Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour

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ARTICLE
Trust in government moderates the association
between fear of COVID-19 as well as empathic
concern and preventive behaviour
With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people
comply with (or not) large-scale cooperative activities. Here we investigated the motives that
underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from
34 countries. We hypothesized that the associations of empathic prosocial concern and fear
of disease with support towards preventive COVID-19 behaviours would be moderated by
trust in the government. Results suggest that the association between fear of disease and
support for COVID-19 preventive behaviours was strongest when trust in the government
was weak (both at individual- and country-level). Conversely, the association with empathic
prosocial concern was strongest when trust in the government was high, but this moderation
was only found at individual-level scores of governmental trust. We discuss how motivations
may be shaped by socio-cultural context, and outline how findings may contribute to a better
understanding of collective action during global crises.
https://doi.org/10.1038/s44271-023-00046-5 OPEN
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The official proclamation of the global coronavirus
(COVID-19) pandemic in March 20201was followed by
the institution of a variety of strategies to limit the spread
of the virus among governments all around the world. For
instance, in many places, lockdowns were initiated, masks were
mandated, social distancing enforced, while vaccination and
isolation requirements were imposed2. However, many citizens
rallied against these public health mandates and recommenda-
tions, motivated by pseudoscientific claims and conspiratorial
beliefs. Public mistrust in governmental measures and policies
grew as a consequence, while many refused to get a vaccine, or
practice safe and preventive behaviors to contain the spread of the
virus (including physical distancing, hand washing, or face
covering)3,4.
The scientific quest to illuminate reasons why citizens across
the world support or undermine safe and protective behaviors,
came as a natural response to the global pandemic. Current
evidence suggests that individuals may support or practice
behaviors that prevent the spread of COVID-19 either for their
self-interest (i.e., fear of becoming infected)5–12, or for other-
oriented prosocial reasons (i.e., fear of infecting others)13–16. Yet,
a deeper understanding of how these motives may interact with
their socio-cultural conditions is lacking.
Such understanding is relevant to identify effective ways to
mobilize support for COVID-19 preventive practices. In parti-
cular, research would benefit from gaining deeper insight on how
self- versus other-oriented concerns affect attitudes towards
preventive behaviors of people with different levels of govern-
mental trust (i.e., individuals with high versus low trust in the
government) and people living in different socio-cultural contexts
(where trust in the government is typically high versus low). The
present study addresses this call and tests how trust in the gov-
ernment (both on individual- and on country-level) may mod-
erate the relationship between self-oriented fear and other-
oriented empathic concerns regarding COVID-19 disease, with
supportive attitudes toward the practice of COVID-19 contain-
ment behaviors.
Mobilizing people to engage in large-scale collective activities is
a complex, multifaceted, and intricate problem. A core challenge
may be the often uncertain or insufficient incentives for truly
global cooperative activities. The incentives for large-scale coop-
eration do not become evident immediately and the level of
unpredictability is typically high. Namely, in large-scale coop-
eration it is often unclear whether one’s contribution will be
tracked, rewarded, reciprocated, or will produce any large-scale
benefit17,18. All of these are conditions that can aggravate coop-
eration, especially if it only serves altruistic goals.
However, cooperation does not only rely on altruistic reasons,
but can also be driven by selfish motives17,18, and this is corro-
borated by evidence from the latest pandemic. Namely, com-
pliance with COVID-19 mitigating behaviors is associated with
both other-oriented concerns such as empathy and
altruism5,7,10,19 and self-oriented concerns such as fear of
infection9,14,20. However, it is less clear how contextual condi-
tions will shape these motives, and under which conditions they
will be more (or less) strongly associated with COVID-19 com-
pliance. For instance, research with samples from Sweden, Ger-
many, United States of America (USA) and Norway suggests that
other-oriented concerns were more influential than fear of
COVID-19 in promoting adherence to containment
measures6,13,21,22. On the other hand, a study by Zirenko and
colleagues found that caring for oneself played a more important
role than caring for others when predicting individuals’decisions
to physically distance in Russia, Azerbaijan, and China23. Hence,
individuals in different contexts may all (more or less) comply
with COVID-19 containment measures, yet their reasons to do so
may be different depending on relevant (perceived) contextual
conditions, with trust in the government being one of the most
important factors.
Cooperation requires trust, especially when it involves unre-
lated others24–26 and when it revolves around large-scale colla-
borations. People are particularly prone to coalesce in larger
groups if they are motivated by trust toward key authority figures
who regulate social norms27 like government officials, state
representatives, or renowned politicians28–30. It is thus likely that
governmental trust is crucial in motivating joint cooperative
action to contain the COVID-19 pandemic. Yet, a notion that
higher trust in the government is associated with higher (like-
lihood for) engagement in COVID-19 containment behaviors
may not hold true unconditionally, despite the emergence of
some supportive evidence31–34. There is also evidence to suggest
that the relationship between trust in the government and
cooperative behaviors during the COVID-19 pandemic is less
clear and seems more complex35,36. For instance, research by
Clark and colleagues37 with data collected from a large interna-
tional sample found that trust in the government was not related
to how much people reported to adhere with governmental
recommendations, while it showed weak associations with taking
private health precautions. Hence, in the present study, we con-
ceptualized trust in the government as a boundary condition that
moderates the association between support for COVID-19 con-
tainment behaviors with other-oriented (i.e., empathic prosocial
concern) and self-oriented (i.e., fear of COVID-19) motives.
As outlined above, mobilizing people for large-scale and long-
term cooperative acts is challenging; mainly due to the high level
of uncertainty it involves. Some individuals (especially those who
feel less vulnerable to the virus) might even experience a social
dilemma whereby cooperating would require them to sacrifice
their concrete, short-term, and selfish interests (e.g., by avoiding
social events, by face masking, etc.) over the more abstract, less
traceable, and long-term goal of protecting other people from
infection. We argue that such a dilemma should become espe-
cially salient, and thus hinder empathy-driven cooperation, under
highly unpredictable conditions where one is doubtful about
whether or not individual sacrifices will promote any long-term
public benefit. The way in which people perceive their govern-
ments, namely whether they view them as competent, protective,
and caring –simply, whether they trust in their government or
not –may represent one of the factors that guide people’s per-
ceptions of (un)predictability. Namely, when people anticipate
their governments to be supportive and to take the needed
measures for protecting their citizens from any threat, individuals
would become more confident that their selfless and empathy-
driven activities will truly result in positive public health out-
comes. On the contrary, when there is little trust in the govern-
ment, unpredictability will increase, and individuals will doubt
whether their individual compliance with public health measures
will produce any public, other-oriented benefit. Even if respective
measures are implemented by the government, those with little
trust would be propelled to suspect that they may be implemented
in a superficial and non-transparent manner.
However, under such volatile conditions cooperation may still
occur, yet for different sets of reasons. The compliance with safe
and preventive COVID-19 behaviors is not only an act that may
be performed for the sake of protecting others from infection, but
also has direct (beneficial) implications on personal health and
may thus be considered as a self-protective act. In circumstances
where trust in the government is low, people would experience
higher uncertainty, feel more susceptible to the virus, and their
cooperation would be more strongly driven by selfish concerns
such as fear of COVID-19. Therefore, we propose that the rela-
tionship between empathic concern and support for COVID-19
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containment behaviors will be stronger when the government is
(generally) perceived as more (compared to less) trustworthy, and
that conversely the association between fear of disease and sup-
port for COVID-19 containment behaviors will be stronger when
the government is (generally) perceived as less (compared to
more) trustworthy.
The current study tests its premises with a large, cross-national
sample of 12,758 individuals recruited across 34 countries which
adds to its robustness, as cooperation against the COVID-19
pandemic is not only determined by individual factors, but also
shaped by the different socio-cultural conditions that exist across
various societies. Such factors range from more general cultural
and developmental differences such as differences in the content
and strictness of social norms, the level of education and
affluence; to more specific COVID-19 associated differences such
as differences in the health care system, the stringency of gov-
ernment policies, and the severity level of the COVID-19 pan-
demic. To embrace such diversity, the 34 nations involved in the
present research consist of both highly developed and affluent
countries, as well as less-developed and relatively poor
countries38, and represent both countries that were severely
affected by the COVID-19 pandemic at time of data collection, as
well as countries that were hardly affected by the pandemic
(Table 1). Hence, nested within such different cultural and socio-
political contexts, the present study proposes and tests the fol-
lowing pre-registered hypotheses (https://osf.io/k2wjr39).
Empathic prosocial concern is more strongly associated with
support for COVID-19 containment behaviors (e.g., physical
distancing, face masking, enhanced hygiene practices) among
individuals with higher levels of trust in the government com-
pared to individuals with lower levels of trust (Hypothesis 1a).
Conversely, fear of COVID-19 is more strongly associated with
the support for COVID-19 containment behaviors among indi-
viduals with low levels of trust in the government compared to
individuals with higher levels of trust (Hypothesis 2a).
