Discrimination in Lending Markets: Status and the Intersections of Gender and Race

Abstract

Research documents that lenders discriminate between loan applicants in traditional and peer-to-peer lending markets, yet we lack knowledge about the mechanisms driving lenders’ behavior. I offer one possible mechanism: When lenders assess borrowers, they are implicitly guided by cultural stereotypes about the borrowers’ status. This systematically steers lenders toward funding higher status groups even when applicants have the same financial histories. In an experimental test, I examine how applicants’ demographic characteristics combine to alter lenders’ status assessments and, thereby, lenders’ decisions in an artificial peer-to-peer lending market. Participants from Amazon’s Mechanical Turk evaluated a series of loan applicants whose gender (female or male) and race (black or white) were manipulated. The results demonstrate that applicants’ gender and race significantly affect lenders’ funding decisions because they alter lenders’ status beliefs about the applicants. This study provides experimental evidence that status is a likely mechanism driving lending discrimination.