20,944 rated or chosen profiles from 2,618 respondents, in up to 4 tasks of 2 profiles varying on 6 attributes.
| Description | 2,618 US citizens (Respondi online quota panel, December 2016) each split $10 million of disaster relief between two hypothetical disaster-hit counties in 4 tasks; counties varied economic damage, fatalities, household income, unemployment and 2012 presidential vote, with the president's party varied per task. Outcome: amount allocated (rating, $ million, 0-10). |
|---|---|
| Reference | Bechtel, M. M., & Mannino, M. (2022). Retrospection, fairness, and economic shocks: How do voters judge policy responses to natural disasters? Political Science Research and Methods, 10(2), 260-278. |
| DOI | 10.1017/psrm.2020.39 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/JRLYVN |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 2,618 |
|---|---|
| Rows (one per respondent, task and profile) | 20,944 |
| Tasks per respondent (most) | 4 |
| Profiles per task | 2 |
| Attributes | 6 |
| Topic | tax welfare economy |
|---|---|
| A profile represents | place |
| Country | US |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | yes (stated by the source): attr_president is randomized per task and held equal for both counties of a task; all county attributes fully randomized |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead.
Outcomes:
| rating | Please distribute a total of $10 million in relief aid between the two affected counties (contribution to region A / region B) (paraphrase) [rating, 0 ($0) to 10 ($10 million)] |
|---|
attr_economic_damageattr_fatalitiesattr_household_incomeattr_presidentattr_presvote_2012attr_unemploymentattrpos_economic_damageattrpos_fatalitiesattrpos_household_incomeattrpos_presvote_2012attrpos_unemploymentcov_agecov_attention_passcov_educationcov_gendercov_left_rightcov_party_idcov_racecov_survey_weightidprofileratingtask
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("bechtel_2022_disaster_relief", source = "conj")
# Python
pip install irw
import irw
df = irw.fetch("bechtel_2022_disaster_relief", source="conj")
@article{Bechtel_2020, title={Retrospection, fairness, and economic shocks: how do voters judge policy responses to natural disasters?}, volume={10}, ISSN={2049-8489}, url={http://dx.doi.org/10.1017/psrm.2020.39}, DOI={10.1017/psrm.2020.39}, number={2}, journal={Political Science Research and Methods}, publisher={Cambridge University Press (CUP)}, author={Bechtel, Michael M. and Mannino, Massimo}, year={2020}, month=Nov, pages={260–278} }
| 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 |