miller_2026_backsliding_jobs

5,304 rated or chosen profiles from 885 respondents, in up to 3 tasks of 2 profiles varying on 6 attributes.

About this table

DescriptionUS job seekers (n = 885) compared pairs of hypothetical state-government jobs (3 pairs each) described by governing party, elite polarization, salary, workforce size, location and a backsliding or other state-government action; they chose the preferred job and rated each job 1-7.
ReferenceMiller, S. M., & Gilad, S. (2026). Democratic backsliding, elite polarization, and job seekers' pursuit of government employment. Regulation & Governance.
DOI10.1111/rego.70138
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/KCEBF9

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

Respondents885
Rows (one per respondent, task and profile)5,304
Tasks per respondent (most)3
Profiles per task2
Attributes6
Tasks with no profile chosen0

Design

Topiclabour hiring, democracy institutions
A profile representsproduct service
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderinferred from row order
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:

choiceWhich of the two state-government jobs do you prefer? (paraphrase) [choice, forced]
ratingHow attractive is working for this state government? (paraphrase) [rating, 1 (unknown) to 7 (unknown)]

Columns

attr_backslidingattr_government_partyattr_job_locationattr_polarizationattr_salaryattr_workforcechoicecov_party_ididprofileratingtask

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("miller_2026_backsliding_jobs", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("miller_2026_backsliding_jobs", source="conj")

How to cite

@article{Miller_2026, title={Democratic Backsliding, Elite Polarization, and Job Seekers’ Pursuit of Government Employment}, ISSN={1748-5991}, url={http://dx.doi.org/10.1111/rego.70138}, DOI={10.1111/rego.70138}, journal={Regulation &amp; Governance}, publisher={Wiley}, author={Miller, Susan M. and Gilad, Sharon}, year={2026}, month=Mar }

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