Using High-Frequency Evaluations to Estimate Discrimination : Evidence from Mortgage Loan Officers

We develop empirical tests for discrimination that use high-frequency evaluations to address the problem of unobserved heterogeneity in a conventional benchmarking test. Our approach to identifying discrimination requires two conditions: (1) the subject pool is time-invariant in a short time horizon and (2) there is high-frequency variation in the extent to which evaluators can rely on their subjective assessments. We bring our approach to the residential mortgage market, using data on the near-universe of U.S. mortgage applications from 1994 to 2018. Monthly volume quotas reduce how much subjectivity loan officers apply to loans they process at the end of the month. As a result, the volume of new originations increases by 150% at the end of the month, while application volume and applicants’ quality are constant within the month. Owing to within-month variation in loan officers’ subjectivity, we estimate that Black mortgage applicants have 3.5% to 5% lower approval rates, which explains at least half of the observed approval gap for Blacks. When we use this approach to evaluate policies, we find that market concentration and FinTech lending have had no effect on lending discrimination, but that shadow banking has reduced discrimination presumably by having a larger presence in under-served communities