37.7 12.1 21.7 4.7 8.2 4.9 5.5 15+ 28.3 42.0 17.0 30.8 7.4 14.3 3.6 5.8 2.6 4.4 Source: Authors. 37­41 years old, to 34.6 percent in 2001, when this cohort was 57­61 years old. Increases in the proportion of pension and retirement income in total income tended to be higher among women, especially for the oldest cohorts and for people with more years of schooling.11 Social Exclusion and Poverty Dynamics 63 Income Rank Correlations over Time This section analyzes the income dynamics of several groups of individuals based on the results of income-ranking correlations over time. The idea is to verify whether groups of individuals or households change their relative positions in income rankings over time. The analysis is conducted by rank- ing the groups by household average per capita real earnings for each year in the sample (1981, 1985, 1989, 1993, 1997, and 2001); computing Pearson correlation measures for the income rankings across years; and studying income rankings and income-ranking correlations for two other dimensions (race and location). The results show a very high correlation of income rankings (greater than 0.968) for all pairs of years in the sample (table 2.14). As expected, rankings of income for adjacent years present the highest correlations (greater than 0.988). Correlations decrease slightly and monotonically with the distance between years but are still very high (0.968) when 1981 is compared with 2001. The positions of each cohort-gender-schooling group in the income rankings changed very little over time. This result is consistent with the well-documented fact that education is a key variable for explaining income differentials in Brazil. The results indicate only a few changes of position in the income rankings, most of them across cohorts with simi- lar schooling levels. In only a few cases did a less-educated group in one cohort surpasses a more-educated group in the same cohort. When race is added, the correlations of income rankings are still very high (greater than 0.962) for all pairs of years in the sample (table 2.15). Rankings of income for adjacent years are lower than those shown in table 2.14, but they still remain very high (greater than 0.962). Most of the few changes in ranking that occurred were across cohorts. In only a few cases did a less-educated group surpass a more-educated group in Table 2.14. Pearson Correlations of Income Rankings among Cohort-Schooling- Gender Groups, 1981­2001 Year 1981 1985 1989 1993 1997 2001 1981 1.0000 1985 0.9890 1.0000 1989 0.9787 0.9878 1.0000 1993 0.9726 0.9834 0.9891 1.0000 1997 0.9761 0.9861 0.9909 0.9927 1.0000 2001 0.9680 0.9803 0.9835 0.9907 0.9928 1.0000 Source: Authors. 64 Bonelli, Gonzaga, and Veiga Table 2.15. Pearson Correlations of Income Rankings among Cohort-Schooling- Gender-Race Groups, 1989­2001 Year 1989 1993 1997 2001 1989 1.0000 1993 0.9788 1.0000 1997 0.9677 0.9678 1.0000 2001 0.9685 0.9792 0.9616 1.0000 Source: Authors. Note: Data on race are available only after 1989. the same cohort or a nonwhite group surpass a white group in the same cohort with the same level of schooling. Of course, within each group, some individuals in each group fare better than others: analysis of averages substantially underestimates the amount of income mobility observed over time. Using averages as a measure of income mobility may hide important within-cell income changes.12 Do these results change if urban-rural location or region (north, north- east, southeast, center-west, and south) is added to the analysis? Adding these variables reduces the credibility of the hypothesis of fixed group membership. Migration starts playing an important role, changing (perhaps substantially) the compositions of each cell across years.A significant drop in income-ranking correlations occurs when regional dimensions are added (although the figures still remain very high). Correlations of income rank- ings of some pairs of years drop below 0.90. Rural women with no school- ing born in 1940­44, for example, moved from the 4th position from the bottom in 1981 to 21st in 2001 (table 2.16).When both urban-rural loca- tion and region are included (table 2.17), income-ranking correlations drop to 0.81 in some pairs of years. (Adding race did not change the results much, with only a small reduction observed in all years.) These results may reflect differential effects of government policies across regions (such as the expansion of social security in rural areas); differential effects of eco- nomic shocks to economic regions over time; or composition effects, given that possible migration would make groups not comparable over time. Income Convergence across Cohorts over Time This section presents the results of a pseudo-panel regression, used to test whether there is any tendency of income convergence across the 90 cohort-schooling-gender groups between 1981 and 2001. Based on the repeated cross-section data, a simple convergence regression, Social Exclusion and Poverty Dynamics 65 Table 2.16. Pearson Correlations of Income Rankings among Cohort-Schooling- Gender-Urban/Rural Groups, 1981­2001 Year 1981 1985 1989 1993 1997 2001 1981 1.0000 1985 0.9125 1.0000 1989 0.9093 0.9524 1.0000 1993 0.8988 0.9197 0.9215 1.0000 1997 0.8969 0.9608 0.9377 0.9246 1.0000 2001 0.9058 0.8822 0.9082 0.9258 0.9051 1.0000 Source: Authors. Table 2.17. Pearson Correlations of Income Rankings among Cohort-Schooling- Gender-Urban/Rural-Regional Groups, 1981­2001 Year 1981 1985 1989 1993 1997 2001 1981 1.0000 1985 0.8912 1.0000 1989 0.8365 0.8844 1.0000 1993 0.8365 0.8751 0.8395 1.0000 1997 0.8480 0.8837 0.8486 0.8494 1.0000 2001 0.8335 0.8434 0.8143 0.8408 0.8760 1.0000 Source: Authors. Table 2.18. Regression Results on Income Divergence between 1981 and 2001 Item Coefficient t-statistic Initial earnings (1981) 0.214 4.76 Constant ­27.18 ­1.00 Source: Authors. is estimated, where g indexes each of the 90 cohort-schooling-gender cells; the dependent variable represents long-term household per capita average income changes (1981­2001); and the initial income variables are measured in 1981. A negative (positive) coefficient would repre- sent income convergence (divergence), meaning that income growth is negatively (positively) correlated with initial income levels--that is, the earnings of the richest groups grew less (more) than those of the poorest. The estimated coefficient is positive and significantly different from zero (table 2.18).This means that the average earnings of the richest house- holds increased more than those of the poorest.This result is perfectly com- patible with the reported high degree of income-ranking correlations across years reported in the previous section. Although there were not many changes in the income positions of groups of individuals over time, the top quintiles enjoyed larger income increases than other quintiles. 66 Bonelli, Gonzaga, and Veiga Estimating the Probability of Being Poor This section presents probit models in which the probability of being poor in selected cohort-schooling groups in 1981 and 2001 is explained by a set of variables reflecting personal and household characteristics. The idea of performing regression for two different years is to evaluate the possibility that the importance of different variables in affecting the probability of being poor changed over time. Studying each cohort-schooling cell sepa- rately allows the relevance of each variable across time to be tracked for each group. In particular, the importance of retirement and pension income is examined in explaining the reduction in poverty for the oldest cohorts. The specification used is yi = a + b Xi + yZi + b Gi + ei, where i indexes individuals in each cohort-schooling group, and yi equals 1 when the individual's household average per capita income is below the R$160 poverty line (the R$80 poverty line was also tested) and zero otherwise. Xi is a vector of personal characteristics, including the ratio of pension and retirement receipts to income from all sources; whether the individual has a working card, is self-employed, is an employer, or is unem- ployed (the omitted variable is working without a working card); gender (1 for women); race (1 for nonwhite); whether the individual was born out- side of the state in which he or she lives (migrant). Zi is a vector of house- hold characteristics, including the dependency ratio (ratio of individuals 17 or younger to the number of parents in the household); the number of children; and whether the household has only one adult. Gi is a vector of geographical variables, including urban/rural location and regional indica- tors (north/center-west, northeast, and south; southeast is omitted).13 Results for individuals with no schooling in the oldest (1940­44) and youngest (1960­64) cohorts in 2001 for both poverty lines (R$160 and R$80) are analyzed here. (The annex presents a complete set of results for all cohorts in 1981 and 2001 for the two educational groups select- ed. Results are also shown for these two cohorts in 1981 and 2001 for the R$160 poverty line, controlling parametrically for schooling.) For both cohorts, the ratio of pension and retirement income to total income significantly decreases the probability that an individual without formal schooling was poor in 2001. This finding confirms the perception that government pension policies were an important factor behind the reduc- tion in poverty rates observed among the oldest cohorts. Most of the results for individuals in these two cohorts are in line with those for people with no schooling (table 2.19). Table 2.19. Determinants of Being Poor among People from Oldest andYoungest Cohorts, 1981 and 2001 1981 2001 Cohort born 1940­44 Cohort born 1960­64 Cohort born 1940­44 Cohort born 1960­64 Explanatory variables Coefficient P-value Coefficient P-value Coefficient P-value Coefficient P-value Individual characteristics Pension/income ratio 0.0383 0.6180 ­0.4132 0.1380 ­0.5862 0.0000 ­0.5590 0.0000 Formal ­0.1024 0.0000 ­0.3582 0.0000 ­0.2497 0.0000 ­0.0558 0.0130 Gender ­0.0557 0.1590 0.0170 0.2180 ­0.1087 0.0130 0.0056 0.8330 Nonwhite -- -- -- -- 0.2308 0.0000 0.2552 0.0000 Migrant (state) -- -- -- -- ­0.1263 0.0010 ­0.1866 0.0000 Migrant (county) -- -- -- -- ­0.1374 0.0000 ­0.0463 0.0260 Employed ­0.2998 0.0000 ­0.2293 0.0000 ­0.2731 0.0000 ­0.4951 0.0000 Head 0.2788 0.0000 0.4145 0.0000 0.0885 0.0460 0.1893 0.0000 Education ­0.1814 0.0000 ­0.1335 0.0000 ­0.1013 0.0000 ­0.1235 0.0000 Household characteristics Dependency ratio 0.4537 0.0000 0.3507 0.0000 0.6178 0.0000 0.5313 0.0000 Number of children 0.0208 0.0610 0.0257 0.0000 0.0421 0.0050 0.0703 0.0000 Single ­0.2328 0.0000 ­0.1926 0.0000 ­0.2882 0.0000 ­0.1505 0.0000 Geographic characteristics Urban ­0.6482 0.0000 ­0.6881 0.0000 ­0.4599 0.0000 ­0.5721 0.0000 Center-west 0.1745 0.0000 0.0926 0.0000 0.0021 0.9690 0.0414 0.1950 Northeast 0.5912 0.0000 0.4997 0.0000 0.5362 0.0000 0.4738 0.0000 North 0.2764 0.0000 0.1642 0.0000 0.2899 0.0000 0.1612 0.0000 South 0.0010 0.9700 ­0.0428 0.0280 ­0.1037 0.0140 ­0.1297 0.0000 Constant 0.4725 0.0000 0.7494 0.0000 0.4046 0.0000 0.8381 0.0000 67 Pseudo R-squared 0.2811 0.2877 0.2763 0.3049 Source: Authors. -- = Not available. 68 Bonelli, Gonzaga, and Veiga In addition to the extension of retirement benefits to rural workers and the lowering of the retirement age, there was a substantial recovery of the real value of the minimum wage after the Real Plan in 1994, with significant real increases in 1995. Since the minimum wage is the value of pensions received by most rural workers, one would expect that the joint impact of the Lei Orga^nica de Assiste^ncia Social and the minimum wage increase significantly affected household earnings, especially in the second half of the 1990s. The effects of individual variables related to labor status on the proba- bility of being poor reported in table 2.19 have the expected signs.Among the oldest cohort, uneducated workers with a formal labor contract have a lower probability than those without a working card of being poor for the older cohort; this effect was not significant for the youngest cohort in 2001. Being self-employed or an employer also decreased the probability of being poor for the older cohort, but it was significant only for the R$80 poverty line in the case of the youngest cohort. Being unemployed increased the probability of being poor for the youngest cohort but not for the oldest cohort. The two variables available only for 2001 (race and migrant status) have significant coefficients: a nonwhite individual has a higher than average probability and a migrant, a lower than average probability of being poor. This finding is consistent with the earlier analysis of race. Gender has a significantly negative effect on being poor for the 1940­44 cohort; it is also significant and negative for the 1960­64 cohort using the R$160 poverty line. Although the specification controls for other characteristics, especially the number of children and the dependency ratio, this result still needs further investigation, particularly given the results for other cohorts (shown in the annex), which indicate that gender is not a significant determinant of poverty. As for the household variables, as expected, the dependency ratio sig- nificantly increases the probability of being poor for both cohorts and poverty lines. The number of children is significant and positive only for the 1960­64 cohort. The regional variables have the expected signs. People with no schooling who live in urban areas have a lower proba- bility of