Endogenous Racial Identity: Evidence from Brazilian RAIS and PNAD Data

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1 Endogenous Racial Identity: Evidence from Brazilian RAIS and PNAD Data Jason M. Rivera University of Georgia Ian M. Schmutte University of Georgia September 2013 Christopher Cornwell University of Georgia Preliminary and incomplete. Abstract When racial discrimination is present, workers and firms may manipulate perceived racial identity for economic gain. Using employer-employee matched data from Brazil, where race is highly subjective, we show that employer reports of their workers races are very different than those from survey data for the same population. Employer reports suggest that the formal work force is than survey data indicate. This whitening may be a result of workers changing racial status when changing jobs for economic benefit, employer misreporting of race on the basis of statistical discrimination, or reporting biases built into the employer survey instrument. We observe workers changing race when changing jobs. Workers are most likely to change race from brown to white when moving to jobs in whiter, smaller, and lower-paying plants. Changing race to white is also associated with a higher level of education and higher earnings growth. Contact author 1

2 1 Introduction/Motivation We study discrepancies in the recording of racial status in employer-provided administrative data and Brazilian survey data to examine how racial categories are constructed in the labor market. In the presence of discrimination, the ability to alter perceived race is valuable. Bertrand and Mullainathan (2004) showed that increasing an employer s perception that an applicant is African-American decreases the probability of interview and employment. Parsons et al., (2011) and Price and Wolfers (2010) show that even in such racially diverse environments as professional baseball and basketball, race and ethnicity can affect the snap judgments of otherwise objective decision makers. While racial designations are generally a matter of ethnicity and heredity in the US and Europe, this is not so in Brazil. Because the overwhelming majority of the Brazilian population is of mixed African and European descent, racial identity is more closely tied to a person s skin tone than his or her ethnic background. Consequently, the choice or assignment of individuals to discrete racial categories may be endogenous to a range of factors associated with educational and labor-market opportunities. This subjectivity is well illustrated in the story of identical twin brothers, Alan and Alex Teixeira da Cunha, who applied to Universidade de Brasilia, seeking admission under newly instituted quotas for black students. Separate admission counselors admitted one brother under affirmative action as black; the other was denied admission, as his counselor considered him to be white (Marotto, 2007). Given a similar set of circumstances, Brazilian workers may provide employers with information about race that would increase their chances of employment and their income. At the same time, Brazilian employers have little incentive to collect accurate data on race. There is no equal opportunity legislation during our sample period, nor any major affirmative action policies. Should they lack information on race, but are under an admin- 2

3 istrative burden to classify workers, employers may report a noisy prediction of a worker s race based on a handful of characteristics such as earnings, occupation, education, employment history, perceived skin tone, and name. The nature and quality of the firm s report may reflect statistical discrimination, their human resource management policies, and their diligence in accurately completing the survey. The administrative data come from the Annual Social Information Survey (RAIS), which is managed by the Brazilian labor ministry and completed at every registered place of employment. RAIS data are employer-employee matched data, and contain detailed information about workers and plants. For workers, RAIS includes information about earnings, race, gender, education and age. We can aggregate worker descriptions to the plant-level to obtain the race, gender and occupational compositions of each plant. RAIS also captures information about plant size, economic sector, location and legal formation. RAIS has observations of every job of every worker and every employee of every employer for the survey year. We compare the assignment of racial identity in RAIS to the self-reported race data from the National Sampled Housing Survey (PNAD). The PNAD is conducted by the Brazilian Institute of Geography and Statistics, and contains detailed demographic information about all members of the surveyed household, including age, race, education, earnings, employment sector and occupation. Unlike in RAIS, the respondent to the PNAD is almost always the subject; if this is not the case, the respondent is at least a member of the subject s household, if not a family member. Because PNAD data are collected directly from workers, we believe these data to be a benchmark for describing the Brazilian population. We use the most current data available: the 2010 wave of RAIS and the 2011 wave of PNAD. First, we document large discrepancies between the two surveys in the reporting of race. RAIS data over-represent the white segment of Brazil s formal labor force by about ten percentage points. The whitening effect of RAIS is closely tied to worker earnings and occupation; workers who earn less and who are in lower-skilled occupations are the least accurately 3

4 represented. Next, we exploit the employer-employee matched nature and comprehensive coverage of RAIS to explore the determinants of racial identity by linking race changes to job changes. We observe a change in race whenever a worker s reported race in a new job differs from the race reported in his previous job. We first regress race change on a rich set of worker characteristics, such as education level, occupation, age and log income, and plant effects. Education is by far the most important predictor and its effect is to whiten. Second, we regress the estimated plant effects on a range of plant characterisitics including plant size, gender compostion, race composition, industry and legal formation. We find that a plant s racial composition explains an overwhelming amount of the variation in plant effects; specifically, plants with relatively more white workers tend to classify new hires as white, regardless of the workers previously recorded race. Plant size and the legal definition of the plant explain less of the variation than racial composition, but still show significant correlation with plant effects. As a plant employs more employees, it is more likely to report its workers as black or brown, regardless of the workers races in previous jobs. Additionally, plants that operate as state owned enterprises, joint public-private enterprises or as corporations are less likely to report a new worker s race as different from his or her previous employer. However, the racial composition effect supercedes these smaller effects. 2 Race in Brazil Racial identification in Brazil has attracted a considerable amount of attention outside of economics. Htun (2004) documents the acknowledgement of racial inequality in educational and labor market outcomes by Brazilian authorities and the implementation of affirmative action policies in higher education. Using data from the 2005 PNAD, Schwartzman (2007) finds that non-white parents tend to classify their children as white when the parents are 4

