The effect of risk framing on support for restrictive government policy regarding the COVID-19 outbreak

Kirill Chmel; Aigul Klimova; Nikita Savin

doi:10.1371/journal.pone.0258132

Abstract

This confirmatory research investigates the influence of risk framing of COVID-19 on support for restrictive government policy based on two web survey experiments in Russia. Using 2x2 factorial design, we estimated two main effects–factors of risk severity (low vs. high) and object at risk (individual losses vs. losses to others). First, focusing on higher risks had a positive effect on support for the government’s restrictive policy. Second, focusing on the losses for others did not produce stronger support for the restrictive policy compared to focusing on personal losses. However, we found a positive moderation effect of such prosocial values as universalism and benevolence. We found that those with prosocial values had a stronger positive effect in the “losses for others” condition and were more willing to support government restrictive policy when others were included. The effects found in our experimental study reveal both positive and negative aspects in risk communication during the pandemic, which may have a great and long-term impact on trust, attitudes, and behavior.

Citation: Chmel K, Klimova A, Savin N (2021) The effect of risk framing on support for restrictive government policy regarding the COVID-19 outbreak. PLoS ONE 16(10): e0258132. https://doi.org/10.1371/journal.pone.0258132

Editor: Akihiro Nishi, UCLA Fielding School of Public Health, UNITED STATES

Received: April 2, 2021; Accepted: September 21, 2021; Published: October 1, 2021

Copyright: © 2021 Chmel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from Kirill Chmel's GitHub repository https://github.com/KirillChmel/covid-risk-framing.

Funding: The article was prepared within the framework of the HSE University Basic Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

As COVID-19 turns into a pandemic, a political debate is simultaneously raging about whether autocracies or democracies are better at fighting epidemics [1]. Media pundits and global health officials praise draconian security measures imposed by the Chinese government to prevent the spread of COVID-19 [2, 3], and severely criticize the Swedish government for being excessively lax and soft about containing the virus [4]. But how many people would rather ’stay at home’ and keep a safe ’social distance’ instead of reaping the benefits of limitless freedom which most of them enjoyed before the COVID-19 outbreak? As of March 2020, approximately 75% of the world population, says Gallup International Association [5]. Indeed, according to a host of public opinion polls, the percentage of those willing to sacrifice some of their human rights to stave off the spread of coronavirus varies from 32% in Japan to 95% in Austria. In Russia, where the first cases had just been registered to that moment, there were 60% [5]. About a year later, in December 2020 the world on average became less willing to sacrifice rights to prevent the spread of the virus—the percentage dropped from 75% to 70%. At the same time, the statistics in Russia dramatically changed, since citizens were hardly willing to sacrifice their rights. Only 39% of Russian citizens were positive about their rights being trampled in order to resolve the public health crisis [6].

One of the possible explanations of willingness to sacrifice some human rights is rooted in the idea of risk perception as a driving force for decision-making. Research argues that risk perception drives support for security policies that infringe on civil liberties. Particularly, willingness to trade off civil liberties for security increases in the aftermath of exogenous shocks including, probably, public health outbreaks. The term, ‘When in danger, turn right’, from Karwowski et al. [7], demonstrates how the COVID-19 threat promotes social conservatism and support for right-wing candidates. Nonetheless, the perception of risk does not develop on its own, so media coverage, for instance, may introduce a disjuncture between perceptions of personal risk and objective estimates of population incidence [8].

Indeed, framing of a message, which is selection and emphasis on some aspects of a message, can have a greater impact on attitudes and behavior than the actual context [9]. Recent studies address the effects of different framings on following the protective measures during the pandemic. For instance, in line with the research on gain-loss framing, scholars found that gain-framed messages are more effective in promoting self-care behaviors [10–12]. At the same time, other scholars either got null results [13], or found that framing effects are observed if conditioned by such individual characteristics as political ideology or socio-demographics [14, 15].

