Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
证明在开发代理家计调查贫困瞄准工具时，优先通过交叉验证和随机集成方法最小化样本外误差，能显著提升工具在样本外的表现，并以美国国际开发署的贫困评估工具为例进行演示。
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction.We present evidence that prioritizing minimal out-of-sample error, identified through cross-validation and stochastic ensemble methods, in PMT tool development can substantially improve the out-of-sample performance of these targeting tools.We take the United States Agency for International Development (USAID) poverty assessment tool and base data for demonstration of these methods; however, the methods applied in this paper should be considered for PMT and other poverty-targeting tool development more broadly.