National identity predicts public health support during a global pandemic

Abstract

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

Full text

ARTICLE
National identity predicts public health support
during a global pandemic
Changing collective behaviour and supporting non-pharmaceutical interventions is an
important component in mitigating virus transmission during a pandemic. In a large inter-
national collaboration (Study 1, N=49,968 across 67 countries), we investigated self-
reported factors associated with public health behaviours (e.g., spatial distancing and stricter
hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during
the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported
identifying more strongly with their nation consistently reported greater engagement in
public health behaviours and support for public health policies. Results were similar for
representative and non-representative national samples. Study 2 (N=42 countries) con-
ceptually replicated the central finding using aggregate indices of national identity (obtained
using the World Values Survey) and a measure of actual behaviour change during the
pandemic (obtained from Google mobility reports). Higher levels of national identification
prior to the pandemic predicted lower mobility during the early stage of the pandemic
(r=−0.40). We discuss the potential implications of links between national identity, lea-
dership, and public health for managing COVID-19 and future pandemics.
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As of October 2021, more than 235 million people world-
wide have been infected by the new coronavirus and
nearly 5 million have died1, making the COVID-19
pandemic one of the greatest health crises of the past century.
Until a vaccine or effective medical treatment is widely admi-
nistered, the public response to the pandemic is largely limited to
non-pharmaceutical interventions, including policy-making and
collective behaviour change2. To reduce virus transmission, it is
crucial that people engage in public health behaviour (e.g.,
maintain spatial distance and improve physical hygiene) and
support COVID-19 protective policies (e.g., limiting travel and
closing bars and restaurants). And even after effective vaccines
are administered, it is critical to convince people to take them.
This is why the Director of the World Health Organization
declared: “That’s why behavioural science is so important –it helps
us to understand how people make decisions, so we can support
them to make the best decisions for their health”3.
In the current investigation, we respond to this call for beha-
vioural science on the pandemic. Specifically, we present the
results from two large-scale global studies across 67 (Study 1) and
42 (Study 2) countries, testing key predictors of public health
support during COVID-19. Focusing on the potential role of
national identity, we examine the role of key social motives in
collective behaviour during the pandemic. This research may help
scholars, health organizations, and political leaders identify
important factors and design more effective behavioural inter-
ventions to increase compliance with actions such as maintaining
spatial distance and restricting travel during a pandemic.
During a global pandemic, leaders and public health officials
need to inform and mobilize the public to avoid behaviours no
longer considered socially responsible. However, recent evidence
suggests this type of leadership requires cultivating a shared sense
of solidarity to increase compliance with recommended health
behaviours4–6. Solidarity with other members of one’s group is a
component of ingroup identification7, that is, the personal sig-
nificance that being part of a group (e.g., nation) holds for an
individual7–10. Identifying with a group is associated with mutual
cooperation and adherence to its norms11–13, motivation to help
other members of their group14,15, and a willingness to engage in
collectively-oriented actions aimed at improving the group’s
welfare10,16–18. Here we test the role of identification with one’s
national group in promoting public health in the COVID-19
pandemic (see ref. 19).
National identity plays an important role in motivating civic
involvement20 and costly behaviours that benefit other members
of their national community21. Accordingly, a strong sense of
shared national identity might help collective efforts to combat the
pandemic within a country (e.g., ref. 22). Moreover, border clo-
sures, travel bans, and national task forces have likely made
national identities even more salient during the pandemic23. The
existence and activation of strong collective identities can allow
political leaders to mobilize large populations to adhere to emer-
gency public health measures. For instance, political leaders and
public health officials often foster a sense that “we are in this
together”and that we can manage the crisis through collective
action18,24. This might be particularly important for counteracting
polarization within countries, which can reduce health behaviour
and increase the risk for infections and mortality19,25,26.
The goal of the current paper is to examine whether national
identification (NI) is associated with global adherence to the
public health measures during a pandemic27–29. Specifically, we
examined the associations between the strength of identification
with one’s nation and whether people adopted public health
behaviours (e.g., limiting travel, spatial distancing, hand washing)
and endorsed public policy interventions (e.g., closing bars and
restaurants). Extensive evidence suggests these actions could
substantially reduce the number of COVID-19 infections2,30–32.
Our primary hypothesis is that stronger NI will be associated with
greater support for and compliance with public health measures.
National identity is distinct from beliefs about national
superiority or collective narcissism (e.g., refs. 33–35). NN is a form
of social identity that involves the belief that one’s group (i.e.,
nation) is exceptional but unappreciated by others36. NI tends to
correlate positively with NN because they both involve a positive
evaluation of one’s nation. However, they are linked to very
different outcomes. For example, outgroup prejudice is negatively
associated with NI but positively with NN37.
People high in collective narcissism are especially concerned
with how their group reflects on them38. For instance, NN is
associated with a greater preoccupation with maintaining a
positive image of the nation than with the well-being of fellow
citizens39,40. Thus, in a crisis, national narcissists may prefer to
invest in short-term image enhancement rather than in the sorts
of long-term solutions that are necessary to sustain public health
during a long pandemic (see also ref. 41). They may then be less
inclined to engage in behaviours to prevent the spread of
COVID-19 (see ref. 42)--or even acknowledge the risks associated
with the pandemic in their home country (e.g., ref. 43). Therefore,
in identifying associations with compliance with public health
measures, we sought to distinguish NI from NN.
In addition, there is some evidence that right-wing political
ideology (PI) is associated both with national identity (e.g., ref. 44)
and NN (e.g., refs. 39,45,46). Moreover, supporters of right-wing
political parties have tended to downplay risks associated with
COVID-19 (e.g., refs. 47–49) and were less likely to comply with
preventative measures compared to left-leaning or liberal
individuals26,48,50. Therefore, we examined whether NI and nar-
cissism were distinct from PI in explaining public health support.
Results
The COVID-19 pandemic is a truly global crisis with over 200
countries reporting infections. To understand the variables that
account for public health support around the globe, we launched
a collaborative, international project in April 2020 collecting
large-scale data from as many nations as possible. In Study 1, we
collected a large sample consisting of citizens from 67 countries.
We analyzed a sample of 49,968 participants (see Fig. 1). See
“Methods”for details about the sample (all reported materials
and data are available at: https://osf.io/y7ckt/).
We analyzed these data using multi-level models in which
persons were treated as nested within countries51. We also
included a measurement level to control for individual differences
in how consistently people responded to items that were meant to
measure the same construct. Our analyses estimated relationships
at the individual level while controlling for country-level differ-
ences. For example, did people who had a stronger NI endorse
public health measures such as spatial distancing (e.g., reducing
physical contact) more strongly than people with a weaker NI? A
set of regression coefficients was estimated for each country, and
the means of these coefficients were tested for statistical sig-
nificance. Moreover, the standard errors of these coefficient
incorporated “Bayesian shrinkage”meaning that less reliable
observations (countries and individuals) influenced parameter
estimates less than more reliable observations.
We also adjusted for the COVID-19 infection and mortality
rates within each country to ensure that public health support was
not merely a function of local risks. Due to the large sample size
in Study 1, we focused our interpretations on the person-level
findings that were statistically significant at the p< 0.001 level.
(The results with the Human Development Index (HDI) are
available in the Supplementary Information).
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Participants generally reported following the guidelines for
contact and hygiene and they supported policies that were
intended to reduce the impact of COVID-19 (i.e., means for all
three measures were above 8, on scales ranging from 0 to 10; see
Table 1). The public health measures were correlated with one
another (estimated correlations > 0.38). Consistent with prior
work, NI was positively correlated with both NN (r=0.38) and
right-wing PI (r=0.18).
We examined relationships between our three measures of
socio-political beliefs and COVID preventative behaviours and
support of public health policies with a series of multi-level
regressions. In these analyses, preventative behaviours and policy
support were outcomes, and the three measures of social-political
beliefs were modelled simultaneously as predictors. This meant
that the relationship between an outcome and each predictor
statistically adjusted for relationships between that outcome and
the other predictors. The results of these analyses are summarized
in Table 2.
NI was significantly and positively related to all public health
measures. Individuals with stronger NI (relative to other people
within their own nation) reported stronger support for increasing
spatial distance and improving physical hygiene and endorsed
COVID-19 public health policies more strongly than individuals
with weaker identification.
We conducted chi-squares tests comparing the size of these
coefficients and found that for all three public health measures,
the coefficients for NI were stronger than the coefficients for NN
and PI (ps < 0.001). Taken together, the three predictors
accounted for 8% of the person-level variance of the contact
measure, for 8% of the person-level variance of the hygiene
Argentine
Australia
Austria
Belgium
Bangladesh
Bulgary
Bolivia
Brazil
Canada
Swiss
Chile
China
Colombia
Costa Rica
Cuba
Denmark
Dominican Republic
Ecuador
Spain
Finland
France
United Kingdom
Ghana
Greece
Guatemala
Croatia
India
Iraq
Israel
Italy
Japan
South Korea
Latvia
Morocco
Mexico
North Macedonia
Nigeria
Nicaragua
Netherlands
Norway
Nepal
New Zealand
Pakistan
Panama
Peru
Philippines
Poland
Paraguay
Romania
Russia
Senegal
Singapore
Sweden
Turkey
Taiwan
Ukraine
Uruguay
United States of America
Venezuela
South Africa
60°S
40°S
20°S
0°
20°N
40°N
60°N
80°N
120°W 60°W 0° 60°E120°E
Longitude
Latitude
0
500
1000
1500
2000
Participants
N = 49,968
67 countries completed the study
Fig. 1 Map of the 67 participating countries and territories with total sample size scaled to colour (we did not obtain samples from countries in grey).
All the worldmaps were produced using R packages. The map is from the package ‘rworldmap’and is licensed-free from South, A. (2011). rworldmap: A
New R package for Mapping Global Data. The R Journal, 3, 35-43.
Table 1 Summary statistics and multi-level correlations for person-level measures.
