An Experiment in Hiring Discrimination via Online Social Networks

Alessandro Acquisti and Christina Fong ()
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Christina Fong: Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Management Science, 2020, vol. 66, issue 3, 1005-1024

Abstract: We investigate whether personal information posted by job candidates on social media sites is sought and used by prospective U.S. employers. We create profiles for job candidates on popular social networks, manipulating information protected under U.S. laws, and submit job applications on their behalf to more than 4,000 employers. We estimate employer search activity and bias in interview callbacks. We find evidence of employers searching online for the candidates. At the national level, we find no significant difference in the callback rates for a Muslim versus a Christian candidate, or for a gay versus a straight candidate. However, employers in Republican areas exhibit significant bias against the Muslim candidate relative to the Christian candidate. This bias is significantly larger than the bias in Democratic areas. The results on callback bias are robust to using state- and county-level data, to controlling for firm, job, and geographical characteristics, to including additional interaction effects in the empirical specification, and to several estimation strategies. The results suggest that the online disclosure of certain personal traits can influence the hiring decisions of some U.S. employers, but the likelihood of hiring discrimination via online searches varies across employers.

Keywords: online social networks; information systems; privacy; field experiments; economics; discrimination (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:66:y:2020:i:3:p:1005-1024

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