Working Paper: NBER ID: w30469
Authors: Takanori Ida; Takunori Ishihara; Koichiro Ito; Daido Kido; Toru Kitagawa; Shosei Sakaguchi; Shusaku Sasaki
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 highlight that the Local Average Treatment Effect (LATE) framework (Imbens and Angrist, 1994) can be used 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.
Keywords: energy rebate programs; targeting; self-selection; social welfare
JEL Codes: C01; Q4; Q48; Q5; Q58
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| treatment assignment (C90) | social welfare (I38) |
| self-selection (C52) | social welfare (I38) |
| observable data + self-selection (C90) | social welfare (I38) |
| self-selection (C52) | LATE for takers (G14) |
| self-selection (C52) | LATE for nontakers (D15) |