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

70 Pages Posted: 2 Mar 2021 Last revised: 23 Mar 2022

See all articles by Marco Giacoletti

Marco Giacoletti

Marshall School of Business

Rawley Heimer

Boston College - Department of Finance

Edison G. Yu

Federal Reserve Bank of Philadelphia

Date Written: March 15, 2022

Abstract

We develop modified benchmarking tests for disparate treatment that we apply to 25 years of mortgage lending. Our tests limit the scope for omitted variables by linking high-frequency mortgage decisions to an economic mechanism—loan officers have volume quotas that constrain subjectivity on loans processed at month-end. We estimate that these quotas reduce the unexplained Black approval gap of 7 ppt by at least half. Applicant characteristics are time-invariant within-month and measures of credit risk do not explain racial differences in approvals. The within-month approval gap is smaller for shadow banks, but not for FinTechs or banks in concentrated markets

Keywords: Performance Incentives, Lending Discrimination, Loan Officers, Mortgages, Shadow Banking

Suggested Citation

Giacoletti, Marco and Heimer, Rawley and Yu, Edison G., Using High-Frequency Evaluations to Estimate Disparate Treatment: Evidence from Mortgage Loan Officers (March 15, 2022). Proceedings of Paris December 2021 Finance Meeting EUROFIDAI - ESSEC, Available at SSRN: https://ssrn.com/abstract=3795547 or http://dx.doi.org/10.2139/ssrn.3795547

Marco Giacoletti

Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

Rawley Heimer

Boston College - Department of Finance ( email )

Carroll School of Management
140 Commonwealth Avenue
Chestnut Hill, MA 02467-3808
United States

Edison G. Yu (Contact Author)

Federal Reserve Bank of Philadelphia ( email )

Ten Independence Mall
Philadelphia, PA 19106-1574
United States

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