# Social and moral psychology of COVID-19 across 69 countries

Flavio Azevedo, Tomislav Pavlović, Gabriel Rêgo, F. Ceren Ay, Biljana

Gjoneska, Tom Etienne, Robert Ross, Philipp Schönegger, Julián

Riaño-Moreno, Aleksandra Cichocka, et al.

► To cite this version:

Flavio Azevedo, Tomislav Pavlović, Gabriel Rêgo, F. Ceren Ay, Biljana Gjoneska, et al. Social and moral psychology of COVID-19 across 69 countries. Scientific Data, 2023, 10 (1), pp.272. 10.1038/s41597-023-02080-8. hal-04312261

HAL Id: hal-04312261

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DATA DESCRIPTOR

# Social and moral psychology of
COVID-19 across 69 countries

Flavio Azevedo et al.#

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

## Background & Summary

Well over two years after the official outbreak¹, it is evident that the COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies² as well as people's physical and mental health³. At the time of writing, the world reached 850 million confirmed infections and up to 18 million deaths⁴. The detrimental effects of the pandemic extend beyond physical health with evidence of increased stress levels⁵ and suicide rates⁶, along with deterioration of general well-being⁷. Such findings reflect the cautionary warnings by Taylor⁸ that the psychological and societal effects are "likely to be more pronounced, more widespread, and longer-lasting than the purely somatic effects of the infection"⁸, p.23.

In the early stages of the pandemic, when vaccines were not yet available, governments introduced non-pharmaceutical interventions to reduce the spread of the SARS-CoV-2 virus⁹. Various contact-restricting policies (e.g., stay-at-home recommendations, curfews, police hours, partial or complete lock-downs) were enacted, and citizens were advised to adhere to public health recommendations (e.g., hand washing, face masks, and spatial distancing). It quickly became clear that behavioural science had a major role to play¹⁰.

On April 11th, a team of researchers launched a call for international collaboration in social and moral psychology. The initiative quickly gained momentum, gathering a consortium of over 250 academics worldwide. The aim of this project was to collect data from as many countries as possible to serve as a public good for the scientific community by allowing future research to draw on this broad database collected during this early phase of the COVID-19 pandemic. The survey, developed by the initial team, was circulated among the national teams, who provided feedback, translated it into 32 languages, and disseminated it online. The project concluded with responses from a total of 51,404 participants from 69 countries, 77 samples, between April 22nd and June 3rd, 2020.

A key goal of the project was to test the hypothesis that national identity predicts support for public health measures during the COVID-19 pandemic, which has since been confirmed¹¹,¹². In addition to collecting variables to test this hypothesis, we collected data on a variety of other social and moral constructs to make of our multi-country large-scale survey a rich resource for future research. The survey focused on the following areas: on a) COVID-19 beliefs and compliance behaviours (COVID-19 public health support, COVID-19 risk perception, COVID-19 conspiracy beliefs, and COVID-19 testing behaviour); b) identity and social attitudes (national identification, national narcissism, and social belonging); c) ideology (political ideology);

<footnote>*A full list of authors and their affiliations appears at the end of the paper.</footnote>
Sample sizes across 69 countries

<figure><img src="image_3.png" /><figcaption>Heat map showing the number of respondents from each country</figcaption></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netlify.app

<caption>**Fig. 1** A world map visualizing the number of participants in each surveyed country. **Note:** This heat map shows the number of respondents from each country. The gray areas are the countries that are not covered by the data, and the colour scale shows the size of the sample in accordance with the scale on the lower left side.</caption>

Sample proportion across 69 countries

<figure><img src="image_4.png" /><figcaption>Heat map showing the proportion of respondents (i.e., country sample/total sample) from each country</figcaption></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netlify.app

<caption>**Fig. 2** International Collaboration on the Social and Moral Psychology of COVID-19: Investigated constructs, items and variables.</caption>

d) health and well-being (subjective physical health, a wealth ladder ranking, and psychological well-being); e) moral beliefs and motivation (generosity, morality as cooperation, moral identity, and moral circle); f) personality traits and dispositions (open-mindedness, self-esteem, trait optimism, trait self-control, narcissism, and cognitive reflection); and g) demographic variables (i.e., sex, age, marital status, number of children, and employment status).

Using this dataset, project team members have pre-registered a variety of secondary hypotheses (see icsmp-covid19.netlify.app/preregistration), several of which have already been published<sup>11-23</sup>. In this paper, we present the complete ICSMP datasets to facilitate its findability, accessibility, interoperability, and reuse (FAIR;<sup>24,25</sup>) and maximize its educational impact<sup>26-28</sup>.
<figure><img src="image_5.png" /></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netlift.app

<caption>**Fig. 3** Gantt Chart illustrating the data collection periods for each surveyed country.</caption>

Methods

When possible, we used articles published in Nature Scientific Data presenting social sciences data as blueprints⁵,²⁹. Given the urgent call for COVID-19 research, this study received an umbrella ethical approval from the University of Kent (see osf.io/ce638) but also complied with local ethics, norms, and regulations in the countries where the data were collected.

**Participants.** A total of 51,404 individuals from 77 samples across 69 countries participated in our survey. The inclusion criteria were the following: being 18 years of age and older, and giving informed consent (although researchers were encouraged to, ideally, recruit representative samples regarding age and gender). Data were collected between April 22nd and June 3rd, 2020. Figure 1 displays *where* the data were collected, coloured according to national sample size. Figure 2 displays the proportion of respondents in relation to the full sample. Figure 3 shows *when* the data were collected in each country.
<table><caption>**Table 1.** Sample size, average proportion of valid answers, age of respondents and the number of data collections in 69 countries (A-M). **Note:** Country = country names in accordance with ISO3 codes, N = number of respondents in each country. <50% and <90% = average proportion of valid (non NA) answers that are below 0.5 and 0.9 respectively in the subject level. μ<sub>Age</sub> = mean age and sd<sub>Age</sub> = standard deviation of the age, Multiple datasets = whether there were multiple data collections in the country. Tables 1, 2 show the number of participants, the mean proportion of non-missing 'valid' answers, and age. When multiple samples were collected within the same country, data were split into numbered subgroups (e.g., for Brazil, which has three samples, they were flagged as Brazil_1, Brazil_2 and Brazil_3). Multiple subsamples can be observed for Brazil, Canada, Colombia, India, Italy, Mexico and Romania. Note that in all the tables, we kept country subsamples separated to highlight they were collected by different teams, often using different sampling methodologies or languages, which impact their characteristics (e.g., representativeness).</caption><thead><tr><th rowspan="2">Sample</th><th rowspan="2">Country</th><th rowspan="2">N</th><th colspan="2">% Valid Answers</th><th colspan="2">Age</th><th colspan="2">Multiple datasets</th></tr><tr><th>&lt;50%</th><th>&lt;90%</th><th>μ<sub>Age</sub></th><th>sd<sub>Age</sub></th><th>per country</th></tr></thead><tbody><tr><td>AR</td><td>Argentina</td><td>721</td><td>1.00</td><td>1.00</td><td>47.38</td><td>15.29</td><td>1</td><td></td></tr><tr><td>AU</td><td>Australia</td><td>2161</td><td>1.00</td><td>1.00</td><td>46.92</td><td>17.59</td><td>1</td><td></td></tr><tr><td>AT</td><td>Austria</td><td>1605</td><td>0.90</td><td>0.87</td><td>49.77</td><td>14.13</td><td>1</td><td></td></tr><tr><td>BD</td><td>Bangladesh</td><td>596</td><td>0.82</td><td>0.67</td><td>31.90</td><td>10.89</td><td>1</td><td></td></tr><tr><td>BE</td><td>Belgium</td><td>1159</td><td>1.00</td><td>1.00</td><td>46.29</td><td>18.67</td><td>1</td><td></td></tr><tr><td>BO</td><td>Bolivia</td><td>29</td><td>1.00</td><td>1.00</td><td>43.41</td><td>12.98</td><td>1</td><td></td></tr><tr><td>BR_1</td><td>Brazil_1</td><td>961</td><td>0.99</td><td>0.99</td><td>39.31</td><td>14.57</td><td>3</td><td></td></tr><tr><td>BR_2</td><td>Brazil_2</td><td>1301</td><td>0.75</td><td>0.67</td><td>34.89</td><td>13.12</td><td>3</td><td></td></tr><tr><td>BR_3</td><td>Brazil_3</td><td>6</td><td>1.00</td><td>1.00</td><td>40.33</td><td>13.14</td><td>3</td><td></td></tr><tr><td>BG</td><td>Bulgaria</td><td>666</td><td>1.00</td><td>0.96</td><td>30.69</td><td>11.13</td><td>1</td><td></td></tr><tr><td>CA_e</td><td>Canada_english</td><td>792</td><td>1.00</td><td>1.00</td><td>42.70</td><td>17.39</td><td>2</td><td></td></tr><tr><td>CA_f</td><td>Canada_french</td><td>171</td><td>1.00</td><td>1.00</td><td>46.83</td><td>16.97</td><td>2</td><td></td></tr><tr><td>CL</td><td>Chile</td><td>97</td><td>1.00</td><td>1.00</td><td>49.21</td><td>15.47</td><td>1</td><td></td></tr><tr><td>CN</td><td>China</td><td>1030</td><td>1.00</td><td>1.00</td><td>43.24</td><td>14.02</td><td>1</td><td></td></tr><tr><td>CO_1</td><td>Colombia_1</td><td>731</td><td>0.99</td><td>0.91</td><td>37.26</td><td>14.68</td><td>2</td><td></td></tr><tr><td>CO_2</td><td>Colombia_2</td><td>546</td><td>1.00</td><td>1.00</td><td>44.91</td><td>15.16</td><td>2</td><td></td></tr><tr><td>CR</td><td>Costa Rica</td><td>25</td><td>1.00</td><td>1.00</td><td>44.64</td><td>12.73</td><td>1</td><td></td></tr><tr><td>HR</td><td>Croatia</td><td>515</td><td>1.00</td><td>1.00</td><td>45.91</td><td>14.56</td><td>1</td><td></td></tr><tr><td>CU</td><td>Cuba</td><td>43</td><td>1.00</td><td>1.00</td><td>48.65</td><td>12.73</td><td>1</td><td></td></tr><tr><td>DK</td><td>Denmark</td><td>566</td><td>1.00</td><td>1.00</td><td>48.69</td><td>17.54</td><td>1</td><td></td></tr><tr><td>DO</td><td>Dominican Republic</td><td>36</td><td>1.00</td><td>1.00</td><td>40.39</td><td>12.46</td><td>1</td><td></td></tr><tr><td>EC</td><td>Ecuador</td><td>148</td><td>1.00</td><td>1.00</td><td>40.63</td><td>11.98</td><td>1</td><td></td></tr><tr><td>SV</td><td>El Salvador</td><td>28</td><td>1.00</td><td>1.00</td><td>46.43</td><td>11.51</td><td>1</td><td></td></tr><tr><td>FI</td><td>Finland</td><td>698</td><td>0.99</td><td>0.98</td><td>38.64</td><td>13.77</td><td>1</td><td></td></tr><tr><td>FR</td><td>France</td><td>1119</td><td>1.00</td><td>0.99</td><td>43.18</td><td>16.20</td><td>1</td><td></td></tr><tr><td>DE</td><td>Germany</td><td>1587</td><td>1.00</td><td>1.00</td><td>49.58</td><td>16.14</td><td>1</td><td></td></tr><tr><td>GH</td><td>Ghana</td><td>390</td><td>0.68</td><td>0.49</td><td>31.46</td><td>7.54</td><td>1</td><td></td></tr><tr><td>GR</td><td>Greece</td><td>640</td><td>1.00</td><td>1.00</td><td>29.77</td><td>11.43</td><td>1</td><td></td></tr><tr><td>GT</td><td>Guatemala</td><td>48</td><td>1.00</td><td>1.00</td><td>44.67</td><td>13.31</td><td>1</td><td></td></tr><tr><td>HN</td><td>Honduras</td><td>24</td><td>1.00</td><td>1.00</td><td>39.25</td><td>14.30</td><td>1</td><td></td></tr><tr><td>HU</td><td>Hungary</td><td>506</td><td>1.00</td><td>1.00</td><td>48.53</td><td>16.54</td><td>1</td><td></td></tr><tr><td>IN_1</td><td>India_1</td><td>312</td><td>0.87</td><td>0.81</td><td>26.94</td><td>8.49</td><td>2</td><td></td></tr><tr><td>IN_2</td><td>India_2</td><td>429</td><td>0.94</td><td>0.84</td><td>36.81</td><td>12.05</td><td>2</td><td></td></tr><tr><td>IQ</td><td>Iraq</td><td>1142</td><td>0.57</td><td>0.48</td><td>31.03</td><td>14.13</td><td>1</td><td></td></tr><tr><td>IF</td><td>Ireland</td><td>785</td><td>0.96</td><td>0.95</td><td>38.23</td><td>14.63</td><td>1</td><td></td></tr><tr><td>IL</td><td>Israel</td><td>1253</td><td>1.00</td><td>1.00</td><td>41.13</td><td>15.25</td><td>1</td><td></td></tr><tr><td>IT_1</td><td>Italy_1</td><td>998</td><td>0.99</td><td>0.99</td><td>46.41</td><td>16.26</td><td>2</td><td></td></tr><tr><td>IT_2</td><td>Italy_2</td><td>284</td><td>1.00</td><td>1.00</td><td>47.35</td><td>18.07</td><td>2</td><td></td></tr><tr><td>JP</td><td>Japan</td><td>1239</td><td>0.96</td><td>0.93</td><td>47.10</td><td>15.21</td><td>1</td><td></td></tr><tr><td>KR</td><td>Korea</td><td>555</td><td>0.92</td><td>0.89</td><td>41.83</td><td>13.90</td><td>1</td><td></td></tr><tr><td>LV</td><td>Latvia</td><td>1008</td><td>1.00</td><td>1.00</td><td>45.60</td><td>14.11</td><td>1</td><td></td></tr><tr><td>MK</td><td>Macedonia</td><td>726</td><td>0.97</td><td>0.96</td><td>38.13</td><td>11.63</td><td>1</td><td></td></tr><tr><td>MX_1</td><td>Mexico_1</td><td>804</td><td>0.94</td><td>0.93</td><td>47.81</td><td>13.89</td><td>2</td><td></td></tr><tr><td>MX_2</td><td>Mexico_2</td><td>507</td><td>1.00</td><td>1.00</td><td>47.77</td><td>13.54</td><td>2</td><td></td></tr><tr><td>MA</td><td>Morocco</td><td>812</td><td>0.81</td><td>0.71</td><td>31.95</td><td>12.27</td><td>1</td><td></td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Sample</th><th rowspan="2">Country</th><th rowspan="2">N</th><th colspan="2">% Valid Answers</th><th colspan="2">Age</th><th rowspan="2">Multiple datasets<br>per country</th></tr><tr><th>&lt;50%</th><th>&lt;90%</th><th>μ<sub>Age</sub></th><th>sd<sub>Age</sub></th></tr></thead><tbody><tr><td>NP</td><td>Nepal</td><td>563</td><td>0.78</td><td>0.61</td><td>28.06</td><td>7.58</td><td>1</td></tr><tr><td>NL</td><td>Netherlands</td><td>1297</td><td>1.00</td><td>0.99</td><td>49.63</td><td>16.83</td><td>1</td></tr><tr><td>NZ</td><td>New Zealand</td><td>510</td><td>1.00</td><td>1.00</td><td>45.76</td><td>17.62</td><td>1</td></tr><tr><td>NI</td><td>Nicaragua</td><td>16</td><td>1.00</td><td>1.00</td><td>42.75</td><td>14.84</td><td>1</td></tr><tr><td>NG</td><td>Nigeria</td><td>608</td><td>0.93</td><td>0.87</td><td>32.08</td><td>10.81</td><td>1</td></tr><tr><td>NO</td><td>Norway</td><td>532</td><td>1.00</td><td>1.00</td><td>47.04</td><td>17.39</td><td>1</td></tr><tr><td>PK</td><td>Pakistan</td><td>565</td><td>0.90</td><td>0.85</td><td>26.94</td><td>8.38</td><td>1</td></tr><tr><td>PA</td><td>Panama</td><td>18</td><td>1.00</td><td>1.00</td><td>44.11</td><td>17.32</td><td>1</td></tr><tr><td>PY</td><td>Paraguay</td><td>16</td><td>1.00</td><td>1.00</td><td>38.94</td><td>9.33</td><td>1</td></tr><tr><td>PE</td><td>Peru</td><td>91</td><td>1.00</td><td>1.00</td><td>46.21</td><td>14.44</td><td>1</td></tr><tr><td>PH</td><td>Philippines</td><td>524</td><td>0.98</td><td>0.96</td><td>36.74</td><td>12.27</td><td>1</td></tr><tr><td>PL</td><td>Poland</td><td>1817</td><td>1.00</td><td>1.00</td><td>46.44</td><td>17.09</td><td>1</td></tr><tr><td>PR</td><td>Puerto Rico</td><td>2</td><td>1.00</td><td>1.00</td><td>64.00</td><td>16.97</td><td>1</td></tr><tr><td>RO_1</td><td>Romania_1</td><td>500</td><td>1.00</td><td>1.00</td><td>42.26</td><td>13.45</td><td>2</td></tr><tr><td>RO_2</td><td>Romania_2</td><td>505</td><td>1.00</td><td>0.99</td><td>42.53</td><td>14.50</td><td>2</td></tr><tr><td>RU</td><td>Russian Federation</td><td>558</td><td>1.00</td><td>1.00</td><td>45.02</td><td>15.46</td><td>1</td></tr><tr><td>SN</td><td>Senegal</td><td>552</td><td>0.62</td><td>0.51</td><td>34.36</td><td>12.43</td><td>1</td></tr><tr><td>RS</td><td>Serbia</td><td>1070</td><td>0.88</td><td>0.71</td><td>42.92</td><td>11.93</td><td>1</td></tr><tr><td>SG</td><td>Singapore</td><td>564</td><td>0.96</td><td>0.92</td><td>43.06</td><td>13.73</td><td>1</td></tr><tr><td>SK</td><td>Slovakia</td><td>1265</td><td>1.00</td><td>1.00</td><td>44.19</td><td>15.88</td><td>1</td></tr><tr><td>ZA</td><td>South Africa</td><td>939</td><td>0.82</td><td>0.56</td><td>39.90</td><td>13.44</td><td>1</td></tr><tr><td>ES</td><td>Spain</td><td>1090</td><td>1.00</td><td>0.99</td><td>46.01</td><td>13.68</td><td>1</td></tr><tr><td>SE</td><td>Sweden</td><td>1568</td><td>1.00</td><td>1.00</td><td>52.90</td><td>15.42</td><td>1</td></tr><tr><td>CH</td><td>Switzerland</td><td>1056</td><td>1.00</td><td>1.00</td><td>47.94</td><td>16.66</td><td>1</td></tr><tr><td>TW</td><td>Taiwan</td><td>833</td><td>1.00</td><td>1.00</td><td>43.99</td><td>13.25</td><td>1</td></tr><tr><td>TR</td><td>Turkey</td><td>1455</td><td>1.00</td><td>0.99</td><td>37.23</td><td>15.24</td><td>1</td></tr><tr><td>UA</td><td>Ukraine</td><td>577</td><td>1.00</td><td>1.00</td><td>37.45</td><td>8.03</td><td>1</td></tr><tr><td>AE</td><td>United Arab Emirates</td><td>313</td><td>0.71</td><td>0.59</td><td>31.77</td><td>8.59</td><td>1</td></tr><tr><td>GB</td><td>United Kingdom</td><td>550</td><td>1.00</td><td>1.00</td><td>45.66</td><td>15.62</td><td>1</td></tr><tr><td>US</td><td>United States of America</td><td>1506</td><td>1.00</td><td>0.99</td><td>44.23</td><td>16.60</td><td>1</td></tr><tr><td>UY</td><td>Uruguay</td><td>49</td><td>1.00</td><td>1.00</td><td>52.88</td><td>13.70</td><td>1</td></tr><tr><td>VE</td><td>Venezuela</td><td>96</td><td>1.00</td><td>1.00</td><td>46.53</td><td>12.97</td><td>1</td></tr></tbody></table>