Congruent with testing these two hypotheses on an individual
level, we further propose that country-level trust scores will
moderate the association between support for COVID-19 con-
tainment behaviors with empathy and fear motivations in such a
way that individuals’empathic concern is more strongly related
to supporting COVID-19 containment behaviors in contexts
where trust in the government is generally high compared to
contexts where trust is generally low (Hypothesis 1b); and that
individuals’fear of COVID-19 is more strongly related to sup-
porting COVID-19 containment behaviors in contexts where
trust in the government is generally low compared to contexts
where trust is generally high (Hypothesis 2b).
Methods
The current study protocol has been reviewed and approved by
the Institutional Review Board of Bahcesehir University (IRB
protocol number: E-8755). When not declared as exempt,
approvals have additionally been obtained from the local insti-
tutional review boards of all other involved countries. All ethical
guidelines were followed when conducting the present research.
Written informed consent to take part in the study was obtained
from each respondent prior to completing the research, and only
respondents who agreed to the study’s conditions were allowed to
proceed with the research.
The present research was first preregistered on September, 9th,
2021 under https://osf.io/k2wjr39; and has been updated on
March 29th, 2023, for the following reasons and aspects, respec-
tively. First, reviewers requested to avoid any directional language
in the hypotheses and to align the variable names more closely
with the measurement procedure and the labeling in the
manuscript. For this reason, the wording of the hypotheses has
been revised. The original hypotheses were preregistered as:
Governmental trust will moderate the effect of other-centered
motives on performing virus containment behaviors (socially
responsive COVID-19 behaviors and vaccination intentions) in
such a way that the effect will be stronger for respondents holding
high compared to low levels of trust (Hypothesis 1); and gov-
ernmental trust will moderate the effect self-centered motives on
performing virus containment behaviors, in such a way that the
effect will be stronger for respondents holding low compared to
high levels of trust (Hypothesis 2).
Second, the reviewers and the editor asked to preregister a
more concrete data analysis plan. Specifically, they asked for
adding relevant individual-level (e.g., gender and age) and
country-level control variables (e.g., number of COVID-19 cases)
when conducting the regression analysis. Hence, the analyses of
the present research were conducted by controlling for relevant
covariate effects. Further, the reviewers requested to conduct the
same analyses with generalized trust (both at individual and
country-level) to examine whether possible moderator effects are
unique to trust in the government or rather related to a more
general notion of trust. The results concerning these analyses are
presented under exploratory analysis. And finally, the reviewers
asked to check the robustness of the results against using a fixed
effects regression model with cluster-robust standard errors. The
results concerning this alternative model are presented under
Supplementary Note 1 and Supplementary Table 1. The pre-
registered analysis plan has been updated to meet these requests.
Procedure. The present study was spearheaded by the Research
Initiatives Working Group (RIWG) of the American Psycholo-
gical Association (APA) Interdivisional Task Force on the Pan-
demic, committed to the advancement of a knowledge base
through a repository and dissemination of materials and
resources40. The data collection was conducted within the fra-
mework of a large-scale collaborative project spanning across
different nations around the globe, which is named as Interna-
tional and Multidimensional Perspectives on the Impact of
COVID-19 across Generations (IMPACT-C19). The project
focused specifically on the impact, perceptions, and experiences of
COVID-19 among young people and established adults in an
international perspective41. Participating researchers were invited
to collect self-report online data that were created with survey
tools such as Google Forms or Qualtrics using the convenience
sampling method.
Participants. In accordance with our sampling strategy, the
minimal number of participants was approximately 150 adult
respondents per country. Namely, we conducted an a-priori
power analysis using G*Power 3.1.942, assuming a medium effect
size of f2=0.10, targeting a power of 0.95 and an alpha level of
0.05, which suggested a sample size of 158 per collection site
(country).
The raw dataset without any data exclusions by January 20th,
2022, comprised data of 27,787 responses. We first deleted
responses from all respondents that were aged below 18 or did
not indicate any age (N=4951); and then list-wise excluded all
data with any missing values on the study variables (N=8161).
Finally, data from 22 countries with less than 150 complete
responses were removed (N=1917) resulting in the final dataset
based on which the analyses were performed. These countries
were Czech Republic, Slovenia, Taiwan, USA, Costa Rica, Niger,
Zambia, Zimbabwe, Afghanistan, Dominican Republic, Uganda,
Mozambique, Argentina, Kazakhstan, Kosovo, Albania, North
Macedonia, Armenia, Guatemala, Bosnia Herzegovina, Qatar,
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and Chile. The raw dataset is made available at https://osf.io/
kws9x/files/43. The final dataset consisted of N=12,758 indivi-
dual responses (M
age
=26.9; 67% women) from adult partici-
pants, collected over a period of approximately one year (from
February 2021 to December 2021) across 34 different nations
(Table 2). Participation to this research was voluntary and no
monetary compensation was given to respondents for completing
this research.
Measures. In terms of socio-demographic background variables,
respondents were asked to indicate their age, educational degree
and gender, with the following answer options for the latter: man,
woman, none of the above, and non-binary. For descriptive
reasons, respondents were further asked to report their nation-
ality and their ethnicity, in the format of an open-ended question.
No exclusions or additional analyses were made on the basis of
respondents’gender identification, nationality or ethnicity; and
none of the socio-demographic background questions was man-
datory to answer.
To assess concepts associated with COVID-19 and its impact
both already established and newly developed scales were
administered. Please note that the entire survey also assessed
concepts that go beyond the scope of the present research.
Examples of the concepts are COVID-19 threat perception,
national identity, hope, mindfulness, and religiousness. The
survey was first developed in English and then translated and
adapted to the local contexts (where necessary) by using the
committee approach44,45. The survey was anonymous and
completed on a voluntary basis (within 25 min approximately).
The measures analyzed for the purpose of this study were: trust
in the government, fear of COVID-19, empathic prosocial
concern, and support for COVID-19 containment behaviors. The
entire survey containing all scales is available at https://osf.io/
kws9x/46.
Table 1 Cultural, socio-political and COVID-19 associated differences between the countries of the present study.
Human Development Index (HDI) (2021) Hospital
beds per
1000,
2017–2020
Month of
data
collection
start in
2021
Govern-
ment
Strin-
gency
level
Daily
new con-
firmed
COVID-
19 cases
Daily
new con-
firmed
COVID-
19
deaths
Cultural
tightness
HDI
Total
Score
Life
expec-
tancy
at birth
Expected
years of
schooling
Mean
years of
schooling
Gross
National
Income
(GNI) per
capita in
USD
Australia 0.944 83.4 22.0 12.7 48,085 3.84 April 46.76 0.52 0 −0.05
Bangladesh 0.632 72.6 11.6 6.2 4976 0.79 April 83.33 31.32 0.27 –
Brazil 0.765 75.9 15.4 8.0 14,263 2.09 March 67.13 262.23 5.72 −0.38
Bulgaria 0.816 75.1 14.4 11.4 23,325 7.45 March 53.70 228.58 7.77 –
Colombia 0.767 77.3 14.4 8.5 14,257 1.71 Sept. 46.30 38.82 1.51 −0.58
Croatia 0.851 78.5 15.2 11.4 28,070 5.54 Sept. 33.80 133.63 0.98 –
Cuba 0.783 78.8 14.3 11.8 8621 5.33 July 65.28 241.8 1.17 –
Cyprus 0.887 81.0 15.2 12.2 38,207 3.40 May 75.00 850.6 2.39 –
Ecuador 0.759 77.0 14.6 8.9 11,044 1.39 May 75.46 94.51 4.52 −0.18
El Salvador 0.673 73.3 11.7 6.9 8359 1.20 Aug. 32.41 42.27 1.82 –
Georgia 0.812 73.8 15.3 13.1 14,429 2.89 Oct. 47.22 394.85 8.15 –
Germany 0.947 81.3 17.0 14.2 55,314 8.00 May 75.00 224.56 2.79 0.13
Honduras 0.634 75.3 10.1 6.6 5308 0.64 April 82.41 60.22 1.35 –
India 0.645 69.7 12.2 6.5 6,681 0.53 May 73.61 266.28 2.38 0.73
Indonesia 0.718 71.7 13.6 8.2 11,459 1.04 April 71.76 18.25 0.5 0.5
Iran 0.783 76.7 14.8 10.3 12,447 1.56 Aug. 68.06 356.33 3.69 0.38
Israel 0.919 83.0 16.2 13.0 40,187 2.98 May 52.78 7.8 0.2 −0.40
Japan 0.919 84.6 15.2 12.9 42,932 12.98 May 49.07 39.94 0.45 0.19
Lebanon 0.744 78.9 11.3 8.7 14,655 2.73 Dec. 31.48 216.93 1.25 –
Lithuania 0.882 75.9 16.6 13.1 35,799 6.43 March 66.67 194.12 4.04 –
Mexico 0.779 75.1 14.8 8.8 19,160 0.98 Feb. 71.76 107.44 9.68 −0.35
New Zealand 0.931 82.3 18.8 12.8 40,799 2.57 July 22.22 0.47 0 –
Pakistan 0.557 67.3 8.3 5.2 5005 0.63 July 63.89 4.3 0.12 –
Peru 0.777 76.7 15.0 9.7 12,252 1.59 Feb. 80.56 189.71 16.68 −0.34
Philippines 0.718 71.2 13.1 9.4 9778 0.99 July 71.76 51.56 0.98 –
Poland 0.880 78.7 16.3 12.5 31,623 6.54 March 73.15 261.22 6.07 −0.32
Portugal 0.864 82.1 16.5 9.3 33,967 3.45 Feb. 76.85 1169.09 28.61 0.10
Serbia 0.806 76.0 14.7 11.2 17,192 5.61 Feb. 56.48 246.9 2.77 –
Singapore 0.938 83.6 16.4 11.6 88,155 2.49 March 50.93 1.81 0 0.36
Slovakia 0.860 77.5 14.5 12.7 32,113 5.70 April 74.07 492.18 12.51 −0.36
Turkey 0.820 77.7 16.6 8.1 27,701 2.85 May 87.04 433.38 4.19 0.29
Ukraine 0.779 72.1 15.1 11.4 13,216 7.46 Sept. 50.93 49.81 1.48 −0.44
United Kingd. 0.932 81.3 17.5 13.2 46,071 2.46 Dec. 46.76 634.01 1.79 −0.21
Vietnam 0.704 75.4 12.7 8.3 7433 2.60 May 69.91 0.16 0 0.39
Notes: Higher Scores on HDI (Human Development Index) represent a higher level of development; scores were extracted from https://hdr.undp.org/data-center/documentation-and-downloads55.
