Assessing wage inequality with machine learning: Approaches for measuring the adjusted gender pay gap

Oliver Plüghan and Katharina-Maria Rehfeld

IU Discussion Papers - Human Resources from IU International University of Applied Sciences

Abstract: This paper investigates the methodological performance of Ordinary Least Squares (OLS) regression and Random Forest machine learning algorithms in measuring adjusted gender pay gaps. The research is motivated by the European Union's Pay Transparency Directive (2023/970), which mandates that employers report adjusted gender pay gaps. While Oaxaca-Blinder Decomposition and the underlying OLS regression have served as the industry standard for gap estimation, this paper examines whether machine learning approaches can better capture complex, nonlinear compensation relationships. Using synthetic datasets with controlled discrimination parameters, the study compares both methods across two sample sizes and multiple discrimination scenarios. Key findings demonstrate that both methods successfully distinguish between occupational segregation and direct wage discrimination at large sample sizes. However, at smaller sample sizes, Random Forest exhibits substantial instability whereas OLS remains slightly more stable. A methodological adjustment, training Random Forest on the larger population before applying predictions to subsets substantially improves small-sample performance. The paper concludes that OLS regression remains preferable for formal regulatory compliance due to its interpretability and stability, while Random Forest can serve as a complementary validation tool for largescale analysis.

Keywords: Gender Pay Gap; Pay Transparency; OLS Regression; Random Forest; Wage Discrimination; Unexplained Wage Gap; Adjusted Gender Pay Gap (search for similar items in EconPapers)
JEL-codes: C13 C45 J16 J31 J71 M52 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iubhhr:340172

DOI: 10.56250/4118

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