Using machine learning methods to estimate the gender wage gap

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Gender Wage Gap (GWG) is a classic topic in labour economics. Simply put, how do we explain the observed gap in earnings between men and women? Traditionally theGWGhas been estimated using regression models based on Mincer-type wage equations controlling for individual, job and firm characteristics. Recently the literature has shifted towards understanding the relevance of methodological choices in estimating the GWG, including new machine learning (ML) techniques. This paper contributes to the discussion by exploring the alternative machine learning techniques to estimate the GWG. Specifically, we illustrate how to implement the proposal of Ahrens et al. (2023) to use “stacking regression” in combination with the “Double-Debiased Machine Learning” methodology of Chernozhukov et al. (2018).
Original languageEnglish
Title of host publication16th International Conference of Thailand Econometric Society
PublisherSpringer
Publication statusAccepted/In press - 1 Feb 2023
Event16th International Conference of Thailand Econometric Society 2023 - Chiang Mai, Thailand
Duration: 4 Jan 20236 Jan 2023

Publication series

NameMachine Learning for Econometrics and Related Topics

Conference

Conference16th International Conference of Thailand Econometric Society 2023
Abbreviated titleTES 2023
Country/TerritoryThailand
CityChiang Mai
Period4/01/236/01/23

Keywords

  • Gender wage gap
  • Labour economics
  • Machine learning
  • Econometrics

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