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 quasiexperimental 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 femininetyped 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.
Bibliographical noteFunding Information:
History: Accepted by Chris Forman, information systems. Funding: The authors acknowledge the financial support from the Hong Kong Research Grants Council [Project 26500514]. SupplementalMaterial: The online appendix is available at https://doi.org/10.1287/mnsc.2017.2756.
© 2017 INFORMS.
- Gender stereotypes
- Hiring bias
- Online labor markets