gazmararian_2024_transition_fair

2,974 rated or chosen profiles from 248 respondents, in up to 6 tasks of 2 profiles varying on 7 attributes.

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

About this table

Description248 adults recruited at two county fairs in Southwest Pennsylvania (July-August 2021) each chose between 2 government proposals to help fossil fuel workers and communities in 6 tasks; proposals varied in retraining program, retrained salary, retraining time, income support, benefit support, relocation support and community investment.
ReferenceGazmararian, A. F. (2024). Fossil fuel communities support climate policy coupled with just transition assistance. Energy Policy, 184, 113880.
DOI10.1016/j.enpol.2023.113880
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/FC9X6H

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

Respondents248
Rows (one per respondent, task and profile)2,974
Tasks per respondent (most)6
Profiles per task2
Attributes7
Tasks with no profile chosen0

Design

Topicclimate energy environment
A profile representspolicy
CountryUS
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceIf you had to choose, which proposal would you prefer the government to pursue? [choice, forced]

Columns

attr_benefit_supportattr_community_investmentattr_free_retraining_programattr_income_support_during_retrainingattr_relocation_supportattr_retrained_worker_salaryattr_worker_retraining_timechoicecov_age_groupcov_attention_passcov_educationcov_faircov_gendercov_party_idcov_survey_weightcov_weight_untrimmedidprofiletasktrial_intro

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("gazmararian_2024_transition_fair", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("gazmararian_2024_transition_fair", source="conj")

How to cite

@article{Gazmararian_2024, title={Fossil fuel communities support climate policy coupled with just transition assistance}, volume={184}, ISSN={0301-4215}, url={http://dx.doi.org/10.1016/j.enpol.2023.113880}, DOI={10.1016/j.enpol.2023.113880}, journal={Energy Policy}, publisher={Elsevier BV}, author={Gazmararian, Alexander F.}, year={2024}, month=Jan, pages={113880} }

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