IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v69y2020icp651-662.html
   My bibliography  Save this article

How do lenders evaluate borrowers in peer-to-peer lending in China?

Author

Listed:
  • Chen, Shiyi
  • Gu, Yan
  • Liu, Qingfu
  • Tse, Yiuman

Abstract

We examine how lenders incorporate borrowers’ demographic characteristics and behaviors into their decisions in peer-to-peer (P2P) lending in China. Using the Renrendai online platform from 2013 to 2015, we find that the P2P market correctly uses the education level of borrowers (but not age, gender, and marital status) to evaluate their creditworthiness and anticipated loan performance. Borrowers with higher education levels have greater success in attracting funding and a lower probability of default, and those who extend funding to them can expect to receive higher returns. Younger female borrowers are less likely to be funded, even though they have a lower probability of default. Some borrowers may use more positive emotional appeals to persuade lenders to extend funding; however, lenders respond adversely to such appeals.

Suggested Citation

  • Chen, Shiyi & Gu, Yan & Liu, Qingfu & Tse, Yiuman, 2020. "How do lenders evaluate borrowers in peer-to-peer lending in China?," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 651-662.
    • Handle: RePEc:eee:reveco:v:69:y:2020:i:c:p:651-662
    DOI: 10.1016/j.iref.2020.06.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056020301519
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2020.06.038?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. 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.
    2. Qingfu Liu & Yiuman Tse & Linlin Zhang, 2018. "Including commodity futures in asset allocation in China," Quantitative Finance, Taylor & Francis Journals, vol. 18(9), pages 1487-1499, September.
    3. An, Ping & Yu, Mengxuan, 2018. "Neglected part of shadow banking in China," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 211-236.
    4. Juanjuan Chen & Yabin Zhang & Zhujia Yin, 2018. "Education Premium In The Online Peer-To-Peer Lending Marketplace: Evidence From The Big Data In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(01), pages 45-64, March.
    5. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    6. Wei Jiang & Ashlyn Aiko Nelson & Edward Vytlacil, 2014. "Liar's Loan? Effects of Origination Channel and Information Falsification on Mortgage Delinquency," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 1-18, March.
    7. Herzenstein, Michal & Dholakia, Utpal M. & Andrews, Rick L., 2011. "Strategic Herding Behavior in Peer-to-Peer Loan Auctions," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 27-36.
    8. Jonathan Guryan & Kerwin Kofi Charles, 2013. "Taste‐based or Statistical Discrimination: The Economics of Discrimination Returns to its Roots," Economic Journal, Royal Economic Society, vol. 123(11), pages 417-432, November.
    9. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    10. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    11. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    12. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    13. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," The Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Davaadorj, Zagdbazar & Enkhtaivan, Bolortuya & Lu, Wenling, 2024. "The role of job titles in online peer-to-peer lending: An empirical investigation on skilled borrowers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    2. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. Natnara Chulawate & Supaporn Kiattisin, 2023. "Success Factors Influencing Peer-to-Peer Lending to Support Financial Innovation," Sustainability, MDPI, vol. 15(5), pages 1-16, February.
    4. Ho, Kung-Cheng & Gu, Yan & Yan, Cheng & Gozgor, Giray, 2024. "Peer effects in the online peer-to-peer lending market: Ex-ante selection and ex-post learning," International Review of Financial Analysis, Elsevier, vol. 92(C).
    5. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Wang, Shaoda & Ye, Dezhu & Liao, Junyun, 2024. "Politeness matters: The role of polite languages in online peer-to-peer lending," Journal of Business Research, Elsevier, vol. 171(C).

    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. Qun Chen & Ji-Wen Li & Jian-Guo Liu & Jing-Ti Han & Yun Shi & Xun-Hua Guo, 2021. "Borrower Learning Effects: Do Prior Experiences Promote Continuous Successes in Peer-to-Peer Lending?," Information Systems Frontiers, Springer, vol. 23(4), pages 963-986, August.
    2. Qun Chen & Ji-Wen Li & Jian-Guo Liu & Jing-Ti Han & Yun Shi & Xun-Hua Guo, 0. "Borrower Learning Effects: Do Prior Experiences Promote Continuous Successes in Peer-to-Peer Lending?," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    3. Li, Jianwen & Hu, Jinyan, 2019. "Does university reputation matter? Evidence from peer-to-peer lending," Finance Research Letters, Elsevier, vol. 31(C), pages 66-77.
    4. Davaadorj, Zagdbazar & Enkhtaivan, Bolortuya & Lu, Wenling, 2024. "The role of job titles in online peer-to-peer lending: An empirical investigation on skilled borrowers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    5. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    6. Chen, Xiao & Huang, Bihong & Shaban, Mohamed, 2022. "Naïve or sophisticated? Information disclosure and investment decisions in peer to peer lending," Journal of Corporate Finance, Elsevier, vol. 77(C).
    7. Yingxiu Zhao & Wei Zhang & Xiangyu Kong, 2019. "Dynamic Cross-Correlations between Participants’ Attentions to P2P Lending and Offline Loan in the Private Lending Market," Complexity, Hindawi, vol. 2019, pages 1-8, December.
    8. Oleksandr Talavera & Haofeng Xu, 2018. "Role of Verification in Peer-to-Peer Lending," Working Papers 2018-25, Swansea University, School of Management.
    9. Liu, He & Qiao, Han & Wang, Shouyang & Li, Yuze, 2019. "Platform Competition in Peer-to-Peer Lending Considering Risk Control Ability," European Journal of Operational Research, Elsevier, vol. 274(1), pages 280-290.
    10. Chen, Xiao & Guo, Gangxing, 2024. "Air pollution and online lender behavior: Evidence from Chinese peer-to-peer lending," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    11. Belleflamme, Paul & Omrani, Nessrine & Peitz, Martin, 2015. "The economics of crowdfunding platforms," Information Economics and Policy, Elsevier, vol. 33(C), pages 11-28.
    12. Ho, Kung-Cheng & Gu, Yan & Yan, Cheng & Gozgor, Giray, 2024. "Peer effects in the online peer-to-peer lending market: Ex-ante selection and ex-post learning," International Review of Financial Analysis, Elsevier, vol. 92(C).
    13. Douglas J. Cumming & Lars Hornuf, 2020. "Marketplace Lending of SMEs," CESifo Working Paper Series 8100, CESifo.
    14. Andreas Hoegen & Dennis M. Steininger & Daniel Veit, 2018. "How do investors decide? An interdisciplinary review of decision-making in crowdfunding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 339-365, August.
    15. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    16. Samuel Ribeiro-Navarrete & Juan Piñeiro-Chousa & M. Ángeles López-Cabarcos & Daniel Palacios-Marqués, 2022. "Crowdlending: mapping the core literature and research frontiers," Review of Managerial Science, Springer, vol. 16(8), pages 2381-2411, November.
    17. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    18. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2019. "The Gender Gap in Peer-to-Peer Lending: Evidence from the People’s Republic of China," ADBI Working Papers 977, Asian Development Bank Institute.
    19. Peng Wang & Haichao Zheng & Dongyu Chen & Liangchao Ding, 2015. "Exploring the critical factors influencing online lending intentions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    20. Dongyu Chen & Xiaolin Li & Fujun Lai, 2023. "Shill bidding in lenders’ eyes? A cross-country study on the influence of large bids in online P2P lending," Electronic Commerce Research, Springer, vol. 23(2), pages 1089-1114, June.

    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:reveco:v:69:y:2020:i:c:p:651-662. 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/inca/620165 .

    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.