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Econometrica Vol. 94 No. 1 2026

Choosing Who Chooses: Selection‐Driven Targeting in Energy Rebate Programs

Takanori Ida1; Takunori Ishihara2; Koichiro Ito3; Daido Kido4; Toru Kitagawa5; Shosei Sakaguchi6; Shusaku Sasaki7

1 Graduate School of Economics, Kyoto University · 2 Faculty of Economics and Business Administration, Kyoto University of Advanced Science · 3 Harris School of Public Policy, University of Chicago () · 4 Department of Economics, Otaru University of Commerce · 5 Department of Economics; Brown University · 6 Faculty of Economics, University of Tokyo · 7 Center for Infectious Disease Education and Research, University of Osaka

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.

DOI
10.3982/ecta21180
Volume
94
Issue
1
Pages
225-247
Language
en
Sources
openalex crossref

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