Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study
Abstract
:1. Introduction
Literature Review
2. Methods
2.1. Inclusion and Exclusion
2.2. Measures
2.2.1. Sociodemographic Characteristics
2.2.2. Social Media Use
2.2.3. Infection
2.2.4. Trust in Public Authorities
2.3. Statistical Analysis
2.4. Ethics
3. Results
3.1. Participants
3.2. Trust in Public Authorities
3.3. Associations with Trust in the Public Authorities’ Information about COVID-19
3.4. Associations with Trust in the Public Authorities’ Financial Measures to Counteract Effects of COVID-19
4. Discussion
5. Study Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Anastasiou, Evgenia, and Marie-Noelle Duquenne. 2021. First-wave COVID-19 pandemic in Greece: The role of demographic, social, and geographical factors in life satisfaction during lockdown. Social Sciences 10: 186. [Google Scholar] [CrossRef]
- Bish, Alison, and Susan Michie. 2010. Demographic and attitudinal determinants of protective behaviours during a pandemic: A review. British Journal of Health Psychology 15: 797–824. [Google Scholar] [CrossRef] [Green Version]
- Bonsaksen, Tore, Mariyana Schoultz, Hilde Thygesen, Mary Ruffolo, Daicia Price, Janni Leung, and Amy Østertun Geirdal. 2021. Loneliness and its associated factors nine months after the covid-19 outbreak: A cross-national study. International Journal of Environmental Research and Public Health 18: 2841. [Google Scholar] [CrossRef]
- Bouckaert, Geert, and Steven Van de Walle. 2001. Government performance and trust in government. Paper presented at Permanent Study Group of Productivity and Quality in the Public Sector at the European Group of Public Administration Annual Conference, Vaasa, Finland, September 5–8. [Google Scholar]
- Charron, Nicholas, and Bo Rothstein. 2016. Does education lead to higher generalized trust? The importance of quality of government. International Journal of Educational Development 50: 59–73. [Google Scholar] [CrossRef]
- Christensen, Thomas, and Per Lægreid. 2005. Trust in government: The relative importance of service satisfaction, political factors, and demography. Public Performance & Management Review 28: 487–511. [Google Scholar]
- Dalton, Russell. 2005. The social transformation of trust in government. International Review of Sociology 15: 133–54. [Google Scholar] [CrossRef]
- Deslatte, Aaron. 2020. The erosion of trust during a global pandemic and how public administrators should counter it. American Review of Public Administration 50: 489–96. [Google Scholar] [CrossRef]
- Devine, Daniel, Jennifer Gaskell, Will Jennings, and Gerry Stoker. 2021. Trust and the coronavirus pandemic: What are the consequences of and for trust? An early review of the literature. Political Studies Review 19: 274–85. [Google Scholar] [CrossRef]
- Easton, David. 1975. A re-assessment of the concept of political support. British Journal of Political Science 5: 435–57. [Google Scholar] [CrossRef] [Green Version]
- Elgin, Ceyhun, Gokce Basbug, and Abdullah Yalaman. 2020. Economic policy responses to a pandemic: Developing the COVID-19 economic stimulus index. Covid Economics: Vetted and Real Time Papers 3: 40–54. [Google Scholar]
- Ellison, Nicole B., Charles Steinfield, and Cliff Lampe. 2007. The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication 12: 1143–68. [Google Scholar] [CrossRef] [Green Version]
- Enria, Luisa, Naomi Waterlow, Nina Trivedy Rogers, Hannah Brindle, Sham Lal, Rosalind M. Eggo, Shelley Lees, and Chrissy H. Roberts. 2021. Trust and transparency in times of crisis: Results from an online survey during the first wave (April 2020) of the COVID-19 epidemic in the UK. PLoS ONE 16: e0239247. [Google Scholar]
- Fielding, Richard, Wendy Lam, Ella Ho, Tai Lam, Anthony Hedley, and Gabriel Leung. 2005. Avian influenza risk perception, Hong Kong. Emerging Infectious Diseases 11: 677–82. [Google Scholar] [CrossRef]
- Freimuth, Vicki, Don Musa, Karen Hilyard, Sandra Crouse Quinn, and Kevin Kim. 2014. Trust during the early stages of the 2009 H1N1 pandemic. Journal of Health Communication 19: 321–39. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Geirdal, Amy stertun, Mary Ruffolo, Janni Leung, Hilde Thygesen, Daicia Price, Tore Bonsaksen, and Mariyana Schoultz. 2021. Mental health, quality of life, wellbeing, loneliness and use of social media in a time of social distancing during the COVID-19 outbreak: A cross-country comparative study. Journal of Mental Health. in press. [Google Scholar] [CrossRef] [PubMed]
