Yellow Vests, Pessimistic Beliefs, and Carbon Tax Aversion (2022): A Comment - Nicholas Rivers; Matt Woerman; Kareman Yassin (Institute for Replication (I4R), 2023)
Douenne and Fabre (2022) implement a representative survey following the Yellow Vests movement in France that started in opposition to the carbon tax in 2018. They find that a majority of French citizens would oppose a carbon tax and dividend program with proceeds paid equally to each adult. The authors further find that respondents have pessimistic beliefs about several aspects of the policy. They then show how informational treatments cause respondents to update these beliefs, and they finally estimate the causal effect of these beliefs on support for the policy. In this note, we focus on the second section of this paper: the causal effects of feedback on beliefs. Based on elicited household characteristics, Douenne and Fabre (2022) estimate whether each household "wins" or "loses" from the carbon tax and dividend reform. They provide this binary (win vs. lose) information to households and subsequently ask households to evaluate whether they believe they would financially benefit from the policy. By exploiting the discontinuity in win vs. lose feedback, they assess the degree to which feedback affects subjective beliefs, finding that a household that is told it will "win" as a result of the reform increases its subjective belief that it will not lose by about 25 percentage points. The subset of households that is part of the Yellow Vests movement, however, revises its subjective belief of not losing upwards by only 10 percentage points after being told that it will "win" from the carbon tax reform. Conversely, households who initially support the tax increase this belief by 41 percentage points when told they will "win." In this note we replicate this second section of the paper-the causal effects of feedback on beliefs- using the processed data provided by the authors. We successfully replicate the average treatment effect, but we find that the heterogeneous treatment effects may be biased due to model misspecification. While our results support the conclusion that these estimated effects depend on a household's attitudes toward the policy, we find that the source of heterogeneity differs. Further, we note two changes to the analysis that we believe are appropriate (which do not affect the conclusions drawn): first, some (1.8%) of observations in the dataset appear to be misclassified-wrongly coded as if a household would "lose" when in fact they would "win"-and second, the main causal analysis is based on a regression discontinuity design, but does not include standard components of such a design (e.g., a RD plot, optimal selection of bandwidth, density analysis, placebo tests). We update the design to address both of these points. We find results that generally support the main conclusions of Douenne and Fabre (2022), but we urge caution when interpreting the heterogeneous treatment effects.
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