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

Author

Listed:
  • Daniel Greenwald
  • Sabrina T. Howell
  • Cangyuan Li
  • Emmanuel Yimfor

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.

Suggested Citation

  • Daniel Greenwald & Sabrina T. Howell & Cangyuan Li & Emmanuel Yimfor, 2023. "Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis," NBER Working Papers 31646, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31646
    Note: CF PR
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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination

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