Variance Correlations
Mean Between Within Alpha 2 3 4 5 6
1. Spatial distancing 8.60 0.21 2.17 0.74 0.43 0.44 0.02 0.15 −0.02
2. Physical hygiene 8.21 0.46 2.32 0.72 0.38 0.12 0.17 0.02
3. Policy support 8.29 0.94 3.45 0.81 0.06 0.13 −0.03
4. National narcissism 5.37 2.10 4.94 0.82 0.38 0.26
5. National identification 8.02 0.80 3.99 0.71 0.18
6. Political ideology 4.98 0.37 5.05 NA
The mean score for each scale is presented along with the variance explained within and between participants and the scale reliability (alpha). There is no alpha for ideology since it is a one-item
measure. Higher scores reflect greater support for each measure (and stronger right-wing political beliefs in the case of ideology).
Table 2 Relationships between outcomes and predictors
(including the slope and t-ratio of each relationship).
National identification was the strongest predictor of all
three COVID-19 public health support measures.
Outcome Predictor Slope t-ratio
Spatial distancing National narcissism −0.007 <1
National identification 0.129* 8.63
Political ideology −0.028* 4.44
Physical hygiene National narcissism 0.060* 6.45
National identification 0.126* 11.20
Political ideology −0.016 2.05
Policy support National narcissism 0.029* 2.89
National identification 0.129* 10.36
Political ideology −0.050* 4.79
*p< 0.001.
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measure, and 5% of the person-level variance of the policy sup-
port measure. The coefficients for individual countries are dis-
played in Figs. 2and 3. (To see the coefficients and confidences
intervals for each variable in each country see Supplementary
Figs. 1, 2, and 3).
Study 1 relied on self-report measures. To test the robustness
of our predictions, we sought to conceptually replicate our
findings using publicly available indices of national identity as
well as actual behaviour change during the pandemic in Study 2.
To this end, we relied on two publicly available datasets: the
World Values Survey52 and the COVID-19 Google Community
Mobility Reports53 which indicate how people’sphysical
movement has changed in response to COVID-19 policies
(available at www.google.com/covid19/mobility/). We created an
index of NI using the two relevant items from the World Value
Survey (i.e., national pride and closeness to their nation) and an
index of physical mobility by averaging community movement
across all available places (i.e., retail and recreation, groceries
and pharmacies, parks, transit stations, workplaces, and resi-
dential). See “Methods”for details about the sample and
measures.
We examined whether countries with higher average NI prior
to the pandemic predicted a stronger change in mobility in
response to COVID-19 restrictions during April and May 2020
(This period mirrored when we collected most of the samples in
Study 1). We conducted our analysis for the full sample of 42
countries in which aggregate data which was publicly available for
both for the NI and the mobility scores.
Replicating the pattern of results from Study 1, NI was asso-
ciated with reduced spatial mobility, r=−0.40, p=0.008 (see
Fig. 4; see Supplementary Information for separate correlations
for each of the places and the two indices of NIs). The observed
association at the aggregate level was moderate to strong. Thus,
we found evidence both at the person-level and country-level
establishing a link between NI and support for and engagement
with public health behaviours.
Discussion
Our research suggests that national identities might play an
important role in the fight against a global pandemic. Following
World War II, early work in social psychology had a tendency to
focus on the negative side of nationalism and leadership per-
suasion, such as destructive obedience to authority54 and group
conformity to incorrect beliefs held by others55. In the decades
since then, research on social identity10 and a “social cure”
approach to mental health56 has revealed that there is also a pro-
social side to group identity. Based on this latter perspective we
predicted, and found, that NI was positively associated with
support for and engagement with public health behaviours
around the globe.
In two global studies combining person-level and country-level
analyses, the strength of national identity robustly predicted
public health support, operationalized as behavioural health
intentions (i.e., physical distance and physical hygiene), support
for COVID-19 policy interventions, and reduced physical
movement patterns during the pandemic. We found this pattern
with self-report measures at the person-level and using measures
of actual mobility at the country level. The fact that national
identity is associated with large-scale behaviour in real life pro-
vides ecologically valid evidence for our main hypothesis. Taken
together, these results are consistent with our hypothesis that NI
is related to greater behaviour change in compliance with public
health policies. We note that the results showing a decline in
mobility should be treated with caution, as in the mobility report
location accuracy and the categorization of places can vary
between countries. In short, people who identified more strongly
with their nation reported greater engagement with critical public
health measures around the globe.
These results are consistent with the social psychological lit-
erature on the benefits of identifying with one’s social groups.
They also underscore a potential benefit of NI, which might be
salient during a national or global health crisis23. Our research
provides evidence that this form of identification might help to
National Identification
Spatial Distancing Physical Hygiene Policy Support
National NarcissismPolitical Ideology
−0.2 0.0 0.2 0.4
Fig. 2 Relationships between collective concerns and public health measures in 67 countries and territories. Heat index depicts the slope coefficients in
each country. Blueish colours indicate negative associations between our predictors and our outcomes while reddish colours indicate positive associations
(higher scores reflect stronger relationships between national identification, greater national narcissism and greater conservatism, and limiting physical
contact, improving hygiene, and supporting public health policies). All the worldmaps were produced using R packages. The map is from the package
‘rworldmap’and is licensed-free from South, A. (2011). rworldmap: A New R package for Mapping Global Data. The R Journal, 3, 35-43.
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understand public health behaviours. However, work in the
United States has found that threats to national identity can lead
to less support for public health initiatives57. As such, mobilizing
people around a shared national identity might require con-
siderable nuance. Future work should examine the impact of
different types of identity appeals during a pandemic and isolate
the causal influence of national identity on real behaviour.
There is reason to believe that other forms of group identifi-
cation can undercut public health. For instance, partisanship
within countries (i.e., when people strongly identify with a spe-
cific political party) is associated with risky behaviour25,26,58. For
example, one study that used geo-tracking data from 15 million
smartphones in the US found that counties that voted for a
Republican (Donald Trump) over a Democrat (Hillary Clinton)
exhibited 14% less spatial distancing during the early stages of the
pandemic26. These partisan gaps in distancing predicted sub-
sequent increases in infections and mortality in counties that
voted for Donald Trump. Moreover, partisanship was a stronger
predictor of distancing than many other economic or social fac-
tors (e.g., county-level income, population density, religion, age,
and state policy). This may be due to leadership, social norms,
and media consumed by people from different identity groups. As
such, stronger group identification is not always associated with
engagement in public-health behaviour.
It is tempting to conclude that PI might account for these
relationships. However, we found that right-wing PI had a posi-
tive, moderate correlation with both NI and NN, but very weak
correlations with support with public health measures in our
multi-country sample. Specifically, right-wing political beliefs
were associated with less support for COVID-19 public health
policies, compared to left-wing political beliefs. This relationship
between political beliefs and compliance has been observed in
several countries (e.g., refs. 48,49,59). Similarly, while NI and NN
were associated positively with support for public health mea-
sures, right-wing PI was negatively associated with these out-
comes. This suggests that a collective identity might be associated
with valuing the protection of the entire group during a pan-
demic, even after adjusting for their ideological differences.
It is also important to note that the relationship between
national identity and public health support was distinct from NN.
In past research, NN has predominantly been linked to proble-
matic attitudes towards both out-group and in-group
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Colombia
Spain
Israel
Argentina
Ecuador
Macedonia
Serbia
Bulgaria
Uruguay
Ireland
Honduras
United Kingdom
Netherlands
Costa Rica
South Africa
Norway
Slovakia
Guatemala
Nigeria
India
Sweden
Bolivia
Croatia
Mexico
Belgium
Paraguay
Ghana
Tur ke y
Dominican Republic
Austria
Venezuela
Canada
Nicaragua
Romania
Chile
Senegal
Greece
Brazil
El Salvador
Pakistan
Peru
Panama
Germany
Singapore
Finland
Nepal
Italy
Switzerland
New Zealand
Morocco
Russian Federation
Bangladesh
Hungary
Iraq
Tai w a n
Australia
France
Latvia
Japan
Republic of Korea
Poland
Cuba
Ukraine
Philippines
United States
China
Denmark
−0.1 0.0 0.1 0.2 0.3 0.4 0.5
Coefficient
Fig. 3 Relation between collective concerns and public health measures in 67 countries and territories. The coefficients reflecting the relation between
national identity and each of the health measures are presented for each country from strongest (top) to weakest (bottom). The relation with physical
contact (red), policy support (green), and hygiene (blue) are colour coded.
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members38,40,60. However, we found that NN was positively
associated with self-reported physical hygiene and support for
COVID-19 preventative policies (cf. ref. 42). Still, these effects
were much smaller than those for national identity and depended
on the context. Future work should thus carefully consider cross-
national differences in human development as well as social
norms associated with national identity.
Our evidence suggests that national identity may have modest
predictive value for people’s endorsement of and adherence to
public health measures in the context of a pandemic. This
information may be leveraged to create a sense of inclusive
nation-based in-groups, potentially increasing engagement with
recommended policies. Political and public health leaders might
develop effective communication strategies to appeal to a sense of
NI. Indeed, this might be particularly helpful in highly polarized
countries where adherence to public health recommendations has
become a partisan issue (see ref. 26). For instance, Canadian
leaders across the political spectrum adopted similar messaging
about the serious risks of the current pandemic which resulted in
a rare moment of cross-partisan consensus among the public61.
Such recategorizations to overarching inclusive national groups
(e.g., ref. 62) may be effective for preventing unhealthy
behaviours. As such, leaders who wish to inspire public health
behaviour might benefit from connecting the issue to feelings of
national identity. Framing these messages at the level of the
nation rather than, for instance, a partisan group, region, or
municipality also makes sense when the response requires
national coordination22,63.