being poor than people who live in rural areas. People in the northeast have the highest probability of being poor, while those in the south and southeast (omitted variable) have the lowest probability of being poor. Social Exclusion and Poverty Dynamics 69 Conclusions About 5.6 million people in Brazil moved out of poverty between 1977 and 2001, a decline of 23.6 percent. Most of the poverty reduction occurred between 1993 and 2001, when some 4.2 million people in the cohorts studied escaped poverty. Poverty reduction was not homogeneous across cohorts: substantial poverty reduction from 1981 to 2001 is consistently clear only for older cohorts and, especially, for those with four years or less of schooling. It seems that the younger the group, the less pronounced the escape from poverty was. The largest reduction in poverty (36.1 percent) was enjoyed by the 1940­44 cohort, followed by the 1960­64 cohort (29.2 percent) and the 1945­49 cohort (22.2 percent). The drop in poverty within the youngest cohort can be partially explained by the fact that this cohort was very young in 1977 (13­17 years old); most of its members did not participate in the labor market, participated with very low earnings, or participated with no remuneration at all. The finding that poverty decreased more for the oldest cohorts can be explained by a number of factors, including returns to work experience; government social policies; pension and retirement income (a special case of government policy); eco- nomic-cycle effects; household-size and age-composition dynamics; and composition bias, reflecting higher mortality rates for the poorest in each cohort-schooling group. Average household per capita income of women in a given cohort- education group did not prove to be statistically lower from that of men in the same cohort-education group. In fact, women showed higher gains in nearly all educational levels and in many cohorts, especially the oldest ones. This finding contrasts with previous results, which indicated no income differences between genders when household per capita average income was considered. One possible explanation is that such differ- ences reflect the fact that women tend to form households with men in the same income bracket. For older cohorts, income gains for women are much larger than for men in all educational groups, especially the least educated. In contrast, in the two youngest cohorts, the effect applies only to the least educated. Education is a key variable for explaining income differentials in Brazil. For people with little education, the incidence of poverty decreased with 70 Bonelli, Gonzaga, and Veiga time--albeit still leaving large proportions of these people below the poverty line. At the end of the period analyzed, the vast majority of peo- ple in households with no schooling still lived in poverty (71.3 percent in 2001). Income inequality, as measured by the Gini index, decreased over time for all cohorts, despite some fluctuations, for this group. For the remaining groups, poverty increased and average incomes decreased over time. For a given level of schooling, the incidence of poverty is much higher among nonwhites than whites. In 2001, for example, the incidence of poverty among people with no schooling was 50­70 percent for whites and 70­85 percent for nonwhites, depending on the cohort. Escape from poverty was also much less common for nonwhites. Most of the few changes in income-ranking positions occurred across cohorts. In only a few cases did a less-educated group surpass a more- educated group in the same cohort or a nonwhite group surpass a white group in the same education-cohort group. A significant drop in income-ranking correlations was observed when regional dimensions were added, although the correlations were still very high. These results may be pointing to different effects of government policies across regions; to different economic shocks to economic regions over time; or to composition effects, given that migration would make groups not comparable over time. Results from the analysis of income-ranking correlations show that the positions of each cohort-gender-schooling-race group in the income rank- ings changed very little across years. The results from the income-conver- gence analysis show that household per capita average incomes diverged from 1981 to 2001--that is, average earnings of the richest Brazilians increased more than those of the poorest. This result is perfectly compat- ible with the reported high degree of income-ranking correlations across years. Although there were not many changes in the income positions of groups of individuals over time, the results point to larger income increas- es for those in the top quintiles of the household per capita average income distribution. Annex : Results for All Levels of Education This annex presents the results for the cohort-schooling groups that were not included in the chapter. The groups are (a) people with incomplete lower-primary education (1­3 years of formal schooling); (b) people with Social Exclusion and Poverty Dynamics 71 Table 2A.1. Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with One toThreeYears of Schooling, by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 Total Number of people (million) 1981 1.631 1.723 1.850 1.816 2.216 9.225 1985 1.591 1.728 1.803 1.794 1.910 8.825 1989 1.494 1.677 1.851 1.719 1.671 8.412 1993 1.344 1.535 1.707 1.687 1.784 8.057 1997 1.259 1.402 1.583 1.612 1.670 7.526 2001 1.144 1.282 1.495 1.555 1.618 7.094 Average per capita household income (R$) 1981 164 156 154 173 153 160 1985 171 159 146 145 148 154 1989 216 190 167 139 157 174 1993 196 174 148 121 125 153 1997 238 207 186 162 140 187 2001 247 233 194 158 138 194 Percentage of people below R$160 poverty line 1981 67.8 69.5 69.1 66.9 72.3 69.1 1985 63.5 69.2 73.2 73.9 74.3 70.8 1989 55.2 60.9 68.3 73.5 73.2 66.2 1993 53.6 61.1 68.8 77.3 78.7 67.9 1997 49.4 54.2 60.3 66.4 72.5 60.5 2001 41.3 47.3 55.8 64.9 71.4 56.1 Percentage of people below R$80 poverty line 1981 36.5 39.2 36.3 33.6 38.7 36.9 1985 32.3 38.2 41.9 42.8 41.2 39.3 1989 26.5 34.4 40.0 45.9 43.7 38.1 1993 27.0 32.6 38.7 47.7 50.5 39.3 1997 23.1 26.3 30.7 36.6 41.6 31.7 2001 15.6 21.8 27.0 33.1 39.7 27.4 Gini coefficient 1981 0.50 0.50 0.47 0.51 0.50 0.49 1985 0.46 0.49 0.49 0.49 0.49 0.49 1989 0.50 0.52 0.55 0.52 0.55 0.53 1993 0.48 0.48 0.49 0.49 0.54 0.50 1997 0.48 0.47 0.48 0.49 0.49 0.48 2001 0.45 0.48 0.47 0.45 0.46 0.46 Source: Authors. 72 Bonelli, Gonzaga, and Veiga incomplete upper-primary education (5­7 years of formal schooling); (c) people with incomplete secondary education (9­10 years of formal schooling); and (d) people with incomplete tertiary education (12­14 years of formal schooling). Cohort Sizes and Main Characteristics of Sample by Level of Education One to Three Years of Schooling (Incomplete Lower Primary School) As expected, the number of people with one to three years of formal schooling is higher the younger the cohort, indicating progress in basic education attainment in Brazil (table 2A.1). Average per capita household incomes are almost twice those of people with no schooling.The incidence of poverty decreased over time, particularly for the older cohorts, especially after 1993 (figure 2A.1). As with people with no schooling, the proportions of people living in poverty were still sizable in 2001 (albeit decreasing over time). These figures range from 41 percent among the 1940­44 cohort to 71 percent among the 1960­64 cohort. The range among this group is wider than Figure 2A.1. Proportion of People with One toThreeYears of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 Source: Authors. Social Exclusion and Poverty Dynamics 73 Table 2A.2. Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with Five to SevenYears of Schooling, by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 Total Number of people 1981 290,210 436,327 781,259 1,292,176 2,635,024 5,434,996 1985 251,647 399,002 710,975 1,154,998 2,040,067 4,556,689 1989 270,636 400,564 733,230 1,142,459 1,865,858 4,412,747 1993 221,597 360,200 651,180 1,149,412 1,839,126 4,221,515 1997 205,649 361,045 654,966 1,102,534 1,858,103 4,182,297 2001 245,364 373,476 653,712 1,064,049 1,755,358 4,091,959 Average per capita household income (R$) 1981 351 331 295 276 226 296 1985 358 316 266 256 230 285 1989 473 353 298 264 231 324 1993 359 295 250 206 195 261 1997 492 403 313 274 235 343 2001 430 373 318 253 207 316 Percentage of people below R$160 poverty line 1981 27.9 29.4 35.2 37.0 48.1 35.5 1985 26.1 32.6 41.9 43.4 48.1 38.4 1989 25.6 35.0 40.3 47.0 49.6 39.5 1993 32.7 40.6 47.2 54.5 58.4 46.7 1997 22.9 24.7 31.7 39.6 47.6 33.3 2001 23.7 28.4 31.9 40.4 52.2 35.3 Percentage of people below R$80 poverty line 1981 8.4 9.9 10.0 10.4 17.2 11.2 1985 9.0 12.0 13.4 15.7 16.2 13.3 1989 9.8 10.9 14.2 19.0 20.5 14.9 1993 13.2 15.7 18.2 22.8 26.4 19.3 1997 5.6 8.5 11.3 14.5 19.6 11.9 2001 6.7 8.5 12.7 14.5 22.3 13.0 Gini coefficient 1981 0.44 0.45 0.44 0.44 0.44 0.44 1985 0.44 46.4 44.3 45.0 43.5 44.7 1989 0.51 48.4 49.4 50.0 47.3 49.3 1993 0.52 50.6 48.6 46.0 47.9 49.0 1997 0.51 45.5 42.9 45.4 46.5 46.2 2001 0.48 48.5 46.1 42.7 44.4 46.0 Source: Authors. 74 Bonelli, Gonzaga, and Veiga that among people with no schooling. No significant (or consistent) changes in income inequality occurred during the period analyzed, with the Gini coefficients remaining fairly constant. Five to Seven Years of Schooling (Incomplete Upper Primary Schooling) Only about 200,000­250,000 Brazilians in the oldest cohort have five to seven years of schooling. Among the youngest cohort, the figure is about 1.8­2.0 million people, indicating a significant increase in years of schooling over time (table 2A.2). Household per capita average income rose between 1981 and 2001 only for the three oldest cohorts.The proportions of poor people increased in all cohorts up to 1993 before decreasing between 1993 and 2001 (figure 2A.2). Income inequality remained fairly stable for all cohorts. Nine to Ten Years of Schooling (Incomplete Secondary Education) The number of Brazilians with some secondary education is higher for younger cohorts, indicating increases in the level of education over time (table 2A.3). Household per capita average income shows a pattern Figure 2A.2. Proportion of People with Five to SevenYears of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 Source: Authors. Social Exclusion and Poverty Dynamics 75 Table 2A.3. Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with 9­10Years of Schooling, by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 Total Number of people 1981 75,591 142,657 272,837 592,333 1,438,093 2,521,511 1985 74,447 118,113 241,091 459,899 868,598 1,762,148 1989 85,746 119,713 239,686 453,051 672,457 1,570,653 1993 50,380 118,968 229,558 412,575 552,309 1,363,790 1997 48,397 97,883 185,517 385,059 509,407 1,226,263 2001 49,856 101,344 217,870 347,791 520,963 1,237,824 Average per capita household income (R$) 1981 518 467 460 388 418 450 1985 532 411 429 378 366 423 1989 654 585 490 392 379 500 1993 436 476 344 302 287 369 1997 724 535 484 416 373 506 2001 572 473 501 416 313 455 Percentage of people below R$160 poverty line 1981 15.6 16.9 17.9 21.4 22.0 18.8 1985 15.8 23.4 22.0 25.5 29.3 23.2 1989 15.7 19.9 27.8 27.7 30.0 24.2 1993 21.1 22.8 33.8 32.0 38.7 29.7 1997 14.5 14.2 22.8 26.6 31.7 22.0 2001 16.3 16.5 19.0 26.2 31.8 21.9 Percentage of people below R$80 poverty line 1981 4.8 6.4 4.5 6.2 5.4 5.5 1985 6.0 7.1 5.5 6.7 7.8 6.6 1989 4.2 7.4 6.5 8.1 8.3 6.9 1993 5.3 7.7 10.7 10.1 13.9 9.6 1997 1.7 3.9 6.1 8.2 10.4 6.0 2001 4.6 4.3 6.6 8.2 11.0 6.9 Gini coefficient 1981 0.47 0.45 0.43 0.42 0.46 0.45 1985 0.44 0.45 0.46 0.44 0.46 0.45 1989 0.56 0.53 0.53 0.48 0.48 0.52 1993 0.45 0.49 0.47 0.45 0.47 0.47 1997 0.41 0.41 0.47 0.48 0.49 0.45 2001 0.44 0.43 0.49 0.50 0.43 0.46 Source: Authors. 76 Bonelli, Gonzaga, and Veiga Figure 2A.3. Proportion of People with 9­10Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 Source: Authors. similar to that observed for people with five to seven years of education and eight years of schooling (not shown), decreasing from 1981 to 1993 (despite a peak in 1989) and increasing from 1993 to 2001. Trajectories varied across cohorts, with larger decreases in income and a weaker recovery for the youngest cohorts. The incidence of poverty is also similar to that found for people with fewer years of schooling, increasing up to 1993 and decreasing there- after. The increases in the incidence of poverty up to 1993 are propor- tionately larger than for people with fewer years of schooling, especially for the youngest cohorts (1950­54, 1955­59, and 1960­64) (figure 2A.3). Income inequality remained roughly constant over time, with the Gini coefficients showing little variance. Twelve to Fourteen Years of Schooling (Incomplete Tertiary Education) The number of people with 12­14 years of schooling increased substan- tially in each cohort in all years, indicating the diffusion of tertiary education in Brazil, particularly between 1993 and 2001(table 2A.4). Average income among people with 12­14 years of schooling decreased Social Exclusion and Poverty Dynamics 77 Table 2A.4. Number of, Average per Capita Household Income of, and Incidence of Poverty among Brazilians with 12­14Years of Schooling, by Cohort, 1981­2001 Indicator/ Cohort born year 1940­44 1945­49 1950­54 1955­59 1960­64 Total Number of people 1981 91,619 165,391 325,812 515,179 274,205 1,372,206 1985 84,675 154,407 269,838 406,808 649,287 1,565,015 1989 76,672 141,654 246,053 354,989 508,344 1,327,712 1993 65,509 116,074 213,888 325,784 395,726 1,116,981 1997 50,713 116,125 219,795 276,871 359,674 1,023,178 2001 59,867 119,491 210,251 302,231 403,475 1,095,315 Average per capita household income (R$) 1981 873 882 896 854 900 881 1985 1,133 903 897 917 855 941 1989 1,144 905 963 879 974 973 1993 1,051 883 813 758 707 842 1997 1,089 1,142 906 916 957 1,002 2001 1,288 1,110 932 849 804 997 Percentage of people below R$160 poverty line 1981 3.6 3.5 4.0 4.1 3.8 3.8 1985 3.1 4.5 4.1 4.7 4.2 4.1 1989 4.1 5.2 5.1 7.9 6.1 5.7 1993 3.2 5.6 7.6 7.8 8.9 6.6 1997 4.6 4.4 5.3 6.7 4.9 5.2 2001 4.2 3.2 3.0 4.5 5.8 4.2 Percentage of people below R$80 poverty line 1981 1.0 1.5 1.3 1.0 1.3 1.2 1985 0.4 1.1 0.6 0.9 1.1 0.8 1989 2.1 0.6 1.3 1.7 1.8 1.5 1993 0.8 0.8 3.0 2.1 2.3 1.8 1997 1.7 1.0 2.9 3.4 1.6 2.1 2001 0.0 2.0 1.0 1.4 1.5 1.2 Gini coefficient 1981 0.42 0.41 0.41 0.42 0.44 0.42 1985 0.54 0.44 0.41 0.44 0.43 0.45 1989 0.45 0.47 0.48 0.46 0.46 0.47 1993 0.50 0.48 0.47 0.48 0.46 0.48 1997 0.42 0.48 0.43 0.49 0.49 0.46 2001 0.45 0.39 0.40 0.43 0.43 0.42 Source: Authors. 