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The gender pay gap revisited: Does machine learning offer new insights?

Stephanie Brieland and Marina Töpfer

No 111, Discussion Papers from Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics

Abstract: This paper analyses gender differences in pay at the mean as well as along the wage distribution. Using data from the German Socio-Economic Panel, we estimate the adjusted gender pay gap applying a machine learning method (post-double-LASSO procedure). Comparing results from this method to conventional models in the literature, we find that the size of the adjusted pay gap differs substantially depending on the approach used. The main reason is that the machine learning approach selects numerous interactions and second-order polynomials as well as different sets of covariates at various points of the wage distribution. This insight suggests that more exible specifications are needed to estimate gender differences in pay more appropriately. We further show that estimates of all models are robust to remaining selection on unobservables.

Keywords: Gender pay gap; Machine Learning; Selection on unobservables (search for similar items in EconPapers)
JEL-codes: J7 J16 J31 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-gen and nep-lma
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