Detailed Gender Wage Gap Decompositions: Controlling for Worker Unobserved Heterogeneity Using Network Theory

TL;DR

This paper introduces a network theory-based method to improve gender wage gap decompositions by controlling for unobserved worker heterogeneity and addressing support overlap issues, applied to Brazil.

Contribution

It develops a novel network approach for controlling unobserved heterogeneity and extends decomposition tools to handle non-overlapping covariate supports.

Findings

Effective control for unobserved skills heterogeneity.

Enhanced decomposition accuracy with support overlap adjustments.

Application reveals detailed gender wage gap insights in Brazil.

Abstract

Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. In this context, building reliable counterfactuals requires using tighter controls to ensure that similar workers are correctly identified by making sure that important unobserved variables such as skills are controlled for, as well as comparing only workers with similar observable characteristics. This paper contributes to the wage decomposition literature in two main ways: (i) developing an economic principled network based 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.