Algorithmically recommending workers to employers for the purpose of recruiting can substantially increase hiring: in an experiment conducted in an online labor market, employers with technical job vacancies that received recruiting recommendations had a 20% higher fill rate compared to the control. There is no evidence that the treatment crowded out hiring of nonrecommended candidates. The experimentally induced recruits were highly positively selected and were statistically indistinguishable from the kinds of workers employers recruit “on their own.” Recommendations were most effective for job openings that were likely to receive a smaller applicant pool.

Firms posting job openings in an online labor market were randomly assigned minimum hourly wages. When facing a minimum wage, fewer firms hired, but those they did hire paid higher wages. Hoursworked fell substantially. Treated firms shifted to hiring more productive workers. Using the platform's imposition of a market-wide minimum wage after the experiment, I find that many of the experimental results also hold in equilibrium, including the substitution towards more productive workers. However, there was also a large reduction in the number of jobs posted for which the minimum wage would likely bind.

We report the results of a field experiment in which treated employers could not observe the compensation history of their job applicants. Treated employers responded by evaluating more applicants and evaluating those applicants more intensively. They also responded by changing what kind of workers they evaluated: treated employers evaluated workers with 5% lower past average wages and hired workers with 13% lower past average wages. Conditional on bargaining, workers hired by treated employers struck better wage bargains for themselves.