Determinants of Occupational Segregation across Race and Gender: Evidence from Sourcing, Screening, and Hiring in IT Firms

  • Prasanna Parasurama
  • Anindya Ghose
  • Panagiotis G. Ipeirotis

Despite the precipitous rise in the use of online professional networking platforms for sourcing passive candidates, the hiring literature has largely focused on the traditional screening channel (i.e. who applies to what jobs; who receives an interview once they apply). In this paper, we broaden this focus by studying the online sourcing channel and its role in occupational segregation. Specifically, we study demand-side employer choices (i.e. who gets contacted on LinkedIn) and supply-side worker choices (i.e. who responds) on LinkedIn, and how these choices contribute to the underrepresentation of women in tech. In addition, we compare these choices to the traditional screening channel to understand the relative contribution of online sourcing to occupational segregation. We address these questions using two novel, large-scale datasets: Applicant Tracking System data from 8 tech firms containing nearly 900k candidates, and a LinkedIn dataset containing 318 million public LinkedIn profiles. We find that employer choices do not differ substantively between screening and sourcing channels as they both favor female candidates. Compared to men, female applicants in the screening channel are more likely to be invited for a phone screen after applying. Similarly, in the sourcing channel, passive female candidates are more likely to be contacted on LinkedIn and invited for a phone screen. Worker choices on the other hand do differ between screening and sourcing. In the screening channel, female applicants are less likely to apply to technical jobs, and more likely to decline a phone screen invitation compared to men. In the sourcing channel, in contrast, female prospects are slightly less likely to decline a phone screen invitation. These results highlight how online sourcing can serve as an effective channel to address female underrepresentation in tech.