Moralizing Physical Distancing During the COVID-19 Pandemic – Personal Motivations Predict Moral Condemnation

Moralizing Physical Distancing during the COVID-19 Pandemic – Personal Motivations Predict Moral Condemnation

Alexander Bor1, Marie Fly Lindholt1, Frederik Jørgensen1, and Michael Bang Petersen1

1Department of Political Science, Aarhus University

December 12, 2020

Abstract Physical distancing is a crucial aspect of most countries’ strategies to manage the COVID- 19 pandemic. However, keeping distance to others in public requires significant changes in conduct and behavior relative to ordinary circumstances. Throughout history, an effective strategy to make people engage in such behavioral change has been to morally condemn those who do not behave in an appropriate way. Accordingly, here, we investigate whether physical distancing has emerged as a moralized issue during the COVID-19 pandemic, potentially explaining the massive changes in behavior that have occurred across societies to halter the spread of the pandemic. Specifically, we utilize time-sensitive, representative survey evidence from eight Western democracies to examine the extent to which people (1) find it justified to condemn those who do not keep a distance to others in public and (2) blame ordinary citizens for the severity of the pandemic. The results demonstrate that physical distancing has indeed become a moral issue in most countries in the early phases of the pandemic. Furthermore, we identify the most important predictors of moralization to be age, behavioral change, social trust, and trust in the government. Except for minor differences, this pattern is observed within all countries in the sample. While moralization was high during the first wave of the pandemic, temporal analyses also indicate that moralization is lower in the second wave of the pandemic, potentially making it more difficult to engage in sufficient behavioral changes.

Word count: 6,420

This working paper has not been peer review yet.

Acknowledgements: This research has been supported by Carlsbergfondet grant CF20-0044 awarded to Michael Bang Petersen. Introduction

During the gradual reopening of societies following the first wave of the COVID-19 pandemic, it became clear that life will not return to normal in the near future. Accordingly, health authorities are seeking to educate citizens on proper behavior: A responsible citizen avoids crowded areas, keeps a safe distance to all others, wears a face mask in public, and avoids touching others. These behavioral changes have not come easy to many people. To begin with, all habits are difficult to change (Marcus, Neuman and MacKuen 2000), but the habits around social interactions tap into the fundamentals of human nature. Humans are social animals, and mingling with others touches on our fundamental instincts (Dunbar 1998). While analyses demonstrated dramatic changes in behavior across the world during the first wave of the COVID-19 pandemic (Jørgensen, Bor and Petersen 2020), public opinion data also shows that compliance decreased by mid-summer in several countries and is not increasing as quickly as the rising number of cases during the fall of 2020.1 Consistent with this, the World Health

Organization makes clear that increasing compliance is not only key but also diﬃcult as the populations in several countries show signs of ”pandemic fatigue” (Michie, West and Harvey

2020).

Yet, widespread and massive behavior change remains the best remedy against the COVID-

19 pandemic until vaccines become widely available (Chu et al. 2020). Given the economic and welfare costs of lockdowns, such containment strategies are becoming increasingly unsustainable as long-term strategies (Clemmensen, Petersen and Sørensen 2020; Fernandes 2020). Thus, the authorities encourage behavioral change through a combination of restrictions, informational campaigns, and nudges. Importantly, however, such top-down strategies from authorities may in themselves have limited eﬀects. Strict enforcement of restrictions that aﬀect the everyday lives of citizens may, for example, not be normatively desirable in democracies or even possible given the private nature of the target behavior. The existing literature on both compliance in general and compliance during the COVID-19 pandemic thus emphasizes how compliance is driven to a large extent by citizen-level perceptions about, for example, the legitimacy of restrictions (Tyler 2006; Chan et al. 2020) and the felt threat from COVID-19 (Harper et al.

1A number of international research projects and survey agencies monitor compliance, e.g., https://www.imperial.ac.uk/global-health-innovation/our-research/covid-19-response/covid-19-behaviour- tracker/, www.hope-project.dk, https://yougov.co.uk/covid-19.

1 2020). In this perspective, compliance is as much shaped by bottom-up processes at the level of citizens as top-down processes.

In this manuscript, we focus on a particular bottom-up process that has been key in previous situations involving rapid behavioral and attitudinal change but has received limited attention in research on the COVID-19 pandemic: moralization. Moralization involves framing an issue as belonging to the domain of morality and, hence, the labeling of particular behavior (e.g., not engaging in physical distancing) as immoral and as the target of condemnation (Rozin 1999; De-

Scioli and Kurzban 2013, 218). Moralization has been crucial in, for example, rapid behavioral change regarding second-hand smoking (Rozin and Singh 1999). Health authorities have leveled information campaigns against smoking for decades, but behavioral change only emerged–and did so quickly–when second-hand smoking was labelled immoral. There is anecdotal evidence that compliance with health advice regarding COVID-19 quickly became a moral issue when the pandemic hit the world in the spring of 2020. This is most clear in the polarization surrounding disease avoidance that emerged in the US across partisan lines (Gollwitzer et al. 2020) but also in the signiﬁcant condemnation of instances of violation of physical distancing or mask wearing that circulated on social media.

In this manuscript, we specifically investigate, first, whether compliance with advice regarding physical distancing has emerged as a moral issue across countries during the COVID-19 pandemic, involving significant condemnation of non-compliers. Second, we investigate who is most likely to engage in moralization of COVID-19-related compliance. Our primary ambition is description, and to the best of our knowledge, we offer the first insights on moralization related to physical distancing, relying on large, quota-sampled online surveys from eight Western democracies (Denmark, Sweden, Germany, France, Italy, Hungary, the UK, and the US). At the same time, however, the results that we present also inform theoretical debates over the nature of moralization by showing that moralization is essentially egotropic in nature. The more likely someone is to benefit from public compliance with physical distancing, the more likely they are to support moralization.

