IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v51y2023ics1544612322006298.html
   My bibliography  Save this article

Asymmetric information in peer-to-peer lending: empirical evidence from China

Author

Listed:
  • Wang, Jin
  • Li, Rui

Abstract

This paper examines how asymmetric information affects peer-to-peer lending in China. We find that default rates rise significantly with interest rates. Specifically, borrowers who select interest rates above the legal maximum private lending rate of 15.4% are more likely to default. A lack of interest rate caps to prevent adverse selection is responsible for the market failure of Chinese peer-to-peer lending. However, there is no strong evidence of the moral hazard effect in relation to interest rate and loan size. In addition, the credit scoring based on applicant characteristics can mitigate the asymmetric information problem, but not eradicate it completely.

Suggested Citation

  • Wang, Jin & Li, Rui, 2023. "Asymmetric information in peer-to-peer lending: empirical evidence from China," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006298
    DOI: 10.1016/j.frl.2022.103452
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322006298
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.103452?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William Adams & Liran Einav & Jonathan Levin, 2009. "Liquidity Constraints and Imperfect Information in Subprime Lending," American Economic Review, American Economic Association, vol. 99(1), pages 49-84, March.
    2. Gregory S. Crawford & Nicola Pavanini & Fabiano Schivardi, 2018. "Asymmetric Information and Imperfect Competition in Lending Markets," American Economic Review, American Economic Association, vol. 108(7), pages 1659-1701, July.
    3. Gao, M. & Yen, J. & Liu, M., 2021. "Determinants of defaults on P2P lending platforms in China," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 334-348.
    4. Wang, Qi & Xiong, Xiong & Zheng, Zunxin, 2021. "Platform Characteristics and Online Peer-to-Peer Lending: Evidence from China," Finance Research Letters, Elsevier, vol. 38(C).
    5. Deng, Jiapin, 2022. "The crowding-out effect of formal finance on the P2P lending market: An explanation for the failure of China's P2P lending industry," Finance Research Letters, Elsevier, vol. 45(C).
    6. Jiaqi Yan & Wayne Yu & J. Leon Zhao, 2015. "How signaling and search costs affect information asymmetry in P2P lending: the economics of big data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    7. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    8. Chen, Xiao & Chong, Zhaohui & Giudici, Paolo & Huang, Bihong, 2022. "Network centrality effects in peer to peer lending," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tarantino, Emanuele & Pavanini, Nicola & Mayordomo, Sergio, 2020. "The Impact of Alternative Forms of Bank Consolidation on Credit Supply and Financial Stability," CEPR Discussion Papers 15069, C.E.P.R. Discussion Papers.
    2. Yannelis, Constantine & Zhang, Anthony Lee, 2023. "Competition and selection in credit markets," Journal of Financial Economics, Elsevier, vol. 150(2).
    3. Simone Lenzu & Francesco Manaresi, 2019. "Sources and implications of resource misallocation: new evidence from firm-level marginal products and user costs," Questioni di Economia e Finanza (Occasional Papers) 485, Bank of Italy, Economic Research and International Relations Area.
    4. Ioannidou, Vasso & Pavanini, Nicola & Peng, Yushi, 2022. "Collateral and asymmetric information in lending markets," Journal of Financial Economics, Elsevier, vol. 144(1), pages 93-121.
    5. Barnett, William A. & Su, Liting, 2020. "Financial Firm Production Of Inside Monetary And Credit Card Services: An Aggregation Theoretic Approach," Macroeconomic Dynamics, Cambridge University Press, vol. 24(1), pages 130-160, January.
    6. Ruyi Ge & Juan Feng & Bin Gu, 2016. "Borrower’s default and self-disclosure of social media information in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-6, December.
    7. Gregory S. Crawford & Nicola Pavanini & Fabiano Schivardi, 2018. "Asymmetric Information and Imperfect Competition in Lending Markets," American Economic Review, American Economic Association, vol. 108(7), pages 1659-1701, July.
    8. Jing Gu & Junyao Wang & Yang Yang & Zeshui Xu, 2019. "Credit Line Models for Supply Chain Enterprises with Channel Background and Soft Information," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    9. Looney, Adam & Yannelis, Constantine, 2022. "The consequences of student loan credit expansions: Evidence from three decades of default cycles," Journal of Financial Economics, Elsevier, vol. 143(2), pages 771-793.
    10. Chodorow-Reich, Gabriel & Darmouni, Olivier & Luck, Stephan & Plosser, Matthew, 2022. "Bank liquidity provision across the firm size distribution," Journal of Financial Economics, Elsevier, vol. 144(3), pages 908-932.
    11. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    12. Miller, Sarah, 2015. "Information and default in consumer credit markets: Evidence from a natural experiment," Journal of Financial Intermediation, Elsevier, vol. 24(1), pages 45-70.
    13. Li, Jianwen, 2023. "MSMEs meet FinTech: Chance or challenge?," Finance Research Letters, Elsevier, vol. 57(C).
    14. Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Working Paper Series rwp09-031, Harvard University, John F. Kennedy School of Government.
    15. Dömötör, Barbara & Ölvedi, Tímea, 2021. "A személyközi hitelezés létjogosultsága a pénzügyi közvetítésben [The relevance of peer-to-peer lending in financial intermediation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 773-793.
    16. Jonathan Zinman, 2014. "Consumer Credit: Too Much or Too Little (or Just Right)?," The Journal of Legal Studies, University of Chicago Press, vol. 43(S2), pages 209-237.
    17. Song Zhang & Liang Han & Konstantinos Kallias & Antonios Kallias, 2022. "Bank switching of US small businesses: new methods and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1573-1616, May.
    18. DeFusco, Anthony A. & Tang, Huan & Yannelis, Constantine, 2022. "Measuring the welfare cost of asymmetric information in consumer credit markets," Journal of Financial Economics, Elsevier, vol. 146(3), pages 821-840.
    19. Gabriela Kuvikova, 2015. "Does Loan Maturity Matter in Risk-Based Pricing? Evidence from Consumer Loan Data," CERGE-EI Working Papers wp538, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    20. Matt Darst & Ehraz Refayet, 2019. "Mixed Signals: Investment Distortions with Adverse Selection," Finance and Economics Discussion Series 2019-044, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    Keywords

    Asymmetric information; Peer-to-peer lending; Adverse selection; Moral hazard; China;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.