5 better educated and white parents tend to classify their children as non-white when the parents are not well educated. In the economic literature, Francis and Tannuri-Pianto (2012) explore the effectiveness of affirmative action policies in Brazil s universities. Francis and Tannuri-Pianto (2013) follow up with a paper in the spirit of Schwartzman, and find that the adoption of affirmative action policies leads to more students claiming to be black or brown. In the Brazilian context, our work builds upon this research by providing a first look at both how adult participants in the labor market self-identify and, importantly, how third parties identify these workers. However, our research applies to studies of race more generally, as we also build upon the previously mentioned work of Bertrand and Mullainathan (2004) and Parsons, et al. (2011), in describing how racial biases may affect the collection of data. Of course, these studies build upon the important work of Arrow (1973) and Phelps (1972), which are important early papers that look at the statistical relationship between labor market characteristics and racial dscrimination. 3 Data 3.1 Sources We obtain our primary data from the Relação Anual de Informações Sociais, or Annual Social Information Survey (RAIS), courtesy of the Brazilian Ministry of Labor and Employment (MTE). The MTE uses the employer-employee matched data from RAIS to administer a Thirteenth Salary, or annual bonus, that is equivalent to one month s earnings to each worker, mandated by the Brazilian constitution. To administer the Thirteenth Salary, the MTE use RAIS to collect information about the identity of each employee and their earnings in each plant where they worked for the calendar year. RAIS also requires employers to 5

6 provide detailed characteristics of every employee, which MTE uses to produce labor market statistics for broader policy objectives. RAIS collects data at the plant, not the firm, level. Plant management reports the data on behalf of the employees; in smaller enterprises the respondent may be the owner, while larger firms likely have an accountant, human resources manager or other administrator submitting the data. Uniquely among other employer-employee matched datasets, RAIS provides universal coverage of the formal labor market. For each plant, RAIS captures every worker in its employ during the survey year; for each worker, RAIS captures each registered employer he or she worked for over the same period. As a benchmark for RAIS, we also use data from the Pesquisa Nacional por Amostra de Domicílios (PNAD), Brazil s national household survey, which is administered by the Brazilian Insitute of Geography and Statistics (IBGE). The PNAD, like US household data, includes many variables that relate to a worker s labor market characteristics, including earnings, education, and demographic information. Importantly, respondents in PNAD are almost always the actual subject of the survey. Even when the respondent is not the subject of the question, he or she is always a member of the same household, if not a direct relation. Thus, the key distinction between PNAD and RAIS is that employee race is self-reported in the former and assigned by a firm representative in the latter. 3.2 A Statistical Description of Formal Labor Market of Brazil Our analysis is based on the 2010 wave of RAIS, the most recent wave available, which contains the over 65 million worker-job observations that define the Brazilian workforce and the 2011 wave of PNAD, which is a representative sample of the Brazilian population 1. Restricting attention to full-time formal workers between 20 and 65 years of age in RAIS gives us about 36.1 million unique workers. Almost 2.8 million plants employ these workers, 1 PNAD was not administered in

7 producing about 42 million worker-plant observations in RAIS. Imposing these age and fulltime restrictions on PNAD results in a sample of 52,000 observations, each of which is a unique worker. Table 1 reports race, occupation classes and education levels in both samples, as well as the category difference in composition between the two sources. Race, as discussed earlier, is a complex issue in Brazil and, unlike in the US or Europe, is defined almost entirely on skin tone. This color-based definition leads to ambiguities that we do not observe in the US, where race is a matter of heredity and ethnicity. Both the PNAD and RAIS use the same racial categories: indigenous, white, black, yellow and brown. With the obvious exception of the indigenous category, these classifications reflect only a person s skin color. By far the most striking contrast between the RAIS and PNAD samples is in their racial compositions. Focusing attention on the last column, RAIS depicts a formal workforce that is almost 10 percentage points whiter, and, correspondingly, 10 percentage points less brown and black. According to the PNAD data, white workers make up about 52 percent of the workforce; in RAIS they are almost 62 percent. There are also disparities between indigenous and yellow worker shares, but these groups are small, comprising only 0.33 percent and 0.55 percent of PNAD and 0.27 percent and 0.75 percent of RAIS. If we believe the PNAD data to record race more accurately because the respondent and subject are the same person, these large differences suggest an issue that extends beyond simple measurement error. With just two exceptions the defense industry and average monthly income no other category produces a discrepancy even half as large. There is virtual agreement on the gender composition of the formal workforce: 65.4 percent male in PNAD versus 64.9 percent male in RAIS. The average worker age in both samples is also very similar. The average worker is about 33.5 years old in PNAD and 34.3 years old in RAIS. Industry categories are aggregations of five-digit codes from PNAD and six-digit codes from RAIS, as specified by the IBGE s National Registry of Economic Activity (CNAE). In 7