2 The Social and Psychological Functions of Moralization

To what extent have physical distancing and observation of other protective behaviors been moralized in the context of the COVID-19 pandemic? And who is most likely to engage in moralization? This requires, ﬁrst, a conceptualization of moralization and, second, an outline of the key psychological factors underlying moralization.

Moralization can be deﬁned as “the process through which preferences are converted into values” (Rozin 1999, 218), and compared to conventional preferences, moralized convictions appear for those who hold them as behavior-guiding principles that are universally and ob- jectively true, independently of whether the authorities mandate them or not (Skitka 2010).

Moralised actions are considered virtuous in and of themselves, often heavily discounting spe- ciﬁc consequences (Ryan 2019). Consequently, people are unwilling to compromise on moral issues (Delton, DeScioli and Ryan 2020; Ryan 2017).

Why do humans moralize? The ultimate function of morality is a hotly debated topic

(e.g. Shackelford and Hansen (2015)), but most accounts agree that morality is rooted in the complexities of humans’ social life. Gintis et al. (2008) see morality as the cornerstone of human altruism. Curry (2016) proposes that morality has evolved as a mechanism to solve a wide variety of coordination and cooperation dilemmas. Meanwhile, DeScioli and Kurzban

(2013) posit that morality is speciﬁcally a tool for choosing sides in conﬂicts.

However, other animals also cooperate and get into conﬂicts, so why do only humans moralize

(to the best of our knowledge)? In a particularly ambitious hypothesis, Gintis (2019) reminds us that whereas in other animals, the rules of cooperation are fairly stable and often genetically encoded, humans owe their success to their remarkably ﬂexible social cooperation. However,

“playing games with socially constructed rules requires a moral sense” (Gintis 2019, 60). It is diﬃcult to think of a better example of human societies being in a dire need to rapidly tweak the rules of cooperation than during a pandemic, and therefore, we should expect moralization to take center stage.

Why could morality be more eﬀective at changing the rules of cooperation? A key diﬀerence between conventional preferences and moralized convictions is intimately tied to condemnation:

We experience outrage when others breach moralized principles and are motivated to inﬂict costs on the perpetrator. Moralization thus fuels what is often termed third-party punishment, that

3 is, punishment from individuals not directly engaged in a given exchange. Imagine that Alex hits Bertie and Bertie retaliates; this is an instance of second-party punishment. If, however,

Casey intervenes and punishes Alex, this is an instance of third-party punishment. If there is no prior relationship between Bertie and Casey, this intervention is motivated by a moral outrage caused by Alex’s norm-breaking behavior.

Third-party punishment often takes a social form rather than a physical or monetary form.

The mere act of condemnation is a form of punishment-by-stigmatization that signals to the target and other audience members that the punisher lowered their valuation of the target and is therefore less likely to act pro-socially towards them in the future. Moreover, condemnation also signals that the punisher is likely to act in similar ways towards any other actor who violates the moralized principle. Thus, moralization incentivizes the observation of speciﬁc behaviors and can therefore lead individuals who are otherwise neutral about the behavior to still follow it for the fear of condemnation. Accordingly, moralization is essentially a tool in the negotiation of norms within human groups, and the goal of moralizing a particular conviction is to ﬁxate it as a norm within the group (DeScioli and Kurzban 2013).

If condemnation is at the heart of moralization, the next question is this: Who are the condemners? Or, more precisely, what are the psychological motivations that lead some individuals to moralize certain behavior-guiding principles? Within moral psychology, there are two perspectives. One perspective focuses on the role of egotropic or self-interested factors (DeScioli and Kurzban 2013). People “could beneﬁt from manipulating the propensity of others to engage in third-party punishment, if this punishment can be directed toward people who challenge the interest of the individual” (Petersen 2013, 79). In other words, a clever way to further one’s interest is to claim that acts that harm the self are immoral and should therefore be socially condemned.

Meanwhile, the other perspective focuses on sociotropic or altruistic factors (Gintis et al.

2008; Richerson and Boyd 2008). According to this view, humans not only cooperate, but punish norm-violators altruistically because doing so could help groups out-compete other groups. This so-called “strong reciprocity” could be quite costly for an individual but yields suﬃcient beneﬁts at the group level. In other words, a clever way to ensure the cohesion and cooperativeness of my group is to claim that acts that harm the group are immoral and should therefore be socially

4 condemned.

Both of these perspectives argue that moral condemnation is motivated by a desire to see others comply with particular norms and, hence, share a number of predictions. In essence, the more one believes that a norm is important, and the more one worries that others will not comply with it, the more likely one is to moralize. However, the two perspectives also diverge with respect to the fundamental driver of these motivations: Do they reﬂect concerns for yourself or for others?

On the basis of the predictions shared by the different theoretical approaches to moralization during the COVID-19 pandemic, we can specify three key factors that could predict who con- demns norm-breaking: (1) people’s own behavior; (2) people’s perception of the sources of the advice to engage in physical distancing; and (3) people’s perception that others are not likely to engage in physical distancing and other protective behavior. Thus, we should expect that people who themselves follow official recommendations are more likely to incentivize others to do the same as a general reflection that such people have processed the recommendations as relevant and important. Furthermore, given that recommendations regarding protective behavior are being promoted by the government and health authorities, we should expect that individuals who trust more in such institutions are more likely to endorse these recommendations and, hence, are motivated to incentivize others to do the same. Finally, we should expect people who view others as likely to not comply with the government’s recommendations to be more motivated to condemn others. A key psychological construct to tap into such perceptions is interpersonal trust. Individual differences in interpersonal trust generally reflect differences in perceptions about whether others are willing to contribute to public goods, such as those stemming from a generalized adherence to health advice (Johnson et al. 2020). Thus, while institutional trust may increase moralization, we should find that people low in interpersonal trust are more motivated to moralize protective behavior in the context of the COVID-19 pandemic and condemn others for not observing the underlying recommendation.

