# National identity predicts public health support during a global pandemic

Jay Van Bavel, Aleksandra Cichocka, Valerio Capraro, Hallgeir Sjåstad, John B. Nezlek, Tomislav Pavlović, Mark Alfano, Michele J. Gelfand, Flavio Azevedo, Michèle D. Birtel, et al.

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## To cite this version:

Jay Van Bavel, Aleksandra Cichocka, Valerio Capraro, Hallgeir Sjåstad, John B. Nezlek, et al., National identity predicts public health support during a global pandemic. Nature Communications, 2022, 13 (1), pp.1-14. 10.1038/s41467-021-27668-9. hal-03875286

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# 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 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.
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 significance 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).
<figure><img src="image_5.png" /><figcaption>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).</figcaption></figure>

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><caption>Table 1 Summary statistics and multi-level correlations for person-level measures.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Variance</th><th colspan="5">Correlations</th></tr><tr><th>Mean</th><th>Between</th><th>Within</th><th>Alpha</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>1. Spatial distancing</td><td>8.60</td><td>0.21</td><td>2.17</td><td>0.74</td><td>0.43</td><td>0.44</td><td>0.02</td><td>0.15</td><td>-0.02</td></tr><tr><td>2. Physical hygiene</td><td>8.21</td><td>0.46</td><td>2.32</td><td>0.72</td><td></td><td>0.38</td><td>0.12</td><td>0.17</td><td>0.02</td></tr><tr><td>3. Policy support</td><td>8.29</td><td>0.94</td><td>3.45</td><td>0.81</td><td></td><td></td><td>0.06</td><td>0.13</td><td>-0.03</td></tr><tr><td>4. National narcissism</td><td>5.37</td><td>2.10</td><td>4.94</td><td>0.82</td><td></td><td></td><td></td><td>0.38</td><td>0.26</td></tr><tr><td>5. National identification</td><td>8.02</td><td>0.80</td><td>3.99</td><td>0.71</td><td></td><td></td><td></td><td></td><td>0.18</td></tr><tr><td>6. Political ideology</td><td>4.98</td><td>0.37</td><td>5.05</td><td>NA</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

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).

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.

<caption>Table 2 Relationships between outcomes and predictors (including the slope and t-ratio of each relationship).</caption>

National identification was the strongest predictor of all three COVID-19 public health support measures.

<table><thead><tr><th>Outcome</th><th>Predictor</th><th>Slope</th><th>t-ratio</th></tr></thead><tbody><tr><td rowspan="3">Spatial distancing</td><td>National narcissism</td><td>-0.007</td><td>&lt;1</td></tr><tr><td>National identification</td><td>0.129*</td><td>8.63</td></tr><tr><td>Political ideology</td><td>-0.028*</td><td>4.44</td></tr><tr><td rowspan="3">Physical hygiene</td><td>National narcissism</td><td>0.060*</td><td>6.45</td></tr><tr><td>National identification</td><td>0.126*</td><td>11.20</td></tr><tr><td>Political ideology</td><td>-0.016</td><td>2.05</td></tr><tr><td rowspan="3">Policy support</td><td>National narcissism</td><td>0.029*</td><td>2.89</td></tr><tr><td>National identification</td><td>0.129*</td><td>10.36</td></tr><tr><td>Political ideology</td><td>-0.050*</td><td>4.79</td></tr></tbody></table>

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 ($p < 0.001$). 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

<footnote>*p < 0.001.</footnote>
<figure><img src="image_6.png" /><figcaption>**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.</figcaption></figure>

measure, and 5% of the person-level variance of the policy support measure. The coefficients for individual countries are displayed in Figs. 2 and 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's physical 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 residential). 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 associated 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 persuasion, 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 provides 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 literature 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
<figure><img src="image_7.png" /><figcaption>**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.</figcaption></figure>

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 considerable 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 identification can undercut public health. For instance, partisanship within countries (i.e., when people strongly identify with a specific 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 subsequent 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 factors (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 positive, 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 measures, right-wing PI was negatively associated with these outcomes. This suggests that a collective identity might be associated with valuing the protection of the entire group during a pandemic, 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 problematic attitudes towards both out-group and in-group
<figure><img src="image_8.png" /><figcaption>**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.</figcaption></figure>

