A synthesis of evidence for policy from behavioural science during COVID-19

Kai Ruggeri*, Friederike Stock, S Alexander Haslam, Valerio Capraro, Paulo Boggio, Naomi Ellemers, Aleksandra Cichocka, Karen M Douglas, David G Rand, Sander van der Linden, Mina Cikara, Eli J Finkel, James N Druckman, Michael J A Wohl, Richard E Petty, Joshua A Tucker, Azim Shariff, Michele Gelfand, Dominic Packer, Jolanda JettenPaul A M Van Lange, Gordon Pennycook, Ellen Peters, Katherine Baicker, Alia Crum, Kim A Weeden, Lucy Napper, Nassim Tabri, Jamil Zaki, Linda Skitka, Shinobu Kitayama, Dean Mobbs, Cass R Sunstein, Sarah Ashcroft-Jones, Anna Louise Todsen, Ali Hajian, Sanne Verra, Vanessa Buehler, Maja Friedemann, Marlene Hecht, Rayyan S Mobarak, Ralitsa Karakasheva, Markus R Tünte, Siu Kit Yeung, R Shayna Rosenbaum, Žan Lep, Yuki Yamada, Sa-Kiera Tiarra Jolynn Hudson, Lucía Macchia, Irina Soboleva, Eugen Dimant, Sandra J Geiger, Hannes Jarke, Tobias Wingen, Jana B Berkessel, Silvana Mareva, Lucy McGill, Francesca Papa, Bojana Većkalov, Zeina Afif, Eike K Buabang, Marna Landman, Felice Tavera, Jack L Andrews, Aslı Bursalıoğlu, Zorana Zupan, Lisa Wagner, Joaquín Navajas, Marek Vranka, David Kasdan, Patricia Chen, Kathleen R Hudson, Lindsay M Novak, Paul Teas, Nikolay R Rachev, Matteo M Galizzi, Katherine L Milkman, Marija Petrović, Jay J Van Bavel, Robb Willer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Original languageEnglish
Pages (from-to)134-147
Number of pages22
Publication statusPublished - 13 Dec 2023


Dive into the research topics of 'A synthesis of evidence for policy from behavioural science during COVID-19'. Together they form a unique fingerprint.

Cite this