**Table 2.** Sample size, average proportion of valid answers, age of respondents and the number of data collections in 69 countries (N-V). **Note:** Country = country names in accordance with ISO3 codes, N = number of respondents in each country. <50% and <90% = average proportion of valid (non NA) answers that are below 0.5 and 0.9 respectively in the subject level. μ<sub>Age</sub> = mean age and sd<sub>Age</sub> = standard deviation of the age, Multiple datasets = whether there were multiple data collections in the country. Tables 1, 2 show the number of participants, the mean proportion of non-missing ‘valid’ answers, and age. When multiple samples were collected within the same country, data were split into numbered subgroups (e.g., for Brazil, which has three samples, they were flagged as Brazil_1, Brazil_2 and Brazil_3). Multiple subsamples can be observed for Brazil, Canada, Colombia, India, Italy, Mexico and Romania. Note that in all the tables, we kept country subsamples separated to highlight they were collected by different teams, often using different sampling methodologies or languages, which impact their characteristics (e.g., representativeness).

Demographic variables across countries are summarised in several tables: Tables 1, 2 show the number of participants, the mean proportion of non-missing ‘valid’ answers, and age. Tables 3, 4 illustrate the distribution of gender; Tables 5, 6 show employment status; and Tables 7–9 show marital status and number of children. When multiple samples were collected within the same country, data were split into numbered subgroups (e.g., for Brazil, which has three samples, they were flagged as Brazil_1, Brazil_2 and Brazil_3). Note that in the tables above, we kept country subsamples separated to highlight they were collected by different teams, often using different sampling methodologies or languages, which impact their characteristics (e.g., representativeness).

For the most part, participants were recruited via professional survey research companies and were incentivised to participate. In countries that, to our knowledge, did not possess polling infrastructure³⁰, incentivising participants was not feasible. To collect data in these countries, leaders of national teams relied on online
<table><caption>**Table 3.** Distribution of sex in 69 countries (A-M). Note: Country = country names in accordance with ISO3 codes, % Female = Proportion of female respondents in the country, % Male = proportion of male respondents, % Other = proportion of non-binary respondents and % NA = proportion of the unreported sex.</caption><thead><tr><th>Country</th><th>% Female</th><th>% Male</th><th>% Other</th><th>% Unreported</th></tr></thead><tbody><tr><td>Argentina</td><td>0.69</td><td>0.31</td><td>0.00</td><td>0.00</td></tr><tr><td>Australia</td><td>0.51</td><td>0.48</td><td>0.01</td><td>0.00</td></tr><tr><td>Austria</td><td>0.46</td><td>0.41</td><td>0.00</td><td>0.13</td></tr><tr><td>Bangladesh</td><td>0.37</td><td>0.31</td><td>0.01</td><td>0.31</td></tr><tr><td>Belgium</td><td>0.41</td><td>0.59</td><td>0.00</td><td>0.00</td></tr><tr><td>Bolivia</td><td>0.59</td><td>0.41</td><td>0.00</td><td>0.00</td></tr><tr><td>Brazil_1</td><td>0.49</td><td>0.50</td><td>0.01</td><td>0.01</td></tr><tr><td>Brazil_2</td><td>0.47</td><td>0.19</td><td>0.00</td><td>0.33</td></tr><tr><td>Brazil_3</td><td>0.83</td><td>0.17</td><td>0.00</td><td>0.00</td></tr><tr><td>Bulgaria</td><td>0.65</td><td>0.34</td><td>0.00</td><td>0.01</td></tr><tr><td>Canada_English</td><td>0.62</td><td>0.38</td><td>0.01</td><td>0.00</td></tr><tr><td>Canada_French</td><td>0.54</td><td>0.46</td><td>0.00</td><td>0.00</td></tr><tr><td>Chile</td><td>0.65</td><td>0.35</td><td>0.00</td><td>0.00</td></tr><tr><td>China</td><td>0.49</td><td>0.51</td><td>0.00</td><td>0.00</td></tr><tr><td>Colombia_1</td><td>0.62</td><td>0.37</td><td>0.00</td><td>0.01</td></tr><tr><td>Colombia_2</td><td>0.63</td><td>0.37</td><td>0.00</td><td>0.00</td></tr><tr><td>Costa Rica</td><td>0.36</td><td>0.64</td><td>0.00</td><td>0.00</td></tr><tr><td>Croatia</td><td>0.52</td><td>0.48</td><td>0.00</td><td>0.01</td></tr><tr><td>Cuba</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Denmark</td><td>0.49</td><td>0.51</td><td>0.00</td><td>0.00</td></tr><tr><td>Dominican Republic</td><td>0.81</td><td>0.19</td><td>0.00</td><td>0.00</td></tr><tr><td>Ecuador</td><td>0.55</td><td>0.45</td><td>0.00</td><td>0.00</td></tr><tr><td>El Salvador</td><td>0.54</td><td>0.46</td><td>0.00</td><td>0.00</td></tr><tr><td>Finland</td><td>0.45</td><td>0.48</td><td>0.05</td><td>0.02</td></tr><tr><td>France</td><td>0.55</td><td>0.45</td><td>0.00</td><td>0.00</td></tr><tr><td>Germany</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Ghana</td><td>0.26</td><td>0.53</td><td>0.00</td><td>0.22</td></tr><tr><td>Greece</td><td>0.35</td><td>0.65</td><td>0.00</td><td>0.00</td></tr><tr><td>Guatemala</td><td>0.44</td><td>0.56</td><td>0.00</td><td>0.00</td></tr><tr><td>Honduras</td><td>0.71</td><td>0.29</td><td>0.00</td><td>0.00</td></tr><tr><td>Hungary</td><td>0.52</td><td>0.48</td><td>0.00</td><td>0.00</td></tr><tr><td>India_1</td><td>0.42</td><td>0.38</td><td>0.02</td><td>0.18</td></tr><tr><td>India_2</td><td>0.31</td><td>0.59</td><td>0.01</td><td>0.10</td></tr><tr><td>Iraq</td><td>0.23</td><td>0.26</td><td>0.01</td><td>0.50</td></tr><tr><td>Ireland</td><td>0.63</td><td>0.31</td><td>0.00</td><td>0.05</td></tr><tr><td>Israel</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Italy_1</td><td>0.50</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Italy_2</td><td>0.66</td><td>0.33</td><td>0.00</td><td>0.01</td></tr><tr><td>Japan</td><td>0.48</td><td>0.46</td><td>0.00</td><td>0.06</td></tr><tr><td>Korea</td><td>0.42</td><td>0.48</td><td>0.00</td><td>0.10</td></tr><tr><td>Latvia</td><td>0.63</td><td>0.37</td><td>0.00</td><td>0.00</td></tr><tr><td>Macedonia</td><td>0.54</td><td>0.43</td><td>0.01</td><td>0.03</td></tr><tr><td>Mexico_1</td><td>0.39</td><td>0.53</td><td>0.00</td><td>0.07</td></tr><tr><td>Mexico_2</td><td>0.61</td><td>0.38</td><td>0.00</td><td>0.00</td></tr><tr><td>Morocco</td><td>0.52</td><td>0.47</td><td>0.01</td><td>0.00</td></tr></tbody></table>

volunteers recruited via media appeals, mailing lists, advertisements on news aggregators, local communities and bloggers, and private messaging apps such as WhatsApp or WeChat.

**Materials.** The measures we used are illustrated in Figs. 4, 5 along with the specific items listed for each measure. In most cases, participants’ responses were collected on a scale from 0 = ‘strongly disagree’ to 10 = ‘strongly agree’, with 5 = ‘neither disagree nor agree’. In some cases, when more appropriate, we used other response scales (e.g., the generosity measure, where a 0–100% response scale was applied to hypothetical donations). In total, we
<table><caption>**Table 4.** Distribution of sex in 69 countries (N-V). *Note:* Country = country names in accordance with ISO3 codes, % Female = Proportion of female respondents in the country, % Male = proportion of male respondents, % Other = proportion of non-binary respondents and % NA = proportion of the unreported sex.</caption><thead><tr><th>Country</th><th>% Female</th><th>% Male</th><th>% Other</th><th>% Unreported</th></tr></thead><tbody><tr><td>Nepal</td><td>0.33</td><td>0.29</td><td>0.01</td><td>0.37</td></tr><tr><td>Netherlands</td><td>0.46</td><td>0.54</td><td>0.00</td><td>0.00</td></tr><tr><td>New Zealand</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Nicaragua</td><td>0.62</td><td>0.38</td><td>0.00</td><td>0.00</td></tr><tr><td>Nigeria</td><td>0.49</td><td>0.51</td><td>0.00</td><td>0.00</td></tr><tr><td>Norway</td><td>0.53</td><td>0.46</td><td>0.00</td><td>0.00</td></tr><tr><td>Pakistan</td><td>0.46</td><td>0.40</td><td>0.00</td><td>0.14</td></tr><tr><td>Panama</td><td>0.67</td><td>0.33</td><td>0.00</td><td>0.00</td></tr><tr><td>Paraguay</td><td>0.88</td><td>0.12</td><td>0.00</td><td>0.00</td></tr><tr><td>Peru</td><td>0.45</td><td>0.55</td><td>0.00</td><td>0.00</td></tr><tr><td>Philippines</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Poland</td><td>0.49</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Puerto Rico</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Romania_1</td><td>0.52</td><td>0.48</td><td>0.00</td><td>0.00</td></tr><tr><td>Romania_2</td><td>0.49</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Russian Federation</td><td>0.53</td><td>0.47</td><td>0.00</td><td>0.00</td></tr><tr><td>Senegal</td><td>0.37</td><td>0.63</td><td>0.01</td><td>0.00</td></tr><tr><td>Serbia</td><td>0.53</td><td>0.19</td><td>0.00</td><td>0.28</td></tr><tr><td>Singapore</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Slovakia</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>South Africa</td><td>0.51</td><td>0.17</td><td>0.00</td><td>0.31</td></tr><tr><td>Spain</td><td>0.33</td><td>0.67</td><td>0.00</td><td>0.00</td></tr><tr><td>Sweden</td><td>0.40</td><td>0.59</td><td>0.00</td><td>0.00</td></tr><tr><td>Switzerland</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Taiwan</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Turkey</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>Ukraine</td><td>0.52</td><td>0.47</td><td>0.00</td><td>0.00</td></tr><tr><td>United Arab Emirates</td><td>0.29</td><td>0.31</td><td>0.00</td><td>0.40</td></tr><tr><td>United Kingdom</td><td>0.51</td><td>0.49</td><td>0.00</td><td>0.00</td></tr><tr><td>United States of America</td><td>0.51</td><td>0.48</td><td>0.00</td><td>0.00</td></tr><tr><td>Uruguay</td><td>0.69</td><td>0.31</td><td>0.00</td><td>0.00</td></tr><tr><td>Venezuela</td><td>0.56</td><td>0.44</td><td>0.00</td><td>0.00</td></tr></tbody></table>

collected 98 unique variables and meta-data. To ensure participants' anonymity, no data that would allow their identification were collected.

