The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?

Strittmatter, Anthony and Wunsch, Conny. (2021) The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter? WWZ Working Paper, 2021 (05). Basel.

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Official URL: https://edoc.unibas.ch/82092/

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The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model exibility when controlling for wage determinants, and iii) the choice of different parametric and semiparametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more exible specication of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Arbeitsmarktökonomie (Wunsch)
12 Special Collections > WWZ Publications > WWZ Discussion Papers and Working Papers
UniBasel Contributors:Wunsch, Conny and Strittmatter, Anthony
Item Type:Working Paper
Number of Pages:41
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
Identification Number:
  • handle: RePEc:bsl:wpaper:2021/05
edoc DOI:
Last Modified:23 Feb 2021 14:59
Deposited On:23 Feb 2021 14:59

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