78 Bonelli, Gonzaga, and Veiga Figure 2A.4. Proportion of People with 12­14Years of Schooling Living below the R$160 Poverty Line, by Cohort, 1981­2001 Source: Authors. between 1981 and 1993, except for the two oldest cohorts, and increased significantly between 1993 and 2001 for all cohorts. Poverty rates were low and showed very little change between 1981 and 2001 (figure 2A.4). Poverty increased from 1981 to 1993 and decreased between 1993 and 2001 for most cohorts. The oldest cohort had the lowest incidences of poverty, except in 2001. Income inequality remained roughly constant over time, with the Gini coefficients showing little variance. Notes 1. Two possibilities may change the size of the fixed groups: deaths of individuals and changes in educational status over time.Working with relative sizes (that is, proportions of the total cohort) solves part of the first problem. Working with adults 25 and older reduces but does not solve the second problem. 2. The use of five-year cohorts increases the robustness of the samples. 3. Some analyses are based on household characteristics, not gender. 4. Income from main job and income from all sources are also calculated, for both men and women. Household per capita average income is a better measure of Social Exclusion and Poverty Dynamics 79 poverty characteristics, however, and is therefore used.To investigate the effects of personal characteristics, the separate gender data are more appropriate. 5. Brazil's government has not defined an "official" poverty line. Different poverty lines derived from slightly different methodologies are equally defensible. In chapter 1 of this book, the extreme poverty line of R$65 was determined by the cost of a minimum food basket, and a poverty line that incorporates no food expenditures was defined as R$132 (World Bank 2001).The difficult issue of deflating nominal values during the years of high inflation was dealt with by using a set of carefully designed and consistent deflators for PNAD data prepared by Corseuil and Foguel (2002). 6. Using different poverty levels, Paes de Barros, Henriques, and Mendonça (2000) report a substantial drop in poverty incidence between 1993 and 1999. 7. Data limitations do not allow investigation of the importance of composition bias in this study. The results should therefore be interpreted with caution. 8. Steeper earnings curves for men are also observed in other countries, capturing the higher incidence of on-the-job training among men, probably as a result of the still heavier load of child-bearing responsibilities that fall on women. 9. Bolsa Ecola is a decentralized conditional cash transfer program that provides cash payments to poor families who send their children to school. First implemented in 1995 in Campinas and the outskirts of Brasilia, the program was federalized in 2001. It is considered one of the most successful condi- tional cash transfer programs in Latin America. 10. The law made coverage universal and reduced the minimum age for eligibility of rural workers to 60 years for men and 55 years for women. 11. See Barros, Piola, and Vianna (1996) for evidence of the growing importance of retirement earnings on total household income with age and over time in Brazil. They also show that on average, the retirement earnings of older people are higher than the per capita income of other members of their households. 12. This point was raised by Francisco Ferreira. 13. Data from the PNAD on migrant status and race are not available for some years. References Attanasio, O., and M. Székely. 2003. "An Introduction to Household Decision- Making: How to Interpret the Data." In The Family in Flux: Household Decision- Making in Latin America, ed. O. Attanasio and M. Székely. Washington, DC: Inter-American Development Bank. Barros, Maria Elizabeth, Sérgio Francisco Piola, and Solon Magalhães Vianna. 1996. "Política de saúde no Brasil: diagnóstico e perspectivas." Texto para discussão 401, Instituto de Pesquisa Econômica Aplicada, Rio de Janeiro. 80 Bonelli, Gonzaga, and Veiga Corseuil, C. H. E., and M. N. Foguel. 2002."Uma sugestão de deflatores para rendas do trabalho obtidas a partir de pesquisas domiciliares."Texto para discussão 897, Instituto de Pesquisa Econômica Aplicada, Rio de Janeiro. Paes de Barros, Ricardo, Ricardo Henriques, and Rosane Mendonça. 2000. "A esta- bilidade inaceitável: Desigualdade e pobreza no Brasil." In Desigualdade e pobreza no Brasil, ed. Ricardo Henriques. Rio de Janeiro: Instituto de Pesquisa Econômica Aplicada. World Bank. 2001. Attacking Brazil's Poverty: A Poverty Report with a Focus on Urban Poverty Reduction Policies. Report 20475-BR. Washington, DC: World Bank. C H A P T E R 3 Legitimization and Acceptance: How Brazilians Survive Inequalities Maria Celi Scalon and Ignacio Cano Many studies address the high level of inequality in Brazil and its conse- quences for social relations. Most analyze objective dimensions related to the acquired or ascribed characteristics of the population, such as edu- cation, occupation, income, race, age, and gender. Very little is known about the perceptions Brazilians have of inequal- ity, however, one of the defining traits of the country. Do Brazilians see their country as unequal? Which factors or individual characteristics do they view as determinants of income inequalities? Are these determi- nants perceived as fair or unfair? These questions can be answered only by understanding how Brazilian society produced and sustained cultural values that legitimize inequality and allow the population to live with one of the highest levels of income disparity in the world. This chapter explores these issues. History of Income Inequality in Brazil Economic growth and industrialization in Brazil after 1945 were constant and rapid until the beginning of the 1980s. It was during the 1970s that the country definitively changed from a rural to an urban 81 82 Scalon and Cano society in which industrial and service sector work far surpassed rural work. By 1980 Brazilian industrial output was the seventh largest in the Western world. Educational opportunities also increased rapidly after the 1950s, particularly since the 1970s; between 1970 and 1975, for example, tertiary enrollments more than doubled. In contrast to the previous decades, the 1980s and 1990s were periods of nearly stagnant economic growth.1 The structural changes until the beginning of the 1980s opened new opportunities, with the creation of jobs outpacing population growth between 1945 and 1985 (Faria 1986). The crises of the past two decades slowed the expansion of these opportunities. Who took advantage of the new opportunities and who suffered as a result of the crisis? How did Brazilian class structure change? Did the social structure become more open, grow more rigid, or remain essentially the same? Studies of industrialization in Brazil claim that the transition to an industrial society is still in progress. The idea that "archaic" or "traditional" societal characteristics parallel "modern" ones is not prevalent, leading to a critique of the very idea that industrialization per se improves well- being.2 Brazil has a rich economy--according to some indices the eighth richest in the world--and one of the highest indices of income and wealth inequality ever measured (Bacha and Klein 1989). The former government economist and scholar Edmar Lisboa coined the sensation- alistic expression "Bel-India" in order to describe Brazil's socioeconomic structure as a mixture of rich and "modern" Belgium with poor and "traditional" India. With per capita gross national income of roughly $3,000 in 2002 (World Bank 2003), Brazil cannot be viewed as a poor country; the poverty of a significant part of the population reflects the unequal distribution of wealth and income. Brazil once enjoyed the fastest economic growth in Latin America. Between 1950 and 1980, the GNP index grew constantly at an average annual rate of 4.3 percent. The state supported the development of the modern sector of the economy and changed the structure of the labor market, stimulating the transfer of the labor force from the rural sector to the industrial and service sectors. Although industrialization in Brazil began in the 1930s, it was only after World War II that it reached rapid and sustained periods of economic growth. The industrial sector of the economy is concentrated in the southeast (São Paulo and Rio de Janeiro), and the new socioeconomic classes--linked to the modern labor market--are mixed with classes of people engaged in more traditional forms of production. This is a common situation in Latin Legitimization and Acceptance: How Brazilians Survive Inequalities 83 America (see Shanin 1978), one that has not changed dramatically over time. In addition, the size of Brazil's (already large) urban population grew more rapidly than its industrial counterpart, making it impossible to incorporate all workers into the formal labor market. The 1990s were marked by the introduction of economic reforms that led to the opening of markets, the privatization of public services and national companies, and many other policies designed to attract interna- tional private capital. The increase in imports added to the restricted capacity of national companies to compete in the international market and led to "deindustrialization." As a result, the labor force moved to employment in the service and commerce sectors, in which there is almost no legal protection or regulation. According to the Instituto Brasileiro de Geografia e Estatística, the level of industrial employment fell 48 percent between 1990 and 1999 (IBGE 1999). Despite the expansion of the informal sector in the past decade, in general the quality of life in Brazil improved considerably. The transfer of labor from the rural to the urban sectors led to a deep transformation in the social structure, which increased the social mobility of many workers and their families. However, diverse forms of inequality--especially income inequality-- continued to grow, creating huge gaps between social groups. In 1997 the 10 percent of the population with the highest salaries accounted for about 47 percent of all income obtained from work, while the 10 percent with the lowest salaries accounted for just 1 percent. In 1998 the 40 percent of workers with the lowest incomes earned an average monthly salary of US$90--less than the legal minimum wage--while the 10 percent with the highest incomes earned an average monthly salary of US$1,800. Among the poorest 40 percent of the population, 32.1 percent were not formally employed and 30.5 percent were self-employed; among the richest 10 percent, just 6.8 percent were not formally employed, and 20.8 percent were self-employed. Only 8.8 percent of the poorest 40 percent of the population work in the industrial sector, while 14.6 per- cent in the highest income decile do so. Survey and Model Results This chapter analyzes people's ideal income-disparity levels and identi- fies the determinants of those levels. It also seeks to understand who is perceived as responsible for diminishing inequality and whether citizens see themselves as having a role in this task. 84 Scalon and Cano The research is based on the results of a survey conducted in 2001 (on a nationally representative sample of 2,000 respondents) on perceptions of inequality. The survey, part of the International Social Survey Programme (ISSP), measures attitudes and opinions about inequality in general.3 The data provide insights into perceptions of fairness and the causes of income differences, past and future mobility and perceptions of mobility, opinions about policies for reducing inequality, and the role of government. Perceptions and Tolerance of Inequality Brazilians see their country as it is: a very unequal society. Asked if differences in income are too large in Brazil, 86 percent of respondents strongly agreed, 10 percent agreed, and only 3 percent disagreed.All social groups share this perspective, regardless of educational level, gender, income, mobility experience or expectation, and rural or urban residence.4 Despite this clear and massive perception of income inequality, there is a normative and legitimating discourse that explains tolerance toward inequality and assigns primary responsibility for redressing this issue to the state. Brazilian society shows a certain degree of acceptance and con- formism that fosters growing