kao_2025_new_residents

20,282 rated or chosen profiles from 2,075 respondents, in up to 5 tasks of 2 profiles varying on 4 attributes.

About this table

DescriptionTaiwanese adults (n=2,075) compared 5 pairs of hypothetical immigrant (New Resident) profiles varying country of origin, ethnicity, occupation/visa status and Mandarin proficiency, choosing one per pair on three questions: admission, national health insurance, and mother-tongue instruction.
ReferenceKao, J. C., & Liu, A. H. (2025). Racial identities, linguistic proficiency, and public attitudes towards immigrants: Evidence from two surveys in Taiwan. Political Research Quarterly, 78(2), 701-719.
DOI10.1177/10659129251319737
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/TTAE29

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

Respondents2,075
Rows (one per respondent, task and profile)20,282
Tasks per respondent (most)5
Profiles per task2
Attributes4
Tasks with no profile chosen0

Design

Topicimmigration asylum
A profile representsperson
CountryTW
Language shownzh
Language of stored levelsen
Randomization restrictionsyes (stated by the source): Ukraine only with ethnicity 'No Info' (never Hakka or Cantonese); level shares unequal: Ukraine 1,527 rows vs about 4,700 for each other country level, ethnicity 'No Info' 7,714 vs about 6,300 (consistent with the restriction; probabilities not documented)
Task orderrecorded in the deposit
Profile positionrecorded 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:

choiceShould the Taiwanese government ease immigration policy for Person A or Person B, making the process for obtaining permanent residency easier? [choice, forced]
choice_health_insuranceNeither Person A nor Person B has permanent residency and is currently not eligible for nationalized healthcare. Should the Taiwanese government offer nationalized healthcare for Person A or Person B? [choice, forced]
choice_mother_tongueShould the Taiwanese government offer mother tongue language classes for Person A's or Person B's children? [choice, forced]

Columns

attr_countryattr_ethnicityattr_mandarinattr_visachoicechoice_health_insurancechoice_mother_tonguecov_age_group_codecov_education_codecov_father_aboriginecov_father_hakkacov_father_mainlandercov_father_minnancov_father_newresidentcov_father_othercov_gender_codecov_incomecov_mother_aboriginecov_mother_hakkacov_mother_mainlandercov_mother_minnancov_mother_newresidentcov_mother_othercov_party_idcov_proficiency_aboriginecov_proficiency_englishcov_proficiency_hakkacov_proficiency_mandarincov_proficiency_minnancov_proficiency_othercov_proficiency_seasiacov_regioncov_tw_economycov_visit_hkcov_visit_mainlandcov_visit_seacov_visit_usacov_work_hkcov_work_mainlandcov_work_seacov_work_usaidprofiletask

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("kao_2025_new_residents", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("kao_2025_new_residents", source="conj")

How to cite

@article{Kao_2025, title={Racial Identities, Linguistic Proficiency, and Public Attitudes Towards Immigrants: Evidence From Two Surveys in Taiwan}, volume={78}, ISSN={1938-274X}, url={http://dx.doi.org/10.1177/10659129251319737}, DOI={10.1177/10659129251319737}, number={2}, journal={Political Research Quarterly}, publisher={SAGE Publications}, author={Kao, Jay C. and Liu, Amy H.}, year={2025}, month=Feb, pages={701–719} }

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