*COVID-19 Beliefs and compliance.* Four constructs: COVID-19 public health support, COVID-19 risk perception, COVID-19 conspiracy theory beliefs, and COVID-19 testing behaviour. The public health support construct, in turn, is composed of three measures: spatial distancing, physical hygiene, and policy support. These are ad-hoc scales that we developed ourselves.

*Identity and social attitudes.* Three constructs: national identification³¹, national narcissism³², and social belonging³³.

*Ideology.* One construct: political ideology. Participants self-reported their political orientation according to a single item on a scale from 0 (“Very left-leaning”) to 10 (“Very right-leaning”). This measure has been shown to account for a significant proportion of the variance in voting intentions in American presidential elections between 1972 and 2004³⁴ and 2016³⁵⁻³⁷. In fact, using a single-item scale to measure political ideology has been a common practice in political psychology literature, providing substantive evidence for the validity of the measure both across national and international research³⁸,³⁹. However, even if the symbolic ideology can be a useful and parsimonious instrument to study political attitudes, when interpreting results, users should be attentive to the political and cultural applicability, psychometric validity, and generalisability of measures of political ideology⁴⁰⁻⁴².
<table><caption>**Table 5.** Distribution of employment status in 69 countries (A-M). Note: Country = country names in accordance with ISO3 codes, % Full = Proportion of full time workers, % Part = proportion of part time workers, % Unemp. = proportion of unemployed respondents, % Student = proportion of students, % Retired = proportion of retirees, % Other = proportion of respondents who do not fit in the mentioned categories and % NA = proportion of the unreported employment status.</caption><thead><tr><th>Country</th><th>% Full</th><th>% Part</th><th>% Unemp.</th><th>% Student</th><th>% Retired</th><th>% Other</th><th>% Unreported</th></tr></thead><tbody><tr><td>Argentina</td><td>0.45</td><td>0.15</td><td>0.02</td><td>0.08</td><td>0.08</td><td>0.22</td><td>0.00</td></tr><tr><td>Australia</td><td>0.36</td><td>0.18</td><td>0.11</td><td>0.05</td><td>0.23</td><td>0.07</td><td>0.00</td></tr><tr><td>Austria</td><td>0.36</td><td>0.13</td><td>0.02</td><td>0.05</td><td>0.12</td><td>0.20</td><td>0.13</td></tr><tr><td>Bangladesh</td><td>0.18</td><td>0.15</td><td>0.08</td><td>0.21</td><td>0.02</td><td>0.04</td><td>0.32</td></tr><tr><td>Belgium</td><td>0.28</td><td>0.04</td><td>0.03</td><td>0.25</td><td>0.25</td><td>0.14</td><td>0.00</td></tr><tr><td>Bolivia</td><td>0.52</td><td>0.14</td><td>0.07</td><td>0.07</td><td>0.00</td><td>0.21</td><td>0.00</td></tr><tr><td>Brazil_1</td><td>0.51</td><td>0.10</td><td>0.11</td><td>0.09</td><td>0.09</td><td>0.09</td><td>0.01</td></tr><tr><td>Brazil_2</td><td>0.25</td><td>0.08</td><td>0.06</td><td>0.16</td><td>0.04</td><td>0.08</td><td>0.33</td></tr><tr><td>Brazil_3</td><td>0.50</td><td>0.00</td><td>0.00</td><td>0.33</td><td>0.00</td><td>0.17</td><td>0.00</td></tr><tr><td>Bulgaria</td><td>0.37</td><td>0.06</td><td>0.06</td><td>0.24</td><td>0.01</td><td>0.23</td><td>0.03</td></tr><tr><td>Canada_English</td><td>0.41</td><td>0.12</td><td>0.09</td><td>0.11</td><td>0.18</td><td>0.09</td><td>0.00</td></tr><tr><td>Canada_French</td><td>0.00</td><td>0.00</td><td>0.63</td><td>0.05</td><td>0.25</td><td>0.08</td><td>0.00</td></tr><tr><td>Chile</td><td>0.40</td><td>0.16</td><td>0.04</td><td>0.04</td><td>0.07</td><td>0.28</td><td>0.00</td></tr><tr><td>China</td><td>0.73</td><td>0.01</td><td>0.01</td><td>0.05</td><td>0.20</td><td>0.00</td><td>0.00</td></tr><tr><td>Colombia_1</td><td>0.42</td><td>0.07</td><td>0.09</td><td>0.26</td><td>0.05</td><td>0.11</td><td>0.02</td></tr><tr><td>Colombia_2</td><td>0.40</td><td>0.15</td><td>0.04</td><td>0.12</td><td>0.07</td><td>0.22</td><td>0.00</td></tr><tr><td>Costa Rica</td><td>0.68</td><td>0.04</td><td>0.12</td><td>0.00</td><td>0.08</td><td>0.08</td><td>0.00</td></tr><tr><td>Croatia</td><td>0.48</td><td>0.03</td><td>0.16</td><td>0.05</td><td>0.24</td><td>0.05</td><td>0.00</td></tr><tr><td>Cuba</td><td>0.74</td><td>0.07</td><td>0.09</td><td>0.02</td><td>0.02</td><td>0.05</td><td>0.00</td></tr><tr><td>Denmark</td><td>0.41</td><td>0.07</td><td>0.07</td><td>0.10</td><td>0.29</td><td>0.07</td><td>0.00</td></tr><tr><td>Dominican Republic</td><td>0.56</td><td>0.14</td><td>0.08</td><td>0.11</td><td>0.03</td><td>0.08</td><td>0.00</td></tr><tr><td>Ecuador</td><td>0.57</td><td>0.10</td><td>0.06</td><td>0.07</td><td>0.05</td><td>0.14</td><td>0.00</td></tr><tr><td>El Salvador</td><td>0.68</td><td>0.07</td><td>0.07</td><td>0.04</td><td>0.00</td><td>0.14</td><td>0.00</td></tr><tr><td>Finland</td><td>0.44</td><td>0.08</td><td>0.09</td><td>0.19</td><td>0.08</td><td>0.10</td><td>0.02</td></tr><tr><td>France</td><td>0.55</td><td>0.07</td><td>0.07</td><td>0.08</td><td>0.18</td><td>0.05</td><td>0.00</td></tr><tr><td>Germany</td><td>0.37</td><td>0.13</td><td>0.05</td><td>0.07</td><td>0.29</td><td>0.09</td><td>0.00</td></tr><tr><td>Ghana</td><td>0.31</td><td>0.08</td><td>0.11</td><td>0.22</td><td>0.01</td><td>0.05</td><td>0.22</td></tr><tr><td>Greece</td><td>0.33</td><td>0.10</td><td>0.14</td><td>0.37</td><td>0.03</td><td>0.03</td><td>0.00</td></tr><tr><td>Guatemala</td><td>0.56</td><td>0.08</td><td>0.04</td><td>0.04</td><td>0.04</td><td>0.23</td><td>0.00</td></tr><tr><td>Honduras</td><td>0.46</td><td>0.38</td><td>0.08</td><td>0.04</td><td>0.00</td><td>0.04</td><td>0.00</td></tr><tr><td>Hungary</td><td>0.44</td><td>0.07</td><td>0.07</td><td>0.05</td><td>0.29</td><td>0.07</td><td>0.00</td></tr><tr><td>India_1</td><td>0.31</td><td>0.05</td><td>0.06</td><td>0.33</td><td>0.01</td><td>0.05</td><td>0.18</td></tr><tr><td>India_2</td><td>0.37</td><td>0.11</td><td>0.09</td><td>0.10</td><td>0.05</td><td>0.19</td><td>0.10</td></tr><tr><td>Iraq</td><td>0.09</td><td>0.08</td><td>0.09</td><td>0.17</td><td>0.01</td><td>0.04</td><td>0.50</td></tr><tr><td>Ireland</td><td>0.42</td><td>0.12</td><td>0.05</td><td>0.18</td><td>0.06</td><td>0.12</td><td>0.05</td></tr><tr><td>Israel</td><td>0.39</td><td>0.13</td><td>0.15</td><td>0.06</td><td>0.09</td><td>0.18</td><td>0.00</td></tr><tr><td>Italy_1</td><td>0.42</td><td>0.12</td><td>0.13</td><td>0.08</td><td>0.17</td><td>0.08</td><td>0.00</td></tr><tr><td>Italy_2</td><td>0.37</td><td>0.07</td><td>0.04</td><td>0.15</td><td>0.25</td><td>0.11</td><td>0.00</td></tr><tr><td>Japan</td><td>0.44</td><td>0.12</td><td>0.16</td><td>0.05</td><td>0.10</td><td>0.06</td><td>0.06</td></tr><tr><td>Korea</td><td>0.49</td><td>0.12</td><td>0.06</td><td>0.08</td><td>0.06</td><td>0.09</td><td>0.10</td></tr><tr><td>Latvia</td><td>0.63</td><td>0.08</td><td>0.06</td><td>0.07</td><td>0.10</td><td>0.08</td><td>0.00</td></tr><tr><td>Macedonia</td><td>0.70</td><td>0.04</td><td>0.07</td><td>0.08</td><td>0.02</td><td>0.06</td><td>0.03</td></tr><tr><td>Mexico_1</td><td>0.45</td><td>0.12</td><td>0.08</td><td>0.03</td><td>0.10</td><td>0.16</td><td>0.07</td></tr><tr><td>Mexico_2</td><td>0.52</td><td>0.15</td><td>0.03</td><td>0.05</td><td>0.07</td><td>0.18</td><td>0.00</td></tr><tr><td>Morocco</td><td>0.38</td><td>0.09</td><td>0.12</td><td>0.29</td><td>0.03</td><td>0.09</td><td>0.01</td></tr></tbody></table>
<table><caption>**Table 6.** Distribution of employment status in 69 countries (N-V). Note: Country = country names in accordance with ISO3 codes, % Full = Proportion of full time workers, % Part = proportion of part time workers, % Unemp. = proportion of unemployed respondents, % Student = proportion of students, % Retired = proportion of retirees, % Other = proportion of respondents who do not fit in the mentioned categories and % NA = proportion of the unreported employment status.</caption><thead><tr><th>Country</th><th>% Full</th><th>% Part</th><th>% Unemp.</th><th>% Student</th><th>% Retired</th><th>% Other</th><th>% Unreported</th></tr></thead><tbody><tr><td>Nepal</td><td>0.25</td><td>0.08</td><td>0.07</td><td>0.19</td><td>0.01</td><td>0.04</td><td>0.37</td></tr><tr><td>Netherlands</td><td>0.31</td><td>0.17</td><td>0.04</td><td>0.08</td><td>0.20</td><td>0.19</td><td>0.00</td></tr><tr><td>New Zealand</td><td>0.40</td><td>0.16</td><td>0.10</td><td>0.05</td><td>0.16</td><td>0.12</td><td>0.00</td></tr><tr><td>Nicaragua</td><td>0.44</td><td>0.25</td><td>0.06</td><td>0.00</td><td>0.06</td><td>0.19</td><td>0.00</td></tr><tr><td>Nigeria</td><td>0.30</td><td>0.14</td><td>0.17</td><td>0.18</td><td>0.01</td><td>0.06</td><td>0.13</td></tr><tr><td>Norway</td><td>0.45</td><td>0.09</td><td>0.03</td><td>0.06</td><td>0.20</td><td>0.15</td><td>0.00</td></tr><tr><td>Pakistan</td><td>0.24</td><td>0.05</td><td>0.07</td><td>0.43</td><td>0.01</td><td>0.06</td><td>0.14</td></tr><tr><td>Panama</td><td>0.50</td><td>0.00</td><td>0.11</td><td>0.06</td><td>0.11</td><td>0.22</td><td>0.00</td></tr><tr><td>Paraguay</td><td>0.62</td><td>0.38</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Peru</td><td>0.49</td><td>0.21</td><td>0.07</td><td>0.08</td><td>0.07</td><td>0.09</td><td>0.00</td></tr><tr><td>Philippines</td><td>0.47</td><td>0.12</td><td>0.15</td><td>0.09</td><td>0.03</td><td>0.11</td><td>0.03</td></tr><tr><td>Poland</td><td>0.37</td><td>0.07</td><td>0.13</td><td>0.07</td><td>0.26</td><td>0.10</td><td>0.00</td></tr><tr><td>Puerto Rico</td><td>0.50</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.50</td><td>0.00</td></tr><tr><td>Romania_1</td><td>0.63</td><td>0.04</td><td>0.08</td><td>0.07</td><td>0.13</td><td>0.05</td><td>0.00</td></tr><tr><td>Romania_2</td><td>0.58</td><td>0.05</td><td>0.08</td><td>0.08</td><td>0.14</td><td>0.08</td><td>0.00</td></tr><tr><td>Russian Federation</td><td>0.26</td><td>0.20</td><td>0.23</td><td>0.05</td><td>0.24</td><td>0.02</td><td>0.00</td></tr><tr><td>Senegal</td><td>0.51</td><td>0.05</td><td>0.06</td><td>0.23</td><td>0.01</td><td>0.13</td><td>0.00</td></tr><tr><td>Serbia</td><td>0.49</td><td>0.03</td><td>0.06</td><td>0.05</td><td>0.05</td><td>0.00</td><td>0.33</td></tr><tr><td>Singapore</td><td>0.63</td><td>0.06</td><td>0.08</td><td>0.04</td><td>0.05</td><td>0.05</td><td>0.07</td></tr><tr><td>Slovakia</td><td>0.48</td><td>0.05</td><td>0.08</td><td>0.07</td><td>0.24</td><td>0.08</td><td>0.00</td></tr><tr><td>South Africa</td><td>0.39</td><td>0.06</td><td>0.04</td><td>0.05</td><td>0.04</td><td>0.10</td><td>0.31</td></tr><tr><td>Spain</td><td>0.54</td><td>0.07</td><td>0.09</td><td>0.05</td><td>0.13</td><td>0.11</td><td>0.00</td></tr><tr><td>Sweden</td><td>0.51</td><td>0.06</td><td>0.03</td><td>0.05</td><td>0.27</td><td>0.09</td><td>0.00</td></tr><tr><td>Switzerland</td><td>0.37</td><td>0.18</td><td>0.06</td><td>0.07</td><td>0.20</td><td>0.11</td><td>0.00</td></tr><tr><td>Taiwan</td><td>0.57</td><td>0.10</td><td>0.06</td><td>0.07</td><td>0.14</td><td>0.07</td><td>0.00</td></tr><tr><td>Turkey</td><td>0.37</td><td>0.07</td><td>0.11</td><td>0.20</td><td>0.10</td><td>0.16</td><td>0.00</td></tr><tr><td>Ukraine</td><td>0.61</td><td>0.14</td><td>0.12</td><td>0.02</td><td>0.02</td><td>0.09</td><td>0.00</td></tr><tr><td>United Arab Emirates</td><td>0.30</td><td>0.02</td><td>0.05</td><td>0.18</td><td>0.00</td><td>0.05</td><td>0.40</td></tr><tr><td>United Kingdom</td><td>0.40</td><td>0.17</td><td>0.11</td><td>0.05</td><td>0.17</td><td>0.10</td><td>0.00</td></tr><tr><td>United States of America</td><td>0.48</td><td>0.11</td><td>0.12</td><td>0.04</td><td>0.18</td><td>0.06</td><td>0.00</td></tr><tr><td>Uruguay</td><td>0.53</td><td>0.14</td><td>0.00</td><td>0.06</td><td>0.12</td><td>0.14</td><td>0.00</td></tr><tr><td>Venezuela</td><td>0.46</td><td>0.12</td><td>0.07</td><td>0.02</td><td>0.03</td><td>0.29</td><td>0.00</td></tr></tbody></table>

**Health and well-being.** Three constructs: subjective physical health, wealth ladder, and psychological well-being. Each of these scales relied on well-validated instruments$^{43–45}$.