Risk communication is critical to managing public health outbreaks because of deep uncertainty and lack of issue localization [16]. Risk messages presented to citizens openly and timely aim to rectify the knowledge gap in understanding an epidemiological crisis and adjust the public’s behavior to cope with the risk proactively [17]. However, not all messages have the same effect on citizens’ behavior. For instance, while the World Health Organization (WHO) proclaimed the importance of being ‘supportive’ and ‘careful’ towards others [18], the Swedish strategy to manage COVID-19 has been largely based on the personal responsibility of the citizens who receive daily information about, and individually targeted instructions for, self-protection. Which strategy—to protect yourself or to take care of others—works better then?

Political effects of high and low risk framing

The psychometric paradigm in risk perception research suggests that the main risk categorizations are the level of dread people feel about the risk and the familiarity with the risk [19, 20]. The main factor was found to be the dread risk. The higher the score on this factor, the more individuals are willing to support any restrictive measures that can reduce this risk [19]. This paradigm emphasizes the importance of affective responses that individuals have towards potential risk. They coined the term ‘affect heuristic’ to describe these affective responses [20, 21]. Heuristics are biases whereby respondents use only part of the information with which they are provided [22]. Researchers found that the higher the level of dread people feel about the risks, especially if the risks are new and they are frequently discussed in media, the higher the perceived risks are [23].

Higher perceived risks are the main source of enforcing authoritarian attitudes. Social threats increase right-wing authoritarianism [24–26]. The trade-off between civil rights and a high threat to society drives up the willingness to sacrifice rights to reinforce social order [27]. This can be found in risk situations where there is a threat to some particular groups or society overall in the aftermath of pandemics [28], violent crimes, political or economic crises [29], terrorist attacks [30], natural disasters [31], or climate change [32]. Overall, individuals tend to preserve collective security at the expense of freedom, autonomy, and rights, when there are high perceived risks to society.

We rely on the literature that emphasizes the psychological mechanisms behind framing effects and the literature which shows the effect of framing on a number of attitudes [33, 34]. Frames provide some meanings to the events, selecting certain aspects of the perceived reality and making them more salient in communication [35]. They make some opinions available for retrieval and accessible while being exposed to them. Following the distinction, which is made by Chong and Druckman [33], between issue and equivalence framing, we use the former approach to increase the ecological validity of research. We promote high-risk and low-risk as different considerations, since they are usually communicated by politicians. Despite their logical similarity they may not be necessarily perceived as a trade-off without public discussion. As a result, we expect to find that the high-risk framing of COVID-19 would increase support for restrictive government policy compared to low-risk framing.

H1: High-risk framing will have a stronger effect on the support for restrictive government policy compared to low-risk framing.

Who is the main object at risk?

The question of ‘object at risk’ is one of the main categorizations which has an effect on risk perception [36]. COVID-19 is widely recognized to pose a threat to all age groups. However, according to statistics and media, as well as according to restrictive policy regarding particular groups of population, older people are particularly vulnerable, thus they are central to the COVID-19 risk [37]. The politicians and media justified restrictive measures by emphasizing that people themselves can be risk objects, which poses a threat to others, especially for those who are at higher risk. That produced the international ‘Stay home. Save lives’ media campaign. The discourse framed the restrictive COVID-19 measures as a way to save the lives of others. The WHO promoted not only protecting oneself from getting sick but others as well [18] along with being ‘supportive’ and ‘careful’ towards others.

Though economic perspective focuses on self-interest, a number of researchers stress the importance of prosocial behavior. Research shows that higher responsibility for oneself and for others regarding risky decisions, decreases risk willingness and risky behavior [38, 39]. There is evidence that people tend to be more risk averse when they make a choice for others, rather than for themselves [40]. Indicating that vaccination is a prosocial behavior, which results not only in direct protection of the vaccinated but also protection of unvaccinated individuals through herd immunity, increases the willingness to be vaccinated [41, 42]. Individuals tend to be more risk-averse and cautious when their risky behavior can have a negative effect on others [43, 44], or are more willing to take risks in order to help others [45].

