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We analyze discrimination in a new type of credit market known as peer-to-peer lending. Specifically, we examine how lenders in this online market respond to signals of characteristics such as race, age, and gender that are conveyed via pictures and text. We find evidence of significant racial disparities; loan listings with blacks in the attached picture are 25 to 35 percent less likely to receive funding than those of whites with similar credit profiles. Conditional on receiving a loan, the interest rate paid by blacks is 60 to 80 basis points higher than that paid by comparable whites. Though less significant than the effects for race, we find that the market also discriminates somewhat against the elderly and the overweight, but in favor of women and those that signal military involvement. Despite the higher average interest rates charged to blacks, lenders making such loans earn a lower net return compared to loans made to whites with similar credit profiles because blacks have higher relative default rates. This pattern of net returns is inconsistent with theories of accurate statistical discrimination (equal net returns) or costly taste-based preferences against loaning money to black borrowers (higher net returns for blacks). It is instead consistent with partial taste-based preferences by lenders in favor of blacks over whites or with systematic underestimation by lenders of relative default rates between blacks and whites.
Pope et al. (Sat,) studied this question.