Turning to predictions where the egotropic and the sociotropic accounts diverge, the key question is whether condemners are mostly concerned about the impact of non-compliance on themselves or whether they are mostly concerned about the impact of non-compliance on others. The key self-interested concern in the context of COVID-19 is the health-related impact

5 on oneself or close family members, but there may also be added personal concerns about, for example, personal employment if the non-compliance of others leads to further restrictions and lockdowns. Independently of the speciﬁc nature of the personal concerns, the egotropic account predicts that people who are more concerned about the impact of COVID-19 on themselves are more likely to engage in moral condemnation. As a modern version of the Nietzschean view of morality (Nietzsche 1994), this perspective entails that while morality is often praised for its virtuousness, it is essentially self-interest in disguise. In contrast, the sociotropic perspective entails that people morally condemn non-compliance to help others. In the context of the

COVID-19 pandemic, there are multiple relevant sociotropic concerns. Most importantly, individuals can be concerned about the health impact of non-compliance and how this generates an overheated hospital system, or they can be concerned about the national economic consequences of renewed lockdowns if individuals do not comply. In addition, concerns can relate to society’s ability to help disadvantaged groups or the impact of continued lockdowns on social unrest.

Methods

Data

To investigate moralization during the COVID-19 pandemic and its potential precursors, we

ﬁelded quota-sampled online surveys in eight countries: Denmark, Sweden, Germany, France,

Italy, Hungary, the UK, and the US. We chose these countries to represent a diverse set of political, economic, and epidemic characteristics. Section OA1 in the online appendix (OA) oﬀers an overview of macro-level indicators with regard to stringency of government response, deaths related to COVID-19, GDP, levels of social expenditure, level of ethnic fractionalization, and level of democracy. Insofar as our analysis reveals common trends across these eight countries, we believe our conclusions generalize to other Western democracies.

In each round of the data collection, the survey company Epinion sampled approximately 500 adult respondents from the population of eligible voters in each country, resembling population margins on age, gender, and geography. In the present analysis, we rely on data from April 9 through November 7. Thus, our data oﬀers insights from all three phases of the pandemic so far: 1) the ﬁrst wave in the spring of 2020, characterized by high and poorly controlled infection numbers and relatively high casualties (except in Hungary); 2) the summer, witnessing successful

6 efforts to contain the virus in most countries, although with significant variation; and finally,

3) the second wave of the pandemic in the fall, when increasing indoors activities, restarting schools, and so on have caused infections to surge but, again, with large variation on how well or poorly countries have managed this challenge. The dashed lines on top of each country panel in Figure 1 depict death rates per 100K residents, which is considered the best measure of pandemic severity.

Our sample is slightly imbalanced across countries as Denmark was oversampled, especially in the ﬁrst months of data collection, for reasons unrelated to this manuscript. We rely on multilevel modeling to reduce the eﬀect of this imbalance. In total, the overall sample includes

93,722 individuals. Section B in the OA oﬀers sample descriptives.

Measures

Outcome Variables

Our analysis relies on two variables tapping into levels of moralization, condemning norm- breakers and blaming ordinary people for their behavior. How can we know if physical distancing has become a moral issue? We could ask whether people have condemned anyone for breaking the social norm. Yet, personal experience with condemnation is likely a rare, costly, and sensitive behavior. Instead, we use an indirect measure, asking to what extent the respondent feels it is

“justiﬁed to condemn those who do not keep a distance to others in public”. This is a much more common, less costly, and less sensitive issue, which could therefore be more accurately assessed in an online survey. We measure this outcome on a standard 5-point Likert scale, which we transform into a 0-1 continuous variable for our analyses.

While our moralization variable taps into the readiness of a society to condemn norm- breakers, it remains mute on how frequently respondents experience that others do not keep their distance in public. Consequently, we employ a secondary outcome variable that taps into the extent to which respondents believe that the corona crisis has become this severe “because lay individuals did not take the virus seriously enough”. Blaming the people is measured on a simple binary yes-no scale. The two outcome variables are weakly correlated (P earson0s r = 0.20).

We report detailed question wording in the OA Section A.1.

7 Correlates of Moral Condemnation

We include four sets of correlates in our statistical models, measuring 1) levels of concern,

2) degree of behavioral change as a response to the pandemic, 3) interpersonal and institutional trust, and ﬁnally, 4) standard demographic covariates.

First, theories of moralization agree that people who are concerned about the consequences of the pandemic are more likely to moralize but disagree on whether it is primarily personal or social concern that drives these effects. We rely on a battery of five items measuring concern on a four-point scale from “not at all” to “to a high degree”. An exploratory factor analysis indicates that the items load on two factors. The first taps into more personal concerns about

1) the self and the family and 2) hospitals’ ability to help the sick. The other taps into more social concerns about 3) society’s ability to help the disadvantaged, 4) social unrest and crime, and 5) the country’s economy.2 We form two indices by averaging these two sets of items for our main analysis but report item-wise correlations in the appendix. We expect higher concern to lead to higher moralization.

Second, to measure more broadly the individual-level motivation to enforce norms around physical distancing, we focus on whether respondents changed their own behavior. Moralization is an eﬀective tool against free riding, and those who pay the price of cooperation are more motivated to ensure that others do, too. Accordingly, we asked the following: “To what degree do you feel that the current situation with the corona virus has made you change your behavior to avoid spreading infection?” and measured answers on the same four-point scale. We expect more behavior change to lead to more moralization.

Third, trust often plays a central role in matters of social cooperation. As explained, moralization about physical distancing during a pandemic is one of the few issues where social and institutional trust are expected to have opposite eﬀects. Social trust renders moralization super-

ﬂuous; if most people can be trusted, there is no need to waste eﬀort on moral condemnation.

We measure social trust with a standard question: “Do you think that most people by and large are to be trusted or that you cannot be too careful when it comes to other people?” on an 11-point scale. Conversely, institutional trust could be considered an indicator of political

2Item 1 (self and family) and item 3 (help the disadvantaged) load on both factors, but for the sake of conceptual clarity and simplicity, we ignore these. Indices based on factor loadings correlate very strongly with our simple averages 0.91 and 0.97 for personal and social concern, respectively.