In words of Mary Douglas [46], during the COVID-19 pandemic, everyone is described as potentially ‘contaminated’, ‘polluted’, and dangerous to others as everyone can be the asymptomatic carrier of COVID-19 without knowing it, as the symptoms can take up to 14 days to manifest themselves. As a result, everyone can be accused of being a threat to others, and society overall, and punished for not following the rules of self-isolation and staying at home, as breaking the rules can be dangerous to the health of others and society.

Recent research on the effect of prosocial messages on COVID-19 prevention behaviors showed some mixed evidence. While some experimental studies found almost no difference between self-focused and prosocial framing on the willingness to self-isolate and wash hands more often [47, 48], other studies found that prosocial framing indeed increased such prevention behavior as social distancing [49–51] and wearing masks [52]. Some other experimental studies found no effect of prosocial framing on clickthrough rates while delivering advertisements on Facebook, compared to self-focused framing [53] and comprehension of the information regarding recommended behaviors [54]. In spite of this mixed evidence, we suggest that the framing which highlights losses to others would increase support for restrictive government policy compared to the condition in which we highlight only personal losses.

H2: The object at risk which focuses on losses to others will have a stronger effect on the support for restrictive government policy compared to the personal losses framing.

Prosocial values as moderators of framing effects

Following the argument made by Chong and Druckman [33], we suggest that the effect of frames on attitudes is moderated by personal values. We assumed that the frame affects only particular types of individuals with strong prosocial attitudes. It was found in previous literature that such variables as values [55, 56] or personality traits [57] could explain the variation in attitudes towards prosocial behavior like moral decision-making or altruism. According to the norm activation theory, different frames would activate particular personal values depending on the individual’s cognitive structure of these values [55]. As a result, the ‘losses to others’ frame may have an effect only if this is in line with personal values. Some studies showed that higher empathy increases willingness to self-isolate and maintain social distancing during the COVID-19 pandemic [58, 59].

We would apply the Schwartz theory of basic human values and his approach to the measurement of values [60]. Schwartz suggested that there are ten basic human values across cultures. Two of them are in the self-transcendence direction (i.e., prosocial values): benevolence and universalism [61]. Both values suggest that individuals are concerned with the welfare and interests of others, which basically means the transcendence of selfish interests. While benevolence enhances the welfare of in-group members, universalism enhances the welfare of all people beyond the in-group. This theory and approach to the measurement of values showed quite a high validity across many cultures and is used in such cross-cultural studies as the European Social Survey [62]. Some authors argue that such self-transcendent values should have a positive effect on a compliance with COVID-19 restrictive government policy though no empirical evidence has been shown [63]. The mechanism behind it is based on the prioritization of the interests of others at some personal cost. Therefore, we suggest that the ‘losses to others’ framing has a stronger effect on individuals with prosocial values, i.e. with stronger benevolence and universalism values.

H2a: The effect of the object at risk, which focuses on losses to others, will have a stronger effect on the support for restrictive government policy compared to the personal losses framing for the individuals with strong prosocial attitudes.

The context of the study: COVID-19 in Russia

Two experiments were conducted in Russia. Experiment 1 was conducted during the so-called first wave of COVID-19. On March 28, 2020 –the day when Experiment 1 started–the confirmed number of cases in Russia was 1,264. Four people had died from COVID-19 up to that day. On April 24, 2020 when Experiment 1 was finished, a total of 68,622 cases were confirmed, and the COVID-19 death toll reached 615. On March 25, 2020 the President, Vladimir Putin, declared a non-working week in all Russian regions from March 28 to April 5, 2020 [64], which was later extended till April 30 [65], and then till May 11, 2020 [66]. He also entrusted regional authorities full powers to adopt restrictive measures depending on the number of cases in a region [65]. For instance, Moscow, Saint-Petersburg and a number of other Russian regions were placed under lockdown on March 30, 2020 by the decision of local authorities. People were allowed to go outside for medical care purposes, shopping for food and medication, and going to work if remote work was not an option. Besides the obligatory closure of schools, universities, gyms, swimming pools, shopping malls and hair salons, a number of regions including Moscow and Saint-Petersburg launched a digital pass system in April to allow residents to leave their homes for essential reasons as well as a smartphone app to monitor coronavirus patients’ movement in self-isolation [67].

