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

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 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. We find that the observed hiring bias diminishes as employers gain more hiring experience on the platform. Sub-analyses show that women are highly preferred in feminine-typed occupations, while men are only slightly preferred in masculine-typed occupations. Interestingly, women gain an advantage in gender-neutral jobs. We further run an experiment to uncover the underlying gender-specific traits that influence hiring outcomes. Our findings provide key insights for several groups of stakeholders including policymakers, platform owners, hiring managers, and workers. Managerial and practical implications are discussed.

BIODATA:

Dr. Jason Chan is an Assistant Professor of Information & Decision Sciences at the Carlson School of Management, University of Minnesota. He holds an interest towards research that has relevant business and policy insights on emerging phenomenon relating Internet platforms and social outcomes, in various areas including healthcare, crime, financial well-being, education, and labor discrimination. In his research, he adopts a variety of quantitative methods including econometric modeling, experiments and technical methods, to extract meaningful relationships that lies within datasets.

His work has been published in top academic journals and conferences such as Management Science, MIS Quarterly, and the International Conference on Information Systems, and has been presented at the National Bureau of Economic Research. His work has also been covered by prominent media outlets, including The Economist, The Washington Post, The Economic Times, NBC News, Newsweek, Forbes, The Daily Beast, and Market Watch. Dr. Chan is a recipient and nominee of several Best Paper Awards in IS conferences and workshops, and has received multiple research grants for his work. He has received the AIS Best Published Paper 2014 (selected by senior editors among top MIS journals), the MISQ Best Paper Award 2014, and is also the winner of the 2015 Nunamaker-Chen Dissertation Award conferred by INFORMS ISS. He serves as a reviewer for multiple top-tier MIS journals, conferences, and has served as Associate Editor at the International Conference of Information Systems (ICIS).