8 legitimacy. People with high institutional trust are likely to believe that the new social norms propagated by the state and government are good and right. This may add an additional level of motivation to moralize beyond mere concern or behavior change. Respondents indicate their level of conﬁdence in the government on a standard 11-point scale.

Fourth, we incorporate a battery of demographics in our models. We include a dummy variable for those who identify as female. We include a continuous variable for age. We add a dummy for having completed tertiary education (level 5 or higher on UNESCO’s International

Standard Classification of Education (ISCED) scale). Finally, we include the vote choice of respondents. Specifically, respondents are asked to answer which party or candidate they voted for at the last first-order national election. This variable is recoded into a variable that indicates whether the respondent voted for a left-wing party, a right-wing party, or neither. All continuous variables are standardized with a mean of 0 and a unit of two standard deviations. This makes the coefficients of continuous variables and binary indicators comparable (Gelman 2008).

Statistical Analysis

A methodological challenge posed by our data is the clustering due to multiple countries and waves. We address this challenge by relying on multilevel regression modeling. Our baseline models rely only on the demographic covariates but include varying intercepts for countries and survey waves. Next, we add the other predictors, first, one-by-one and, then, all in a single model. All coefficients remain substantively similar in the pooled model. Therefore, we rely on this for further analyses. Yet, these models assume that the correlation of the psychological predictors is the same across all countries. We relax this assumption by adding varying slopes for the five psychological correlates. At each step of increasing model complexity, we verify that the model fit is improved by standard information criteria (AIC and BIC). Full model details are reported in Section C.1 of the OA.

All of our models include post-stratification weights, which ensures that our samples are informative of the population. Weights have been calculated by the data provider and include data on party choice, region, education, age and gender interactions, house type, and house- hold size. Because the continuous variables are re-scaled with a mean of zero and a unit of two standard deviations, coefficient estimates could be interpreted as the difference between

9 a respondent one standard deviation below average and a respondent one standard deviation above average on a predictor. Given our large sample sizes, traditional significance estimates are uninformative (most estimates are significant). Therefore, our results section focuses on substantive effect sizes.

As robustness checks, we rerun our main models without post-stratiﬁcation weights. More- over, by adding various slopes for survey rounds, we investigated whether the strength of the associations between our outcomes and the psychological predictors changes in time. We found no meaningful time trends in the strength of these associations. See Section C.2 in the OA for details.

Results

Figure 1 displays the two outcome variables for each country across the study period. The countries are ordered by the overall level of moralization and blame in the sample. A number of important descriptive patterns emerge. 1) It is clear that a large majority of our respondents readily condemn norm breaking related to physical distancing. The average value is around 0.7 across the entire period in all countries, except the US, where it is around 0.6. 2) At the same time, consistently fewer respondents believe that lay people are to blame. Still, for most of the study period, the sample means range between 0.35 and 0.6. It is notable that blaming the people varies much more across countries than moralization, suggesting that respondents every- where moralize physical distancing but disagree about how large the problem of non-compliance is. 3) Both moralization and blame show some temporal dynamics. Both measures were at their peek in the spring, witnessing the highest death rates in most countries and characterized by a global sense of urgency and fear, often paired with stringent government restrictions. Moraliza- tion has decreased substantially over the summer in Italy, Hungary, Denmark, and Germany.

This trend is always more profound in blaming the people than in condemning norm breakers.

Interestingly, there is little to no decrease in moralization in the countries hit most hard by the pandemic: Sweden, France, the UK, and the US. Although the absolute levels of moralization are not the highest in these countries, the lack of a decline may mean that these countries stayed on edge. Meanwhile, despite surging case counts in Hungary, Italy, and Germany, there is no evidence of an increase in moralization in these countries so far.

10 Figure 1: Time Trends in Levels of Moralization – Condemning Norm Breakers in Red, Blam- ing the People in Blue – and COVID-19-Related Deaths across the Eight Countries

Italy Hungary United Kingdom Denmark 9 2 6 10 5 3 5 1 0 0 0 0 May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov

Germany France Sweden USA 8 2 10 6 5 1 5 4 0 0 0 2 May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov May Jul Sep Nov

Note: Red and blue points denote weighted sample means. Error bars denote 95% conﬁdence intervals. Smooth lines display loess curves. Dashed black lines denote COVID-19-related deaths per 1 million citizens.

Next, we turn our attention to the individual-level correlates of moralization. Who is more likely to condemn norm breakers and blame the people for the pandemic? Figure 2 reports the

ﬁxed eﬀects from the multilevel regression model, partially pooling data across all eight countries in our sample. These estimates can be interpreted as the average correlations across the eight countries. First, it is apparent that there are relatively little asymmetries by demographic correlates, with the exception of age and moralization. Elderly respondents condemn about

10 percentage points more than young respondents. Interestingly, however, there is no age asymmetry when it comes to blaming the people, perhaps because the elderly follow physical distancing guidelines strictly (Jørgensen, Bor and Petersen 2020) and see the same behavior among their peers. Although the coeﬃcient estimates for sex, education, and partisanship

11 often reach statistical signiﬁcance because of our large sample size, these associations remain substantively small at around two percentage points or less (i.e., less than 7% of a standard deviation in the outcomes).

Figure 2: Individual-Level Correlates of Moralization and Blaming the People

● Age ●

● Female ● Demographics

● Higher ed. ●

● Party − Left ●

● Party − Right ●

● Concern Personal ●

● Social ● Behavior

● B. Change ●

● Institutional ● Trust

● Social ●

−0.15 −0.10 −0.05 0.00 0.05 0.10

● Condemn norm−breakers ● Blame the people

Note: Fixed effect coefficient estimates from linear multilevel regression models corresponding to a two-standard deviation change in the independent variables. The outcomes are coded 0-1. Error bars are 95% confidence intervals.