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 protecting 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 cosmopolitanism or other supranational identities and ideologies may play a role that bolsters what we have seen in the case of national identity⁶⁵.
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 democratic 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 relationship 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 pandemics. 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 manipulate 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 participants recruited from the UEA where it was difficult to establish participant nationality. This resulted in a sample of 51,089 participants. For the current analysis, 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 1 shows 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 continents except for Antarctica. Due to our open call for collaborators, some continents 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. ⁹ and 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 proportion 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, consisting of five items, as, for example, “During the days of the coronavirus (COVID-19) pandemic, I have been washing my hands longer than usual”. A policy 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 universities”. We used an 11-point “slider scale with three labels: 0 = “strongly disagree”, 50 = “neither agree nor disagree”, 100 = “strongly agree”, which was re-coded to a scale from 0 to 10.
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): $y_{ijk} = \pi_{0jk} + e_{ijk}$

Level 2 (person-level): $\pi_{0jk} = b_{00k} + r_{0jk}$

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, $\pi_{0jk}$ is a random coefficient representing the mean response for person j in country k, $b_{00k}$ is a random coefficient representing the mean of y for 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 data (see Table 1).

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 presented 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 randomly 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): $\pi_{0jk} = b_{00k} + b_{01k}*(NN) + b_{02k}*(NI) + b_{03k}*(PI) + r_{0jk}$

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. 2 for 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 collective 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 distancing ($g_{011} = -0.03$, $t = 2.93$, *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 -1 SD on the HDI was 0.002, functionally 0). We note that these effects were not statistically significant at *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
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 identification 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;
Published online: 26 January 2022

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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 [Fidor] 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) - 13381 [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 Razvoja (Ministry of Education, Science and Technological Development of the Republic of Serbia) - 47010 [Todosijićević] 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] Gouvernement 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 Foundation) - CF20-0044 [Jorgensen] Gouvernement du Canada | Social Sciences and Humanities Research Council of Canada (Conseil de recherches en sciences humaines du Canada) - SSHRC-506547 [Long] Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung) - 13381 [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
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 Technological 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) [Bonicinelli] 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 collection, 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 documentation. Flavio Azevedo designed, populated, and maintained the ICMP 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 Arua, Sahba Nomvula Besharati, Alexander Bor, Becky L. Choma, Charles David Crabtree, William A. Cunningham, Koustav De, Waqas Ejaz, Christian T. Elbaek, Andrej Fidor, Daniel Flichtentrei, Renata Franc, Biljana Gjoneska, June Gruber, Estrella Gualda, Yusaku Horiuchi, Toan Luu Duc Huynh, Agustin Ibanez, Mostak Ahamed Imran, Jacob Israelashvili, 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. Umbres, 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, Mouhamadou Hady Ba, Sergio Barbosa, Brock Bastian, Anton Berg, Maria P. Bernal-Zárate, Michael Bernstein, Michal Bialek, Ennio Bilancini, Natalia Bogatyreva, Leonardo Boncinelli, 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 Crouzevalle, Jo Cutler, Marzena Cypryńska, Justyna Dabrowska, Michael A. Daniels, Victoria H. Davis, Pamala N Dayley, Sylvain Delouvee, Ognjan Denkovski, Guillaume Dezecache, Nathan A. Dhaliwal, Alenie 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. Fogelsang, 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, Rafael 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 Yogeeswaran, 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.

