Gender and Race Preferences in Hiring in the Age of Diversity Goals: Evidence from Silicon Valley Tech Firms
56 Pages Posted: 27 Aug 2020
Date Written: August 12, 2020
We study the heterogeneous effects of race and gender on hiring outcomes in the context of organizational diversity efforts. Against the backdrop of increasing scrutiny around diversity issues in tech companies and the concomitant growing response of organizational efforts to increase workforce diversity, we revisit the age-old question of whether race and gender preferences (continue to) exist in hiring decisions. We address this question using two novel, large-scale datasets: Applicant Tracking System data from 8 Silicon Valley firms containing nearly 900k applicants, and a LinkedIn dataset containing 300 million public LinkedIn profiles. Using matched sample analyses and controlling for a rich set of job and applicant attributes found in applicants’ resumes and LinkedIn profiles, we find that women are 9-10% more likely to receive a callback compared to men, whereas Black, Hispanic, and Asian applicants are 8-13% less likely to receive a callback compared to White applicants. These outcome gaps do not cancel-out in the later stages, as female and White applicants are more likely to receive an interview and offer. To further address endogeneity concerns, we perform quasi-experimental analysis involving applicants whose race and gender are ambiguous to the recruiter in the initial application review stage, but are later revealed in the phone screen stage. We find that ambiguity in applicants’ race and gender attenuates the main effects of race and gender on receiving a callback – that is, the outcome gap in callback disappears for applicants whose race and gender are ambiguous to the recruiter. We discuss these results in light of theories of statistical discrimination, value-in- diversity, and institutional norms around diversity, and highlight how diversity efforts may not categorically benefit all underrepresented minorities.
Keywords: hiring, discrimination, diversity, gender inequality, race inequality, tech, text analytics, big data
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