hillenbrand_2025_asylum_reform

30,954 rated or chosen profiles from 2,211 respondents, in up to 7 tasks of 2 profiles varying on 4 attributes.

About this table

DescriptionPaired conjoint: 2,211 German adults (TGM panel, December 2024) made 7 choices between refugee-policy packages varying admission policy (third-country transfer and resettlement), refugee selection criteria, partner-country conditions and welfare access, and rated each package 1-7; respondents had first been assigned one of three framing primes.
ReferenceHillenbrand, T. (2025). Reforms welcome? Evidence on the nature of asylum backlash and orderly admissions as a remedy (UNU-MERIT Working Paper 2025-026). UNU-MERIT.
DOI10.53330/PZPD3802
LicenceCC0 1.0
Source datahttps://doi.org/10.34894/YDL4EZ

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

Respondents2,211
Rows (one per respondent, task and profile)30,954
Tasks per respondent (most)7
Profiles per task2
Attributes4
Tasks with no profile chosen0

Design

Topicimmigration asylum
A profile representspolicy
CountryDE
Language shownde
Language of stored levelsen
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceIf you had to choose a policy mix for Germany, which of the two options would you prefer? (translated) [choice, forced]
ratingAnd how would you rate the two options? (translated) [rating, 1 (very negative) to 7 (very positive)]

Columns

attr_admissionattr_cooperationattr_selectionattr_welfareattrpos_admissionattrpos_cooperationattrpos_selectionattrpos_welfarechoicecov_agecov_attention_passcov_eastcov_gender_codecov_incomecov_migration_backgroundcov_party_preferencecov_statecov_unemployedcov_university_educidprofileratingtasktrial_prime

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("hillenbrand_2025_asylum_reform", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("hillenbrand_2025_asylum_reform", source="conj")

How to cite

@book{Hillenbrand_2025, title={Reforms welcome? Evidence on the nature of asylum backlash and orderly admissions as a remedy}, url={http://dx.doi.org/10.53330/PZPD3802}, DOI={10.53330/pzpd3802}, institution={UNU-MERIT}, author={Hillenbrand, Tobias}, year={2025}, month=Nov }

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