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The strategy of protest against Covid‐19 containment policies in Germany

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  • Thomas Plümper
  • Eric Neumayer
  • Katharina Gabriela Pfaff

Abstract

Objectives The article analyzes the epidemiological and political logics of protest against containment policies in Germany. To maximize the mobilization potential, protest organizers organize more protest events when Covid‐19 mortality rates are low, the stringency of containment policies is high, and in districts in which the vote share of mainstream parties is traditionally low. Method Using a negative binomial model, we analyze the number of protest events in a sample of 401 German districts over the period from March to May 2020. Results We find robust positive predicted effects of the stringency of containment policies and negative predicted effects for the mortality rate and the strengths of mainstream parties. Conclusion We interpret these findings as evidence of the strategic behavior of protest organizers that target protest participation and mobilization to keep the movement alive and potentially grow it.

Suggested Citation

  • Thomas Plümper & Eric Neumayer & Katharina Gabriela Pfaff, 2021. "The strategy of protest against Covid‐19 containment policies in Germany," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2236-2250, September.
    • Handle: RePEc:bla:socsci:v:102:y:2021:i:5:p:2236-2250
    DOI: 10.1111/ssqu.13066
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    References listed on IDEAS

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    1. Martin Lange & Ole Monscheuer, 2022. "Spreading the disease: Protest in times of pandemics," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2664-2679, December.
    2. Nicolas Berman & Mathieu Couttenier & Nathalie Monnet & Rohit Ticku, 2020. "Shutdown Policies and Worldwide Conflict," Working Papers 20-16, Chapman University, Economic Science Institute.
    3. Francesco Iacoella & Patricia Justino & Bruno Martorano, 2021. "Do pandemics lead to rebellion? Policy responses to COVID-19, inequality, and protests in the USA," WIDER Working Paper Series wp-2021-57, World Institute for Development Economic Research (UNU-WIDER).
    4. Michael J. Hanmer & Kerem Ozan Kalkan, 2013. "Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models," American Journal of Political Science, John Wiley & Sons, vol. 57(1), pages 263-277, January.
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    Cited by:

    1. Lorena Barberia & Thomas Plümper & Guy D. Whitten, 2021. "The political science of Covid‐19: An introduction," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2045-2054, September.
    2. Walter, Dror & Ophir, Yotam & Lokmanoglu, Ayse D. & Pruden, Meredith L., 2022. "Vaccine discourse in white nationalist online communication: A mixed-methods computational approach," Social Science & Medicine, Elsevier, vol. 298(C).

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