When it comes to concern, we ﬁnd that personal concern is a consistently strong correlate of moralization, whereas social concern (once personal concern is controlled for) is not. Respon- dents with higher levels of concern are on average 10 percentage points more likely to approve condemnation and to blame the people.

Looking at self-reported behavior change to mitigate the eﬀects of the pandemic, we ﬁnd a remarkably similar picture. People who report higher behavior change are 10 percentage points

12 more likely to moralize compared to people reporting lower behavior change. Importantly, we

ﬁnd these strong associations despite both concern and behavior change variables being skewed, with most respondents reporting some or high levels of concern or behavior change.

Finally, consistent with our predictions, we find that both institutional and social trust are meaningfully correlated with our outcomes, albeit in opposite directions. Respondents who trust institutions more are about 7.5 percentage points more likely to condemn others and five percentage points more likely to blame the people. That said, it is worth noting that these estimates have much higher uncertainty than other variables in our model, presumably because of larger cross-country variance (see below). Accordingly, the estimate for blaming the people does not reach statistical significance. Meanwhile, trusting other people is negatively correlated with moralization. Respondents with higher social trust morally condemn eight percentage points less and blame other people 11 percentage points less.

To investigate cross-country heterogeneity, we display the correlations by country (i.e., the varying slopes) in Figure 3. Overall, the results are strikingly uniform across countries. For personal concern, social concern, and behavior change, the coeﬃcient estimates are substantively similar in all eight countries. As mentioned above, social and institutional trust stand out. In the US, people higher versus lower on social trust are equally likely to moralize, whereas in the other seven countries, these relationships are large and negative. Meanwhile, institutional trust is associated with condemnation but not blaming the people in France and the UK; it is not associated with either outcome in Sweden; and it is not associated with condemnation but negatively associated with blaming the people in the US. One potential (post-hoc) explanation for these patterns could be late or controversial governmental response to COVID-19, which these countries share.

Our analyses so far have limited internal validity. We find that people who are more concerned, changed their behavior more, and trust institution more and other people less are also more likely to moralize. Yet, we do not know to what extent these psychological factors cause moralization. As a final test of our predictions, we repeat our analyses among the 15,088 respondents who completed the survey at multiple times (total N = 39,907). Two-way fixed effects regression models purge variation across respondents and survey waves. These models adjust for all omitted variables that do not change for respondents between the waves or do not

13 Figure 3: Cross-Country Heterogeneity in Psychological Correlates of Moralization

Social concern Personal concern Behav. change

Denmark ●● ●● ● ●

France ● ● ●● ●●

Germany ●● ● ● ● ●

Hungary ●● ●● ● ●

Italy ●● ● ● ● ●

Sweden ● ●● ●●

United Kingdom ● ● ● ● ● ●

USA ●● ● ● ●●

−0.2 −0.1 0.0 0.1 0.2 Social trust Inst. trust

Denmark ● ● ● ●

France ● ● ● ●

Germany ●● ● ●

● ● Hungary ● ● ● Condemn norm−breakers

Italy ● ● ● ● ● Blame the people

Sweden ● ● ●●

United Kingdom ● ● ● ●

USA ● ● ● ●

−0.2 −0.1 0.0 0.1 0.2−0.2 −0.1 0.0 0.1 0.2

Note: Random effect coefficient estimates from linear multilevel regression models corresponding to a two- standard deviation change in the independent variables. The outcomes are coded 0-1. Error bars are 95% confidence intervals.

change for waves between respondents. In other words, these models zoom in on the unique within-respondent variation (Mummolo and Peterson 2018). As usual with these models, the strength of the associations are greatly diminished, yet retain statistical signiﬁcance. The average within-respondent change in our independent variables correspond to 2-7 percentage points of a standard deviation change in the residualized (i.e., demeaned) condemnation variable. That said, for those who change their psychological motivations the most in our sample, this predicted change is 16-51 percentage points of a standard deviation in the outcome. These associations are similar for blaming the people, amounting to 5-7 percentage points of a standard deviation in the residualised outcome for an average within-individual change in the predictors and 31-55 percentage points of a standard deviation change for maximum within-individual change in the

14 predictors. The only exception is institutional trust, which is not associated with blaming the people in our panel samples controlling for respondent and wave fixed effects. Overall, the main takeaway from these fixed effects models is that the association reported in our multilevel models are unlikely to be a simple statistical artefact and likely reflect real causal associations between the variables.

Conclusions and Discussion

Several pieces of anecdotal evidence suggest that the COVID-19 pandemic has activated moralization processes. Most governments and public health experts have sought to re-educate the public on what constitutes proper and responsible behavior. These elite actors warned people that amidst a pandemic, it is not acceptable to initiate close physical contacts, especially against the wish of others in society. For many, the new norm(al) is frequent hand washing, wearing face masks in public, and avoiding crowded public spaces such as public transport and queues. Importantly, moralization is not necessarily a top-down process; regular citizens may have acted as voluntary agents of the authorities. For example, several countries and states in the US have set up ”corona hotlines” that allow people to report suspected violations of physical distancing regulations. Moralization has been especially conspicuous in societies where the pandemic has been politicised, thereby inducing moralization not only by proponents of physical distancing, but also its opponents. This has led to several – sometimes even fatal – incidents between people condemning no physical distancing and people condemning physical distancing.3

To the best of our knowledge, this manuscript offers the first systematic investigation of this phenomenon, providing empirical evidence on both the levels and predictors of physical distancing moralization. We investigated this issue comparatively, relying on quantitative survey data across eight Western democratic countries during the COVID-19 pandemic. We found that in all countries, most respondents think it is justified to condemn those who break the new norms of physical distancing. A somewhat smaller majority of respondents believe that laypeople are to blame for the pandemic. Thus, while there is cross-national moralization of physical distancing, different populations differ in the perceived magnitude of the problem of

3See, e.g., these reports from Michigan, New York, and Budapest, Hungary (last accessed on 2020-Nov-03).