- Goldfinch, Shaun, Ross Taplin, and Robin Gauld. 2021. Trust in government increased during the COVID-19 pandemic in Australia and New Zealand. Australian Journal of Public Administration 80: 3–11. [Google Scholar] [CrossRef]
- Gozgor, Giray. 2021. Global evidence on the determinants of public trust in governments during the COVID-19. Applied Research in Quality of Life, 1–20. [Google Scholar] [CrossRef]
- Guillon, Marlène, and Pauline Kergall. 2020. Attitudes and opinions on quarantine and support for a contact-tracing application in France during the COVID-19 outbreak. Public Health (London) 188: 21–31. [Google Scholar] [CrossRef]
- Hale, Thomas, Anna Petherick, Toby Phillips, and Samuel Webster. 2020. Variation in Government Responses to COVID-19. Oxford: Oxford University. [Google Scholar]
- Hanfling, Dan, Bruce Altevogt, Kristin Viswanathan, and Lawrence Gostin. 2012. Committee on Guidance for Establishing Crisis Standards of Care for Use in Disaster Situations. Crisis Standards of Care: A Systems Framework for Catastrophic Disaster Response. Washington, DC: Institute of Medicine, National Academies Press. [Google Scholar]
- Haselhuhn, Michael, Jessica Kennedy, Laura Kray, Alex Van Zant, and Maurice Schweitzer. 2015. Gender differences in trust dynamics: Women trust more than men following a trust violation. Journal of Experimental Social Psychology 56: 104–09. [Google Scholar] [CrossRef] [Green Version]
- Huang, Peter. 2020. Pandemic Emotions, Public Health, Financial Economics, Law, and Leadership. University of Colorado Law Legal Studies Research Paper No. 20–14. Available online: https://papers-ssrn-com.proxy.lib.umich.edu/sol3/papers.cfm?abstract_id=3575101 (accessed on 12 September 2021).
- Jakovljevic, Miro, Sarah Bedov, Filip Mustac, and Ivan Jakovljevic. 2020. COVID-19 infodemic and public trust from the perspective of public and global mental health. Psychiatria Danubina 32: 449–57. [Google Scholar] [CrossRef]
- Keele, Luke. 2007. Social capital and the dynamics of trust in government. American Journal of Policitcal Science 51: 241–54. [Google Scholar] [CrossRef]
- Kim, Do Kyun David, and Gary Kreps. 2020. An analysis of government communication in the United States during the COVID-19 pandemic: Recommendations for effective government health risk communication. World Medical and Health Policy 12: 398–412. [Google Scholar] [CrossRef] [PubMed]
- Kreps, Gary, Kenneth Alibek, Linda Neuhauser, Katherine Rowan, and Lisa Sparks. 2005. Emergency/risk communication to promote public health and respond to biological threats. In Global Public Health Communications: Challenges, Perspectives, and Strategies. Edited by Muhiuddin Haider. Sudbury: Jones & Bartlett Publishers, pp. 349–62. [Google Scholar]
- Levi, Margaret, and Laura Stoker. 2000. Political trust and trustworthiness. Annual Review of Political Science 3: 475–507. [Google Scholar] [CrossRef]
- Liu, Brooke Fisher, J. Suzanne Horsley, and Kaifeng Yang. 2012. Overcoming negative media coverage: Does government communication matter? Journal of Public Administration Research and Theory 22: 597–621. [Google Scholar] [CrossRef]
- McDermott, Monika L., and David R. Jones. 2020. Gender, sex, and trust in government. Politics & Gender, 1–24. [Google Scholar] [CrossRef]
- Newton, Kenneth. 2020. Government communications, political trust and compliant social behaviour: The politics of COVID-19 in Britain. The Political Quarterly 91: 502–13. [Google Scholar] [CrossRef] [PubMed]
- OECD. 2013. Trust in government, policy effectiveness and the governance agenda. In Government at a Glance. Paris: OECD Publishing. [Google Scholar] [CrossRef]
- Oude Groeniger, Joost, Kjell Noordzij, Jeroen Van Der Waal, and Willem De Koster. 2021. Dutch COVID-19 lockdown measures increased trust in government and trust in science: A difference-in-differences analysis. Social Science & Medicine 275: 113819. Available online: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/ (accessed on 12 September 2021).