8 2010/2011, almost 50 percent of Brazilian workers were employed in the trade and repair and production sector by either data source. There is a discrepance of 2.2 percentage points in the trade and repair category, but the difference between RAIS and PNAD is generally less than one percentage point in other sectors. For only one sector, defense/social security, does the emplyment share differ by more than 2.5 percentage points. However, PNAD records no workers in this sector for Turning to occupation shares, service and production workers each make up more than a quarter of Brazil s workforce; combined these job classifications account for about 55 percent of the Brazilian workforce according to both surveys. Agricultural workers, production, repair and maintenance workers, or the remaining blue-collar occupations, account for an additional 10.5 percent of formal workers in both surveys. The remaining white-collar and middle-skill occupations public administrations, professionals, artists, scientists, mid-level technicians and administrative workers make up between 32 and 35 percent of the workforce, depending on the source. Typically we find a difference of two or four percentage points between the two sources for any given occupation class. As with industry, these occupation classes are one-digit aggregations of PNAD s four-digit and RAIS s six-digit occupation codes from the IBGE s Brazilian Code of Occupations (CBO 2002). Educational attainment is recorded differently in PNAD than in RAIS, which makes comparisons in this category more difficult. Nevertheless, aggregating into the seven common categories produces similar workforce compositions by education level. The largest differences appear at the elementary school and bachelor s degree levels. RAIS over-represents workers with no more than an elementary school education by about 4.5 percentage points and understates college graduates by about 4.3 percentage points. As noted above, there is also a substantial discrepancy between PNAD and RAIS in mean income; the average monthly labor income is R$1, in the 2011 PNAD and R$1, in the 2010 RAIS, a difference of about 12 percent. Previous work has shown household 8

9 surveys to be less accurate than administrative surveys when reporting income (Juhn and McCue, 2010). Additionally, incomes in the 2010 RAIS and 2011 PNAD have not yet been adjusted for inflation or earnings growth. Overall, the high degree of comparability between RAIS and PNAD in non-race characteristics places the discrepancies in reporting race in sharp relief. Race stands apart as misrepresented 2. Next, we examine the discrpancy in racial composition by worker characteristics. 3.3 Differences in Racial Compositions by Gender, Earnings, Occupation and Industry To better understand the whitening of the Brazilian labor force in RAIS, we examine racial composition differences by gender, income quartile, occupation and industry. Table 2 reports the percentage-point difference between PNAD and RAIS for white, black and brown workers by these characteristics; we omit yellow and indigenous workers as they comprise a very small portion of the formal labor force as shown in Table 1 breaks down the difference for each racial category by these characteristics. Broadly, we find that the PNAD-RAIS racial composition discrepancy is essentially the same for men and women, but varies substantially by earnings, occupation and income 3. The relationship with monthly earnings is the most striking. The whitening effect of RAIS grows as you move from the top to the bottom earnings quartiles. For each of the lowest two quartiles, RAIS overstates the white share of the labor force by more than 12 2 These statistics are also available for RAIS 2003 and PNAD 2003, and are similar in both sign and magnitude. Please contact the author for 2003 statistics and results. 3 An interesting feature of this decomposition is that regardless of the data source, female workers report more whites and fewer minorities than their male counter-parts. This statistic may be an artifact of selection into the formal workforce; Gasparini and Tornarolli (2009) notes that the informal labor-force of Brazil is dominated by both women and Henley et al. (2009) notes that both women and minorities are more likely to participate in the informal market. 9

10 percentage points. Overstatement of the white shares exacly mirrors understatement of black and brown shares in each quartile. Similarly, the whitening effect of RAIS is greater in lower-skilled, non-professional occupations. From mid-level technicians down, the white share of occupation exceeds 10 percentage points. The lower-skilled blue-collar jobs show the greatest whitening. The agricultural worker category is 14.1 percentage points whiter in RAIS; the two categories of production workers are 13.1 and 15.7 percentage points whiter. In contrast, the government worker and professional categories are no more than five percentage points whiter in RAIS. Given that the whitening effect of RAIS is concentrated in lower-skilled occupations, if would not be surprising to find that industries employing large numbers of low-skilled workers are relatively whiter according to RAIS. In agriculture and fishing, minig, utilities and food and lodging whites are over-represented by 11.5 to 16.3 percentage points. These are industries that we might expect to have large concentration of low-skilled blue-collar workers. The large white shares in mining, utilities and international sectors may result from their small PNAD samples. Industries that employ a more occupationally varied workforce have white shares that are overstated typically by less than 10 percentage points. 4 Race Change Employer-provided administrative data depicts a Brazilian workforce that is significantly whiter than the self-reported race information collected by the government s national survey. in addition, the white bias of RAIS is concentrated among lower-skill jobs and lower-paid workers. Now, we exploit the employer-employee matching in RAIS to identify factors that might account for the white bias. The matching feature allows us to observe within-year job changes, which, in turn, reveal changes in racial status. In 2010, of the 1.8 million workers initially classified as brown or black who change jobs, over a third are classified as white 10