**Reprints and permission information** is available at http://www.nature.com/reprints

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© The Author(s) 2022, corrected publication 2022
Jay J. Van Bavel ID 1, Aleksandra Cichocka ID 2, Valerio Capraro 3, Hallgeir Sjåstad ID 4, John B. Nezlek ID 5,6, Tomislav Pavlović ID 7, Mark Alfano ID 8, Michele J. Gelfand 9, Flavio Azevedo ID 10, Michèle D. Birtel 11, Aleksandra Cislak ID 5, Patricia L. Lockwood ID 12,13, Robert Malcolm Ross ID 14, Koen Abts ID 15, Elena Agadullina ID 16, John Jamir Benzon Aruta ID 17, Sahba Nomvula Besharati ID 18, Alexander Bor ID 19, Becky L. Choma 20, Charles David Crabtree ID 21, William A. Cunningham 22, Koustav De ID 23, Waqas Ejaz ID 24, Christian T. Elbaek ID 25, Andrej Findor ID 26, Daniel Flichtentrei 27, Renata Franc ID 7, Biljana Gjoneska ID 28, June Gruber 29, Estrella Gualda 30,31, Yusaku Horiuchi 21, Toan Luu Duc Huynh ID 32, Agustin Ibanez ID 33,34, Mostak Ahamed Imran ID 35, Jacob Israelashvili 36, Katarzyna Jasko 37, Jaroslaw Kantorowicz ID 38, Elena Kantorowicz-Reznichenko 39, André Krouwel ID 40, Michael Laakasuo ID 41, Claus Lamm ID 42, Caroline Leygue ID 43, Ming-Jen Lin ID 44,45, Mohammad Sabbir Mansoor ID 46, Antoine Marie ID 19, Lewend Mayiwar 47, Honorata Mazepus 48,49, Cillian McHugh ID 50, John Paul Minda ID 51, Panagiotis Mitkidis ID 25,52, Andreas Olsson ID 53, Tobias Otterbring ID 54,55, Dominic J. Packer 56, Anat Perry ID 36, Michael Bang Petersen ID 19, Arathy Puthillam 57, Julián C. Riaño-Moreno ID 58,59, Tobias Rothmund ID 10, Hernando Santamaría-García 60, Petra C. Schmid ID 61, Drozdstoy Stoyanov ID 62, Shruti Tewari 63, Bojan Todosijević ID 64, Manos Tsakiris ID 65,66,67, Hans H. Tung ID 68,45, Radu G. Umbreş 69, Edmunds Vanags ID 70, Madalina Vlasceanu ID 71, Andrew Vonasch 72, Meltem Yucel ID 73,74, Yucheng Zhang ID 75, Mohcine Abad ID 76, Eli Adler 36, Narin Akrawi 77, Hamza Alaoui Mdarhri ID 76, Hanane Amara ID 78, David M. Amodio 1,79, Benedict G. Antazo ID 80, Matthew Apps ID 13, F. Ceren Ay 81,82, Mouhamadou Hady Ba 83, Sergio Barbosa ID 84,85, Brock Bastian ID 86, Anton Berg 41, Maria P. Bernal-Zárate ID 58, Michael Bernstein 87, Michał Białek ID 88, Ennio Bilancini 89, Natalia Bogatyreva 16, Leonardo Boncinelli ID 90, Jonathan E. Booth ID 91, Sylvie Borau ID 92, Ondrej Buchel ID 93,94, C. Daryl Cameron 95,96, Chrissie F. Carvalho 97, Tatiana Celadin 98, Chiara Cerami 99,100, Hom Nath Chalise ID 46, Xiaojun Cheng 101, Luca Cian ID 102, Kate Cockcroft ID 18, Jane Conway ID 103, Mateo Andres Córdoba-Delgado ID 60, Chiara Crespi 100,104, Marie Crouzevialle ID 61, Jo Cutler ID 12,13, Marzena Cypryańska 5, Justyna Dabrowska ID 105, Michael A. Daniels 106, Victoria H. Davis ID 22, Pamala N. Dayley ID 107, Sylvain Delouvee ID 108, Ognjan Denkovski 79, Guillaume Dezecache 109, Nathan A. Dhaliwal 106, Alelie B. Diato 110, Roberto Di Paolo ID 89, Marianna Drosinou 41, Uwe Dulleck ID 111,112,113,114, Jānis Ekmanis ID 70, Arhan