References
Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68.
Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households’ residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data.
Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443.
The article develops a novel empirical test of racial bias based on comparisons between forward-looking, expectations-based credit scores and backward-looking, repayment-history-based credit scores. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias. If anything, forward-looking credit-score models under-predict the rate of payment delinquency among minority-owned businesses.
Swanton, Mary. (2010). Background bias: EEOC steps up pressure on employers that reject applicants based on criminal records and credit scores.(LABOR). InsideCounsel, 26.
Pope, D., & Sydnor, J. (2011). Implementing Anti-Discrimination Policies in Statistical Profiling Models. American Economic Journal: Economic Policy, 3(3), 206-231.
In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics is often contentious. The article provides a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. The article illustrates the value of our proposed method using data from the Worker Profiling and Reemployment Services system.
O’Neil, C. (2016). Weapons of math destruction : How big data increases inequality and threatens democracy (First ed.).
Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547.
Although credit scoring offers benefits to lenders and borrowers, its use raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. The evidence from a national sample of credit‐bureau records suggests that concerns about omitted‐variable bias may be justified, as local economic factors show significant correlations with credit scores.
