15,010 rated or chosen profiles from 1,501 respondents, in up to 5 tasks of 2 profiles varying on 4 attributes.

About this table

Description	New Delhi residents (Ipsos online quota samples, two waves Nov-Dec 2017, n = 1,501) chose between and rated (1-7) 5 pairs of vehicle-pollution policy bundles combining a fuel tax, an odd-even rule, a ban on old vehicles and a vehicle ownership permit.
Reference	Beiser-McGrath, L. F., Bernauer, T., & Prakash, A. (2022). Command and control or market-based instruments? Public support for policies to address vehicular pollution in Beijing and New Delhi. Environmental Politics, 32(4), 586-618.
DOI	10.1080/09644016.2022.2113608
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/JVDNTD

Notes

Conjoint table (irw_conjoint). choice = which of two policy bundles the government should adopt and implement (forced, one per task); rating = 1-7 support for each bundle (1 strongly oppose, 7 strongly support). No opt-out; four attributes, randomized independently by the article, attribute order randomized once per respondent (not recorded). Profile position inferred from row order. Beijing sample of the same deposit not included (screen text unknown). N 1,501 (article: 750 per wave).

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	1,501
Rows (one per respondent, task and profile)	15,010
Tasks per respondent (most)	5
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	climate energy environment
A profile represents	policy
Country	IN
Language shown	en
Language of stored levels	en
Randomization restrictions	unknown
Task order	recorded in the deposit
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:

choice	Which of the two policies should the government adopt and implement? [choice, forced]
rating	Please rate the two policy measures on a scale from 1 to 7, where 1 indicates that you "strongly oppose" and 7 indicates that you "strongly support" the policy measure. [rating, 1 (strongly oppose) to 7 (strongly support)]

Columns

attr_fuel_taxattr_odd_evenattr_vehicle_banattr_vopchoicecov_agecov_educ_codecov_gendercov_gov_perfcov_income_codecov_owncaridprofileratingtasktrial_wave

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("beisermcgrath_2022_vehicle_delhi", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("beisermcgrath_2022_vehicle_delhi", source="conj")

How to cite

@article{Beiser_McGrath_2022, title={Command and control or market-based instruments? Public support for policies to address vehicular pollution in Beijing and New Delhi}, volume={32}, ISSN={1743-8934}, url={http://dx.doi.org/10.1080/09644016.2022.2113608}, DOI={10.1080/09644016.2022.2113608}, number={4}, journal={Environmental Politics}, publisher={Informa UK Limited}, author={Beiser-McGrath, Liam F. and Bernauer, Thomas and Prakash, Aseem}, year={2022}, month=Sept, pages={586–618} }

Version and provenance

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