Data on hospital beds per thousand were extracted from https://ourworldindata.org/grapher/hospital-beds-per-1000-people75. Higher scores on government stringency represent more stringent and
severe COVID-19 restrictions; each country’s score represents the average stringency level for the month where data collection started; data on stringency level were extracted from https://covidtracker.
bsg.ox.ac.uk/stringency-map76. The scores for daily new COVID-19 cases and deaths represent a 7-days rolling average per million people, assessed at the start of data collection; data were extracted
from https://ourworldindata.org/explorers/coronavirus-data-explorer?uniformYAxis=0&pickerSort=asc&pickerMetric=location&Metric=Cases+and+deaths&Interval=7-day+rolling
+average&Relative+to+Population=true&Color+by+test+positivity=false77. Higher scores on cultural tightness represent more tight cultures; scores for cultural tightness were extracted from
https://www.thelancet.com/action/showPdf?pii=S2542-5196%2820%2930301-678.
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Table 2 Participating countries and their descriptive statistics.
Country NGender (% of
women; men;
& other/non-
binary)
Mean
age
(years)
Individual-level
trust in
government;
M(SD) (1 =low;
5=high)
Fear of
COVID-19;
M(SD)
(1 =low;
5=high)
Empathic
prosocial
concern;
M(SD)
(1 =low;
7=high)
Support for COVID-
19 containment
Beh. M(SD)
(1 =low; 5 =high)
Country-level
trust in
government; %
with high trust
Australia 301 76% f; 23%
m; 1% o
23.3 2.72 (0.81) 2.06 (0.80) 5.51 (1.31) 3.27 (0.77) 30.3
Bangladesh 714 53% f; 47%
m
22.7 3.04 (0.94) 3.21 (0.79) 5.29 (1.23) 4.30 (0.73) 81.1
Brazil 502 73% f; 26%
m; 1% o
33.6 1.62 (0.81) 2.97 (0.87) 6.00 (1.12) 4.30 (0.56) 22.4
Bulgaria 294 63% f; 37%
m
38.3 2.49 (1.08) 2.34 (0.88) 5.13 (1.12) 3.46 (1.00) 19.9
Colombia 339 62% f; 38%
m
24.2 2.62 (0.97) 2.62 (0.81) 5.66 (1.10) 3.94 (0.62) 11.9
Croatia 540 63% f; 37%
m
25.4 2.23 (0.86) 1.83 (0.71) 4.82 (1.41) 2.47 (0.75) 9.6
Cuba 301 52% f; 48%
m
24.1 2.60 (1.13) 2.67 (0.98) 4.42 (1.73) 3.58 (1.03) –
Cyprus 173 66% f; 34%
m
37.1 2.33 (0.96) 2.30 (0.77) 5.77 (1.00) 3.59 (0.77) 33.4
Ecuador 199 67% f; 33%
m
27.5 2.51 (0.98) 2.82 (0.96) 5.21 (1.26) 4.00 (0.66) 31.8
El Salvador 261 58% f; 42%
m
27.7 2.12 (0.99) 2.69 (0.90) 5.30 (1.32) 4.03 (0.56) –
Georgia 179 83% f; 17% m 25.6 2.26 (1.00) 2.49 (0.85) 5.50 (1.20) 3.73 (0.75) 37.4
Germany 930 77% f; 22%
m; 1% o
31.1 2.79 (0.87) 1.86 (0.70) 5.31 (1.26) 3.00 (0.76) 44.2
Honduras 718 62%f; 38% m 26.0 1.52 (0.88) 2.78 (1.03) 5.05 (1.70) 4.09 (0.84) –
India 251 36% f; 64%
m
21.2 3.42 (0.89) 3.04 (0.84) 5.65 (0.80) 4.53 (0.52) –
Indonesia 196 76% f; 24%
m
23.1 2.88 (0.75) 2.72 (0.75) 5.59 (1.03) 4.13 (0.49) 78.8
Iran 245 61% f; 39%
m
31.5 2.15 (0.98) 2.46 (0.86) 5.60 (1.11) 4.12 (0.72) 51.7
Israel 201 50% f; 50%
m
30.1 2.45 (0.98) 2.00 (0.88) 5.32 (1.55) 3.66 (0.87) –
Japan 874 52% f; 48%
m
36.3 2.28 (0.83) 2.84 (0.80) 4.28 (1.39) 3.79 (0.60) 39.9
Lebanon 208 73% f; 27%
m
25.4 1.24 (0.56) 2.27 (0.81) 5.49 (1.30) 3.50 (0.79) 19.8
Lithuania 342 81% f; 19% m 32.0 2.67 (0.84) 2.19 (0.79) 4.62 (1.29) 3.59 (0.77) 39.0
Mexico 205 87% f; 13% m 24.1 1.59 (0.74) 2.78 (0.99) 5.81 (1.27) 4.30 (0.48) 17.4
New Zealand 188 80% f; 19%
m; 2% o
19.6 3.14 (0.81) 2.13 (0.78) 5.32 (1.33) 2.82 (0.81) 50.0
Pakistan 218 93% f; 7% m 21.3 3.12 (0.85) 2.62 (0.83) 4.82 (1.29) 3.08 (1.22) 62.3
Peru 705 72% f; 28%
m
24.2 1.99 (0.88) 3.03 (1.04) 5.70 (1.38) 4.32 (0.73) 10.6
Philippines 475 64% f; 36%
m
21.4 2.39 (1.00) 3.31 (0.84) 6.00 (1.01) 4.46 (0.43) 81.6
Poland 923 59% f; 41%
m
27.4 1.57 (0.79) 2.11 (0.78) 4.74 (1.40) 3.38 (0.88) 23.1
Portugal 185 69% f; 30%
m; 1% o
39.2 2.69 (0.99) 2.51 (0.86) 5.98 (1.02) 4.35 (0.54) 34.4
Serbia 569 85% f; 15%
m
22.6 2.07 (0.83) 2.12 (0.74) 5.50 (1.29) 3.11 (0.83) 28.7
Singapore 347 71% f; 29%
m
25.4 3.49 (0.68) 2.10 (0.69) 5.08 (1.23) 3.32 (0.58) 80.6
Slovakia 152 63% f; 37%
m
18.4 2.28 (0.82) 2.26 (0.74) 4.92 (1.35) 3.42 (0.83) 30.4
Turkey 405 80% f; 20%
m
21.9 1.89 (0.98) 2.64 (0.96) 5.36 (1.09) 4.21 (0.61) 68.8
Ukraine 167 63%f; 37% m 20.2 2.40 (1.00) 2.07 (0.88) 4.43 (1.65) 2.92 (0.89) 18.9
United Kingd. 152 77% f, 18%
m, 5% o
24.3 1.96 (0.85) 2.33 (0.81) 5.69 (1.23) 3.08 (0.92) 29.3
Vietnam 286 67% f; 33%
m
23.2 3.60 (0.71) 3.04 (0.85) 4.68 (1.21) 4.28 (0.59) 92.9
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Trust in the government (individual level) was assessed with
four items adapted from Kerr and colleagues47. Respondents were
asked to rate items (e.g., My government is there for me when I
need it) on a five-point Likert scale ranging (from 1=strongly
disagree to 5=strongly agree). The internal consistency was
calculated as Cronbach’s alpha =0.95 for the total sample, and
ranged between 0.85 and 0.97 across the different countries.
