Regulatory arbitrage or random errors? Implications of race prediction algorithms in fair lending analysis

• 成果类型: Article
• 署名作者: Greenwald, Daniel L.; Howell, Sabrina T.; Li, Cangyuan; Yimfor, Emmanuel
• 署名单位: New York University; Columbia University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2024.103857
• 发表日期: 2024
• 关键词: racial disparities; Disparate impact analysis; Race-conscious policies; Race prediction; Small business lending; BISG

摘要

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.