Unpicking the gender hiring bias in online labor markets

Online labor marketplaces (OLMs) allow businesses and private individuals to contract with freelancers around the globe. Recent studies suggest OLMs suffer a gender bias in hiring that favors women. Analyzing a unique proprietary dataset obtained from a leading European OLM, which includes all correspondence between sellers and buyers, we confirm this headline result: but shed important new light on ‘why’ we observe this bias, by determining exactly ‘where’ it exists. The gender hiring bias is not observed across all project types, decreasing monotonically in significance and size, as we consider higher valued projects. This result holds across all categories of project, in developing and developed country contexts and when buyers are male or female. Our findings question previous explanations for this hiring bias and draw on evidence of differences in attitudes to risk and competition between men and women, to consider alternative policies that may increase the efficiency of OLMs.

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Unpicking the gender hiring bias in online labor markets
Cerqua, A.; Urwin, P. (2018), 'Unpicking the gender hiring bias in online labor markets', Academy of Management Proceedings, 1.

Metadata

  • professional services
  • EU28
  • 2018
  • Research publication
  • work organisation, worker demographics, health and safety, employability and career progression
  • English
  • Academy of Management Proceedings (Publisher)
  • Quantitative research
  • Subscription
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