Meeting Link: https://umich.zoom.us/j/99929959678
Passcode: csaaw

Abstract: Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. It is possible to decompose the wage gap into (1) a factor explained by differences in workers' covariates and (2) a residual portion potentially due to discrimination and/or to unobservable factors dictating workers' productivitiy. This work proposes a method to leverage the information contained in the labor market network, in order to enhance controls for the wage gap decomposition exercise. More precisely, we contribute to the wage decomposition literature in two main ways: (i) developing an economic-principled network theory approach to control for unobserved worker skills heterogeneity in the presence of potential discrimination; and (ii) extending existing generic decomposition tools to accommodate for potential lack of overlapping supports in covariates between groups being compared, which is likely to be the norm in more detailed decompositions. We illustrate the methodology by decomposing the gender wage gap in Brazil.