12,564 rated or chosen profiles from 1,047 respondents, in up to 6 tasks of 2 profiles varying on 4 attributes.

About this table

Description	Online respondents in China (SSI, fall 2017; 1,047 business elites and nonelites, 532 in the authors' screened sample) chose between six pairs of tax-reform proposals varying tax type, tax rate, the government services funded and the institutional political influence granted.
Reference	Kao, J. C., Lü, X., & Queralt, D. (2024). Do gains in political representation sweeten tax reform in China? It depends on who you ask. Political Science Research and Methods, 12(1), 146-165.
DOI	10.1017/psrm.2022.58
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/TKEWPJ

Notes

Conjoint table (irw_conjoint). choice: which of two tax-reform proposals the respondent prefers (paraphrase: 'choose which is most preferred'), forced, no opt-out; 6 tasks, 4 attributes (tax type, rate 1-20%, government services, institutional political influence), levels as the authors' value labels (respondents saw Chinese). 57 respondents with no choices omitted; the article's screened sample = cov_elite_t10 non-missing. Taiwan shadow-case file not built (tasks not recoverable).

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

Design

Topic	tax welfare economy
A profile represents	policy
Country	CN
Language shown	zh
Language of stored levels	en
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Choose which of the two tax reform proposals is most preferred (paraphrase) [choice, forced]

Columns

attr_government_servicesattr_political_influenceattr_tax_rateattr_tax_typechoicecov_agecov_ccp_membercov_educationcov_elite_sourcecov_elite_t1cov_elite_t10cov_elite_t5cov_gendercov_household_incomecov_marriedcov_money_todaycov_sectorcov_tax_bargaincov_trust_govcov_vat_burdencov_years_paying_taxidprofiletask

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("kao_2024_tax_reform", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("kao_2024_tax_reform", source="conj")

How to cite

@article{Kao_2022, title={Do gains in political representation sweeten tax reform in China? It depends on who you ask}, volume={12}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2022.58}, DOI={10.1017/psrm.2022.58}, number={1}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Kao, Jay C. and Lü, Xiaobo and Queralt, Didac}, year={2022}, month=Nov, pages={146–165} }

Respondents	1,047
Rows (one per respondent, task and profile)	12,564
Tasks per respondent (most)	6
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

IRW version	v584
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 v44.0

kao_2024_tax_reform