1,997 rated or chosen profiles from 1,997 respondents, in up to 1 tasks of 1 profile varying on 3 attributes.
One of 2 tables from the same source; this one is the welfare_sanctions table. The others are listed under Related tables.
| Description | Single vignette per respondent about a Universal Credit claimant in the UK: name (6), reason for redundancy (2) and sanction for not meeting conditions (none or three severities). Ratings: support for sanctions, likelihood the claimant and the respondent would accept a job, fairness of the JobCentre. YouGov, July 2025, about 2,000 respondents. |
|---|---|
| Reference | O'Grady, T. (2026). Deterrence and retribution as motives for supporting punitive public policies. [Journal not stated in the deposit.] |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/9ILS3D |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 1,997 |
|---|---|
| Rows (one per respondent, task and profile) | 1,997 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 3 |
| Topic | tax welfare economy |
|---|---|
| A profile represents | scenario |
| Country | GB |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | inferred from row order |
| Profile position | inferred from row order |
Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| rating_claimant_accept_job | How likely is a claimant like this one to accept a job offer? (paraphrase of WelfareV_AcceptOffer) [rating, 1 (most likely) to 4 (least likely)] |
|---|---|
| rating_fair_jobcentre | How fair is the JobCentre's treatment of the claimant? (paraphrase of WelfareV_FairJobCentre) [rating, 1 (fairest) to 4 (least fair)] |
| rating_self_accept_job | How likely would you yourself be to accept a job offer in this situation? (paraphrase of WelfareV_AttendInterview, renamed AcceptOfferSelf by the authors) [rating, 1 (most likely) to 4 (least likely)] |
| rating_support_sanctions | How much do you support or oppose sanctioning benefit claimants who do not meet the conditions? (paraphrase of UCL1_1) [rating, 1 (strongly support) to 5 (strongly oppose)] |
attr_claimant_nameattr_sanctionattr_unemployment_reasoncov_agecov_gendercov_survey_weightidprofilerating_claimant_accept_jobrating_fair_jobcentrerating_self_accept_jobrating_support_sanctionstasktrial_basetext_variant
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.
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("ogrady_2026_welfare_sanctions", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("ogrady_2026_welfare_sanctions", source="conj")
@misc{https://doi.org/10.7910/dvn/9ils3d,
doi = {10.7910/DVN/9ILS3D},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/9ILS3D},
author = {O'Grady, Tom},
keywords = {Social Sciences, welfare, immigration, deterrence},
title = {Replication Data for: Deterrence and Retribution as Motives for Supporting Punitive Public Policies},
publisher = {Harvard Dataverse},
year = {2026}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint_2 v1.4 |
| Redivis dataset DOI | 10.57761/hk97-wv30 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |