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Does Microfinance Reduce Poverty in Bangladesh? New Evidence from Household Panel Data

  • Katsushi S. Imai

    (Economics, School of Social Sciences, University of Manchester (UK) and RIEB, Kobe University (Japan))

  • Md. Shafiul Azam

    (Economics, School of Social Sciences, University of Manchester, UK)

The purpose of the present study is to examine whether microfinance reduces poverty in Bangladesh drawing upon the nationally representative household panel data covering 4 rounds from 1997 to 2005. A special attention was drawn to the issue of endogeneity by applying treatment effects model and propensity score matching (PSM) for the participants and non-participants of microfinance programmes. It has been found by treatment effects model applied to panel data that the simple household access to general loans from microfinance institutions (MFIs) did not increase per capita household income significantly, but household access to loans for productive purposes from MFIs significantly increased per capita household income. This suggests that the purpose and monitoring of how clients use the loans is important for increasing household income, and thus decreasing household poverty. However, the application of treatment effects model and PSM to each cross-sectional component of the panel data shows that the poverty reducing effect of MFI on poverty was significantly reduced over the years. This suggests the importance of more attention to the primary purpose of microcredit, that is, poverty reduction, and also to monitoring loan usages in the situations where the profits of MFIs became increasingly squeezed and their activities became more commercialised under severe competitions among MFIs in recent years.

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File URL: http://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2010-24.pdf
File Function: Revised version, 2011
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Paper provided by Research Institute for Economics & Business Administration, Kobe University in its series Discussion Paper Series with number DP2010-24.

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Length: 34 pages
Date of creation: Sep 2010
Date of revision: Nov 2011
Handle: RePEc:kob:dpaper:dp2010-24
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  1. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  3. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," World Bank Economic Review, World Bank Group, vol. 19(2), pages 263-286.
  4. Jonathan Morduch, 1998. "Does Microfinance Really Help the Poor? New Evidence from Flagship Programs in Bangladesh," Working Papers 198, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  5. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
  6. Besley, T. & Coate, S., 1991. "Group Lending, Repayment Incentives And Social Collateral," Papers 152, Princeton, Woodrow Wilson School - Development Studies.
  7. Katsushi S. Imai & Samuel Kobina Annim, 2010. "Microfinance and Household Poverty Reduction: New evidence from India," Discussion Paper Series DP2010-14, Research Institute for Economics & Business Administration, Kobe University.
  8. David Roodman & Jonathan Morduch, 2014. "The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence," Journal of Development Studies, Taylor & Francis Journals, vol. 50(4), pages 583-604, April.
  9. Mark M. Pitt & Shahidur R. Khandker, 1998. "The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 958-996, October.
  10. Christian Ahlin & RobertM. Townsend, 2007. "Using Repayment Data to Test Across Models of Joint Liability Lending," Economic Journal, Royal Economic Society, vol. 117(517), pages F11-F51, 02.
  11. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
  12. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
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