Experiment 2 was conducted during the so-called second wave of COVID-19. As compared with the first wave, the situation with COVID-19 in Russia had largely changed. On November 13, 2020 when Experiment 2 started, the confirmed number of cases in Russia was 1,880,551 and the COVID-19 death toll reached 32,443. During Experiment 2 no lockdown was in place in Russia, as well as there were no non-working weeks. The system of digital passes, which was abolished in all Russian regions by June 9, was no longer used. However, in November 2020 a series of restrictive measures, which were lifted in summer, had been reintroduced across Russian regions to contain the spread of the coronavirus. Nevertheless, such measures as mandatory wearing of face masks and gloves, prohibition of mass events and mass gatherings, reduced capacity of theaters, cinemas, and restaurants, and maintaining social distance had never been lifted from the times of the first wave. The head of Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor), Anna Popova, called for restrictive measures in the regions with the highest numbers of the COVID-19 active cases; and Russian Prime Minister, Mikhail Mishustin, supported this idea [68]. Following this order, for instance, in Moscow, such measures as distance learning and remote work were introduced.

By November 2020 two vaccines against COVID-19 were officially registered in Russia. On 11 August 2020, Russian president Vladimir Putin announced the official approval of the Sputnik-V vaccine. Two month later, on 14 October 2020 another vaccine, EpiVacCorona, was officially registered. However, according to Russian public opinion polls, the vaccine did not make citizens less fearful of the virus. While about 57% of Russians were afraid of getting sick with COVID-19 during the first wave in March 2020, in October 2020 the percentage increased up to 64% [69].

Experiment 1

Participants

Current undergraduate or graduate students of the HSE University were eligible to take part in the experiment, except for students of Political Science and Sociology departments. The consent was obtained in a written form. We invited students via their group emails, which are used for communication between lecturers and students. As a result, according to AAPOR [70] standards, response rates cannot be computed. The number of completed interviews was 762 (N = 762). Completed interviews were determined as those which had more than 80% of the essential questions answered [70].

No course credits were provided for the survey participation and students signed a consent form in which they were told they were free to withdraw from participation at any time they wanted. As an incentive for survey completion, we offered participation in a lottery in which students could win the smart home device, Yandex. Station (the price of around $150 U.S.). The break-off rate was 46% (N = 760), about half of the breakoffs were at the introduction page. Some individuals who reported that they are not students of the HSE University were screened out (N = 118). On average, it took 23 minutes to complete the survey (M = 23.12, SD = 12.69). The baseline characteristics of the final sample are given in S1 File.

Although we used a convenience sample, the use of such samples does not appear to consistently generate false negatives, false positives, or inaccurate effect sizes [71]. Kühberger [34] also finds that the behavior of student participants does not significantly differ from the behavior of non-student participants. However, we cannot estimate conditional average treatment effects (CATEs) using a convenience sample of students for the following reason. We did not expect to observe large variation in values in a convenience sample; there is some evidence that the differences in prosocial values among students are relatively small [72]. Since Mullinix et al. [71] suggest that estimation of CATEs in the experimental studies is problematic when there is lack of variance of the moderator, especially among convenience samples of students, we did not test hypothesis H2a in Experiment 1.

