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From Credit Risk to Social Impact: On the Funding Determinants in Interest-Free Peer-to-Peer Lending

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Abstract

Based on a unique data set on US direct microloans, we study the funding determinants of interest-free peer-to-peer crowdlending aimed at borrowers in the US. By performing logistic regressions on funding success and Tobit regressions on the reversed funding time, the existence of a social underwriting by a third-party trustee and information in the description texts fostering the investors’ trust are shown to be the main predictors of successful funding. Regarding social impact, the possibility to empower women and groups of borrowers appeals to the investors, whereas empowerment of the family or community beyond the borrowers themselves appears to remain unappreciated. When examining the vulnerability of the borrowers as a predictor, the results manifest differences amongst the attitudes of the investors towards social impact. In the subsample of non-endorsed loans, the investors appear to prefer to support borrowers with an immigration background. In contrast, this is not the case with endorsed loans.

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Notes

  1. The internal due diligence process includes a review of the financial history, a verification of the identity and a validation of the business. Also, all applicants are screened through the Office of Foreign Assets Control terrorism database due to national security reasons.

  2. Note that for our analysis the private fundraising does not play a significant role because every loan application fulfills this requirement (typically approximately 10% to 15% of the loan amount is pre-funded).

  3. See Kiva (2019b). It’s even lower than that of usual P2P lending. As an example, the average repayment rates for the German P2P lending platforms, Auxmoney and Smava, are 88% and 86.2% (Dorfleitner et al. 2016).

  4. See descriptive statistics in Section “Data and methodology”.

  5. In Kiva’s intermediary-based model, the investors can see credit profiles of the MFIs, including default rate, delinquency rate, loans at risk rate, etc. Moreover, they can also see whether a special social performance badge is assigned to the MFI (Kiva 2019c).

  6. The interest rate a potential borrower is willing to accept can signal the creditworthiness of the borrower in the sense that high interest rates are only accepted by borrowers with low creditworthiness, which corresponds to the idea of lemon markets (Akerlof 1970).

  7. If not otherwise specified, the coeff. and st.err. in parentheses are from model III.

  8. As 7 loan applications are funded within one day, the survival time is multiplied by 100 to avoid negative logarithmic values.

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Authors and Affiliations

  1. Department of Finance, University of Regensburg, 93040, Regensburg, Germany

    Gregor Dorfleitner, Eva-Maria Oswald & Rongxin Zhang

  2. CERMi (Centre for European Research in Microfinance), Bruxelles, Belgium

    Gregor Dorfleitner

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  1. Gregor Dorfleitner
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Dorfleitner, G., Oswald, EM. & Zhang, R. From Credit Risk to Social Impact: On the Funding Determinants in Interest-Free Peer-to-Peer Lending. J Bus Ethics 170, 375–400 (2021). https://doi.org/10.1007/s10551-019-04311-8

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