Table 2 shows the results from standard Oaxaca-Blinder decompositions of log earnings, comparing in turn men and women within race, whites and nonwhites within gender, and white men to nonwhite women.<sup>11</sup> Women tend to receive a relatively higher payoff from economic attainment than do men (with the notable exception of white women in Guatemala).

Table 2 shows that Guyana has noticeably small wage differentials by race/ethnicity, while Bolivia has the smallest wage differential by gender within the dominant group and Guatemala has the widest gender differentials. Notably, while racial/ethnic differences (controlling for gender) have a large characteristics component,<sup>12</sup> gender differences (controlling for race/ethnicity) have a large differences-in-treatment component. Indeed, women’s characteristics in both Bolivia and Brazil (and in Guyana for Afro-origin women) would contribute to lowered earnings inequality if it were not for the offsetting effects of differences in coefficients. While the “endowed” differences between races/ethnicities are by region and education, the differences between men and women are primarily in employment position. The difference in wages by race/ethnicity that can be attributed to returns to endowments (i.e., the differences in coefficients) is primarily due to education. The returns to education are also important for explaining the gender wage gap, but returns to other factors also emerge as important.

A focus on decompositions such as provided in Table 2 makes it appear as though earnings differences would decline notably if differences in treatment (i.e., differences in coefficients) were eradicated, and also that race/ethnicity differences would diminish substantially in all countries save Guyana if differences in characteristics were narrowed.

11 This paper’s technical appendix, available from this World Bank website, contains the full regression results used to create these decompositions, including the first stage multinomial logit results and the final stage OLS earnings regressions for the multi-stage simulation described below. The surveys vary in sample size, availability of variables, and goodness of fit of the earnings equations. While the four country surveys used herein were chosen in part because they had relatively good and also relatively similar data available, the specifications are not identical due to data limitations and coding differences. However, some standard patterns of returns occur across all four countries, namely the constancy of positive returns to higher educational attainment, and a traditional quadratic relationship between age and earnings that is remarkably similar across the four countries. There are positive relationships to earnings of being in urban rather than rural settings, and having employment in a relatively more formal sector.

12 This has been found in other studies of Latin American data, including MacIsaac and Patrinos (1995), using Peruvian data, which finds fifty to seventy percent of the log earnings gap between indigenous and nonindigenous men due to differences in characteristics; Patrinos, Velez, and Psacharopoulos (1994), which finds seventy-eight percent of the difference between Spanish and Guarani speakers due to differences in characteristics; and Psacharopoulos and Patrinos (1994), although Patrinos (1997) points out variation in the proportion attributable to characteristics among the various ethnic groups within Guatemala. Our result is partly consistent with Soares (2000), 5