Gender Wage Gaps Reconsidered: A Structural Approach Using Matched Employer-Employee Data

Abstract

In this paper I propose and estimate an equilibrium search model using matched employer-employee data to study the extent to which wage differentials between men and women can be explained by differences in productivity, disparities in friction patterns, segregation or wage discrimination. The availability of matched employer-employee data is essential to empirically disentangle differences in workers productivity across groups from differences in wage policies toward those groups. The model features rent splitting, on-the-job search and two-sided heterogeneity in productivity. It is estimated using German microdata. I find that female workers are less productive and more mobile than males. Female workers have on average slightly lower bargaining power than their male counterparts. The total gender wage gap is 42 percent. It turns out that most of the gap, 65 percent, is accounted for by differences in productivity, 17 percent of this gap is driven by segregation while differences in destruction rates explain 9 percent of the total wage-gap. Netting out differences in offer-arrival rates would increase the gap by 13 percent. Due to differences in wage setting, female workers receive wages 9 percent lower than male ones.labor market discrimination; search frictions; structural estimation; matched employer-employee data