HETEROGENEOUS TREATMENT EFFECTS OF NUDGE AND REBATE: CAUSAL MACHINE LEARNING IN A FIELD EXPERIMENT ON ELECTRICITY CONSERVATION

B-Tier
Journal: International Economic Review
Year: 2022
Volume: 63
Issue: 4
Pages: 1779-1803

Authors (4)

Kayo Murakami (Kwansei Gakuin University) Hideki Shimada (not in RePEc) Yoshiaki Ushifusa (not in RePEc) Takanori Ida (not in RePEc)

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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 the rebate is 4%, whereas that from the 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.

Technical Details

RePEc Handle
repec:wly:iecrev:v:63:y:2022:i:4:p:1779-1803
Journal Field
General
Author Count
4
Added to Database
2026-01-26