Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias

Management Science (Forthcoming)

63 Pages Posted: 21 Jun 2015 Last revised: 24 Dec 2016

See all articles by Jason Chan

Jason Chan

University of Minnesota - Twin Cities - Carlson School of Management

Jing Wang

Hong Kong University of Science & Technology

Date Written: December 20, 2016

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, as the hiring decisions made on these online platforms implicate the incomes of millions of workers worldwide. Despite this importance, limited effort has been devoted to understanding whether potential hiring biases exist in online labor platforms and how they may 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 holistic set of job and worker controls, a matched sample approach, and a quasi-experimental technique, we find evidence of a positive hiring bias in favor of female workers. An experiment was used to uncover the underlying gender-specific traits that could influence hiring outcomes. We find that the observed hiring bias diminishes as employers gain more hiring experience on the platform. In addition, the female hiring bias appears to stem solely from the consideration of applicants from developing countries, and not those from developed countries. Sub-analyses show that women are preferred in feminine-typed occupations while men do not enjoy higher hiring likelihoods in masculine-typed occupations. We also find that female employers are more susceptible to the female hiring bias compared to male employers. Our findings provide key insights for several groups of stakeholders including policymakers, platform owners, hiring managers, and workers. Managerial and practical implications are discussed.

Keywords: Online Labor markets, hiring bias, gender stereotypes, propensity score matching, quasi-experiment

Suggested Citation

Chan, Jason and Wang, Jing, Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias (December 20, 2016). Management Science (Forthcoming). Available at SSRN: https://ssrn.com/abstract=2619922 or http://dx.doi.org/10.2139/ssrn.2619922

Jason Chan (Contact Author)

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

HOME PAGE: http://carlsonschool.umn.edu/faculty/jason-chan

Jing Wang

Hong Kong University of Science & Technology ( email )

Lee Shau Kee Business Building
Clearwater Bay
Kowloon
Hong Kong

HOME PAGE: http://www.bm.ust.hk/isom/faculty-and-staff/directory/jwang

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