11 in their next job. First we describe the job and race changers in comparison to workers in general. Then, we attempt to sort out the contributions of individual and employer charracteristics to changes in racial identity. 4.1 Race Changers and Their Employers Table 3 reports the racial, occupational and educational compositions of the entire workforce in 2010, and of job and race changers in that year. The mean age and monthly income of each group are also given. A job change occurs when we observe a worker associated with more than one plant in 2010; race change occurs when the reported race for a job changer differs between jobs. All figures are for workers in their first reported job in 2010 by hire date. In 2010, almost 5 million workers, roughly 13.8 percent of the workforce, change jobs or take on a second job. Nearly 1.6 million workers, 31.2 percent of job changers, change race. The overall workforce is about 62 percent white, 31 percent brown and almost 6 percent black. The racial composition of job changers is essntially the same. However, race changers look very different; only about 44 percent of workers who change race are white; 42 percent are brown and 11 percent are black. Table 4 describes the share of race changes between the white, black and brown classifications among all job changes and race changes. Changes between these three classifications are present among almost 30 percent of all job changes and account for over 95 percent of all race changes. Moreover, changes from white to brown and from brown to white make up over 73 percent of all race changes. Job changers are representative of the overall workforce except they are more likely in Production I occupations, have high-school level educations and tend to be younger. Production I workers are a larger share of job changers, 31.8 percent, than the overall workforce by about 3.5 percentage points. Job changers also have a slightly larger share of 11

12 workers who stopped their schooling after completing high-school, 45.8 percent, a difference of about 3 percentage points. Job changers are also about 2.5 years younger, with a mean age of 31.8 years. Compared to job changers, race changers are even more likely to have Production I occupations; these workers are over 35 percent of race changers, about 3.5 percentage points more than for job changers in general. Race changers are also less likely to be in Public Administration/Management and Professional occupations. Only 1.5 percent of race changers are in public administration occupations, compared to 2.6 percent of job changers. The share of professionals among race changers is only 2.7 percent, 1.3 percentage points less than job changers as a whole. Race changers are also more likely to have less than high-school level education with 46.8 percent of race changers not completing high school, a 5.3 percentage point increase compared to job changers. Unsurprisingly, we see that race changers only earn about 88 percent of what job changers as whole do, about 1,180 reais a month. Workers only represent one side of the race changing phenomenon; the plants where they work are the other. In 2010, RAIS records over two million distinct plants. Table 5 provides a description of these plants in terms of worker demographic and occupational compositions, industry affiliation and size. On average, just over a third of a plant s workforce consists of service workers and vendors. An additional 20 percent are administrative and clerical workers. Other occupations make up between 2.5 and 9 percent of a plant s workforce, except the military and police category which is 0.01 percent. The share of firms in a given size category decreases as the plant size decreases. Very small plants of one to four workers make up over 64 percent of all plants, and nearly 92 percent of plants have less than 20 workers. Nearly 41 percent of plants are in the trade and repair industry, the largest sector by number of plants. Real estate, production and agriculture are the next largest sectors, making up between 9.5 and 12 percent of plants each. The typical labor force of a plant is 12

13 roughly 68 percent white. The mean age of a plant s employees is almost about 35 years old and about 42 percent of a plant s employees are women. 4.2 An Empirical Model of Race Change To identify the factors that explain changes in race, we take a two-step approach. First, we relate race change to a range of worker characteristics and plant effects, estimating models of the form Race i,r = x i,r β + ψ i,j,r + ε i,r, (1) where Race i,r is a race-change indicator for worker i with race of origin r, x i,r is a vector of worker characteristics, ψ i,j,r is a fixed eggect for destination plant j and ε i,r is a mean-zero error term. The set of worker characteristics includes origin and destination log earnings, origin and destination occupation, origin industry, age and education level. We carry out this step separately for the cases where the race of origin is white (W ) and black or brown (B), using the sample of workers who changed jobs in To gauge the relative importance of the employment destination on race changes, we estimate (1) both with and without plant effects. We compute standard errors that are clustered at the plant level. Second, we link the estimated destination plant effects, ˆψ j,r to the plant s racial composition and other observable characteristics: ˆψ j,r = δwhite j + z j α + u j,r, (2) where white j is the white share of plant employment at the beginning of the year and z j contains the average worker age, average salary. Again, we estimate the white to black/brown and black/brown to white cases separately. For this step, we compute heteroskedastic-robust standard errors. 13

