Minding the Gap: Aid Effectiveness, Project Ratings and Contextualization
利用机器学习构建新数据集，分析发展项目文件，发现项目对发展成果的贡献最强预测因素是适应国家情境的程度，而非捐助者的自我评级，尤其在制度薄弱环境中的大型项目评级与影响差异最大。
Abstract This paper applies novel techniques to long-standing questions of aid effectiveness. It constructs a new data set using machine-learning methods to encode aspects of development project documents that would be infeasible with manual methods. It then uses that data set to show that the strongest predictor of these projects’ contributions to development outcomes is not the self-evaluation ratings assigned by donors, but their degree of adaptation to country context and that the largest differences between ratings and actual impact occur in large projects in institutionally weak settings. It also finds suggestive evidence that the content of ex post reviews of project effectiveness may predict sector outcomes, even if ratings do not.