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

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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
Date created September 10, 2021

Creators/Contributors

Author Giacoletti, Marco
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
Genre Text
Genre Working paper
Genre Grey literature

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License
This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

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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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