24,012 rated or chosen profiles from 3,037 respondents, in up to 8 tasks of 2 profiles varying on 13 attributes.

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

About this table

Description	3,037 Facebook-recruited Ukrainian adults (2020, survey in Ukrainian or Russian) compared 4 pairs of healthcare (Petro hurt his leg; doctor Ruslana announces a long wait at a state clinic) scenarios varying 11 attributes (wait time, need, substitutes, bribe size, detection risk, enforceability, past/future interaction, other bribers, first mover, multiple officials) plus randomized citizen/official names; forced choice of the scenario where the citizen is more likely to bribe, and 1-7 ratings of the citizen's and the respondent's own likelihood of bribing.
Reference	Erlich, A., Gans-Morse, J., & Nichter, S. (2025). Selective bribery: When do citizens engage in corruption? Comparative Political Studies, 58(5), 996-1036.
DOI	10.1177/00104140241259444
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/EVEOQN

Notes

Conjoint table (irw_conjoint). choice = in which of two side-by-side scenarios (A/B) Petro would be more likely to pay the doctor a bribe (forced, no opt-out); rating = how likely Petro would be to pay a bribe to get treatment more quickly, rating_self = how likely the respondent would be in Petro's position, both 1 = definitely no to 7 = definitely yes (mapped from Russian answer text; higher = more likely to bribe). Attribute levels are stored as displayed in Ukrainian or Russian, so each level has two spellings and cross-language level pairs never co-occur. Tasks without a forced-choice answer dropped; 3,037 of the 3,060 analysed respondents have this scenario, matching the authors' long file.

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

Design

Topic	corruption accountability
A profile represents	scenario
Country	UA
Language shown	uk, ru
Language of stored levels	uk, ru
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Please read the additional information below about scenario A and scenario B and then indicate in which scenario - A or B - Petro would be more likely to pay a bribe in order to receive treatment more quickly. (translated) [choice, forced]
rating	On a [7-point] scale from 'definitely no' to 'definitely yes,' how likely would Petro be to pay a bribe to the doctor to receive treatment more quickly? (translated) [rating, 1 (Абсолютно нет (definitely no)) to 7 (Абсолютно да (definitely yes))]
rating_self	Using the same scale, if you were in Petro's position, how likely would you be to pay a bribe to the doctor to receive treatment more quickly? (translated) [rating, 1 (Абсолютно нет (definitely no)) to 7 (Абсолютно да (definitely yes))]

Columns

attr_bribe_sizeattr_citizen_nameattr_enforcedattr_first_moverattr_futureattr_needattr_official_nameattr_other_bribersattr_other_officialattr_pastattr_red_tapeattr_riskattr_substituteschoicecov_agecov_attention_pass_1cov_attention_pass_2cov_city_sizecov_duration_seccov_educationcov_gendercov_incomecov_native_languagecov_oblastcov_survey_languagecov_survey_weightidprofileratingrating_selftasktrial_block_first

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("erlich_2025_bribery_clinic", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("erlich_2025_bribery_clinic", source="conj")

How to cite

@article{Erlich_2024, title={Selective Bribery: When Do Citizens Engage in Corruption?}, volume={58}, ISSN={1552-3829}, url={http://dx.doi.org/10.1177/00104140241259444}, DOI={10.1177/00104140241259444}, number={5}, journal={Comparative Political Studies}, publisher={SAGE Publications}, author={Erlich, Aaron and Gans-Morse, Jordan and Nichter, Simeon}, year={2024}, month=Aug, pages={996–1036} }

Respondents	3,037
Rows (one per respondent, task and profile)	24,012
Tasks per respondent (most)	8
Profiles per task	2
Attributes	13
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

erlich_2025_bribery_clinic