nelson_2022_backsliding_students

10,030 rated or chosen profiles from 337 respondents, in up to 15 tasks of 2 profiles varying on 6 attributes.

One of 2 tables from the same source; this one is the students table. The others are listed under Related tables.

About this table

DescriptionStudents at a large US public university (Sept 2019; n=337) compared 15 pairs of hypothetical job offers described by salary, firm size, location type, workplace culture, the state's presidential partisanship and a state democratic-backsliding event; outcomes are a forced choice of offer and a 5-point rating of each.
ReferenceNelson, M. J., & Witko, C. (2022). The economic costs of democratic backsliding? Backsliding and state location preferences of US job seekers. The Journal of Politics, 84(2), 1233-1238.
DOI10.1086/715601
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/YOBUVS

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

Respondents337
Rows (one per respondent, task and profile)10,030
Tasks per respondent (most)15
Profiles per task2
Attributes6
Tasks with no profile chosen0

Design

Topiclabour hiring
A profile representsproduct service
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceselect which of the two jobs they prefer (paraphrase) [choice, forced]
ratingrate the attractiveness of each job offer on a 5-point scale (paraphrase) [rating, 1 (Not at all attractive) to 5 (Very attractive)]

Columns

attr_backslidingattr_cultureattr_locationattr_salaryattr_sizeattr_state_partisanshipchoicecov_birth_yearcov_party_id7_codeidprofileratingtask

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("nelson_2022_backsliding_students", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("nelson_2022_backsliding_students", source="conj")

How to cite

@article{Nelson_2022, title={The Economic Costs of Democratic Backsliding? Backsliding and State Location Preferences of US Job Seekers}, volume={84}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/715601}, DOI={10.1086/715601}, number={2}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Nelson, Michael J. and Witko, Christopher}, year={2022}, month=Apr, pages={1233–1238} }

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