In this paper we study the relationship between ethnic exclusion and earnings in Urban Peru. Our approach to the concept of ethnicity involves the usage of instruments in many of its several dimensions: mother tongue, parental background, religion, migration events and race. In order to approximate what can be called racial differences in a context like the Peruvian in which “racial mixture” is the main characteristic of the population, we use a scorebased procedure to capture both the differences and the mixtures. By means of this procedure each individual is assigned intensities by pollsters in each of the four categories that correspond to the most easily recognized distinct racial groups in the Peruvian society: Asiatic, White, Indigenous, and Black. We find that the multidimensional race indicator is correlated with several human capital and physical capital assets, as well as with access to public services. Using an extension of the Blinder-Oaxaca (B-O) decompositions to allow comparisons among infinitely many groups, we find that a substantial part of the earnings differences between racial groups cannot be explained by differences in individual characteristics. The results suggests that among wage earners after controlling for a large set of characteristics, there are racially related earnings differences in favor of predominantly White individuals. In the case of the self-employed, none of the empirical distributions of earning differences attributable to race is substantially above zero.
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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number
980.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Horowitz, Joel L., 2001.
"The Bootstrap,"
Handbook of Econometrics,
in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228
Elsevier.
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