Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey for white workers in these high-paying firms, which suggests discriminatory practices in hiring and retention of workers. Moreover, within firm differences in wage premiums across races account for another 5-6% of the total racial wage gap. The wage losses due to unexplained differences in sorting and wage-setting are larger among workers with higher skills, as measured by workers fixed-effects, suggesting that discrimination may be important in explaining differences at the top of the distribution. We may therefore conjecture that the observed decrease in firm heterogeneity during the 1990s and 2000s documented by Alvarez et al. (2018) may be somehow related with the reduction in the wage gap across races. However, without causal models it is not possible to pin down the exact direction of this effect: was it the reduction in firms’ heterogeneity that led to a decrease in racial wage gaps, or was it a reduction in discrimination that contributed partially to the compression of the firm-specific pay premium?

Machado et al. (2018) find that the gender earnings gap in Brazil is diminishing across generations, at least within the formal sector. If taste-based discrimination is also behind the observed gender gap, then the trade liberalization episode in the early 1990s may be also responsible for part of the reduction in the gender gap. Morchio and Moser (2019) develop an equilibrium search model that includes firm and worker heterogeneity, as well as gender-specific amenities and employer taste for discrimination. Using matched data for firms and workers in the formal Brazilian sector, they find evidence for taste-based discrimination, as it explains around 2/3 of the observed raw gender gap in earnings<sup>17</sup> of 15 log points. The gender earnings gap conditional on a richer set of dummies - years of schooling, interacted with potential experience dummies, and state, industry and occupation dummies - increases the earnings gap to 22 log points,<sup>18</sup> meaning that women are on average endowed with more characteristics that earn higher returns, such as years of education. As Gerard et al. (2018) did for races, they also find that firm heterogeneity plays an important role in explaining differences of payment across genders. They decompose the overall gender gap into a within- and between-firm pay gap, finding that half of the differences is explained by each of these components. The importance of firms effects, together with occupation and industry effects, is also documented by Machado et al. (2018), with a larger impact of these covariates on the gender gap among more educated workers. Again, given the recent decrease in firms fixed-effects documented by Alvarez et al. (2018), it is not clear whether this was partially caused by a reduction in gender differences in payment or the reduction in the gender gap was the result of decreasing inequality in payment policies across firms.

Considerable differences in the lifecycle income profile of men and women are an

> 17Earnings include differences in hours worked. For hourly wages, the gap is 7.8 log points.

> 1820 log points for hourly wages.

20