View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Papers in Economics Ibero-Amerika Institut für Wirtschaftsforschung Instituto Ibero-Americano de Investigaciones Económicas Ibero-America Institute for Economic Research (IAI) Georg-August-Universität Göttingen (founded in 1737) Diskussionsbeiträge · Documentos de Trabajo · Discussion Papers Nr. 121 Earnings Inequalities and Educational Mobility in Brazil over Two Decades Denis Cogneau, Jérémie Gignoux October 2005 Platz der Göttinger Sieben 3 ⋅ 37073 Goettingen ⋅ Germany ⋅ Phone: +49-(0)551-398172 ⋅ Fax: +49-(0)551-398173 e-mail: [email protected] ⋅ http://www.iai.wiwi.uni-goettingen.de EARNINGS INEQUALITIES AND EDUCATIONAL MOBILITY IN BRAZIL OVER TWO DECADES1 Denis Cogneau IRD-Paris, DIAL [email protected] Jérémie Gignoux INED, IEP-Paris, Université Paris-IX Dauphine, LEA/INRA [email protected] Document de travail DIAL Juin 2005 ABSTRACT This paper studies the impact of changes in educational opportunities on various definitions of labour market inequalities in Brazil over two decades (1976-96). Using four editions of the nationally representative PNAD survey, we analyze the evolution of overall inequalities and inequalities of opportunity in 40-49 year old males’ earnings. We design and implement semiparametric decompositions of the respective effects of (i) schooling expansion, (ii) changes in the structure of earnings, and (iii) changes in intergenerational educational mobility. Earnings inequalities varied little over the period, with a peak in the late 1980s that can be imputed to hyperinflation. First of all, the decompositions show that changes in the distribution of education contributed to the increase in both overall earnings inequalities and inequalities of opportunity among the oldest generations, before sharply reducing them among the post-WWII cohorts. Secondly, the decrease in returns to education also contributed to equalizing labour market opportunities in the 1988-96 period. Thirdly and lastly, the changes in educational mobility were not large enough to significantly affect earnings inequalities, whereas it is shown that they should play a prominent role in equalizing opportunities in the future. Key words: Equality of opportunities, Labour market, Inequality decomposition, Brazil JEL Code: D32, D63, J62, O15 RESUME Ce papier étudie les conséquences des changements dans les opportunités scolaires sur plusieurs définitions des inégalités sur le marché du travail au Brésil sur deux décennies. En utilisant quatre éditions de l’enquête nationale représentative PNAD, nous analysons l’évolution des inégalités globales et de l’inégalité des chances de rémunérations des hommes de 40 à 49 ans. Nous construisons et mettons en œuvre des décompositions semi-paramétriques des effets respectifs de (i) l’expansion de la scolarisation (ii) les changements dans la structure des rémunérations, et (iii) les changements dans la mobilité scolaire intergénérationnelles. Les inégalités de rémunération ont peu varié sur l’ensemble de la période, avec un pic à la fin des années 1980 attribuable à l’hyperinflation. Premièrement, les décompositions montrent que les changements dans la distribution de l’éducation ont contribué à l’accroissement des inégalités de rémunération globales et de l’inégalité des chances dans les générations les plus anciennes, avant de les réduire fortement parmi les cohortes nées dans l’après- guerre. Deuxièmement, la baisse des rendements de l’éducation a aussi contribué à égaliser les opportunités sur le marché du travail pendant la période 1988-96. Troisièmement et enfin, les évolutions de la mobilité scolaire n’ont pas été suffisamment importantes pour affecter significativement les inégalités de rémunération, alors qu’il est montré qu’elles devraient jouer un rôle primordial pour l’égalisation des opportunités dans le futur. Mots clés : Egalité des chances, Marché du travail, Décomposition des inégalités, Brésil 1 The authors would like to thank Chico Ferreira, Michael Grimm, Marc Gurgand, Stephan Klasen, Sylvie Lambert, Christophe Muller and Petra Todd, and participants at an Education Day at INED-Paris, at the Ibero-America Conference in Göttingen, and at the 1st ECINEQ conference in Palma de Mallorca. The views expressed in this paper are those of the authors alone. 1 Contents 1. INTRODUCTION.......................................................................................................................4 2. DATA...........................................................................................................................................5 3. GROWTH IN WAGE INEQUALITIES OVER THE 1976-1996 PERIOD..........................7 3.1. Overall earnings inequalities......................................................................................................7 3.2. The inequality of labour market opportunities........................................................................8 4. INTERGENERATIONAL EDUCATIONAL MOBILITY...................................................10 5. THE EFFECTS OF EDUCATIONAL CHANGES ON EARNINGS INEQUALITIES FROM 1976 TO 