**Moral beliefs and motivation.** Four constructs: generosity$^{46}$, morality as cooperation$^{47}$, moral identity$^{48}$, and moral circle$^{49}$.

**Personality traits.** Six constructs: open-mindedness$^{50}$, self-esteem$^{51}$, trait optimism$^{52}$, trait self-control$^{53}$, narcissism$^{54}$, and cognitive reflection$^{55}$.

**Demographics.** Six questions: age, number of children, employment status, marital status, gender, and urbanicity.

**Metadata and attention check.** An attention check was used to mitigate negative impact on data quality from potential non-human responses and the likelihood of biasing data and subsequent analysis of low base-rate outcomes—such as endorsement of COVID-19 conspiracies. We collected typical questionnaire metadata (e.g., start, record, and end dates, duration, and language). In addition, we created an internal participant ID, added ISO2 and ISO3 country codes, and sample representativeness.
<table><caption>**Table 7.** Distribution of marital status and number of children in 69 countries (A-H). *Note:* Country = country names in accordance with ISO3 codes, Columns 2-5 shows the proportion of different marital status, NA(MS) = unreported marital status, Columns 6-12 shows proportion of respondents by the number of children they have and NA(Child.) = proportion of unreported number of children.</caption><thead><tr><th rowspan="2">Country</th><th colspan="4">Marital Status</th><th colspan="6">Number of Children</th></tr><tr><th>Single</th><th>Relation</th><th>Married</th><th>Unreported (MS)</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>≥4</th><th>Unreported (Child.)</th></tr></thead><tbody><tr><td>Argentina</td><td>0.29</td><td>0.27</td><td>0.44</td><td>0.00</td><td>0.37</td><td>0.16</td><td>0.26</td><td>0.14</td><td>0.05</td><td>0.02</td><td>0.00</td></tr><tr><td>Australia</td><td>0.37</td><td>0.15</td><td>0.48</td><td>0.00</td><td>0.44</td><td>0.15</td><td>0.24</td><td>0.10</td><td>0.04</td><td>0.02</td><td>0.00</td></tr><tr><td>Austria</td><td>0.20</td><td>0.24</td><td>0.43</td><td>0.13</td><td>0.32</td><td>0.17</td><td>0.23</td><td>0.11</td><td>0.03</td><td>0.01</td><td>0.13</td></tr><tr><td>Bangladesh</td><td>0.33</td><td>0.04</td><td>0.31</td><td>0.32</td><td>0.36</td><td>0.07</td><td>0.10</td><td>0.03</td><td>0.00</td><td>0.01</td><td>0.43</td></tr><tr><td>Belgium</td><td>0.37</td><td>0.26</td><td>0.36</td><td>0.00</td><td>0.57</td><td>0.12</td><td>0.19</td><td>0.08</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>Bolivia</td><td>0.38</td><td>0.10</td><td>0.52</td><td>0.00</td><td>0.41</td><td>0.14</td><td>0.28</td><td>0.10</td><td>0.07</td><td>0.00</td><td>0.00</td></tr><tr><td>Brazil_1</td><td>0.40</td><td>0.14</td><td>0.45</td><td>0.01</td><td>0.44</td><td>0.23</td><td>0.20</td><td>0.09</td><td>0.02</td><td>0.01</td><td>0.01</td></tr><tr><td>Brazil_2</td><td>0.24</td><td>0.21</td><td>0.22</td><td>0.33</td><td>0.45</td><td>0.10</td><td>0.09</td><td>0.03</td><td>0.00</td><td>0.00</td><td>0.33</td></tr><tr><td>Brazil_3</td><td>0.17</td><td>0.33</td><td>0.50</td><td>0.00</td><td>0.67</td><td>0.33</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Bulgaria</td><td>0.40</td><td>0.37</td><td>0.21</td><td>0.02</td><td>0.67</td><td>0.16</td><td>0.13</td><td>0.01</td><td>0.00</td><td>0.01</td><td>0.02</td></tr><tr><td>Canada_English</td><td>0.40</td><td>0.21</td><td>0.39</td><td>0.00</td><td>0.57</td><td>0.15</td><td>0.16</td><td>0.08</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>Canada_French</td><td>0.49</td><td>0.22</td><td>0.29</td><td>0.00</td><td>0.58</td><td>0.16</td><td>0.17</td><td>0.06</td><td>0.02</td><td>0.01</td><td>0.00</td></tr><tr><td>Chile</td><td>0.38</td><td>0.18</td><td>0.44</td><td>0.00</td><td>0.32</td><td>0.12</td><td>0.27</td><td>0.20</td><td>0.07</td><td>0.02</td><td>0.00</td></tr><tr><td>China</td><td>0.11</td><td>0.05</td><td>0.84</td><td>0.00</td><td>0.20</td><td>0.74</td><td>0.06</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Colombia_1</td><td>0.40</td><td>0.29</td><td>0.30</td><td>0.01</td><td>0.55</td><td>0.16</td><td>0.18</td><td>0.07</td><td>0.02</td><td>0.01</td><td>0.02</td></tr><tr><td>Colombia_2</td><td>0.32</td><td>0.21</td><td>0.47</td><td>0.00</td><td>0.39</td><td>0.16</td><td>0.27</td><td>0.12</td><td>0.03</td><td>0.02</td><td>0.00</td></tr><tr><td>Costa Rica</td><td>0.44</td><td>0.12</td><td>0.44</td><td>0.00</td><td>0.56</td><td>0.08</td><td>0.04</td><td>0.12</td><td>0.16</td><td>0.04</td><td>0.00</td></tr><tr><td>Croatia</td><td>0.21</td><td>0.14</td><td>0.61</td><td>0.04</td><td>0.34</td><td>0.17</td><td>0.34</td><td>0.10</td><td>0.02</td><td>0.02</td><td>0.01</td></tr><tr><td>Cuba</td><td>0.23</td><td>0.21</td><td>0.56</td><td>0.00</td><td>0.19</td><td>0.40</td><td>0.23</td><td>0.12</td><td>0.05</td><td>0.02</td><td>0.00</td></tr><tr><td>Denmark</td><td>0.28</td><td>0.26</td><td>0.46</td><td>0.00</td><td>0.39</td><td>0.16</td><td>0.31</td><td>0.09</td><td>0.02</td><td>0.02</td><td>0.00</td></tr><tr><td>Dominican Republic</td><td>0.44</td><td>0.25</td><td>0.31</td><td>0.00</td><td>0.50</td><td>0.28</td><td>0.14</td><td>0.08</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Ecuador</td><td>0.36</td><td>0.16</td><td>0.48</td><td>0.00</td><td>0.43</td><td>0.18</td><td>0.26</td><td>0.09</td><td>0.03</td><td>0.03</td><td>0.00</td></tr><tr><td>El Salvador</td><td>0.39</td><td>0.11</td><td>0.50</td><td>0.00</td><td>0.36</td><td>0.25</td><td>0.21</td><td>0.14</td><td>0.00</td><td>0.04</td><td>0.00</td></tr><tr><td>Finland</td><td>0.37</td><td>0.34</td><td>0.27</td><td>0.02</td><td>0.63</td><td>0.11</td><td>0.15</td><td>0.05</td><td>0.02</td><td>0.03</td><td>0.02</td></tr><tr><td>France</td><td>0.35</td><td>0.30</td><td>0.35</td><td>0.00</td><td>0.58</td><td>0.20</td><td>0.15</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.00</td></tr><tr><td>Germany</td><td>0.38</td><td>0.19</td><td>0.43</td><td>0.00</td><td>0.48</td><td>0.19</td><td>0.23</td><td>0.08</td><td>0.01</td><td>0.01</td><td>0.00</td></tr><tr><td>Ghana</td><td>0.36</td><td>0.11</td><td>0.32</td><td>0.21</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>Greece</td><td>0.45</td><td>0.38</td><td>0.16</td><td>0.00</td><td>0.86</td><td>0.07</td><td>0.06</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Guatemala</td><td>0.29</td><td>0.25</td><td>0.46</td><td>0.00</td><td>0.40</td><td>0.17</td><td>0.25</td><td>0.10</td><td>0.06</td><td>0.02</td><td>0.00</td></tr><tr><td>Honduras</td><td>0.38</td><td>0.17</td><td>0.46</td><td>0.00</td><td>0.58</td><td>0.12</td><td>0.00</td><td>0.04</td><td>0.17</td><td>0.08</td><td>0.00</td></tr><tr><td>Hungary</td><td>0.30</td><td>0.27</td><td>0.43</td><td>0.00</td><td>0.39</td><td>0.25</td><td>0.26</td><td>0.08</td><td>0.01</td><td>0.01</td><td>0.00</td></tr></tbody></table>

**Translation.** The survey instrument was drafted in English and translated into other languages using the standard forward-backward method (i.e., members of national teams were advised to split members into forward-translating the survey into the local language and back-translating it into English, and then have the two groups discuss and resolve discrepancies). In total, the survey instrument was translated into 32 languages, including adaptations of region-specific dialects or vernaculars. Specifically, from English into Arabic, Bengali, Bulgarian, Croatian, Danish, Dutch, Finnish, French, German, Greek, Hebrew, Hungary, Italian, Japanese, Korean, Kurdish, Latvian, Macedonian, Mandarin simplified, Mandarin traditional, Nepali, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Spanish, Swedish, Turkish, and Ukrainian (see osf.io/tfsza at sub-folder *Translations*).

**Data cleaning.** We received individual data files from each national team. To merge these raw data, minor modifications were introduced, which we delineate in this section. First, we renamed columns to match across data sets, reordered variables alphabetically, and standardised variable labels. Furthermore, all missing values and values denoting the absence of a response were converted to NAs (not available). When ambiguous date formats were found (e.g., on start date, end date, and record date), we manually specified the correct format and standardised them. At the second stage, we introduced multiple modifications to clean the data for research. Some modifications were introduced to every national data set, while others were introduced to specific national data sets
<table><caption>**Table 8.** Distribution of marital status and number of children in 69 countries (I-R). *Note:* Country = country names in accordance with ISO3 codes, Columns 2-5 shows the proportion of different marital status, NA(MS) = unreported marital status, Columns 6-12 shows proportion of respondents by the number of children they have and NA(Child.) = proportion of unreported number of children.</caption><thead><tr><th rowspan="2">Country</th><th colspan="4">Marital Status</th><th colspan="6">Number of Children</th></tr><tr><th>Single</th><th>Relation</th><th>Married</th><th>Unreported (MS)</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>≥4</th><th>Unreported (Child.)</th></tr></thead><tbody><tr><td>India_1</td><td>0.55</td><td>0.14</td><td>0.13</td><td>0.18</td><td>0.69</td><td>0.03</td><td>0.07</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.20</td></tr><tr><td>India_2</td><td>0.29</td><td>0.07</td><td>0.55</td><td>0.10</td><td>0.20</td><td>0.29</td><td>0.18</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.31</td></tr><tr><td>Iraq</td><td>0.26</td><td>0.04</td><td>0.20</td><td>0.50</td><td>0.30</td><td>0.03</td><td>0.05</td><td>0.04</td><td>0.03</td><td>0.03</td><td>0.52</td></tr><tr><td>Ireland</td><td>0.32</td><td>0.28</td><td>0.34</td><td>0.05</td><td>0.52</td><td>0.10</td><td>0.17</td><td>0.09</td><td>0.05</td><td>0.02</td><td>0.06</td></tr><tr><td>Israel</td><td>0.24</td><td>0.11</td><td>0.55</td><td>0.09</td><td>0.38</td><td>0.12</td><td>0.20</td><td>0.18</td><td>0.06</td><td>0.05</td><td>0.00</td></tr><tr><td>Italy_1</td><td>0.26</td><td>0.25</td><td>0.49</td><td>0.00</td><td>0.44</td><td>0.25</td><td>0.25</td><td>0.05</td><td>0.01</td><td>0.00</td><td>0.00</td></tr><tr><td>Italy_2</td><td>0.23</td><td>0.30</td><td>0.46</td><td>0.00</td><td>0.49</td><td>0.20</td><td>0.25</td><td>0.06</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Japan</td><td>0.35</td><td>0.05</td><td>0.54</td><td>0.06</td><td>0.46</td><td>0.14</td><td>0.23</td><td>0.08</td><td>0.02</td><td>0.00</td><td>0.07</td></tr><tr><td>Korea</td><td>0.35</td><td>0.07</td><td>0.49</td><td>0.10</td><td>0.44</td><td>0.16</td><td>0.25</td><td>0.03</td><td>0.01</td><td>0.00</td><td>0.10</td></tr><tr><td>Latvia</td><td>0.34</td><td>0.25</td><td>0.42</td><td>0.00</td><td>0.00</td><td>0.32</td><td>0.19</td><td>0.31</td><td>0.12</td><td>0.05</td><td>0.00</td></tr><tr><td>Macedonia</td><td>0.30</td><td>0.19</td><td>0.48</td><td>0.03</td><td>0.50</td><td>0.17</td><td>0.26</td><td>0.04</td><td>0.00</td><td>0.00</td><td>0.04</td></tr><tr><td>Mexico_1</td><td>0.26</td><td>0.18</td><td>0.49</td><td>0.07</td><td>0.34</td><td>0.13</td><td>0.25</td><td>0.14</td><td>0.04</td><td>0.03</td><td>0.07</td></tr><tr><td>Mexico_2</td><td>0.31</td><td>0.19</td><td>0.50</td><td>0.00</td><td>0.29</td><td>0.18</td><td>0.32</td><td>0.15</td><td>0.04</td><td>0.02</td><td>0.00</td></tr><tr><td>Morocco</td><td>0.57</td><td>0.09</td><td>0.33</td><td>0.01</td><td>0.70</td><td>0.09</td><td>0.10</td><td>0.06</td><td>0.01</td><td>0.01</td><td>0.02</td></tr><tr><td>Nepal</td><td>0.36</td><td>0.05</td><td>0.21</td><td>0.37</td><td>0.46</td><td>0.08</td><td>0.06</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.39</td></tr><tr><td>Netherlands</td><td>0.29</td><td>0.27</td><td>0.43</td><td>0.00</td><td>0.41</td><td>0.12</td><td>0.29</td><td>0.13</td><td>0.03</td><td>0.02</td><td>0.00</td></tr><tr><td>New Zealand</td><td>0.39</td><td>0.20</td><td>0.41</td><td>0.00</td><td>0.41</td><td>0.16</td><td>0.21</td><td>0.13</td><td>0.06</td><td>0.04</td><td>0.00</td></tr><tr><td>Nicaragua</td><td>0.19</td><td>0.25</td><td>0.56</td><td>0.00</td><td>0.25</td><td>0.12</td><td>0.25</td><td>0.19</td><td>0.12</td><td>0.06</td><td>0.00</td></tr><tr><td>Nigeria</td><td>0.42</td><td>0.11</td><td>0.34</td><td>0.13</td><td>0.51</td><td>0.10</td><td>0.12</td><td>0.08</td><td>0.03</td><td>0.02</td><td>0.13</td></tr><tr><td>Norway</td><td>0.32</td><td>0.26</td><td>0.42</td><td>0.00</td><td>0.41</td><td>0.15</td><td>0.24</td><td>0.16</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>Pakistan</td><td>0.51</td><td>0.10</td><td>0.24</td><td>0.14</td><td>0.66</td><td>0.07</td><td>0.07</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.15</td></tr><tr><td>Panama</td><td>0.33</td><td>0.17</td><td>0.50</td><td>0.00</td><td>0.44</td><td>0.11</td><td>0.28</td><td>0.11</td><td>0.00</td><td>0.06</td><td>0.00</td></tr><tr><td>Paraguay</td><td>0.56</td><td>0.31</td><td>0.12</td><td>0.00</td><td>0.44</td><td>0.06</td><td>0.31</td><td>0.06</td><td>0.12</td><td>0.00</td><td>0.00</td></tr><tr><td>Peru</td><td>0.40</td><td>0.14</td><td>0.46</td><td>0.00</td><td>0.35</td><td>0.20</td><td>0.29</td><td>0.13</td><td>0.01</td><td>0.02</td><td>0.00</td></tr><tr><td>Philippines</td><td>0.44</td><td>0.15</td><td>0.38</td><td>0.03</td><td>0.46</td><td>0.21</td><td>0.17</td><td>0.09</td><td>0.02</td><td>0.02</td><td>0.03</td></tr><tr><td>Poland</td><td>0.29</td><td>0.21</td><td>0.50</td><td>0.00</td><td>0.33</td><td>0.22</td><td>0.31</td><td>0.10</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>Puerto Rico</td><td>0.00</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.50</td><td>0.50</td><td>0.00</td><td>0.00</td></tr><tr><td>Romania_1</td><td>0.32</td><td>0.13</td><td>0.55</td><td>0.00</td><td>0.65</td><td>0.22</td><td>0.11</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Romania_2</td><td>0.27</td><td>0.19</td><td>0.54</td><td>0.00</td><td>0.40</td><td>0.32</td><td>0.22</td><td>0.04</td><td>0.01</td><td>0.01</td><td>0.00</td></tr><tr><td>Russian Federation</td><td>0.41</td><td>0.15</td><td>0.44</td><td>0.00</td><td>0.39</td><td>0.28</td><td>0.26</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.00</td></tr></tbody></table>