S. Ertan 115, Tom W. Etienne ID 116, Hapsa Hossain Farhana 35, Fahima Farkhari ID 10, Harry Farmer ID 11, Ali Fenwick ID 117, Kristijan Fidanovski 118, Terry Flew ID 119, Shona Fraser 120, Raymond Boadi Frempong ID 121, Jonathan A. Fugelsang 122, Jessica Gale ID 72, E. Begoña Garcia-Navarro ID 30, Prasad Garladinne 63, Oussama Ghajjou ID 123, Theofilos Gkinopoulos 124, Kurt Gray 125, Siobhán M. Griffin ID 50, Bjarki Gronfeldt 2, Mert Gümren 126, Ranju Lama Gurung 46, Eran Halperin ID 36, Elizabeth Harris 1, Volo Herzon ID 41, Matej Hruška 26, Guanxiong Huang ID 127, Matthias F. C. Hudecek ID 128,129, Ozan Isler ID 111,112, Simon Jangard ID 53, Frederik J. Jørgensen ID 19, Frank Kachanoff 125, John Kahn ID 21, Apsara Katuwal Dangol 46, Oleksandra Keudel 130, Lina Koppel ID 131, Mika Koverola ID 41, Emily Kubin 132, Anton Kunnari 41, Yordan Kutiyski 116, Oscar Laguna 116, Josh Leota ID 133, Eva Lermer ID 129,134,135, Jonathan Levy 136,137, Neil Levy 8, Chunyun Li 91, Elizabeth U. Long 22, Chiara Longoni 138, Marina Maglić ID 7, Darragh McCashin 139, Alexander L. Metcalf ID 140, Igor Mikloušič 7, Soulaimane El Mimouni 78, Asako Miura ID 141, Juliana Molina-Paredes 60, César Monroy-Fonseca ID 142, Elena Morales-Marente ID 30, David Moreau ID 143, Rafał Muda 144, Annalisa Myer 74,145, Kyle Nash 133, Tarik Nesh-Nash ID 78, Jonas P. Nitschke ID 42, Matthew S. Nurse ID 146, Yohsuke Ohtsubo 147, Victoria Oldemburgo de Mello 22, Cathal O'Madagain 76, Michal Onderco 148, M. Soledad Palacios-Galvez 30, Jussi Palomäki ID 41, Yafeng Pan ID 53, Zsófia Papp 149, Philip Pärnamets 53, Mariola Paruzel-Czachura 150, Zoran Pavlović ID 151, César Payán-Gómez ID 152, Silva Perander ID 41, Michael Mark Pitman ID 18, Rajib Prasad 153, Joanna Pyrkosz-Pacyna ID 154, Steve Rathje 155, Ali Raza ID 156,157,
Gabriel G. Rêgo¹⁵⁸, Kasey Rhee¹⁵⁹, Claire E. Robertson¹, Iván Rodríguez-Pascual³⁰, Teemu Saikkonen¹⁶⁰, Octavio Salvador-Ginez⁴³, Waldir M. Sampaio¹⁵⁸, Gaia C. Santi⁹⁹, Natalia Santiago-Tovar¹⁶¹, David Savage¹⁶², Julian A. Scheffer⁹⁵, Philipp Schönegger¹⁶³,¹⁶⁴, David T. Schultner⁸⁰, Enid M. Schutte¹⁸, Andy Scott¹³³, Madhavi Sharma⁴⁶, Pujan Sharma⁴⁶, Ahmed Skali¹⁶⁵, David Stadelmann¹²¹, Clara Alexandra Stafford⁵¹,¹⁶⁶,¹⁶⁷, Dragan Stanojević¹⁶⁸, Anna Stefaniak¹⁶⁹, Anni Sternisko¹, Agustin Stoica¹⁷⁰, Kristina K. Stoyanova¹⁷¹, Brent Strickland⁷⁶,¹⁷², Jukka Sundvall¹⁴¹, Jeffrey P. Thomas⁸⁶, Gustav Tinghög¹³¹, Benno Torgler¹¹¹,¹¹²,¹⁷³, Iris J. Traast⁸⁰, Raffaele Tucciarelli¹⁷⁴,¹⁷⁵, Michael Tyrala¹⁷⁶, Nick D. Ungson¹⁷⁷, Mete S. Uysal¹⁷⁸, Paul A. M. Van Lange¹⁷⁹, Jan-Willem van Prooijen¹⁷⁹, Dirk van Rooy¹⁸⁰, Daniel Västfjäll¹⁸¹, Peter Verkoeijen¹⁸², Joana B. Vieira⁵³, Christian von Sikorski¹⁸³, Alexander Cameron Walker¹²², Jennifer Watermeyer¹⁸⁴, Erik Wetter¹⁸⁵, Ashley Whillans¹⁸⁶, Robin Willardt⁶¹, Michael J. A. Wohl¹⁶⁹, Adrian Dominik Wójcik¹⁸⁷, Kaidi Wu¹⁸⁸, Yuki Yamada¹⁸⁹, Onurcan Yilmaz¹⁹⁰, Kumar Yogeeswaran⁷², Carolin-Theresa Ziemer¹⁰, Rolf A. Zwaan¹⁸² & Paulo S. Boggio¹⁵⁸