1996................................................................................................................12 5.1. Methodology..............................................................................................................................12 5.1.1 Construction of the counterfactual inequalities.............................................................13 5.1.2 Construction of counterfactual educational mobility.....................................................14 5.1.3 Semiparametric decomposition.....................................................................................15 5.1.4 Empty cells and selection biases....................................................................................16 5.2. Findings: historical decomposition of growth in earnings inequalities from 1976 to 1996...............................................................................................................................16 6. THE POTENTIAL EFFECTS OF AN INCREASE IN EDUCATION MOBILITY ON EARNINGS INEQUALITIES..........................................................................................22 6.1. Methodology..............................................................................................................................22 6.2. Findings: impact of perfect educational mobility on earnings inequalities.........................23 7. CONCLUSION..........................................................................................................................24 BIBLIOGRAPHY..................................................................................................................27 List of tables Table 1: Measurements of overall inequalities in hourly earnings.....................................................................7 Table 2: Measurements of the inequality of economic opportunities (128 types of origin)................................9 Table 3: Measurements of the inequality of economic opportunities (9 types of origin)....................................9 Table 4: Distribution of education levels by year.............................................................................................11 Table 5: Educational mobility reproduction coefficients..................................................................................12 Table A Frequencies of the assumed distribution observed in t.......................................................................15 Table B Frequencies of the assumed distribution observed in t'......................................................................15 Table C Frequencies of the distribution simulated with the marginal distribution of t and education mobility of t'........................................................................................................................................15 Table D Frequencies of the distribution simulated with the marginal distribution of t...................................15 Table 6: Historical decomposition 1976-1982.................................................................................................19 Table 7: Historical decomposition 1982-1988.................................................................................................20 Table 8: Historical decomposition1988-1996..................................................................................................21 2 Table E Frequencies of the assumed distribution observed in t and average earnings by level of education and type of origin...............................................................................................................23 Table F Frequencies of the distribution simulating perfect educational mobility...........................................23 Table 9: Simulations of perfect educational mobility.......................................................................................26 List of figures Figure 1: Variations in hourly earnings densities................................................................................................8 Figure 2: Counterfactual variation in earnings densities from 1988 to 1996....................................................18 Figure 3: Differences between observed densities and simulated densities with perfect educational mobility..24 List of appendices Appendix A 1: Educational mobility tables........................................................................................................29 Appendix A 2: Average hourly earnings by level of education and type of origin..............................................31 Appendix A 3: Impact of measurement errors on the decomposition of the inequality indices..........................33 Appendix A 4: Per capita GDP and inflation rate (World Development Indicators 2003)................................35 3 1. INTRODUCTION Brazilian society can be regarded as one of the most inegalitarian in the world from a number of points of view and special attention is consequently paid to it. The South American and Caribbean societies are particularly inegalitarian. This characteristic has now been related