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Supporting Carbon Taxes and the Role of Justice
Last registered on October 08, 2019


Trial Information
General Information
Supporting Carbon Taxes and the Role of Justice
Initial registration date
October 08, 2019
Last updated
October 08, 2019 1:46 PM EDT

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Primary Investigator
RWI - Leibniz Institute for Economic Research
Other Primary Investigator(s)
PI Affiliation
Potsdam-Institute for Climate Impact Research (PIK)
PI Affiliation
Institute for New Economic Thinking at the Oxford Martin School and Environmental Change Institute, School of Geography and the Environment, University of Oxford
Additional Trial Information
In development
Start date
End date
Secondary IDs
While much research in economics has studied the implications of environmental taxation for income inequality, research about the perceived justice of carbon pricing reforms is scarce. In this paper, we analyze the importance of preferences about justice on the support for carbon prices and its interaction with revenue recycling schemes using a discrete-choice experiment with randomized information treatments. We illustrate different fairness conceptions in a representative sample covering about 6,000 German household heads and measure the effect on the acceptability of a carbon tax.
External Link(s)
Registration Citation
Mattauch, Linus , Michael Pahle and Stephan Sommer. 2019. "Supporting Carbon Taxes and the Role of Justice." AEA RCT Registry. October 08. https://doi.org/10.1257/rct.4829-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
willingness to accept the implementation of a carbon tax
preference for revenue recycling scheme
willingness to accept the implementation of a carbon tax if designed according to respondent's preference
preference for revenue recycling scheme after learning about fairness conceptions
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
See Pre Analysis Plan
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
6,000 household heads
Sample size: planned number of observations
6,000 household heads
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our experimental design provides for 6,000 respondents who are randomly split into two groups, i.e. 3,000 respondents each. Based on Baranzani and Carratini (2017), we assume that half of the respondents would accept higher carbon taxes (SD= 0.5). Using the standard significance level of 0.05, the standard power level of 0.80 and a two-sided test, the minimum detectable treatment effect amounts to roughly three percentage points. Carratini et al. (2017) analyze the preferences for different redistribution schemes and find that 18.6% of the respondents prefer the redistribution of revenues from carbon pricing to households and the industry (SD=0.389), while 11.5% prefer social cushioning (SD=0.320). Given our sample size and the above parametrization, we can identify minimum effects of about two percentage points.
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents

MD5: 149ca1704050895020a40ad32dc812ac

SHA1: 3624ee8a9d67259d12e958148eecbb7c4c8564b9

Uploaded At: October 08, 2019