Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature
Rachel Maeger (LSE, UK)
This paper develops methods to aggregate evidence on distributional treatment effects from multiple studies conducted in
different settings, and applies them to the microcredit literature. Several randomized trials of expanding access to
microcredit found substantial effects on the tails of household outcome distributions, but the extent to which these findings
generalize to future settings was not known. Aggregating the evidence on sets of quantile effects poses additional challenges
relative to average effects because distributional effects must imply monotonic quantiles and pass information
across quantiles. Using a Bayesian hierarchical framework, I develop new models to aggregate distributional effects and
assess their generalizability. For continuous outcome variables, the methodological challenges are addressed by applying
transforms to the unknown parameters. For partially discrete variables such as business profits, I use contextual
economic knowledge to build tailored parametric aggregation models. I find generalizable evidence that microcredit
has negligible impact on the distribution of various household outcomes below the 75th percentile, but above this point
there is no generalizable prediction. Thus, there is strong evidence that microcredit typically does not lead to worse
outcomes at the group level, but no generalizable evidence on whether it improves group outcomes.
Households with previous business experience accoun tfor the majority of the impact in the tails and see large
increases in the upper tail of the consumption distribution in particular.