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Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation

Kayo Murakami, Hideki Shimada, Yoshiaki Ushifusa and Takanori Ida

Discussion papers from Graduate School of Economics , Kyoto University

Abstract: This study investigates the different impacts of monetary and nonmonetary incentives on energy-saving behaviors using a field experiment conducted in Japan. We find that the average reduction in electricity consumption from rebate is 4%, while that from nudge is not significantly different from zero. Applying a novel machine learning method for causal inference (causal forest) to estimate heterogeneous treatment effects at the household level, we demonstrate that the nudge intervention’s treatment effects generate greater heterogeneity among households. These findings suggest that selective targeting for treatment increases the policy efficiency of monetary and nonmonetary interventions.

Keywords: Causal Forest; Rebate,Nudge; Randomized Controlled Trial; Energy; Machine Learning (search for similar items in EconPapers)
JEL-codes: D9 C93 Q4 (search for similar items in EconPapers)
Pages: 48
Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-ene, nep-exp and nep-reg
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