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

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  • Bonaccolto-Töpfer, Marina
  • Briel, Stephanie

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.

Suggested Citation

  • Bonaccolto-Töpfer, Marina & Briel, Stephanie, 2022. "The gender pay gap revisited: Does machine learning offer new insights?," Labour Economics, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:labeco:v:78:y:2022:i:c:s0927537122001130
    DOI: 10.1016/j.labeco.2022.102223
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    1. Bonaccolto-Töpfer, Marina & Castagnetti, Carolina & Rosti, Luisa, 2023. "Changes in the gender pay gap over time: the case of West Germany," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-11.

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    More about this item

    Keywords

    Gender pay gap; Machine learning; Model selection; Unconditional quantile regression;
    All these keywords.

    JEL classification:

    • J7 - Labor and Demographic Economics - - Labor Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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