(both of which are thoroughly reported in the Data Records section). To each national data set, we recoded the attention check (attcheck) into pass (1) or fail (0); standardised generosity items (generosity1-3), recoded CRT items into intuitive (2), correct (1), and incorrect (0); converted the number of children (children) into a variable with a fixed range from zero to ten or more; recoded all participants declaring being older than 100 years old as 100; and we excluded all duplicates (i.e., in case multiple participants were recorded with identical inputs within a national database, only the first input was retained).

## Data Records

All materials associated with the ICSMP COVID-19 project can be found on the project's repository (comprising five folders) hosted by the Open Science Framework (OSF, https://doi.org/10.17605/osf.io/tfsza)⁵⁶,⁵⁷. The folder named *Code* includes an R Markdown document (ICSMP official data.Rmd; osf.io/dwpng) that loads multiple data files (from each national team), cleans them up, merges them into a single data file, generates a data-driven code-book, and saves all outputs. It also includes a reproducible report with all reported numbers, analyses and graphs in this article (Analyses-SciData.html; osf.io/s5c4p; Analyses SciData.Rmd; osf.io/9suyb). The folder named *Data* includes three sub-folders. The *Raw data* sub-folder contains the original and unmodified data files from each national team (country data files.zip; osf.io/dqmut). The sub-folder named *Cleaned data* contains the merged and cleaned dataset, which is provided in a non-proprietary (ICSMP_cleaned_data.csv; osf.io/ypkrc) and a labelled (ICSMP_cleaned_data.sav; at osf.io/8tyj9) file formats. In addition, we included in a sub-folder a dataset that removes observations failing the attention check or filled out less than 50% of the items, both in a non-proprietary (ICSMP_cleaned_data_nobots.csv; osf.io/98fex) and a labelled (ICSMP_cleaned_data_nobots.sav; at osf.io/3yjga) file formats. The *Metadata* sub-folder provides a thorough itemised description
<table><caption>**Table 9.** Distribution of marital status and number of children in 69 countries (S-V). *Note:* Country = country names in accordance with ISO3 codes, Columns 2-5 shows the proportion of different marital status, NA(MS) = unreported marital status, Columns 6-12 shows proportion of respondents by the number of children they have and NA(Child.) = proportion of unreported number of children.</caption><thead><tr><th rowspan="2">Country</th><th colspan="4">Marital Status</th><th colspan="6">Number of Children</th></tr><tr><th>Single</th><th>Relation</th><th>Married</th><th>Unreported (MS)</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>≥4</th><th>Unreported (Child.)</th></tr></thead><tbody><tr><td>Senegal</td><td>0.48</td><td>0.08</td><td>0.44</td><td>0.00</td><td>0.50</td><td>0.13</td><td>0.11</td><td>0.12</td><td>0.06</td><td>0.07</td><td>0.01</td></tr><tr><td>Serbia</td><td>0.19</td><td>0.15</td><td>0.38</td><td>0.28</td><td>0.28</td><td>0.16</td><td>0.21</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.29</td></tr><tr><td>Singapore</td><td>0.31</td><td>0.08</td><td>0.53</td><td>0.07</td><td>0.44</td><td>0.18</td><td>0.21</td><td>0.08</td><td>0.01</td><td>0.01</td><td>0.07</td></tr><tr><td>Slovakia</td><td>0.28</td><td>0.25</td><td>0.47</td><td>0.00</td><td>0.37</td><td>0.18</td><td>0.31</td><td>0.11</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>South Africa</td><td>0.23</td><td>0.16</td><td>0.30</td><td>0.32</td><td>0.32</td><td>0.11</td><td>0.16</td><td>0.07</td><td>0.01</td><td>0.01</td><td>0.32</td></tr><tr><td>Spain</td><td>0.24</td><td>0.27</td><td>0.49</td><td>0.00</td><td>0.49</td><td>0.17</td><td>0.27</td><td>0.06</td><td>0.01</td><td>0.00</td><td>0.00</td></tr><tr><td>Sweden</td><td>0.27</td><td>0.27</td><td>0.46</td><td>0.00</td><td>0.29</td><td>0.14</td><td>0.33</td><td>0.16</td><td>0.05</td><td>0.03</td><td>0.00</td></tr><tr><td>Switzerland</td><td>0.31</td><td>0.28</td><td>0.41</td><td>0.00</td><td>0.46</td><td>0.19</td><td>0.25</td><td>0.08</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>Taiwan</td><td>0.35</td><td>0.11</td><td>0.54</td><td>0.00</td><td>0.46</td><td>0.16</td><td>0.27</td><td>0.09</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>Turkey</td><td>0.37</td><td>0.06</td><td>0.57</td><td>0.00</td><td>0.42</td><td>0.12</td><td>0.26</td><td>0.11</td><td>0.03</td><td>0.05</td><td>0.00</td></tr><tr><td>Ukraine</td><td>0.19</td><td>0.12</td><td>0.62</td><td>0.07</td><td>0.33</td><td>0.39</td><td>0.24</td><td>0.03</td><td>0.01</td><td>0.00</td><td>0.00</td></tr><tr><td>United Arab Emirates</td><td>0.26</td><td>0.15</td><td>0.20</td><td>0.40</td><td>0.40</td><td>0.08</td><td>0.07</td><td>0.04</td><td>0.00</td><td>0.01</td><td>0.40</td></tr><tr><td>United Kingdom</td><td>0.33</td><td>0.24</td><td>0.42</td><td>0.00</td><td>0.50</td><td>0.15</td><td>0.24</td><td>0.07</td><td>0.03</td><td>0.01</td><td>0.00</td></tr><tr><td>United States of America</td><td>0.41</td><td>0.11</td><td>0.48</td><td>0.00</td><td>0.47</td><td>0.18</td><td>0.23</td><td>0.08</td><td>0.02</td><td>0.02</td><td>0.00</td></tr><tr><td>Uruguay</td><td>0.29</td><td>0.14</td><td>0.57</td><td>0.00</td><td>0.27</td><td>0.22</td><td>0.37</td><td>0.08</td><td>0.06</td><td>0.00</td><td>0.00</td></tr><tr><td>Venezuela</td><td>0.28</td><td>0.23</td><td>0.49</td><td>0.00</td><td>0.31</td><td>0.19</td><td>0.30</td><td>0.15</td><td>0.05</td><td>0.00</td><td>0.00</td></tr></tbody></table>

of the data cleaning process in both text (Data Cleaning.docx; osf.io/7udpt) and human-readable change-log (human-readable change log ICSMP.xlsx; osf.io/fydx2).

We also provide a data-driven codebook detailing how each measure was collected—e.g., listing variable names, variable labels, and label values (dt.codebook.xlsx; osf.io/ecva2). The IRB folder contains both the Internal Review Board Ethics application (ICSMP Kent Ethics application full.pdf; osf.io/xt9gr) and Ethics approval (ICSMP Kent Ethics approval.pdf; osf.io/ce638). The folder *Sample Type & Representativeness* includes the documentation for an internal survey conducted with national team leaders about the employed survey methodology for the data provided (Sample Type & Representativeness.zip; osf.io/fj5xn). The folder *Survey Instrument* contains the initial English version of our survey instrument along with its Qualtrics.qsf for reproducibility (Survey Instrument.zip; osf.io/nf48q). In the sub-folder *Translations*, we archived all 32 translated survey instruments along with a report on the languages of conducted surveys per country (i.e., several countries had their surveys in multiple languages per country; Country and language.xlsx; osf.io/wj7d2).

**Potential for future research.** The data contains four measures of COVID-19 beliefs and compliance, 17 social and moral psychological constructs, and six sociodemographic characteristics, amounting to 27 socially-relevant variables. To quantify the potential of this dataset—and assuming a typical research paper uses between three to five key main constructs plus sociodemographics and controls—we calculated the number of combinations of 17 constructs, taken three, four, and five at a time, yielding a grand total of 9248 possible unique designs. As a demonstration of the broad scope of the ICSMP data, published studies cover a broad range of psychological disciplines, including social psychology¹³,¹⁴, cognitive psychology¹⁵,¹⁷, political psychology¹⁶, moral psychology¹⁶,¹⁸, economic psychology¹⁹ and health sciences²⁰, among others. They explore different populations in reference to the COVID-19 pandemic in terms of age (e.g., older adults see²¹, marital status¹⁹ or nationality (e.g., for a study on the Spanish population, see;²² for Swedish and Chinese population see²³), and other socio-demographic characteristics. These all attest to the great potential of the ICSMP data to inspire further research. In sum, the present dataset affords numerous opportunities for cross-cultural research on a plethora of hypotheses. We encourage researchers who consider reusing ICSMP data to examine the list of pre-registrations before beginning a new project so as to avoid duplication (see icsmp-covid19.netlify.app/preregistration).

**Data visualisation interface.** In addition to the raw data, a dedicated Web application was developed to provide a general overview of the dataset (icsmp.shinyapps.io/icsmp_covid19). The application is based on an R shiny server (rstudio.com/products/shiny), together with the *leaflet*⁵⁸ and *ggplot2*⁵⁹ graphical libraries to generate dynamic plots. All the generated figures can be exported as .png files, and all tables can be exported as .csv files. The Web application allows easy and dynamic generation of illustrations like the figures with maps for each construct with zoomable world maps and static figures and plots for sample and country characteristics. In addition, all tables are embedded with dynamic features for sorting and filtering. To make it more accessible
<table><thead><tr><th>SUPERORDINATE CONSTRUCT</th><th>CONSTRUCT</th><th>ITEMS</th></tr></thead><tbody><tr><td rowspan="10">Covid-19 beliefs and compliance</td><td rowspan="5">Public health support</td><td><strong>Spatial distancing</strong><br>During the days of the coronavirus (Covid-19) pandemic...<br>- I have been staying at home as much as practically possible.<br>- Visiting friends, family, or colleagues outside my home.<br>- Keeping the number of grocery store visits at an absolute minimum.<br>- Keeping physical distance from all other people outside my home.<br>- Avoiding handshaking with people outside my home.<br><br><strong>Physical hygiene</strong><br>During the days of the coronavirus (Covid-19) pandemic...<br>- I have been washing my hands longer than usual.<br>- Washing my hands (with soap) more thoroughly than usual.<br>- Washing my hands immediately after returning home.<br>- Disinfecting frequently used objects, such as mobile phones and keys.<br>- Sneezing and coughing into my upper sleeve.<br><br><strong>Policy support</strong><br>During the days of the coronavirus (Covid-19) pandemic...<br>- In favor of closing all schools and universities.<br>- In favor of closing all bars and restaurants.<br>- In favor of closing all parks.<br>- In favor of forbidding all public gatherings where many people are gathered at one place (sports and culture).<br>- In favor of forbidding all non-necessary travel.</td></tr><tr><td rowspan="2">COVID-19 risk perception</td><td>- By April 30, 2021: How likely do you think it is that you will get infected by the Coronavirus (Covid-19)?<br>- By April 30, 2021: How likely do you think it is that the average person in [INSERT COUNTRY] will get infected by the Coronavirus (Covid-19)?</td></tr><tr><td>- The coronavirus (Covid-19) is a bioweapon engineered by scientists.<br>- The coronavirus (Covid-19) is a conspiracy to take away citizens rights for good and establish an authoritarian government.<br>- The coronavirus (Covid-19) is a hoax invented by interest groups for financial gains.<br>- The coronavirus (Covid-19) was created as a cover up for the impending global economic crash.</td></tr><tr><td rowspan="2">COVID-19 conspiracy beliefs</td><td>- Have you tested positive for the Coronavirus (Covid-19), meaning that you (now or earlier) have had a medically confirmed case of this disease?<br>- Has anyone you know well (friend, partner, family, colleague etc.) tested positive for the Coronavirus (Covid-19)?</td></tr><tr><td>- I identify as [nationality].<br>Being a [nationality] is an important reflection of who I am.</td></tr><tr><td rowspan="3">Identity and social attitudes</td><td>National identification</td><td>(My national group) deserves special treatment.<br>Not many people seem to fully understand the importance of [my national group].<br>Not many people seem to fully understand the importance of [my national group].</td></tr><tr><td>National narcissism</td><td>- I feel connected with others.<br>- When I am with other people, I feel included.<br>- I feel accepted by others.<br>- I have close bonds with family and friends.</td></tr><tr><td>Social belonging</td><td>- I feel connected with others.<br>- When I am with other people, I feel included.<br>- I feel accepted by others.<br>- I have close bonds with family and friends.</td></tr><tr><td>Ideology</td><td>Political ideology</td><td>Overall, how would you best describe yourself in terms of political ideology?</td></tr><tr><td rowspan="3">Health and well-being</td><td>Subjective physical health</td><td>In general, how would you rate your physical health as it is today?</td></tr><tr><td>Wealth ladder</td><td>Where would you place yourself on this ladder to represent where you think you stand at this time in your life, compared to other people in [country]?</td></tr><tr><td>Psychological well-being</td><td>- In general, to what extent do you feel happy these days?<br>- Please imagine a ladder, with steps numbered 0 at the bottom and 10 at the top. The top represents the best possible life for you, and the bottom represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 4 International Collaboration on the Social and Moral Psychology of COVID-19: Investigated constructs, items and variables.</figcaption></figure>