Experimental design

The experimental design was approved by the Council of Peers at Ronald F. Inglehart Laboratory for Comparative Social Research (№ER-2020-01). It is confirmed that the proposed research project conforms to ethical standards in modern social sciences. We designed a web-based experiment with a 2 (risk severity: high vs. low) X 2 (object at risk: individual losses vs. losses to others) factorial design. The subjects were randomly assigned to one of the four conditions: (1) High-risk X Individual losses, (2) High-risk X Losses to others, (3) Low-risk X Individual losses, (4) High-risk X Losses to others. The randomization process was carried out at the individual level and was conducted within the flow of the survey. No restrictions were placed on randomization, and we did not employ blocking. To ensure the effectiveness of randomization we checked for the covariate balance and put these results in S2 File. The p-values of joint orthogonality tests indicate that the group differences are insignificant, except for the probability of COVID-19 infection in the manipulation of the risk severity factor (for the details of statistical analysis, see S2 File). The data collection started on March 28, 2020 and finished on April 24, 2020. As is recommended in Reporting Guidelines for Experimental Research, the CONSORT flow diagram is provided in S3 File.

Materials

The vignettes were structured as the set of rubrics with essentially similar content, yet different framing. The information on the pandemic, scale of the issue, probability of infection / recovery, medical treatment, long-term negative consequences for personal health (yes / no), incubation period, and advice on how to protect yourself / take care of others, were described in texts. The statistics and information in the vignettes were taken from official websites such as the World Health Organization, Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, and Russian media sources, in March-April 2020. The length of text varied from 214 to 265 words. Vignettes can be found in Table 1. To ensure that the independent variable had effectively been manipulated and the participants understood risk framing in the way we wanted them to, we used three manipulation checks. Further details of manipulation checks are given in S8 File.

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Table 1. Experimental vignettes used in Experiment 1.

https://doi.org/10.1371/journal.pone.0258132.t001

Outcome measures and covariates

We had three dependent variables mainly used to ensure the robustness. Descriptive statistics of the dependent variable measures and pre-treatment covariates, which are described in S4 File, are given in Table 2. First, we asked the participants ‘How willing are you to sacrifice some of your rights if this helps prevent the spread of coronavirus in Russia?’, and measured their willingness to sacrifice rights on a 5-point scale (1 –‘not at all willing’; 5 –‘fully willing’).

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Table 2. Descriptive statistics of dependent variables and pre-treatment covariates.

https://doi.org/10.1371/journal.pone.0258132.t002

Second, we provided the participants with a list of 22 restrictive government policies which either had already been adopted by the Russian government or were being discussed in order to prevent the further spread of COVID-19. The complete list of security measures is given in S5 File. So, we asked the participants ‘To what extent do you support the following measures to prevent the spread of coronavirus in Russia’ and measured their level of support on a 5-point scale from 1 –‘do not support at all’ to 5 –‘fully support’. We then added up the points from respondents’ answers and used the resulting sum as the measure of participants’ support for restrictive government policy (Cronbach’s alpha, α = 0.91; [0.90; 0.92]).

Third, we asked the participants ‘To what extent do you support the introduction of criminal liability for violation of the quarantine in Russia?’ and measured their support for criminal liability on a 5-point scale (1 –‘do not support at all’; 5 –‘fully support’). This question appeared separately from other questions on restrictive government policies, since the intention of the Russian government to adopt it was harshly criticized in various media sources. Many people found this measure too severe and violating human rights, which resulted in public outrage on social media.

Results

Group means comparisons are summarized in Table 3 and Fig 1. As expected, we found statistically significant differences in the support for restrictive government policy (F(3, 762) = 4.68, p < 0.01) and support for criminal liability for the quarantine violation (F(3, 762) = 2.72, p < 0.05) between four treatment conditions. Though we did not find evidence of significant differences in means of the willingness to sacrifice rights (F(3, 762) = 1.82, p = 0.142) between experimental conditions, the effect of risk framing was proven to be statistically significant in 2 of 3 measures for the dependent variable. Pairwise comparisons of experimental conditions and be found in S6 File.

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Fig 1.

