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The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies

In: Race, Ethnicity, and Economic Statistics for the 21st Century

Author

Listed:
  • Alexander Berry
  • Molly Maloney
  • David Neumark
Abstract
Stronger enforcement of discrimination laws can help to reduce disparities in economic outcomes with respect to race, ethnicity, and gender in the United States. However, the data necessary to detect possible discrimination and to act to counter it is not publicly available – in particular, data on racial, ethnic, and gender disparities within specific companies. In this paper, we explore and develop methods to use information extracted from publicly available LinkedIn data to measure the racial, ethnic, and gender composition of company workforces. We use predictive tools based on both names and pictures to identify race, ethnicity, and gender. We show that one can use LinkedIn data to obtain reasonably reliable measures of workforce demographic composition by race, ethnicity, and gender, based on validation exercises comparing estimates from scraped LinkedIn data to two sources: ACS data, and company diversity or EEO-1 reports. We apply our methods to study the race, ethnic, and gender composition of workers who were hired and those who experienced mass layoffs at two large companies. Finally, we explore using LinkedIn data to measure race, ethnic, and gender differences in promotion. In our analyses of layoffs and promotions, we find suggestive evidence of discrimination at some of the companies we study, including evidence of “intersectional” discrimination against black and Hispanic women.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Alexander Berry & Molly Maloney & David Neumark, 2024. "The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14962
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    References listed on IDEAS

    as
    1. Pierre-Philippe Combes & Bruno Decreuse & Morgane Laouénan & Alain Trannoy, 2016. "Customer Discrimination and Employment Outcomes: Theory and Evidence from the French Labor Market," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 107-160.
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    More about this item

    JEL classification:

    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J7 - Labor and Demographic Economics - - Labor Discrimination

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