The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?

TL;DR

This study uses a large Swiss dataset to show that methodological choices significantly influence estimates of the unexplained gender pay gap, with advanced methods reducing the gap estimates substantially.

Contribution

It demonstrates how big data enables more flexible and comparable analyses, revealing that traditional methods may overestimate the gender pay gap.

Findings

Blinder-Oaxaca estimates decrease by up to 39% with better comparability and flexibility.

Semi-parametric matching estimates are up to 50% smaller than traditional methods.

Methodological choices greatly affect the estimated size of the gender pay gap.

Abstract

The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included.