选择选择者: 能源返利项目中由选择驱动的定向

Econometrica · 2026 · [{"name": "Takanori Ida", "affiliation": ["Kyoto University"]}, {"name": "Takunori Ishihara", "affiliation": ["Kyoto University of Advanced Science"]}, {"name": "Koichiro Ito", "affiliation": ["University of Chicago"]}, {"name": "Daido Kido", "affiliation": ["Otaru University of Commerce"]}, {"name": "Toru Kitagawa", "affiliation": ["John Brown University"]}, {"name": "Shosei Sakaguchi", "affiliation": ["Japan University of Economics", "The University of Tokyo"]}, {"name": "Shusaku Sasaki", "affiliation": ["Osaka International Cancer Institute"]}]

中文摘要

我们提出了一种最优政策分配规则, 它整合了经济学中常用的两种独特方法——基于可观测变量的瞄准和通过自我选择的瞄准。该方法可与实验或准实验数据结合使用, 以识别哪些人应当接受处理、哪些人应不接受处理, 以及哪些人应通过自我选择接受处理, 从而实现政策制定者的目标。将该方法应用于一项针对居民能源返利计划的随机对照试验, 我们发现, 最优地同时利用可观测数据与自我选择的瞄准优于传统瞄准。我们采用局部平均处理效应 (LATE) 框架 (Imbens and Angrist, 1994) 来考察本方法的作用机制。通过基于实验所产生的随机变异估计若干关键的 LATE, 我们展示了本方法如何使政策制定者能够识别: 哪些人的自我选择对社会福利有价值, 哪些人的自我选择则有损社会福利。

Abstract

We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observables and targeting through self‐selection . Our method can be used with experimental or quasi‐experimental data to identify who should be treated, be untreated, and self‐select to achieve a policymaker's objective. Applying this method to a randomized controlled trial on a residential energy rebate program, we find that targeting that optimally exploits both observable data and self‐selection outperforms conventional targeting. We use the Local Average Treatment Effect (LATE) framework (Imbens and Angrist (1994)) to investigate the mechanism in our approach. By estimating several key LATEs based on the random variation created by our experiment, we demonstrate how our method allows policymakers to identify whose self‐selection would be valuable and harmful to social welfare.