Asymmetric Information, Reputation, and Welfare in Online Credit Markets

Abstract

This paper studies the impact of reputation/feedback systems on the operation of online credit markets using data from Prosper.com. The ability of lenders to recover their loans is one of the main concerns in these markets, where the problems of asymmetric information are two-fold. On the one hand, borrowers differ in their inherent risks; on the other hand, additional incentives are necessary to motivate borrowers to exert effort. In this paper, I investigate the channels through which reputation/feedback systems improve the total welfare of market participants when both adverse selection and moral hazard are present. A finite-horizon dynamic model of a credit market in which borrowers and lenders interact repeatedly over time is developed and then estimated. I prove the identification of the distribution of borrowers' private types and utility primitives based on variations in borrowers' repayment histories, transitions of their characteristics, and interest rates. In the counterfactual analysis, I find that 22 percent of welfare loss from asymmetric information is due to adverse selection, while 78 percent is due to moral hazard. Furthermore, I find that 95 percent of the inefficiency induced by asymmetric information is eliminated by the reputation system. I consider a policy intervention that protects borrowers from accidental loss of reputation. My results suggest that incorporating a payment protection insurance into the market further improves total welfare.

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