1,730 rated or chosen profiles from 221 respondents, in up to 4 tasks of 2 profiles varying on 4 attributes.

About this table

Description	Block Development Officers in Telangana, India (elite sample, n=221) chose which of two hypothetical village heads (sarpanches) they would help get work done, varying gender, seat reservation, education and family background; up to 4 tasks.
Reference	Purohit, B. (2026). Bureaucratic discretion against female politicians. Comparative Political Studies.
DOI	10.1177/00104140261448427
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/LEGBP2

Notes

Outcome: which of two sarpanches the officer would help "get work done" for (exact wording in the article, not the deposit), forced choice, no opt-out. Attribute levels are the author's short labels (e.g. UR/BC/SC reservation). 221 of the 253 surveyed officials appear in the conjoint file; only respondent gender is kept to limit re-identification. (irw#2887)

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	221
Rows (one per respondent, task and profile)	1,730
Tasks per respondent (most)	4
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	public administration
A profile represents	person
Country	IN
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	Bureaucrats were shown pairs of hypothetical sarpanch profiles and asked which they would be more likely to help ... the one they'd "get work done" for. (paraphrase) [choice, forced]

Columns

attr_educationattr_family_backgroundattr_genderattr_reservationchoicecov_genderidprofiletask

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("purohit_2026_bureaucrat_help", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("purohit_2026_bureaucrat_help", source="conj")

How to cite

@article{Purohit_2026, title={Bureaucratic Discretion Against Female Politicians}, ISSN={1552-3829}, url={http://dx.doi.org/10.1177/00104140261448427}, DOI={10.1177/00104140261448427}, journal={Comparative Political Studies}, publisher={SAGE Publications}, author={Purohit, Bhumi}, year={2026}, month=May }

Version and provenance

IRW version	v607
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 v47.0