Reducing Perceptions of Discrimination
This project focuses on workers’ perceptions of discrimination, specifically (1) providing evidence on the effectiveness of hiring and promotion mechanisms that could reduce perceptions of discrimination; (2) how perceived discrimination affects job satisfaction and performance; and (3) whether anticipated discrimination affects selection into the labor market.
I will answer these research questions using two studies on the online gig economy platform MTurk. First, I will answer the first and second research questions using an experiment that varies whether 4000 workers believe they were not promoted by a potentially biased manager who knows their demographics, a manager who is blinded to their demographics, or a fair computer algorithm. A second study will elicit the value that 400 workers place on jobs with promotion mechanisms that vary in the extent to which they allow for discrimination. Both studies will over-sample racial minorities. Based on piloting and prior research, I hypothesize that workers will perceive algorithms and demographic-blinded humans as less discriminatory than a human decision-maker who knows demographics, and that workers will select into jobs accordingly. In turn, I expect reducing perceived discrimination to increase effort, job satisfaction, and retention.