Using High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan Officer
Abstract/Contents
- Abstract
We develop tests for discrimination that we apply to 25 years of mortgage lending. Our tests limit the scope for omitted variables in a conventional benchmarking test by combining high-frequency mortgage evaluations with the notion that economic incentives can mitigate subjective biases. Loan officers have monthly volume quotas that constrain their subjectivity on loans processed at month-end. Concurrently, applicant characteristics are time-invariant within-month. We estimate that loan officers’ subjectivity contributes
to at least half of the unexplained Black approval gap. The within-month approval gap is smaller for shadow banks, but not for FinTech lenders or banks in concentrated markets.
Description
Type of resource | text |
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Date created | September 10, 2021 |
Creators/Contributors
Author | Giacoletti, Marco |
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Author | Heimer, Rawley |
Author | Yu, Edison |
Organizer of meeting | Diamond, Rebecca |
Organizer of meeting | van Dijk, Winnie |
Organizer of meeting | Schneider, Martin |
Organizer of meeting | Tsivanidis, Nick |
Subjects
Subject | economics |
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Genre | Text |
Genre | Working paper |
Genre | Grey literature |
Bibliographic information
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- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Giacoletti, M., Heimer, R., and Yu, E. (2022). Using High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan Officer. Stanford Digital Repository. Available at https://purl.stanford.edu/xq272nn6652
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SITE Conference 2021
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