to the institutions left over from the colonial period (Engerman and Sokoloff, 2000). The level of inequalities in Brazil is much greater even than the average on the sub-continent with, for example, a Gini index one-third higher than Argentina (UN/WIDER data), and is at the same level as in South Africa (Lam, 1999). The colonial legacy probably weighs heavy from this point of view, since Brazil was the region’s main slave country. Correlatively, the Brazilian economy and society display an extremely high degree of dualism, visible both in the education system (private/state) and on the labour market (official/unofficial). Brazil also has among the lowest intergenerational educational mobility and equality of social and economic opportunities in the world (Dunn, 2003). A series of nationally representative annual surveys based on large samples (PNAD, Pesquisa Nacional por Amostra de Domicilios) provides a fairly accurate observation of the change in inequalities in Brazil over nearly thirty years. These data show that income inequalities remained remarkably stable, whether gaps in individual earnings or household standards of living. The 1976, 1982, 1988 and 1996 PNAD surveys also include certain information on individuals’ social origin. Sociologists have used these data to produce the first quantitative analyses of intergenerational social mobility in Brazil (Pastore, 1982; Pastore and Valle Silva, 2000; Picanço, 2003). Economists have also recently looked into the impact of family origins and inequalities of educational and labour market opportunities (Lam and Schoeni, 1993; Arias, Yamada and Tejerina, 2002; Bourguignon, Ferreira and Menendez, 2003; Andrade, Ferreira, Madalozzo and Veloso, 2003; Dunn, 2003; Ferreira and Veloso, 2003). The main question put in these papers is the contribution of education to the reduction of economic inequalities. This analytic question ties in with a contemporary political issue, since Brazil set up extensive means- based transfer programmes in 1999 conditional on sending children to school (Bolsa Escola) and stopping child labour (PETI). These programmes have now been combined into a single programme called Bolsa Familia and are reaching cruising speed with widespread coverage. However, it is not easy to evaluate the impact of these programmes since, unlike the Mexican Progresa programme, no randomly allocated pilot set-up has been implemented. An ex-ante evaluation of the Bolsa Escola programme using a structural micro-econometric model finds that the transfers have a significantly positive, albeit modest, impact on school enrolment and child labour. The sums of transfers distributed are moreover lower than in the Progresa programme. Hence they only have a marginal impact on income inequalities and poverty (Bourguignon, Ferreira and Leite, 2004). An ex-post evaluation of the programme is underway using data from the PNAD surveys, which identify the recipient households (Leite, 2005). Whatever the impact of these programmes on the education of the most underprivileged children, a second question arises as to the long-run impact of a decrease in educational inequalities on the distribution of income in Brazil. As regards the reduction of income inequalities, the hopes raised by the huge surge in the average level of education have not yet been realized, contrary to optimistic forecasts by Lam and Levison (1991) (see Ferreira & Paes de Barros, 2000 and 2004 on household income poverty). A recent paper by Bourguignon, Ferreira and Menendez (2003) applies microsimulation techniques to the 1996 PNAD survey to analyze the contribution of inequalities of educational and income opportunities to the formation of inequalities in an urban environment. It concludes that the cancelling out of inequalities due to social origin variables (race, region of birth, and parental education and occupation) would reduce the Gini index of individual incomes by just over 15% (10 points) and the Theil index by approximately 30%. The impact on household incomes would be slightly greater (15 Gini points), given the impacts on female labour force participation and fertility. A large part of the impact of factors of origin on individual earnings is associated with paternal education. The microsimulations also show that a minimal six years of education would produce more or less similar effects on the following generation. Lastly, half of this impact can be 4 imputed to the direct effect of factors of origin on earnings while the other half corresponds to the indirect effect of education, i.e. the equalization of schooling opportunities. The study’s authors deem these findings disappointing since they only bring Brazil down to an average level of inequality by Latin American standards and a level way above comparable Asian countries. This paper addresses the same type of question using a different set of data and other econometric methods (semiparametric decompositions). We use four PNAD surveys in 1976, 1982, 1988 and 1996 to focus on individual earnings inequalities among men aged 40 to 49 and to conduct a historical decomposition of the