However, the effective application of these appeals requires
future research as national identity is also implicated in inter-
group conflict. This is more likely in the case of NN36,60,which
tends to be associated with lower solidarity with other groups in
crisis (e.g., ref. 64). In the absence of collective narcissism,
national identity could reflect not only concerns about pro-
tecting one’s own country, but also into concern for other
nations. Indeed, prior research has found that NI is associated
with more positive attitudes towards other nations—especially
when adjusting for NN37,45. Thus, the nature of national identity
might be an important determinant of the effectiveness of
identity and the potential for international cooperation. In
addition, it could turn out that a commitment to cosmopoli-
tanism or other supranational identities and ideologies may play
arolethatbolsterswhatwehaveseeninthecaseofnational
identity65.
Argentina
Australia
Bangladesh
Bolivia
Brazil
Chile
Colombia
Germany
Ecuador
Egypt
Greece
Guatemala
Hong Kong SAR
Indonesia
Iraq
Jordan
Japan
Kazakhstan
Kyrgyzstan
South Korea
Lebanon
Mexico
Myanmar
Malaysia
Nigeria
Nicaragua
New Zealand
Pakistan
Peru
Philippines
Puerto Rico
Romania
Russia
Serbia
Thailand
Tajikistan
Turkey
Taiwan ROC
Ukraine United States
Vietnam
Zimbabwe
2.0
2.5
−60 −40 −20 0
Mobility Index
National Identification
Fig. 4 Relation between national identification (y-axis) and community mobility (x-axis) in 42 countries and territories. Google mobility is depicted as a
mean change in mobility during April and May 2020 (i.e., blueish colours indicate a greater reduction of mobility during this period while reddish colours
indicate a smaller reduction of mobility). Grey shading is the 95% confidence interval.
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One major strength of our paper is the scope of nations we
included in our samples. The first study included data from 67
nations and the second study included data from 42 countries.
The vast majority of published research in psychology and social
sciences has been conducted in so-called WEIRD cultures66,
typically restricted to the narrow western and educational setting
of American or European university students, and non-
representative participants from industrialized, rich and demo-
cratic countries. The COVID-19 pandemic, however, is a truly
global issue underscoring the importance of gathering samples
outside these WEIRD cultures. Moreover, it was striking to see
that the same person-level association between NI and our public
health measures was in the same direction in almost every
country we studied. Although we managed to collect data from a
wide variety of countries and territories, we were unable to obtain
samples from every nation (especially in Africa and the middle
east). As such, we encourage future research in these countries to
see if the same dynamics are at play.
Another element of our paper was an attempt to collect
representative or stratified samples in Study 1. While most studies
in psychology focus on convenience samples (e.g., undergraduate
or MTurk participants), it is important to gather samples that are
more diverse with regards to gender, age, and other key risk
factors during a pandemic. Collecting representative samples
affords the opportunity to help make better generalizations to the
wider population within each country as well as the broader
sample of countries around the globe. Due to funding constraints,
we were not able to obtain representative samples from most
nations. As such, we are unable to make strong generalizations
about the populations in those countries. But note that we did
directly compare the findings in more vs. less representative
samples and found no significant difference in the overall rela-
tionship between NI and all three public health measures (see
Supplementary Information for details).
This research was correlational and conducted during the early
phase of the pandemic. Although a causal relation between NI
and public health behaviour makes sense from a theoretical
perspective, we cannot rule out the possibility that public health
behaviour causes NI, or that both are caused by a third variable
(e.g., ref. 23). Moreover, we have no evidence whether this pattern
would apply during later stages of the current or future pan-
demics. Indeed, national identity may increase during times of
crisis as people recognize their duty as citizens to help respond to
this issue. We encourage future work to experimentally manip-
ulate the salience of NI or frame health messages in a way that
highlights the link between identification and the public health
measures. Another limitation is the exclusive focus on national
groups rather than, for instance, identification with a city, region,
religion, or ethnic group—or, for that matter, all of humanity.
Some research suggests that local leaders may be ineffective if
their advice contradicts a national leader (see ref. 26). In the
current pandemic, nations have been among the most important
actors for implementing policy or promoting national health
guidelines, but sub-national units and international organizations
such as the World Health Organization also play an
important role.
The COVID-19 pandemic spreading across the world is one of
the most devastating global health crises of the past century. Until
a verifiably safe and effective vaccine or therapeutic treatment is
universally administered, efforts to inspire collective action for
greater compliance with public health measures remain a central
challenge when mitigating the transmission of the SARS-CoV-2
virus (e.g., spatial distancing, physical hygiene, and support for
health policies). Moreover, understanding social identity and
collective behaviour likely plays a key role in vaccination efforts67.
Our large-scale studies suggest that identification with one’s
nation is positively associated with support for and engagement
in critical behavioural public health measures. Understanding the
role of social identity appears to be an important issue when
addressing public health crises.
Methods
In Study 1, we launched a call using social media to collect data all over the world
on psychological factors that might be related to COVID-19 pandemic response,
with public health support as the primary outcome in April 2020. Each team was
asked to collect data from at least 500 participants, representative with respect to
gender and age, in their own country or territory. We created a survey in English
(see below) that we sent to each team. The survey was approved by the ethics board
at the University of Kent (each research team was allowed to include additional
items after the main survey under their own institutional protocol). We have
complied with all relevant ethical regulations and all participants were asked to give
informed consent. Where necessary, each team translated the survey into the local
language, using the standard forward-backward translation method, and then
collected the data. The datasets were then collated and analyzed using multi-level
models. We report how we determined our sample size, all data exclusions (if any),
all manipulations, and all measures in the study (see Supplementary Information).
All materials and data are available at: https://osf.io/y7ckt/.
Raw data we obtained from all collaborators were cleaned to exclude any
duplicate answers as well as those younger than 18 years or older than 100 years.
We then excluded data from two participants from Puerto Rico and 313 partici-
pants recruited from the UEA where it was difficult to establish participant
nationality. This resulted in a sample of 51,089 participants. For the current ana-
lysis, we also excluded participants who had missing data on all six key variables of
interest. We were left with a sample of 49,968 for analyses (Mean age =43; Gender
=52% females). Figure 1shows the geographical distribution of countries included
in the project (For a full list and sample characteristics from each country, please
see Supplementary Information). The sample includes countries from all con-
tinents except for Antarctica. Due to our open call for collaborators, some con-
tinents are overrepresented (e.g., Europe, Americas) while others are
underrepresented (e.g., Africa, Middle East).
We encouraged teams to collect nationally representative samples. Of the 67
countries in which data were collected, representative samples were collected in 28,
convenience samples were collected in 36, and both types of sampling were used in
three countries. To determine if the relationships that were the focus of our paper
varied as a function of the type of sample, we conducted analyses that compared
coefficients for countries that had the three types of samples. These analyses found
only one difference as a function of type of sample. Type of sample moderated the
slope between spatial distancing and national identity. The overall mean slope was
0.12, and the estimated slope for countries that collected representative samples
was 0.16, whereas it was 0.08 for countries that collected convenience samples.
Importantly, both were statistically significant from 0 (p< 0.001).
Questionnaires were administered online. Each participant completed a series of
psychological measures and self-reported public health behaviours (see complete
survey with all items in Supplementary Information). Participants completed the
scales in random order.
For the current paper, we focused on three potential predictors of public health
support. Our primary predictor was a two-item NI measure (which included one
item from ref. 9and an additional item measuring identity centrality from ref. 8): “I
identify as (nationality)”and “Being a (nationality) is an important reflection of
who I am”. Our secondary predictor was a three-item NN scale36, which included
the following sample item: “My (national group) deserves special treatment.”The
nationalities were provided by the survey researchers. These measures used an 11-
point slider scale with three labels items: 0 =“strongly disagree”,5=“neither agree
nor disagree”,10=“strongly agree”.
As a third predictor, we included a one-item measure of PI:“Overall, how would
you best describe yourself in terms of PI?”. This measure used a scale from 0 =
extremely liberal/left-leaning to 10 =extremely conservative/right-leaning). This
single-item measure of ideology has been found to account for a significant pro-
portion of the variance in presidential voting intentions in American National
Election studies between 1972 and 200468. We included the terms left-leaning and
right-leaning to make the item generalizable to numerous political systems.
As the primary outcome variable, we included three measures of public health
support. A spatial distancing scale, consisting of five items, as, for example, “During
the days of the coronavirus (COVID-19) pandemic, I have been staying at home as
much as practically possible”. (Prior to conducting our analyses, we learned that
the five-item scale had low reliability (α=0.002). However, after dropping one bad
item the scale had acceptable reliability (α=0.72). As such, all analyses reported in
the paper use this four-item version of the scale.) A physical hygiene scale, con-
sisting of five items, as, for example, “During the days of the coronavirus (COVID-
19) pandemic, I have been washing my hands longer than usual”.Apolicy support
scale, consisting of five items, as, for example, “During the days of the coronavirus
(COVID-19) pandemic, I have been in favour of closing all schools and uni-
versities”. We used an 11-point “slider scale with three labels: 0 =“strongly dis-
agree”,50=“neither agree nor disagree”, 100 =“strongly agree”, which was re-
coded to a scale from 0 to 10.
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To see if these relationships varied as a function of socio-economic factors and
the state of the pandemic in each country, we examined several country-level
factors. Specifically, we included the 2018 (most recent available) HDI (ranging
from 0 to 1), which represents a combined index of life expectancy at birth, level of
education (mean years of schooling for adults over 25 and expected years of
schooling for children), and national wealth (gross national income per capita69).
To ensure our results were not confounded with the pandemic rate across
countries, we measured the total COVID-19 infection and mortality cases (as well
as the infection and mortality rate per capita) in each country at the start of data
collection for this project. Our main findings did not vary as a function of total
infections and deaths as well as infections and deaths per capita at the start of data
collection for this project70 (April 17, 2020). These variables had very little impact
on the results and are not discussed further. All measures will be made publicly
available upon publication at the Open Science Framework website.