- Pak, Anton, Emma McBryde, and Oyelola A. Adegboye. 2021. Does high public trust amplify compliance with stringent covid-19 government health guidelines? A multi-country analysis using data from 102,627 individuals. Risk Management and Healthcare Policy 14: 293–302. [Google Scholar] [CrossRef]
- Pew Research Center. 2019. Trust and Distrust in America. Washington, DC: Pew Research Center, July 22, Available online: https://www.pewresearch.org/politics/2019/07/22/trust-and-distrust-in-america/ (accessed on 12 September 2021).
- Reinhardt, G. 2015. First-hand experience and second-hand information: Changing trust across three levels of government. The Review of Policy Research 32: 345–64. [Google Scholar] [CrossRef] [Green Version]
- Ruffolo, Mary, Daicia Price, M. Schoultz, Janni Leung, Tore Bonsaksen, Hilde Thygesen, and Amy Østertun Geirdal. 2021. Employment uncertainty and mental health during the COVID-19 pandemic initial social distancing implementation: A cross-national study. Global Social Welfare. [Google Scholar] [CrossRef]
- Schwerter, Frederik, and Florian Zimmermann. 2020. Determinants of trust: The role of personal experiences. Games and Economic Behavior 122: 413–25. [Google Scholar] [CrossRef]
- Seeger, Matthew, Laura Pechta, Simani Price, Keri Lubell, Dale Rose, Saloni Sapru, Melanie Chansky, and Belinda Smith. 2018. A conceptual model for evaluating emergency risk communication in public health. Health Security 16: 193–203. [Google Scholar] [CrossRef]
- Skidmore, Max J. 2016. Presidents, Pandemics, and Politics. New York: Palgrave Macmillan. [Google Scholar]
- Smith, Maxwell J., and Diego S. Silva. 2015. Ethics for pandemics beyond influenza: Ebola, drug-resistant tuberculosis, and anticipating future ethical challenges in pandemic preparedness and response. Monash Bioethics Review 33: 130–47. [Google Scholar] [CrossRef] [PubMed]
- Trumbo, Craig W., and Katherine A. McComas. 2003. The function of credibility in information processing for risk perception. Risk Analysis: An International Journal 23: 343–53. [Google Scholar] [CrossRef] [PubMed]
- Wachinger, Gisela, Ortwin Renn, Chloe Begg, and Christian Kuhlicke. 2013. The risk perception paradox—Implications for governance and communication of natural hazards. Risk Analysis: An International Journal 33: 1049–65. [Google Scholar] [CrossRef] [PubMed]
- Wong, Catherine Mei Ling, and Olivia Jensen. 2020. The paradox of trust: Perceived risk and public compliance during the COVID-19 pandemic in Singapore. Journal of Risk Research 23: 1021–30. [Google Scholar] [CrossRef]