evolution of these inequalities (Bourguignon et al. conduct static microsimulations by cohorts on the 1996 survey). For the first time in the case of Brazil, we construct and calculate inequality of opportunity indicators in keeping with the axiomatics proposed by Roemer (1996 and 1998) and Van De Gaer et al. (2000). We also look at the impact of educational mobility on the income distribution, i.e. the Bourguignon et al. ‘indirect’ effect, which is the variable for Bolsa Escola programme action. Lastly, as an alternative to the microsimulation techniques, we propose a semiparametric estimation of the respective effects of schooling expansion, educational mobility and the structure of earnings on changes in individual earnings inequalities, covering as much overall inequalities as inequalities of opportunity. We use decompositions of educational mobility furnished by the log-linear model and nonparametric reweighting techniques inspired by Di Nardo, Fortin and Lemieux (1996). The two types of earnings inequalities for men aged 40 to 49 (1927-1956 cohorts) displayed a similar growth path including a peak in the late 1980s with the end of the dictatorship (1985) and the height of the inflationary crisis (Cruzado, Bresser, Summer and Collor plans; see also Appendix 4). All things considered, overall inequalities rose slightly from the beginning to the end of the period, while inequalities of opportunity posted a slight drop. Intergenerational educational mobility also recorded a very slight upturn. As regards the equality of economic opportunities, changes in the distribution of education levels initially had an inegalitarian effect before becoming equalizing as of the late 1980s. The first sub- period analyzed shows a rise in secondary and higher education immediately following the war, i.e. for the generations educated from 1945 to 1965, which benefited mainly the children of the upper classes. The second sub-period corresponds to the generations educated from 1955 to 1975. This period was marked by the expansion of primary schooling, which benefited more the children of the underprivileged classes. Moreover, the change in the structure of earnings by education level and type of social origin had an egalitarian effect mainly at the end of the period, in particular in the form of a decrease in returns to education. As regards the overall earnings inequalities, the expansion of education likewise initially had an inegalitarian effect before becoming equalizing at the end of the period. However, other factors, especially macroeconomic shocks, with soaring inflation and a drop in the minimum wage in real terms, provoked a sharp rise in earnings inequalities from 1982 to 1988 (see also Bittencourt, 2005). Yet this increase was virtually absorbed in the 40-49 year old age bracket from 1988 to 1996 due to the expansion of primary education. Lastly, the historical growth in intergenerational educational mobility for the generations born from 1927 to 1956 was too small to play a significant part in the developments observed. This explains the persisting inequalities of economic opportunity at a high level. We confirm that the majority of the inequalities of opportunity on the labour market can be imputed to this factor. However, as found by Bourguignon et al. (2003), this factor only plays a modest role in overall inequalities. We nevertheless put forward that this last evaluation is highly sensitive to earnings measurement errors. 2. DATA We use the data from four editions of the national survey of households (PNAD) conducted by the Brazilian Institute of Statistics (IBGE) in 1976, 1982, 1988 and 1996. The PNAD surveys cover a large sample since the data concern nearly 100,000 households every year. The sample is 5 representative of the population of Brazil, but excludes the rural areas in the northern region (the Amazon)2. These four editions contain information on the adults’ social origins, collected for the head of household and his spouse3. This concerns the father’s level of education and occupation when the individual started working4. In addition, a question on migration provides information on the individual’s place of birth (Federative Republic State)5 and the questionnaire on demographic characteristics provides information on the individual’s colour. Overall, therefore, we use four data on social origins. We restrict the sample to men aged 40 to 49 and subsequently disregard age effects on the assumption that such effects are negligible within this group. We limit the sample to men declared as the head of household or, more rarely, the spouse of the head (who combined represent 92% to 94% of this age bracket depending on the edition) and to employed individuals (93% to 89%, with this proportion decreasing over time) for whom information on social mobility, working hours and earned income is provided. Our samples cover 2,860 observations in 1976, 18,833 in 1982, 11,304 in 1988 and 14,096 in 1996. We construct an hourly earnings rate variable based on the information on monthly incomes in the different economic activities, wage and non-wage combined6, and