19,857 rated or chosen profiles from 5,126 respondents, in up to 4 tasks of 1 profile varying on 6 attributes.
| Description | Cint quota-sample adults in Australia, Canada, the UK and the US (March 2022; n=5,126 with usable answers in the conjoint arm) each rated four single scenarios of a manufacturing firm automating, which varied the product and its price change, high- and low-skilled job counts and wages; outcomes are perceived fairness and whether they would decide the same as CEO (both 1-5). |
|---|---|
| Reference | Magistro, B., Loewen, P., Bonikowski, B., Borwein, S., & Lee-Whiting, B. (2024). Attitudes toward automation and the demand for policies addressing job loss: The effects of information about trade-offs. Political Science Research and Methods, 12(4), 783-798. |
| DOI | 10.1017/psrm.2024.1 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/AJGHMH |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 5,126 |
|---|---|
| Rows (one per respondent, task and profile) | 19,857 |
| Tasks per respondent (most) | 4 |
| Profiles per task | 1 |
| Attributes | 6 |
| Topic | technology ai privacy, labour hiring |
|---|---|
| A profile represents | scenario |
| Country | AU, CA, GB, US |
| Language shown | en, fr, es |
| Language of stored levels | en, fr |
| Randomization restrictions | yes (stated by the source): price after innovation is drawn from the product's own levels (same, 20% or 50% cheaper than the product's before-price); low-skilled workers after is 150 or 50 (never the before value 200); wage levels differ by country (currency and amount) |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.
Outcomes:
| rating | The company's decision to introduce the new technology is fair [rating, 1 (strongly disagree) to 5 (strongly agree)] |
|---|---|
| rating_ceo | Would make the same decision if you were the CEO of the company (paraphrase) [rating, 1 (unknown) to 5 (unknown)] |
attr_high_skilled_wage_afterattr_high_skilled_workers_afterattr_low_skilled_wage_afterattr_low_skilled_workers_afterattr_price_afterattr_productcov_agecov_attention_passcov_collegecov_countrycov_employmentcov_gendercov_incomecov_languagecov_lrcov_policy_automationtaxcov_policy_basicincomecov_policy_jobguaranteecov_policy_reskillcov_policy_skilledcov_policy_socialspendcov_policy_tradecov_policy_unskilledidprofileratingrating_ceotask
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("magistro_2024_automation_fairness", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("magistro_2024_automation_fairness", source="conj")
@article{Magistro_2024, title={Attitudes toward automation and the demand for policies addressing job loss: the effects of information about trade-offs}, volume={12}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2024.1}, DOI={10.1017/psrm.2024.1}, number={4}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Magistro, Beatrice and Loewen, Peter and Bonikowski, Bart and Borwein, Sophie and Lee-Whiting, Blake}, year={2024}, month=Feb, pages={783–798} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint v4.2 |
| Redivis dataset DOI | 10.57761/qkye-q530 |
| Manifest pin for this IRW version | v4.2 |
| Metadata source | irw_meta v44.0 |