inequalities between classes. At the same time, a cognitive discourse explains both the clear perception of the high levels of income inequality and the mechanisms that impede breaking it down. This difference between the normative and cognitive levels helps explain why people identify the large gaps in equality but nevertheless assume that the rules are fair and are willing to play by them. Agreement with the idea that there is high income inequality in Brazil is a "moral" statement only in the sense that society perceives equality as desir- able and fair. Moreover, there is a predominant political discourse in defense of equality as a modern value of social justice. Thus, the discourse in favor of equality and the legitimization of inequality are not incompatible. Brazilians tolerate high levels of inequality in salaries across occupa- tions. The ISSP questionnaire asked respondents what the income differ- ences should be for a list of occupations with different levels of status and prestige.Their responses indicate the level of income dispersion each person is willing to accept as just (table 3.1). The results show large differences in Brazil, Chile, and the Russian Federation, countries that also have high Gini coefficients. Brazilians rank occupations that reflect education and credentials--doctor, lawyer, and judge--higher relative to the baseline than respondents in any other countries. This phenomenon can be explained by the fact that Brazilians Table 3.1. Level of Income Dispersion Considered"Just"in Selected Countries Occupation Brazil Chile Hungary Portugal Russian Federation Spain Sweden United States Doctor in general practice 5.93 4.37 2.56 3.42 2.35 1.81 1.93 4.66 Chair of large corporation 11.89 15.42 7.58 5.97 13.16 2.54 2.96 6.48 Lawyer 6.34 5.28 4.13 4.10 4.50 2.01 2.29 4.14 Cabinet minister 8.86 7.32 4.96 5.31 9.34 2.56 2.32 3.52 Owner-manager of factory 8.29 23.70 9.61 4.35 9.27 2.54 3.14 5.05 Judge 10.03 7.41 5.18 5.74 8.71 2.57 2.63 4.66 Shop assistant 1.00 1.98 1.14 1.14 1.29 1.02 1.02 1.03 Skilled worker in factory 1.88 1.00 1.34 1.40 2.28 1.19 1.18 1.48 Unskilled worker in factory 1.34 2.80 1.00 1.00 1.00 1.00 1.00 1.00 Your occupation 1.48 1.17 1.57 1.49 2.28 1.23 1.31 1.83 Source: Authors. Note: The survey posed the question"What do you think people in these jobs ought to be paid--how much do you think they should earn before taxes, regardless of what they actually get?"The occupation with the lowest average across countries was used as the baseline (and therefore takes the value 1). The other values indicate the factor by which earnings in each occu- pation exceed those in the baseline occupation. 85 86 Scalon and Cano identify education as the main factor responsible for income differentia- tion: 84 percent of respondents agree that people study in order to enter occupations that can provide much higher incomes than "ordinary" jobs. Brazilians value credentials and diplomas as signs of distinction, a norm that may reflect the country's colonial past. Until the 19th century, Brazil was a society of bachelors: the sons of sugar plantation owners, and later coffee producers, studied in Europe and returned as engineers, medical doctors, and lawyers to rule the country. Brazilians' deference toward credentials and diplomas may be a legacy of this era. In order to understand how Brazilians deal with the very high levels of concentration of wealth in their country, it is necessary to under- stand what determines an individual's assessment of income inequality. One hypothesis is that an individual's own position in the social pyra- mid helps explain his or her tolerance of inequality, with the rich accepting higher income disparities than the poor. The literature also stresses the importance of other factors, including the expectation of future upward mobility or the experience of past mobility. Another hypothesis is therefore that people who perceive themselves as having an upward trajectory, past or future, tend to legitimize the system that has allowed or will allow them to move ahead and to tend to accept higher income disparities. Hirschman (1973) coined the term tunnel effect to describe this phe- nomenon, which sheds light on why rising inequality may be tolerated in rapidly developing countries (see Suhrcke 2001) or among poor people who see themselves as able to experience social ascension. Certainly, individuals will use both their own experience of mobility and the general mobility pattern in society when evaluating opportunities for status attainment. The legitimacy of the stratification criteria is also linked to the acceptance of income differences in society. A society in which income inequality is the result of an individual's own effort or talent will be seen as fair and wealth differences within it as legitimate. As Suhrcke (2001) argues, "To the extent that the `moral' entitlement to one's income is stronger if his or her income was generated by factors the individual is entirely responsible for, the importance of personal hard work may justify income inequality." Conversely, when income dispar- ity is seen as a result of factors outside the individual's control, it will be perceived as unjust, and inequality will be less tolerated. This reflects the tension between meritocratic and nonmeritocratic (or inheritance) criteria for income rewards. Legitimization and Acceptance: How Brazilians Survive Inequalities 87 The ISSP data were used to test these hypotheses.The first model used was a multiple linear regression formulated to test the hypotheses regard- ing tolerance of income inequality. The dependent variable was extracted from the salaries people believe different occupations should receive.That is, respondents were given a list of occupations and asked what salary peo- ple in that occupation ought to be paid. The dependent variable for the equation was operationalized as the standard deviation of the salaries accorded by the respondent to different occupations or group of occupa- tions.5 This variable is labeled "limits of income inequality." The mean response to this question was R$4,288, the median R$2,580, and the standard deviation R$6,214. The list of independent variables included unemployment, gender, age, race, education, per capita house- hold income, rural/urban area of residence, observed intergenerational mobility, observed intragenerational mobility, perceived intergenerational mobility, perceived intragenerational mobility, perceived future mobility, and legitimacy of stratification. The three perceived mobility variables were measured by the perception respondents have of their own experi- ence of mobility in relation to their parents', their situation 10 years ago, and what they expect over the next 10 years. Legitimacy of stratification was measured by the importance given to acquired or ascribed individual characteristics in order to achieve a higher social position. This is a key variable, because it represents the tension between factors such as intelli- gence, skills, and effort ("meritocratic values") and factors, such as origin and networks, that reflect individuals' place in the social structure. Independent variables have very limited power to explain tolerance toward income inequality. The only significant variable is "real" intergen- erational mobility. This factor, and not the perception of mobility, affects tolerance of inequality. This could be interpreted in the predicted sense, that people who experience upward mobility will be more likely to tolerate greater inequality of income within a system they have suppos- edly benefited from. It may reflect sheer self-interest and the reluctance to accept income redistribution if one is doing well, but it may also reflect the belief that Brazil is a permeable and fluid society. If this is the case, one would also expect an indirect effect through the perceived legitimacy of stratification criteria. Thus, people with higher mobility should be expected to view the stratification criteria as more legitimate and those who accord more legitimacy to the system should be expected to accept greater inequalities. In fact, this correlation is not confirmed by the model: the legitimacy of stratification criteria does not appear to be significant. Moreover, social class, perception of prospects for individual 88 Scalon and Cano mobility, or even opinion regarding the legitimacy of stratification criteria have no perceptible impact on the tolerance of inequality. Perceptions of the legitimacy of the social structure. Are the factors that are perceived as determining upward social mobility viewed as fair? The perception that they are fair could confer legitimacy on income inequal- ity, no matter how wide it may be. To analyze this dimension, a second multiple linear regression model was developed with "legitimacy of stratification" as the dependent variable. This variable is an index of the belief in the prevalence of meritocracy in Brazil. The independent variables were the same as those used in the first model. This model explains only 4 percent of the variance in tolerance of inequality. However, two variables are significant--race and expectation of future mobility--although the effect of the second is weak. Being non- white decreases the perception of legitimacy of stratification criteria. The perspective of future mobility has an inverse relation with the meritocratic view--that is, as expected, people with higher expectations of future mobility tend to believe more in the fairness of stratification criteria. It is interesting to compare these results with those of the previous model. In this model, legitimacy of stratification is explained by the expectation of upward mobility (that is, by the perception that social ascension is possible), while the tolerance of inequality is built on the individual's real experience of mobility in relation to his or her parents. Nevertheless, based on the low R-squared and the reduced significance of explanatory variables, sociodemographic variables and the percep- tion of mobility appear to have very little impact on the perceived legitimacy of stratification. Perceptions of mobility. Brazil experienced high rates of structural mobility in the 1970s. Even after the economic crisis of the 1980s, struc- tural mobility remained a feature of Brazilian society, with the transfer of the labor force from rural occupations to the lower strata of the urban occupational structure. This phenomenon probably affected people's perception of mobility. The dependent variable in this third model is the perception of indi- vidual mobility in the previous 10 years--in short, perceived intragener- ational mobility.This variable reveals how individuals evaluate their own trajectories.This model explains only about 3 percent of the total variance, although the influence of mobility is observable. The variables that have a significant effect on perceptions of an individual's trajectory are the Legitimization and Acceptance: How Brazilians Survive Inequalities 89 perception of an individual's own past mobility compared with that of his or her father (perception of intergenerational mobility) and the per- ception of the individual's own future mobility. Actual mobility--inter- generational or intragenerational--does not have a significant impact on one's perception of being higher or lower in the social structure than 10 years earlier. What accounts for the model results? The most important characteristic of Brazilian society that emerges from the three models is the similarity in the perception of inequality across social groups. How can this relative homogeneity of opinion and attitudes toward inequality be explained, given the extreme inequality in Brazil? One explanation could be the historical process of social exclusion and a concomitant process of internalization and acceptance of inequality. Modern institutions, such as the state and the market, were imported from Europe to a setting with completely different social conditions. In 1808 the Portuguese crown moved to Brazil, and the ports were opened to trade. Imported modernization arrived in a rural society based on the intensive exploitation of slave labor. As a result, the process of modern- ization in Brazil was one of exclusion and segregation. At the same time, however, the population completely internalized the values and ethos that constitute the core of modern institutions--that is, belief in the meritocratic order and the logic of the market, in which differential performance and individual initiative are rewarded.6 The impersonal nature of these values and ethics made it easy to internalize them--even when they were not implemented in practice. The lack of significant differences in the perceptions of different social groups in terms of their tolerance of inequality and the legitimization of stratification may reflect the relative homogeneity in the adoption of such values and ethics in Brazil. This does not mean that people in general believe that Brazil is a fair society. Although most Brazilians, across social groups, believe that skill and intelligence are rewarded, only a minority feel that personal effort is rewarded (figure 3.1). A sizable minority of people (about 39 percent) feel that it is impor- tant to come from a wealthy family in order to prosper in life (figure 3.2). Having the right connections is perceived as even more important, with more than half of respondents considering it "essential" or "very important." Interestingly, there is no negative correlation between the perceived