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Information and default in consumer credit markets: Evidence from a natural experiment

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  • Miller, Sarah

Abstract

Despite the prominent role that information plays in the economic theory of credit markets, no direct evidence exists on the causal relationship between the availability of information about loan applicants and loan performance. This paper provides such evidence by exploiting an unanticipated change in the amount of information visible in an online market for loans to measure the impact of lender information on loan outcomes. Conditional on data available in both periods, allowing lenders to access more borrower credit information substantially reduced default rates among high-risk borrowers by 17percentage points on average but had almost no effect on low-risk borrowers. Immediate lender returns increased by about 12percentage points and took 5weeks to decay. Among high-risk loans, returns converged within credit grade bins. Using panel information on lenders, I find that the information improved loan performance in two ways: first, it significantly improved the screening performed by lenders already active on the website. Second, it attracted new lenders who were better at screening loan applicants and earned higher returns. I test whether the reform resulted in selection among loan applicants using data that is unobserved by lenders in both periods. I find that there was no change in unobserved credit quality among loan applicants, but that the information improved lenders’ ability to select the (unobservably) higher quality borrowers from the pool of applicants. I also find suggestive evidence that lenders’ beliefs about loan applicants, as measured by the minimum interest rate at which they were willing to lend, converged.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jfinin:v:24:y:2015:i:1:p:45-70
    DOI: 10.1016/j.jfi.2014.06.003
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    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.
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    1. Oren Rigbi, 2013. "The Effects of Usury Laws: Evidence from the Online Loan Market," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1238-1248, October.
    2. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2018. "The performance of marketplace lenders: Evidence from lending club payment data," CFS Working Paper Series 598, Center for Financial Studies (CFS).
    3. Torres Pena, Maria Veronica & Breidbach, Christoph F., 2021. "On emergence in service platforms: An application to P2P lending," Journal of Business Research, Elsevier, vol. 135(C), pages 337-347.
    4. 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.
    5. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    6. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    7. Inessa Liskovich & Maya Shaton, 2017. "Borrowers in Search of Feedback : Evidence from Consumer Credit Markets," Finance and Economics Discussion Series 2017-049, Board of Governors of the Federal Reserve System (U.S.).
    8. Goodell, John W., 2016. "Do for-profit universities induce bad student loans?," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 173-184.
    9. Zhu, Zongyuan, 2018. "Safety promise, moral hazard and financial supervision: Evidence from peer-to-peer lending," Finance Research Letters, Elsevier, vol. 27(C), pages 1-5.
    10. Oleksandr Talavera & Haofeng Xu, 2018. "Role of Verification in Peer-to-Peer Lending," Working Papers 2018-25, Swansea University, School of Management.
    11. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
    12. Cummins, Mark & Mac an Bhaird, Ciarán & Rosati, Pierangleo & Lynn, Theo, 2020. "Institutional investment in online business lending markets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    13. Martin Hauptfleisch, 2019. "Financial Decision-Making Using Data," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 6-2019, January-A.
    14. 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.
    15. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.

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