15 non-compliance. Furthermore, we demonstrated that variables indicating a personal motivation to maintain the social norms around physical distancing are the strongest correlates of both condemning norm breakers and blaming laypeople for the pandemic. We found that this finding was remarkably consistent across countries, and using a panel sample, we demonstrated that the associations are robust to focusing on within-individual variation in a two-way fixed effects regression.

Our manuscript is subject to a number of limitations. While our two-way fixed effects models increase our confidence in a causal relationship between personal concern, behavioral change, and moralization, we cannot rule out that time-variant confounders bias the estimated relationship. Thus, future research may seek to employ experimental methods to increase the internal validity of the present findings. Furthermore, our analysis hones in on individual-level psychological processes but remains mute on macro-level trends. What makes Italians and Hun- garian moralize more (on average) than Americans? Why do Swedes blame laypeople more than

French people do? Future research should explore how elite dynamics and cultural diﬀerences

(among other things) shaped these processes. It is also beyond the scope of our analysis to test whether higher moralization indeed yields more compliance with physical distancing regulations. The literature on moralization offers solid evidence that moralization is an important tool for compliance (Gintis et al. 2005), but especially in polarized societies, condemning some for not following the new norms may even backfire. Backfiring may not just take the form of intensified conflict, but the stigmatization associated with moralization may also negatively influence the motivation to be tested and facilitate the tracing of close contacts. Stigmatization has thus been raised as a key obstacle to test-and-trace strategies in relation to other epidemics, such as sexually transmitted diseases (Villa et al. 2020).

These limitations notwithstanding, our manuscript oﬀers important implications of both theory and practice. Regarding the former, our results are most consistent with strategic and egoistic accounts of moralization. People who are concerned for themselves are often also concerned for society as a whole, but once the eﬀects of these variables are simultaneously assessed, the partial correlation between social concern and condemning norm breakers or blaming the people drops to zero. Meanwhile, the personal concerns and behavior change are consistently strong predictors of moralization. It appears that people are most likely to moralize if they

16 themselves can beneﬁt from it and if they are already paying the steep costs associated with following the new norms.

For practitioners, our manuscript offers an important reminder that the human mind con- tains a suit of psychological mechanisms designed to aid large-scale cooperation – even (or especially) when the rules of cooperation are changing rapidly. This insight is especially relevant in Western democracies, which lack the capacity or willingness to enforce all regulations through coercion. While the potential for backfiring implies that moralization should be treated with care, we nonetheless believe that bottom-up moralization could be supported such that regular citizens would not only be more likely to comply with regulations, but also take an active role in “policing” compliance among others. Assuming that this insight is correct, policy-makers should seek to design policies that are easy to moralize. This means that clear rules, which make norm violation obvious, such as “thou shall wear a face mask on public transport”, could be much more effective than vague rules, such as “thou shall wear a face mask on public transport if the vehicle is crowded”. The COVID-19 pandemic is the greatest global challenge of our generation. To successfully fight it, it is imperative to understand and utilize the psychological processes through which rapid, voluntary compliance with new norms emerge.

Availability of Data and Computer Code

All data and required code is publicly available in a repository at the webpage of the Open

Science Framework: https://osf.io/byrh7/ [Anonymised]

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20 Online Appendix

Contents

A Supplementary Materials 22

A.1 Question Wording ...... 22

B Descriptive Statistics 23

B.1 Macro-Level Statistical Indicators ...... 23

B.2 Sample Demographics with and without Weighting ...... 24

C Supplementary Results 25

C.1 Full Model Details ...... 25

C.2 Additional Analyses ...... 29

C.2.1 Main Results without Weights ...... 29

C.2.2 Temporal Dynamics ...... 31

21 A Supplementary Materials

A.1 Question Wording

Outcomes: Condemn

To what extent do you agree with the following statements? “It is completely justiﬁed to condemn those who do not keep a distance to others in public.”

[Completely agree, Somewhat agree, Neither agree nor disagree, Somewhat disagree, Com- pletely disagree]

Outcomes: Blame

In your opinion, why did the corona crisis become so severe? Please select all that applies.

“Because lay individuals did not take the virus seriously enough.”

[Yes / No]

Concern about COVID-19

To what degree are you concerned about the consequences of the coronavirus . . .

1. . . . for you and your family? 2. . . . for hospitals’ ability to help the sick? 3. . . . for society’s ability to help the disadvantaged? 4. . . . on social unrest and crime? 5. . . . on the country’s economy?

[To a high degree, To a certain degree, To a lesser degree, Not at all]

Note that the ﬁrst two items in italics form the personal concern scale, whereas the other three items form the social concern scale.

Changing behavior

To what degree do you feel that the current situation with the Corona virus has made you change your behaviour to avoid spreading infection?

[To a high degree, To a certain degree, To a lesser degree, Not at all]

Institutional trust

Give your assessment on a scale from 0 to 10, where 0 indicates that you have no conﬁdence in the government at all and 10 indicates that you have full conﬁdence in the government.

[0 - No conﬁdence at all . . . 10 - Full conﬁdence]

Social trust

22 Do you think that most people by and large are to be trusted or that you cannot be too careful when it comes to other people?

