An Experiment in Hiring Discrimination via Online Social Networks

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. This paper was accepted by Chris Forman, information systems.

The Willingness to Sell Personal Data

“…Acquisti and Fong (2015) report that a considerable share of US employers use data from social networks to discriminate against certain groups. 28 Another reason why this can be only a rough estimate of a lower bound, is that the data sold during our experiments were disclosed to only one company and not to all companies.C The editors of The Scandinavian Journal of Economics 2017.…”

Disparate Impact? Race, Sex, and Credit Reports in Hiring

“…For example, many employers now screen job applicants by searching for information about them online and looking at their social media profiles and posts (Society for Human Resource Management 2016). While scholars have considered how such information may reveal otherwise occluded personal traits, such as religious belief and sexual orientation (Acquisti and Fong 2016), no one has yet investigated whether patterns of behavior revealed on such sites-like drinking, drug use, frequent dating, and "partying"-might be interpreted differently based on an applicant's race, gender, class, and other social characteristics. Although we might anticipate, even hope, that providing more and more highly personalized information about individuals will lessen the impact of ascriptive or social characteristics on employment outcomes, inequalities may persist if how that information is assessed is contingent on whom it is associated with, as we find to be the case in this article.…”

Contour-as-Face Framework: A Method to Preserve Privacy and Perception

“…Studies have shown that job allocation decisions are affected by perceptions of employees’ gender and sexual dimorphism (e.g., Altonji and Blank 1999; Landau 1995). For example, Acquisti and Fong (2020) found that access to information about candidates’ sexual dimorphism and ethnicity via online social network profiles could bias employers’ callback decisions. The use of face information in automated hiring software is facing widespread public backlash, with fears that it may lead to more biased hiring decisions (Martin 2018).…”