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DTSTART:20210328T010000
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DTSTART;TZID=Europe/Helsinki:20210420T150000
DTEND;TZID=Europe/Helsinki:20210420T161500
DTSTAMP:20260712T072237
CREATED:20210211T075508Z
LAST-MODIFIED:20241104T133241Z
UID:10645-1618930800-1618935300@crest.science
SUMMARY:Anthony STRITTMATTER (CREST)  - "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter? "
DESCRIPTION:Microeconometrics Seminar: Every Tuesday \nTime: 03:00 pm – 4:15 pm\nDate: 20th of April 2021\nby visio\nAnthony STRITTMATTER (CREST) – “ The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter? ” \nAbstract: 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 restrictive linear Blinder-Oaxaca estimates\, are up to 50% smaller and also less sensitive to the way wage determinants are included. \nOrganizers:\n\nBenoît SCHMUTZ (Pôle d’économie du CREST)\nAnthony STRITTMATTER (Pôle d’économie du CREST)\nSponsors:\nCREST \n\n
URL:https://crest.science/event/anthony-strittmatter-2/
CATEGORIES:Applied Seminar,Economics,Microeconometrics,Seminars
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