2,492 rated or chosen profiles from 178 respondents, in up to 7 tasks of 2 profiles varying on 8 attributes.

About this table

Description	World Bank staff (mostly task team leaders; elite online survey, 2022, n=178) rated 7 pairs of hypothetical development policy loans with eight binary features (prior-action conditions, amount, environmental/social and macroeconomic risk, gender-disaggregated targets, recipient CPIA score, past US opposition, income status) on the likelihood of board approval and of positive development impact (1-10 each).
Reference	Heinzel, M., Weaver, C., & Jorgensen, S. (2025). Bureaucratic representation and gender mainstreaming in international organizations: Evidence from the World Bank. American Political Science Review, 119(1), 332-348.
DOI	10.1017/S0003055424000376
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/U090WG

Notes

Conjoint table (irw_conjoint). Paired profiles without forced choice, 7 tasks. rating_approval: 'In your personal opinion, how likely is each project to get board approval?' and rating_impact: 'In your personal opinion, how likely is each project to have a positive impact on development outcomes in the recipient country?', each 1 = Very unlikely to 10 = Very likely. Eight binary features independently randomized; levels as in Appendix Table A2. Survey weight kept (weights_final); alternative robustness weights dropped.

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

Design

Topic	international economy
A profile represents	policy
Country	unknown
Language shown	en
Language of stored levels	en
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

rating_approval	In your personal opinion, how likely is each project to get board approval? [rating, 1 (Very unlikely) to 10 (Very likely)]
rating_impact	In your personal opinion, how likely is each project to have a positive impact on development outcomes in the recipient country? [rating, 1 (Very unlikely) to 10 (Very likely)]

Columns

attr_amountattr_cpiaattr_env_social_riskattr_gender_targetsattr_incomeattr_macro_riskattr_prior_actionsattr_us_opposedcov_gender_expertisecov_influence_cdscov_influence_edscov_influence_othcov_influence_pmscov_influence_ttlscov_influence_vpscov_region_codecov_sector_codecov_survey_weightcov_womenidprofilerating_approvalrating_impacttask

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("heinzel_2024_world_bank_dpl", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("heinzel_2024_world_bank_dpl", source="conj")

How to cite

@article{HEINZEL_2024, title={Bureaucratic Representation and Gender Mainstreaming in International Organizations: Evidence from the World Bank}, volume={119}, ISSN={1537-5943}, url={http://dx.doi.org/10.1017/S0003055424000376}, DOI={10.1017/s0003055424000376}, number={1}, journal={American Political Science Review}, publisher={Cambridge University Press (CUP)}, author={HEINZEL, MIRKO and WEAVER, CATHERINE and JORGENSEN, SAMANTHA}, year={2024}, month=Apr, pages={332–348} }

Respondents	178
Rows (one per respondent, task and profile)	2,492
Tasks per respondent (most)	7
Profiles per task	2
Attributes	8

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

heinzel_2024_world_bank_dpl