for the readers, both tables and figures are downloadable. The Shiny app has two tabs giving general information about the project and the international consortium. The first tab contains sample descriptions such as sample size, missing data, and attention checks for each country with a Gantt chart showing the dates of data collection. The second tab displays world maps of spatial distancing, policy support, national identity, conspiracy beliefs, national narcissism and morality as cooperation as well as all tables reported in dynamic formats.

Technical Validation

To support the technical quality of the dataset, we conducted an analysis to showcase its reliability (and its diverse applicability to research questions in social sciences and beyond). For completeness, in the analyses that follow, we examined all samples-including those with very few observations, such as Puerto Rico (N = 2), Brazil_3 (N = 6), and Panama (N = 12).

We evaluated the adopted survey methodology utilised by national teams by conducting an internal survey to ensure the accuracy of reported sample types. The inspection showed that 28 samples were quota-based nationally representative samples (36%), 6 used post hoc weights to achieve an approximate level of national representation (8%) which nonetheless should be seen as convenience samples, and 43 were convenience samples
<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td rowspan="10">Moral beliefs and Motivation</td><td>Morality as cooperation</td><td>Proportion of the daily wage in [country] you would keep for yourself / give to a national charity / give to an international charity.<br>When you decide whether something is right or wrong, to what extent are the following considerations relevant to your thinking?<br>- Whether or not someone helped a member of their family.<br>- Whether or not someone worked to unite a community.<br>- Whether or not someone kept their promise.<br>- Whether or not someone showed courage in the face of adversity.<br>- Whether or not someone deferred to those in authority.<br>- Whether or not someone kept the best part for themselves.<br>- Whether or not someone kept something that didn't belong to them.</td></tr><tr><td>Moral identity</td><td>- It would make me feel good to be a person who has these characteristics.<br>- Being someone who has these characteristics is an important part of who I am.<br>- I often wear clothes that identify me as having these characteristics.<br>- I would be ashamed to be a person who had these characteristics.<br>- The types of things I do in my spare time (e.g. hobbies) clearly identify me as having these characteristics.<br>- The kinds of books and magazines that I read identify me as having these characteristics.<br>- Having these characteristics is not really important to me.<br>- The fact that I have these characteristics is communicated to others by my membership in certain organizations.<br>- I am actively involved in activities that communicate to others that I have these characteristics.<br>- I strongly desire to have these characteristics.</td></tr><tr><td>Moral circle</td><td>Please select the number that represents the extent of your moral circle. Note that, in this scale, the number you select includes all the numbers below it as well. So, for example, if you select 10 (all mammals) you are also including number 1-9 (up to all people on all continents) in your moral circle.</td></tr><tr><td>Open mindedness</td><td>- I think that paying attention to people who disagree with me is a waste of time.<br>- I feel no shame learning from someone who knows more than me.<br>- If I do not know much about some topic, I don't mind being taught about it, even if I know about other topics.<br>- Even when I have high status, I don't mind learning from others who have lower status.<br>- Only wimps admit that they've made mistakes.<br>- I don't take people seriously if they're very different from me.</td></tr><tr><td>Self-esteem</td><td>I have high self-esteem.</td></tr><tr><td>Personality traits</td><td>Trait optimism<br>- As a person, I am optimistic for my future.<br>- Overall, I expect more good things to happen to me than bad.</td></tr><tr><td></td><td>Trait self-control<br>- I am good at resisting temptation.<br>- I am able to work effectively toward long-term goals.<br>- I have a hard time breaking bad habits.<br>- I am lazy.</td></tr><tr><td></td><td>Narcissism<br>- I react annoyed if another person steals the show from me.<br>- I deserve to be seen as a great personality.<br>- I want my rivals to fail.<br>- Being a very special person gives me a lot of strength.<br>- I manage to be the center of attention with my outstanding contributions.<br>- Most people are somehow losers.</td></tr><tr><td></td><td>Cognitive reflection test<br>- A postcard and a pen cost 150 cents in total. The postcard costs 100 cents more than the pen. How many cents does the pen cost?<br>- If it takes 3 nurses 3 minutes to measure the blood pressure of 3 patients, how long would it take 300 nurses to measure the blood pressure of 300 patients?<br>- Sally is making some tea. Every hour, the concentration of the tea doubles. If it takes 8 hours for the tea to be ready, how many hours would it take for the tea to reach half of the final concentration?</td></tr><tr><td>Demographics</td><td>Demographics<br>- How old are you?<br>- How many children do you have? If none, please type 0.<br>- How would you describe your current employment status?<br>- What is your current marital status?<br>- What is your gender?<br>- Which of the following best describes the area you live in?</td></tr><tr><td>Metadata and attention check</td><td>Attention check<br>Help us get rid of bots. Please write the number 213 into the comment box.</td></tr><tr><td></td><td>Survey measures<br>- Country code.<br>- Response date.<br>- Response duration.<br>- Language.<br>- Subject identifier number.</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 5** International Collaboration on the Social and Moral Psychology of COVID-19: Investigated constructs, items and variables.</figcaption></figure>

(56%), many of which were from low and middle-income countries⁶⁰. We codified the results of this survey into the cleaned data as the variable 'sample_coding' and present a summary in Table 10. National representativeness for the 28 quota-based samples relate to an approximation of the demographic characteristics of age and gender only for each country.

Regarding individual-level data quality, Fig. 6 shows a world map of the 69 countries from which data were collected, coloured according to overall percentages of missing data (overall mean = 6.0%). Overall, 95.6% of participants had less than 50% missing data, 92.8% participants had less than 10% missing data, and 24.7% of participants had 0% missing data. Another indicator of data quality is the rate of attention check fails per country. On the last screen of the survey, participants were given the following instructions: “Help us get rid of bots: Please write the number 213 into the comment box.” Participants who wrote “213” were coded as passing the attention check, participants who wrote anything else were coded as failing the attention check, and those who did not reach this screen of the survey were coded as missing data. Figure 6 also shows (bottom plot) a world map coloured according to the rate of attention-check fails across countries. Overall, 90.1% of participants passed the attention check (1.0% failed), and 8.0% did not reach the final screen with the attention check.

The full dataset presents N = 51,404 cases across 69 countries (from 77 samples, 28 of which are quota-based nationally representative), with an average sample size of 745 (SD = 549) and a proportion of valid answers of 95%. The mean age of respondents was 42.93 (SD = 16.04) years, and 50.9% were women (44% males, 0.3%
<table><caption>**Table 10.** Overview of the samples.</caption><thead><tr><th>Sample Coding</th><th>Samples (Countries)</th><th>N Samples</th><th>N Respondents</th><th>% Countries</th><th>% Respondents</th></tr></thead><tbody><tr><td>Continued on next page Quota-based nationally representative</td><td>AU, BR_1, CA_e, CA_f, CH, CN, DE, FR, HR, HU, IL, IT_1, JP, KR, LV, NG, NO, NZ, PH, PL, RO_1, RU, SG, SK, TR, TW, GB, US</td><td>28</td><td>26173</td><td>0.36</td><td>0.51</td></tr><tr><td>Post-hoc weights</td><td>AT, DK, ES, NL, SE, UA</td><td>6</td><td>6703</td><td>0.08</td><td>0.13</td></tr><tr><td>Convenience</td><td>AE, BD, BE, BG, BR_2, BR_3, CO_1, FI, GH, GR, IE, IN_1, IN_2, IT_2, IQ, CO_2, AR, CL, MX_2, PE, VE, CR, PY, BR_3, EC, GT, UY, BO, SV, PA, HN, CU, NI, DO, PR, MA, MK, MX_1, MX_2, NP, PK, RO_2, RS, SN, ZA</td><td>43</td><td>18528</td><td>0.56</td><td>0.36</td></tr><tr><td>Total</td><td></td><td>77</td><td>51404</td><td>1.00</td><td>1.00</td></tr></tbody></table>

## Overall percentages of missing data by country

<figure><img src="image_13.png" /></figure>

## Rate of attention-check fails across countries

<figure><img src="image_14.png" /></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netli fy.app

**Fig. 6** Data quality indicators for each surveyed country. **Note:** The percentage of missing data considered all the questions in the survey (i.e., all sociodemographics and psychological scales"). We calculated, for each country, the mean of the participants' proportion of missing data across all survey questions, including sociodemographics (this information is also provided in our reproducible report of Fig. 6, where the R code is provided).
Cross-cultural differences in Social & Moral Psychology of COVID-19

<figure><img src="image_15.png" /><figcaption>**Fig. 7** Cross-cultural differences in Social & Moral Psychology of COVID-19 across 69 countries. **Note:** Each world heat map in the figure shows the mean score, at the country level, for constructs in the survey. Conspiracy Beliefs - participant's beliefs in conspiracy theories regarding COVID-19; Morality as Cooperation - participant's moral concern based on the morality-as-cooperation theory; Spatial Distancing - participant's support for spatial distancing as a strategy against COVID-19; Collective Narcissism - participant's narcissism, i.e., an inflated view regarding their ingroup (in this research we focused on nationality); National Identity - participant's identity attached to belonging to a nation; Policy Support - participant's support to public policies (e.g., closing parks or schools) as a strategy against COVID-19.</figcaption></figure>

others, and 4.8% unreported). The employment status breakdown shows 44.8% employed full-time, 10.6% part-time, 8.1% unemployed, 10% students, 10.1% retired, 11% other, and 5.3% unreported. The overall marital status shows 33% of respondents were single, 18.7% in a relationship, 42.7% married, and 5.5% unreported. The majority of our participants reported having no children (41.6%), with 16.7% having one child, 20.1%, 9.2%, and 3.9% with two, three and four children, respectively, and 1.7% had five or more children (6.9% unreported). We break down these aggregated results per country. Tables 1, 2 show the number of cases and valid answers, Table 3,4 summarises the distribution of sex, Tables 5, 6 display employment status, and Tables 7–9 illustrate both marital statuses and the number of children.

We also examined cross-cultural differences in conspiracy beliefs, morality as cooperation, spatial distancing, national narcissism, national identification, and policy support for preventative measures across 69 countries in Fig. 7. Additionally, we showcase patterns of associations between these moral and psychological constructs across gender, ideology and age in Figs. 8, 9. For the association pattern analysis, we excluded samples with less than 490 respondents as recommended for stable correlations⁶¹, as well as for the subsequent consistency measure analysis.

To examine internal consistency for the main scales, we calculated Cronbach's Alpha, Omega, Guttman split-half reliability, and proportion of variance explained by a unidimensional factor. This table is available at osf.io/ed7yg and shows indices of internal consistency by country for measures of conspiracy beliefs, morality as cooperation, spatial distancing, national narcissism, national identification, and policy support for preventative
<figure><img src="image_16.png" /><figcaption>Fig. 8 Cross-cultural differences in associations of Social & Moral Psychology of COVID-19 across sex and ideology in 69 countries.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 9 Cross-cultural differences in associations of Social & Moral Psychology of COVID-19 across age in 69 countries.</figcaption></figure>
Policy support

<figure><img src="image_18.png" /><figcaption>Heat map showing consistency measures for each country</figcaption></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netilijy.app

<caption>Fig. 10 Support for policies in 69 countries.</caption>

Collective narcissism

<figure><img src="image_19.png" /><figcaption>Heat map showing consistency measures for each country</figcaption></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netilijy.app

<caption>Fig. 11 Collective narcissism in 69 countries.</caption>

measures, respectively. We found that the spatial distancing construct, on average, has the lowest Cronbach's alpha, followed by morality as cooperation. On average, conspiracy beliefs have the highest Cronbach's alpha, followed by policy support. These patterns hold for the Omega measures, but when considering Guttman's split-half reliability, collective narcissism and national identity yield the lowest values. Figures 9–15 show these patterns visually.

Usage Notes

The datasets are shared, cleaned, and ready for analysis. We recommend that interested researchers use the cleaned version of the data (available at https://doi.org/10.17605/osf.io/tfsza)⁵⁶. The use of the labelled data is also suggested for convenience as it has all variable levels encoded, thus eliminating the need to consult the codebook when using the.csv format.
Spatial distancing

<figure><img src="image_20.png" /><figcaption>Fig. 12 Spatial distancing in 69 countries.</figcaption></figure>

Morality as cooperation

<figure><img src="image_21.png" /><figcaption>Fig. 13 Morality as cooperation in 69 countries.</figcaption></figure>

The Data were imported and cleaned using the R software for statistical analysis⁶² and packages *readr*⁶³, *haven*⁶⁴, *readxl*⁶⁵, *dplyr*⁶⁶, *psych*⁶⁷, *htmltools*⁶⁸, *mime*⁶⁹, *xfun*⁷⁰, *labelled*⁷¹, *sjlabelled*⁷², *codebook*⁷³, *lubridate*⁷⁴.

As previously noted⁵, those wishing to approximate national representativeness can apply the appropriate survey weights to demographic and countries of interest when random sampling is used (e.g., sex: https://ourworldindata.org/gender-ratio; age: http://data.un.org/Data.aspx?d=POP&f=tableCode%3A22; education: https://ourworldindata.org/global-education; marital status: https://ourworldindata.org/marriages-and-divorces).

