9,660 rated or chosen profiles from 805 respondents, in up to 6 tasks of 2 profiles varying on 7 attributes.

About this table

Description	805 Malawian respondents each chose between 2 hypothetical candidates in 6 contests; candidates varied in disaster-prevention and relief effort and effectiveness, asking for relief, constituency visits and honesty. Outcome is the supported candidate.
Reference	Hartmann, F. (2026). Do voters value relief over preparedness? Evidence from disaster policies in Malawi. The Journal of Politics.
DOI	10.1086/736446
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/ECUDW4

Notes

Conjoint table (irw_conjoint). choice is the candidate the respondent supported in each of 6 pairs (exact wording not deposited); exactly one profile chosen per pair, no opt-out. Attribute text is the author's short English labels, not the wording respondents saw; effort and effectiveness levels are crossed freely and restrictions are not documented. trial_disaster_prime is the respondent's arm in a disaster prime shown before the conjoint; unlabelled covariate codes and village are dropped.

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	805
Rows (one per respondent, task and profile)	9,660
Tasks per respondent (most)	6
Profiles per task	2
Attributes	7
Tasks with no profile chosen	0

Design

Topic	candidate choice
A profile represents	person
Country	MW
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	Which candidate would you support? (paraphrase) [choice, forced]

Columns

attr_ask_reliefattr_honestyattr_prevention_effectiveattr_prevention_effortattr_relief_effectiveattr_relief_effortattr_visitschoicecov_ageidprofiletasktrial_disaster_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("hartmann_2026_disaster_relief", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("hartmann_2026_disaster_relief", source="conj")

How to cite

@article{Hartmann_2026, title={Do Voters Value Relief over Preparedness? Evidence from Disaster Policies in Malawi}, volume={88}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/736446}, DOI={10.1086/736446}, number={4}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Hartmann, Felix}, year={2026}, month=Oct, pages={1674–1680} }

Version and provenance

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