The country-level scores for trust in the government were
extracted from the most recent wave of the World Values
Survey48. The World Values Survey asks nationally representative
respondents to indicate their level of confidence in their
government with answer options ranging from none at all, not
very much,toquite a lot, and a great deal (additionally there were
the answer options don’t know and no answer). The data used for
the present analyses were all collected in 2017 and 2020. We used
the sum of the percentage of respondents who reported to have a
great deal and quite a lot of confidence in their governments as an
indicator of the general level of governmental trust in the
different study contexts, (for a similar procedure see ref. 49, for
summary of our results see Table 2).
The three items from the research by Pfatteicher and
colleagues19 were used to assess people’s empathic concern for
others in times of the COVID-19 pandemic. Using a Likert scale
ranging from 1=strongly disagree to 7=strongly agree, respon-
dents were asked to indicate how much they agree, for instance,
with the following statement: I am very concerned about those
most vulnerable to COVID-19. Cronbach’s alpha for the overall
sample was 0.91, and ranged between 0.63 and 0.97 across the
countries.
The seven-item fear of COVID-19 scale50 was used to assess
respondents’fear. On a five-point Likert scale ranging from
1=strongly disagree to 5=strongly agree respondents were asked
to indicate how much they agree with, such as for instance: I am
most afraid of COVID-19. Cronbach’s alpha was. 90 for the total
sample, and ranged between 0.83 and 0.93 across the different
countries.
Support for COVID-19 containment behaviors was used to
assess participants’engagement in COVID-19 containment
behaviors, an extended version of the measure developed by
Tepe and Karakulak51 was used. With 13 items, respondents were
asked to report how important it is to engage in a particular
COVID-19 preventive behavior (such as wearing a mask, not
going outside, trying to stay at home, or frequently washing
hands) in the examined period. Response options ranged from
1=not at all important to 5=very important. Internal
consistencies were calculated as Cronbach’s alpha =0.94 for the
total sample, and ranged between 0.81 and 0.95 across the
different countries.
Data analysis. The hypotheses of the present research were tested
with two separate multi-level regression analyses using the
maximum likelihood estimator. First a stepwise linear regression
nested within the study countries was carried out to test the
proposed associations on an individual-level; and second a
mixed-level stepwise linear regression was carried out to test the
proposed interaction with country-level scores of trust in the
government. The regression analyses were carried out across four
steps. First, a null model without any predictors, and only the
criterion variable, was carried out. Second, a fixed-effects model
that specified random intercepts and fixed slopes for the main
effects of the predictor variables was calculated. Third, a random
slopes model testing the predictors’main effects was carried out;
and finally, the hypothesized two-way interactions were added to
the random slopes regression model. All predictor variables
entered into the model were group-mean centered, except for the
country-level score of trust in the government which was grand-
mean centered. All analyses (from the second step and onwards)
were conducted by entering covariate effects of respondents’age
and their gender, and country-level scores of HDI, number of
hospital beds per 1000, month of data collection, government
stringency level, and the number of new daily COVID-19 cases
and deaths by the time of data collection. Due to the large sample
size of the present study, the data distribution was not formally
tested for normality, but assumed to be normal. The descriptive
statistics, histogram and Q-Q- plot of the dependent variable are
presented in the Supplementary Information under Supplemen-
tary Note 2, Supplementary Table 2, Supplementary Figs. 1 and 2.
Exploratory analysis. We conducted additional analyses to
examine (1) whether our findings remain robust when general-
ized trust is added as another covariate to the analyses, and (2)
whether the proposed moderation effects can also be obtained
with generalized (instead of governmental) trust. The scores for
individual-level generalized trust were obtained by averaging the
scores that respondents provided to the following two questions:
“In general, most people in our community can be trusted”, and
“Most people in our community are fair and do not take
advantage of you”(1 =totally disagree,5=totally agree,
r=0.78). The scores for country-level generalized trust were
extracted from the most recent wave of the World Values
Survey48.
Reporting summary. Further information on research design is
available in the Nature Portfolio Reporting Summary linked to
this article.
Results
We first tested our hypotheses at the individual level with the
pooled individual-level data nested within countries, and then ran
an additional test with country-level scores of trust in the gov-
ernment extracted from the World Values Survey48 as a moder-
ating factor. Analyses were conducted with the program jamovi
2.052.
Individual level analyses. The overall dataset comprises complete
responses from 12,758 individuals living in 34 countries. Table 2
illustrates the number of responses, information about partici-
pants’age and gender, and the descriptive statistics of the study
variables per country. Table 3shows the Pearson product-
moment correlations for the study variables based on the pooled
dataset.
We conducted a multi-level linear regression model to estimate
individual-level differences, nested within 34 countries. Results
from this analysis using the maximum likelihood estimator are
summarized in Table 4. First, a null model without any
predictors, and the support for COVID-19 containment beha-
viors as the only criterion variable, was implemented53. In this
model the ICC(1) for the criterion variable was 0.34, suggesting
that 34% of the variance in supporting COVID-19 containment
behaviors existed between the different countries, which justifies
the use of a mixed-level approach that takes this between-level
variance into account. In the second step, we ran a fixed-effects
model that specified random intercepts and fixed slopes for the
main effects of the predictor variables: individuals’trust in their
governments, their empathic prosocial concern, and their fear of
COVID-19. We used group-mean centering for these scores as
grand-mean centering creates inappropriate level-1 estimators
that reflect a mixture of within and between group variations54.
Additionally, to account for possible socio-demographic, country-
and pandemic-specific effects, we entered respondents’age and
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their gender, and country-level scores of the Human Develop-
ment Index (HDI, a composite score reflecting the level of a
country’s overall development in the domains of economy,
education, and health)55, number of hospital beds per 1000,
month of data collection, government stringency level, and the
number of new daily COVID-19 cases and deaths by the time of
data collection as covariates to the analyses. The analyses
suggested that a higher level of trust in the government
(B(SE) =0.05 (0.01), 95% CI =[0.04, 0.06], t(12705.1) =7.60,
p< 0.001, Cohen’sf=0.07), more empathic prosocial concern
(B(SE) =0.15 (0.05), 95% CI =[0.14, 0.16], t(12705.3) =32.04,
p< 0.001, Cohen’sf=0.28), and stronger fear of COVID-19
(B(SE) =0.29 (0.01), 95% CI =[0.27, 0.30], t(12705.3) =39.94,
p< 0.001, Cohen’sf=0.35) were all significantly associated with
stronger support for COVID-19 containment behaviors.
In the next step (step 3), we repeated the same analysis using
the random slopes model whereby the slopes (the strength of the
main effects) of trust in the government, empathic concern and
fear of COVID-19 were allowed to vary across clusters. This is
because the different preconditions that exist across countries
(i.e., number of daily infections, mortality rates, lockdown
regulations, etc.) may affect how strongly support for COVID-
19 containment behaviors is associated with trust in the
government, empathic prosocial concern, and fear of COVID-
19. Results again confirmed that all three predictor variables were
significantly and positively associated with support for COVID-
19 containment behaviors, (B(SE) =0.05 (0.01), 95% CI =[0.03,
0.08], t(34.5) =4.11, p< 0.001, and Cohen’sf=0.66 for trust in
the government; B(SE) =0.14 (0.01), 95% CI =[0.12, 0.15],
t(30.4) =18.08, p< 0.001, and Cohen’sf=3.17 for empathic
concern; and B(SE) =0.30 (0.02), 95% CI =[0.26, 0.35],
t(33.1) =12.71, p< 0.001, and Cohen’sf=2.14 for fear of
COVID-19).