To minimize misclassification of text-based responses to the cognitive reflection test (CRT) and the attention check, we used multiple steps of data cleaning using REGEX (regular expressions) as fully detailed in (ICSMP official data.Rmd; osf.io/dwpng) located in the folder named *Code*. First, we coded the predefined numerical and
## National identity

<figure><img src="image_22.png" /><figcaption>Heat map showing consistency measures for each country</figcaption></figure>

Note: National Identity refers to the participant's identity attached to belonging to a nation.

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netlify.app

<caption>**Fig. 14** National identity in 69 countries.</caption>

## Conspiracy beliefs

<figure><img src="image_23.png" /><figcaption>Heat map showing consistency measures for each country</figcaption></figure>

International Collaboration on the Social & Moral Psychology of COVID-19
https://icsmp-covid19.netli fy.app

<caption>**Fig. 15** Cross-cultural differences in Internal Consistency Coefficients (Cronbach's alpha, McDonald's Omega, Guttman Split-Half), and variance explained of Social & Moral Psychology Constructs in 69 countries.</caption>

**Note:** internal consistency typically refers to correlations between different items on the same test to evaluate the extent to which latent indicators comprising the scale measure the same construct.

text values as correct (in the case of CRT, also the values predefined as intuitive). Then, iteratively, we screened the remaining responses and, using REGEX, updated answers. Remaining responses were recoded as incorrect.

## Code availability

All raw and cleaned data—as well as the R-code—used for standardising national-teams data, merging, and cleaning them are available at https://doi.org/10.17605/osf.io/tfsza⁵⁶.

Received: 31 May 2022; Accepted: 16 March 2023;

Published online: 11 May 2023
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## Acknowledgements

The ICSMP consortium would like to acknowledge the additional contributions of numerous friends and collaborators in translating and sharing the COVIDISTRESS survey, even if contributions were small or the person did not wish their name included as a member of the consortium.

## Author contributions

Conceptualization: F.A. Data curation: F.A., T.P., W.M.S. and G.R. Formal analysis: F.A., F.C.A., T.P., T.E. and J.C.R. Investigation: F.A. Methodology: F.A. Project administration: F.A. Resources: F.A. Software: F.A. and T.P.

Supervision: F.A. Validation: F.A. and R.M.R. Visualization: F.A., F.C.A., T.E., H.F.C., L.C., C.L. and J.C.R. Writing - original draft: F.A., B.G., R.M.R. and P.S. Writing - review & editing: ICSMP Collaborators.

## Funding

Open Access funding enabled and organized by Projekt DEAL.

## Competing interests

André Krouwel (ownership and stocks in Kieskompas BV, a data collector in this project). No payment was received by the author. No other authors reported a competing interest.

## Additional information

Correspondence and requests for materials should be addressed to F.A.

Reprints and permissions information is available at www.nature.com/reprints.

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Flavio Azevedo<sup>1,2,3</sup>, Tomislav Pavlović<sup>3</sup>, Gabriel G. Rêgo<sup>4</sup>, F. Ceren Ay<sup>5,6</sup>, Biljana Gjoneska<sup>7</sup>, Tom W. Etienne<sup>8,9</sup>, Robert M. Ross<sup>10</sup>, Philipp Schönegger<sup>11,12</sup>, Julián C. Riaño-Moreno<sup>13,14</sup>, Aleksandra Cichocka<sup>15</sup>, Valerio Capraro<sup>16</sup>, Luca Cian<sup>17</sup>, Chiara Longoni<sup>18</sup>, Ho Fai Chan<sup>19,20</sup>, Jay J. Van Bavel<sup>21</sup>, Hallgeir Sjåstad<sup>22</sup>, John B. Nezlek<sup>23,24</sup>, Mark Alfano<sup>25</sup>, Michele J. Gelfand<sup>26</sup>, Michèle D. Birtel<sup>27</sup>, Aleksandra Cislak<sup>23</sup>, Patricia L. Lockwood<sup>28,29</sup>, Koen Abts<sup>30</sup>, Elena Agadullina<sup>31</sup>, John Jamir Benzon Aruta<sup>32</sup>, Sahba Nomvula Besharati<sup>33</sup>, Alexander Bor<sup>34</sup>, Becky L. Choma<sup>35</sup>, Charles David Crabtree<sup>36</sup>, William A. Cunningham<sup>37</sup>, Koustav De<sup>38</sup>, Waqas Ejaz<sup>39</sup>, Christian T. Elbaek<sup>40</sup>, Andrej Findor<sup>41</sup>, Daniel Flichtentrei<sup>42</sup>, Renata Franc<sup>3</sup>, June Gruber<sup>43</sup>, Estrella Gualda<sup>44,45</sup>, Yusaku Horiuchi<sup>36</sup>, Toan Luu Duc Huynh<sup>46</sup>, Agustin Ibanez<sup>47,48,49</sup>, Mostak Ahamed Imran<sup>50</sup>, Jacob Israelashvili<sup>51</sup>, Katarzyna Jasko<sup>52</sup>, Jaroslaw Kantorowicz<sup>53</sup>, Elena Kantorowicz-Reznichenko<sup>54</sup>, André Krouwel<sup>55</sup>, Michael Laakasuo<sup>56</sup>, Claus Lamm<sup>57</sup>, Caroline Leygue<sup>58</sup>, Ming-Jen Lin<sup>59,60</sup>, Mohammad Sabbir Mansoor<sup>61</sup>, Antoine Marie<sup>34</sup>, Lewend Mayiwar<sup>62</sup>, Honorata Mazepus<sup>63,64</sup>, Cillian McHugh<sup>65</sup>, John Paul Minda<sup>66</sup>, Panagiotis Mitkidis<sup>40,67</sup>, Andreas Olsson<sup>68</sup>, Tobias Otterbring<sup>69,70</sup>, Dominic J. Packer<sup>71</sup>, Anat Perry<sup>51</sup>, Michael Bang Petersen<sup>34</sup>, Arathy Puthillam<sup>72</sup>, Tobias Rothmund<sup>2</sup>, Hernando Santamaría-García<sup>73</sup>, Petra C. Schmid<sup>74</sup>, Drozdstoy Stoyanov<sup>75</sup>, Shruti Tewari<sup>76</sup>, Bojan Todosijević<sup>77</sup>, Manos Tsakiris<sup>78,79,80</sup>, Hans H. Tung<sup>60,81</sup>, Radu G. Umbres<sup>82</sup>, Edmunds Vanags<sup>83</sup>, Madalina Vlasceanu<sup>84</sup>, Andrew Vonasch<sup>85</sup>, Meltem Yucel<sup>86,87</sup>, Yucheng Zhang<sup>88</sup>, Mohcine Abad<sup>89</sup>, Eli Adler<sup>51</sup>, Narin Akrawi<sup>90</sup>, Hamza Alaoui Mdarhri<sup>89</sup>, Hanane Amara<sup>91</sup>, David M. Amodio<sup>21,92</sup>, Benedict G. Antazo<sup>93</sup>, Matthew Apps<sup>29</sup>, Mouhamadou Hady Ba<sup>94</sup>, Sergio Barbosa<sup>95,96</sup>, Brock Bastian<sup>97</sup>, Anton Berg<sup>56</sup>, Maria P. Bernal-Zárate<sup>13</sup>, Michael Bernstein<sup>98</sup>, Michał Białek<sup>99</sup>, Ennio Bilancini<sup>100</sup>, Natalia Bogatyreva<sup>31</sup>, Leonardo Boncinelli<sup>101</sup>, Jonathan E. Booth<sup>102</sup>, Sylvie Borau<sup>103</sup>, Ondrej Buchel<sup>104,105</sup>, C. Daryl Cameron<sup>106,107</sup>, Chrissie F. Carvalho<sup>108</sup>, Tatiana Celadin<sup>109</sup>, Chiara Cerami<sup>110,111</sup>, Hom Nath Chalise<sup>61</sup>, Xiaojun Cheng<sup>112</sup>, Kate Cockcroft<sup>33</sup>, Jane Conway<sup>113</sup>, Mateo Andres Córdoba-Delgado<sup>73</sup>, Chiara Crespi<sup>111,114</sup>, Marie Crouzevialle<sup>74</sup>, Jo Cutler<sup>28,29</sup>, Marzena Cypryańska<sup>23</sup>, Justyna Dabrowska<sup>115</sup>, Michael A. Daniels<sup>116</sup>, Victoria H. Davis<sup>37</sup>, Pamala N. Dayley<sup>117</sup>, Sylvain Delouvée<sup>118</sup>, Ognjan Denkovski<sup>92</sup>, Guillaume Dezecache<sup>119</sup>, Nathan A. Dhaliwal<sup>116</sup>, Alelie B. Diato<sup>120</sup>, Roberto Di Paolo<sup>100</sup>, Marianna Drosinou<sup>56</sup>, Uwe Dulleck<sup>19,20,121,122</sup>, Jānis Ekmanis<sup>83</sup>, Arhan S. Ertan<sup>123</sup>, Hapsa Hossain Farhana<sup>50</sup>, Fahima Farkhari<sup>2</sup>, Harry Farmer<sup>27</sup>, Ali Fenwick<sup>124</sup>, Kristijan Fidanovski<sup>125</sup>, Terry Flew<sup>126</sup>, Shona Fraser<sup>127</sup>, Raymond Boadi Frempong<sup>128</sup>, Jonathan A. Fugelsang<sup>129</sup>, Jessica Gale<sup>85</sup>, E. Begoña Garcia-Navarro<sup>44</sup>, Prasad Garladinne<sup>76</sup>, Ouussama Ghajjou<sup>130</sup>, Theofilos Gkinopoulos<sup>131</sup>, Kurt Gray<sup>132</sup>, Siobhán M. Griffin<sup>65</sup>, Bjarki Gronfeldt<sup>15</sup>, Mert Gümren<sup>133</sup>, Ranju Lama Gurung<sup>61</sup>, Eran Halperin<sup>51</sup>, Elizabeth Harris<sup>21</sup>, Volo Herzon<sup>56</sup>, Matej Hruška<sup>41</sup>, Guanxiong Huang<sup>134</sup>, Matthias F. C. Hudecek<sup>135</sup>, Ozan Isler<sup>19,20</sup>, Simon Jangard<sup>68</sup>, Frederik J. Jorgensen<sup>34</sup>, Frank Kachanoff<sup>132</sup>, John Kahn<sup>36</sup>, Apsara Katuwal Dangol<sup>61</sup>, Oleksandra Keudel<sup>136</sup>, Lina Koppel<sup>137</sup>, Mika Koverola<sup>56</sup>, Emily Kubin<sup>138</sup>, Anton Kunnari<sup>56</sup>, Yordan Kutiyski<sup>8</sup>, Oscar Moreda Laguna<sup>8</sup>, Josh Leota<sup>139</sup>, Eva Lermer<sup>140,141</sup>, Jonathan Levy<sup>142,143</sup>, Neil Levy<sup>25</sup>, Chunyun Li<sup>102</sup>, Elizabeth U. Long<sup>37</sup>, Marina Maglić<sup>3</sup>, Darragh McCashin<sup>144</sup>, Alexander L. Metcalf<sup>145</sup>, Igor Mikloušić<sup>3</sup>, Soulaimane El Mimouni<sup>91</sup>, Asako Miura<sup>146</sup>, Juliana Molina-Paredes<sup>73</sup>, César Monroy-Fonseca<sup>147</sup>, Elena Morales-Marente<sup>44</sup>, David Moreau<sup>148</sup>, Rafał Muda<sup>149</sup>, Annalisa Myer<sup>87,150</sup>, Kyle Nash<sup>139</sup>, Tarik Nesh-Nash<sup>91</sup>, Jonas P. Nitschke<sup>57</sup>, Matthew S. Nurse<sup>151</sup>, Yohsuke Ohtsubo<sup>152</sup>, Victoria Oldemburgo de Mello<sup>37</sup>, Cathal O'Madagain<sup>89</sup>, Michal Onderco<sup>153</sup>, M. Soledad Palacios-Galvez<sup>44</sup>, Jussi Palomöki<sup>56</sup>, Yafeng Pan<sup>68</sup>, Zsófia Papp<sup>154</sup>, Philip Pärnamets<sup>68</sup>, Mariola Paruzel-Czachura<sup>155,156</sup>, Zoran Pavlović<sup>157</sup>, César Payán-Gómez<sup>158</sup>, Silva Perander<sup>56</sup>, Michael Mark Pitman<sup>33</sup>, Rajib Prasad<sup>159</sup>, Joanna Pyrkosz-Pacyna<sup>160</sup>, Steve Rathje<sup>1</sup>, Ali Raza<sup>161,162</sup>, Kasey Rhee<sup>163</sup>, Claire E. Robertson<sup>21</sup>, Iván Rodríguez-Pascual<sup>44</sup>, Teemu Saikkonen<sup>164</sup>, Octavio Salvador-Ginez<sup>58</sup>, Gaia C. Santi<sup>110</sup>, Natalia Santiago-Tovar<sup>165</sup>, David Savage<sup>166</sup>, Julian A. Scheffer<sup>106</sup>, David T. Schultner<sup>92</sup>, Enid M. Schutte<sup>33</sup>, Andy Scott<sup>139</sup>, Madhavi Sharma<sup>61</sup>, Pujan Sharma<sup>61</sup>, Ahmed Skali<sup>167</sup>, David Stadelmann<sup>128</sup>, Clara Alexandra Stafford<sup>66,168,169</sup>, Dragan Stanojević<sup>170</sup>, Anna Stefaniak<sup>171</sup>, Anni Sternisko<sup>21</sup>, Augustin Stoica<sup>172</sup>, Kristina K. Stoyanova<sup>173</sup>, Brent Strickland<sup>89,174</sup>, Jukka Sundvall<sup>56</sup>, Jeffrey P. Thomas<sup>97</sup>, Gustav Tinghög<sup>137</sup>, Benno Torgler<sup>19,20,175</sup>, Iris J. Traast<sup>92</sup>, Raffaele Tucciarelli<sup>176,177</sup>, Michael Tyrala<sup>178</sup>, Nick D. Ungson<sup>179</sup>, Mete S. Uysal<sup>180</sup>,
Paul A. M. Van Lange¹⁸¹, Jan-Willem van Prooijen¹⁸¹, Dirk van Rooy¹⁸², Daniel Västfjäll¹⁸³, Peter Verkoeijen¹⁸⁴, Joana B. Vieira⁶⁸, Christian von Sikorski¹⁸⁵, Alexander Cameron Walker¹²⁹, Jennifer Watermeyer¹⁸⁶, Erik Wetter¹⁸⁷, Ashley Whillans¹⁸⁸, Katherine White¹¹⁶, Rishad Habib³⁵, Robin Willardt⁷⁴, Michael J. A. Wohl¹⁷¹, Adrian Dominik Wójcik¹⁸⁹, Kaidi Wu¹⁹⁰, Yuki Yamada¹⁹¹, Onurcan Yilmaz¹⁹², Kumar Yogeeswaran⁸⁵, Carolin-Theresa Ziemer², Rolf A. Zwaan¹⁸⁴, Paulo S. Boggio⁴ & Waldir M. Sampaio⁴

