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Trust Predicts Compliance with COVID-19 Containment Policies: Evidence from Ten Countries Using Big Data

Author

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  • Sarracino, Francesco

    (University of California)

  • Greyling, Talita

    (University of Johannesburg)

  • O'Connor, Kelsey J.

    (STATEC Research – National Institute of Statistics and Economic Studies)

  • Peroni, Chiara

    (STATEC Research – National Institute of Statistics and Economic Studies)

  • Rossouw, Stephanié

    (Auckland University of Technology)

Abstract

Previous evidence indicates trust is an important correlate of compliance with COVID-19 containment policies. However, this conclusion hinges on two crucial assumptions: first, that compliance does not change over time, and second, that mobility or self-reported measures are good proxies for compliance. This study is the first to use a time-varying measure of compliance to study the relationship between compliance and trust in others and institutions over the period from March 2020 to January 2021 in ten mostly European countries. We calculate a time-varying measure of compliance as the association between containment policies and people's mobility behavior using data from the Oxford Policy Tracker and Google. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. We test the predictive role of our trust measures using various panel estimation techniques. Our findings demonstrate that compliance does change over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates compliance should not be taken for granted, and confirms the importance of cultivating trust in others. Nurturing trust in others, through ad-hoc policies such as community activity programs and urban design to facilitate social interactions, can foster compliance with public policies.

Suggested Citation

  • Sarracino, Francesco & Greyling, Talita & O'Connor, Kelsey J. & Peroni, Chiara & Rossouw, Stephanié, 2022. "Trust Predicts Compliance with COVID-19 Containment Policies: Evidence from Ten Countries Using Big Data," IZA Discussion Papers 15171, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15171
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    Cited by:

    1. Greyling, Talita & Rossouw, Stephanié, 2024. "Vaccination uptake, happiness and emotions: using a supervised machine learning approach," GLO Discussion Paper Series 1482, Global Labor Organization (GLO).
    2. Rossouw, Stephanié & Greyling, Talita, 2022. "Collective emotions and macro-level shocks: COVID-19 vs the Ukrainian war," GLO Discussion Paper Series 1210, Global Labor Organization (GLO).
    3. Costa-Font, Joan & Vilaplana-Prieto, Cristina, 2023. "Health System Trust and Compliance with COVID-19 Restrictions," IZA Discussion Papers 15961, Institute of Labor Economics (IZA).
    4. Costa-Font, Joan & Vilaplana-Prieto, Cristina, 2023. "Trusting the health system and COVID 19 restriction compliance," LSE Research Online Documents on Economics 118267, London School of Economics and Political Science, LSE Library.
    5. Costa-Font, Joan & Vilaplana-Prieto, Cristina, 2023. "Trusting the Health System and COVID 19 Restriction Compliance," Economics & Human Biology, Elsevier, vol. 49(C).
    6. Sarracino, Francesco & Slater, Giulia, 2024. "The trust paradox," MPRA Paper 120053, University Library of Munich, Germany.

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    More about this item

    Keywords

    Twitter; big data; trust; COVID-19; compliance;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management

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