14 5 Results 5.1 Race Change and Worker Characteristics Table 6 reproduces select results from our estimation of equation (1). The first two columns present the case where a worker s race changes from white to black/brown; the last two columns give the black/brown to white case. Perhaps the most important finding of this step is the importance of plant effects on race changing. The R 2 coefficient for column 1 is just and for column 3. But controlling for plant effects increases the explanatory power of the model greatly; adding plant effects to the sees the R 2 coefficients increase to and for the to black/brown and to white models. The effects of education on race change are interesting; the lower education classifications are correlated to an increased probability of change from white to black or brown, and decreased probability of change in the other direction. At the same time, workers with above high-school educations have decreased probabilities of changing from white and increased probabilities of changing to white. These results seem at odds with what we know about the whitening of workers in RAIS compared to PNAD. The lack of clarity for the relationship between race-changing and education is disheartening, especially given the work of other researchers (Htun, 2007 and Schwartzman, 2012) indicating a strong relationship between race and education in general. Fortunately, the complete model, which includes plant effects, brings some clarity to our understanding of the relationship between educational attainment and race-changing probabilities. The education coefficients present as two sides of the same coin: in the first estimation, we find that black or brown workers that are more highly educated have increased probabilities of changing race to white in their second jobs. The flip side is that white workers who are more highly educated have decreased probabilities of changing race to black or brown, and therefore increased probabilities of staying white. This is an important result, in that it 14

15 coalesces with previous research which suggests that education whitens. However, where previous research has focused on how educated parents classify their children, our analysis presents evidence that education whitens not just within families inter-generationally, but for the same person over in as little as a year. Further complicating our understanding is the effect of earnings on race change. Increased earnings in a worker s new job decreases the probability that he changes to black or brown if his previous employer classified him as white and, in the complete model, increases the probability he changes to white if his previous employer classified him as black or brown. Additionally, workers new and previous occupations follow suit and those occupations where we saw the greatest whitening agriculture and production seem to have the largest positive coefficients for changes to black or brown and the largest negative coefficients for changes to white. While these results remain at odds with our comparison to PNAD, they do suggest evidence of statistical discrimination in the vein of Arrow (1973) and Phelps (1972). 5.2 Race Change and the New Employer We now turn to the plant-level analysis. Figures 1 and 2 show the distributions of the ˆψ j,r for the white to black/brown and black/brown to white cases. The horizontal axis measures the magnitudes of plant effects on the probability of race change. Both figures show that plant effects are highly bimodal. For plant effects on changes from white to black/brown, we see a very large mode for firms with negative plant effects of approximately -0.2; these plants are less likely to change a white worker s race to black or brown. The other, smaller, mode is centered around 0.75, and consists of plants that increase the probability that a worker s race is changed from white to black or brown. We see a similar pattern in the plant effects on changes from black or brown to white. There is a large mode of plants with negative 15

16 effects around -0.3 and a second mode with a center near 0.6. These figures show us that, for a given type of race change, there are many plants that actually decrease the probability of a race change. When reporting race, these plants are at least consistent with their workers previous employers, if not more accurate. However, there is a significant group of plants that seem to almost actively change workers races. The very large, positive mode in Figure 2 may help explain why we see a bias towards whitening in RAIS. We know from our estimation of equation (1) that plant effects explain a much larger amount of the variation in race changes than any worker characteristics do. The large positive mode in this figure indicates a larger number of firms that regularly classify black and brown workers as white, since this mode is much larger than the positive mode for changes to black or brown. The variance depcited in the figures is linked to plant characteristics in (2), and table 7 presents the results from estimating that relationship. Again, the first column reports the white to black/brown case and the second column reports the black/brown to white case. Clearly the share of white workers in a plant has a tremendous effect on a plant s propensity to change a worker s race. The coefficient on this term is for plant effects changing from white to black or brown and almost 0.84 for the opposite effect. While it is difficult to make a causal statement regarding this coefficient, it is far and away the largest and most telling effect. Plant size appears to be somewhat correlated with these plant effects, with increased plant size positively correlated with changes to black and brown and negatively correlated with changes to white. However, we do not observe the strong monotonicity we would expect. A plant s legal formation seems to correlate with its plant effect. Relative to limited liability corporations, which make up well over 50 percent of all plants, state-owned corporations (2011), joint state-private corporations (2038), public corporations (2046) and private corporations (2054) all have statistically significant negative coefficients for both model spec- 16

