Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments

Despite evidence from multiple randomized evaluations of microcredit, questions about external validity have impeded consensus on the results. I jointly estimate the average effect and the heterogeneity in effects across seven studies using Bayesian hierarchical models. I find the impact on household business and consumption variables is unlikely to be transformative and may be negligible. I find reasonable external validity: true heterogeneity in effects is moderate, and approximately 60 percent of observed heterogeneity is sampling variation. Households with previous business experience have larger but more heterogeneous effects. Economic features of microcredit interventions predict variation in effects better than studies' evaluation protocols.

[1]  J. Griffin,et al.  Some Priors for Sparse Regression Modelling , 2013 .

[2]  L. Fahrmeir,et al.  Bayesian Regularisation in Structured Additive Regression Models for Survival Data , 2008 .

[3]  Jonathan Zinman,et al.  Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco , 2014 .

[4]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[5]  Joshua D. Angrist,et al.  Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants , 1998 .

[6]  Guido Imbens,et al.  Site Selection Bias in Program Evaluation , 2014 .

[7]  C. McCulloch,et al.  Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter , 2011, 1201.1980.

[8]  Dean Karlan,et al.  Six Randomized Evaluations of Microcredit: Introduction and Further Steps † , 2015 .

[9]  Bruce Wydick Microfinance on the margin: why recent impact studies may understate average treatment effects , 2016 .

[10]  Amanda E. Kowalski Doing More When You&Apos;Re Running Late: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments , 2016 .

[11]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[12]  Rajeev Dehejia,et al.  Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs With Grouped Data , 2003 .

[13]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[14]  Dean Karlan,et al.  A multifaceted program causes lasting progress for the very poor: Evidence from six countries , 2015, Science.

[15]  Sophia Rabe-Hesketh,et al.  A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models , 2013, Psychometrika.

[16]  M. Ahmad Distant voices: the views of the field workers of NGOs in Bangladesh on microcredit , 2003 .

[17]  Rachael Meager Understanding the Average Impact of Microcredit Expansions : A Bayesian Hierarchical Analysis of 7 Randomized Experiments WORKING PAPER , 2016 .

[18]  Lucy H. Jones,et al.  Scoring for Impact Evaluation Microcredit in Theory and Practice : Using Randomized Credit , 2011 .

[19]  Guido W. Imbens,et al.  External Validity in Fuzzy Regression Discontinuity Designs , 2014, Journal of Business & Economic Statistics.

[20]  J. Angrist,et al.  Extrapolate-Ing: External Validity and Overidentification in the Late Framework , 2010 .

[21]  A. Banerjee,et al.  The Miracle of Microfinance? Evidence from a Randomized Evaluation , 2013 .

[22]  A. Banerjee,et al.  Microcredit Under the Microscope: What Have We Learned in the Past Two Decades, and What Do We Need to Know? , 2013 .

[23]  M. Burke,et al.  Climate and Conflict , 2014 .

[24]  Rachael Meager Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature , 2017, American Economic Review.

[25]  Andrew Gelman,et al.  The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It , 2018, Personality & social psychology bulletin.

[26]  A. V. D. Vaart,et al.  Asymptotic Statistics: Frontmatter , 1998 .

[27]  B. Efron,et al.  Data Analysis Using Stein's Estimator and its Generalizations , 1975 .

[28]  Dean S. Karlan,et al.  Six Randomized Evaluations of Microcredit : Introduction and Further Steps , 2015 .

[29]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[30]  Isaiah Andrews,et al.  Identification of and Correction for Publication Bias , 2017, American Economic Review.

[31]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[32]  D. Rubin Estimation in Parallel Randomized Experiments , 1981 .

[33]  G. David Roodman Due Diligence: An Impertinent Inquiry into Microfinance , 2011 .

[34]  Orazio Attanasio,et al.  The Impacts of Microfinance: Evidence from Joint-Liability Lending in Mongolia , 2015 .

[35]  Lant Pritchett,et al.  Learning from Experiments When Context Matters , 2015 .

[36]  P. Diaconis Finite forms of de Finetti's theorem on exchangeability , 1977, Synthese.

[37]  Sophia Rabe-Hesketh,et al.  Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models , 2015 .

[38]  James G. Scott,et al.  The horseshoe estimator for sparse signals , 2010 .

[39]  Cedric E. Ginestet ggplot2: Elegant Graphics for Data Analysis , 2011 .

[40]  Erica Field,et al.  Does the Classic Microfinance Model Discourage Entrepreneurship Among the Poor? Experimental Evidence from India † , 2013 .

[41]  M. Betancourt,et al.  Hamiltonian Monte Carlo for Hierarchical Models , 2013, 1312.0906.

[42]  James G. MacKinnon,et al.  Wild Bootstrap Inference for Wildly Different Cluster Sizes , 2017 .

[43]  L. Hedges,et al.  Vote-counting methods in research synthesis. , 1980 .

[44]  Andrew Gelman,et al.  Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models , 2006, Technometrics.

[45]  Esther Duflo,et al.  Estimating the Impact of Microcredit on Those Who Take it Up: Evidence from a Randomized Experiment in Morocco , 2014 .

[46]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[47]  A. Gelman Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

[48]  Alessandro Tarozzi,et al.  The Impacts of Microcredit : Evidence from Ethiopia , 2014 .

[49]  Rajeev Dehejia,et al.  From Local to Global: External Validity in a Fertility Natural Experiment , 2015, Journal of Business & Economic Statistics.

[50]  H J Eysenck,et al.  Systematic Reviews: Meta-analysis and its problems , 1994, BMJ.

[51]  Joseph P. Kaboski,et al.  A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative , 2008, Econometrica : journal of the Econometric Society.