@techreport{60e35bbfa379458a97a0e1f443676fab,
title = "Credit Reports as R{\'e}sum{\'e}s: The Incidence of Pre-Employment Credit Screening",
abstract = "We study recent bans on employers' use of credit reports to screen job applicants – a practice that has been popular among employers, but controversial for its perceived disparate impact on racial minorities. Exploiting geographic, temporal, and job-level variation in which workers are covered by these bans, we analyze these bans' effects in two datasets: the panel dimension of the Current Population Survey (CPS); and data aggregated from state unemployment insurance records. We find that the bans reduced job-finding rates for blacks by 7 to 16 log points, and increased subsequent separation rates for black new hires by 3 percentage points, arguably contrary to the bans' intended effects. Results for Hispanics and whites are less conclusive. We interpret these findings in a statistical discrimination model in which credit report data, more so for blacks than for other groups, send a high-precision signal relative to the precision of employers' priors. ",
keywords = "Unemployment, Employment Discrimination, Signaling, Hiring, Firing, Policy Analysis",
author = "Alexander Bartik and Nelson, {Scott T}",
year = "2016",
month = apr,
day = "8",
doi = "10.2139/ssrn.2759560",
language = "English (US)",
series = "MIT Department of Economics Graduate Student Research Paper 16-01",
type = "WorkingPaper",
}