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Asymmetric Information, Reputation, and Welfare in Online Credit Markets

Xin, Yi (2018) Asymmetric Information, Reputation, and Welfare in Online Credit Markets. . (Unpublished)

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This paper studies the impact of reputation/feedback systems on the operation of online credit markets using data from 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.

Item Type:Report or Paper (Discussion Paper)
Additional Information:I thank my dissertation advisors Yingyao Hu, Elena Krasnokutskaya, and Yuya Takahashi for their guidance and encouragement throughout the research process. This work has benefited particularly from the comments of Yuya Sasaki, Robert Moffitt, Jian Ni, Richard Spady, Jorge Balat, Ying Chen, Nicholas Papageorge, Matthew Backus, John Cawley, Ruli Xiao, and Yajing Jiang. I also thank seminar and conference participants at the Johns Hopkins University, WEAI Graduate Student Workshop (2017), NASM (2017), and Washington DC IO Conference (2017). The usual disclaimer applies.
Subject Keywords:asymmetric information, reputation/feedback system, credit markets
Classification Code:JEL Code: L14, D82, G21, C14
Record Number:CaltechAUTHORS:20180613-125308182
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87068
Deposited By: George Porter
Deposited On:13 Jun 2018 20:58
Last Modified:03 Oct 2019 19:51

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