Labor Market Effects of Algorithmic Hiring: Evidence from Field and Natural Experiments
Publication Date
1-1-2016
Grant Type
Early Career Research Award
Description
What are the labor market effects of using algorithms for hiring? How can digitization improve hiring outcomes for employers and job candidates? What shortcomings in human judgment do such algorithms correct? I evaluate these questions using a series of field- and natural- experiments showing productivity gains from IT adoption in screening of job applicants. Through a field experiment, I show that algorithmic hiring technology decrease both Type I and Type II errors compared to the incumbent human screening process, and improves match quality both as perceived by the firm and the applicants. The mechanism of this gain appears to be the reduction of behavioral weaknesses and shortcomings in human judgements and decision making processes.
Grant Product
Bias and Productivity in Humans and Machines
Upjohn Institute Working Paper No. 19-309, 2019