importance of origin and connections on the one hand and skill and effort on the other.7 90 Scalon and Cano Figure 3.1. Perceptions that Socioeconomic Rewards Are Reaped from Effort and Intelligence/Skills 35 33.7 29.5 30 26.5 25 21.5 19.5 20 19.2 18.3 cent 14.3 per15 10.3 10 7.2 5 0 strongly agree agree neither agree disagree strongly disagree nor disagree effort intelligence/skills Source: Authors. Figure 3.2. Perceptions that Family and Social Networks Are Important for Getting Ahead 35 32 30 26.8 25 23.1 21.7 20.3 20 18.9 17.3 cent 15.9 per 15 12.5 11.5 10 5 0 essential very important fairly important not very not important important at all come from a wealthy family knowing the right people Source: Authors. Brazilians believe strongly in the importance of intelligence and skills for upward social mobility. Comparative data for eight countries indi- cate that Brazilians have the strongest belief in these characteristics and that they attach less importance to origin and networks than people in other countries. Legitimization and Acceptance: How Brazilians Survive Inequalities 91 Responsibility for Diminishing Inequality The vast majority of Brazilians believe that the state bears primary responsibility for reducing inequality: 62 percent of respondents indicate that the government is responsible, and another 12 percent identify Parliament as responsible. Only 4 percent of respondents believe that "people like me" can do anything to reduce inequality. Nevertheless, two- thirds of respondents agreed that the persistence of inequality reflects the fact that people do not join forces to fight it.This result opens a space for reflections on participation and civic culture. Brazilians seem to believe that collective action has an important role to play in reducing inequal- ities, but they do not see themselves, individually, as relevant actors in the process. A logistic model was developed using as the dependent variable whether the respondent chose the government as the main actor in dimin- ishing inequality. The independent variables were unemployment, gender, age, race, education, rural/urban area of residence, unionism, observed intergenerational mobility, observed intragenerational mobility, perceived intergenerational mobility, perceived intragenerational mobility, and perceived future mobility. No independent variable yielded a significant result, suggesting that there are no clear fractures across social groups in the perceptions of the government's central role in reducing inequality. Another logistic regression was applied to the dichotomous variable of whether "people like me" was among the top three entities identified as responsible for diminishing inequality. The independent variables were the same as in the previous regression.Three variables--years of schooling, race, and perception of intragenerational mobility--revealed a significant impact. More-educated people, nonwhites, and people who perceive that they have experienced upward mobility in the past 10 years believe that they are more responsible for diminishing inequality than people with less education, whites, and those who have not experienced upward mobil- ity. The effect of education and mobility could be explained by the idea that those in more favorable positions feel better able to intervene in social realities. The influence of race could be credited to a more political attitude, perhaps even reflecting discussions of affirmative action that are dominating the debate on racial relations in Brazil today. Preferences for Policies for Reducing Inequality Respondents were given a list of six possible policies for reducing inequality and asked to pick the three most important ones. Improving public services was the most popular choice, reflecting Brazilians' longing 92 Scalon and Cano for an efficient welfare state. The second-most popular policy was land reform, which refers to a program of land redistribution that govern- ments have undertaken in recent years. The third choice was workers' participation in company benefits. Do these views differ across social groups? To find out, contingency tables were computed and significance testing (chi-squared and phi) was conducted (table 3.2). Gender has a significant impact, with men preferring land reform and women preferring better public services. Area of residence is also signif- icant, with rural inhabitants preferring land reform and urban inhabi- tants opting for better public services. The impact of schooling seems to be explained by the fact that more-educated people believe that better public services are the key. All groups seem to indicate preferences for the policies that are closest to their interests: peasants want land and women demand services, because they are the main users. Perceptions of Group Conflicts To analyze the perception of conflicts between social groups, the survey asked respondents, "Are there differences, or even conflicts, between different social groups? In your opinion, how strong are the conflicts between the following social groups?" The groups examined were rich and poor, entrepreneurs and workers, nonwhites and whites, and young and old. Table 3.2. Frequency Distribution of Most Important Policy Options for Reducing Inequality Cumulative Indicator Frequency Percent Valid percent percent Valid Land reform 540 27.0 28.6 28.6 Improving public services 755 37.8 40.0 68.6 Control population growth 100 5.0 5.3 73.9 Privatize public enterprises 34 1.7 1.8 75.7 Increase taxes on the wealthy 134 6.7 7.1 82.8 Workers'participation in benefits 306 15.3 16.2 99.0 Others 18 0.9 1.0 100.0 Subtotal 1,887 94.4 100.0 Missing Didn't know 91 4.6 Didn't respond 22 1.1 Subtotal 113 5.7 Total 2,000 100.0 Source: Authors. Legitimization and Acceptance: How Brazilians Survive Inequalities 93 Class conflicts are perceived as the most important, with 60.7 percent agreeing that such conflicts are strong or very strong (figure 3.3). A slightly lower proportion (58.6 percent) held the same view about ten- sions between rich and poor. Racial and, particularly, generational con- flicts are perceived as less acute. In fact, more than a third of respondents deny the existence of generational conflict. It is possible that conflicts are perceived or evaluated differently by the social groups that actually or potentially participate in them. This aspect was explored through several significance tests using income, race, or age as independent variables. Conflict between poor and rich did not vary significantly with per capita family income; the perception of the intensity of class conflict does not seem to change across income groups. In contrast, nonwhites are much more likely than whites to strongly agree that racial conflicts exist in Brazil (figure 3.4). This significant result could reflect the influence of other variables, such as income or education, which are correlated with race.To check this, mul- tivariate analysis was conducted, in the form of a logistic regression (table 3.3). The dependent variable was whether or not the person strongly or very strongly perceived the presence of racial conflict. Independent vari- ables included race, schooling, and area of residence (rural/urban). The only significant variables (at the p = 0.05 level) were the racial ones: blacks are more likely to perceive conflict than other groups, and mulattoes are more likely to perceive conflict than whites. Older people tend to perceive generational conflict significantly more than young people. Figure 3.3. People's Perceptions of the Severity of Conflicts betweenVarious Social Groups young and older 11.1 22.1 26.9 34.3 blacks and whites 20.2 29.7 22.7 21.8 management and workers 28.5 32.2 20.8 13.6 poor and rich 26.6 32.0 20.0 15.6 0 20 40 60 80 100 percent very severe severe not very severe there are no conflicts Source: Authors. 94 Scalon and Cano Figure 3.4. Perceptions of the Degree of Racial Conflict, byWhites and Blacks 35 33.4 29.8 30 25.8 24.5 25 23.1 22.0 21.5 19.9 20 cent per 15 10 5 0 very strong strong not very strong there are no conflicts whites blacks Source: Authors. Table 3.3. Logistic Regression Coefficients for Determinants of Racial Conflict, byVariable in the Equation Variable B S.E. Wald df Sig. Exp(B) Race1 (black) 0.402 0.151 7.086 1 0.008 1.495 Race2 (mulatto) 0.221 0.106 4.349 1 0.037 1.248 ANESTUa ­0.012 0.013 0.866 1 0.352 0.988 Areab 0.227 0.127 3.217 1 0.073 1.255 Constant ­0.146 0.137 1.143 1 0.285 0.864 Source: Authors. Note: For Race1: 1 = black; 0 = all other groups. For Race2: 1= mulatto, 0 = all other groups. a. Variable for schooling. b. Variable for area of residence. Conclusion Across social groups, Brazilians perceive the legitimacy of the social structure in similar ways, perhaps because all groups have internalized and accepted the same social values regarding inequality. Even percep- tions of class conflict are similar across social strata. This acceptance may be what allows Brazilians to live with extreme levels of inequality and what prevents social exclusion from being challenged. It may also affect the tendency of Brazilians to feel that it is not up them, but rather to the state, to reduce inequality. If one accepts the idea that Brazil went through a process of selective modernization that resulted in the internalization and acceptance of Legitimization and Acceptance: How Brazilians Survive Inequalities 95 inequalities, the promotion of greater equality can be achieve only through policies of inclusion for all social groups. Promoting equal chances in the competition for space in the social structure would mean realizing and practicing the values that are already entrenched in principle in Brazil. For many people, it is difficult to understand how it is possible that in a highly unequal country, a perception also exists­­even among those who are at the bottom and thus most affected by inequality­­that people must be unequally rewarded for their unequal assets and talents. But one must also ask how a country could accept extraordinary gaps in equality for decades without adopting an ideology that permits and legitimizes this inequality. This seems perverse, and it is: unequal societies tend to be more tolerant toward inequality, which in turns perpetuates that inequal- ity. Acceptance of inequality should not be interpreted to mean that Brazilians "like" inequality or think their society is fair; they do not. There is, however, a culture of acceptance of it (Wilson 1992). A shared political culture that is complicit with the acceptance of inequality makes it hard to break patterns in a society. The problem of inequality will be solved only if social policies are implemented that increase self-confidence--and as a consequence, the capacity for associ- ation and mobilization. It is important that Brazilians believe they can change their country's unfair structures. Unfortunately, the data show that they do not share this belief. Inequality will not be reduced before Brazilians are able to see it as something odd, looking at it with foreigners' eyes--that is, the eyes of unfamiliarity or strangeness. Notes 1. For a history of economic policy in Brazil, see Abreu (1990). 2. This idea is very common in Brazilian sociology, at least since Fernandes (1977). Bacha (1978) and Bacha and Klein (1989) present a more recent version of this idea. 3. The ISSP brings together preexisting social science projects and coordinates research goals, thereby adding a cross-national perspective to national studies. About 40 countries participate in the program. 4. Two variables--employment status and race--revealed a slight significance in the chi-squared test. The results were in an unexpected direction, how- ever, with whites and the employed more likely to agree strongly that inequality is too great. Nevertheless, the high proportion (96 percent) of people who perceive that inequality exists and the low significance in all tests used to verify group differences suggest that differences among social groups are not noteworthy. 96 Scalon and Cano 5. Respondents were asked about the ideal salary for 10 different occupations. However, when the index (standard deviation) was calculated over all 10 values, the number of missing values was very high. In order to lose fewer cases, categories of occupations were created and an average salary was calcu- lated for each category. Occupations were lumped into the same category when their average ideal salary was similar and they loaded on the same fac- tor in a principal components analysis. 6. For a detailed analysis of this theory, see Souza (2000). 7. The correlation coefficients between these two groups of variables are not significant. References Abreu, Marcelo, ed. 1990. A ordem do progresso: Cem anos de política econômica republicana, 1889­1989. Rio de Janeiro: Campus. Bacha, Edmar Lisboa. 1978. Política econômica e distribuição de renda. Rio de Janeiro: Paz e Terra. Bacha, Edmar Lisboa, and Herbert S. Klein. 1989. Social Change in Brazil 1945­1985: The Incomplete Transition. Albuquerque: University of New Mexico Press. Faria, Vilmar Evangelhista. 1986. "Mudanças na composição do emprego e na estrutura das ocupações." In A transição incomplete, vol. 1, ed. Edmar Lisboa Bacha and Herbert S. Klein, 72­112. Rio de Janeiro: Paz e Terra. Fernandes, Florestan. 1977. A revolucão burguesa no Brazil. Ensaio de interpre- tacão sociológica. Rio de Janeiro: Zahar Editores. Hirschman, Albert. 1973. "The Changing Tolerance for Income Inequality in the Course of Economic Development," with a mathematical appendix by Michael Rothschild. Quarterly Journal of Economics 87 (4): 544­66. IBGE (Instituto Brasileiro de Geografia e Estatística). 