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Home | Events Archive | Monitoring Recruiters at Work: Ethnic Discrimination on an Online Recruitment Platform

Monitoring Recruiters at Work: Ethnic Discrimination on an Online Recruitment Platform

  • Series
  • Speaker(s)
    Michael Siegenthaler (ETH Zürich, Switzerland)
  • Field
    Empirical Microeconomics
  • Location
    Tinbergen Institute (Gustav Mahlerplein 117), Room 1.01
  • Date and time

    February 25, 2020
    16:00 - 17:15

Women (compared to men) and ethnic minorities (compared to natives) face inferior labor market outcomes in many economies, but the extent to—and the channels through—which discrimination is responsible for these effects remains unclear. We introduce a new approach to investigating hiring discrimination that combines tracking of recruiters’ search behavior on employment websites and supervised machine learning to control for all job-seeker characteristics that are visible to recruiters. We apply this methodology to the Swiss government-affiliated, online recruitment platform. Based on more than 3 million decisions, we find that, depending on their country of origin, ethnic minorities face 3–19% lower contact rates than otherwise identical natives. These ethnic penalties are larger during the hours just before noon and towards the end of the workday, when recruiters spend less time evaluating each CV. Testing for attention discrimination, we find that employers spend less time on the profiles of certain ethnic groups, but the economic effect is small. We also find that skills and labor market tightness moderate discrimination: ethnic penalties are larger for job seekers with low employability and limited German skills, and if there is a larger pool of candidates to choose from. Lastly, we find that obtaining a Swiss passport substantially reduces discrimination against immigrant job seekers. Our approach provides a widely applicable, non-intrusive, and cost-efficient tool that researchers and policy-makers can employ to continuously monitor hiring discrimination, and to inform approaches to counter it. oint with Andreas Beerli, Jan Ruffner, and Giovanni Peri.

Here is a link to the description of the paper.