We conceptualized the data as a multi-level data structure in which persons were
nested within countries, and we analyzed the data with a series of multi-level
models (MLM) using the programme HLM71 (see ref. 51 for a description of using
MLM to analyze data from multinational studies). The analyses examined within-
country (person-level) relationships between behavioural health-protective
responses to COVID-19 (i.e., spatial distancing, physical hygiene, and policy
support) and individual differences in collective concerns (i.e., NI, NN, and PI). We
also examined the moderating effects of country‑level differences on these person-
level relationships. For instance, we examined if these person-level relationships
between collective concerns and health-protective measures varied as a function of
between-country differences in overall human development as measured by the
HDI or national rates of COVID-19 infections and mortality.
Before examining relations between COVID-19 protection and socio-political
attitudes, we examined the reliability of our measures (with the exception of PI,
which was measured with only one item). These analyses consisted of models in
which the i items in a scale were nested within j persons, which were nested within
k countries. Such analyses provide the multi-level equivalent of a Cronbach’s
alpha72,73. The model is below.
Level 1 (item level): y
ijk
=π
0jk
+e
ijk
Level 2 (person-level): π
0jk
=b
00k
+r
0jk
Level 3 (country-level): b
00k
=g
000
+u
00k
In the level 1 model, y
ijk
is response i, for person j, in country k,π
0jk
is a random
coefficient representing the mean response for person jin country k,b
0j
is a
random coefficient representing the mean of yfor country k(across the j persons in
each country), e
ijk
represents the error associated with each measure, and the
variance of r
ijk
constitutes the within-country variance. In multi-level modelling,
the coefficients from one level of analysis are passed up to the next. In the level 3
model, g
000
represents the grand mean of the country-level means (b
00ks
) from the
person-level model, u
00k
represents the error of b
00k
, and the variance of u
00k
constitutes the level 3, country-level variance.
These analyses suggested that, with the exception of spatial distancing, our scales
were at least “moderately”reliable74 (α> 0.60). The reliability estimates and
descriptive statistics are presented in Table 1. For spatial distancing, follow-up
analyses indicated that a reliable scale could be created from items 1, 3, 4, and 5.
Item 2 asking about visiting friends, family or colleagues was therefore dropped
from the final analyses.
The estimated means suggest that people generally reported following the
guidelines for contact and hygiene and they supported policies that were intended
to reduce the impact of COVID-19 (i.e., means for all three measures were above 8,
on scales ranging from 0 to 10). Moreover, although the majority of variance in NI,
NN, and PI was within-country, there was also notable between-country variance.
This justified further analyses of relations between country-level means of these
measures and HDI. We calculated scale means and used Mplus75 to estimate multi-
level correlations for person-level measures, controlling for the nested structure of
the data (see Table 1).
The next set of analyses examined relations between scores on the HDI and the
means of the person-level measures. This model was a variant of the unconditional
model. HDI scores were entered as a predictor in the country-level model pre-
sented above (level 3). MLM analyses do not estimate standardized coefficients, and
to simplify the interpretation of the results, HDI scores were standardized prior to
analysis (and, therefore, were entered uncentered). Note that these analyses
account for the reliability of scales. By nesting items within persons, we estimated a
latent mean for each construct.
The results of these analyses are presented in Table 2. For all measures, except
PI, there were negative relationships between HDI scores and country-level means.
Note that the coefficients in the table represent the change in a country-level mean
associated with a 1SD increase in HDI scores. In other words, citizens in countries
with higher scores on the global HDI also reported less support for COVID-19
public health measures. Effect sizes are defined as the percent reduction in the
country-level variance of a null model (Table 2) associated with the inclusion of
HDI scores at the country level. Because PI was measured with only one item, the
variance estimates and effect size for PI are from a two-level model (persons nested
within countries). Estimating effect sizes for multi-level analyses such as those used
in the present study are discussed in Nezlek51.
Next, we examined person-level relationships between the three COVID-19
protection measures (modelled as outcomes) and NI, NN, and PI (modelled as
predictors). Predictors were defined as the mean scores for each scale. To account
for relationships among the predictors, all predictors were entered at the person
level of the model. Predictors were entered group-mean centred and were modelled
as randomly varying. Again, because this was done using a three-level model in
which the first level was a measurement level, outcomes were modelled as
latent means.
Entering predictors group-mean centred meant that estimates of coefficients
controlled for country-level differences in means51. Entering predictors as ran-
domly varying meant that the model account for the possibility that slopes varied
between countries. In essence, a regression equation, consisting of an intercept and
a set of slopes, was estimated for each country, and these estimates were “passed
up”to the country level where they were tested for significance. The model is below
(item level is not shown).
Level 2 (person-level): π
0jk
=b
00k
+b
01k
*(NN) +b
02k
*(NI) +b
03k
*(PI) +r
0jk
Level 3 (intercept): b
00k
=g
000
+u
00k
Level 3 (NN slope): b
00k
=g
010
+u
01k
Level 3 (NI slope): b
00k
=g
020
+u
02k
Level 3 (PI slope): b
00k
=g
030
+u
03k
The hypothesis of interest was tested by assessing the significance of the g
010
,
g
020
, and g
030
coefficients in this model. Was the mean slope between an outcome
and a predictor significantly different from 0? These unstandardized coefficients
represent the expected change in an outcome for a one-unit increase in a predictor,
i.e., an increase of one on a scale (out of 11). Also, the random error terms for all
predictors were significant at p< 0.001.
According to these analyses, NI was the most reliable and strongest predictor of
our COVID-19 public health support measures (see Fig. 2for the coefficients in
each country as well as Supplementary Figures 1, 2, and 3 for the coefficients with
95% confidence intervals). It was significantly and positively related to all three
measures (even after adjusting for NN and PI). Individuals with stronger NI
(relative to other people within their own nation) reported stronger support for
limiting physical distance and improving physical hygiene than individuals with
weaker identification, and they also endorsed COVID-19 public health policies to a
greater extent.
NN was significantly positively related to two of the three protective measures
(albeit weakly). Individuals scoring higher in NN supported recommendations for
physical hygiene and endorsed COVID-19 related policies more strongly compared
to individuals with lower levels of NN.
The relationships between PI and public health support were negative (albeit
weakly) for all three measures, indicating that individuals with more left-leaning or
liberal political orientation tended to endorse recommendations for contact and
hygiene and supported COVID-19-related policies more strongly than those with
more right-leaning or conservative political orientation.
Effect sizes were estimated using a similar procedure to that used for estimating
effect sizes at the country-level. Effect sizes were defined as the percent reduction in
the person-level variance of a null model (Table 2) associated with the inclusion of
the three predictors (collective narcissism, NI, and PI) at the person level. The three
predictors accounted for 8% of the person-level variance of the contact measure,
for 7% of the person-level variance of the hygiene measure, and 5% of the person-
level variance of the policy support measure.
Next, we modelled country-level factors, such as the HDI to examine whether
the relations between person-level factors, like NI, and public health support would
remain after adjusting for the general health and standard of living in a country.
The HDI is a measure of achievement in key dimensions of human development: a
long and healthy life, being knowledgeable, and having a decent standard of living.
The HDI is the mean of normalized indices for each of the three dimensions (see
ref. 76). Specifically, we examined if person-level relations (slopes) between col-
lective concerns and COVID-19 public health support varied across countries as a
function of HDI by adding HDI scores to the level 3 model that examined slopes.
The relationships between NI and each of the three public health measures were
not heavily impacted or moderated by HDI. Indeed, we observed only two modest
moderating effects.
We found that HDI moderated the relationships between NN and spatial dis-
tancing (g
011
=−0.03, t=2.93, p< 0.01). The relationship between NN and spatial
distancing was negative in countries that had higher HDI scores (the estimated
slope for a country +1 SD on the HDI was 0.037) but positive in countries that had
lower HDI scores (the estimated slope for a country −1 SD on the HDI was 0.027).
We also found that HDI moderated the relationship between PI and hygiene
(g
031
=−0.016, t=2.16 p=0.034). The overall negative relationship between
right-wing PI and hygiene was stronger in countries that had higher HDI scores
(the estimated slope for a country +1 SD on the HDI was −0.031) than in
countries that had lower HDI scores (the estimated slope for a country −1SDon
the HDI was 0.002, functionally 0). We note that these effects were not statistically
significant at the p< 0.001 threshold we used for Study 1 so we recommend
interpreting them with caution.
In Study 2, we accessed data from two publicly available datasets: the World
Values Survey52 and the COVID-19 Google Community Mobility Reports53 which
indicate how people’s physical movement has changed in response to COVID-19
policies (available at www.google.com/covid19/mobility/). We examined whether
countries with higher average NI would also show stronger change in mobility in
response to COVID-19 restrictions during April and May 2020. We created an
index of NI using the two relevant items from the World Value Survey (i.e.,
national pride and closeness to their nation) and an index of physical mobility by
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averaging community movement across all available places (i.e., retail and
recreation, groceries and pharmacies, parks, transit stations, workplaces, and
residential). We analyzed all 42 countries in which aggregate data was publicly
available for both for NI and mobility scores. The study was approved by the ethics
board at the University of Kent. All materials and data are available at: https://
osf.io/y7ckt/.
NI was computed based on indices from the first release of data from Wave 7 of
the World Value Survey. The surveys were conducted between early 2017 to mid-
2020. All countries employed random probability representative samples of the
adult population (We computed country averages using default weights applied in
the World Values Survey dataset. However, our results are very similar whether are
not these weights are applied). Our analysis focused on two indices. First, we used
the national pride question: “How proud are you to be [country’s nationality]? 1 =
Very proud,2=Quite proud,3=Not very proud,4=Not at all proud, and 5 =I
am not [country’s nationality]. (In some countries, this source item actually refers to
pride of “being a citizen [of the country].”A response choice was available for
respondents who were not citizens of the country where they were interviewed in
Wave 7 of the World Value Survey. While some countries differ in terms of their
ethnic or civic-based notions of citizenship, we used NI to denote overall identifi-
cation with a specific national polity.) We excluded the latter category and re-coded
the remaining responses on a scale from 0 =Not at all proud to 3 =Very proud.