17 ifications. These types of plants, at least compred to LLCs, seem the most truthful with regards to their employees races. Other plant characteristics such as occupational composition, gender composition, industry, mean salary and average worker age either have very small coefficients or are not statistically significant. 6 Conclusion The double-headed issue of race and ethnicity, and its pervasiveness through the world s labor markets, continues to motivate economic and social researchers. Our research furthers this issue by addressing questions of self-identification and strategic information asymmetries as they pertain to race in Brazil. In comparing the data of RAIS to those of PNAD, we find an unsettling discrepancy in the racial distribution of the formal labor force of Brazil. White workers are over-represented in RAIS data by about ten percentage points compared to PNAD. This disparity stands in stark contrast to the relative similarity of the two datasets with regard to other demographic and labor market descriptive characteristics such as earnings, occupation, industry, gender and education. A closer examination of the statistics reveals that, contrary to anecdotes and urban legend, in the upper end of the earnings distribution and for higher skilled professions, the difference in the percentage of white workers between RAIS and PNAD may be much smaller; however for workers with below-mean earnings or in lower-skilled occupations, the difference is even greater. As earnings and occupational skill level decrease, the discrepancy between the data sets grows. We exploit how RAIS captures all of a worker s jobs within a year to explore the consistency of race reporting within RAIS. We construct a two-period panel of workers who change jobs within We find that a significant number of workers races change when the work- 17

18 ers change jobs. In order to better describe this phenomenon, we develop a two-step model of race change. Estimation results from the first step reveal that race change is closely related to worker education levels, and increased education is associated with increased whiteness. However, the dominating relationship in the first step is that between race change, as defined in two different ways, and plant-level effects. In the second step we explore the relationship between the estimated plant-level effects and plant characteristics. The plant characteristics of legal formation and plant size are found to have significant relationships regardless of how race change is defined. However, neither of these have the explanatory power of the plant s racial composition. Plants with more white workers are more likely to change a brown or black worker s race to white, and less likely to change a white worker s race to brown or black. In order to clarify the interpretation, our future plans for this research involve controlling for both worker and plant fixed effects. That analysis should allow us to tease out any strategic behavior on the part of the worker. 18

19 References Arrow, K. (1973). The Theory of Discrimination, in O. Ashenfelter and A. Rees (eds), Discrimination in Labor Markets, Princeton University Press, Princeton, NJ. Bertrand, M. and Mullainathan, S. (2004). Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination, American Economic Review 94(4): Francis, A. M. and Tannuri-Pianto, M. (2012). Using Brazil s Racial Continuum to Examine the Short-Term Effects of Affirmative Action in Higher Education, Journal of Human Resources 47(3): Francis, A. M. and Tannuri-Pianto, M. (2013). Endogenous Race in Brazil : Affirmative Action and the Construction of Racial Identity among Young Adults, Economic Development and Cultural Change (Forthcoming): Gasparini, L. and Tornarolli, L. (2009). Labor Informality in Latin America and the Caribbean: Patterns and Trends from Household Survey Microdata, Desarrollo Y Sociedad 63(1): Henley, A., Arabsheibani, G. R. and Carneiro, F. G. (2009). On Defining and Measuring the Informal Sector : Evidence from Brazil On Defining and Measuring the Informal Sector : Evidence from Brazil October, World Development 37(5): Htun, M. (2004). From Racial Democracy to Affirmative Action: Changing State Policy on Race in Brazil, Latin American Research Review 39(1): Juhn, C. and McCue, K. (2010). Comparing Measures of Earnings Instability Based on Survey and Administrative Reports. 19

20 Parsons, C. A., Sulaeman, J., Yates, M. C. and Hamermesh, D. S. (2011). Strike Three: Discrimination, Incentives, and Evaluation, American Economic Review 101(June): Phelps, E. S. (1972). The Statistical Theory of Racism and Sexism, The American Economic Review 62(4): Price, J. and Wolfers, J. (2010). Racial Discrimination Among NBA Referees, Quarterly Journal of Economics 125(4): Schwartzman, L. F. (2007). Does Money Whiten? Intergenerational Changes in Racial Classification in Brazil, American Sociological Review 72(December 2007):

21 7 Tables and Figures Table 1: Frequencies, Compositions and Differences PNAD 2011 RAIS 2010 Diff Variable Freq. Percent Freq. Percent Pct Pts Color or Race Indigenous , White 26, ,376, Black 4, ,021, Yellow , Brown 19, ,352, Gender & Age Male 33, ,426, Age (years) % Education No Schooling 2, , Some Elementary 9, ,129, Elementary 5, ,611, Some High School 3, ,178, High School 21, ,476, Some University 3, ,432, Bachelor s (+) 6, ,062, Industry Ag/Fishing 1, ,945, Mining , Production 11, ,965, Utilities , Construction 4, ,193, Trade/Repair 14, ,052, Food/Lodging 3, ,729, Transp./Storage/Comm. 4, ,287, Finance/Banking 1, , Real Estate 6, , Defense/Soc.Sec ,395, Total 51, ,119, Source: PNAD 2011, IBGE; RAIS 2010, MTE. PNAD data include only adults ages with full-time employment. Occupation in PNAD and RAIS reclassified according to single digit CBO 2002, provided by the IBGE. Monthly income and age reported reported in reais and years and difference is percentage difference. Continued on next page. 21