[0 - You cannot be too careful . . . 10 - Most people are to be trusted]

B Descriptive Statistics

B.1 Macro-Level Statistical Indicators

Table OA1: Country Level Macro Statistics Reﬂecting Considerable Variablity in Our Case Selection

Denmark France Germany Hungary Italy Sweden UK USA Stringency (Apr 7) 72 88 77 77 92 65 80 73 Stringency (Nov 9) 40 79 59 57 67 56 75 63 Deaths (Apr 7) 2.4 13.4 2.3 1.0 9.7 8.7 13.7 5.3 Deaths (Nov 9) 0.6 7.6 1.5 9.1 6.4 2.1 5.1 2.9 GDP $60K $49K $56K $34K $44K $56K $49K $65K Welfare state 28 31.2 25.1 19.4 27.9 26.1 20.6 18.7 (%GDP) Ethnic fraction. 0.08 0.10 0.17 0.15 0.11 0.06 0.12 0.49 FH Dem-cy Score 97 90 94 70 89 100 94 86 Notes: 1) Stringency: Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. 0-100 scale, higher number indicate more restrictive COVID-19 policies 2) Deaths: 7-day rolling average of COVID-19 related deaths per million citizens via European Centre for Disease Prevention and Control. 3) GDP: World Bank’s estimates of per capita Gross Domestic Product at purchasing power parity (2019). 4) Welfare state: Social expenditure as percentage of GDP from OECD. 5) Ethnic fractionalization: an index developed by Alberto Aleseina; et. al (2003). J of Econ Growth 8, 155–194. The numbers reﬂect the probability that two randomly drawn individuals from a country are not from the same group 6) Freedom House’s Democracy Scores: Freedom in the world 2020 report. Aggregate scores reﬂecting both political rights and civil liberties: 0 = least free, 100 = most free

23 B.2 Sample Demographics with and without Weighting

Table OA2: Sample Characteristics by Country Group

variable Denmark France Germany Hungary Italy Sweden UK USA Age 48 (19) 46 (15) 48 (15) 45 (15) 42 (13) 47 (16) 42 (14) 41 (14) Age – weighted 49 (18) 48 (16) 49 (16) 47 (15) 47 (14) 48 (17) 46 (15) 45 (15) Female 0.5 (0.5) 0.52 (0.5) 0.51 (0.5) 0.52 (0.5) 0.52 (0.5) 0.5 (0.5) 0.52 (0.5) 0.53 (0.5) Female – weighted 0.51 (0.5) 0.52 (0.5) 0.51 (0.5) 0.53 (0.5) 0.52 (0.5) 0.5 (0.5) 0.51 (0.5) 0.51 (0.5) Higher education 0.55 (0.5) 0.45 (0.5) 0.41 (0.49) 0.37 (0.48) 0.35 (0.48) 0.4 (0.49) 0.48 (0.5) 0.7 (0.46) Higher education – weighted 0.33 (0.47) 0.33 (0.47) 0.29 (0.45) 0.26 (0.44) 0.17 (0.38) 0.37 (0.48) 0.39 (0.49) 0.58 (0.49) Left 0.51 (0.5) 0.39 (0.49) 0.36 (0.48) 0.2 (0.4) 0.18 (0.38) 0.36 (0.48) 0.44 (0.5) 0.33 (0.47) Left – weighted 0.45 (0.5) 0.35 (0.48) 0.32 (0.47) 0.16 (0.37) 0.19 (0.39) 0.33 (0.47) 0.4 (0.49) 0.35 (0.48) Right 0.36 (0.48) 0.28 (0.45) 0.35 (0.48) 0.35 (0.48) 0.52 (0.5) 0.43 (0.5) 0.34 (0.47) 0.32 (0.47)

24 Right – weighted 0.41 (0.49) 0.32 (0.47) 0.43 (0.5) 0.4 (0.49) 0.5 (0.5) 0.47 (0.5) 0.36 (0.48) 0.33 (0.47) Abstain 0.13 (0.34) 0.34 (0.47) 0.29 (0.45) 0.46 (0.5) 0.3 (0.46) 0.21 (0.41) 0.22 (0.42) 0.34 (0.48) Abstain – weighted 0.13 (0.34) 0.33 (0.47) 0.25 (0.43) 0.44 (0.5) 0.31 (0.46) 0.2 (0.4) 0.24 (0.43) 0.32 (0.47) N 16,430 11,052 11,214 11,141 11,159 10,858 10,940 10,928 C Supplementary Results

C.1 Full Model Details

Tables OA3 and OA4 report full details for the seven multilevel models in the stepwise regression-building procedures. In both tables, model 8 denote the ﬁnal models constituting the basis of Figures 2 and 3 in the main text.

Table OA3: Individual Level Correlates of Condemning Norm-breakers

Dependent variable: Condemning norm-breakers Pooled model Varying slopes (1) (2) (3) (4) (5) (6) (7) Age 0.1 0.1 0.1 0.1 0.1 0.1 0.1 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Female −0.001 0.003 −0.001 0.01 0.01 −0.01 −0.01 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Higher ed. −0.001 −0.003 −0.01 −0.01 −0.000 −0.001 −0.002 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Party:Left 0.03 0.03 0.02 0.02 0.04 0.02 0.01 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Party:Right 0.03 0.02 0.02 0.02 0.03 0.02 0.02 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Pers.conc. 0.2 0.1 0.1 (0.002) (0.002) (0.01)

Soc.conc 0.1 0.005 0.01 (0.002) (0.002) (0.01)

Beh.change 0.1 0.1 0.1 (0.002) (0.002) (0.01)

Inst. trst 0.1 0.1 0.1 (0.002) (0.002) (0.02)

Soc. trst −0.1 −0.1 −0.1 (0.002) (0.002) (0.01)

Constant 0.7 0.7 0.7 0.7 0.7 0.7 0.7 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)

Observations 91,464 91,464 91,464 91,464 91,464 91,464 91,464 Akaike Inf. Crit. 32,854.0 37,218.0 33,450.9 38,436.4 38,177.1 27,664.7 26,079.5

25 Table OA4: Individual Level Correlates of Blaming Regular People

Dependent variable: Blaming laypeople for pandemic Pooled model Varying slopes (1) (2) (3) (4) (5) (6) (7) Age 0.02 0.01 0.01 0.01 0.01 0.01 0.01 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Female 0.02 0.02 0.02 0.03 0.02 0.001 0.000 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Higher ed. 0.01 0.01 −0.002 0.01 0.02 0.01 0.01 (0.003) (0.004) (0.003) (0.004) (0.004) (0.003) (0.003)