on the weekly hours worked in these activities7. The incomes are discounted to September 2002 Brazilian Reals using the IBGE deflators derived from the INPC national consumer price index. Ferreira et al. (2003) posit that the PNAD underestimates agricultural earnings due to the lack of information on income in kind and production for own consumption and overestimates the production of family businesses due to the lack of information on their expenditure on inputs. Overall, they deem that the incomes are underestimated in the rural areas. This would appear to be borne out by a comparison with the incomes measured by the 1996-1997 Pesquisa Sobre Padrões de Vida (PPV) living standards measurement survey containing more information on these points. Despite these potential measurement errors, we do not limit the sample to urban areas as done by Bourguignon et al. (2004). Analyzing intergenerational mobility based on an urban sub-sample can result in substantial selection biases. We believe that such biases are greater than those caused by the underestimation of incomes in rural areas. Disregarding spatial variations in purchasing power constitutes another source of potential bias in the measurement of incomes. Ferreira et al. (2003) propose a series of regional deflators based on data from the 1996 Pequisa de Orçamento Familiar (POF) household budget survey. We have tested these deflators in our empirical analyses for this year and observed that the findings changed little. We therefore do not correct these potential biases in the rest of this work. The variable used for the education level of the individuals in the sample corresponds to the highest education level attained numbers of years as of entry into primary school, which is normally at seven years old8. We use a discrete decomposition of this variable into nine education levels (0, 1, 2, 3, 4, 5- 7, 8, 9-11 and 12 or more years of education). We use two characterizations of the social origins of the individuals in the sample. The first consists of four categorical variables: a colour variable coded into two categories (“white” for individuals declared as being white or Asian and “black” for individuals declared as being black, mixed-race or Indian); a birth region variable coded into four categories (covering respectively the individuals born in a) the Federal District and the State of Sao Paulo, b) the states of the south, centre-west and west of the Northern region, c) the Rio Grande do Sul, the states of the south-east and south of the 2 Only the rural areas of Tocantins State were covered in this region. 3 For 1976, this information was collected solely for a sub-sample representing approximately 25% of the total sample. 4 In 1976, the question concerned the father’s education when the individual was 15 years old. 5 This information was not collected by the 1982 PNAD. 6 The information on earned income is collected by a single question covering both wage and non-wage activities. 7 We thank Pierre-Emmanuel Couralet for his help in building the databases. 8 Since the 1990s, the first two levels of the Brazilian education system have been the elementary level (equivalent to primary education), lasting for eight years and normally covering children aged 7 to 14, and the intermediate level (equivalent to secondary education), lasting for three years and normally covering children aged 15 to 17. However, when the cohorts studied in this paper were educated, a basic level also existed covering the first four years of the elementary level. 6 Northeastern region, and d) the states of the north of the Northeastern region and east of the Northern region); a father’s level of education variable coded into four categories (covering respectively the individuals whose father a) never went to school, b) is literate or for whom the interviewee is unable to give an answer, c) completed one of the first four years of primary education, and d) completed at least the fifth year of primary education); and a father’s occupation variable coded into four categories (covering respectively the individuals whose father was a) a farmer, b) employed in a traditional industry, a domestic employee, whose occupation is poorly defined or for whom the interviewee is unable to give an answer, c) employed in a modern industry, an unincorporated entrepreneur or employed in a service sector, and d) in a skilled profession, an employer, administrator or manager). These four variables identify 128 groups of potential social origins. The second characterization of social origins consists of a nine-category classification based on the father’s level of education and occupation (covering respectively the individuals whose father a) never went to school and was a farmer, b) never went to school and had another occupation, c) was merely literate and was a farmer, d) was literate and had another occupation, e) completed one of the first four years of primary education and was a farmer, f) completed one of the first four years of primary education and had another occupation, g) completed one of the four years of upper primary education (5-8), h) completed nine or more years of education, and i) the interviewee was unable to answer). We use resampling techniques (bootstrapping) to estimate the accuracy of the statistics calculated, including our decompositions. For this, we take into account the sample design used for the PNAD surveys (Nascimento Silva et al., 2002), i.e. the stratification of the sample into 36 “natural” strata corresponding to 27 Brazilian states and nine metropolitan regions (Bertail and Combris, 1997)9. 