Gender Differences in Comparative Advantage Matches: Evidence from Linked Employer-Employee Data

TL;DR

This paper introduces a novel Gaussian mixture and k-Means clustering method to analyze how firm-worker match effects contribute to the gender wage gap, revealing significant roles of firm characteristics and employment patterns.

Contribution

It develops a new decomposition approach to quantify the impact of match effects on the gender wage gap using large administrative data.

Findings

Match effects account for about 17% of the gender wage gap.

Larger firms and higher human capital are associated with bigger match effects.

Women are less likely to work in firms with higher returns, increasing the firm contribution to the wage gap.

Abstract

In this paper, I introduce a novel decomposition method based on Gaussian mixtures and k-Means clustering, applied to a large Brazilian administrative dataset, to analyze the gender wage gap through the lens of worker-firm interactions shaped by comparative advantage. These interactions generate wage levels in logs that exceed the simple sum of worker and firm components, making them challenging for traditional linear models to capture effectively. I find that these ``complementarity effects'' account for approximately 17% of the gender wage gap. Larger firms, high human capital, STEM degrees, and managerial roles are closely related to it. For instance, among managerial occupations, the match effect goes as high as one-third of the total gap. I also find women are less likely to be employed by firms offering higher returns to both human capital and firm-specific premiums, resulting in a significantly larger firm contribution to the gender wage gap than previously estimated. Combined, these factors explain nearly half of the overall gender wage gap, suggesting the importance of understanding firm-worker matches in addressing gender-based pay disparities.