alvarado_2024_tax_fairness_us

20,426 rated or chosen profiles from 2,081 respondents, in up to 5 tasks of 2 profiles varying on 3 attributes.

One of 4 tables from the same source; this one is the us table. The others are listed under Related tables.

About this table

DescriptionUS respondents (Amazon Mechanical Turk, October 2017; 2,081 respondents, 1,946 in the author's sample) chose which of two hypothetical individuals should pay a higher tax rate, over 5 pairs varying annual income, share of income paid in sales taxes and source of income (effort, family connections, government bailout, lottery).
ReferenceAlvarado, M. (2024). Compensation and tax fairness: Evidence from four countries. British Journal of Political Science, 54(4), 1023-1042.
DOI10.1017/S0007123423000698
LicenceCC0 1.0
Source datahttps://doi.org/10.7910/DVN/A6115X

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

Respondents2,081
Rows (one per respondent, task and profile)20,426
Tasks per respondent (most)5
Profiles per task2
Attributes3
Tasks with no profile chosen0

Design

Topictax welfare economy
A profile representsperson
CountryUS
Language shownen
Language of stored levelsen
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choiceWhich of the two individuals would you personally prefer to charge a higher tax rate to? [choice, forced]

Columns

attr_incomeattr_income_sourceattr_sales_taxattrpos_incomeattrpos_income_sourceattrpos_sales_taxchoicecov_in_authors_sampleidprofiletask

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("alvarado_2024_tax_fairness_us", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("alvarado_2024_tax_fairness_us", source="conj")

How to cite

@article{Alvarado_2024, title={Compensation and Tax Fairness: Evidence From Four Countries}, volume={54}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123423000698}, DOI={10.1017/s0007123423000698}, number={4}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Alvarado, Mariana}, year={2024}, month=Mar, pages={1023–1042} }

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