The second item captured closeness to one’s country: “People have different
views about themselves and how they relate to the world. Using this card, would
you tell me how close do you feel to [country]?”1=Very close,2=Close,3=Not
very close,4=Not close at all. We re-coded the responses on a scale from 0 =Not
close at all proud to 3 =Very close. (Note that participants can refuse to respond or
indicate “I don’t know”to both items. These responses were coded as missing.) The
two items were positively correlated at country-level (r=0.31, p=0.049), so we
averaged them to create a composite index of NI (M=2.38, SD =0.24).
Community mobility was computed based on Google Community Mobility
Reports, which indicate how people’s aggregate physical movement has changed
over time. The reports show movement trends over time across different categories
of places: retail and recreation, groceries and pharmacies, parks, transit stations,
workplaces, and residential. Percentage change for each day is computed relative to
a baseline, which is a median value, for the corresponding day of the week, during
the 5-week period from Jan 3 to Feb 6, 2020. To create our overall index of
reductions in community mobility, we computed average indices for each of the
places over April and May 2020 (to roughly match the time frame of Study 1). We
then created a composite index of mobility by averaging mobility across all places,
with residential mobility reverse-coded (α=0.91, M=−34.87, SD =15.15). This
translates to a 35% reduction in movement from the start of the calendar year to
the spring in these 42 nations.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The data generated during and/or analyzed during the current study are available on the
Open Science Framework repository, https://osf.io/y7ckt/. The publicly available datasets
that support the results of this study, The World Values Survey and the COVID-19
Google Community Mobility Reports, are available from https://
www.worldvaluessurvey.org/wvs.jsp, and www.google.com/covid19/mobility/,
respectively.
Received: 22 December 2020; Accepted: 14 October 2021;
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Acknowledgements
The authors wish to thank Katie Eilish Brown for constructive comments throughout the
editorial process. We also acknowledge the following funding sources: John Templeton
Foundation (JTF) - 61378 [Van Bavel] Narodowe Centrum Nauki (National Science
Centre) - 2018/29/B/HS6/02826 [Cislak] RCUK | Medical Research Council (MRC) -
MR/P014097/1 [Lockwood] Economic Social Research Council Impact Acceleration
Award, University of Oxford [Lockwood] Gouvernement du Canada | Social Sciences
and Humanities Research Council of Canada (Conseil de recherches en sciences
humaines du Canada) - 130760 [Choma] Gouvernement du Canada | Social Sciences and
Humanities Research Council of Canada (Conseil de recherches en sciences humaines du
Canada) - SSHRC-506547 [Cunningham] Agentúra na Podporu Výskumu a Vývoja
(Slovak Research and Development Agency) - APVV-17-0596 [Findor] Narodowe
Centrum Nauki (National Science Centre) - 2015/19/B/HS6/01253 [Jasko] Academy of
Finland (Suomen Akatemia) [Laakasuo] Austrian Science Fund (Fonds zur Förderung
der Wissenschaftlichen Forschung) - I3381 [Lamm] Universität Wien (University of
Vienna) [Lamm] Ministry of Science and Technology, Taiwan (Ministry of Science and
Technology of Taiwan) [Lin] Aarhus Universitets Forskningsfond (Aarhus University
Research Foundation) - AUFF-E-201 9-9-4 [Mitkidis] Vetenskapsrådet (Swedish
Research Council) - 2018-00877 [Olsson] Aarhus Universitets Forskningsfond (Aarhus
University Research Foundation) - 28207 [Otterbring] Carlsbergfondet (Carlsberg
Foundation) - CF20-0044 [Petersen] Ministarstvo Prosvete, Nauke i Tehnološkog Raz-
voja (Ministry of Education, Science and Technological Development of the Republic of
Serbia) - 47010 [Todosijević] NOMIS Stiftung (NOMIS Foundation) [Tsakiris] Ministry
of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)
[Tung] National Natural Science Foundation of China (National Science Foundation of
China) - 71972065 [Zhang] National Natural Science Foundation of China (National
Science Foundation of China) - 71602163 [Zhang] RCUK | Biotechnology and Biological
Sciences Research Council (BBSRC) - BB/R010668/1 [Apps] Agence Nationale de la
Recherche (French National Research Agency) - ANR-17-EURE-0010 [Conway] Gou-
vernement du Canada | Social Sciences and Humanities Research Council of Canada
(Conseil de recherches en sciences humaines du Canada) - SSHRC-506547 [Davis]
Deutsche Forschungsgemeinschaft (German Research Foundation) - EXC 2052/1 –
390713894 [Frempong] Gouvernement du Canada | Natural Sciences and Engineering
Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie
du Canada) [Fugelsang] Agentúra na Podporu Výskumu a Vývoja (Slovak Research and
Development Agency) - APVV-17-0596 [Hruška] Carlsbergfondet (Carlsberg Founda-
tion) - CF20-0044 [Jørgensen] Gouvernement du Canada | Social Sciences and Huma-
nities Research Council of Canada (Conseil de recherches en sciences humaines du
Canada) - SSHRC-506547 [Long] Austrian Science Fund (Fonds zur Förderung der
Wissenschaftlichen Forschung) - I3381 [Nitschke] Universität Wien (University of
Vienna) [Nitschke] Deutsche Forschungsgemeinschaft (German Research Foundation) -
EXC 2052/1 –390713894 [Stadelmann] Agence Nationale de la Recherche (French
National Research Agency) - ANR-10-IDEX-0001-02 PSL [Strickland] Agence Nationale
de la Recherche (French National Research Agency) - ANR-17-EURE-0017 [Strickland]
Vetenskapsrådet (Swedish Research Council) [Tinghög] HKUST IEMS research grant
ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-27668-9
10 NATURE COMMUNICATIONS | (2022) 13:517 | https://doi.org/10.1038/s41467-021-27668-9 | www.nature.com/naturecommunications
Content courtesy of Springer Nature, terms of use apply. Rights reserved

project, funded by EY [Tyrala] Vetenskapsrådet (Swedish Research Council) [Västfjäll]
Gouvernement du Canada | Social Sciences and Humanities Research Council of Canada
(Conseil de recherches en sciences humaines du Canada) - 435-2012-1135 [Wohl]
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal
Agency for the Support and Evaluation of Graduate Education) - 88887.310255/2018
[Boggio] Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian
Federal Agency for the Support and Evaluation of Graduate Education) - 1133/2019
[Boggio] Ministry of Science, Technology and Innovation | Conselho Nacional de
Desenvolvimento Científico e Tecnológico (National Council for Scientific and Tech-
nological Development) - 309905/2019-2 [Boggio] Research Council of Norway through
its Centres of Excellence Scheme, FAIR project No 262675 [Sjåstad] Institute of Social
Sciences Ivo Pilar [Pavlović] J. William Fulbright Program [Azevedo] Institute for
Lifecourse Development, University of Greenwich [Birtel] Institute of Social Sciences Ivo
Pilar [Franc] Project Pro.Co.P.E., IMT School (PAI2019) [Bilancini] Italian Ministry of
University and Research - PRIN 2017 (20178293XT) [Boncinelli] Princeton Graduate
Student Research Funding (Program in Cognitive Science) [Vlasceanu] Corruption
Laboratory on Ethics, Accountability, and the Rule of Law (CLEAR), University of
Virginia [Yucel] Institute for Lifecourse Development, University of Greenwich [Farmer]
Research Council of Norway through its Centres of Excellence Scheme, FAIR project No
262675 [Ay] Charles Koch Foundation, Center for the Science of Moral Understanding
[Gray] Australian Research Council (DP180102384) [Levy] JSPS KAKENHI
(JP16H03079, JP17H00875, JP18K12015, JP20H04581, and 21H03784) [Yamada] St
Andrews and Stirling Graduate Programme Research Funding [Schönegger] Institute of
Social Sciences Ivo Pilar [Maglić] São Paulo Research Foundation - FAPESP (2019/
27100-1) [Sampaio] Institute of Social Sciences Ivo Pilar [Mikloušić] Seele Neuroscience
Social Projects Fund (2020/004) [Monroy-Fonseca] São Paulo Research Foundation -
FAPESP (2019/26665-5) [Rego]
Author contributions
Jay J. Van Bavel launched the project, oversaw the design of the study, data collection,
curation and analysis, and wrote the manuscript. Aleksandra Cichocka and Paulo S.
Boggio oversaw the design of the study, data collection, curation, and analysis, conducted
data analysis, and wrote the manuscript. Aleksandra Cichocka coordinated ethics
approval. Valerio Capraro and Hallgeir Sjåstad designed the study, oversaw data collec-
tion, and contributed to writing the manuscript. John B. Nezlek, Flavio Azevedo, Tomislav
Pavlović, Gabriel G. Rêgo, and Waldir M. Sampaio conducted data analysis. Flavio
Azevedo coordinated data curation efforts. Flavio Azevedo, Tomislav Pavlović, Gabriel G.
Rêgo, and Waldir M. Sampaio prepared the dataset and its metadata. Flavio Azevedo,
Tomislav Pavlović, Gabriel G. Rêgo and Aleksandra Cichocka organised project doc-
umentation. Flavio Azevedo designed, populated, and maintained the ICSMP project’s
website. Bjarki Gronfeldt, Anni Sternisko, Hans H. Tung and Ming-Jen Lin assisted with
data preparation, documentation and analysis. Mark Alfano, Michele J. Gelfand, Flavio
Azevedo, Michèle D. Birtel, Aleksandra Cislak, Claus Lamm, Patricia L. Lockwood, Robert
Malcolm Ross, Biljana Gjoneska, and F. Ceren Ay contributed to various phases of the
organisation, including writing the manuscript. Waqas Ejaz, Annalisa Myer, and Valerio
Capraro were responsible for creating the authors’list, as well as the contributions
statement. Valerio Capraro, Koen Abts, Elena Aguadullina, John Jamir Benzon Aruta,
Sahba Nomvula Besharati, Alexander Bor, Becky L. Choma, Charles David Crabtree,
William A. Cunningham, Koustav De, Waqas Ejaz, Christian T. Elbaek, Andrej Findor,
Daniel Flichtentrei, Renata Franc, Biljana Gjoneska, June Gruber, Estrella Gualda, Yusaku
Horiuchi, Toan Luu Duc Huynh, Agustin Ibanez, Mostak Ahamed Imran, Jacob Israe-
lashvili, Katarzyna Jasko, Jaroslaw Kantorowicz, Elena Kantorowicz-Reznichenko, André
Krouwel, Michael Laakasuo, Claus Lamm, Caroline Leygue, Ming-Jen Lin, Mohammad
Sabbir Mansoor, Antoine Marie, Lewend Mayiwar, Honorata Mazepus, Cillian McHugh,
John Paul Minda, Panagiotis Mitkidis, Andreas Olsson, Tobias Otterbring, Dominic J.
