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Perceptive Risk Clusters of European Citizens and NPI Compliance in face of the Covid-19 Pandemics

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  • Jacques Bughin
  • Michele Cincera
  • Dorota Reykowska
  • Marcin Zyszkiewicz
  • Rafal Ohme

Abstract

Despite promising announcements on an effective vaccine, the control of the covid-19 pandemic is critically dependent on the maximal compliance of citizens to a set of non-pharmaceutical interventions (NPI for short). We use statistical clustering to partition European citizens with regards to their perceptive risks and social attitudes during the first wave of the covid-19 pandemic and find ten segments to predict, both the extent and mix of protective behaviors adopted. Those segments demonstrate a clear divide in the population, with on one extreme, a segment (standing for 8% of the population) that is self-centered and exhibits low self-risk perception as well as low NPI compliance. The other extreme is composed of a segment (11% of the population) that is more socially oriented, and quite responsive to all protective measures. As data are survey-based, we adjust responses based on information gap (by reaction time, RT, measurement) of both worry expression and NPI compliance, to confirm the robustness of our results. Further, we extend the notion of worries to be not only health-related but to include financial risk (like losing a job) as well as psychological worries (e.g. feeling alone, or being unable to meet with family and friends), as they prove to drive different NPI behaviors among the population.

Suggested Citation

  • Jacques Bughin & Michele Cincera & Dorota Reykowska & Marcin Zyszkiewicz & Rafal Ohme, 2020. "Perceptive Risk Clusters of European Citizens and NPI Compliance in face of the Covid-19 Pandemics," iCite Working Papers 2020-42, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:ict:wpaper:2013/316087
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    References listed on IDEAS

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    1. Lunn, Peter D. & Timmons, Shane & Belton, Cameron A. & Barjaková, Martina & Julienne, Hannah & Lavin, Ciarán, 2020. "Motivating social distancing during the COVID-19 pandemic: An online experiment," Social Science & Medicine, Elsevier, vol. 265(C).
    2. Paul T E Cusack, 2020. "Anxiety Disorders," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24255-24260, October.
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    Cited by:

    1. Jacques Bughin & Michele Cincera & Dorota Reykowska & Rafal Ohme, 2021. "Big data is decision science: The case of COVID-19 vaccination," ULB Institutional Repository 2013/342494, ULB -- Universite Libre de Bruxelles.

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    More about this item

    Keywords

    Covid-19 pandemics; Clustering; Non-pharmaceutical interventions; Europe;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods

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