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.

We study the investment effects of market integration on renewable energy expansion. Our theory highlights that market integration not only improves allocative efficiency by gains from trade but also incentivizes new investment in renewable power plants. To test our theoretical predictions, we examine how recent grid expansions in the Chilean electricity market changed electricity production, wholesale prices, generation costs, and renewable investments. We then build a structural model of power plant entry to quantify the impact of market integration with and without the investment effects. We find that the market integration in Chile increased solar generation by around 180%, saved generation costs by 8%, and reduced carbon emissions by 5%. A substantial amount of renewable entry would not have occurred in the absence of market integration. Our findings suggest that ignoring these investment effects would substantially understate the benefits of market integration and its important role in expanding renewable energy.