18,654 rated or chosen profiles from 1,168 respondents, in up to 8 tasks of 2 profiles varying on 4 attributes.

About this table

Description	UK adults on Prolific chose between two hypothetical life-saving policies that varied by lives saved, policy area, cost and how long until the payoff (stated relative to the respondent's lifetime). 1,168 respondents (qualitative-delay version only), 8 paired forced-choice tasks.
Reference	Barnfield, M. (2023). Policy Discounting Across and Beyond the Lifespan, 2023 [Data collection]. UK Data Service.
Licence	CC BY 4.0
Source data	https://doi.org/10.5255/ukda-sn-856886

Notes

Conjoint table (irw_conjoint_2). Paired forced choice between two life-saving policies (lives saved, policy area, cost, payoff delay); no opt-out. Respondents saw either a qualitative or a quantitative (years) delay; only the qualitative version is included because the years shown are not stored. 1,168 of 2,405 respondents; 38 respondents have 7 tasks and 1 has 4.

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

Size and shape

Respondents	1,168
Rows (one per respondent, task and profile)	18,654
Tasks per respondent (most)	8
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	tax welfare economy
A profile represents	policy
Country	GB
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which of the two policies would you choose if you had the choice? (paraphrase) [choice, forced]

Columns

attr_costattr_delayattr_lives_savedattr_policy_areachoicecov_agecov_age_groupcov_gender_codeidprofiletask

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("barnfield_2023_policy_discounting", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("barnfield_2023_policy_discounting", source="conj")

How to cite

@misc{https://doi.org/10.5255/ukda-sn-856886,
  doi = {10.5255/UKDA-SN-856886},
  url = {http://reshare.ukdataservice.ac.uk/id/eprint/856886},
  author = {Barnfield, Matthew},
  title = {Policy Discounting Across and Beyond the Lifespan, 2023},
  publisher = {UK Data Service},
  year = {2023}
}

Version and provenance

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