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Concentrated Burdens: How Self-Interest and Partisanship Shape Opinion on Opioid Treatment Policy



When does self-interest influence public opinion on contentious public policies? The bulk of theory in political science suggests that self-interest is only a minor force in public opinion. Using nationally representative survey data, we show how financial and spatial self-interest and partisanship all shape public opinion on opioid treatment policy. We find that a majority of respondents support a redistributive funding model for treatment programs, while treatment funded by taxation based on a community’s overdose rate is less popular. Moreover, financial self-interest cross-pressures lower-income Republicans, closing the partisan gap in support by more than half. We also experimentally test how the spatial burden of siting treatment clinics alters policy preferences. People across the political spectrum are less supportive when construction of a clinic is proposed closer to their home. These results highlight how partisanship and self-interest interact in shaping preferences on public policy with concentrated burdens.


Corresponding author

*Justin de Benedictis-Kessner, Assistant Professor, Department of Political Science, Boston University,
Michael Hankinson, Assistant Professor, Department of Political Science, Baruch College, CUNY,


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Both authors contributed equally. For comments, suggestions, and advice, we thank Karl Kronebusch, Stéphane Lavertu, Brendan Nyhan, Justin Phillips, Melissa Sands, and participants at the 2018 Local Political Economy APSA pre-conference, the 2018 APSA Annual Meeting, the 2018 APPAM Fall Research Conference, and seminars at the Marxe School of Public & International Affairs, Baruch College, and the Northeastern University School of Public Policy and Urban Affairs. We appreciate the research assistance of Cody Edgerly, Aaron Henry, and Claudia Scott, and funding from Time-sharing Experiments for the Social Sciences (TESS). Replication files are available at the American Political Science Review Dataverse:



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Supplementary materials

De Benedictis-Kessner and Hankinson Dataset

Supplementary materials

De Benedictis-Kessner and Hankinson supplementary material
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