英文标题：
《Understanding the Impact of Microcredit Expansions: A Bayesian
  Hierarchical Analysis of 7 Randomised Experiments》
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作者：
Rachael Meager
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最新提交年份：
2016
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英文摘要：
  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.
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中文摘要：
贝叶斯层次模型是一种从异构环境中聚合和合成数据的方法，广泛应用于统计学和其他学科。我将这一框架应用于7个扩大小额信贷获取的随机实验的证据，以评估干预对家庭结果的总体影响以及不同地点影响的异质性。结果表明，小额信贷的影响可能是积极的，但相对于对照组的平均水平而言，影响很小，不能排除产生负面影响的可能性。相比之下，汇集所有数据而不评估异质性的常见元分析方法会误导6个家庭结果中的2个产生“统计显著”结果。研究的标准汇集指标表明，平均60%的治疗效果汇集在一起，这表明特定地点的影响在外部是合理有效的，因此可以为彼此和一般情况提供信息。交叉研究的异质性几乎完全是由在小额信贷扩张之前曾经营过企业的27%家庭的异质性效应产生的，尽管这一群体整体上可能会受到更大的影响。用岭回归方法评估位点特异性协变量与治疗效果之间的相关性表明，剩余的异质性与经济变量的差异密切相关，但与研究设计方案的差异无关。平均利率和平均贷款规模与治疗效果的相关性最强，且均为负。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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