Dynamic Targeting: Experimental Evidence from Energy Rebate Programs

Economic policies often involve dynamic interventions, where individuals receive repeated interventions over multiple periods. This dynamics makes past responses informative to predict future responses and ultimate outcomes depend on the history of interventions. Despite these phenomena, existing economic studies typically focus on static targeting, possibly overlooking key information from dynamic interventions. We develop a framework for designing optimal dynamic targeting that maximizes social welfare gains from dynamic policy intervention. Our framework can be applied to experimental or quasi-experimental data with sequential randomization. We demonstrate that dynamic targeting can outperform static targeting through several key mechanisms: learning, habit formation, and screening effects. We then propose methods to empirically identify these effects. By applying this method to a randomized controlled trial on a residential energy rebate program, we show that dynamic targeting significantly outperforms conventional static targeting, leading to improved social welfare gains. We observe significant heterogeneity in the learning, habit formation, and screening effects, and illustrate how our approach leverages this heterogeneity to design optimal dynamic targeting.