Hiring biases in online labor markets: The case of gender stereotyping

Jason Chan, Jing Wang

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

Online labor marketplaces facilitate the efficient matching of employers and workers across geographical boundaries. The exponential growth of this nascent online phenomenon holds important social and economic implications. Despite this importance, limited effort has been devoted to understand whether potential hiring biases exist in online labor platforms and how they affect hiring outcomes. Using a novel proprietary dataset from a leading online labor platform, we investigate the impact of gender-based stereotypes on hiring outcomes. After accounting for endogeneity via a matched sample approach and quasi-experimental technique, we find evidence of a positive hiring bias towards female workers at the aggregate level. Sub-category analyses show that women are preferred in female-dominated occupations, while men are preferred in male-dominated occupations. Interestingly, women also gain an advantage in gender neutral jobs. We find that the observed hiring bias diminishes as employers gain more hiring experience on the platform. Managerial and practical implications are discussed.

Original languageEnglish (US)
StatePublished - Jan 1 2014
Event35th International Conference on Information Systems: Building a Better World Through Information Systems, ICIS 2014 - Auckland, New Zealand
Duration: Dec 14 2014Dec 17 2014

Other

Other35th International Conference on Information Systems: Building a Better World Through Information Systems, ICIS 2014
CountryNew Zealand
CityAuckland
Period12/14/1412/17/14

    Fingerprint

Keywords

  • Gender stereotypes
  • Hiring bias
  • Matching
  • Online labor markets
  • Quasi-experiment

Cite this

Chan, J., & Wang, J. (2014). Hiring biases in online labor markets: The case of gender stereotyping. Paper presented at 35th International Conference on Information Systems: Building a Better World Through Information Systems, ICIS 2014, Auckland, New Zealand.