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The Effects of Usury Laws: Evidence from the Online Loan Market

  • Oren Rigbi

    ()

    (Ben-Gurion University of the Negev)

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    Usury laws cap the interest rates that lenders can charge. Using data from Prosper.com (an online lending marketplace), I show how interest rate caps affect: 1) the probability that a loan is funded; 2) the amount a borrower requests; 3) the interest rate at which a loan is funded; and 4) loan repayments. The key to my empirical strategy is that there initially was substantial variability in states' interest rate caps, according to which Prosper borrowers from different states faced caps ranging from 6 to 36%. A behind-the-scenes change in loan origination, however, suddenly increased the cap to 36% in all but one state. This change, which was not pre-announced, creates \treatment" states where caps rose and a few control states where caps remained unchanged. I find that higher interest rate caps increase the probability that a loan will be funded, especially if the borrower is risky and previously was just \outside the money." I do not find, however, that borrowers change the loan amounts they request or that their probability of default rises. On the other hand, the interest rate paid rises slightly, probably because online lending is substantially, yet imperfectly, integrated with the general credit market.

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    File URL: http://www.ec.bgu.ac.il/monaster/admin/papers/1204.pdf
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    Paper provided by Ben-Gurion University of the Negev, Department of Economics in its series Working Papers with number 1204.

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    Length: 40 pages
    Date of creation: 2012
    Date of revision:
    Handle: RePEc:bgu:wpaper:1204
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    1. Reint Gropp & John Karl Scholz & Michelle White, 1996. "Personal Bankruptcy and Credit Supply and Demand," NBER Working Papers 5653, National Bureau of Economic Research, Inc.
    2. Maurice B. Goudzwaard, 1968. "Price Ceilings And Credit Rationing," Journal of Finance, American Finance Association, vol. 23(1), pages 177-185, 03.
    3. Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2013. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Working Paper Series rwp13-017, Harvard University, John F. Kennedy School of Government.
    4. Alessie, Rob & Hochguertel, Stefan & Weber, Guglielmo, 2001. "Consumer Credit: Evidence from Italian Micro Data," CEPR Discussion Papers 3071, C.E.P.R. Discussion Papers.
    5. Scott Carrell & Jonathan Zinman, 2008. "In harm’s way? Payday loan access and military personnel performance," Working Papers 08-18, Federal Reserve Bank of Philadelphia.
    6. James W. Hardin, 2002. "The robust variance estimator for two-stage models," Stata Journal, StataCorp LP, vol. 2(3), pages 253-266, August.
    7. Villegas, Daniel J, 1982. " An Analysis of the Impact of Interest Rate Ceilings," Journal of Finance, American Finance Association, vol. 37(4), pages 941-54, September.
    8. Seth M. Freedman & Ginger Zhe Jin, 2011. "Learning by Doing with Asymmetric Information: Evidence from Prosper.com," NBER Working Papers 16855, National Bureau of Economic Research, Inc.
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