¹Department of Psychology and Neural Science, New York University, New York, NY, USA. ²School of Psychology, University of Kent, Canterbury, England. ³Department of Economics, Middlesex University London, London, England. ⁴Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway. ⁵SWPS University of Social Sciences and Humanities, Poznań, Poland. ⁶Department of Psychological Sciences, College of William and Mary, Williamsburg, VA, USA. ⁷Institute of Social Sciences Ivo Pilar, Zagreb, Croatia. ⁸Department of Philosophy, Macquarie University, Sydney, NSW, Australia. ⁹Stanford Graduate School of Business, Stanford University, Stanford, CA, USA.

¹⁰Institute of Communication Science, Friedrich-Schiller University Jena, Jena, Germany. ¹¹School of Human Sciences, Institute for Lifecourse Development, University of Greenwich, London, England. ¹²Department of Experimental Psychology, University of Oxford, Oxford, England. ¹³Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, England. ¹⁴Department of Psychology, Macquarie University, Sydney, NSW, Australia. ¹⁵KU Leuven, Leuven, Belgium. ¹⁶National Research University Higher School of Economics (HSE), Moscow, Russia. ¹⁷De La Salle University, Manila, Philippines. ¹⁸Department of Psychology, University of the Witwatersrand, Johannesburg, South Africa. ¹⁹Department of Political Science, Aarhus University, Aarhus, Denmark. ²⁰X University, Toronto, Canada. ²¹Department of Government, Dartmouth College, Hanover, NH, USA. ²²Department of Psychology, University of Toronto, Toronto, ON, Canada. ²³Gatton College of Business and Economics, University of Kentucky, Lexington, KY, USA. ²⁴Department of Mass Communication, National University of Science and Technology (NUST), Islamabad, Pakistan. ²⁵Department of Management, Aarhus University, Aarhus, Denmark. ²⁶Faculty of Social and Economic Sciences, Comenius University, Bratislava, Slovakia. ²⁷IntraMed, Buenos Aires, Argentina. ²⁸Macedonian Academy of Sciences and Arts, North Macedonia, Republic of North Macedonia. ²⁹University of Colorado Boulder, Boulder, CO, USA. ³⁰ESEIS/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. ³¹Faculty of Social Work, University of Huelva, Huelva, Spain. ³²WHU - Otto Beisheim School of Management, Vallendar, Germany.

³³Latin American Brain Health Institute (BrainLat), Adolfo Ibáñez University, Santiago, Chile. ³⁴Global Brain Health Institute, University of San Andrés, Buenos Aires, Argentina. ³⁵Department of Educational and Counselling Psychology, University of Dhaka, Dhaka, Bangladesh. ³⁶Psychology Department, The Hebrew University of Jerusalem, Jerusalem, Israel. ³⁷Institute of Psychology, Jagiellonian University, Kraków, Poland. ³⁸Institute of Security and Global Affairs, Leiden University, The Hague, Netherlands. ³⁹Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, Netherlands. ⁴⁰Department of Political Science, Vrije University (VU) Amsterdam, Amsterdam, Netherlands. ⁴¹Department of Digital Humanities, University of Helsinki, Helsinki, Finland. ⁴²Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria. ⁴³School of Psychology, National Autonomous University of Mexico, Mexico City, Mexico. ⁴⁴Department of Economics, National Taiwan University, Taipei, Taiwan. ⁴⁵Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan.