1999. Pesquisa industrial mensal de emprego e salário (Junho). Rio de Janeiro. Shanin, Theodor. 1978. "The Third Stage: Marxist Historiography and the Origins of Our Time." Journal of Contemporary Asia 6 (3). Souza, Jessé. 2000. A modernização seletiva: Uma reinterpretação do dilema brasileiro. Brasília: Edunb. Suhrcke, M. 2001. "Preferences for Inequality: East vs. West." Innocenti Working Paper 89, UNICEF, Florence. Wilson, R. W. 1992. Compliance Ideologies: Rethinking Political Culture. Cambridge, UK: Cambridge University Press. World Bank. 2003. World Development Indicators 2003. Washington, DC: World Bank. C H A P T E R 4 Conclusions and Recommendations Estanislao Gacitúa Marió The data and analyses presented in this volume summarize the most recent scholarly work on poverty exclusion, in Brazil and elsewhere. The findings are consistent with those of other research on poverty and inequality in Brazil. Comparisons between Brazil and other economies at similar levels of per capita income reveal that the level of poverty is considerably above the average and that the main obstacle to overcoming poverty is not the insufficiency of resources but the extremely unequal distribution of resources. Economic growth and macroeconomic reforms have significantly contributed to the stabilization of the economy and the alleviation of poverty. Nevertheless, there is an increasing consensus among Brazilian policy makers, scholars, legislators, and civil society organizations that growth and successful macroeconomic policies have not resolved the problems of inequality and social exclusion that generate and sustain poverty. Inequality and social exclusion persist as the main challenges facing Brazilian society today. The research reported in this volume examines specific processes of social exclusion and barriers to mobility that have a direct impact on moving out of poverty for certain social groups. It shows that certain social groups are most vulnerable to social exclusion processes based on their place of residence, race, gender, and age. 97 98 Gacitúa Marió Poverty in Brazil has decreased over time, but poverty reduction has not been homogeneous across social groups. Only certain social groups-- whites, men, people with more education, people employed in the indus- trial sector--have enjoyed sustained average income gains. Of course, some individuals within each of these groups experience mobility while others do not. However, to understand the processes of exclusion and the causes of sustained inequality and poverty, the focus cannot be on individuals but on the social group to which an individual belongs. While there are chances for individual mobility in Brazil, certain social groups systematically encounter barriers to mobility. The analysis identifies some of the factors contributing to the lack of mobility and the increased vulnerability of the poor, including the trend toward the informalization of labor markets. Certain policy instruments (such as pensions) have contributed to reducing poverty. But two main processes of social exclusion continue to prevent mobility by the extreme poor: procedural barriers to access and politico-institutional and sociocultural barriers to inclusion and mobility, including the process of (ideological and psychological) legitimization of inequality and the resulting roles for restitution assigned to the state and civil society. There is an emerging consensus that the fight against poverty in Brazil cannot be dissociated from the fight against inequality; indeed, income inequality is a main cause of poverty in Brazil. Redistributive policies are necessary, not only to improve economic conditions for the poor but also to increase the political and social stability of the country as a whole. Political and civil rights are insufficient when large social inequalities exist, in effect keeping those rights from being exercised. Promoting sustainable development and poverty reduction in Brazil in order to integrate those groups left out requires looking closely at the social dimensions of development. It requires focusing on developing market, political, social, and cultural institutions that will sustain progress toward a more inclusive, accountable, and cohesive society. In addition to overall redistributive policies, the focus should be on specific social groups--marginal children and youth, poor nonwhites, and people in rural areas (primarily the landless)--and processes that hinder equal chances of participation in the economy, polity, and society. The recommendations emerging from this research can be grouped into three categories: improving mechanisms to facilitate participation in the labor market and increase access to productive assets; enhancing access to social security; and improving human and social capital through education and social organization. This chapter summarizes the report's main conclusions in each of these areas. Conclusions and Recommendations 99 Improving Access to Labor Markets and Assets Labor markets are one of the main channels for economic inclusion in modern societies. Policy recommendations will likely need to address issues related to institutional factors that limit or condition participation in the labor market. This report finds that structural factors such as race-based discrimination are at the root of labor market exclusion and that informal arrangements are more prevalent among groups with less social mobility. In such cases, some combination of affirmative-action-type policies and mechanisms to minimize the risks and vulnerability associated with living and working in the informal sector may be warranted. Making the Labor Laws More Flexible and Increasing Job Opportunities for Youth The labor market in Brazil has changed in important ways in the past decade. Recent changes have led to increasing demands for more flexi- ble labor laws. There is no consensus among specialists on the policies needed to deal with the trends in the labor market or to diminish their perverse impacts on the most vulnerable sectors of the workforce. Many proposals, however, focus on the legal constraints to hiring and dismissing workers, as well as on the legal incentives driving the informalization of labor markets. Another set of proposals focuses on the need to provide targeted public services to the unemployed and the poorest people in the informal sector and to improve the qualifications and skills of the labor force. The review of the literature shows that the sectors most affected by recent changes are people with few skills, few years of formal education, or no work experience; people who are not protected by labor unions and labor contracts; and nonwhite women. Labor conditions in the formal sector have deteriorated. But the greater threat to the poor is the growth of the informal sector, which they perceive as increasing their social vulnerability. Although unem- ployment is not widespread, it is nonetheless considered to be one of the main problems affecting poor people's daily lives, as well as the most important cause of poverty. The rise in both underemployment and the informal sector points to growing vulnerability, and uncertainty, precari- ousness of employment, and dualization of urban labor markets. Businesspeople have long argued that the high labor costs in Brazil, together with the poor quality of the services and benefits provided to workers in return for their contributions, provide incentives to hire workers illegally. Some have suggested that workers themselves often seek informal employment, because signing a work card requires them 100 Gacitúa Marió to contribute to the social security system, which workers do not perceive as advantageous (Amadeo, Gill, and Neri 2002). In fact, the motivation for participating in the informal sector is different at different points on the earnings distribution; for the most vulnerable sectors, it does not constitute a choice (Tannuri-Pianto and Pianto 2002). The trend toward the informalization of labor markets has been observed in rural areas as well as urban regions. In the northeast, the share of salaried workers in agriculture fell from 41 percent in 1981 to 32 percent in 1997; during the same period, the percentage of unpaid fam- ily workers rose from 22 percent to 30 percent. Throughout the 1980s and 1990s, only 28 percent of the agricultural labor force was engaged in formal employment and earning a regular wage (World Bank 2001b). The demands for greater flexibility in labor markets led the govern- ment to propose a series of changes in the Consolidated Labor Code. These changes--regarding working hours, hiring rules, and earnings in the private sector--were approved by the National Congress in 2001. The justifications presented for the changes were based on the positive impacts that greater flexibility measures would have on the creation of formal sector jobs and, as a consequence, upward social mobility. Most actors agree that comprehensive and coherent reform of the labor codes is necessary to provide a better environment for the creation of more and better jobs, but the goals and the proposals put forward are very different from one another. Private actors divide themselves roughly between those who wish to further democratize the labor relations system but maintain the rigidity of the labor law and those who empha- size the need to lower labor costs by reducing payroll contributions and making labor legislation more flexible. Workers with formal contracts have a lower probability of being poor. The executive and legislative authorities will have to address the incen- tives to hire workers illegally, as well as the demands of further democra- tizing labor relations and the fear of labor unions that the deregulation of rights will lead to more precarious forms of work, and, ultimately, more social exclusion. Other types of changes--such as modifications in the rules regarding union finances and organization; minimum wage­setting procedures; individual benefits, such as vacation and maternity leave; and firing costs for unjustified dismissals--require constitutional changes. They are therefore much more difficult to implement. An issue on which there is considerable consensus, and thus greater probability that policies may be successful, is the need to provide greater incentives for employers to hire young people. One of the great obstacles Conclusions and Recommendations 101 to insertion of youth into the labor market is the requirement of previous experience, which creates a vicious cycle of lack of opportunity, unem- ployment, and social exclusion. Several recent programs sponsored by the government and civil society organizations have acknowledged the need to provide incentives for employers to give youth their first jobs. It is still too early to evaluate these initiatives. Increasing Access to Land Access to assets is a main avenue for increasing social inclusion and dimin- ishing inequality. The data presented in this report confirm evidence from other research that living in a rural area (particularly in the north- east) increases the probability of being poor.1 Most of the poor live in farm households located in remote, sparsely populated, low-productivity areas, for whom income from farming and agricultural labor represents about 70 percent of total household income. Previous research has shown that the most vulnerable groups include the landless and farmers with less than 10 hectares, who are often unable to support their families or generate the surpluses that would enable them to move out of poverty (World Bank 2001b). In the case of landless rural dwellers, the issue is compounded by the lack of alter- native off-farm income-generating opportunities. Promoting social inclusion (increased mobility and poverty alleviation) within this sector requires facilitating labor market insertion and improving access to land and public services. People trapped in poverty face tremendous obsta- cles to social mobility, because they cannot benefit from opportunities in commercial agriculture or technological innovation, and they lack access to adequate public services.2 For Brazil's rural poor, the land reform program is probably the most important public policy being implemented. Land reform has improved the living conditions of the rural poor (Heredia and others 2002). It is essential to expand and consolidate land redistribution through the cur- rent scheme or other mechanisms that allow the rural landless to be set- tled and small farmers to either buy or rent land to increase the size of their production units to make them viable. Moving landless people to agricultural settlements is not enough, how- ever: many of these people face obstacles encountered by the rural poor as a whole.3 Access to education is usually limited to the primary level, and only a fifth of settlements have their own health centers. Access to credit has improved, but there is a pressing problem of delays in the release of resources (Heredia and others 2002). Moreover, in almost half 102 Gacitúa Marió of all settlements, productivity is lower than on the average for all farms of the region (Heredia and others 2002). Enhancing Access to Social Security Expenditures on social security have primarily benefited the wealthiest Brazilians, with only about 8 percent of total spending on pensions and benefits to public servants reaching the poorest income quintile in 1997 (Ministério