Total Sample (n = 3474) | USA (n = 2130) | UK (n = 640) | Norway (n = 547) | Australia (n = 157) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Characteristics | Inform. | Financial | Inform. | Financial | Inform. | Financial | Inform. | Financial | Inform. | Financial |
Age group | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) |
18–29 years | 341 (53.9) | 184 (29.1) | 209 (51.5) | 86 (21.2) | 35 (28.7) | 34 (27.9) | 80 (93.0) | 54 (63.5) | 17 (89.5) | 10 (52.6) |
30–39 years | 365 (51.7) | 217 (30.8) | 220 (46.1) | 104 (21.8) | 38 (35.2) | 35 (32.4) | 90 (89.1) | 63 (63.0) | 17 (85.0) | 15 (75.0) |
40–49 years | 266 (47.4) | 197 (35.1) | 106 (36.4) | 63 (21.6) | 43 (33.6) | 44 (34.4) | 103 (84.4) | 77 (63.1) | 14 (70.0) | 13 (65.0) |
50–59 years | 235 (53.3) | 167 (38.0) | 62 (31.5) | 37 (18.9) | 49 (43.8) | 39 (34.8) | 100 (98.0) | 73 (72.3) | 24 (80.0) | 18 (60.0) |
60–69 years | 210 (47.4) | 150 (33.9) | 90 (31.9) | 51 (18.1) | 26 (49.1) | 19 (35.8) | 69 (92.0) | 59 (78.7) | 25 (75.8) | 21 (63.6) |
70 years + years | 151 (52.2) | 120 (42.0) | 67 (34.5) | 49 (25.5) | 14 (60.9) | 11 (47.8) | 60 (98.4) | 51 (85.0) | 10 (90.9) | 9 (81.8) |
p | 0.13 | 0.001 | <0.001 | 0.47 | <0.01 | 0.52 | <0.01 | <0.01 | 0.57 | 0.57 |
Gender identity | ||||||||||
Male | 312 (44.3) | 216 (30.7) | 137 (30.5) | 73 (16.3) | 45 (41.7) | 34 (31.5) | 112 (94.9) | 95 (81.2) | 18 (60.0) | 14 (46.7) |
Female | 1242 (53.4) | 814 (35.1) | 606 (44.5) | 313 (23.0) | 157 (36.0) | 146 (33.5) | 389 (91.5) | 282 (66.8) | 90 (88.2) | 73 (71.6) |
p | <0.001 | <0.05 | <0.001 | <0.01 | 0.28 | 0.69 | 0.22 | <0.01 | <0.001 | <0.05 |
Education level | ||||||||||
High school/tech. degree or lower | 368 (43.1) | 250 (29.3) | 144 (29.6) | 77 (15.8) | 74 (38.7) | 71 (37.2) | 121 (85.8) | 81 (57.9) | 29 (80.6) | 21 (58.3) |
Bachelor’s degree | 568 (51.6) | 357 (32.5) | 286 (42.8) | 139 (20.8) | 68 (35.8) | 58 (30.5) | 176 (90.7) | 131 (68.2) | 38 (77.6) | 29 (59.2) |
Master’s/doctoral degree | 645 (56.1) | 436 (38.1) | 334 (44.6) | 179 (25.1) | 63 (37.1) | 54 (31.8) | 205 (96.7) | 165 (78.2) | 43 (84.3) | 38 (74.5) |
p | <0.001 | <0.001 | <0.001 | 0.001 | 0.84 | 0.35 | 0.001 | <0.001 | 0.69 | 0.18 |
Size of place | ||||||||||
Rural/farming | 179 (38.4) | 126 (27.1) | 96 (30.8) | 64 (20.6) | 46 (41.4) | 41 (36.9) | 36 (85.7) | 21 (50.0) | -- | -- |
Town/suburb | 716 (49.2) | 457 (31.5) | 443 (42.5) | 226 (21.7) | 81 (41.1) | 79 (40.1) | 179 (90.9) | 140 (71.4) | 13 (72.2) | 12 (66.7) |
City | 687 (58.2) | 459 (39.1) | 225 (43.5) | 105 (20.4) | 79 (32.9) | 63 (26.3) | 287 (93.5) | 215 (70.7) | 96 (82.8) | 76 (65.5) |
p | <0.001 | <0.001 | <0.001 | 0.81 | 0.14 | <0.01 | 0.17 | <0.05 | 0.50 | 0.39 |
