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

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  • Kayo MURAKAMI
  • Hideki SHIMADA
  • Yoshiaki USHIFUSA
  • Takanori IDA

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.

Suggested Citation

  • Kayo MURAKAMI & Hideki SHIMADA & Yoshiaki USHIFUSA & Takanori IDA, 2020. "Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation," Discussion papers e-20-003, Graduate School of Economics , Kyoto University.
  • Handle: RePEc:kue:epaper:e-20-003
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    More about this item

    Keywords

    Causal Forest; Rebate,Nudge; Randomized Controlled Trial; Energy; Machine Learning;
    All these keywords.

    JEL classification:

    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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