Packer, Anat Perry, Michael Bang Petersen, Arathy Puthillam, Julián C. Riaño-Moreno,
Tobias Rothmund, Hernando Santamaría-García, Petra C. Schmid, Drozdstoy Stoyanov,
Shruti Tewari, Bojan Todosijević, Manos Tsakiris, Hans H. Tung, Radu G. Umbreș,
Edmunds Vanags, Madalina Vlasceanu, Andrew Vonasch, Meltem Yucel, Yucheng Zhang
acted as national team leaders and were responsible for data collection in their own
country. Mohcine Abad, Eli Adler, Narin Akrawi, Hamza Alaoui Mdarhri, Hanane
Amara, David M. Amodio, Benedict G. Antazo, Matthew Apps, F. Ceren Ay, Mouha-
madou Hady Ba, Sergio Barbosa, Brock Bastian, Anton Berg, Maria P. Bernal-Zárate,
Michael Bernstein, MichałBiałek, Ennio Bilancini, Natalia Bogatyreva, Leonardo Bonci-
nelli, Jonathan E. Booth, Sylvie Borau, Ondrej Buchel, Chrissie F. Carvalho, Tatiana
Celadin, Chiara Cerami, Hom Nath Chalise, Xiaojun Cheng, Luca Cian, Kate Cockcroft,
Jane Conway, Mateo Andres Córdoba-Delgado, Chiara Crespi, Marie Crouzevialle, Jo
Cutler, Marzena Cypryańska, Justyna Dabrowska, Michael A. Daniels, Victoria H. Davis,
Pamala N Dayley, Sylvain Delouvee, Ognjan Denkovski, Guillaume Dezecache, Nathan A.
Dhaliwal, Alelie B. Diato, Roberto Di Paolo, Marianna Drosinou, Uwe Dulleck, Jānis
Ekmanis, Arhan S. Ertan, Tom W Etienne, Hapsa Hossain Farhana, Fahima Farkhari,
Harry Farmer, Ali Fenwick, Kristijan Fidanovski, Terry Flew, Shona Fraser, Raymond
Boadi Frempong, Jonathan A. Fugelsang, Jessica Gale, E. Begoña Garcia-Navarro, Prasad
Garladinne, Oussama Ghajjou, Theofilos Gkinopoulos, Kurt Gray, Siobhán M. Griffin,
Bjarki Gronfeldt, Mert Gümren, Ranju Lama Gurung, Eran Halperin, Elizabeth Harris,
Volo Herzon, Matej Hruška, Guanxiong Huang, Matthias F. C. Hudecek, Ozan Isler,
Simon Jangard, Frederik Juhl Jørgensen, Frank Kachanoff, John Kahn, Apsara Katuwal
Dangol, Oleksandra Keudel, Lina Koppel, Mika Koverola, Emily Kubin, Anton Kunnari,
Yordan Kutiyski, Oscar Laguna, Josh Leota, Eva Lermer, Jonathan Levy, Neil Levy,
Chunyun Li, Elizabeth U. Long, Chiara Longoni, Marina Maglić, Darragh McCashin,
Alexander L Metcalf, Igor Mikloušić, Soulaimane El Mimouni, Asako Miura, Juliana
Molina-Paredes, César Monroy-Fonseca, Elena Morales-Marente, David Moreau, Rafał
Muda, Annalisa Myer, Kyle Nash, Tarik Nesh-Nash, Jonas P. Nitschke, Matthew S. Nurse,
Yohsuke Ohtsubo, Victoria Oldemburgo de Mello, Cathal O’Madagain, Michal Onderco,
M. Soledad Palacios-Galvez, Jussi Palomäki, Yafeng Pan, Zsófia Papp, Philip Pärnamets,
Mariola Paruzel-Czachura, Zoran Pavlović, César Payán-Gómez, Silva Perander, Michael
Mark Pitman, Rajib Prasad, Joanna Pyrkosz-Pacyna, Steve Rathje, Ali Raza, Gabriel G.
Rêgo, Kasey Rhee, Claire E. Robertson, Iván Rodríguez-Pascual, Teemu Saikkonen,
Octavio Salvador-Ginez, Waldir M. Sampaio, Gaia C. Santi, Natalia Santiago-Tovar,
David Savage, Philipp Schönegger, David T. Schultner, Enid M. Schutte, Andy Scott,
Madhavi Sharma, Pujan Sharma, Ahmed Skali, David Stadelmann, Clara Alexandra
Stafford, Dragan Stanojević, Anna Stefaniak, Anni Sternisko, Augustin Stoica, Kristina K.
Stoyanova, Brent Strickland, Jukka Sundvall, Jeffrey P. Thomas, Gustav Tinghög, Benno
Torgler, Iris J. Traast, Raffaele Tucciarelli, Michael Tyrala, Nick D. Ungson, Mete S. Uysal,
Paul A. M. Van Lange, Jan-Willem van Prooijen, Dirk van Rooy, Daniel Västfjäll,
Peter Verkoeijen, Joana B. Vieira, Christian von Sikorski, Alexander Cameron Walker,
Jennifer Watermeyer, Erik Wetter, Ashley Whillans, Robin Willardt, Michael J. A. Wohl,
Adrian Dominik Wójcik, Kaidi Wu, Yuki Yamada, Onurcan Yilmaz, Kumar Yogees-
waran, Carolin-Theresa Ziemer, Rolf A. Zwaan were involved in the translation of the
survey in their local language and in data collection. All authors revised and approved the
final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
André Krouwel (ownership and stocks in Kieskompas BV, data collector in this project).
No payment was received by the author. No other authors reported a competing interest.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-021-27668-9.
Correspondence and requests for materials should be addressed to Jay J.Van Bavel or
Flavio Azevedo.
Peer review information Nature Communications thanks Jonas Kunst, Elizabeth Theiss-
Morse and the other, anonymous, reviewer(s) for their contribution to the peer review of
this work. Peer reviewer reports are available.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved

Jay J. Van Bavel 1✉, Aleksandra Cichocka 2, Valerio Capraro3, Hallgeir Sjåstad 4, John B. Nezlek 5,6,
Tomislav Pavlović7, Mark Alfano 8, Michele J. Gelfand9, Flavio Azevedo 10✉, Michèle D. Birtel11,
Aleksandra Cislak 5, Patricia L. Lockwood 12,13, Robert Malcolm Ross 14, Koen Abts 15,
Elena Agadullina 16, John Jamir Benzon Aruta 17, Sahba Nomvula Besharati 18, Alexander Bor 19,
Becky L. Choma20, Charles David Crabtree 21, William A. Cunningham22, Koustav De 23, Waqas Ejaz 24,
Christian T. Elbaek 25, Andrej Findor 26, Daniel Flichtentrei27, Renata Franc 7, Biljana Gjoneska 28,
June Gruber29, Estrella Gualda30,31, Yusaku Horiuchi21, Toan Luu Duc Huynh 32, Agustin Ibanez 33,34,
Mostak Ahamed Imran 35, Jacob Israelashvili36, Katarzyna Jasko37, Jaroslaw Kantorowicz 38,
Elena Kantorowicz-Reznichenko39, André Krouwel 40, Michael Laakasuo 41, Claus Lamm 42,
Caroline Leygue 43, Ming-Jen Lin 44,45, Mohammad Sabbir Mansoor 46, Antoine Marie 19,
Lewend Mayiwar47, Honorata Mazepus48,49, Cillian McHugh 50, John Paul Minda 51,
Panagiotis Mitkidis 25,52, Andreas Olsson 53, Tobias Otterbring 54,55, Dominic J. Packer56, Anat Perry 36,
Michael Bang Petersen 19, Arathy Puthillam57, Julián C. Riaño-Moreno 58,59, Tobias Rothmund 10,
Hernando Santamaría-García60, Petra C. Schmid 61, Drozdstoy Stoyanov 62, Shruti Tewari63,
Bojan Todosijević64, Manos Tsakiris 65,66,67, Hans H. Tung 68,45, Radu G. Umbreș69,
Edmunds Vanags 70, Madalina Vlasceanu 71, Andrew Vonasch72, Meltem Yucel 73,74, Yucheng Zhang 75,
Mohcine Abad 76, Eli Adler36, Narin Akrawi77, Hamza Alaoui Mdarhri 76, Hanane Amara 78,
David M. Amodio1,79, Benedict G. Antazo 80, Matthew Apps 13, F. Ceren Ay81,82, Mouhamadou Hady Ba83,
Sergio Barbosa 84,85, Brock Bastian 86, Anton Berg41, Maria P. Bernal-Zárate 58, Michael Bernstein87,
MichałBiałek 88, Ennio Bilancini89, Natalia Bogatyreva16, Leonardo Boncinelli 90, Jonathan E. Booth 91,
Sylvie Borau 92, Ondrej Buchel 93,94, C. Daryl Cameron95,96, Chrissie F. Carvalho97, Tatiana Celadin98,
Chiara Cerami99,100, Hom Nath Chalise 46, Xiaojun Cheng101, Luca Cian 102, Kate Cockcroft 18,
Jane Conway 103, Mateo Andres Córdoba-Delgado 60, Chiara Crespi100,104, Marie Crouzevialle 61,
Jo Cutler 12,13, Marzena Cypryańska5, Justyna Dabrowska 105, Michael A. Daniels106, Victoria H. Davis 22,
Pamala N. Dayley 107, Sylvain Delouvee 108, Ognjan Denkovski79, Guillaume Dezecache109,
Nathan A. Dhaliwal106, Alelie B. Diato110, Roberto Di Paolo 89, Marianna Drosinou41, Uwe Dulleck 111,112,113,114,
Jānis Ekmanis 70, Arhan S. Ertan115, Tom W. Etienne 116, Hapsa Hossain Farhana35, Fahima Farkhari 10,
Harry Farmer 11, Ali Fenwick 117, Kristijan Fidanovski118, Terry Flew 119, Shona Fraser120,
Raymond Boadi Frempong 121, Jonathan A. Fugelsang122, Jessica Gale 72, E. Begoña Garcia-Navarro 30,
Prasad Garladinne63, Oussama Ghajjou 123, Theofilos Gkinopoulos124, Kurt Gray125, Siobhán M. Griffin50,
Bjarki Gronfeldt2, Mert Gümren126, Ranju Lama Gurung46, Eran Halperin 36, Elizabeth Harris1,
