Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data

TL;DR

This study applies advanced causal inference techniques to large academic salary data, revealing a gender wage gap of about 6% among faculty, with disparities varying by career stage and productivity.

Contribution

It demonstrates the application of modern causal inference methods, like propensity score matching and causal forests, to quantify gender wage disparities in academia.

Findings

Female faculty earn approximately 6% less than male colleagues.

Wage gap varies across career stages and research productivity levels.

Causal inference methods can effectively analyze structural disparities in observational data.

Abstract

Observational studies often present challenges for causal inference due to confounding and heterogeneity. In this paper, we illustrate how modern causal inference methods can be applied to large-scale academic salary data. Using records from 12,039 tenure-track faculty in the University of North Carolina system, linked with bibliometric indicators and institutional classifications, we estimate the causal effect of gender on faculty salaries. Our analysis combines propensity score matching with causal forests to adjust for rank, discipline, research productivity, and career experience. Results indicate that female faculty earn approximately 6% less than comparable male colleagues, with variation in the gap across career stages and levels of research productivity. This case study demonstrates how causal inference methods for observational data can provide insight into structural disparities in complex social systems.