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Understanding the Distributional Aspects of Microcredit Expansions

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  • Melvyn Weeks
  • Tobias Gabel Christiansen

Abstract

Various poverty reduction strategies are being implemented in the pursuit of eliminating extreme poverty. One such strategy is increased access to microcredit in poor areas around the world. Microcredit, typically defined as the supply of small loans to underserved entrepreneurs that originally aimed at displacing expensive local money-lenders, has been both praised and criticized as a development tool (Banerjee et al., 2015b). This paper presents an analysis of heterogeneous impacts from increased access to microcredit using data from three randomised trials. In the spirit of recognising that in general the impact of a policy intervention varies conditional on an unknown set of factors, particular, we investigate whether heterogeneity presents itself as groups of winners and losers, and whether such subgroups share characteristics across RCTs. We find no evidence of impacts, neither average nor distributional, from increased access to microcredit on consumption levels. In contrast, the lack of average effects on profits seems to mask heterogeneous impacts. The findings are, however, not robust to the specific machine learning algorithm applied. Switching from the better performing Elastic Net to the worse performing Random Forest leads to a sharp increase in the variance of the estimates. In this context, methods to evaluate the relative performing machine learning algorithm developed by Chernozhukov et al. (2019) provide a disciplined way for the analyst to counter the uncertainty as to which algorithm to deploy.

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  • Melvyn Weeks & Tobias Gabel Christiansen, 2020. "Understanding the Distributional Aspects of Microcredit Expansions," Papers 2011.10509, arXiv.org.
  • Handle: RePEc:arx:papers:2011.10509
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Meager, Rachael & Sturdy, Jennifer, 2017. "Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature," MetaArXiv 7tkvm, Center for Open Science.
    3. Michael C. Lovell, 1963. "Seasonal Adjustment of Economic Time Series and Multiple Regression," Cowles Foundation Discussion Papers 151, Cowles Foundation for Research in Economics, Yale University.
    4. Manuela Angelucci & Dean Karlan & Jonathan Zinman, 2015. "Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 151-182, January.
    5. Jacob M. Montgomery & Brendan Nyhan & Michelle Torres, 2018. "How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It," American Journal of Political Science, John Wiley & Sons, vol. 62(3), pages 760-775, July.
    6. Rachael Meager, 2019. "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 57-91, January.
    7. Meager, Rachael, 2019. "Understanding the average impact of microcredit expansions: a Bayesian hierarchical analysis of seven randomized experiments," LSE Research Online Documents on Economics 88190, London School of Economics and Political Science, LSE Library.
    8. World Bank, 2018. "Poverty and Shared Prosperity 2018 [Rapport 2018 sur la pauvreté et la prospérité partagée]," World Bank Publications - Books, The World Bank Group, number 30418.
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