⁴⁶Tribhuvan University, Kirtipur, Nepal. ⁴⁷Department of Leadership and Organizational Behavior, BI Norwegian Business School, Oslo, Norway. ⁴⁸Institute of Security and Global Affairs, Leiden University, Leiden, Netherlands. ⁴⁹Faculty of Governance and Global Affairs, Leiden University, Leiden, Netherlands. ⁵⁰Department of Psychology, University of Limerick, Limerick, Ireland. ⁵¹Department of Psychology, The University of Western Ontario, London, ON, Canada. ⁵²Center for Advanced Hindsight, Duke University, Durham, NC, USA. ⁵³Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden. ⁵⁴Department of Management, University of Agder, Kristiansand, Norway. ⁵⁵Institute of Retail Economics, Stockholm, Sweden. ⁵⁶Department of Psychology, Lehigh University, Bethlehem, PA, USA. ⁵⁷Department of Psychology, Monk Prayogshala, Mumbai, India. ⁵⁸Medicine Faculty, Cooperative University of Colombia, Villavicencio, Colombia. ⁵⁹Department of Bioethics, El Bosque University, Bogotá, Colombia. ⁶⁰Faculty of Medicine, Pontifical Javeriana University, Bogotá, Colombia. ⁶¹Department of Management, Technology, and Economics, ETH Zürich, Zürich, Switzerland. ⁶²Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria. ⁶³Humanities and Social Sciences, Indian Institute of Management, Indore, India. ⁶⁴Institute of Social Sciences, Belgrade, Serbia.

⁶⁵Department of Psychology, Royal Holloway, University of London, London, England. ⁶⁶Center for the Politics of Feelings, School of Advanced Study, University of London, London, England. ⁶⁷Department of Behavioral and Cognitive Sciences, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Luxembourg City, Luxembourg. ⁶⁸Department of Political Science, National Taiwan University, Taipei, Taiwan.

⁶⁹Faculty of Political Science, National School for Political Studies and Public Administration, Bucharest, Romania. ⁷⁰Department of Psychology, University of Latvia, Riga, Latvia. ⁷¹Department of Psychology, Princeton University, Princeton, NJ, USA. ⁷²Department of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand. ⁷³Department of Psychology and Neuroscience, Duke University, Durham, NC, USA. ⁷⁴Department of Psychology, University of Virginia, Charlottesville, VA, USA. ⁷⁵School of Economics and Management, Hebei University of Technology, Tianjin, PR China. ⁷⁶School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco. ⁷⁷Institute for Research and Development-Kurdistan, Middle East, Iraq. ⁷⁸Impact For Development, North Africa, Morocco. ⁷⁹Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. ⁸⁰Department of Psychology, Jose Rizal University, Mandaluyong, Philippines. ⁸¹Department of Economics, Norwegian School of Economics, Bergen, Norway. ⁸²Telenor Research, Oslo, Norway. ⁸³Department of Philosophy, University Cheikh

⁸⁰Department of Psychology, Jose Rizal University, Mandaluyong, Philippines. ⁸¹Department of Economics, Norwegian School of Economics, Bergen, Norway. ⁸²Telenor Research, Oslo, Norway. ⁸³Department of Philosophy, University Cheikh

⁸⁵Department of Psychology, Royal Holloway, University of London, London, England. ⁸⁶Department of Behavioral and Cognitive Sciences, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Luxembourg City, Luxembourg. ⁸⁷Department of Political Science, National Taiwan University, Taipei, Taiwan. ⁸⁸Department of Psychology, University of Virginia, Charlottesville, VA, USA. ⁸⁹School of Economics and Management, Hebei University of Technology, Tianjin, PR China. ⁹⁰School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco. ⁹¹Institute for Research and Development-Kurdistan, Middle East, Iraq. ⁹²Impact For Development, North Africa, Morocco. ⁹³Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. ⁹⁴Department of Psychology, Jose Rizal University, Mandaluyong, Philippines. ⁹⁵Department of Economics, Norwegian School of Economics, Bergen, Norway. ⁹⁶Telenor Research, Oslo, Norway. ⁹⁷Department of Philosophy, University Cheikh
Anta Diop, Dakar, Senegal. ⁸⁴School of Medicine and Health Sciences, University of Rosario, Bogotá, Colombia. ⁸⁵Moral Psychology and Decision Sciences Research Incubator, University of Rosario, Bogotá, Colombia. ⁸⁶School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia. ⁸⁷Department of Psychological and Social Sciences, Penn State Abington, Abington, PA, USA. ⁸⁸Institute of Psychology, University of Wrocław, Wrocław, Poland. ⁸⁹IMT School for Advanced Studies Lucca, Lucca, Italy. ⁹⁰Department of Economics and Management, University of Florence, Florence, Italy. ⁹¹Department of Management, London School of Economics and Political Science, London, England. ⁹²Toulouse Business School, University of Toulouse, Toulouse, France. ⁹³Social Policy Institute of the Ministry of Labor, Family and Social Affairs of the Slovak Republic, Bratislava, Slovakia. ⁹⁴Department of Sociology, Tilburg University, Tilburg, Netherlands. ⁹⁵Department of Psychology, Penn State University, University Park, PA, USA. ⁹⁶Rock Ethics Institute, Penn State University, University Park, PA, USA. ⁹⁷Department of Psychology, Federal University of Santa Catarina, Florianópolis, Brazil. ⁹⁸Department of Economics, University of Bologna, Bologna, Italy. ⁹⁹IUSS Cognitive Neuroscience (ICoN) Center, Institute for Advanced Study of Pavia, Pavia, Italy. ¹⁰⁰Cognitive Computational Neuroscience Research Unit, Neurological Institute Foundation Casimiro Mondino, Pavia, Italy. ¹⁰¹School of Psychology, Shenzhen University, Shenzhen, PR China. ¹⁰²Darden School of Business, University of Virginia, Charlottesville, VA, USA. ¹⁰³Institute for Advanced Study in Toulouse, Université Toulouse 1 Capitole, Toulouse, France.

