Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis

Daniel Greenwald, Sabrina T. Howell, Cangyuan Li and Emmanuel Yimfor

No 31646, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending—where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm—we document large prediction errors among Black Americans. The errors bias measured racial disparities in loan approval rates downward by 43%, with greater bias for traditional vs. fintech lenders. Regulation using self-identified race would increase lending to Black borrowers, but also shift lending toward affluent areas because errors correlate with socioeconomics. Overall, using race proxies in policymaking and research presents challenges.

JEL-codes: C81 G21 G23 G28 J15 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-ban and nep-ure
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Published as Daniel L. Greenwald & Sabrina T. Howell & Cangyuan Li & Emmanuel Yimfor, 2024. "Regulatory arbitrage or random errors? Implications of race prediction algorithms in fair lending analysis," Journal of Financial Economics, vol 157.

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