In step four, the hypothesized two-way interactions were added
to the regression model. First, we compared the deviance scores
(calculated as -2*loglikelihood) of the random coefficient model
with the main effects only (step 3; null model) with the random
coefficients model that also included the two-way interactions
(step 4; alternative model) to test whether one or the other model
explained significantly more variance56. The deviance score of the
null model was 24,864. The deviance score of the alternative
model including the interactions was smaller with 24,789. Results
of the Chi-square test suggest that the difference between these
two models was significant [χ2(3, N=12,758) =75, p< 0.001],
indicating that the model including the interactions was
significantly better at explaining the variance in supporting
COVID-19 containment behaviors. The results for this regression
model show that individuals’trust in their government
significantly interacted with both the individuals’empathic
prosocial concern (B(SE) =0.01 (0.005), 95% CI =[0.003, 0.02],
t(12352.8) =2.56, p=0.011, Cohen’sf=0.02), and their fear of
COVID-19 (B(SE) =-0.02 (0.01), 95% CI =[-0.03, -0.005],
t(12435.6) =-2.64, p=0.008, Cohen’sf=0.02). We decomposed
the significant interactions and examined the associations
between support for COVID-19 with empathic prosocial concern
and fear of COVID-19 under low (-1SD), medium (mean) and
high ( +1SD) levels of trust in the government. Empathic
prosocial concern was significantly associated with the support
for COVID-19 containment behaviors across all three levels of
trust in the government. As illustrated in Fig. 1, the association of
empathic prosocial concern and support for COVID-19 contain-
ment measures was strongest when trust in the government was
high (β=0.14, t(59.0) =15.8, p< 0.001, 95% CI [0.12, 0.16],
Cohen’sf=2.01), and weakest when trust in the government was
low (β=0.12, t(47.5) =14.1, p< 0.001, 95% CI [0.10, 0.14],
Cohen’sf=1.99), which supports Hypothesis 1a. Fear of
COVID-19 was also significantly associated with support for
COVID-19 containment behaviors at all levels of trust in the
government. Conversely to empathic concern, the association of
fear of COVID-19 and support for COVID-19 containment
behaviors was strongest when trust in the government was low
(β=0.32, t(38.1) =13.0, p< 0.001, 95% CI [0.27, 0.37], Cohen’s
f=2.04), and weakest when trust in the government was high
(β=0.29, t(38.1) =11.6, p< 0.001, 95% CI [0.24, 0.34], Cohen’s
f=1.83), which supports Hypothesis 2a (Fig. 2).
Mixed-level analyses with country-level scores of trust in the
government. We repeated the same regression analysis as
described above, using country-level scores for trust in the gov-
ernment (see Table 5). The mixed-level linear regression analysis
comprised the data of 11,026 responses from 29 countries (the
data from five countries that did not participate in the World
Values Survey had to be excluded from the analyses. These
countries were Honduras, India, Israel, El Salvador, and Cuba).
The null model without any predictor variables was the same as
in the previous analysis and showed that 34% of the variance in
the support for COVID-19 containment behaviors existed
between the different countries, ICC(1) =0.34. In the second
step, we ran a fixed-effects model and entered the main effects of
the above-described covariates and the predictor variables into
our model: group-mean centered scores of empathic prosocial
concern and fear of COVID-19 were used, while the country-level
trust in the government score was grand-mean centered. The
results show that both empathic prosocial concern (B(SE) =0.15
(0.01), 95% CI =[0.14, 0.16], t(10978.2) =29.67, p< 0.001,
Cohen’sf=0.28) and fear of COVID-19 (B(SE) =0.29 (0.01),
95% CI =[0.28, 0.31], t(10978.2) =37.17, p< 0.001, Cohen’s
f=0.35) were significantly associated with supporting COVID-
19 containment behaviors, whereas no evidence was found for an
association with country-level trust in the government
(B(SE) =0.004 (0.004), 95% CI =[-0.03, 0.01], t(29.1) =1.20,
p=0.24, Cohen’sf=0.22).
In step three, we assumed a random slopes model, whereby the
effects of governmental trust, empathic prosocial concern and
fear of COVID-19 could freely vary across countries. Again,
supporting COVID-19 containment behaviors was significantly
associated with both empathic prosocial concern (B(SE) =0.14
(0.01), 95% CI =[0.13, 0.16], t(24.5) =17.60, p< 0.001, Cohen’s
f=3.41), and fear of COVID-19 (B(SE) =0.31 (0.03), 95%
CI =[0.25, 0.36], t(28.5) =11.06, p< 0.001, Cohen’sf=2.00),
while no evidence was found for a significant association with
country-level trust in the government (B(SE) =0.002 (0.004),
95% CI =[−0.007, 0.01], t(17.5) =0.40, p=0.69, Cohen’s
f=0.09). In the final step, we tested the proposed moderator
effect of trust in the government as a context variable in
accordance with our hypotheses and added the two-way
interactions to the regression model. Again, the deviance scores
Table 3 Pearson product-moment correlations of the study
variables.
1. 2. 3. 4.
1. Support for COVID-19 Containment
Behaviors
1 0.36*** 0.54*** 0.05***
2. Empathic Prosocial Concern 1 0.29*** 0.04***
3. Fear of COVID-19 1 0.07***
4. Trust in the Government
(Individual-level)
1
N=12,758 independent responses.
***p< 0.001.
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of the random coefficients model at step 3 (=21,254) was
compared with that of the model including the interactions at
step 4 ( =21,191)56. Results of the Chi-square test showed that
the latter model explained significantly more variance, χ2
(3, N=11,026) =63, p< 0.001. There was no significant inter-
action between country-level scores of trust in government and
empathic prosocial concern in the regression analysis (B(SE) =
−0.0003 (0.0003), 95% CI =[−0.0009, 0.0004], t(24.8) =−0.82,
p=0.42, Cohen’sf=0.16). Hence, we found no credible evidence
to support Hypothesis 1b. For fear of COVID-19, a significant
interaction with country-level trust in the government was found
(B(SE) =−0.002 (0.0008), 95% CI =[−0.004, −0.0007],
t(25.9) =−2.92, p=0.007, Cohen’sf=0.52). Examination of
the simple effects showed that the effect of fear of COVID-19 was
significantly associated with supporting COVID-19 containment
behaviors across low (−1SD), medium (mean) and high (+1SD)
levels of trust in the government. However, the association
between fear of COVID-19 and support for COVID-19 contain-
ment behaviors was strongest in contexts where trust in the
government is generally low (β=0.37, t(37.3) =11.26, p< 0.001,
95% CI [0.30, 0.43], Cohen’sf=1.79), and weakest in contexts
where trust in the government is generally high (β=0.26,
t(35.9) =8.06, p< 0.001, 95% CI [0.19, 0.32], Cohen’sf=1.30),
which supports Hypothesis 2b (Fig. 3).
Exploratory analysis. We examined whether adding individual-
level scores of generalized trust as another covariate into the
mixed-linear regression model (at step 4) will lead to substantial
changes in the obtained results. Results revealed that indivi-
duals’trust in their government significantly interacted with
both the individuals’empathic prosocial concern (B(SE) =0.013
(0.005), 95% CI [0.003, 0.02], t(12080.2) =2.65, p=0.008,
Cohen’sf=0.02), and their fear of COVID-19 (B(SE) =−0.02
(0.007), 95% CI [−0.003, −0.03], t(12130.0) =−2.42, p=0.015,
Cohen’sf=0.02); both in the proposed direction. Moreover, the
same analyses were repeated by controlling for effects of
country-level generalized trust. Again, the original analysis
results did not change, and analyses supported that trust in the
government moderated the association between fear of COVID-
19 and support for COVID-19 containment behaviors
(B(SE) =−0.002 (0.001), 95% CI [−0.004, −0.0001],
t(26.8) =−2.09, p=0.047, Cohen’sf=0.34) in the expected
direction; while we found no evidence for a significant mod-
erationfortheassociationbetweenempathicconcernand
support for COVID-19 containment behaviors (B(SE) =
−0.0003 (0.0003), 95% CI [−0.0008, 0.0003], t(30.2) =−0.97,
p=0.34, Cohen’sf=0.17).
We further tested the hypotheses of the present research on the
basis of generalized trust (instead of governmental trust) as a
moderator of self- versus other-oriented motives. The analysis
results for this regression model did not provide credible evidence
for a significant interaction; neither between generalized trust and
empathic concern (B(SE) =−0.0004 (0.004), 95% CI [−0.009,
0.009], t(12389.5) =0.07, p=0.95, Cohen’sf=0.001), nor
between generalized trust and fear of COVID-19 (B(SE) =
−0.0003 (0.0003), 95% CI [−0.02, 0.005], t(12394.3) =−1.28,
p=0.20, Cohen’sf=0.01). Second, the same analyses were
repeated on the country-level with generalized trust scores
extracted from the World Values Survey. Again, the results
obtained from the regression analysis did not provide credible
support for the proposed interactions (H1b and H2b). Both the
interaction between generalized trust and empathic concern
(B(SE) =−0.0007 (0.0006), 95% CI [−0.002, 0.0004],
t(30.8) =−1.26, p=0.22, Cohen’sf=0.23) and between general-
ized trust and fear of COVID-19 (B(SE) =0.003 (0.002), 95% CI
[−0.0003, 0.006], t(27.7) =1.77, p=0.09, Cohen’sf=0.33) were
not found as significant.
Table 4 Mixed-level regression results with individual-level scores for trust in the government.
