Filtering the labor pool: How digitalization redistributes hiring costs

I theorize that candidate filters, a core technology in digital labor markets considered central to cost-effective matching, redistributes costs from the search-and-screening stage to the job-offer stage of hiring. Filters cause employers to focus on applicants who display common preferred characteristics. At the search-and-screening stage, filters reduce the number of applicants interviewed and alter the composition of the interviewed pool by removing both lower- and higher-quality candidates. At the job-offer stage, filters lead employers to make job offers to candidates with easily observable signals of quality and higher wage demands. Additionally, I theorize that if many firms target the same signals of quality, they may all pursue similar candidates. This increases bargaining power for those applicants, leading to more rejected job offers and higher wage demands. A randomized field experiment in a large online labor market confirms that filters redistribute hiring costs. The experiment shows that employers with access to filters interview 3.4% fewer applicants. Using a machine learning algorithm to proxy for unobservable-to-the-employer applicant quality, I show that filters cause employers to interview 28.9% fewer lower-quality applicants and 36.9% fewer high-quality applicants per job. Among employers who actively used filters, I find suggestive evidence of worse job-offer outcomes: offers are 8.9% more likely to be rejected, and contracted wages are 2.5% higher. I discuss the implications of this cost redistribution for organizations, labor markets, and strategy research.