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Parametric versus nonparametric methods in risk scoring: an application to microcredit

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  • Manuel Hernandez
  • Maximo Torero

Abstract

The importance of credit access to improve economic opportunities in developing markets is well established in the literature. However, there exists a strong need to mitigate adverse selection problems in microlending. A risk scoring model that more accurately predicts the likelihood of repayment of potential borrowers can help address this market imperfection and to benefit both lenders and borrowers. This paper compares the performance of nonparametric versus semiparametric and traditional parametric risk scoring models based on default probabilities. We show the advantages of relying on less structured, data-driven methods for risk scoring using both simulated data and data from credit loans granted to small and microenterprises in rural Peru. The estimation results indicate that nonparametric methods lead to a better evaluation of credit worthiness and can help prevent including potential “bad” borrowers and excluding “good” borrowers from sensitive microcredit markets. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Manuel Hernandez & Maximo Torero, 2014. "Parametric versus nonparametric methods in risk scoring: an application to microcredit," Empirical Economics, Springer, vol. 46(3), pages 1057-1079, May.
    • Handle: RePEc:spr:empeco:v:46:y:2014:i:3:p:1057-1079
    DOI: 10.1007/s00181-013-0703-8
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    References listed on IDEAS

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    5. Pascaline Dupas & Jonathan Robinson, 2013. "Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya," American Economic Journal: Applied Economics, American Economic Association, vol. 5(1), pages 163-192, January.
    6. de Janvry, Alain & McIntosh, Craig & Sadoulet, Elisabeth, 2010. "The supply- and demand-side impacts of credit market information," Journal of Development Economics, Elsevier, vol. 93(2), pages 173-188, November.
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    8. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," The World Bank Economic Review, World Bank, vol. 19(2), pages 263-286.
    9. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-378, July.
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    Cited by:

    1. Hernandez, Manuel A. & Torero, Maximo, 2018. "A poverty-sensitive scorecard to prioritize lending and grant allocation: Evidence from Central America," Food Policy, Elsevier, vol. 77(C), pages 81-90.
    2. Maria Patricia Durango‐Gutiérrez & Juan Lara‐Rubio & Andrés Navarro‐Galera, 2023. "Analysis of default risk in microfinance institutions under the Basel III framework," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1261-1278, April.
    3. Debadutta Panda & Sriharsha Reddy, 2020. "Predictors of microcredit default in Indian self‐help groups," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 91(2), pages 303-318, June.

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    More about this item

    Keywords

    Risk scoring; Microcredit; Default models; Nonparametric methods; C14; O16; G17;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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