3,354 rated or chosen profiles from 559 respondents, in up to 3 tasks of 2 profiles varying on 3 attributes.

One of 2 tables from the same source; this one is the departments table. The others are listed under Related tables.

About this table

Description	Paired conjoint with 559 mid-level bureaucrats in six Ugandan ministries (2023): in 3 tasks respondents chose between two hypothetical departments varying donor exposure, allowance inequity and coordination; outcome is a forced choice.
Reference	Nagawa, M. (2026). Foreign aid and the performance of bureaucrats. International Organization.
DOI	10.1017/s0020818326101489
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/G2KIZY

Notes

Conjoint table (irw_conjoint). Three paired forced-choice tasks (3 attributes, displayed sentences); choice = preferred department (paraphrase), no opt-out. Follow-up hours-of-effort items (asked only for the chosen profile) are stored as unlabelled codes and not kept. N = 559 after dropping 3 incomplete surveys, as in the paper.

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	559
Rows (one per respondent, task and profile)	3,354
Tasks per respondent (most)	3
Profiles per task	2
Attributes	3
Tasks with no profile chosen	0

Design

Topic	public administration, international economy
A profile represents	organisation
Country	UG
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which department would you prefer to work in? (paraphrase) [choice, forced]

Columns

attr_coordinationattr_donor_exposureattr_inequitychoicecov_contractcov_dfp_evercov_dfp_org_freq_codecov_educationcov_gendercov_ministrycov_rankcov_regionidprofiletask

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("nagawa_2026_aid_departments", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("nagawa_2026_aid_departments", source="conj")

How to cite

@article{Nagawa_2026, title={Foreign Aid and the Performance of Bureaucrats}, ISSN={1531-5088}, url={http://dx.doi.org/10.1017/s0020818326101489}, DOI={10.1017/s0020818326101489}, journal={International Organization}, publisher={Cambridge University Press (CUP)}, author={Nagawa, Maria}, year={2026}, month=Sept, pages={1–33} }

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