cover image: Dynamic Targeting: Experimental Evidence from Energy Rebate Programs

Dynamic Targeting: Experimental Evidence from Energy Rebate Programs

13 Jun 2024

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.
energy econometrics experimental design industrial organization public economics estimation methods labor studies environment and energy economics environmental and resource economics

Authors

Takanori Ida, Takunori Ishihara, Koichiro Ito, Daido Kido, Toru Kitagawa, Shosei Sakaguchi, Shusaku Sasaki

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Acknowledgements & Disclosure
We would like to thank Linnea Holy for her exceptional research assistance and Christopher Costello, Michael Greenstone, Ryan Kellogg, Anna Russo and seminar participants at the Coase Project Conference for their helpful comments. We thank the Japanese Ministry of Environment for their collaboration in realizing this study. Kitagawa and Sakaguchi gratefully acknowledge financial support from the ERC grant (number 715940) and the ESRC Centre for Microdata Methods and Practice (CeMMAP) (grant number RES-589-28-0001). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
DOI
https://doi.org/10.3386/w32561
Published in
United States of America

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