Download data and study materials from OSF
Principal investigators:
Michael Hankinson
George Washington University
Email: hankinson@gwu.edu
Homepage: http://mhankinson.com/
Justin de Benedictis-Kessner
Harvard University
Email: jdbk@hks.harvard.edu
Homepage: https://scholar.harvard.edu/jdbk/home
Sample size: 3112
Field period: 03/16/2019-07/16/2019
H1: We expect that the race, gender, and location in a rural or non-rural location of policy beneficiaries depicted in a media story will affect support for treatment and punitive policies, operationalized as support for increasing funding for opioid treatment policy and punitive policy. Specifically, for the full sample, we expect a decrease in support for funding after reading about a black policy beneficiary compared to a white policy beneficiary.
H2: We expect that respondents will be more sympathetic to policy beneficiaries who share identities with the respondent -- e.g., black respondents will be more sympathetic to black policy beneficiaries depicted in the media, while white respondents will be more sympathetic to white policy beneficiaries. Viewing a profile with a shared identity will increase respondent support for funding treatment policy.
H3: We expect that any experimental treatment which increases respondent support for funding treatment policy will also decrease respondent support for funding law enforcement to arrest and prosecute drug users.
H4: We expect the 'blame' outcome variable to negatively correlate with support for funding treatment policy and positively correlate with support for funding punitive policy.
H5: We expect that respondents will be more sympathetic to policy beneficiaries who become addicted to opioids via legally obtained prescription drugs linked to a legitimate medical need, e.g. knee surgery. This will make them more supportive of increased funding for treatment. Therefore, as discussed above, respondents will also be less supportive of increased punitive measures and less likely to believe that individuals are to blame for their own addiction.
H6: We expect that when people receive treatment for addiction from private insurance, respondents will be more supportive of treatment funding than when it is provided by ACA-subsidized insurance or Medicaid.
H7: We expect that support for the ACA will be higher when the news article features a recovering addict who is able to obtain treatment via ACA-subsidized insurance.
H8: We expect that when a black policy recipient is depicted in the story Republicans will be relatively less supportive of treatment program funding than when a white person is depicted in comparison to Democrats (i.e. a more negative treatment effect among Republicans).
H9: We expect that when a rural policy recipient is depicted in the story Republicans will be relatively more supportive of treatment program funding than when a non-rural person is depicted in comparison to Democrats (i.e. a more positive treatment effect among Republicans).
H10: We expect that when people began their addiction following surgery and a legal prescription for painkillers, Republicans will be relatively more supportive of treatment program funding than when addiction began with drugs offered at a party in comparison to Democrats (i.e. a more negative treatment effect among Republicans). This hypothesis stems from the Republican emphasis on personal responsibility when evaluating addiction.
H11: We expect that when people began their addiction with heroin, Republicans will be relatively less supportive of treatment program funding than when it began with non-heroin painkillers in comparison to Democrats (i.e. a more negative treatment effect among Republicans). This hypothesis stems from the historically racialized nature of heroin and other `street drugs'.
H12: We expect that when people receive treatment for addiction from private insurance, Republicans (Democrats) will be more (less) supportive of treatment program funding than when it is provided by ACA-subsidized insurance or Medicaid.
H13: We expect that respondents who have personally known someone who has struggled with addiction will express greater support for addiction treatment funding.
List of the profiled individual's attributes which we will randomize in the news article and how each attribute is operationalized.
Race - name and use of dark-skinned or light-skinned hand in photo. We use names from the lowest education quartile and highest education quartile within race to mitigate any socio-economic effects outside of race: Black woman - Lakisha (lowest quartile), Janae (highest quartile); White woman - Angie, Katelyn; Black man - DaShawn, Darius; White man - Ronny, Jake
Gender - name and use of he/she pronouns
Urban/non-urban identity: a rural farm; a quiet suburb; an urban downtown center
Pathway to addiction: He/she injured his/her knee and needed surgery. His/her doctor prescribed him/her OxyContin pills for the pain during his/her recovery; His/her friend illegally gave him/her OxyContin pain pills at a party; His/her friend gave him/her heroin at a party.
Drug paraphrenalia - matched to the `pathway to addiction', e.g., when subject begins using opioids via OxyContin pills, the image will show a hand holding pills: Image of hand holding pills; Image of hand holding syringe
Pathway to insurance: insurance purchased from a private provider; insurance purchased through the Affordable Care Act/Obamacare marketplace; insurance coverage from the state's Medicaid expansion, funded by the Affordable Care Act/Obamacare
1. If you were making up the budget for the federal government this year, would you increase, decrease, or keep spending the same for treatment for those addicted to opioids? "Increase a lot" to "Decrease a lot"
2. If you were making up the budget for the federal government this year, would you increase, decrease, or keep spending the same for law enforcement to arrest and prosecute those addicted to opioids? "Increase a lot" to "Decrease a lot"
3. Given what you know about the Affordable Care Act (also known as ``Obamacare"), do you have a generally favorable or unfavorable opinion of it? "Strongly favorable" to "Strongly unfavorable"
4. Would you agree or disagree that individuals addicted to opioids are to blame for their own addiction? "Strongly agree" to "Strongly disagree"