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

Marina Bonaccolto-Töpfer and Stephanie Briel

Labour Economics, 2022, vol. 78, issue C

Abstract: This paper analyses gender differences in pay at the mean as well as along the wage distribution in Germany. 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 estimated 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 covariates at various points of the distribution. This insight suggests that more flexible specifications are needed to estimate gender differences in pay more appropriately.

Keywords: Gender pay gap; Machine learning; Model selection; Unconditional quantile regression (search for similar items in EconPapers)
JEL-codes: J16 J31 J7 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:78:y:2022:i:c:s0927537122001130

DOI: 10.1016/j.labeco.2022.102223

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