IDEAS home Printed from https://ideas.repec.org/p/jgu/wpaper/1115.html
   My bibliography  Save this paper

Demasking the impact of microfinance

Author

Listed:
  • Helke Waelde

    (KfW Entwicklungsbank, Frankfurt am Main, Germany)

Abstract

We reconsider data from a randomized control trial study in India. The data reveal the impact of a microloan program. We extend the often used randomized impact evaluation and di¤erence-in-di¤erence approach by quantile regression and the consideration of the quantile treatment effects. The use of additional, more advanced, evaluation methods allows a more detailed consideration of borrowers at the lower and at the upper end of the wealth distribution. We find a strong negative and signi.cant time-trend. Furthermore, we observe a negative impact of the provi- sion of microfinance loans such that the overall impact is even more negative. This is particularly well seen for entrepreneurs in the lower and in the higher quantiles. As we learn that poor entrepreneurs use microloans for consumption, we doubt that micro.nance is the right instrument for them. The data suggest that providing microloans for average entrepreneurs, who can hire very poor entrepreneurs, might be an effective solution for that dilemma.

Suggested Citation

  • Helke Waelde, 2011. "Demasking the impact of microfinance," Working Papers 1115, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 09 Nov 2011.
    • Handle: RePEc:jgu:wpaper:1115
    as

    Download full text from publisher

    File URL: https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1115.pdf
    File Function: First version, 2011
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    3. Ghatak, Maitreesh, 1999. "Group lending, local information and peer selection," Journal of Development Economics, Elsevier, vol. 60(1), pages 27-50, October.
    4. Shahidur R. Khandker & Gayatri B. Koolwal & Hussain A. Samad, 2010. "Handbook on Impact Evaluation : Quantitative Methods and Practices," World Bank Publications - Books, The World Bank Group, number 2693.
    5. Markus Frölich & Blaise Melly, 2013. "Unconditional Quantile Treatment Effects Under Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 346-357, July.
    6. 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.
    7. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    8. 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.
    9. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2007. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program," NBER Working Papers 12831, National Bureau of Economic Research, Inc.
    10. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    11. Behrman, Jere R., 2010. "The International Food Policy Research Institute (IFPRI) and the Mexican PROGRESA Anti-Poverty and Human Resource Investment Conditional Cash," World Development, Elsevier, vol. 38(10), pages 1473-1485, October.
    12. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    13. Bitler, Marianne P. & Gelbach, Jonah B. & Hoynes, Hilary W., 2008. "Distributional impacts of the Self-Sufficiency Project," Journal of Public Economics, Elsevier, vol. 92(3-4), pages 748-765, April.
    14. Kenneth I. Wolpin & Petra E. Todd, 2006. "Assessing the Impact of a School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility," American Economic Review, American Economic Association, vol. 96(5), pages 1384-1417, December.
    15. Shahidur R. Khandker & Zaid Bakht & Gayatri B. Koolwal, 2009. "The Poverty Impact of Rural Roads: Evidence from Bangladesh," Economic Development and Cultural Change, University of Chicago Press, vol. 57(4), pages 685-722, July.
    16. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, January.
    17. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    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. Mathilde Maîtrot & Miguel Niño-Zarazúa, 2017. "Poverty and wellbeing impacts of microfinance: What do we know?," WIDER Working Paper Series 190, World Institute for Development Economic Research (UNU-WIDER).
    2. Mathilde Maîtrot & Miguel Niño-Zarazúa, 2017. "Poverty and wellbeing impacts of microfinance: What do we know?," WIDER Working Paper Series wp-2017-190, World Institute for Development Economic Research (UNU-WIDER).

    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. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
    3. Słoczyński, Tymon, 2012. "New Evidence on Linear Regression and Treatment Effect Heterogeneity," MPRA Paper 39524, University Library of Munich, Germany.
    4. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    5. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    6. Antecol, Heather & Eren, Ozkan & Ozbeklik, Serkan, 2013. "The effect of Teach for America on the distribution of student achievement in primary school: Evidence from a randomized experiment," Economics of Education Review, Elsevier, vol. 37(C), pages 113-125.
    7. Ozkan Eren & Serkan Ozbeklik, 2014. "Who Benefits From Job Corps? A Distributional Analysis Of An Active Labor Market Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 586-611, June.
    8. Maier, Michael, 2011. "Tests for distributional treatment effects under unconfoundedness," Economics Letters, Elsevier, vol. 110(1), pages 49-51, January.
    9. Daniel Zaga, 2014. "The Impact of Three Mexican Nutritional Programs: The Case of Dif-Puebla," CFD Working Papers 09-2015, Centre for Finance and Development, The Graduate Institute.
    10. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
    11. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    12. Gustavo A. Crespi & Alessandro Maffioli & Pierre Mohnen & Gonzalo Vázquez, 2011. "Evaluating the Impact of Science, Technology and Innovation Programs: a Methodological Toolkit," SPD Working Papers 1104, Inter-American Development Bank, Office of Strategic Planning and Development Effectiveness (SPD).
    13. Liao, Wen-Chi & Zhao, Daxuan, 2019. "The selection and quantile treatment effects on the economic returns of green buildings," Regional Science and Urban Economics, Elsevier, vol. 74(C), pages 38-48.
    14. Sørensen, Kenneth Lykke, 2016. "Heterogeneous impacts on earnings from an early effort in labor market programs," Labour Economics, Elsevier, vol. 41(C), pages 266-279.
    15. 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.
    16. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    17. Gamper-Rabindran, Shanti & Khan, Shakeeb & Timmins, Christopher, 2010. "The impact of piped water provision on infant mortality in Brazil: A quantile panel data approach," Journal of Development Economics, Elsevier, vol. 92(2), pages 188-200, July.
    18. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    19. Brantly Callaway & Weige Huang, 2020. "Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 808-842, August.
    20. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.

    More about this item

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

    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:jgu:wpaper:1115. 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: Research Unit IPP (email available below). General contact details of provider: https://edirc.repec.org/data/vlmaide.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.