Model
Step 1
Null model
B (SE)
Step 2
Random intercept and fixed
slopes
B (SE)
Step 3
Random intercept and random
slopes
B (SE)
Step 4
Two-way interactions
B (SE)
Intercept 3.71*** (0.09) 3.78*** (0.09) 3.78*** (0.09) 3.80*** (0.09)
Main effects
Trust in government (TG) 0.05*** (0.01) 0.05*** (0.01) 0.05*** (0.01)
Empathic concern (EC) 0.15*** (0.005) 0.14*** (0.01) 0.13*** (0.01)
Fear of COVID-19 (FoC) 0.29*** (0.01) 0.30*** (0.02) 0.31*** (0.02)
Interactions
TG × EC 0.01* (0.005)
TG × FoC −0.02** (0.01)
EC × FoC −0.04*** (0.005)
Variance Components
Within-country variance 0.553 0.419 0.403 0.401
Between-country variance 0.283 0.127 0.136 0.136
TG between-country var. 0.004 0.004
EC between-country var. 0.001 0.001
FoC between-country var. 0.017 0.017
Additional Information
−2 *log likelihood (FIML) 28,772 25,221 24,864 24,789
R2marginal 0.000 0.37 0.33 0.33
N=12,758 independent responses nested in 34 countries. Coefficients presented for main effects and interactions represent the unstandardized regression weights (B); the value in brackets refers to the
Standard Error (SE). All analyses from step 2 onwards were performed by entering covariate effects of gender, age, HDI (Human Development Index), hospital beds per 1000, month of data collection,
government stringency level, and the number of new daily COVID-19 cases and deaths by the time of data collection. For reasons of simplicity, the covariate effects are not displayed in the table. They
can be obtained from the analysis documentation at https://osf.io/kws9x/files43.
TG Trust in Government, EC Empathic Concern, FoC Fear of COVID-19.
*p< 0.05; **p< 0.01; ***p< 0.001.
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Finally, we tested the robustness of our results when using a
different methodological approach, namely a fixed effects
regression model with cluster-robust standard errors. For this
purpose, two additional analyses were performed: first, a simple
OLS regression; and second, an OLS regression with the clustered
standard errors correction. A table comparing the results
obtained from these approaches, and a brief discussion concern-
ing the differences in the results can be found in the Supplemen-
tary Information under Supplementary Note 1 and
Supplementary Table 1.
Discussion
The present multinational study provides an individual- and
country-level perspective on the role of governmental trust as a
moderator for motivations to support COVID-19 containment
behaviors. Ever since its emergence in 2020, the pandemic has
been a major reason for worry and concern globally, so govern-
ments and local officials strove to efficiently implement varying
strategies to combat the spread of the virus. So far, only few
studies have examined how the motives of people to comply with
public health guidelines may differ in accordance with various
boundary conditions37,57,58. The present study tried to shed light
on the ways in which individuals with low versus high levels of
trust in the government (or in context where trust is low versus
high) respond to the COVID-19 pandemic, and how their
motivations for supporting COVID-19 containment behaviors
may differ accordingly.
First, we hypothesized that the positive association between
empathic concern and the support of COVID-19 containment
behaviors would be stronger at high compared to low levels of
trust in the government. Our evidence supported this hypothesis
at the individual-level but not the country-level. Namely, support
for COVID-19 containment behaviors was more strongly asso-
ciated with empathic concern when people reported high (com-
pared to low) trust in their government. Second, and as
hypothesized, we found that support for COVID-19 containment
behaviors was more strongly associated with fear of COVID-19
when people reported low (compared to high) trust in their
government, as well as within contexts where trust in the gov-
ernment is typically low (versus high).
In regard to empathic concern, it should be noted, that despite
the significant interaction effect, the strength of the association
between empathic concern and support for COVID-19 contain-
ment behaviors were almost identical under high (Cohen’s
f=2.01) versus low trust in the government (Cohen’sf=1.99).
Fig. 1 Association between empathic prosocial concern and support for COVID-19 containment behaviors under low, medium and high scores of trust
in the government. Notes: The graphic is based on N=12,758 independent responses nested in 34 countries; the black line represents the strength of the
association between empathic prosocial concern and support for COVID-19 containment behaviors under high levels of trust in the government (one
standard deviation above the mean); the blue dashed line represents the strength of the association between empathic prosocial concern and support for
COVID-19 containment behaviors under medium levels of trust in the government; the red dashed line represents the strength of the association between
empathic prosocial concern and support for COVID-19 containment behaviors under low levels of trust in the government (one standard deviation below
the mean). The gray shadowed parts represent the respective 95% Confidence Intervals; support for COVID-19 containment behaviors were assessed on a
scale ranging from 1to 5, with higher scores representing higher support. The scores for empathic prosocial concern (x-axis) represent the group-mean
centered scores.
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This aligns with the notion that people’s empathic concern may
similarly operate across various conditions. Thus, even though
the association between empathy and support for large-scale
cooperative action may become more pronounced when indivi-
duals perceive their governments as more (compared to less)
trustworthy, such interplay with trust seems less salient for
empathic concern (compared to fear of COVID-19). Overall, our
results seem in line with past findings that empathy is consistently
linked with prosocial behaviors59,60, and that highly empathetic
people usually show a higher sense of community and civic
engagement. These people are more likely to take on the
responsibility for the community’s well-being57, and could thus
also be more prone to respond sensitively and favorably to issues
of protecting citizens’health regardless of whether they find
themselves in a context where trust in the government is (gen-
erally) low or high.
At both levels of analysis, our results support that fear of
COVID-19 was related to preventive behaviors, especially when
trust in the government was low. According to a number of
authors8,61,62, in times of epidemic outbreak, people, especially
those at risk of infection, are extremely vulnerable, emotionally
fragile, and feel ambivalent emotions. They feel fear and panic,
but also skepticism and disregard. Under these circumstances,
individuals have difficulty processing information and may think
of and behave for themselves as individuals rather than as con-
nected to others. The same may also apply when people perceive
their governments as not trustworthy, or when they are living in
surroundings that are characterized by low levels of trust in the
government. Under such conditions, individuals may feel a
stronger need to curb their fear through personal protective
behaviors, and thus their self-protection motives may play a
stronger role for their support of COVID-19 containment beha-
viors. This is also in line with previous research showing that
cooperation seems to rely on selfish (instead of altruistic) reasons,
especially when deliberate cognitive processes are involved in the
decision of whether to cooperate or not17,18. Yet, it should be
noted that our results do not indicate that empathy would not
operate in low trust settings, or that fear would not operate in
high trust settings. Instead, our results support the view that both
motivational pathways seem relevant for mobilizing cooperative
action across individuals and contexts (i.e., as main effects were
consistently recorded across various levels of governmental trust),
but suggest that empathy-driven cooperation is likely to unfold
optimally when trust in the government is perceived as high,
while fear-driven cooperation is likely to become most pro-
nounced in contexts where trust in the government is generally
low, or when trust in the government is individually perceived
as low.
Fig. 2 Association between fear of COVID-19 and support for COVID-19 containment behaviors under low, medium and high scores of trust in the
government. Notes: The graphic is based on N=12,758 independent responses nested in 34 countries; the black line represents the strength of the
association between fear of COVID-19 and support for COVID-19 containment behaviors under high levels of trust in the government (one standard
deviation above the mean); the blue dashed line represents the strength of the association between fear of COVID-19 and support for COVID-19
containment behaviors under medium levels of trust in the government; the red dashed line represents the strength of the association between fear of
COVID-19 and support for COVID-19 containment behaviors under low levels of trust in the government (one standard deviation below the mean). The
gray shadowed parts represent the respective 95% Confidence Intervals; support for COVID-19 containment behaviors were assessed on a scale ranging
from 1to 5, with higher scores representing higher support. The scores for fear of COVID-19 (x-axis) represent the group-mean centered scores.
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Another question to be addressed concerns the role of gen-
eralized trust (i.e., how much people trust each other in general),
as much of our argumentation in regard to governmental trust
seem also applicable to generalized trust, and since generalized
trust has also been found to be a relevant predictor of compliance
during the COVID-19 pandemic63. Overall, the evidence
obtained from our exploratory analysis suggest that the findings
obtained with trust in the government are relatively robust
against inter-individual and contextual variations of generalized
trust, plus specific to the concept of trust in the government.
Hence, evidence from the present research emphasizes the unique
role of trust in the government and indicates that supporting
cooperative action during the pandemic may take more than just
citizens’goodwill, and may especially depend on the govern-
ments’activities and on how much people find them
trustworthy64. This is also in line with previous research, con-
firming the central role that systemic trust has played for coop-
erative action during the COVID-19 pandemic, by showing that
not only interpersonal trust but also –and more strongly –trust
in politicians was associated with COVID-19 vaccination58,65,66.