22 Table 1, cont d: Frequencies, Compositions and Differences PNAD 2011 RAIS 2010 Diff Variable Freq. Percent Freq. Percent Pct Pts Education 1, , Health/Soc.Serv 1, ,061, Other Soc./Pers. Serv 1, ,306, Domestic 0 0 5, Int l Org , Occupation Military 0 0 3, Public Admin/Mgmt 4, ,204, Professionals/Arts/Sci 3, ,708, Mid-level Techs 4, ,642, Admin 6, ,213, Service/Vendors 15, ,419, Ag/Fish/Forestry 1, ,070, Production I 12, ,162, Production II 2, ,627, Repair/Mainten. 1, ,067, Monthly Income (R$) 1,534-1, % Total 51, ,119, Source: PNAD 2011, IBGE; RAIS 2010, MTE. PNAD data include only adults ages with full-time employment. Occupation in PNAD and RAIS reclassified according to single digit CBO 2002, provided by the IBGE. Monthly income and age reported reported in reais and years and difference is percentage difference. 22

23 Table 2: Differences in Racial Composition by Gender, Earnings, Industry and Occupation Race White Black Brown Yellow Indigenous Gender Male Female Earnings Quartile 1 = Top = Bottom Occupation Military n/a n/a n/a n/a n/a Public Admin/Mgmt Professionals/Arts/Sc Mid-level Techs Admin Service/Vendors Ag/Fish/Forestry Production I Production II Repair/Mainten Industry Ag/Fishing Mining Production Utilities Construction Trade/Repair Source: RAIS 2010, MTE and PNAD 2011, IBGE RAIS observations: 36,119,295. PNAD observations: 51,673 No workers were observed in PNAD 2011 for this category. No workers were observed in RAIS 2010 for this category. Continued on next page. 23

24 Table 2, cont d: Differences in Racial Composition by Gender, Earnings, Industry and Occupation Race White Black Brown Yellow Indigenous Food/Lodging Transp./Storage/Comm Finance/Banking Real Estate Defense/Soc.Sec. n/a n/a n/a n/a n/a Education Health/Soc.Serv Other Soc./Pers. Serv Domestic n/a n/a n/a n/a n/a Int l Org Source: RAIS 2010, MTE and PNAD 2011, IBGE RAIS observations: 36,119,295. PNAD observations: 51,673 No workers were observed in PNAD 2011 for this category. No workers were observed in RAIS 2010 for this category. 24

25 Table 3: Workforce Composition All Workers Job Changers Race Changers (%) (%) (%) Race, color Indigenous White Black Yellow Brown Occupation Military Public Admin/Mgmt Professionals/Arts/Sci Mid-level Techs Admin Service/Vendors Ag/Fish/Forestry Production I Production II Repair/Mainten Education No Schooling Some Elementary Elementary Some High School High School Some College Bachelor s (+) Age (years) Income (R$) 1, , , Observations 36,011,053 4,973,255 1,551,069 Source: RAIS 2010, MTE Computed from workers first observed job in 2010 by hire date. 25

26 Table 4: Race Change Types among Job and Race Changes Direction % of Job Changes % of Race Changes White to Black White to Brown Black to White Black to Brown Brown to White Brown to Black Observations 5,077,866 1,587,208 Source: RAIS 2010, MTE 26

27 Table 5: Plant Occupation, Race, Age and Gender Variable Freq. Variable Freq. Occupation Industry Military 0.01 Ag/Fishing 9.61 Public Admin/Mgmt 4.89 Mining 0.25 Professionals/Arts/Sci 3.60 Production Mid-level Techs 5.96 Utilities 0.15 Admin Construction 3.37 Service/Vendors Trade/Repair Ag/Fish/Forestry 9.06 Food/Lodging 5.87 Production I Transp./Storage/Comm Production II 2.66 Finance 1.41 Repair/Mainten Real Estate Plant Size (no. workers) Defense/Soc.Sec to Education to Health/Soc.Serv to Other Soc./Pers. Serv to Domestic to Int l Org to Pct White to Pct. Female to Mean Age (Yrs) ,000 or more 0.10 Salary (R$) 1, Observations 2,772,993 Source: RAIS 2010, MTE All variables reported as percentages except age (years), salary (Reais) and total observations (plants). 27

28 Table 6: Estimated Effects on Race Change (1) (2) (3) (4) to Black/Brown to Black/Brown to White to White Education No Schooling (10.14) (11.86) (-10.10) (-9.70) < Elementary (17.33) (42.25) (-12.97) (-33.46) Elementary (0.41) (22.34) (3.62) (-21.47) Some HS (2.29) (17.42) (-0.22) (-18.43) High School (.) (.) (.) (.) Some University (-22.70) (-28.38) (9.64) (14.43) Bachelor s (+) (-15.67) (-29.96) (14.66) (32.03) New Log Income (-12.97) (-64.07) (-5.04) (3.44) Previous Log Income (-11.49) (-19.67) (7.06) (2.15) New Occupation Public Aministration/Mgmt Source: RAIS 2010, MTE. Indicates model includes plant effects. t statistics in parentheses. p < 0.05, p < 0.01, p < Continued on next page. 28

