levis_2026_carbon_messenger

22,344 rated or chosen profiles from 3,192 respondents, in up to 7 tasks of 1 profile varying on 4 attributes.

About this table

DescriptionAdults in Germany and Poland (n = 3,192) read seven short vignettes about a hypothetical EU fuel tax (cost increase of 10/50/100%, sector cars/aviation/heating, backed by the European Commission, the German or the Polish government) and rated their support on a 1-6 scale.
ReferenceLevis, A., Smith, E. K., & Pianta, S. (2026). Attitudes towards carbon taxation in the EU: Messenger effects vs. content effects. Journal of Public Policy.
DOI10.1017/S0143814X26101226
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/FFTR4S

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents3,192
Rows (one per respondent, task and profile)22,344
Tasks per respondent (most)7
Profiles per task1
Attributes4

Design

Topicclimate energy environment
A profile representspolicy
CountryDE, PL
Language shownde, pl
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positioninferred from row order

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

ratingSupport for the described EU fuel tax policy, 1-6 (paraphrase: question text and answer labels are not in the deposit; the Rmd counts >= 4 as support) [rating, 1 (unknown) to 6 (unknown)]

Columns

attr_costattr_messengerattr_policyattr_wordingcov_age_groupcov_countrycov_educationcov_gendercov_survey_weightidprofileratingtasktrial_repeat_of

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("levis_2026_carbon_messenger", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("levis_2026_carbon_messenger", source="conj")

How to cite

@article{Levis_2026, title={Attitudes towards carbon taxation in the EU: messenger effects vs. content effects}, ISSN={1469-7815}, url={http://dx.doi.org/10.1017/S0143814X26101226}, DOI={10.1017/s0143814x26101226}, journal={Journal of Public Policy}, publisher={Cambridge University Press (CUP)}, author={Levis, Alessio and Smith, E. Keith and Pianta, Silvia}, year={2026}, month=Aug, pages={1–24} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint_2 v1.4
Redivis dataset DOI10.57761/hk97-wv30
Manifest pin for this IRW versionv1.4
Metadata sourceirw_meta v44.0