59,772 rated or chosen profiles from 3,027 respondents, in up to 10 tasks of 2 profiles varying on 20 attributes.

One of 3 tables from the same source; this one is the municipal table. The others are listed under Related tables.

About this table

Description	3,027 US respondents (2022 wave (Lucid)) each chose between two candidates in 10 tasks (House vs city council candidates, party labels shown to a random half (trial_show_party)); each candidate took affirmative or negated positions on 4 policies drawn from 10 national and 9 municipal policies. Outcome is the preferred candidate.
Reference	Holliday, D. E., & Rudkin, A. (2026). DC on my mind: Voter evaluations of nationalized policy positions in state and local elections. The Journal of Politics.
DOI	10.1086/741831
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/HKVNTU

Notes

Conjoint table (irw_conjoint). Forced choice of the preferred candidate (no opt-out), "Given this choice, which federal House of Representatives candidate would you prefer?" in tasks 1-5 and the same for a city council candidate in tasks 6-10 (trial_office). Each task shows 4 policies, the same 4 for both candidates, drawn from a pool; other policy attributes are (not shown) and the row order of the 4 is not kept. Party labels shown to a random half of respondents (trial_show_party). cov_baseline_<policy> is the respondent's own stance (1 = affirmative), which the authors use to code agreement; cov_survey_weight is the raked weight. Respondents are those left after the authors' speeder and attention-check drops.

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

Design

Topic	candidate choice
A profile represents	person
Country	US
Language shown	en
Language of stored levels	en
Randomization restrictions	yes (stated by the source): each task shows 4 policies from the pool and both candidates are described on the same 4; every other policy attribute is (not shown) for that task; how the 4 were drawn is not documented
Task order	recorded in the deposit
Profile position	recorded in the deposit

Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.

Outcomes:

choice	Given this choice, which federal House of Representatives candidate would you prefer? (tasks 1-5); Given this choice, which city council candidate would you prefer? (tasks 6-10) [choice, forced]

Columns

attr_affordable_houseattr_business_tax_breakattr_deportationattr_dreamersattr_height_restrictionattr_housing_loansattr_israelattr_medicare_for_allattr_military_sizeattr_parkingattr_partyattr_path_to_citizenattr_population_limitattr_public_optionattr_public_safetyattr_public_transitattr_sanctuaryattr_saudi_weaponsattr_tariffs_chinaattr_tariffs_euchoicecov_age_groupcov_baseline_affordable_housecov_baseline_business_tax_breakcov_baseline_deportationcov_baseline_dreamerscov_baseline_height_restrictioncov_baseline_housing_loanscov_baseline_israelcov_baseline_medicare_for_allcov_baseline_military_sizecov_baseline_parkingcov_baseline_path_to_citizencov_baseline_population_limitcov_baseline_public_optioncov_baseline_public_safetycov_baseline_public_transitcov_baseline_sanctuarycov_baseline_saudi_weaponscov_baseline_tariffs_chinacov_baseline_tariffs_eucov_educationcov_gendercov_hispaniccov_household_incomecov_party_idcov_racecov_regioncov_survey_weightidprofiletasktrial_officetrial_show_party

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 needed Browse on Redivisexplore and query Croissant 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("holliday_2026_policy_municipal", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("holliday_2026_policy_municipal", source="conj")

How to cite

@article{Holliday_2026, title={DC On My Mind: Voter Evaluations of Nationalized Policy Positions in State and Local Elections}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/741831}, DOI={10.1086/741831}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Holliday, Derek Edward and Rudkin, Aaron}, year={2026}, month=Apr }

IRW version	v584
Redivis dataset	irw_conjoint v4.2
Redivis dataset DOI	10.57761/qkye-q530
Manifest pin for this IRW version	v4.2
Metadata source	irw_meta v44.0

holliday_2026_policy_municipal