Volo Herzon 41, Matej Hruška26, Guanxiong Huang 127, Matthias F. C. Hudecek 128,129, Ozan Isler 111,112,
Simon Jangard 53, Frederik J. Jørgensen 19, Frank Kachanoff125, John Kahn 21, Apsara Katuwal Dangol46,
Oleksandra Keudel130, Lina Koppel 131, Mika Koverola 41, Emily Kubin132, Anton Kunnari41, Yordan Kutiyski116,
Oscar Laguna116, Josh Leota 133, Eva Lermer 129,134,135, Jonathan Levy136,137, Neil Levy8, Chunyun Li91,
Elizabeth U. Long22, Chiara Longoni138, Marina Maglić7, Darragh McCashin139, Alexander L. Metcalf 140,
Igor Mikloušić7, Soulaimane El Mimouni78, Asako Miura 141, Juliana Molina-Paredes60,
César Monroy-Fonseca 142, Elena Morales-Marente 30, David Moreau 143, RafałMuda144,
Annalisa Myer74,145, Kyle Nash133, Tarik Nesh-Nash 78, Jonas P. Nitschke 42, Matthew S. Nurse 146,
Yohsuke Ohtsubo147, Victoria Oldemburgo de Mello22, Cathal O’Madagain76, Michal Onderco148,
M. Soledad Palacios-Galvez30, Jussi Palomäki 41, Yafeng Pan 53, Zsófia Papp149, Philip Pärnamets53,
Mariola Paruzel-Czachura150, Zoran Pavlović151, César Payán-Gómez 152, Silva Perander 41,
Michael Mark Pitman 18, Rajib Prasad153, Joanna Pyrkosz-Pacyna 154, Steve Rathje155, Ali Raza 156,157,
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12 NATURE COMMUNICATIONS | (2022) 13:517 | https://doi.org/10.1038/s41467-021-27668-9 | www.nature.com/naturecommunications
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Gabriel G. Rêgo158, Kasey Rhee 159, Claire E. Robertson1, Iván Rodríguez-Pascual 30, Teemu Saikkonen 160,
Octavio Salvador-Ginez43, Waldir M. Sampaio 158, Gaia C. Santi99, Natalia Santiago-Tovar161, David Savage162,
Julian A. Scheffer95, Philipp Schönegger 163,164, David T. Schultner 80, Enid M. Schutte18, Andy Scott 133,
Madhavi Sharma46, Pujan Sharma46, Ahmed Skali 165, David Stadelmann 121,
Clara Alexandra Stafford 51,166,167, Dragan Stanojević168, Anna Stefaniak 169, Anni Sternisko 1,
Agustin Stoica 170, Kristina K. Stoyanova171, Brent Strickland76,172, Jukka Sundvall 41, Jeffrey P. Thomas86,
Gustav Tinghög 131, Benno Torgler 111,112,173, Iris J. Traast80, Raffaele Tucciarelli 174,175, Michael Tyrala 176,
Nick D. Ungson177, Mete S. Uysal178, Paul A. M. Van Lange 179, Jan-Willem van Prooijen 179,
Dirk van Rooy180, Daniel Västfjäll 181, Peter Verkoeijen182, Joana B. Vieira53, Christian von Sikorski 183,
Alexander Cameron Walker 122, Jennifer Watermeyer 184, Erik Wetter185, Ashley Whillans 186,
Robin Willardt 61, Michael J. A. Wohl 169, Adrian Dominik Wójcik 187, Kaidi Wu 188, Yuki Yamada 189,
Onurcan Yilmaz 190, Kumar Yogeeswaran72, Carolin-Theresa Ziemer 10, Rolf A. Zwaan182 &
Paulo S. Boggio158
1
Department of Psychology and Neural Science, New York University, New York, NY, USA.
2
School of Psychology, University of Kent,
Canterbury, England.
3
Department of Economics, Middlesex University London, London, England.
4
Department of Strategy and Management,
Norwegian School of Economics, Bergen, Norway.
5
SWPS University of Social Sciences and Humanities, Poznań, Poland.
6
Department of
Psychological Sciences, College of William and Mary, Williamsburg, VA, USA.
7
Institute of Social Sciences Ivo Pilar, Zagreb, Croatia.
8
Department
of Philosophy, Macquarie University, Sydney, NSW, Australia.
9
Stanford Graduate School of Business, Stanford University, Stanford, CA, USA.
10
Institute of Communication Science, Friedrich-Schiller University Jena, Jena, Germany.
11
School of Human Sciences, Institute for Lifecourse
Development, University of Greenwich, London, England.
12
Department of Experimental Psychology, University of Oxford, Oxford, England.
13
Center
for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, England.
14
Department of Psychology, Macquarie University,
Sydney, NSW, Australia.
15
KU Leuven, Leuven, Belgium.
16
National Research University Higher School of Economics (HSE), Moscow, Russia.
17
De La
Salle University, Manila, Philippines.
18
Department of Psychology, University of the Witwatersrand, Johannesburg, South Africa.
19
Department of
Political Science, Aarhus University, Aarhus, Denmark.
20
X University, Toronto, Canada.
21
Department of Government, Dartmouth College,
Hanover, NH, USA.
22
Department of Psychology, University of Toronto, Toronto, ON, Canada.
23
Gatton College of Business and Economics,
University of Kentucky, Lexington, KY, USA.
24
Department of Mass Communication, National University of Science and Technology (NUST),
Islamabad, Pakistan.
25
Department of Management, Aarhus University, Aarhus, Denmark.
26
Faculty of Social and Economic Sciences, Comenius
University, Bratislava, Slovakia.
27
IntraMed, Buenos Aires, Argentina.
28
Macedonian Academy of Sciences and Arts, North Macedonia, Republic of
North Macedonia.
29
University of Colorado Boulder, Boulder, CO, USA.
30
ESEIS/COIDESO [ESEIS, Social Studies and Social Intervention Research
Center; COIDESO, COIDESO, Center for Research in Contemporary Thought and Innovation for Social Development], University of Huelva,
Huelva, Spain.
31
Faculty of Social Work, University of Huelva, Huelva, Spain.
32
WHU –Otto Beisheim School of Management, Vallendar, Germany.
33
Latin American Brain Health Institute (BrainLat), Adolfo Ibáñez University, Santiago, Chile.
34
Global Brain Health Institute, University of San
Andrés, Buenos Aires, Argentina.
35
Department of Educational and Counselling Psychology, University of Dhaka, Dhaka, Bangladesh.
36
Psychology
Department, The Hebrew University of Jerusalem, Jerusalem, Israel.
37
Institute of Psychology, Jagiellonian University, Kraków, Poland.
38
Institute of
Security and Global Affairs, Leiden University, The Hague, Netherlands.
39
Erasmus School of Law, Erasmus University Rotterdam,
Rotterdam, Netherlands.
40
Department of Political Science, Vrije University (VU) Amsterdam, Amsterdam, Netherlands.
41
Department of Digital
Humanities, University of Helsinki, Helsinki, Finland.
42
Department of Cognition, Emotion, and Methods in Psychology, University of Vienna,
Vienna, Austria.
43
School of Psychology, National Autonomous University of Mexico, Mexico City, Mexico.
44
Department of Economics, National
Taiwan University, Taipei, Taiwan.
45
Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan.
46
Tribhuvan University, Kirtipur, Nepal.
47
Department of Leadership and Organizational Behavior, BI Norwegian Business School, Oslo, Norway.
48
Institute of Security and Global Affairs, Leiden University, Leiden, Netherlands.
49
Faculty of Governance and Global Affairs, Leiden University,
Leiden, Netherlands.
50
Department of Psychology, University of Limerick, Limerick, Ireland.
51
Department of Psychology, The University of Western
Ontario, London, ON, Canada.
52
Center for Advanced Hindsight, Duke University, Durham, NC, USA.
53
Department of Clinical Neuroscience,
Karolinska Institute, Solna, Sweden.
54
Department of Management, University of Agder, Kristiansand, Norway.
55
Institute of Retail Economics,
Stockholm, Sweden.
56
Department of Psychology, Lehigh University, Bethlehem, PA, USA.
57
Department of Psychology, Monk Prayogshala,
Mumbai, India.
58
Medicine Faculty, Cooperative University of Colombia, Villavicencio, Colombia.
59
Department of Bioethics, El Bosque University,
Bogotá, Colombia.
60
Faculty of Medicine, Pontifical Javeriana University, Bogotá, Colombia.
61
Department of Management, Technology, and
Economics, ETH Zürich, Zürich, Switzerland.
62
Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv,
Plovdiv, Bulgaria.