Employment | ||||||||||
Full-time or part-time | 469 (47.4) | 307 (31.2) | 540 (43.7) | 273 (22.1) | 141 (35.0) | 131 (32.5) | 149 (87.6) | 271 (72.1) | 74 (81.3) | 59 (64.8) |
No employment | 1108 (52.6) | 734 (34.9) | 220 (35.1) | 120 (19.3) | 65 (43.6) | 52 (34.9) | 353 (93.6) | 106 (63.5) | 35 (79.5) | 29 (65.9) |
p | <0.01 | <0.05 | <0.001 | 0.16 | 0.06 | 0.60 | <0.05 | <0.05 | 0.81 | 0.90 |
Infected | ||||||||||
Infected | 74 (32.9) | 51 (22.7) | 46 (31.5) | 27 (18.5) | 20 (30.3) | 17 (25.8) | 7 (70.0) | 6 (60.0) | -- | -- |
Not infected | 1506 (43.3) | 991 (34.5) | 717 (41.6) | 368 (21.4) | 186 (38.4) | 166 (34.2) | 495 (92.2) | 371 (69.6) | 108 (81.8) | 86 (65.2) |
p | <0.001 | <0.001 | <0.05 | 0.41 | 0.21 | 0.17 | <0.05 | 0.51 | 0.10 | 0.29 |
Social media use | ||||||||||
<10 min | 51 (66.2) | 38 (50.7) | 6 (25.0) | 4 (16.7) | 7 (50.0) | 3 (21.4) | 36 (100.0) | 30 (88.2) | -- | -- |
10–30 min | 142 (52.2) | 116 (42.8) | 49 (33.8) | 32 (22.1) | 19 (40.4) | 20 (42.6) | 65 (94.2) | 55 (80.9) | 9 (81.8) | 9 (81.8) |
31–60 min | 249 (50.7) | 192 (39.1) | 86 (32.3) | 57 (21.4) | 41 (45.6) | 38 (42.2) | 100 (92.6) | 78 (72.2) | 22 (81.5) | 19 (70.4) |
1–2 h | 475 (55.4) | 322 (37.6) | 224 (45.0) | 129 (26.0) | 46 (35.1) | 40 (30.5) | 174 (92.1) | 131 (69.3) | 31 (79.5) | 22 (56.4) |
2–3 h | 253 (44.7) | 134 (23.7) | 192 (44.9) | 80 (18.7) | 36 (33.6) | 33 (30.8) | 1 (50.0) | 2 (100.0) | 24 (82.8) | 19 (65.5) |
<3 h | 361 (50.3) | 208 (29.1) | 180 (42.0) | 80 (18.7) | 39 (31.0) | 34 (27.0) | 126 (88.1) | 81 (57.0) | 16 (80.0) | 13 (65.0) |
p | <0.001 | <0.001 | 0.001 | 0.07 | 0.24 | 0.11 | 0.05 | 0.001 | 0.99 | 0.52 |
Independent Variables | Total Sample | USA | UK | Norway | Australia |
---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Higher age | 0.97 (0.92–1.03) | 0.86 (0.79–0.91) *** | 1.17 (1.00–1.36) * | 1.21 (0.95–1.54) | 0.79 (0.52–1.20) |
Female gender | 1.41 (1.18–1.70) *** | 1.49 (1.16–1.90) ** | 0.79 (0.49–1.28) | 0.75 (0.30–1.91) | 8.60 (2.83–26.12) *** |
Bachelor’s degree education 1 | 1.25 (1.03–1.52) * | 1.48 (1.13–1.95) ** | 1.11 (0.68–1.79) | 1.18 (0.54–2.60) | 0.71 (0.19–2.59) |
Master’s/doctoral degree education 1 | 1.45 (1.20–1.77) *** | 1.86 (1.42–2.43) *** | 1.04 (0.65–1.66) | 2.59 (0.96–6.96) | 1.74 (0.43–7.00) |
Having employment | 1.02 (0.85–1.22) | 0.94 (0.73–1.19) | 0.68 (0.44–1.05) | 2.33 (1.10–4.94) * | 0.65 (0.18–2.30) |
Town/suburb 2 | 1.37 (1.09–1.71) ** | 1.31 (0.98–1.76) | 1.03 (0.61–1.72) | 1.78 (0.62–5.08) | - |
City 2 | 1.96 (1.55–2.48) *** | 1.28 (0.93–1.77) | 0.70 (0.41–1.19) | 2.18 (0.78–6.12) | - |
