Prosociality predicts health behaviors during the COVID-19 pandemic
Open accessAbstract
Socially responsible behavior is crucial for slowing the spread of infectious diseases. However, economic and epidemiological models of disease transmission abstract from prosocial motivations as a driver of behaviors that impact the health of others. In an incentivized study, we show that a large majority of people are very reluctant to put others at risk for their personal benefit. Moreover, this experimental measure of prosociality predicts health behaviors during the COVID-19 pandemic, measured in a separate and ostensibly unrelated study with the same people. Prosocial individuals are more likely to follow physical distancing guidelines, stay home when sick, and buy face masks. We also find that prosociality measured two years before the pandemic predicts health behaviors during the pandemic. Our findings indicate that prosociality is a stable, long-term predictor of policy-relevant behaviors, suggesting that the impact of policies on a population may depend on the degree of prosociality.
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Achieving the five pillars of a successful fight against emerging infectious diseases requires an ethical framework of prosociality, meaning that individuals and government regulations prioritise societal needs over narrow individual interests.
“To accomplish these five pillars requires an ethical framework of prosociality—the orientation of individuals and government regulations to the needs of society as a whole, rather than to narrow individual interests.”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [8]
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- Authors
- Campos-Mercade, P., Meier, A. N., Schneider, F. H., & Wengström, E.
- Affiliations
- University of Basel (CH)
- University of Zurich (CH)
- Lund University (SE)
- Corresponding author
- Schneider, Florian H.
- Venue
- Journal of Public Economics
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 195
- Pages
- 104367
- ISSN
- 0047-2727 (ISSN-L)
- PMC
- PMC7842154
- OpenAlex
- W3026358667
- Cited by
- 242
- References
- 107
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 29 · 2023: 48 · 2022: 63 · 2021: 56 · 2020: 9
- FWCI
- 61.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Public Economics (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion)
- edoc (University of Basel) (acceptedVersion)
- IRIS (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Danmarks Grundforskningsfond (DNRF134)
- Torsten Söderbergs Stiftelse
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (100018_185176, P2BSP1_181847)
- Universität Zürich
- Universität Basel
- Københavns Universitet
- University of Chicago
- Topics
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- prosocial behavior, COVID-19 pandemic, health behavior, social distancing, masking, socially responsible behavior, disease transmission, behavioral stability, public health policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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