6,470 rated or chosen profiles from 647 respondents, in up to 5 tasks of 2 profiles varying on 5 attributes.

About this table

Description	Residents of the Federated States of Micronesia (offline interviews, 2023, n = 647) chose, in 5 paired tasks, the scenario in which they would be more likely to migrate to the US (health, family, job, climate and income conditions randomized) and rated each scenario's migration likelihood 1-7.
Reference	Gest, J., Núñez, L., Drinkall, S., & Micky, K. (2025). The effect of slow-onset climate change on migration decisions. International Migration Review.
DOI	10.1177/01979183251343879
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/6EOCPF

Notes

Conjoint table (irw_conjoint). Forced choice of the scenario in which the respondent would be more likely to migrate to the United States (no opt-out), then a 1-7 rating of each scenario (1 highly unlikely .. 7 highly likely, raw); scenarios were read aloud by interviewers. Attribute row order shuffled per task (attrpos_). trial_prime marks an experimental arm whose content is not documented in the deposit. One preview record and one respondent without choices dropped.

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

Design

Topic	immigration asylum, climate energy environment
A profile represents	scenario
Country	FM
Language shown	unknown
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	The following describes two scenarios. Listen closely as I read their full descriptions to you. Please select the scenario in which you would be more likely to migrate to the United States. [choice, forced]
rating	On a scale from 1 (highly unlikely) to 7 (highly likely), how likely would you be to migrate to the US, based on the scenario Scenario [1/2]? [rating, 1 (highly unlikely) to 7 (highly likely)]

Columns

attr_climateattr_familyattr_healthattr_jobattr_moneyattrpos_climateattrpos_familyattrpos_healthattrpos_jobattrpos_moneychoicecov_agecov_climate_perception_1cov_climate_perception_2cov_climate_perception_3cov_duration_seccov_educationcov_family_incomecov_family_uscov_gendercov_internetcov_interview_languagecov_interview_placecov_lives_terraincov_lives_wherecov_marital_statuscov_passportcov_pers_risk_1cov_pers_risk_2cov_pers_risk_3cov_plan_movecov_religioncov_remittancescov_risk_takingidprofileratingtasktrial_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 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("gest_2025_climate_migration", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("gest_2025_climate_migration", source="conj")

How to cite

@article{Gest_2025, title={The Effect of Slow-Onset Climate Change on Migration Decisions}, ISSN={1747-7379}, url={http://dx.doi.org/10.1177/01979183251343879}, DOI={10.1177/01979183251343879}, journal={International Migration Review}, publisher={SAGE Publications}, author={Gest, Justin and Núñez, Lucas and Drinkall, Scott and Micky, Kapiolani}, year={2025}, month=May }

Respondents	647
Rows (one per respondent, task and profile)	6,470
Tasks per respondent (most)	5
Profiles per task	2
Attributes	5
Tasks with no profile chosen	0

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

gest_2025_climate_migration