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

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.