Replication Data for: "Overcoming COVID-19 vaccination resistance when alternative policies affect the dynamics of conformism, social norms, and crowding out"

What is an effective vaccination policy to end the COVID-19 pandemic? We address this question in a model of the dynamics of policy effectiveness drawing upon the results of a large panel survey implemented in Germany during the first and second waves of the pandemic. We observe increased opposition to vaccinations were they to be legally required. In contrast, for voluntary vaccinations, there was higher and undiminished support. We find that public distrust undermines vaccine acceptance, apparently because it is associated with a belief that the vaccine is ineffective and, if enforced, compromises individual freedom. We model how the willingness to be vaccinated may vary over time in response to the fraction of the population already vaccinated and whether vaccination has occurred voluntarily or not. A negative effect of enforcement on vaccine acceptance (of the magnitude observed in our panel or even considerably smaller) could result in a large increase in the numbers that would have to be vaccinated unwillingly in order to reach a herd-immunity target. Costly errors may be avoided if policy makers understand that citizens’ preferences are not fixed but will be affected both by the crowding-out effect of enforcement and by conformism. This data set provides the data and Stata code used for the article. A detailed description of the variables is available from the corresponding publication. Please cite our paper if you use the data.

Non-probability Sample - Quota Sample

Web-based interviewInterview.WebBased

Webbasiertes InterviewInterview.WebBased

Identifier
Source https://search.gesis.org/research_data/SDN-10.7802-2272?lang=de
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=86d5c1ab8a28b9d6dc3f39be366c357570a8ee231cd7eff3f1149d9559146ad1
Provenance
Creator Schmelz, Katrin; Bowles, Samuel
Publisher GESIS Data Archive for the Social Sciences; GESIS Datenarchiv für Sozialwissenschaften
Publication Year 2021
Funding Reference [Funded by the Deutsche Forschungsgemeinschaft (DFG – German Research Foundation) under Germany‘s Excellence Strategy – EXC-2035/1 – 390681379]
Rights Free access (without registration) - The research data can be downloaded directly by anyone without further limitations. CC BY-SA 4.0: Attribution – ShareAlike (https://creativecommons.org/licenses/by-sa/4.0/deed.de); Freier Zugang (ohne Registrierung) - Die Forschungsdaten können von jedem direkt heruntergeladen werden. CC BY-SA 4.0: Attribution – ShareAlike (https://creativecommons.org/licenses/by-sa/4.0/deed.de)
OpenAccess true
Contact http://www.gesis.org/
Representation
Language English; German
Discipline Social Sciences
Spatial Coverage Germany; Germany