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

نویسندگان

چکیده

The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions Blinder-Oaxaca decomposition. Using a notably rich and large data set 1.7 million employees in Switzerland, we investigate how methodological improvements made possible by such big affect estimates gap. We study sensitivity with regard to i) availability observationally comparable men women, ii) model flexibility when controlling for wage determinants, iii) choice different parametric semi-parametric estimators, including variants make machine learning methods. find these three factors matter greatly. decline up 39% enforce comparability between women more flexible specification equation. Semi-parametric matching yields compared estimates, are 50% smaller also less sensitive way determinants included.

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ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2021

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.3798933