29 Table 6, Continued: Estimated Effects on Race Change (1) (2) (3) (4) to Black/Brown to Black/Brown to White to White (-5.47) (-4.11) (-10.85) (-4.95) Professionals/Arts/Science (-1.15) (-2.67) (-0.59) (1.90) Administrative (-0.58) (-3.36) (-7.20) (0.83) Service/Vendors (3.62) (14.98) (-4.09) (-1.28) Agriculture (6.64) (22.97) (-6.85) (-8.58) Production I (7.12) (28.22) (-3.00) (-5.79) Production II (-1.15) (15.87) (0.14) (-3.85) Repair/Maintenance (5.82) (13.31) (-2.39) (-4.51) Previous Occupation Public Administration/Mgmt (-2.96) (-0.14) (-5.60) (-1.11) Professionals/Arts/Science (-2.38) (-4.42) (-2.45) (5.46) Administrative Source: RAIS 2010, MTE. Indicates model includes plant effects. t statistics in parentheses. p < 0.05, p < 0.01, p < Continued on next page. 29

30 Table 6, Continued: Estimated Effects on Race Change (1) (2) (3) (4) to Black/Brown to Black/Brown to White to White (-7.10) (-8.69) (-2.08) (-2.02) Service/Vendor (1.36) (5.80) (1.03) (-4.08) Agriculture (5.97) (14.44) (2.42) (-3.91) Production I (2.11) (19.33) (3.18) (-5.95) Production II (0.11) (12.16) (2.87) (-5.25) Repair/Maintenance (9.12) (13.54) (0.19) (-3.90) Previous Industry Agriculture/Fishing (-20.84) (-11.37) (5.22) (-5.05) Mining (-8.49) (-10.44) (-2.73) (-7.39) Production (-35.58) (-18.99) (9.39) (-5.38) Utilities (-22.67) (-6.85) (-9.93) (-7.32) Trade/Repair Source: RAIS 2010, MTE. Indicates model includes plant effects. t statistics in parentheses. p < 0.05, p < 0.01, p < Continued on next page. 30

31 Table 6, Continued: Estimated Effects on Race Change (1) (2) (3) (4) to Black/Brown to Black/Brown to White to White (-29.78) (-22.35) (5.62) (-0.61) Food/Lodging (-21.76) (-3.81) (9.46) (0.20) Transp./Storage/Comm (-20.00) (-15.65) (3.93) (-0.96) Finance/Banking (-33.18) (-8.40) (-10.58) (-7.01) Real Estate (-13.09) (9.22) (11.83) (10.45) Defense/Soc. Sec (-10.46) (-0.93) (-1.10) (-2.23) Education (-24.16) (-6.23) (3.55) (-3.46) Health/Social Serv (-19.06) (-10.34) (2.19) (-2.85) Other Social/Personal Serv (-17.73) (-1.43) (4.93) (2.28) Domestic Org (-6.00) (0.73) (3.72) (0.55) Int l Org (-0.06) (-1.02) (1.44) (0.75) Source: RAIS 2010, MTE. Indicates model includes plant effects. t statistics in parentheses. p < 0.05, p < 0.01, p < Continued on next page. 31

32 Table 6, Continued: Estimated Effects on Race Change (1) (2) (3) (4) to Black/Brown to Black/Brown to White to White Age (years) (-10.98) (-25.25) (-9.91) (-0.79) Constant (43.32) (132.13) (13.04) (51.88) Observations R Adjusted R Source: RAIS 2010, MTE. Indicates model includes plant effects. t statistics in parentheses. p < 0.05, p < 0.01, p <

33 Table 7: Estimated Effects of Plant Characteristics on Plant Effects, 2010 (1) (2) Plant Effect - To Black/Brown Plant Effect - To White White share ( ) (389.69) Plant Size 5 to (2.93) (-2.57) 10 to (3.03) (-1.99) 20 to (4.23) (-2.16) 50 to (2.25) (-5.99) 100 to (4.97) (-7.41) 250 to (6.24) (-5.96) 500 to (8.95) (-4.07) 1,000 or more (10.55) (-4.26) Legal Formation Source: RAIS 2010, MTE. t statistics in parentheses p < 0.05, p < 0.01, p < Continued on next page. 33

34 Table 7, Continued: Estimated Effects of Plant Characteristics on Plant Effects (1) (2) Plant Effect - To Black/Brown Plant Effect - To White (-1.32) (1.63) (-2.06) (0.03) (-4.01) (0.91) (-88.31) (101.67) (-2.16) (2.00) (-2.67) (0.08) (-6.61) (0.27) (-1.23) (-5.39) (-3.85) (-1.01) (-1.56) (1.29) (-6.13) Source: RAIS 2010, MTE. t statistics in parentheses p < 0.05, p < 0.01, p < Continued on next page. 34

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