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Business Training Allocation and Credit Scoring: Theory and Evidence from Microcredit in France

Author

Listed:
  • Renaud Bourlès
  • Anastasia Cozarenco
  • Dominique Henriet
  • Xavier Joutard

Abstract

The microcredit market, where inexperienced micro-borrowers meet experienced microfinance institutions (MFIs), is subject to reversed asymmetric information. Thus, MFIs' choices can shape borrowers' beliefs and their behavior. We analyze how this mechanism may influence microfinance institution decisions to allocate business training. By means of a theoretical model, we show that superior information can lead the MFI not to train (or to train less) riskier borrowers. We then investigate whether this mechanism is empirically relevant, using data from a French MFI.Confirming our theoretical reasoning, we find a non-monotonic relationship between the MFI's decision to train and the risk that micro-borrowers represent.

Suggested Citation

  • Renaud Bourlès & Anastasia Cozarenco & Dominique Henriet & Xavier Joutard, 2015. "Business Training Allocation and Credit Scoring: Theory and Evidence from Microcredit in France," Working Papers CEB 15-030, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:sol:wpaper:2013/206494
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    References listed on IDEAS

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    1. Myerson, Roger B, 1983. "Mechanism Design by an Informed Principal," Econometrica, Econometric Society, vol. 51(6), pages 1767-1797, November.
    2. Dean Karlan & Martin Valdivia, 2011. "Teaching Entrepreneurship: Impact of Business Training on Microfinance Clients and Institutions," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 510-527, May.
    3. Luminita Postelnicu & Niels Hermes & Ariane Szafarz, 2013. "Defining Social Collateral in Microfinance Group Lending," Working Papers CEB 13-050, ULB -- Universite Libre de Bruxelles.
    4. Bertrand Villeneuve, 2000. "The Consequences for a Monopolistic Insurance Firm of Evaluating Risk Better than Customers: The Adverse Selection Hypothesis Reversed," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 25(1), pages 65-79, June.
    5. DeYoung, Robert & Glennon, Dennis & Nigro, Peter, 2008. "Borrower-lender distance, credit scoring, and loan performance: Evidence from informational-opaque small business borrowers," Journal of Financial Intermediation, Elsevier, vol. 17(1), pages 113-143, January.
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    Cited by:

    1. Emmanuel Tetteh Jumpah & Emmanuel Kojo Tetteh & Abdulai Adams, 2018. "Microcredit repayment among smallholder farmers: what microfinance institutions need to know," Asian Journal of Agriculture and rural Development, Asian Economic and Social Society, vol. 8(2), pages 74-91, July.

    More about this item

    Keywords

    microcredit; reversed asymmetric information; looking-glass self; bivariate probit; scoring model;

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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