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This study will develop new methodology for meta-analyses of distributional treatment effects in order to produce a new meta-analysis of microcredit interventions. Bayesian hierarchical models provide the framework for aggregation of quantile treatment effects and variance treatment effects, allowing for heterogeneous effects across studies while also estimating a generalized effect. I will also develop accompanying metrics of external validity, by extending the existing Bayesian pooling metrics to assess the heterogeneity in distributional effects across sites. I will consider a variety of modeling choices in order to derive robust and flexible hierarchical models and evaluate their relative performance using Monte Carlo simulation studies where appropriate. The resulting analysis should reveal the full distributional impact of microcredit access, and thus inform future policy decisions regarding microfinance institutions.