Implications. The present findings underscore that adherence to
guidelines aimed at preventing the COVID-19 pandemic spread
depends on the interplay between personal individual-level
resources (like fear and empathy), and contextual resources
(like governmental trust). Mirroring results from extant
research6,21, the present findings highlight the strong and robust
role of empathic concern for mobilizing large-scale cooperation
that was found to be unaffected by country-level trust in the
government. Moreover, the present research underlines that self-
centered motives may also play a role for large-scale cooperative
endeavors. Fear of disease was associated with individuals’sup-
port for COVID-19 containment measures, and that this was
especially pronounced when individuals hardly trusted their
governments, or when the general level of governmental trust
within a community was low. However, this evidence should not
be understood as proof for an underlying causal relationship
between governmental distrust and fear-related compliance with
COVID-19 mitigating behaviors. Neither should it be regarded as
a call for utilizing fear-arousing messages as a way to promote
cooperation under such conditions. In fact, while fear of infection
may be associated with stronger compliance to COVID-19 health
measures14,20, there is also growing evidence about the negative
mental health consequences of enhanced fear of infection67,68,
suggesting that promoting large-scale cooperation via fear would
represent a costly and harmful strategy. Promoting empathy, on
the contrary, may work as a promising and no-risk strategy to
enhance the efficacy of policy recommendations in collective
crisis situations across various conditions. However, to benefit
from empathy to the utmost, governments should take action in
increasing individuals’perceptions of trust in the government.
Limitations. The present research allowed us to examine the links
between trust and acceptance of imposed restrictions during
times of COVID-19, albeit in a correlational rather than causal
manner. Though correlational research is central for scientific
progress69, it is but an intermediate step in the process. Corre-
lational research such as the one presented in this study should
ideally be supported by ensuing causal confirmations, or at least
with corroborated results from independent samples (also see
many labs project70). Until then, we should interpret the present
findings with due caution.
The present data are sufficiently inclusive in terms of cultures
of the world, and contain answers from a sufficiently large sample
for conducting complex analyses such as hierarchical regression.
Nonetheless, there is concern, as is the case usually in survey
research71. The sampled population might not be representative
for the phenomenon and therefore poses questions about the
Table 5 Mixed-level regression results with country-level scores for trust in the government.
Model
Step 1
Null model
B (SE)
Step 2
Random intercept and fixed
slopes
B (SE)
Step 3
Random intercept and random
slopes
B (SE)
Step 4
Two-way interactions
B (SE)
Intercept 3.71*** (0.09) 3.72*** (0.09) 3.80*** (0.09) 3.75*** (0.09)
Main effects
TG (Country-Level) 0.004 (0.004) 0.002 (0.04) 0.01 (0.004)
EC 0.15*** (0.01) 0.14*** (0.01) 0.13*** (0.01)
FoC 0.29*** (0.01) 0.31*** (0.03) 0.31*** (0.03)
Interactions
TG (Country-Lev.) ×EC −0.0003 (0.0003)
TG (Country-Lev.) ×FoC −0.002** (0.001)
EC × FoC −0.04*** (0.01)
Variance Components
Within-country variance 0.553 0.410 0.396 0.394
Between-country variance 0.283 0.124 0.071 0.076
TG between-country var. 0.0003 0.0004
EC between-country var. 0.001 0.001
FoC between-country var. 0.020 0.018
Additional Information
−2 *log likelihood (FIML) 28,815 21,549 21,254 21,191
R2marginal 0.000 0.37 0.25 0.34
Notes: N=11,026 independent responses nested in 29 countries. Coefficients presented for main effects and interactions represent the unstandardized regression weights (B); the value in brackets
refers to the Standard Error (SE). All analyses from step 2 onwards were performed by entering covariate effects of gender, age, HDI (Human Development Index), hospital beds per 1000, month of data
collection, government stringency level, and the number of new daily COVID-19 cases and deaths by the time of data collection. For reasons of simplicity, the covariate effects are not displayed in the
table. They can be obtained from the analysis documentation at https://osf.io/kws9x/files43.
TG Trust in Government, EC Empathic Concern, FoC Fear of COVID-19.
** p< 0.01; ***p< 0.001.
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generalizability of our results. This is especially concerning
considering that certain populations who were more at risk, such
as older people and people with a migration background, could
not be adequately represented. It is noteworthy that the general
level of fear of COVID-19 was rather low across the study
populations (only in five out of the 34 nations the means for fear
of COVID-19 were slightly above the scale midpoint), while the
level of empathic concern was consistently high (in all study
populations the means for empathy were above the scale
midpoint). The results therefore might be biased toward the
younger, relatively low-risk and local populations with internet
access at the time of data collection.
Moreover, we are aware that the self-reported support for
COVID-19 containment behaviors which served as the dependent
variable of the present research may not allow conclusions on
how individuals would actually behave72. For the present
research, the choice of a self-report measure was driven by the
multi-national nature of the study, and the limited resources
available for data collection during the pandemic. Moreover, it
should be noted that we deliberately chose to assess the support
for practicing COVID-19 containment measures instead of
assessing self-reported behavior intentions or past behavior
frequencies. That is, because behavior intentions may also be
determined by other obligations or situational constraints. For
instance, people working in hospitals would not report avoiding
hospitals, even though they may consider it important to do so
(for others).
Last, we note that the moderation effects, though significant,
are small in magnitude, especially the ones obtained from the
individual level analysis (e.g., Cohen’sf=0.02). However, this is
not in itself a concern due to the explorative nature of the study.
It could become a concern if these results were taken at face-value
without subsequent testing. Hence, even though the findings of
the present research seem overall in line with the literature, they
may not necessarily apply to other global crises, or if additional
independent data were to be collected. We do appreciate the
benefits of conducting qualitative research at this stage73,74, and
recommend using both experimental and qualitative research
methods to further probe the links between trust and various
motivations.
Data availability
The data generated and/or analyzed during the current study are available on the Open
Science Framework repository, https://osf.io/kws9x/46. The World Values Survey data are
available at https:// www.worldvaluessurvey.org/wvs.jsp48. Scores on HDI were extracted
Fig. 3 Association between fear of COVID-19 and support for COVID-19 containment behaviors under low, medium and high scores of country-level
trust in the government. Notes: The graphic is based on N=11,026 independent responses nested in 34 countries; the black line represents the strength
of the association between fear of COVID-19 and support for COVID-19 containment when the country-level trust in the government was high (one
standard deviation above the mean); the blue dashed line represents the strength of the association between fear of COVID-19 and support for COVID-19
containment behaviors when the country-level trust in the government was medium; the red dashed line represents the strength of the association
between fear of COVID-19 and support for COVID-19 containment behaviors when the country-level trust in the government was low (one standard
deviation below the mean). The gray shadowed parts represent the respective 95% Confidence Intervals; support for COVID-19 containment behaviors
were assessed on a scale ranging from 1to 5, with higher scores representing higher support. The scores for fear of COVID-19 (x-axis) represent the group-
mean centered scores.
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12 COMMUNICATIONS PSYCHOLOGY | (2023) 1:43 | https://doi.org/10.1038/s44271-023-00046-5 | www.nature.com/commspsychol

from https://hdr.undp.org/data-center/documentation-and-downloads55. Data about
hospital beds per thousand were extracted from https://ourworldindata.org/grapher/
hospital-beds-per-1000-people75. Data about government stringency level were extracted
from https://covidtracker.bsg.ox.ac.uk/stringency-map76. The scores for daily new
COVID-19 cases and deaths represent a 7-days rolling average per million people and
were extracted from https://ourworldindata.org/explorers/coronavirus-data-explorer?
uniformYAxis=0&pickerSort=asc&pickerMetric=location&Metric=Cases+and
+deaths&Interval=7-day+rolling+average&Relative+to+Population=true&Color+by
+test+positivity=false77.
Code availability
We shared all data and material relevant for this study by using widely-known/standard
file formats, and used open tools for data interpretation/re-use. The analysis outputs
together with the codes for reproducing these analyses can be accessed under https://osf.
io/kws9x/files/43.
Received: 6 February 2023; Accepted: 1 December 2023;
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Acknowledgements
We would like to thank Valentina Romo, Jallene Jia En Chua, Melis Yetkin, Emren Burak
Ömür, Miguel Landa Blanco, Christine Kraus, Raymond Langley, Youmna Haddad, Abida
Sultana, Sadiya Alam Surma, Nishat Tamanna Omi, Asmaul Husna Chara, Monir Hossen,
Mohima Akter, Nargees Akter, Khondoker Shahriar Islam, Md Johirul Islam, Joti Saha,
Sk.Sabbir Islam Mahi, Tayeba Sultana Sonia, Md. Fakhrul Islam Maruf, Md Mahiuddin
Howlader, Shakila Umme Noor, Thaher Uddin Raju, Farha Hossain, Tasfia Tabassum, Md
Refat Hossain, Nazia Sultana, Mahbubur Rahman, Meghna Chakravartty, Bikona Ghosh,
Airthie Chakma, Esrat Jahan Bani, Khondoker Shahriar Islam, Md Ferdous Shanto, Md. Seikh
Sadiul Islam Tanvir, Rowell P. Nitafan, Saima Fariha, Md. Sazzad Hossain, Mahima Ranjan
Acharjee, Arnab Bose, Saima Sultana, Istiaq Ahmed, Sami Murshed, Md. Rifat Al Mazid
Bhuiyan,Md.AsifurRahaman,Mst.FarihaSultana,KimberlyAlmonte,ClaudiaNúñezfor
helping with the survey translations, data collection and data preparation. We further
acknowledge the contribution of the following grants that supported the realization of this
research by funding the work of several co-authors. The Cooperative University of Colombia
INV3092 provided funding for M.M.-C.; the ANID –Millennium Science Initiative Program
(NCS2021_081) provided funding for J.G.; the GESIS-Leibniz Institute for the Social Sciences
provided funding for A.S.; the Systemic Risk Institute (LX22NPO5101) funded by European
Union - Next Generation EU (Ministry of Education, Youth and Sports, NPO: EXCELES)
provided funding for I.P.Š., M.K.-B., and S.S.; the RA Science Committee (Project 21T-5A203)
provided funding for H.A., the Council for Scientific and Technological Development
(Conselho Nacional de Desenvolvimento Científico e Tecnológico/CNPq - Processo: 401749/
2022-3) provided funding for S.G., R.C.F.G., C.M., and J.R. The funders had no role in the
design of the study, data collection, preparation of the manuscript or decision to publish.
