3,000 rated or chosen profiles from 3,000 respondents, in up to 1 tasks of 1 profile varying on 2 attributes.

About this table

Description	3,000 Japanese adults each read one vignette about a 32-year-old Chinese man applying for a 3-5 year working visa, varying his skill (computer-science master's and systems engineer vs technical high school and plumber) and extra information (none, wants a convenience-store job, chronic asthma and health insurance use, teen shoplifting arrest); they said whether they would approve the visa.
Reference	Igarashi, A., Miwa, H., & Ono, Y. (2022). Why do citizens prefer high-skilled immigrants to low-skilled immigrants? Identifying causal mechanisms of immigration preferences with a survey experiment. Research & Politics, 9(2).
DOI	10.1177/20531680221091439
Licence	CC0 1.0
Source data	https://doi.org/10.7910/dvn/lg1ly9

Notes

Conjoint table (irw_conjoint_2). One text vignette per respondent (Japan, 2020): skill of a Chinese applicant (education and former job) x extra information (none, wants a convenience-store job, chronic asthma and health insurance, teen shoplifting arrest); choice = 1 if the respondent would approve his visa, 0 if not (single-profile accept/reject, opt-out). Attribute level text taken from the English vignettes in the authors' RIETI discussion paper appendix (B.2); the deposit stores group codes only, so levels are paraphrased from that translation. 'No extra information' is coded (not shown).

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	3,000
Rows (one per respondent, task and profile)	3,000
Tasks per respondent (most)	1
Profiles per task	1
Attributes	2
Tasks with no profile chosen	1,250

Design

Topic	immigration asylum
A profile represents	person
Country	JP
Language shown	ja
Language of stored levels	en
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:

choice	If you were an immigration officer, would you approve his visa? (1. Approve his visa / 2. Do not approve his visa) (translated) [choice, opt-out allowed]

Columns

attr_added_infoattr_skillchoicecov_agecov_educationcov_gendercov_ideologycov_incomeidprofiletask

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("igarashi_2022_skilled_immigrant", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("igarashi_2022_skilled_immigrant", source="conj")

How to cite

@article{Igarashi_2022, title={Why do citizens prefer high-skilled immigrants to low-skilled immigrants? Identifying causal mechanisms of immigration preferences with a survey experiment}, volume={9}, ISSN={2053-1680}, url={http://dx.doi.org/10.1177/20531680221091439}, DOI={10.1177/20531680221091439}, number={2}, journal={Research &amp; Politics}, publisher={SAGE Publications}, author={Igarashi, Akira and Miwa, Hirofumi and Ono, Yoshikuni}, year={2022}, month=Apr }

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