Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 flexibility
when controlling for wage determinants, and iii) the choice of different
parametric and semi-parametric 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 flexible
specification 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.

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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: quant-ph