da Previdência e Assistência Social do Brasil 2000). Despite this poor targeting, the expansion of spending on pensions has had a sig- nificant effect on poverty alleviation, particularly among the most vul- nerable. For this reason, the government should consider expanding this program, making sure that it reaches the poor. Expansion of social security to the rural poor represents one of the most important public programs in Brazil, providing a safety net for the most vulnerable segments of the rural poor. The benefits paid to rural households as income support to retired workers, spouses and children of deceased workers, and the temporarily injured and permanently disabled steadily increased during the 1990s. The rural pensions program constitutes an increasing share of household income among the rural poor. It has reduced the incidence of poverty. In contrast to other safety net policies, such as unemployment insurance, which covers only formal sector workers, these benefits are available to any worker who reaches the age of 70. Brazil's new minimum income programs are believed to have a greater impact on social mobility than programs that distribute in-kind benefits, such as food distribution programs.4 One such case is the Bolsa Escola program. A preliminary assessment of Bolsa Escola (World Bank 2001a) concludes that the program is well targeted and has had a positive impact on educational outcomes, as well as poverty reduction and social mobility (see also Camargo and Ferreira 2001). However, coverage remains limited and varies widely across localities. Improving Human and Social Capital In 2001, 12.4 percent of Brazil's population had never attended school, most of them poor. Among children six years old and younger, 48.8 percent of the richest quintile and just 26.6 percent of the poorest quintile were in school. Among people who completed secondary school (11 years of education), only 11.9 percent come from families that Conclusions and Recommendations 103 earned less than half of the minimum wage per capita; 74.6 percent come from families that earned more than twice the minimum wage per capita (IBGE 2002b). The data in this volume confirm the relevance of education to reducing poverty. Public opinion surveys demonstrate that Brazilians view education as the most important tool with which to fight social exclusion and inequal- ity. Social exclusion cannot be fought through education alone, however. Between 1982 and 1998, the earnings of workers who had completed primary school fell 26 percent, while those with some lower-secondary education fell 35 percent. Returns to completed secondary education did not change significantly, while returns to tertiary education increased 24 percent. Two main recommendations for improving human and social capital emerge from this report.5 First, there is a need to continue expanding cov- erage of basic and secondary education, but it is also essential to strengthen vocational and tertiary education, in order to meet the demands of the job market for ever more qualified workers. Second, the quality of the educa- tion the poor receive needs to be improved. Better-quality education would increase economic opportunities for the poor, inspire more-active citizenship, and promote greater social inclusion. Increasing Opportunities for Poor Young People The findings of this study indicate that marginal youth face severe vulner- ability and exclusion in Brazil. Access to education, the job market, and social services is weak, and politico-institutional participation is low. On average, young people have higher educational attainment than their pred- ecessors did, but increased schooling has not translated into better jobs, higher salaries, or greater participation in society. Labor market dynamics have set a cap on the salaries people without a tertiary education can earn. After investing in education, most poor youth see that their opportunities are not much different from--or perhaps even worse than--those of their parents. At the same time, there is a lack of social recognition of the roles and rights of young people, reinforced by the predominance of stereotypes and negative images of youth in the media. Developing entrepreneurial skills. These findings point to the need for developing the entrepreneurial capacity and skills of young people by facilitating access to productive resources and assets and developing man- agerial skills among poor youth. More also needs to be done to reduce vulnerability to the most important social risks facing youth--dropping 104 Gacitúa Marió out of school and becoming pregnant. Civic participation (effective representation) of poor youth in policy decision making and civil society organizations also needs to be increased. Programs such as Primeiro Emprego--which facilitates the labor market entry of young people constrained by lack of opportunities, experience, or skills--should be strengthened. Incentives could also be provided to the private sector to hire young people and develop their skills. Financial support is needed to facilitate access to resources for income-generating projects by young people, through microcredit schemes or grants for productive enterprises, and to provide training to increase the entrepreneurial and managerial capacity of poor youth. The Bolsa Escola program needs to be strengthened and expanded, to provide further incentives for families to keep their children in school, particularly in rural areas. Dropouts need to be reintegrated into the formal education system by providing them with alternative skills or vocational training. Preventing teenage pregnancy. Other reforms could focus on improving health insurance options, facilitating youth access to reproductive health services, and developing educational programs that provide professional counseling, sex education, and steady access to contraceptives, particularly for teenage girls. The results of this study indicate that the dependency ratio is a key factor contributing to poverty. Preventing teenage pregnancy is thus critical. Policy makers need to recognize, however, that pregnancy among poor teenagers is not caused only by a lack of information about or access to contraceptives; many teenage girls seek to become pregnant in order to assert their position in society. Therefore, it is important to combine access to health services (insurance and reproductive health attention) with programs that encourage youth, particularly teenage girls, to search for participation and representation within different social spheres. Including marginal youth. To increase the social inclusion of marginal youth, it is important to develop social communication and outreach campaigns involving the media to counter negative social representa- tions of them as violent, lazy, and irresponsible--images that provide the basis for the social moratorium, or lack of social engagement, in which most young people live.Youth programs and projects should develop the social, cultural, and symbolic capital of individuals, in order to prepare them to function as competent and integrated members of the society. Conclusions and Recommendations 105 Simultaneously, special strategies should be developed for the poorest and most excluded youth. These programs should acknowledge the virtues and strengths of the groups and focus on developing the abilities and talents of young people. Reducing Racial Inequality and Discrimination The greater acknowledgment of the existence and importance of gender- and race-based inequality and discrimination by state officials and Brazilian society at large has led to controversies over how to deal with these problems.Without specific policies targeted at diminishing gender- and race-based inequality, improvements occur too slowly, if at all. For this reason, many people support affirmative action proposals. The debate over affirmative action is healthy and necessary.6 Past poli- cies, such as the establishment of gender-based quotas for candidates in elections, have had limited results. Quotas based on race are difficult to design and implement. This does not mean that affirmative action poli- cies cannot be implemented; it means that the importance of policies designed to address other issues may not be the best way forward, as controversies over the use of quotas at the university in Rio de Janeiro have proven. The controversy over quotas probably reflects the attitude of Brazilian elites in the face of distributive and compensatory policies. Less controversial policies can also be implemented, targeted at the most vulnerable groups: black women, black youth, pregnant women (especially those living in the northeast and north), and working girls, most of whom live in rural areas. Changes in the labor law that widen the scope of the labor rights of domestic workers, most of whom are young black women, as well as an awareness campaign to promote these rights among employers and employees could be helpful. Other important changes are already under way, such as the modifi- cation of public school curricula to include more African history. Civil society initiatives, such as programs that help black students gain entry to universities, have been adopted by the Ministry of Education as federal policy. Strengthening Participation and Citizenship This report identifies the mechanisms of social exclusion that are triggered at two interrelated levels: (a) politico-institutional mechanisms based on differential access and control of resources and (b) sociocultural mecha- nisms that foster inequality and discrimination based on race, gender, place of residence, place of origin, and age.As the "Voices of the Poor" report 106 Gacitúa Marió reveals (World Bank 2000), many poor people agree that there have been great improvements in their well-being and in the provision of public services. However, those interviewed also report that social exclusion takes place through a plethora of mechanisms. Among the obvious politico-institutional constraints to social inclusion are those related to participation mechanisms that have failed to generate ownership and social responsibility and discrimination practices that have prevented the most vulnerable groups from accessing resources and exer- cising their rights. Rich and poor alike view the state as the only agent responsible for reducing poverty and inequality. But Brazilians view state organisms and public officials with great distrust and skepticism and consider them incapable of fulfilling this important role. Exclusionary processes are directly related to lack of political repre- sentation and voice. The most vulnerable groups--particularly the rural poor, poor women, people of African descent, indigenous populations, and poor and marginal youth--lack appropriate channels of representa- tion and participation. In the past, labor unions, which represent formal sector workers, represented the urban poor.7 Current legislation, which still bestows monopoly status on local unions for collecting dues and negotiating wages and benefits, does not reflect the needs of informal workers and the self-employed, who need proper representation. The fact that labor unions represent only the interests of formal sector workers may represent an obstacle to extending benefits, such as social security, to workers in the growing informal sector. Corporatist organizations are essential, but there is also a need to sup- port civil society organizations. These groups can serve a very important purpose by providing better information, equalizing representation, educating citizens, and providing a governance alternative that involves citizens in the management of public activities. Few policies or programs have sought to systematically elicit contin- uous participation and develop ownership. In order to foster and strengthen social capital, Brazil needs to establish new forms of association and noninstitutionalized forms of participation and control that are directly linked to the exercise of public authority, such as participatory budgeting. More channels of dialogue need to be opened and more partnerships between state and civil society created, lending more trans- parency to decision-making processes and giving poor communities a voice in these processes. These kinds of initiatives help increase efficiency, fight clientelism, dissociate poverty from powerlessness, and develop ownership. Developing ownership is particularly important, because it Conclusions and Recommendations 107 could help modify the predominant perception of the state as the only agent responsible for decreasing poverty and inequality. As long as Brazilians perceive that the state is the only institution that can reduce social exclusion and poverty, change will be slow. For this reason, a trans- formation in attitudes may be as important as policy changes in ensuring equal opportunities for all Brazilians. Notes 1. See World Bank (2003). On the impact of the agrarian reform, see Heredia and others (2002). 2. In this regard, the case of the quilombos (hidden communities of black slaves) is paradigmatic, because it combines racial and economic inequities.There are 724 quilombos in Brazil. The largest single concentration of such communities is in the northeastern state of Bahia, home to 259 quilombos. Only 33 of these 259 communities have received land titles as a result of the land reform process. Moreover, even people living in quilombos with land titles have not moved out of poverty (Heredia and others 2002; Silberling 2003). 