Party:Left 0.03 0.03 0.02 0.03 0.05 0.02 0.02 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Party:Right −0.02 −0.02 −0.02 −0.02 −0.01 −0.02 −0.02 (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Pers.conc. 0.1 0.1 0.1 (0.003) (0.004) (0.01)

Soc.conc 0.1 0.000 0.004 (0.003) (0.004) (0.01)

Beh.change 0.1 0.1 0.1 (0.003) (0.004) (0.01)

Inst. trst 0.03 0.1 0.05 (0.003) (0.004) (0.03)

Soc. trst −0.1 −0.1 −0.1 (0.003) (0.004) (0.02)

Constant 0.4 0.4 0.5 0.4 0.4 0.5 0.5 (0.03) (0.03) (0.02) (0.03) (0.03) (0.03) (0.03)

Observations 93,166 93,166 93,166 93,166 93,166 93,166 93,166 Akaike Inf. Crit. 146,493.1 147,671.8 146,717.5 148,280.2 147,150.6 144,648.4 143,879.5

26 Tables OA5 and OA6 report full model details for two-way fixed effects regression models, regressing condemning norm breakers and blaming laypeople, respectively, on psychological predictors. Table OA7 in turn reports the average (SD) and maximum (Max) change in the residualised predictors, that is, zooming in on within-individual differences over and above broad national changes and how much these independent variables vary. The table then reports the effect size estimates scaled to these average or maximum within-individual changes.

Table OA5: Two-way Fixed Eﬀects Models on Condemning Norm-breakers

Dependent variable: Condemning norm-breakers (1) (2) (3) (4) (5) (6) Beh.change 0.03∗∗∗ 0.02∗∗∗ (0.004) (0.004)

Pers.conc. 0.04∗∗∗ 0.04∗∗∗ (0.004) (0.004)

Soc.conc 0.02∗∗∗ 0.01∗ (0.004) (0.004)

Inst. trst 0.02∗∗ 0.02∗∗∗ (0.01) (0.01)

Soc. trst −0.02∗∗∗ −0.03∗∗∗ (0.004) (0.004)

Observations 39,225 39,225 39,225 39,225 39,225 39,225 Adjusted R2 0.57 0.57 0.57 0.57 0.57 0.57 Note: ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001

27 Table OA6: Two-way Fixed Eﬀects Models on Blaming Laypeople

Dependent variable: Blaming laypeople (1) (2) (3) (4) (5) (6) Beh.change 0.03∗∗∗ 0.03∗∗∗ (0.01) (0.01)

Pers.conc. 0.05∗∗∗ 0.03∗∗∗ (0.01) (0.01)

Soc.conc 0.03∗∗∗ 0.02∗ (0.01) (0.01)

Inst. trst −0.002 0.01 (0.01) (0.01)

Soc. trst −0.03∗∗∗ −0.03∗∗ (0.01) (0.01)

Observations 39,907 39,907 39,907 39,907 39,907 39,907 Adjusted R2 0.37 0.37 0.37 0.37 0.37 0.37 Note: ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001

Table OA7: Scaled Two-way Fixed Eﬀects Eﬀect Size Estimates

Variable SD Max Condemn average Condemn max Blame average Blame max Beh.change 0.26 1.49 0.05 0.30 0.06 0.33 Pers.conc. 0.23 1.75 0.07 0.51 0.07 0.55 Soc.conc 0.24 1.89 0.04 0.32 0.06 0.43 Inst. trst 0.16 1.30 0.02 0.16 0 -0.02 Soc. trst 0.22 1.45 -0.04 -0.25 -0.05 -0.31

28 C.2 Additional Analyses

C.2.1 Main Results without Weights

Table OA8 demonstrates that adding post-stratification weights to correct for sampling bias does not drive any of our results. Models 1 and 3 reproduce our main results from varying slopes models using the pooled sample. Meanwhile, models 2 and 4 report the same models but omit post-stratification weights. Across models 1-2 and 3-4, the partial regression coefficients are almost identical.

29 Table OA8: Rerunning Main Multilevel Regression Models without Weights

Dependent variable: Condemning norm-breakers Blaming laypeople Weights No weights Weights No weights (1) (2) (3) (4) Age 0.1 0.1 0.01 0.001 (0.002) (0.002) (0.003) (0.003)

Female −0.01 −0.01 0.000 0.01 (0.002) (0.002) (0.003) (0.003)

Higher ed. −0.002 −0.003 0.01 0.01 (0.002) (0.002) (0.003) (0.003)

Party:Left 0.01 0.02 0.02 0.02 (0.002) (0.002) (0.004) (0.004)

Party:Right 0.02 0.02 −0.02 −0.02 (0.002) (0.002) (0.004) (0.004)

Pers.conc. 0.1 0.1 0.1 0.1 (0.01) (0.01) (0.01) (0.01)

Soc.conc 0.01 0.004 0.004 −0.002 (0.01) (0.01) (0.01) (0.01)

Beh.change 0.1 0.1 0.1 0.1 (0.01) (0.01) (0.01) (0.01)

Inst. trst 0.1 0.1 0.05 0.05 (0.02) (0.02) (0.03) (0.03)

Soc. trst −0.1 −0.1 −0.1 −0.1 (0.01) (0.02) (0.02) (0.02)

Constant 0.7 0.7 0.5 0.5 (0.02) (0.02) (0.03) (0.03)

Observations 91,464 91,464 93,166 93,166 Akaike Inf. Crit. 26,079.5 11,760.7 143,879.5 128,248.4

30 C.2.2 Temporal Dynamics

Figure OA1: We ﬁnd no meaningful time trends in the relationship btw the outcomes and the psychological predictors

Behav. change Personal concern Social concern

0.1

0.0

−0.1

−0.2 Apr Jul Oct Inst. trust Social trust

0.1

0.0 Condemn norm−breakers Blame the people

−0.1

−0.2 Apr Jul Oct Apr Jul Oct