Arbeitspapier

The gender pay gap revisited with big data: Do methodological choices matter?

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.

Sprache
Englisch

Erschienen in
Series: WWZ Working Paper ; No. 2021/05

Klassifikation
Wirtschaft
Wage Level and Structure; Wage Differentials
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Thema
Gender Inequality
Gender Pay Gap
Common Support
Model Specification
Matching Estimator
Machine Learning

Ereignis
Geistige Schöpfung
(wer)
Strittmatter, Anthony
Wunsch, Conny
Ereignis
Veröffentlichung
(wer)
University of Basel, Center of Business and Economics (WWZ)
(wo)
Basel
(wann)
2021

DOI
doi:10.5451/unibas-ep82092
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Strittmatter, Anthony
  • Wunsch, Conny
  • University of Basel, Center of Business and Economics (WWZ)

Entstanden

  • 2021

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