3. In addition to the impact on social mobility, it is important to consider the sociopolitical dimension of this issue. The discontent of the landless and rural poor has been expressed by their mobilization and protests. The growing political force of the organizations representing these groups has to be recog- nized if conflicts are not to escalate. Trust in democratic, accountable institu- tions and the government will remain low and governance will be threatened if social and economic inclusion does not become a reality for the landless. 4. See, for example, Lavinas' (2000) evaluation of Prodea (Programa de Distribuição Emergencial de Alimentos), a federal compensatory program instituted in 1993 through which food is distributed to municipalities. The author argues that this program is inefficient and that these kinds of policies should be replaced by programs that transfer income directly to poor families. 5. These recommendations are fully consistent with those of Blom, Holm-Nielsen, and Verner (2001). 6. For a review of the debate over affirmative action in Brazil, see Jaccoud and Beghin (2002). 7. 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See racial reduction in poverty rates, 1981­2001, issues 57­68 age groups. See cohorts rural vs. urban populations, agricultural land, access to, 101­2, 107n3 55­57, 64 analysis of long-term income dynamics in subgroups selected, 38­39 Brazil, 35­79 years of schooling and poverty age group factors. See cohorts incidence. See under education conclusions drawn from, 69­70 Arbix, Glauco, x gender-related income differences, 39­40, 39t, 51t, 53t income convergence across cohorts over B time, 64­65, 65t income-ranking correlations over time, "Bel-India," 82 63­64t Birdsall, Nancy, xvii life-cycle earning patterns, 58­59, Blom, Andreas, 13 60­61f Bonelli, Regis, xiii, 35­79 129 130 Index Brazil, social exclusion and mobility in. See D social exclusion and mobility in Brazil development in Brazil, 7­8, 81­83 Brazilian National Household Survey dynamics of income, long-term. See analysis (Pesquisa Nacional por Amostra de of long-term income dynamics in Domicílios or PNAD), 6, 35, 36 Brazil E C East Asian financial crisis, 8 Cano, Ignacio, xiii, 81­96 economic growth and development in children and youth Brazil, 7­8, 81­83 access to labor market by young people, education 99­101 Brazilian valuation of credentials and education of. See education diplomas, 84­86 girls, pregnancy, and education, as exclusionary dynamic, 12­13 27n27, 104 girls, pregnancy, and education, importance of focusing on, 23 27n27, 104 marginalized youth, providing importance of access to, 15, 23, 102­5 opportunities for, 103­5 labor market demands and, 14 poverty, illiteracy, and mortality rates, on racial issues, 105 14­15 in rural areas, 11 Chile, perceptions and tolerance of years of schooling and poverty incidence inequality in, 84, 85t across cohorts, 41­54 citizenship and participation, strengthening, no schooling, 42, 43t 24, 105­7 one to three years (incomplete lower class conflict, perceptions of, 92­94, 93f primary education), 71t, 72­74, 72f cohorts (age groups) four years (lower primary school), estimating probability of being poor by, 42­44, 45t 66­68, 67t five to seven years (incomplete upper gender-related income distribution primary education), 73t, 74f across, 39­40, 39t eight years (upper primary school), income convergence across cohorts over 44, 46t time, 64­65, 65t nine to ten years (incomplete second- income differentials, 48­54, 49­51t ary education), 74­76, 75t, 76f income-ranking correlations over time, eleven years (secondary school), 44, 47t 63­64t twelve to fourteen years (incomplete life-cycle earning patterns, 58­59, tertiary education), 76­78, 77t, 78f 60­61f fifteen years or more (tertiary oldest cohorts, highest rates of poverty education), 48, 49t reduction among (1981­2001), estimating probability of being poor, 57­62 66­68, 67t poverty across, 40­41, 41t, 48, 50t by gender, 51t, 53t selection of, 37 income differentials, 48­54 size of household by, 51, 52t as key variable, 69­70 sizes of cohorts, 38­39, 38f life-cycle earnings, 58­59, 60­61f years of schooling and poverty by race, 55­58f incidence. See under education size of household, 51, 52t conflict between social groups, perceptions urban vs. rural populations, 59f of, 92­94, 93­94f, 94t elasticities of poverty to economic growth, "conservative modernization" in 8, 10t Brazil, 7­8 entrepreneurial skills, development of, Cunningham, Wendy, 15 103­4 Index 131 estimating probability of being poor, nature of Brazilian poverty and income 66­68, 67t inequality, 3­4 ethnic discrimination. See racial issues perceptions of, 20­21, 84­88 exclusion. See social exclusion and mobility poverty and inequality, relationship in Brazil between, xvii­xix preferences as to policies for reducing, F 91­92, 92t responsibility for diminishing inequality favelas, 12, 26n19 in Brazil, attribution of, 91 G social exclusion exacerbating. See social exclusion and mobility Gacitúa Marió, Estanislao, xiii, 1­34, in Brazil 97­108 informal labor market, 10, 14, 100 gender Instituto Brasileiro de Geografia e estimating probability of being poor and, Estatística (IBGE), 83 66­68, 67t Instituto de Pesquisa Econômica Aplicada girls, pregnancy, and education, (IPEA), x 27n27, 104 International Social Survey Programme income differences by, 39­40, 39t, (ISSP), 84, 87 51t, 53t income-ranking correlations L over time, 63 labor market and, 15­16 labor laws, need for flexibility in, life-cycle earning patterns, 59, 60­61f 99­101 policies for reducing inequality, Brazilian labor markets preferences as to, 92t access to, 22, 99­102 globalization, 25n6 conflict between social groups, Gonzaga, Gustavo, xiv, 25­79 perceptions of, 92­94, 93f Graham, Carol, xvii education and demands of, 14 group conflict, perceptions of, 92­94, gender bias in, 15­16 93­94f, 94t informal, 10, 14, 100 growth and development in Brazil, 7­8, land, access to, 101­2, 107n3 81­83 legitimation of inequality in Brazil, 81­96 H historical background, 81­83 intelligence and skills vs. personal effort, Hirschman, Albert, 5, 86 belief in importance of, 89­90, 90f Holm-Nielsen, Lauritz, 13 internalization of historical process of I social exclusion and, 89 meritocracy, mythos of, xviii, 5, 21, IBGE (Instituto Brasileiro de Geografia e 28n32, 86, 87 Estatística), 83 perceptions income dynamics, long-term. See analysis of of inequality, 20­21, 84­88 long-term income dynamics in Brazil of mobility, 88­89 income inequality. See inequality in Brazil of social conflict, 92­94, 93­94f, 94t industrialization in Brazil, 7­8, 81­83 preferences as to policies for reducing inequality in Brazil, xvii­xix inequality, 91­92, 92t history of, 81­83 responsibility for diminishing inequality, legitimation of. See legitimation of attribution of, 91 inequality in Brazil social structure, views on legitimacy mechanisms of, 8­19 of, 88 meritocracy, mythos of, xviii tolerance of inequality, 84­88, 85t 132 Index Lei Orgânica de Assistência Social, 57 Portugal, perceptions and tolerance of life-cycle earnings patterns, 58­59, 60­61f inequality in, 85t Lisboa, Edmar, 82 poverty in Brazil, xvii­xix long-term income dynamics. See analysis across cohorts, 40­41, 41t, 48, 50t of long-term income dynamics analysis of. See analysis of long-term in Brazil income dynamics in Brazil elasticities of poverty to economic M growth, 8, 10t macroeconomic stabilization, 8 indicators for, 8, 9t marginalized youth, providing opportunities inequality and poverty, relationship for, 103­5 between, 98 mechanisms of exclusion, 8­19 mechanisms of exclusion reinforcing, meritocracy, mythos of, xviii, 5, 21, 28n32, 8­19 86, 87 nature of poverty and income mobility. See social exclusion and mobility inequality, 3­4 in Brazil perceptions of conflict between rich and poor, 92­94, 93f N probability of being poor, estimating, 66­68, 67t nonwhites. See racial issues regional component of, 8, 11, 11t1 social exclusion exacerbating. See social O exclusion and mobility origin, discrimination based on place of, 12 in Brazil years of schooling and incidence of. See P under education youth living in poverty, providing participation and citizenship, strengthening, opportunities for, 103­5 24, 105­7 pregnancy, teenage, 27n27, 104 pension and retirement/social security probability of being poor, estimating, income, 12, 17­19, 18t, 23, 59­62, 66­68, 67t 62t, 102 public services, access to, 10­11, 17­19, perceptions 18t, 22­23, 102 of inequality, 20­21, 84­90 of mobility, 88­89 Q of social conflict, 92­94, 93­94f, 94t PNAD (Pesquisa Nacional por Amostra de quilombos, 107n2 Domicílios or Brazilian National R Household Survey), 6, 35, 36 policies for reducing inequality, 21­24, racial issues, xix 97­108 estimating probability of being poor, Brazilian preferences as to, 91­92, 92t 66­68, 67t citizenship and participation, household heads by race and income strengthening, 24, 105­7 decile, 16f education, access to, 23, 102­5 income differences, race-related, 54­55, labor market and assets, improving 55­58f access to, 21­24, 97­108 income-ranking correlations over time, public services and social security, access 63­64 to, 10­11, 22­23, 102 in labor market, 15, 16­17 racial discrimination, reducing, perceptions of conflict between racial 23­24, 105 groups, 92­94, 93­94f, 94t youth living in poverty, providing policy recommendations regarding, opportunities for, 103­5 23­24, 105 Index 133 quilombos, 107n2 perceptions of inequality, 20­21, 84­90 Ravallion, Martin, 25n6 perceptions of mobility, 88­89 Real Plan, 8, 19, 25­26n14, 68 policy recommendations regarding. See regional component of poverty in Brazil, 8, policies for reducing inequality 11, 11t poverty and inequality, relationship to, residence, discrimination based on, 12, xvii­xix 26n19 theoretical basis of study, 4­5 responsibility for diminishing inequality in social security/pension and retirement Brazil, attribution of, 91 income, 12, 17­19, 18t, 23, 59­62, retirement and pension/social security 62t, 102 income, 12, 17­19, 18t, 23, 59­62, social services, access to, 10­11, 17­19, 18t, 62t, 102 22­23, 102 rural vs. urban populations social structure, views on legitimacy of, 88 analysis of long-term income dynamics, Spain, perceptions and tolerance of 55­57 inequality in, 85t estimating probability of being poor, Suhrcke, M., 86 66­68, 67t Sweden, perceptions and tolerance of favelas (urban slums), 12, 26n19 inequality in, 85t income-ranking correlations, 64 social exclusion affecting, 10­12, 11t T Russian Federation, perceptions and teenage pregnancy, 27n27, 104 tolerance of inequality in, 84, 85t tolerance of inequality, 84­88, 85t S tunnel effect, 86 Scalon, Maria Celi, xiv, 81­96 U Sen, Amartya, xvii unemployment and underemployment, sex discrimination. See gender 13­14 size of household, by cohort and years of United States, mythos of meritocracy in, schooling, 51, 52t xviii, 25n5 social conflict, perceptions of, 92­94, urban populations. See rural vs. urban 93­94f, 94t populations social exclusion and mobility in Brazil, ix­x, 1­34 V analysis of dynamics of. See analysis of long-term income dynamics in Brazil Veiga, Alinne, xiv, 35­79 economic growth and development in Verner, Dorte, 13 Brazil, 7­8, 81­83 von Bulow, Marisa, 1­34 hypotheses regarding, 5­7 internalization of process of, 89 W legitimation of. See legitimation of inequality in Brazil women. See gender low social mobility of poorest people, Woolcock, Michael, xiv, 1­34 19­20 World Bank, x, 1­2 mechanisms of exclusion, 8­19 Y methodology of study, 5­7 nature of Brazilian poverty, 3­4 youth. See children and youth ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving Saved: endangered forests and natural resources. The · 5 trees Office of the Publisher has chosen to print · 4 million BTUs of total Social Exclusion and Mobility in Brazil on energy recycled paper with 30 percent postconsumer · 443 lbs. of net fiber in accordance with the recommended greenhouse gases standards for paper usage set by the Green · 1,838 gallons of waste Press Initiative, a nonprofit program support- water ing publishers in using fiber that is not sourced · 236 lbs. of solid waste from endangered forests. For more informa- tion, visit www.greenpressinitiative.org. Brazil is a country of sharp disparities. The gap between the richest and the poorest citizens is one of the largest in the world. Inequality in Brazil is well-known, but its low mobility is not. Until now, few studies have sought to investigate how forms of social exclusion constrain socioeconomic mobility. Why do particular groups remain excluded and trapped in poverty for generations? What do Brazilians themselves think about income inequality and social mobility? This study explores these issues, provides a set of options to redress them, and promotes a national dialogue for action. In addition to reviewing pertinent literature, Social Exclusion and Mobility in Brazil examines the changing income dynamics among homogeneous groups over a 20-year period. With respect to mobility, it tracks changes in the relative positions of social groups with similar characteristics. The analysis derives factors affecting the probability that certain groups will continue to lack equal access to the economic, cultural, and polit- ical resources that would improve their living standards. The current political climate in Brazil offers a unique opportunity to open a new and more informed conversation on the dynamics of exclusion and mobility. This book contributes to that conversation. ISBN 978-0-8213-7219-7 SKU 17219