¹Department of Psychology, University of Cambridge, Cambridge, England. ²Institute of Communication Science, Friedrich-Schiller University Jena, Jena, Germany. ³Institute of Social Sciences Ivo Pilar, Zagreb, Croatia. ⁴Social and Cognitive Neuroscience Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil. ⁵Department of Economics, Norwegian School of Economics, Bergen, Norway. ⁶Telenor Research, Oslo, Norway. ⁷Macedonian Academy of Sciences and Arts, Skopje, Republic of North Macedonia. ⁸Kieskompas - Election Compass, Amsterdam, Netherlands. ⁹Department of Political Science & Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA. ¹⁰Department of Psychology, Macquarie University, Sydney, NSW, Australia. ¹¹Department of Philosophy, University of St Andrews, St Andrews, Scotland. ¹²School of Economics and Finance, University of St Andrews, St Andrews, Scotland. ¹³Medicine Faculty, Cooperative University of Colombia, Villavicencio, Colombia. ¹⁴Department of Bioethics, El Bosque University, Bogotá, Colombia. ¹⁵School of Psychology, University of Kent, Canterbury, England. ¹⁶Department of Economics, Middlesex University London, London, England. ¹⁷Darden School of Business, University of Virginia, Charlottesville, VA, USA. ¹⁸Questrom School of Business, Boston University, Boston, MA, USA. ¹⁹School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, Australia. ²⁰Center for Behavioural Economics, Society and Technology, Queensland University of Technology, Brisbane, QLD, Australia. ²¹Department of Psychology and Neural Science, New York University, New York, NY, USA. ²²Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway. ²³SWPS University of Social Sciences and Humanities, Warsaw, Poland. ²⁴Department of Psychological Sciences, College of William and Mary, Williamsburg, VA, USA. ²⁵Department of Philosophy, Macquarie University, Sydney, NSW, Australia. ²⁶Stanford Graduate School of Business, Stanford University, Stanford, CA, USA. ²⁷School of Human Sciences, Institute for Lifecourse Development, University of Greenwich, London, England. ²⁸Department of Experimental Psychology, University of Oxford, Oxford, England. ²⁹Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, England. ³⁰KU Leuven, Leuven, Belgium. ³¹National Research University Higher School of Economics (HSE), Moscow, Russia. ³²De La Salle University, Manila, Philippines. ³³Department of Psychology, University of the Witwatersrand, Johannesburg, South Africa. ³⁴Department of Political Science, Aarhus University, Aarhus, Denmark. ³⁵Toronto Metropolitan University, Toronto, Canada. ³⁶Department of Government, Dartmouth College, Hanover, NH, USA. ³⁷Department of Psychology, University of Toronto, Toronto, ON, Canada. ³⁸Gatton College of Business and Economics, University of Kentucky, Lexington, KY, USA. ³⁹Department of Mass Communication, National University of Science and Technology (NUST), Islamabad, Pakistan. ⁴⁰Department of Management, Aarhus University, Aarhus, Denmark. ⁴¹Faculty of Social and Economic Sciences, Comenius University, Bratislava, Slovakia. ⁴²IntraMed, Buenos Aires, Argentina. ⁴³University of Colorado Boulder, Boulder, CO, USA. ⁴⁴ESEIS/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. ⁴⁵Faculty of Social Work, University of Huelva, Huelva, Spain. ⁴⁶WHU - Otto Beisheim School of Management, Vallendar, Germany. ⁴⁷Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile. ⁴⁸Cognitive Neuroscience Center (CNC), University of San Andrés, Buenos Aires, Argentina. ⁴⁹Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin (TCD), Dublin, Ireland. ⁵⁰Department of Educational and Counselling Psychology, University of Dhaka, Dhaka, Bangladesh. ⁵¹Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel. ⁵²Institute of Psychology, Jagiellonian University, Kraków, Poland. ⁵³Institute of Security and Global Affairs, Leiden University, The Hague, Netherlands. ⁵⁴Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, Netherlands. ⁵⁵Department of Political Science, Vrije University (VU) Amsterdam, Amsterdam, Netherlands. ⁵⁶Department of Digital Humanities, University of Helsinki, Helsinki, Finland. ⁵⁷Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria. ⁵⁸School of Psychology, National Autonomous University of Mexico, Mexico City, Mexico. ⁵⁹Department of Economics, National Taiwan University, Taipei, Taiwan. ⁶⁰Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan. ⁶¹Tribhuvan University, Kirtipur, Nepal. ⁶²Department of Leadership and Organizational Behavior, BI Norwegian Business School, Oslo, Norway. ⁶³Institute of Security and Global Affairs, Leiden University, Leiden, Netherlands. ⁶⁴Faculty of Governance and Global Affairs, Leiden University, Leiden, Netherlands. ⁶⁵Department of Psychology, University of Limerick, Limerick, Ireland. ⁶⁶Department of Psychology, The University of Western Ontario, London, ON, Canada. ⁶⁷Center for Advanced Hindsight, Duke University, Durham, NC, USA. ⁶⁸Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden. ⁶⁹Department of Management, University of Agder, Kristiansand, Norway. ⁷⁰Institute of Retail Economics, Stockholm, Sweden. ⁷¹Department of Psychology, Lehigh University, Bethlehem, PA, USA. ⁷²Department of Psychology, Monk Prayogshala, Mumbai, India. ⁷³Faculty of Medicine, Pontifical Javeriana University, Bogotá, Colombia. ⁷⁴Department of Management, Technology, and Economics, ETH Zürich, Zürich, Switzerland. ⁷⁵Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria. ⁷⁶Humanities and Social Sciences, Indian Institute of Management, Indore, India. ⁷⁷Institute of Social Sciences, Belgrade, Serbia. ⁷⁸Department of Psychology, Royal Holloway, University of London, London, England. ⁷⁹Center for the Politics of Feelings, School of Advanced Study, University of London, London, England. ⁸⁰Department of Behavioral and Cognitive Sciences, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Luxembourg City, Luxembourg. ⁸¹Department of Political Science, National Taiwan University, Taipei,
Taiwan. ⁸²Faculty of Political Science, National School for Political Studies and Public Administration, Bucharest, Romania. ⁸³Department of Psychology, University of Latvia, Riga, Latvia. ⁸⁴Department of Psychology, Princeton University, Princeton, NJ, USA. ⁸⁵Department of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand. ⁸⁶Department of Psychology and Neuroscience, Duke University, Durham, NC, USA. ⁸⁷Department of Psychology, University of Virginia, Charlottesville, VA, USA. ⁸⁸School of Economics and Management, Hebei University of Technology, Tianjin, PR China. ⁸⁹School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco. ⁹⁰Institute for Research and Development-Kurdistan, Middle East, Iraq. ⁹¹Impact For Development, North Africa, Rabat, Morocco. ⁹²Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. ⁹³Department of Psychology, Jose Rizal University, Mandaluyong, Philippines. ⁹⁴Department of Philosophy, University Cheikh Anta Diop, Dakar, Senegal. ⁹⁵School of Medicine and Health Sciences, University of Rosario, Bogotá, Colombia. ⁹⁶Moral Psychology and Decision Sciences Research Incubator, University of Rosario, Bogotá, Colombia. ⁹⁷School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia. ⁹⁸Department of Psychological and Social Sciences, Penn State Abington, Abington, PA, USA. ⁹⁹Institute of Psychology, University of Wrocław, Wrocław, Poland. ¹⁰⁰IMT School for Advanced Studies Lucca, Lucca, Italy. ¹⁰¹Department of Economics and Management, University of Florence, Florence, Italy. ¹⁰²Department of Management, London School of Economics and Political Science, London, England. ¹⁰³Toulouse Business School, University of Toulouse, Toulouse, France. ¹⁰⁴Social Policy Institute of the Ministry of Labor, Family and Social Affairs of the Slovak Republic, Bratislava, Slovakia. ¹⁰⁵The Institute for Sociology of the Slovak Academy of Sciences, Bratislava, Slovakia. ¹⁰⁶Department of Psychology, Penn State University, University Park, PA, USA. ¹⁰⁷Rock Ethics Institute, Penn State University, University Park, PA, USA. ¹⁰⁸Department of Psychology, Federal University of Santa Catarina, Florianópolis, Brazil. ¹⁰⁹Department of Economics, University of Bologna, Bologna, Italy. ¹¹⁰IUSS Cognitive Neuroscience (ICoN) Center, Institute for Advanced Study of Pavia, Pavia, Italy. ¹¹¹Cognitive Computational Neuroscience Research Unit, Neurological Institute Foundation Casimiro Mondino, Pavia, Italy. ¹¹²School of Psychology, Shenzhen University, Shenzhen, PR China. ¹¹³Institute for Advanced Study in Toulouse, Université Toulouse 1 Capitole, Toulouse, France. ¹¹⁴Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. ¹¹⁵Cracow University of Economics, Kraków, Poland. ¹¹⁶UBC Sauder School of Business, University of British Columbia, Vancouver, BC, Canada. ¹¹⁷Psychology Department, University of California - Los Angeles, Los Angeles, CA, USA. ¹¹⁸Laboratory of Psychology: Cognition, Behavior, and Communication (LP3C), Rennes 2 University, Rennes, France. ¹¹⁹Laboratory of Social and Cognitive Psychology, Clermont Auvergne University, CNRS, Clermont-Ferrand, France. ¹²⁰Cavite State University-General Trias City Campus, Cavite, Philippines. ¹²¹Crawford School of Public Policy, Australian National University, Canberra, ACT, Australia. ¹²²CESifo, University of Munich, Munich, Germany. ¹²³Department of International Trade, Boğaziçi University, Istanbul, Turkey. ¹²⁴Hult International Business School Dubai, Dubai, UAE. ¹²⁵Department of Social Policy and Intervention, University of Oxford, Oxford, England. ¹²⁶Department of Media and Communications, University of Sydney, Sydney, NSW, Australia. ¹²⁷Department of Psychiatry, University of the Witwatersrand, Johannesburg, South Africa. ¹²⁸University of Bayreuth, Bayreuth, Germany. ¹²⁹Department of Psychology, University of Waterloo, Waterloo, ON, Canada. ¹³⁰Department of Peace Studies, University of Bradford, Bradford, England. ¹³¹Philosophy and Social Studies Department, Rethymno, Greece. ¹³²Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ¹³³Department of Economics, Koc University, Istanbul, Turkey. ¹³⁴Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong. ¹³⁵University of Regensburg, Regensburg, Germany. ¹³⁶Graduate School for Transnational Studies, Free University of Berlin, Berlin, Germany. ¹³⁷Department of Management and Engineering, Linköping University, Linköping, Sweden. ¹³⁸Department of Psychology, University of Koblenz-Landau, Landau, Germany. ¹³⁹Department of Psychology, University of Alberta, Edmonton, Canada. ¹⁴⁰LMU Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany. ¹⁴¹Augsburg University for Applied Sciences, Augsburg, Germany. ¹⁴²Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel. ¹⁴³Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. ¹⁴⁴School of Psychology, Dublin City University, Dublin, Ireland. ¹⁴⁵University of Montana, Missoula, MT, USA. ¹⁴⁶Graduate School of Human Sciences, Osaka University, Suita, Japan. ¹⁴⁷SEELE Neuroscience, Mexico City, Mexico. ¹⁴⁸School of Psychology, University of Auckland, Auckland, New Zealand. ¹⁴⁹Faculty of Economics, Maria Curie-Skłodowska University, Lublin, Poland. ¹⁵⁰Department of Psychology, The City University of New York (CUNY) Graduate Center, New York, NY, USA. ¹⁵¹Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, ACT, Australia. ¹⁵²Department of Social Psychology, Graduate School of Humanities and Sociology, University of Tokyo, Tokyo, Japan. ¹⁵³Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands. ¹⁵⁴Center for Social Sciences, Hungarian Academy of Sciences Center of Excellence, Budapest, Hungary. ¹⁵⁵Institute of Psychology, University of Silesia, Katowice, Poland. ¹⁵⁶Complutense University in Madrid, Madrid, Spain. ¹⁵⁷Department of Psychology, University of Belgrade, Belgrade, Serbia. ¹⁵⁸Universidad Nacional de Colombia, Sede de La Paz, La Paz, Colombia. ¹⁵⁹Vidyasagar College For Women, Kolkata, India. ¹⁶⁰AGH University of Science and Technology, Kraków, Poland. ¹⁶¹Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA. ¹⁶²Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA. ¹⁶³Stanford University, Stanford, CA, USA. ¹⁶⁴Department of Biology, University of Turku, Turku, Finland. ¹⁶⁵Cooperative University of Colombia, Bogotá, Colombia. ¹⁶⁶Newcastle Business School, University of Newcastle, Callaghan, NSW, Australia. ¹⁶⁷Department of Global Economics and Management, University of Groningen, Groningen, Netherlands. ¹⁶⁸Brain and Mind Institute, University of Western Ontario, London, ON, Canada. ¹⁶⁹Western Interdisciplinary Research Building, University of Western Ontario, London, ON, Canada. ¹⁷⁰Department of Sociology, University of Belgrade, Belgrade, Serbia. ¹⁷¹Department of Psychology, Carleton University, Ottawa, ON, Canada. ¹⁷²National University of Political Studies and Public Administration (SNSPA), Bucharest, Romania. ¹⁷³Research Institute at Medical University of Plovdiv, Division of Translational Neuroscience, Plovdiv, Bulgaria. ¹⁷⁴Department of Cognitive Science, ENS, EHESS, CNRS, Institut Jean Nicod, PSL Research University, Paris, France. ¹⁷⁵CREMA - Center for Research in Economics, Management and the Arts, Basel,
Switzerland. ¹⁷⁶The Warburg Institute, School of Advanced Study, University of London, London, England.

¹⁷⁷Institute of Cognitive Neuroscience, University College London, London, England. ¹⁷⁸Institute for Emerging Market Studies, The Hong Kong University of Science and Technology, Kowloon, Hong Kong. ¹⁷⁹Department of Psychology, Susquehanna University, Selinsgrove, PA, USA. ¹⁸⁰Psychology Department, Dokuz Eylül University, Izmir, Turkey. ¹⁸¹Department of Experimental and Applied Psychology, VU Amsterdam, Amsterdam, Netherlands.

¹⁸²Faculty of Design Sciences, University of Antwerp, Antwerpen, Belgium. ¹⁸³Department of Behavioural Sciences and Learning (IBL), Linköping University, Linköping, Sweden. ¹⁸⁴Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands. ¹⁸⁵University of Koblenz-Landau, Landau, Germany. ¹⁸⁶Health Communication Research Unit, School of Human and Community Development, University of the Witwatersrand, Johannesburg, South Africa. ¹⁸⁷Department of Entrepreneurship, Innovation, and Technology, Stockholm School of Economics, Stockholm, Sweden. ¹⁸⁸Harvard Business School, Harvard University, Cambridge, MA, USA. ¹⁸⁹Nicolaus Copernicus University, Toruń, Poland. ¹⁹⁰University of California, San Diego, La Jolla, CA, USA.

¹⁹¹Kyushu University, Fukuoka, Japan. ¹⁹²Department of Psychology, Kadir Has University, Istanbul, Turkey.

e-mail: fa441@cam.ac.uk