IDEAS home Printed from https://ideas.repec.org/p/mos/moswps/2008-29.html
   My bibliography  Save this paper

Who Benefits From Microfinance? The Impact Evaluation Of Large Scale Programs In Bangladesh

Author

Listed:
  • Asadul Islam Author-X-Name-Asadul

Abstract

This paper evaluates the impact of microfinance on household consumption using a new, large and unique cross-section data set from Bangladesh. The richness of the data and program eligibility criterion allow the use of a number of non-experimental impact evaluation techniques, in particular instrumental variable (IV) estimation and propensity score matching (PSM). Estimates from both IV and PSM strategies have been interpreted as average causal effects that are valid for various groups of participants in microfinance. The overall results indicate that the effects of micro loans are not robust across all groups of poor household borrowers. It appears that the poorest of the poor participants are among those who benefit most. The impact estimates are lower, or sometimes even negative, for those households marginal to the participation decision. The effects of participation are, in general, stronger for male borrowers. These results hold across different specifications and methods, including correction for various sources of selection bias (including possible spill-over effects).

Suggested Citation

  • Asadul Islam Author-X-Name-Asadul, 2008. "Who Benefits From Microfinance? The Impact Evaluation Of Large Scale Programs In Bangladesh," Monash Economics Working Papers 29/08, Monash University, Department of Economics.
    • Handle: RePEc:mos:moswps:2008-29
    as

    Download full text from publisher

    File URL: http://www.buseco.monash.edu.au/eco/research/papers/2008/2908microfinanceislam.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    2. Angelucci, Manuela & Attanasio, Orazio, 2006. "Estimating ATT Effects with Non-Experimental Data and Low Compliance," IZA Discussion Papers 2368, Institute of Labor Economics (IZA).
    3. Dean Karlan & Jonathan Zinman, 2010. "Expanding Credit Access: Using Randomized Supply Decisions to Estimate the Impacts," The Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 433-464, January.
    4. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. repec:pri:rpdevs:morduch_microfinance_poor is not listed on IDEAS
    7. Jonathan Morduch, 1999. "The Microfinance Promise," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1569-1614, December.
    8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    9. Charles Michalopoulos & Howard S. Bloom & Carolyn J. Hill, 2004. "Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 156-179, February.
    10. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    11. 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.
    12. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    13. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    14. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    15. Coleman, Brett E., 1999. "The impact of group lending in Northeast Thailand," Journal of Development Economics, Elsevier, vol. 60(1), pages 105-141, October.
    16. Anna Fruttero & Varun Gauri, 2003. "Location decisions and nongovernmental organization motivation : evidence from rural Bangladesh," Policy Research Working Paper Series 3176, The World Bank.
    17. 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..
    18. Joshua D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
    19. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 533-575.
    20. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    21. David Card, 1992. "Do Minimum Wages Reduce Employment? A Case Study of California, 1987–89," ILR Review, Cornell University, ILR School, vol. 46(1), pages 38-54, October.
    22. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    23. Chowdhury, Prabal Roy, 2005. "Group-lending: Sequential financing, lender monitoring and joint liability," Journal of Development Economics, Elsevier, vol. 77(2), pages 415-439, August.
    24. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    25. 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.
    26. Joshua Angrist & Stacey Chen, 2007. "Long-Term Effects of Vietnam-Era Conscription: Schooling, Experience and Earnings," Working Papers 07-23, Center for Economic Studies, U.S. Census Bureau.
    27. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2005. "Evaluating the effect of education on earnings: models, methods and results from the National Child Development Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 473-512, July.
    28. Daniel S. Hamermesh & Stephen J. Trejo, 2000. "The Demand for Hours of Labor: Direct Evidence from California," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 38-47, February.
    29. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    30. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    31. Joseph P. Kaboski & Robert M. Townsend, 2005. "Policies and Impact: An Analysis of Village-Level Microfinance Institutions," Journal of the European Economic Association, MIT Press, vol. 3(1), pages 1-50, March.
    32. Joshua D. Angrist & Stacey H. Chen, 2007. "Long-term consequences of vietnam-era conscription: schooling, experience, and earnings," NBER Working Papers 13411, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdul Wadud, 2013. "Impact of Microcredit on Agricultural Farm Performance and Food Security in Bangladesh," Working Papers 14, Institute of Microfinance (InM).
    2. Asadul Islam & Chongwoo Choe, 2013. "Child Labor And Schooling Responses To Access To Microcredit In Rural Bangladesh," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 46-61, January.
    3. Thai, Pham Huu Hong, 2018. "Does household credit benefit child schooling for the poorest ethnic minorities? New evidence from a transitional economy," Children and Youth Services Review, Elsevier, vol. 89(C), pages 103-112.
    4. Md. Thuhid Noor & Md. Rabiul Auwul & Saha Forid, 2018. "Impact of Micro-Credit on Poor Households in Kurigram District," Applied Economics and Finance, Redfame publishing, vol. 5(1), pages 102-124, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    2. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    3. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    4. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    5. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    6. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    7. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    8. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).
    9. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2008. "Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 61, pages 3895-3962, Elsevier.
    10. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    11. James J. Heckman & Petra E. Todd, 2009. "A note on adapting propensity score matching and selection models to choice based samples," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 230-234, January.
    12. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    13. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    14. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    15. Stephen L. Morgan & David J. Harding, 2006. "Matching Estimators of Causal Effects," Sociological Methods & Research, , vol. 35(1), pages 3-60, August.
    16. Richard Disney & Eleonora Fischera & Trudy Owens, 2010. "Has the Introduction of Microfinance Crowded-out Informal Loans in Malawi?," Discussion Papers 10/08, University of Nottingham, CREDIT.
    17. Islam, Asadul & Nguyen, Chau & Smyth, Russell, 2015. "Does microfinance change informal lending in village economies? Evidence from Bangladesh," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 141-156.
    18. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    19. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
    20. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.

    More about this item

    Keywords

    Microfinance; treatment effect; Matching; Consumption.;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General
    • L30 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - General
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mos:moswps:2008-29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Simon Angus (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.