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

Jason Chan () and Jing Wang ()
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Jason Chan: Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Jing Wang: School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

Management Science, 2018, vol. 64, issue 7, 2973-2994

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 data set 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. Subanalyses 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 policy makers, platform owners, hiring managers, and workers. Managerial and practical implications are discussed.

Keywords: online labor markets; hiring bias; gender stereotypes; quasi-experiment; econometrics (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (27)

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