37,044 rated or chosen profiles from 6,174 respondents, in up to 3 tasks of 2 profiles varying on 2 attributes.

About this table

Description	Residents of 223 slums in Bangalore, Jaipur and Patna (India; n = 6,174) chose between two hypothetical ward-leader candidates in each of 3 pairs, each candidate described by one party/identity/education trait and one benefit-provision trait.
Reference	Rains, E., & Wibbels, E. (2023). Informal work, risk, and clientelism: Evidence from 223 slums across India. British Journal of Political Science, 53(1), 1-24.
DOI	10.1017/S0007123422000011
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/ADZECE

Notes

Conjoint table (irw_conjoint). choice = respondent prefers this hypothetical ward-leader candidate over the other (codebook paraphrase; original wording and language not deposited), no opt-out; 3 pairs per respondent, task inferred from row order and verified (one choice per pair), position within pair not recorded. Levels are the codebook's English descriptions. 1,704 of 7,878 respondents whose candidate traits were not saved 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	6,174
Rows (one per respondent, task and profile)	37,044
Tasks per respondent (most)	3
Profiles per task	2
Attributes	2
Tasks with no profile chosen	0

Design

Topic	candidate choice
A profile represents	person
Country	IN
Language shown	unknown
Language of stored levels	en
Randomization restrictions	unknown
Task order	inferred from row order
Profile position	unknown

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choice	Which of the two hypothetical ward leader candidates do you prefer? (paraphrase) [choice, forced]

Columns

attr_trait_aattr_trait_bchoicecov_occlowidprofiletask

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("rains_2023_ward_candidate", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("rains_2023_ward_candidate", source="conj")

How to cite

@article{Rains_2022, title={Informal Work, Risk, and Clientelism: Evidence from 223 Slums across India}, volume={53}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123422000011}, DOI={10.1017/s0007123422000011}, number={1}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Rains, Emily and Wibbels, Erik}, year={2022}, month=Apr, pages={1–24} }

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