kjoller_2025_council_positions

28,386 rated or chosen profiles from 2,141 respondents, in up to 7 tasks of 2 profiles varying on 4 attributes.

About this table

DescriptionCandidates in the 2021 Danish municipal elections (n=2,141; 1,939 in the authors' completed-in-time sample) made 7 forced choices between two hypothetical council positions varying in position/influence, remuneration, workload and working environment (respect vs harassment vs sexism).
ReferenceKjøller, F. K., & Pedersen, L. H. (2025). The gendered cost of politics. British Journal of Political Science, 55, e59.
DOI10.1017/S0007123424000310
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/SOLAAJ

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

Respondents2,141
Rows (one per respondent, task and profile)28,386
Tasks per respondent (most)7
Profiles per task2
Attributes4
Tasks with no profile chosen0

Design

Topicdemocracy institutions
A profile representsproduct service
CountryDK
Language shownda
Language of stored levelsda
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceForestil dig, at du tilbydes følgende to poster som medlem af byrådet. Sæt dig venligst godt ind i de to poster. Hvilken post vil du vælge? [choice, forced]

Columns

attr_environmentattr_positionattr_remunerationattr_workloadattrpos_environmentattrpos_positionattrpos_remunerationattrpos_workloadchoicecov_birth_yearcov_educationcov_elected_2021cov_gendercov_harassed_1cov_harassed_2cov_harassed_3cov_harassed_4cov_harassed_5cov_harassment_riskcov_in_authors_samplecov_left_rightcov_maritalcov_political_hourscov_survey_statusidprofiletask

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("kjoller_2025_council_positions", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("kjoller_2025_council_positions", source="conj")

How to cite

@article{Kj_ller_2025, title={The Gendered Cost of Politics}, volume={55}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123424000310}, DOI={10.1017/s0007123424000310}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Kjøller, Frederik Klaaborg and Pedersen, Lene Holm}, year={2025} }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v44.0