¹⁰⁴Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. ¹⁰⁵Cracow University of Economics, Kraków, Poland. ¹⁰⁶UBC Sauder School of Business, University of British Columbia, Vancouver, BC, Canada. ¹⁰⁷Psychology Department, University of California - Los Angeles, Los Angeles, CA, USA. ¹⁰⁸Laboratory of Psychology: Cognition, Behavior, and Communication (LP3C), Rennes 2 University, Rennes, France. ¹⁰⁹Laboratory of Social and Cognitive Psychology, Clermont Auvergne University, CNRS, Clermont-Ferrand, France. ¹¹⁰Cavite State University-General Trias City Campus, Cavite, Philippines. ¹¹¹School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, Australia. ¹¹²Center for Behavioural Economics, Society and Technology, Queensland University of Technology, Brisbane, QLD, Australia. ¹¹³Crawford School of Public Policy, Australian National University, Canberra, ACT, Australia. ¹¹⁴CESifo, University of Munich, Munich, Germany. ¹¹⁵Department of International Trade, Boğaziçi University, Istanbul, Turkey. ¹¹⁶Kieskompas - Election Compass, Amsterdam, Netherlands. ¹¹⁷Hult International Business School Dubai, Dubai, UAE. ¹¹⁸Department of Social Policy and Intervention, University of Oxford, Oxford, England. ¹¹⁹Department of Media and Communications, University of Sydney, Sydney, NSW, Australia. ¹²⁰Department of Psychiatry, University of the Witwatersrand, Johannesburg, South Africa.

¹²¹University of Bayreuth, Bayreuth, Germany. ¹²²Department of Psychology, University of Waterloo, Waterloo, ON, Canada. ¹²³Department of Peace Studies, University of Bradford, Bradford, England. ¹²⁴Philosophy and Social Studies Department, Rethymno, Greece. ¹²⁵Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ¹²⁶Department of Economics, Koc University, Istanbul, Turkey.

