Quantile Selection in the Gender Pay Gap

TL;DR

This paper introduces a semiparametric method using instrumental variables to estimate selection-corrected quantiles of the gender wage gap, revealing nuanced patterns of positive selection among different education groups.

Contribution

It develops a new approach for quantile estimation of the gender pay gap that accounts for selection bias without parametric restrictions, applicable to labor supply models.

Findings

Positive selection among less-educated women widens the gender gap at lower earnings.

Strong positive selection among highly educated men narrows the gap at upper quantiles.

Method applied to German data reveals complex selection patterns in full-time earnings.

Abstract

We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide semiparametric identification of the quantile parameters without imposing parametric restrictions on the selection probability, derive the asymptotic distribution of the proposed estimator based on constrained selection probability weighting, and demonstrate how the approach applies to the Roy model of labor supply. Using German administrative data, we analyze the distribution of the gender gap in full-time earnings. We find pronounced positive selection among women at the lower end, especially those with less education, which widens the gender gap in this segment, and strong positive selection among highly educated men at the top, which narrows the gender wage gap at upper quantiles.