Firms often receive many more applicants than can be reasonably reviewed in detail: Recruiters spend considerable time and resources filtering the pool of applicants into a much smaller set of candidates who will receive an interview. Thus, an automated process that can be fine-tuned to evaluate candidates for open positions offers significant promise. Although the discussion of the use of algorithms and similar innovations is a hot topic for firms and policymakers, we have little empirical evidence regarding two central questions: (1) How prevalent is the use of algorithms in screening job applicants in the labor market? (2) Are candidates from underrepresented backgrounds differentially affected by the use of screening algorithms versus traditional candidate screening techniques?
Research assistance is sought to clean data that has already been collected for this project and to collect new data under Prof. Botelho’s guidance, along with other tasks that may arise related to this project.