ono_2025_bicameral_gender

45,340 rated or chosen profiles from 2,267 respondents, in up to 20 tasks of 1 profile varying on 8 attributes.

About this table

DescriptionJapanese voters (Rakuten Insight online panel, July 2020, n=2,267) rated 20 hypothetical Diet candidates one at a time on an 8-point favorability scale, 10 for the House of Representatives and 10 for the House of Councillors (chamber order randomized; half primed on the lower house's power to elect the PM); 8 attributes with real-world level probabilities (gender, party, age, education, occupation, hometown, experience, dynasty).
ReferenceOno, Y., Kasuya, Y., & Miwa, H. (2025). Analyzing gender gaps in bicameral legislatures: How asymmetrical institutions affect the supply and demand for female candidates. Legislative Studies Quarterly, 50(3), e12493.
DOI10.1111/lsq.12493
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/8SX9CK

Notes

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

Size and shape

Respondents2,267
Rows (one per respondent, task and profile)45,340
Tasks per respondent (most)20
Profiles per task1
Attributes8

Design

Topiccandidate choice
A profile representsperson
CountryJP
Language shownja
Language of stored levelsja
Randomization restrictionsunknown
Task orderinferred from row order
Profile positionrecorded in the deposit

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

Outcomes:

ratingTo what extent is this candidate favorable as a [HoR/HoC] member? Please evaluate this candidate using an eight-point scale from "not favorable at all" to "very favorable." (translated) [rating, 1 (not favorable at all) to 8 (very favorable)]

Columns

attr_ageattr_dynastyattr_educationattr_experienceattr_genderattr_hometownattr_occupationattr_partyattrpos_ageattrpos_dynastyattrpos_educationattrpos_experienceattrpos_genderattrpos_hometownattrpos_occupationattrpos_partycov_agecov_educationcov_gendercov_importance_hoccov_importance_horcov_importance_localcov_party_idcov_prefectureidprofileratingtasktrial_chambertrial_priming

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 neededBrowse on Redivisexplore and queryCroissant 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("ono_2025_bicameral_gender", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("ono_2025_bicameral_gender", source="conj")

How to cite

@article{Ono_2025, title={Analyzing gender gaps in bicameral legislatures: How asymmetrical institutions affect the supply and demand for female candidates}, volume={50}, ISSN={1939-9162}, url={http://dx.doi.org/10.1111/lsq.12493}, DOI={10.1111/lsq.12493}, number={3}, journal={Legislative Studies Quarterly}, publisher={Wiley}, author={Ono, Yoshikuni and Kasuya, Yuko and Miwa, Hirofumi}, year={2025}, month=Mar }

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint v4.2
Redivis dataset DOI10.57761/qkye-q530
Manifest pin for this IRW versionv4.2
Metadata sourceirw_meta v44.0