Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature

• 成果类型: Article
• 署名作者: Meager, Rachael
• 署名单位: University of London; London School Economics & Political Science
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20181811
• 发表日期: 2022
• 页码: 1818-1847
• 关键词: microfinance evidence; seasonal migration; impacts; MODEL; neighborhoods; Heterogeneity; quantile; exposure; miracle; WEALTH

摘要

Expanding credit access in developing contexts could help some households while harming others. Microcredit studies show different effects at different quantiles of household profit, including some neg-ative effects; yet these findings also differ across studies. I develop new Bayesian hierarchical models to aggregate the evidence on these distributional effects for mixture-type outcomes such as household profit. Applying them to microcredit, I find a precise zero effect from the fifth to seventy-fifth quantiles, and uncertain yet large effects on the upper tails, particularly for households with business experience. These quantile estimates are more reliable than averages because the data are fat tailed. (JEL G21, G51, L25, O16, P34)