3. GROWTH IN WAGE INEQUALITIES OVER THE 1976-1996 PERIOD In this section, we describe the changes in the measurements of overall inequalities and inequalities of opportunity regarding the hourly earnings of men aged 40 to 49. 3.1. Overall earnings inequalities Table 1 presents the growth in overall inequalities in the distribution of hourly earnings as measured by the Gini and Theil indices. Table 1: Measurements of overall inequalities in hourly earnings 1976 1982 1988 1996 Gini index 0.570 (0.009) 0.585 * (0.004) 0.623 * (0.005) 0.599 * (0.005) Theil index 0.625 (0.027) 0.687 (0.017) 0.772 * (0.018) 0.719 (0.028) (Per capita GDP) 100 105.4 114.9 120.4 Source: PNAD surveys, IBGE. Coverage: Men aged 40 to 49. Reading: Indices on inequality in the distribution of hourly earnings. (*): Variation significant at 5% compared with the previous year (In brackets): bootstrap standard deviations. The Gini index remains close to 0.60 for the entire period. It increases significantly from 1976 to 1988, then falls from 1988 to 1996 before returning to a level slightly above, but not significantly different to, its 1976 level. The Theil index displays similar growth. It rises more sharply than the Gini index from 1976 to 1988, and decreases from 1988 to 1996 to a level significantly higher than in 1976. The difference between the growth in the two indices shows that overall inequalities changed little over these twenty years, but that inequalities rose to the detriment of the bottom of the earnings distribution. These trends are illustrated by Figure 1, which presents the smoothed density differences in hourly earnings from 1976 to 1996. 9 A second stratification at the level of the municipalities of the metropolitan strata, the main municipalities and grouping of municipalities of the other strata cannot be taken into account since the data do not enable these strata to be identified. 7 3.2. The inequality of labour market opportunities We construct the inequality of labour market opportunity indices in keeping with the two main economic literature proposals on economic justice and equality of opportunities (Roemer 1996 and 1998; Van de Gaer, 2000). For a given outcome variable (here hourly earnings), both proposals distinguish between what is due to “circumstances”, defined as an individual characteristics that influence his outcome but over which he has no control (here social origin), and what is due to “effort” for which the individual is held responsible. More generally, we use this latter term to cover all the outcome factors considered irrelevant to the establishment of illegitimate inequalities. Figure 1: Variations in hourly earnings densities 5 0 . s e c n e er Diff 0 y sit n e D d e 5 h 0 ot -. o m S 1 -. -4 -2 0 2 4 6 lnsalh2 Actual difference 76-82 Actual difference 82-88 Actual difference 88-96 Method: Double smoothing by a Gaussian kernel function (bandwidth 0.2) The first approach proposed by Roemer considers that only the relative “efforts” in each group of “circumstances” (called types by this author) are comparable. The inequalities between types are then measured by comparing individuals with the same relative level of effort. The inequality of opportunity is measured at different points of the distribution of relative levels of effort and these measurements are then aggregated into a single index. We implement this type of measurement by estimating decile regressions of earnings using dummy variables for the different types of social origin. For this, we assume that the effects of the origin variables are additive10. The predictions made based on these estimates at the different deciles measure the hourly earnings deciles conditional on the types of origin. We hence reconstitute the earnings distributions predicted by the origin variables at each relative level of effort. We calculate the inequality indices at each decile and aggregate them taking their average11. These “Roemer” indices are written: ROE = 1/10 . ∑ I { w } (1) π o, π where o is an index for the different types of social origins, w is the predicted wage at decile π for o, π type o, and I is an index of inequality (here Gini or Theil) between the different types o. 10 The parametric assumptions about the additivity of the decile regressions enable us to estimate a decile level for a large number of types when considering the four social origin variables (see Section 2). In this latter case, direct nonparametric estimates are in effect impossible. 11 This average can be weighted to place more weight on the inequality of opportunity at the bottom or top of the distribution of relative efforts. 8