|Author Name||IDA Takanori (Kyoto University) / ISHIHARA Takunori (Kyoto University of Advanced Science) / ITO Koichiro (Visiting Fellow, RIETI) / KIDO Daido (Kyoto University) / KITAGAWA Toru (Brown University / UCL) / SAKAGUCHI Shosei (University of Tokyo) / SASAKI Shusaku (Osaka University)|
|Creation Date/NO.||February 2023 23-E-011|
|Download / Links|
We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observable characteristics and targeting through self-selection. Our method uses experimental or quasi-experimental data to identify who should be treated, untreated, and who should 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 leverages both observable data and self- selection outperforms conventional targeting for a standard utilitarian welfare function and welfare functions that balance the equity-efficiency trade-off. We highlight that the LATE framework (Imbens and Angrist, 1994) can be used to investigate the mechanism behind our approach. By introducing new estimators called the LATEs for takers and non-takers, we show that our method allows policymakers to identify whose self-selection would be valuable and harmful to social welfare.