Understanding the Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of 7 Randomised Experiments

TL;DR

This paper uses Bayesian hierarchical models to analyze data from seven randomized microcredit experiments, revealing small positive effects with significant heterogeneity influenced by household characteristics and economic variables.

Contribution

It introduces a Bayesian hierarchical approach to synthesize heterogeneous microcredit experiment data, highlighting the importance of accounting for heterogeneity and covariate effects.

Findings

Microcredit effects are likely positive but small.

Heterogeneity is mainly driven by households with pre-existing businesses.

Interest rate and loan size negatively correlate with treatment effects.

Abstract

Bayesian hierarchical models are a methodology for aggregation and synthesis of data from heterogeneous settings, used widely in statistics and other disciplines. I apply this framework to the evidence from 7 randomized experiments of expanding access to microcredit to assess the general impact of the intervention on household outcomes and the heterogeneity in this impact across sites. The results suggest that the effect of microcredit is likely to be positive but small relative to control group average levels, and the possibility of a negative impact cannot be ruled out. By contrast, common meta-analytic methods that pool all the data without assessing the heterogeneity misleadingly produce "statistically significant" results in 2 of the 6 household outcomes. Standard pooling metrics for the studies indicate on average 60% pooling on the treatment effects, suggesting that the site-specific effects are reasonably externally valid, and thus informative for each other and for the general case. The cross-study heterogeneity is almost entirely generated by heterogeneous effects for the 27% households who previously operated businesses before microcredit expansion, although this group is likely to see much larger impacts overall. A Ridge regression procedure to assess the correlations between site-specific covariates and treatment effects indicates that the remaining heterogeneity is strongly correlated with differences in economic variables, but not with differences in study design protocols. The average interest rate and the average loan size have the strongest correlation with the treatment effects, and both are negative.