Infected | 0.46 (0.33–0.63) *** | 0.63 (0.42–0.96) * | 0.80 (0.44–1.46) | 0.15 (0.03–0.66) * | 0.19 (0.01–2.88) |
Social media use | 0.91 (0.86–0.96) ** | 1.05 (0.97–1.14) | 0.90 (0.78–1.04) | 0.85 (0.66–1.09) | 0.95 (0.62–1.44) |
Cox Snell R2 Nagelkerke R2 | 4.1% 5.4% | 5.3% 7.2% | 4.4% 5.9% | 5.2% 12.6% | 16.5% 26.5% |
Independent Variables | Total Sample | USA | UK | Norway | Australia |
---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Higher age | 1.10 (1.04–1.16) ** | 1.00 (0.92–1.09) | 0.99 (0.85–1.15) | 1.20 (1.04–1.39) * | 1.00 (0.73–1.37) |
Female gender | 1.31 (1.08–1.59) ** | 1.44 (1.07–1.94) * | 1.23 (0.74–2.04) | 0.56 (0.33–0.95) * | 3.15 (1.24–8.01) * |
Bachelor’s degree education 1 | 1.08 (0.88–1.34) | 1.33 (0.96–1.84) | 0.69 (0.42–1.12) | 1.22 (0.74–2.00) | 0.84 (0.30–2.38) |
Master’s/doctoral degree education 1 | 1.22 (0.99–1.49) | 1.57 (1.14–2.16) ** | 0.77 (0.48–1.24) | 1.71 (1.01–2.89) * | 1.69 (0.58–4.96) |
Having employment | 1.25 (1.03–1.51) * | 1.11 (0.83–1.48) | 0.87 (0.55–1.37) | 1.74 (1.08–2.81) * | 0.84 (0.31–2.30) |
Town/suburb 2 | 1.15 (0.90–1.47) | 0.89 (0.64–1.24) | 1.10 (0.66–1.84) | 2.45 (1.20–5.00) * | – |
City 2 | 1.68 (1.30–2.15) *** | 0.85 (0.58–1.23) | 0.57 (0.33–0.97)* | 2.34 (1.17–4.67) * | – |
Infected | 0.60 (0.42–0.85) ** | 0.88 (0.54–1.43) | 0.74 (0.39–1.38) | 0.59 (0.15–2.31) | 0.23 (0.02–3.41) |
Social media use | 0.82 (0.78–0.88) *** | 0.91 (0.83–1.00) | 0.87 (0.76–1.01) | 0.83 (0.72–0.96) * | 0.98 (0.71–1.36) |
Cox Snell R2 Nagelkerke R2 | 3.9% 5.4% | 1.3% 2.0% | 4.1% 5.7% | 9.3% 13.1% | 9.4% 13.1% |
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Price, D.; Bonsaksen, T.; Ruffolo, M.; Leung, J.; Chiu, V.; Thygesen, H.; Schoultz, M.; Geirdal, A.O. Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study. Soc. Sci. 2021, 10, 349. https://doi.org/10.3390/socsci10090349
Price D, Bonsaksen T, Ruffolo M, Leung J, Chiu V, Thygesen H, Schoultz M, Geirdal AO. Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study. Social Sciences. 2021; 10(9):349. https://doi.org/10.3390/socsci10090349
Chicago/Turabian StylePrice, Daicia, Tore Bonsaksen, Mary Ruffolo, Janni Leung, Vivian Chiu, Hilde Thygesen, Mariyana Schoultz, and Amy Ostertun Geirdal. 2021. "Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study" Social Sciences 10, no. 9: 349. https://doi.org/10.3390/socsci10090349
APA StylePrice, D., Bonsaksen, T., Ruffolo, M., Leung, J., Chiu, V., Thygesen, H., Schoultz, M., & Geirdal, A. O. (2021). Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study. Social Sciences, 10(9), 349. https://doi.org/10.3390/socsci10090349