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A Guilt-Free Strategy Increase Self-Reported Non-Compliance with COVID-19 Preventive Measures: Experimental Evidence from 12 Countries

Author

Listed:
  • Daoust, Jean-François
  • Bélanger, Éric

    (McGill University)

  • Dassonneville, Ruth
  • Lachapelle, Erick
  • Nadeau, Richard
  • Becher, Michael
  • Brouard, Sylvain
  • Foucault, Martial
  • Hönnige, Christoph
  • Stegmueller, Daniel

Abstract

Studies of citizens’ compliance with COVID-19 preventive measures routinely rely on survey data. While essential, public health restrictions provide clear signals of what is socially desirable in this context, creating a potential source of response bias in self-reported measures of compliance. In this research, we examine whether the results of a guilt-free strategy that was used by Daoust et al. (2020) to loosen this constraint are generalizable across twelve countries, and whether the treatment effect varies across subgroups. Our findings show that the guilt-free strategy is a very useful tool in every country included, increasing respondents’ proclivity to report non-compliance by 9 to 16 percentage points. This effect holds for different subgroups based on gender, age and education. We conclude that the inclusion of this strategy should be the new standard for survey research that aims to provide crucial data on the current pandemic.

Suggested Citation

  • Daoust, Jean-François & Bélanger, Éric & Dassonneville, Ruth & Lachapelle, Erick & Nadeau, Richard & Becher, Michael & Brouard, Sylvain & Foucault, Martial & Hönnige, Christoph & Stegmueller, Daniel, 2020. "A Guilt-Free Strategy Increase Self-Reported Non-Compliance with COVID-19 Preventive Measures: Experimental Evidence from 12 Countries," SocArXiv tkrs7_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:tkrs7_v1
    DOI: 10.31219/osf.io/tkrs7_v1
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    References listed on IDEAS

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    1. Daoust, Jean-François & Nadeau, Richard & Dassonneville, Ruth & Lachapelle, Erick & Bélanger, Éric & Savoie, Justin & van der Linden, Clifton, 2020. "How to survey citizens’ compliance with COVID-19 public health measures? Evidence from three survey experiments," SocArXiv gursd, Center for Open Science.
    2. Jonathan de Quidt & Johannes Haushofer & Christopher Roth, 2018. "Measuring and Bounding Experimenter Demand," American Economic Review, American Economic Association, vol. 108(11), pages 3266-3302, November.
    3. Diana C. Mutz & Robin Pemantle & Philip Pham, 2019. "The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data," The American Statistician, Taylor & Francis Journals, vol. 73(1), pages 32-42, January.
    4. Marc Höglinger & Ben Jann, 2018. "More is not always better: An experimental individual-level validation of the randomized response technique and the crosswise model," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-22, August.
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