Responsible researcher: Eduarda Miller de Figueiredo
Authors: François Gerard, Lorenzo Lagos, Edson Severnini and David Card
Intervention Location: Southeast Region of Brazil
Sample Size:
Sector: Job Market
Variable of Main Interest: Hourly wage log
Type of Intervention: Salary
Methodology: Other
Summary
The political debate surrounding racial differences in education levels between whites and non-whites is on the rise in Latin American countries, noting that non-whites continue to be underrepresented in high-paying sectors. Analyzing data from PNAD and RAIS for the Southeast region of Brazil, the article estimates wage differences between races and genders, considering individual and establishment effects. The results demonstrate a wage disparity between races.
Salary differentiation between whites and non-whites occurs in many countries around the world. Randomized studies show that employer callback rates are lower for minority job applicants, implying that some companies set a higher standard for admitting non-white applicants or avoid hiring minorities. Also suggesting that employers assign non-White workers to low-paying occupations, accounting for some of the racial pay disparities within companies (Penner, 2008; Giuliano, Leonard, and Levine, 2009, 2011). However, it is unclear how much these patterns contribute to the disparities in average wages between whites and non-whites (Lang and Lehmann, 2012).
Understanding this differentiated hiring is particularly relevant in Brazil, a country where almost half of all workers identify as non-white.
In Brazil, around 50% of men and women of working age are white, 42% consider themselves mixed (“pardo”) and 8% are black. In the southeast region, 57% are white and 33% are mixed. On average, 45% of Brazilian men employed in the private sector during the study period had completed high school, with a higher rate for whites (53%) than for non-whites (38%).
When looking at average log hourly wage statistics, a few factors stand out: (i) white workers of both sexes earn about 30% to 35% more than non-white workers; (ii) wage levels are more than ten log points higher in the southeast than in the country as a whole, but wage disparities remain similar; and (iii) average salaries for brown and black people are just a few percentage points apart.
The informal sector in Brazil is large, with only 80% of private sector employees claiming to have a valid work card, which is an indication of formal work in the country.
The main analysis of the study uses administrative data for formal sector workers in the southeast region of Brazil, which includes Espírito Santo, Minas Gerais, Rio de Janeiro and São Paulo. Through the National Household Sample Survey (PNAD), annual information was collected on the labor market for formal and informal workers. Data from men and women aged between 25 and 54 years old, with at least one year of experience in the labor market and employees in the private sector were used.
To estimate the impacts of company policies on racial wage disparities, the authors used the Annual Social Information List (RAIS), which provides universal coverage of formal employment data in the country (Ministry of Labor, 2015).
To assess the issue of informality, simple linear probability models were estimated for the incidence of formality. The models accurately suggest null effects of nonwhite race on the probability of formality. Furthermore, the size of the unexplained Whitenon-White wage differences was compared based on samples that include all employees in the private sector and only those in the formal sector.
Results were estimated based on the AKM model (Abowd, Kramarz, and Margolis, 1999), with the dependent variable being the log of the hourly wage paid to worker i in race-gender group g in December of year t . A fixed effect per person was added, as well as a set of time-varying controls. The results were also estimated through instrumental variables, using the estimated wage premium for workers of the same racial group but of the opposite gender, as an instrument for the group's set of wages in each establishment.
According to the authors, it is important to emphasize that the person effects estimated in an AKM model incorporate any unobserved components of human capital, such as differences in school quality or choice of higher education course. Therefore, differences in school or degree quality between whites and nonwhites were likely reflected in skill-based measures but did not influence residual classification measures.
Estimates suggest that personal effects account for 51% to 62% of wage variation, while establishment effects account for 20% to 23%. Worker and firm effects are positively correlated within each gender-race group, which accounts for 8% to 11% of the overall wage variation for non-whites and 18% for whites. Together, differences in wages paid by different establishments and the strong pattern of matching between workers and establishments explain about 30% to 40% of the variation in wages for all gender and racial groups. These estimates are similar to those reported by Card, Heining and Kline (2013) for the analysis of Germany and by Lavetti and Schmutte (2016) for Brazil.
When performing the racial wage decomposition into personal and establishment effects, the authors find a wage difference of 15.5 percentage points for men and 23.8 percentage points for women, with most of this gap between wages being attributed to differences in personal effects. Therefore, person effects and time-varying covariates account for 79% and 75% of the overall white-nonwhite wage gap for men and women, respectively.
For education, estimates were made for three categories of education: workers with less than a high school education, high school graduates but who did not complete college, and college graduates. The results demonstrate that the wage gap between whites and non-whites increases sharply in all three categories for both men and women, ranging from about 5 percentage points for workers without a high school education to 19-22 percentage points for those with a college degree. The results also demonstrate that the increase in individual effects and establishment effects, for higher education levels, are more pronounced for whites than for non-whites for both sexes. And that, in conjunction with differences in establishment effects, increases the overall wage gap by race for high school and college graduates.
Lastly, estimates demonstrate that workers with greater transferable skills are more likely to work in establishments that pay higher premiums and that whites tend to have greater transferable skills than non-whites, earning higher wages for a given level. of skill.
The results of this article refer to political debates that are active across Latin American countries, in which racial differences in education levels are persistent and non-whites continue to be underrepresented in high-paying sectors. It has been shown that non-whites are less likely to be employed in high-value locations, even in the absence of any discriminatory employment practices.
References
GERARD, François et al. Assortative matching or exclusionary hiring? the impact of employment and pay policies on racial wage differences in brazil. American Economic Review , vol. 111, no. 10, p. 3418-57, 2021.
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