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Signaling in Online Credit Markets

Author

Listed:
  • Kosuke Uetake

    (Northwestern University)

  • Ken ONISHI

    (Northwestern University)

  • Kei Kawai

    (New York University)

Abstract

This paper studies how signaling can facilitate the functioning of a market with classical adverse selection problems. Using data from Prosper.com, an online credit market where loans are funded through auctions, we provide evidence that reserve interest rates that borrowers post can serve as a signaling device. We then develop and estimate a structural model of borrowers and lenders where low reserve interest rates can credibly signal low default risk. Announcing a high reserve interest rate increases the probability of receiving funding at the cost of higher expected interest payments conditional on obtaining a loan. Borrowers regard this trade-off differentially, which results in a separating equilibrium. Using the estimated parameters of the model, we compare the credit supply curve and welfare under three alternative market designs in our counterfactual policy experiment -- a market with signaling, a market without signaling, and a market with no asymmetric information. We find that the cost of adverse selection can be as much as 16% of the total surplus created under no asymmetric information, up to 95% of which can be restored with signaling. We also estimate the credit supply curves for each of the three market designs and find backward-bending supply curves for some of the markets, consistent with the prediction of Stiglitz and Weiss (1981).

Suggested Citation

  • Kosuke Uetake & Ken ONISHI & Kei Kawai, 2013. "Signaling in Online Credit Markets," 2013 Meeting Papers 516, Society for Economic Dynamics.
  • Handle: RePEc:red:sed013:516
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    References listed on IDEAS

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    Cited by:

    1. Minkyung Kim & K. Sudhir & Kosuke Uetake & Rodrigo Canales, 2016. "Multidimensional Sales Incentives in CRM Settings: Customer Adverse Selection and Moral Hazard," Cowles Foundation Discussion Papers 2085, Cowles Foundation for Research in Economics, Yale University.
    2. Freedman, Seth & Jin, Ginger Zhe, 2017. "The information value of online social networks: Lessons from peer-to-peer lending," International Journal of Industrial Organization, Elsevier, vol. 51(C), pages 185-222.
    3. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    4. Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2020. "Buying reputation as a signal of quality: Evidence from an online marketplace," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 965-988, December.
    5. Nicolas Eschenbaum & Helge Liebert, 2021. "Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions," Papers 2107.11314, arXiv.org.
    6. Zhao, Yunhui, 2016. "Got Hurt for What You Paid? Revisiting Government Subsidy in the U.S. Mortgage Market," MPRA Paper 81083, University Library of Munich, Germany, revised 01 Aug 2017.
    7. Bryan Bollinger & Song Yao, 2018. "Risk transfer versus cost reduction on two-sided microfinance platforms," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 251-287, September.
    8. Matthew Backus & Tom Blake & Steven Tadelis, 2015. "Cheap Talk, Round Numbers, and the Economics of Negotiation," NBER Working Papers 21285, National Bureau of Economic Research, Inc.
    9. Li, Zhiyong & Zhang, Haiyang & Yu, Mei & Wang, Hairan, 2019. "Too long to be true in the description? Evidence from a Peer-to-Peer platform in China," Research in International Business and Finance, Elsevier, vol. 50(C), pages 246-251.
    10. Alexander W. Butler & Jess Cornaggia & Umit G. Gurun, 2017. "Do Local Capital Market Conditions Affect Consumers’ Borrowing Decisions?," Management Science, INFORMS, vol. 63(12), pages 4175-4187, December.
    11. Runshan Fu & Yan Huang & Param Vir Singh, 2020. "Crowd, Lending, Machine, and Bias," Papers 2008.04068, arXiv.org.

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