2,019 rated or chosen profiles from 2,019 respondents, in up to 1 tasks of 1 profile varying on 4 attributes.
| Description | 2,019 online-panel respondents in Argentina each read one vignette about a re-election-seeking mayor's food-box programme, randomizing mayor gender, information on whether distribution was biased, mayor's party and whether the mayor's name appeared on the box. Seven ratings 1-4 (vote for mayor, programme helps re-election, satisfaction, need-based distribution, corruption, patronage, vote buying). |
|---|---|
| Reference | Diaz, G., Oliveros, V., Weitz-Shapiro, R., & Winters, M. S. (2025). Ignoring women's performance: A survey experiment on policy implementation in Argentina. British Journal of Political Science. |
| DOI | 10.1017/S0007123424000668 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/dvn/swipbz |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,019 |
|---|---|
| Rows (one per respondent, task and profile) | 2,019 |
| Tasks per respondent (most) | 1 |
| Profiles per task | 1 |
| Attributes | 4 |
| Topic | candidate choice |
|---|---|
| A profile represents | person |
| Country | AR |
| Language shown | es |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | inferred from row order |
| Profile position | inferred from row order |
Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.
Outcomes:
| rating_buyvotes | How likely is it that the mayor buys votes (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
|---|---|
| rating_corrupt | How likely is it that the mayor was corrupt (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
| rating_help_reelect | How likely is it that the programme helps the mayor win re-election (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
| rating_need_based | How likely is it that the food boxes go to those most in need (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
| rating_patronage | How likely is it that the mayor offered patronage (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
| rating_satisfied | How likely would you be to be satisfied with the programme if it ran in your municipality (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
| rating_vote_mayor | How likely would you be to vote for the mayor (paraphrase) [rating, 1 (Not at all likely) to 4 (Very likely)] |
attr_distribution_infoattr_mayor_genderattr_mayor_partyattr_name_on_boxcov_agecov_duration_seccov_gendercov_provincecov_sesidprofilerating_buyvotesrating_corruptrating_help_reelectrating_need_basedrating_patronagerating_satisfiedrating_vote_mayortask
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.
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("diaz_2025_mayor_credit", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("diaz_2025_mayor_credit", source="conj")
@article{Diaz_2025, title={Ignoring Women’s Performance: A Survey Experiment on Policy Implementation in Argentina}, volume={55}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123424000668}, DOI={10.1017/s0007123424000668}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Diaz, Gustavo and Oliveros, Virginia and Weitz-Shapiro, Rebecca and Winters, Matthew S.}, year={2025} }
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint_2 v1.4 |
| Redivis dataset DOI | 10.57761/hk97-wv30 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |