{"title":"为什么小企业贷款市场存在种族差异","authors":"A. Rakshit, J. Peterson","doi":"10.2139/ssrn.3889590","DOIUrl":null,"url":null,"abstract":"We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Why are there Racial Disparities in the Small Business Loan Market\",\"authors\":\"A. Rakshit, J. Peterson\",\"doi\":\"10.2139/ssrn.3889590\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.\",\"PeriodicalId\":166384,\"journal\":{\"name\":\"PSN: Politics of Race (Topic)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PSN: Politics of Race (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3889590\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PSN: Politics of Race (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3889590","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Why are there Racial Disparities in the Small Business Loan Market
We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.