 Do algorithms discriminate against African Americans in lending?Jérémie Bertrand and Laurent Weill Economic Modelling, 2021, vol. 104, issue C Abstract: Vast evidence exists of discrimination in lending against African Americans. Does such widespread discrimination persist in peer-to-peer lending, in the absence of information about borrowers’ ethnicity? To investigate this question, we gather data from a large peer-to-peer lender that uses algorithms and no face-to-face interviews to determine loan approval and conditions. With data from 3.6 million loan applications and 817,000 granted loans during 2016 and 2017, we perform regressions of loan acceptance and loan conditions on the percentage of African American residents by 3-digit zip area. The evidence shows the persistence of discrimination in peer-to-peer lending against African American borrowers. In areas with higher proportions of African American residents, the results reveal higher loan rejection, higher loan spread, and shorter loan maturity. Discrimination in peer-to-peer lending also affects Native American and Asian borrowers. Thus, the expansion of peer-to-peer lending cannot end discrimination in lending. Keywords: Discrimination; Fintech; Peer-to-peer lending; Loans (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:104:y:2021:i:c:s026499932100208x DOI: 10.1016/j.econmod.2021.105619 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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