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Empirical Economics

, Volume 46, Issue 3, pp 1057–1079 | Cite as

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

  • Manuel A. HernandezEmail author
  • Maximo Torero
Article

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.

Keywords

Risk scoring Microcredit Default models Nonparametric methods 

JEL Classification

C14 O16 G17 

Notes

Acknowledgments

We would like to thank Qi Li, Carlos Martins-Filho, Robert Kunst, and two anonymous referees for their valuable comments. We also thank Christopher Marciniak for his valuable research assistance.

References

  1. Armendariz B, Morduch J (2005) The economics of microfinance. MIT Press, CambridgeGoogle Scholar
  2. Banerjee A, Duflo E, Glennerster R, Kinnan C (2010) The miracle of microfinance? Evidence from a randomized evaluation. Working paper, MIT Poverty Action LabGoogle Scholar
  3. Capon N (1982) Credit scoring systems: a critical analysis. J Market 46(2):82–91CrossRefGoogle Scholar
  4. Coleman B (2006) Microfinance in Northeast Thailand: who benefits and how much? World Dev 34(9):1612–1638CrossRefGoogle Scholar
  5. de Janvry A, McIntosh C, Sadoulet E (2010) The supply- and demand-side impacts of credit market information. J Dev Econ 93(2):173–188CrossRefGoogle Scholar
  6. Dupas P, Robinson J (2009) Savings constraints and microenterprise development: evidence from a field experiment in Kenya. NBER Working Paper No. 14693Google Scholar
  7. Fan J, Gijbels I (1996) Local polynomial modeling and its applications. Chapman and Hall, LondonGoogle Scholar
  8. Ghosh P, Mookherjee D, Ray D (2000) Credit rationing in developing countries: an overview of the theory. In: Mookherjee D, Ray D (eds) Readings in the theory of development economics. Blackwell, LondonGoogle Scholar
  9. Hand D, Henley W (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Ser A 160(3):523–541CrossRefGoogle Scholar
  10. Ichimura H (1993) Semiparametric least squares (SLS) and weighted SLS estimation of single-index models. J Econom 58(1–2):71–120CrossRefGoogle Scholar
  11. Karlan D, Zinman J (2011) Microcredit in theory and practice: using randomized credit scoring for impact evaluation. Science 332:1278–1284CrossRefGoogle Scholar
  12. Khandker S (2005) Microfinance and poverty: evidence using panel data from Bangladesh. World Bank Econ Rev 19(2):263–286CrossRefGoogle Scholar
  13. Klein R, Spady R (1993) An efficient semiparametric estimator for binary response models. Econometrica 61(2):387–421CrossRefGoogle Scholar
  14. Li Q, Racine J (2004) Nonparametric estimation of regression functions with both categorical and continuous data. J Econom 119(1):99–130CrossRefGoogle Scholar
  15. Li Q, Racine J (2006) Nonparametric econometrics: theory and practice. Princeton University Press, PrincetonGoogle Scholar
  16. Luoto J, McIntosh C, Wydick B (2007) Credit information systems in less developed countries: a test with microfinance in Guatemala. Econ Dev Cult Change 55(2):313–334CrossRefGoogle Scholar
  17. Maes J, Reed L (2012) State of the Microcredit Summit Campaign Report 2012. Microcredit Summit CampaignGoogle Scholar
  18. McFadden D, Puig C, Kirschner D (1977) Determinants of the long-run demand for electricity. Proc Am Stat Assoc 1:109–117Google Scholar
  19. Pregibon D (1979) Data analytic methods for generalized linear models. PhD dissertation, University of TorontoGoogle Scholar
  20. Racine J (1997) Consistent significance testing for nonparametric regression. J Bus Econ Stat 15(3):369–378Google Scholar
  21. Racine J (2008) Nonparametric econometrics: a primer. Found Trends Econom 3(1):1–88CrossRefGoogle Scholar
  22. Racine J, Hart J, Li Q (2006) Testing the significance of categorical predictor variables in nonparametric regression models. Econom Rev 25(4):523–544CrossRefGoogle Scholar
  23. Schreiner M (2000) Credit scoring for microfinance: can it work? J Microfinance 2(2):105–118Google Scholar
  24. Tukey J (1949) One degree of freedom for non-additivity. Biometrics 5(3):232–242CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Markets, Trade and Institutions DivisionIFPRIWashingtonUSA

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