Author contributions
A. Karakulak played a lead role in initiating and conceptualizing the project, performing
data curation, formal analysis, investigation, methodology design, project administration,
visualization, and writing of the original draft, as well as writing review and editing. B.T.
played a major role in the conceptualization, investigation, methodology, and writing the
original draft and R.D. provided valuable support for the project. M.R. played a sup-
porting role in performing supplemental analyses. These authors contributed equally by
supporting the investigation and project administration at the different study sites:
M.K.A., P.A., R.A., Y.A.A., A. Amin, D.A.L.A., A. Andres, J.J.B.R.A., M.A., H.A., N.A.,
M.B.-S., R.K.B., B.B., S.B., D.B., I.B., A.K.B., J.B., K.B., Y.B.-P., C.B., R.C., M.M.C., B.-B.C.,
G.R.D., D.C.D., A.d.C.D.E., W.G.E., N.F., R.F.-M., J.G., Y.G., S.G., R.C.F.G., M.-T.F., S.G.,
B.G., J.C.G., M.d.P.G., C.H., G.H., S.H., M.S.H., M.S.I., A.J., N.J., V.J., R.S.K., N.B.A.K.,
J.K., D.K., Z.K., T.D.K., M.K., R.K., M.K., M.K.-B., A. Kozina, S.E.K., R.L., K.L., A.L.-W.,
Y.-H.L., A.M., S.M., D.M.-M., S.M., B.M., E.A.M., M.M., S.M., J.M.-K., D.M., F.M., R.M.-
H., C.M., M.M., P.M., A.N., A.N., F.N., J.N., L.M.A.P., H.O.-A., C.I.O., L.M.O., S.K.M.,
J.P., I.P., E.A.P., P.P., S.P., F.P., J.R., R.M.R., B.P.D.R., A.S., T.Z.S., D.S., F.S., P.S., S.S.,
K.S., I.P.Š., O.S.-K., A.S., D.S., L.C.L.S., M.S., J.S., L.F.S., K.S., M.S.S., A.O.S., E.T., L.T.-E.,
L.D.T., F.U., R.P.V., B.W., G.W.W., P.-J.Y., E.Y., Y.Y., M.A.M.Y. and M.Z.d.S. All authors
agreed to the manuscript contents, their authorship and its order.
Competing interests
J.A. is an Editorial Board Member for Communications Psychology but was not involved
in the editorial review of, nor the decision to publish this article. The other authors
declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s44271-023-00046-5.
Correspondence and requests for materials should be addressed to Arzu Karakulak.
Peer review information Communications Psychology thanks Andrea Martinangeli and
the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Primary Handling Editor: Antonia Eisenkoeck. A peer review file is available.
Reprints and permission information is available at http://www.nature.com/reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
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© The Author(s) 2023
Arzu Karakulak 1,2✉, Beyza Tepe 2, Radosveta Dimitrova3, Mohamed Abdelrahman 4,5, Plamen Akaliyski6,
Rana Alaseel7, Yousuf Abdulqader Alkamali8, Azzam Amin4, Danny A. Lizarzaburu Aguinaga 9,
Andrii Andres 10, John Jamir Benzon R. Aruta11, Marios Assiotis12, Hrant Avanesyan 13, Norzihan Ayub 14,
Maria Bacikova-Sleskova 15, Raushan Baikanova16, Batoul Bakkar7, Sunčica Bartoluci17, David Benitez18,
Ivanna Bodnar 19, Aidos Bolatov16, Judyta Borchet20, Ksenija Bosnar17, Yunier Broche-Pérez21, Carmen Buzea
22, Rosalinda Cassibba23, Marta Martín Carbonell 24, Bin-Bin Chen25, Gordana Ristevska Dimitrovska26,27,
Dương Công Doanh28, Alejandra del Carmen Dominguez Espinosa29, Wassim Gharz Edine7, Nelli Ferenczi 30,
Regina Fernández-Morales 31,32, Jorge Gaete 33,34, Yiqun Gan 35, Suely Giolo 36,
Rubia Carla Formighieri Giordani 36, Maria-Therese Friehs 37, Shahar Gindi 38, Biljana Gjoneska 39,
Juan Carlos Godoy40, Maria del Pilar Grazioso41, Camellia Hancheva42,43, Given Hapunda44,45, Shogo Hihara
46,47, Mohd Saiful Husain48, Md Saiful Islam 49,50, Anna Janovská17, Nino Javakhishvili51, Veljko Jovanović
52, Russell Sarwar Kabir 46, Nor Ba’yah Abdul Kadir53, Johannes Karl54,55, Darko Katović17,
Zhumaly Kauyzbay 56, Tinka Delakorda Kawashima46, Maria Kazmierczak20, Richa Khanna57, Meetu Khosla58,
Martina Klicperová-Baker 59, Ana Kozina60, Steven Eric Krauss61, Rodrigo Landabur62,
Katharina Lefringhausen63, Aleksandra Lewandowska-Walter20, Yun-Hsia Liang64, Ana Makashvili 51,
Sadia Malik65, Denisse Manrique-Millones 66, Stefanos Mastrotheodoros67,68, Breeda McGrath69,
Enkeleint A. Mechili 70, Marinés Mejía 41, Samson Mhizha 71, Justyna Michalek-Kwiecien 20,
Diana Miconi72, Fatema Mohsen7,73, Rodrigo Moreta-Herrera 74, Camila Muhl 36, Maria Muradyan 13,
Pasquale Musso 23, Andrej Naterer75, Arash Nemat76,77, Felix Neto78, Joana Neto79,
Luz Marina Alonso Palacio80, Hassan Okati-Aliabad81, Carlos Iván Orellana 82, Ligia María Orellana83,
Sushanta Kumar Mishra 84, Joonha Park 85, Iuliia Pavlova 19, Eddy Peralta 86, Petro Petrytsa87,
SašaPišot88, Franjo Prot17, José Rasia36, Rita Rivera18,89, Benedicta Prihatin Dwi Riyanti90, Adil Samekin91,
Telman Seisembekov16, Danielius Serapinas92, Fabiola Silletti23, Prerna Sharma93, Shanu Shukla 94,95,
Katarzyna Skrzypińska20,96, Iva Poláčková Šolcová59, Olga Solomontos-Kountouri12, Adrian Stanciu97,
Delia Stefenel98, Lorena Cecilia López Steinmetz 40,99, Maria Stogianni 100, Jaimee Stuart101,102,
Laura Francisca Sudarnoto90, Kazumi Sugimura46, Sadia Sultana49, Angela Oktavia Suryani90, Ergyul Tair 103,
Lucy Tavitian-Elmadjan100,104, Luciana Dutra Thome105, Fitim Uka106,107, Rasa PilkauskaitėValickienė92,
Brett Walter46, Guilherme W. Wendt 108, Pei-Jung Yang109, Ebrar Yıldırım110, Yue Yu111,112,
Maria Angela Mattar Yunes113, Milene Zanoni da Silva 114 & Maksim Rudnev 115
1
Istanbul Policy Center, Sabanci University, Istanbul, Turkey.
2
Department of Psychology, MEF University, Istanbul, Turkey.
3
Department of
Psychology, Stockholm University, Stockholm, Sweden.
4
Social Psychology Department, Doha Institute for Graduate Studies, Doha, Qatar.
5
Mokhtass for Consultations and Research, Doha, Qatar.
6
Department of Sociology and Social Policy, Lingnan University, Hong Kong SAR, China.
7
Faculty of Medicine, Syrian Private University, Damascus, Syrian Arab Republic.
8
United Private School, Athaiba, Oman.
9
Universidad César
COMMUNICATIONS PSYCHOLOGY | https://doi.org/10.1038/s44271-023-00046-5 ARTICLE
COMMUNICATIONS PSYCHOLOGY | (2023) 1:43 | https://doi.org/10.1038/s44271-023-00046-5 | www.nature.com/commspsychol 15