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

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  • Brieland, Stephanie
  • Töpfer, Marina

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.

Suggested Citation

  • Brieland, Stephanie & Töpfer, Marina, 2020. "The gender pay gap revisited: Does machine learning offer new insights?," Discussion Papers 111, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    • Handle: RePEc:zbw:faulre:111
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    2. Olga Takács & János Vincze, 2023. "Where is the pain the most acute? The market segments particularly affected by gender wage discrimination in Hungary," CERS-IE WORKING PAPERS 2304, Institute of Economics, Centre for Economic and Regional Studies.

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    More about this item

    Keywords

    Gender pay gap; Machine Learning; Selection on unobservables;
    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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