¹²⁷Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong. ¹²⁸University of Regensburg, Regensburg, Germany. ¹²⁹FOM University of Applied Sciences, Essen, Germany. ¹³⁰Graduate School for Transnational Studies, Free University of Berlin, Berlin, Germany. ¹³¹Department of Management and Engineering, Linköping University, Linköping, Sweden. ¹³²Department of Psychology, University of Koblenz-Landau, Landau, Germany. ¹³³Department of Psychology, University of Alberta, Edmonton, Canada. ¹³⁴LMU Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany. ¹³⁵Ansbach University for Applied Sciences, Ansbach, Germany. ¹³⁶Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya (IDC), Herzliya, Israel. ¹³⁷Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. ¹³⁸Questrom School of Business, Boston University, Boston, MA, USA. ¹³⁹School of Psychology, Dublin City University, Dublin, Ireland. ¹⁴⁰University of Montana, Missoula, MT, USA. ¹⁴¹Graduate School of Human Sciences Human Sciences, Osaka University, Suita, Japan. ¹⁴²SEELE Neuroscience, Mexico City, Mexico. ¹⁴³School of Psychology, University of Auckland, Auckland, New Zealand. ¹⁴⁴Faculty of Economics, Maria Curie-Skłodowska University, Lublin, Poland. ¹⁴⁵Department of Psychology, The City University of New York (CUNY) Graduate Center, New York, NY, USA. ¹⁴⁶Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, ACT, Australia. ¹⁴⁷Department of Social Psychology, Graduate School of Humanities and Sociology, University of Tokyo, Tokyo, Japan. ¹⁴⁸Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands. ¹⁴⁹Center for Social Sciences, Hungarian Academy of Sciences Center of Excellence, Budapest, Hungary. ¹⁵⁰Institute of Psychology, University of Silesia, Katowice, Poland. ¹⁵¹Department of Psychology, University of Belgrade, Belgrade, Serbia. ¹⁵²Department of Biology, Faculty of Natural Sciences, Universidad del Rosario, Bogotá, Colombia. ¹⁵³Vidyasagar College For Women, Kolkata, India. ¹⁵⁴AGH University of Science and Technology, Kraków, Poland. ¹⁵⁵Department of Psychology, University of Cambridge, Cambridge, England. ¹⁵⁶Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA. ¹⁵⁷Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA. ¹⁵⁸Social and Cognitive Neuroscience Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil. ¹⁵⁹Stanford University, Stanford, CA, USA. ¹⁶⁰Department of Biology, University of Turku, Turku, Finland. ¹⁶¹Cooperative University of Colombia, Bogotá, Colombia. ¹⁶²Newcastle Business School, University of Newcastle, Callaghan, NSW, Australia. ¹⁶³Department of Philosophy, University of St Andrews, St Andrews, Scotland. ¹⁶⁴School of Economics and Finance, University of St Andrews, St Andrews, Scotland. ¹⁶⁵Department of Global Economics and Management, University of Groningen, Groningen, Netherlands. ¹⁶⁶Brain and Mind Institute, University of Western Ontario, London, ON, Canada. ¹⁶⁷Western Interdisciplinary Research Building, University of Western Ontario, London, ON, Canada. ¹⁶⁸Department of Sociology, University of Belgrade, Belgrade, Serbia. ¹⁶⁹Department of Psychology, Carleton University, Ottawa, ON, Canada. ¹⁷⁰National University of Political Studies and Public Administration (SNSPA), Bucharest, Romania. ¹⁷¹Research Institute at Medical University of Plovdiv), Division of Translational Neuroscience, Plovdiv, Bulgaria. ¹⁷²Department of Cognitive Science, ENS, EHESS, CNRS, Institut Jean Nicod, PSL Research University, Paris, France. ¹⁷³CREMA - Center for Research in Economics, Management and the Arts, Basel, Switzerland. ¹⁷⁴The Warburg Institute, School of Advanced Study, University of London, London, England.

¹⁷⁵Institute of Cognitive Neuroscience, University College London, London, England. ¹⁷⁶Institute for Emerging Market Studies, The Hong Kong University of Science and Technology, Kowloon, Hong Kong. ¹⁷⁷Department of Psychology, Susquehanna University, Selinsgrove, PA, USA.

¹⁷⁸Psychology Department, Dokuz Eylül University, Izmir, Turkey. ¹⁷⁹Department of Experimental and Applied Psychology, VU Amsterdam, Amsterdam, Netherlands. ¹⁸⁰Research School of Psychology, Australian National University, Canberra, ACT, Australia. ¹⁸¹Department of Behavioural Sciences and Learning (IBL), Linköping University, Linköping, Sweden. ¹⁸²Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands. ¹⁸³University of Koblenz-Landau, Landau, Germany. ¹⁸⁴Health Communication Research Unit, School of Human and Community Development, University of the Witwatersrand, Johannesburg, South Africa. ¹⁸⁵Department of Business Administration, Stockholm School of Economics, Stockholm, Sweden. ¹⁸⁶Harvard Business School, Harvard University, Cambridge, MA, USA. ¹⁸⁷Nicolaus Copernicus University, Toruń, Poland. ¹⁸⁸University of California, San Diego, La Jolla, CA, USA. ¹⁸⁹Kyushu University, Fukuoka, Japan. ¹⁹⁰Department of Psychology, Kadir Has University, Istanbul, Turkey. ⁸⁹email: jay.vanbavel@nyu.edu; flavio.azevedo@uni-jena.de