From left to right, the group means with 95% error bars in a) the willingness to sacrifice rights, b) the support for restrictive government policy, and c) the support for criminal liability for quarantine violation.

https://doi.org/10.1371/journal.pone.0258132.g001

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Table 3. Group means of experimental conditions in a completely randomized 2x2 factorial design.

https://doi.org/10.1371/journal.pone.0258132.t003

To estimate average treatment effects of the main factors, we ran t-tests. We found strong support of H1 for all three measures of the dependent variable. The differences between means of participants’ willingness to sacrifice rights in high-risk (M = 3.41, SD = 0.92) and low-risk (M = 3.26, SD = 0.96) conditions are statistically significant (t(760) = 2.222, p < 0.05), so the ATE of risk severity on the willingness to sacrifice rights is 0.15 ([0.02; 0.29]) on a 5-point scale, Cohen’s d = 0.16. The same is true for the measure of support for restrictive government policy. The high-risk group mean (M = 94.72, SD = 11.97) and the low-risk group mean (M = 91.23, SD = 14) are significantly different (t(760) = 3.707, p < 0.001), meaning that the ATE of risk severity on the support for restrictive government policy is 3.49 ([1.64; 5.34]), Cohen’s d = 0.27. Regarding the support for criminal liability for the quarantine violation, we also found a statistically significant difference (t(760) = 2.805, p < 0.01) between the high-risk (M = 3.00, SD = 1.31) and low-risk groups (M = 2.73, SD = 1.34), so the ATE is 0.27 ([0.08; 0.46]) on a 5-point scale, Cohen’s d = 0.20.

OLS regression models (see Table 4; see also S7 File) confirm the results of t-tests. Overall, these results prove that the high-risk framing has a stronger effect compared to the low-risk framing on willingness to sacrifice rights (β = 0.152, p < 0.05), the support for restrictive measures (β = 3.489, p < 0.01), and the support for criminal liability for the quarantine violation (β = 0.269, p < 0.01).

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Table 4. OLS regression models estimates of main effects and interactions; pre-treatment covariates as controls are not included.

https://doi.org/10.1371/journal.pone.0258132.t004

In contrast, we did not find any evidence of H2. We found that the difference in willingness to sacrifice rights between the ‘individual losses’ (M = 3.36, SD = 0.95) and the ‘losses to others’ (M = 3.31, SD = 0.94) groups is not statistically significant (t(760) = 0.708, p = 0.479). There is also no evidence that the support for restrictive government policy is any different (t(760) = 0.080, p = 0.937) for those who were exposed to personal losses framing (M = 93.09, SD = 13.32) in comparison with those who were shown the object at risk, which indicates losses to others (M = 93.01, SD = 12.87). The support for criminal liability for the quarantine violation was not proved to be appreciably different (t(760) = 0.274, p = 0.784) between the ‘individual losses’ (M = 2.86, SD = 1.36) and the ‘losses to others’ (M = 2.88, SD = 1.30) conditions. Therefore, there are no grounds for accepting the second hypothesis. We conclude that the object at risk which indicates losses to others does not have a stronger effect on the support for restrictive government policy compared to the personal losses framing. OLS regression models (see Table 4; see also S7 File) confirm the results of t-tests; there are no statistically significant effects of ‘losses to others’ frame compared to ‘individual losses’ on willingness to sacrifice rights (β = -0.05 p = 0.465), the support for restrictive measures (β = -0.109, p = 0.908), and the support for criminal liability for the quarantine violation (β = 0.024, p = 0.804).

We also estimated interaction effects between main experimental factors. Models 2, 4, and 6 in Table 4 demonstrate that there are no statistically significant interaction effects between risk severity factor and object at risk, neither on willingness to sacrifice rights (β = -0.000, p = 0.999), nor on the support for restrictive government policy (β = 1.074, p = 0.569), nor on the support for criminal liability for the quarantine violation (β = 0.097, p = 0.612).

Interestingly enough, we found that the participants who were exposed to the ‘low-risk’ framing (M = 2.91, SD = 0.59) were less convinced of the credibility of the information (t(760) = -4.938, p < 0.001) than those who were in the ‘high-risk group’ (M = 3.1, SD = 0.49) (see S8 File for more details). We found that the CATEs (conditional average treatment effects) of risk severity framing increase for those who perceived the information as credible (see S8 File).