63
Humanities and Social Sciences, Indian Institute of Management, Indore, India.
64
Institute of Social Sciences, Belgrade, Serbia.
65
Department of Psychology, Royal Holloway, University of London, London, England.
66
Center for the Politics of Feelings, School of Advanced
Study, University of London, London, England.
67
Department of Behavioral and Cognitive Sciences, Faculty of Humanities, Education and Social
Sciences, University of Luxembourg, Luxembourg City, Luxembourg.
68
Department of Political Science, National Taiwan University, Taipei, Taiwan.
69
Faculty of Political Science, National School for Political Studies and Public Administration, Bucharest, Romania.
70
Department of Psychology,
University of Latvia, Riga, Latvia.
71
Department of Psychology, Princeton University, Princeton, NJ, USA.
72
Department of Psychology, Speech, and
Hearing, University of Canterbury, Christchurch, New Zealand.
73
Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
74
Department of Psychology, University of Virginia, Charlottesville, VA, USA.
75
School of Economics and Management, Hebei University of
Technology, Tianjin, PR China.
76
School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco.
77
Institute for
Research and Development-Kurdistan, Middle East, Iraq.
78
Impact For Development, North Africa, Morocco.
79
Department of Psychology,
University of Amsterdam, Amsterdam, Netherlands.
80
Department of Psychology, Jose Rizal University, Mandaluyong, Philippines.
81
Department of
Economics, Norwegian School of Economics, Bergen, Norway.
82
Telenor Research, Oslo, Norway.
83
Department of Philosophy, University Cheikh
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Anta Diop, Dakar, Senegal.
84
School of Medicine and Health Sciences, University of Rosario, Bogotá, Colombia.
85
Moral Psychology and Decision
Sciences Research Incubator, University of Rosario, Bogotá, Colombia.
86
School of Psychological Sciences, University of Melbourne, Parkville, VIC,
Australia.
87
Department of Psychological and Social Sciences, Penn State Abington, Abington, PA, USA.
88
Institute of Psychology, University of
Wrocław, Wrocław, Poland.
89
IMT School for Advanced Studies Lucca, Lucca, Italy.
90
Department of Economics and Management, University of
Florence, Florence, Italy.
91
Department of Management, London School of Economics and Political Science, London, England.
92
Toulouse Business
School, University of Toulouse, Toulouse, France.
93
Social Policy Institute of the Ministry of Labor, Family and Social Affairs of the Slovak Republic,
Bratislava, Slovakia.
94
Department of Sociology, Tilburg University, Tilburg, Netherlands.
95
Department of Psychology, Penn State University,
University Park, PA, USA.
96
Rock Ethics Institute, Penn State University, University Park, PA, USA.
97
Department of Psychology, Federal University
of Santa Catarina, Florianópolis, Brazil.
98
Department of Economics, University of Bologna, Bologna, Italy.
99
IUSS Cognitive Neuroscience (ICoN)
Center, Institute for Advanced Study of Pavia, Pavia, Italy.
100
Cognitive Computational Neuroscience Research Unit, Neurological Institute
Foundation Casimiro Mondino, Pavia, Italy.
101
School of Psychology, Shenzhen University, Shenzhen, PR China.
102
Darden School of Business,
University of Virginia, Charlottesville, VA, USA.
103
Institute for Advanced Study in Toulouse, Université Toulouse 1 Capitole, Toulouse, France.
104
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
105
Cracow University of Economics, Kraków, Poland.
106
UBC Sauder
School of Business, University of British Columbia, Vancouver, BC, Canada.
107
Psychology Department, University of California - Los Angeles, Los
Angeles, CA, USA.
108
Laboratory of Psychology: Cognition, Behavior, and Communication (LP3C), Rennes 2 University, Rennes, France.
109
Laboratory
of Social and Cognitive Psychology, Clermont Auvergne University, CNRS, Clermont-Ferrand, France.
110
Cavite State University-General Trias City
Campus, Cavite, Philippines.
111
School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, Australia.
112
Center for
Behavioural Economics, Society and Technology, Queensland University of Technology, Brisbane, QLD, Australia.
113
Crawford School of Public Policy,
Australian National University, Canberra, ACT, Australia.
114
CESifo, University of Munich, Munich, Germany.
115
Department of International Trade,
Boğaziçi University, Istanbul, Turkey.
116
Kieskompas - Election Compass, Amsterdam, Netherlands.
117
Hult International Business School Dubai,
Dubai, UAE.
118
Department of Social Policy and Intervention, University of Oxford, Oxford, England.
119
Department of Media and Communications,
University of Sydney, Sydney, NSW, Australia.
120
Department of Psychiatry, University of the Witwatersrand, Johannesburg, South Africa.
121
University of Bayreuth, Bayreuth, Germany.
122
Department of Psychology, University of Waterloo, Waterloo, ON, Canada.
123
Department of Peace
Studies, University of Bradford, Bradford, England.
124
Philosophy and Social Studies Department, Rethymno, Greece.
125
Department of Psychology
and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
126
Department of Economics, Koc University, Istanbul, Turkey.
127
Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
128
University of Regensburg,
Regensburg, Germany.
129
FOM University of Applied Sciences, Essen, Germany.
130
Graduate School for Transnational Studies, Free University of
Berlin, Berlin, Germany.
131
Department of Management and Engineering, Linköping University, Linköping, Sweden.
132
Department of Psychology,
University of Koblenz-Landau, Landau, Germany.
133
Department of Psychology, University of Alberta, Edmonton, Canada.
134
LMU Center for
Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany.
135
Ansbach University for Applied Sciences,
Ansbach, Germany.
136
Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya (IDC), Herzliya, Israel.
137
Department of Neuroscience
and Biomedical Engineering, Aalto University, Espoo, Finland.
138
Questrom School of Business, Boston University, Boston, MA, USA.
139
School of
Psychology, Dublin City University, Dublin, Ireland.
140
University of Montana, Missoula, MT, USA.
141
Graduate School of Human Sciences Human
Sciences, Osaka University, Suita, Japan.
142
SEELE Neuroscience, Mexico City, Mexico.
143
School of Psychology, University of Auckland,
Auckland, New Zealand.
144
Faculty of Economics, Maria Curie-Skłodowska University, Lublin, Poland.
145
Department of Psychology, The City
University of New York (CUNY) Graduate Center, New York, NY, USA.
146
Australian National Centre for the Public Awareness of Science, Australian
National University, Canberra, ACT, Australia.
147
Department of Social Psychology, Graduate School of Humanities and Sociology, University of
Tokyo, Tokyo, Japan.
148
Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands.
149
Center for
Social Sciences, Hungarian Academy of Sciences Center of Excellence, Budapest, Hungary.
150
Institute of Psychology, University of Silesia,
Katowice, Poland.
151
Department of Psychology, University of Belgrade, Belgrade, Serbia.
152
Department of Biology, Faculty of Natural Sciences,
Universidad del Rosario, Bogotá, Colombia.
153
Vidyasagar College For Women, Kolkata, India.
154
AGH University of Science and Technology,
Kraków, Poland.
155
Department of Psychology, University of Cambridge, Cambridge, England.
156
Department of Computer Science, University of
Colorado Boulder, Boulder, CO, USA.
157
Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.
158
Social and Cognitive
Neuroscience Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil.
159
Stanford University, Stanford, CA, USA.
160
Department of Biology,
University of Turku, Turku, Finland.
161
Cooperative University of Colombia, Bogotá, Colombia.
162
Newcastle Business School, University of Newcastle,
Callaghan, NSW, Australia.
163
Department of Philosophy, University of St Andrews, St Andrews, Scotland.
164
School of Economics and Finance,
University of St Andrews, St Andrews, Scotland.
165
Department of Global Economics and Management, University of Groningen,
Groningen, Netherlands.
166
Brain and Mind Institute, University of Western Ontario, London, ON, Canada.
167
Western Interdisciplinary Research
Building, University of Western Ontario, London, ON, Canada.
168
Department of Sociology, University of Belgrade, Belgrade, Serbia.
169
Department of
Psychology, Carleton University, Ottawa, ON, Canada.
170
National University of Political Studies and Public Administration (SNSPA),
Bucharest, Romania.
171
Research Institute at Medical University of Plovdiv), Division of Translational Neuroscience, Plovdiv, Bulgaria.
172
Department
of Cognitive Science, ENS, EHESS, CNRS, Institut Jean Nicod, PSL Research University, Paris, France.
173
CREMA ‐Center for Research in Economics,
Management and the Arts, Basel, Switzerland.
174
The Warburg Institute, School of Advanced Study, University of London, London, England.
175
Institute of Cognitive Neuroscience, University College London, London, England.
176
Institute for Emerging Market Studies, The Hong Kong
University of Science and Technology, Kowloon, Hong Kong.
177
Department of Psychology, Susquehanna University, Selinsgrove, PA, USA.
178
Psychology Department, Dokuz Eylül University, İzmir, Turkey.
179
Department of Experimental and Applied Psychology, VU Amsterdam,
Amsterdam, Netherlands.
180
Research School of Psychology, Australian National University, Canberra, ACT, Australia.
181
Department of Behavioural
Sciences and Learning (IBL), Linköping University, Linköping, Sweden.
182
Department of Psychology, Education and Child Studies, Erasmus University
Rotterdam, Rotterdam, Netherlands.
183
University of Koblenz-Landau, Landau, Germany.
184
Health Communication Research Unit, School of Human
and Community Development, University of the Witwatersrand, Johannesburg, South Africa.
185
Department of Business Administration, Stockholm
School of Economics, Stockholm, Sweden.
186
Harvard Business School, Harvard University, Cambridge, MA, USA.
187
Nicolaus Copernicus University,
Toruń, Poland.
188
University of California, San Diego, La Jolla, CA, USA.
189
Kyushu University, Fukuoka, Japan.
190
Department of Psychology, Kadir
Has University, Istanbul, Turkey. ✉email: jay.vanbavel@nyu.edu;flavio.azevedo@uni-jena.de
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