论文标题
针对社会援助的混合方法
A hybrid approach to targeting social assistance
论文作者
论文摘要
代理意味着测试（PMT）和基于社区的目标（CBT）是针对发展中国家社会援助的两种主要方法。在本文中，我们提出了一种混合靶向方法，该方法将CBT的重点放在本地信息和偏好上，并与PMT对可验证指标的依赖相结合。具体而言，我们概述了一个类似于PMT的贝叶斯框架，该框架是基于社会人口统计学特征的加权总和。然而，我们提出了从社区定位练习中校准重量排名的权重，这意味着我们方法使用的权重反映了潜在的受益人本身如何在做出目标决策时替代社会人口统计学特征。我们讨论了该模型的几个实际扩展，包括每个社区对多个排名的概括，精英捕获的调整，一种合并潜在受益人的辅助信息的方法以及动态更新过程。我们使用布基纳法索和印度尼西亚的数据进一步提供了经验说明。
Proxy means testing (PMT) and community-based targeting (CBT) are two of the leading methods for targeting social assistance in developing countries. In this paper, we present a hybrid targeting method that incorporates CBT's emphasis on local information and preferences with PMT's reliance on verifiable indicators. Specifically, we outline a Bayesian framework for targeting that resembles PMT in that beneficiary selection is based on a weighted sum of sociodemographic characteristics. We nevertheless propose calibrating the weights to preference rankings from community targeting exercises, implying that the weights used by our method reflect how potential beneficiaries themselves substitute sociodemographic features when making targeting decisions. We discuss several practical extensions to the model, including a generalization to multiple rankings per community, an adjustment for elite capture, a method for incorporating auxiliary information on potential beneficiaries, and a dynamic updating procedure. We further provide an empirical illustration using data from Burkina Faso and Indonesia.
