Chapter 5: Lost in Translation: Narrative Salience of Fear > Hope in Prevention of COVID-19

Holly L. Peterson*, University of South Alabama
Chad Zanocco, Stanford University
Aaron Smith-Walter, University of Massachusetts Lowell

 

 

 

Abstract

Using short, policy-image-like narratives, we explore the relationship between narrative agreement and narrative impacts in the case of COVID-19 in the US. Building upon previous research which identified attention narratives focusing on problems “stories of fear” and those focusing on solutions “stories of hope,” we use a narrative survey experiment of the general public (n=1000) to test the salience of problem and solution narratives and if they impact agreement with Center for Disease Control (CDC) prevention guidelines. Our findings are 1) fear story agreement is partisan but hope story agreement is not 2) fear story is the more salient of the two, 3) narrative agreement for both fear and hope were related to CDC safety guideline agreement, but were partisan, and 4) exposure to neither narrative impacted likelihood to agree with the guidelines as compared to a control group. Our findings are consistent with previous work indicating a Democratic party preference for stories of fear, where Democrats were more likely to support policy action. While we find that agreement with our narratives and guidelines is related, neither narrative treatment successfully altered support for CDC guidelines, suggesting a potential limit for the influence of narratives to either change or reorder existing preferences in highly salient and partisan issue areas like COVID-19 and suggesting a need for more research into the dynamics of narrative attention.

 

*Corresponding author: hollypeterson@southalabama.edu

To cite the chapter: Peterson, Holly L., Chad Zanocco, and Aaron Smith-Walter. 2022. “Lost in Translation: Narrative Salience of Fear>Hope in Prevention of COVID-19”, in Narratives and the Policy Process: Applications of the Narrative Policy Framework, Michael D. Jones, Mark K. McBeth, and Elizabeth A. Shanahan (eds.), Montana State University Library, 113-134. doi.org/10.15788/npf5

 

Introduction

     The COVID-19 pandemic is one of the most salient events of the 21st century. With almost 30 million reported cases of the virus and approximately 550,000 deaths in the United States alone in early 2021 (New York Times, 2021) and a broad patchwork of state, local, and federal policy responses (e.g., Xu & Basu, 2021; Kincaid & Leckrone, 2021), it is difficult to imagine how anyone could remain untouched by the pandemic. Most people in the United States are likely to have either had the disease (e.g., Angulo et al., 2021), know someone who has (Smith, 2021), observed disruptions to their daily routines and activities (e.g., Zanocco et al., 2021), and/or have experienced economic consequences from the pandemic (e.g., Horowitz et al., 2021).

     Despite evidence that people are paying attention to the problem of coronavirus transmission (e.g., Jones, 2021) and its catastrophic impacts on public health and the world economy (Cutter & Summers, 2021), there is not agreement in the U.S. on the adoption of prevention measures as many people refuse to abide by health guidelines to reduce transmission (e.g., Yamamoto et al., 2021). Some feel that taking virus transmission precautions inhibits their freedom (e.g., Taylor & Asmundson, 2021). They feel they should not have to socially distance or wear a mask simply because they have been ordered to by public officials and public officials should not have the authority to direct their behavior in this way (e.g., Koplowitz, 2021; Sharp, 2020). For instance, in Alabama, Governor Kay Ivey initially indicated sympathy with this argument. Ivey, who has in the past avoided even hurricane evacuation mandates (Carson, 2020), said that people should practice safety measures by their own volition because it was the right thing to do to protect themselves andothers and to help the state’s economy (Gore, 2020). However, much to Ivey’s disappointment, her appeal to “personal responsibility” was not sufficient to motivate enough Alabamians to agree to undertake preventive measures. Ivey eventually did issue a stay-at-home order, the closing of public spaces where large groups congregated (Carson, 2020), and mandate mask wearing (Gore, 2020). Many other states implemented similar policies despite the unpopularity with the electorate (Mervosh et al., 2020). After failed appeals to personal responsibility, public officials were forced to act in the face of rising case numbers (e.g., Grossman et al., 2020).

     Given the reality of this disconnect between public policy communication and public behavior (e.g., Gollust et al., 2020), we ask, can narratives help people to comply with simple measures like those recommended by the CDC to prevent transmission of disease? The Narrative Policy Framework (NPF) proposes a narrative lens for understanding the policymaking process. For the NPF, narratives, or stories with characters, matter because they can persuade people (Shanahan et al., 2018). This has important implications for public policymaking, including public opinion (e.g., Jones & Song, 2014) and voting intention (e.g., McMorris et al., 2018) as well as an individual’s risk perceptions (e.g., Shanahan et al., 2019,;Guenther & Shanahan, 2020). But recent research also proposes narratives might function by shifting attention as opposed to persuading individuals (Peterson, 2018).

     In the case of a highly salient topic like COVID-19, most people probably have established opinions about whether or not they agree with the CDC’s guidance (e.g., Czeisler et al., 2020). Especially given the highly partisan nature of many topics and events in 2020 (e.g., Schaefer, 2020) and the role politics plays in framing the response to safety recommendations (e.g., Grossman et al., 2020); for example, former President Trump often understated the impact of COVID-19 and did not adhere to safety guidelines (e.g., Bump, 2020). Given the degree of national, and often partisan focus, on the COVID-19 pandemic and subsequent safety guidelines, is it reasonable to think that narratives can impact people’s agreement with safety guidelines? In this chapter, we explore these questions by testing the roles of narratives, narrative agreement (e.g., salience), and partisanship on agreement with CDC COVID-19 safety guidelines.

Narrative Attention

      Narrative attention is a recent idea elaborated from the NPF by way of synthesis with the information processing and agenda-setting approach of Bryan D. Jones and Frank R. Baumgartner (e.g., Jones, 1994; Jones & Baumgartner, 2005; Baumgartner & Jones, 2010). The question of narrative attention simply asks the NPF to take its framework of thought and expectations about the existence and persuasive function of narratives and alter them to account for changes in theoretical expectations when it makes more sense to expect attention shifts as opposed to persuasion shifts, or perhaps even shift in both attention and persuasion (Peterson, 2018).

The Narrative Policy Framework (NPF)

      The creators of the NPF identify a policy narrative as a character and policy referent and potentially many other elements and/or content (Shanahan et al., 2018). They conceptualize narratives as consisting of elements including characters, plot, setting, moral of the story, and other various kinds of content. Characters may include heroes (who help), villains (who hurt), and victims (who are harmed). Plot is the causal or temporal explanation that ties together the other narrative components. Setting refers to relevant contextual information. The moral of the story refers to the policy solution. According to Shanahan et al. (2018), narrative content includes expressions of beliefs (often identified or inferred from a theory or concept, like ideology) and potentially strategic information crafting (like emphasizing the negative traits of an opponent).

      The NPF proposes three levels of analysis: micro, meso, and macro (Shanahan et al., 2018). The micro level of analysis focuses on how narratives impact individuals, encapsulated in the homo narrans model of individual cognition (Jones, McBeth and Shanahan 2014). The meso level of analysis explores narratives across and within groups, especially those working together for a policy-related outcome (i.e., coalitions) within issue- or geographic-specific subsystems, agora narrans. The macro-level of analysis is less developed but focuses on institutions and policy change (e.g., Shanahan et al., 2018)—polis narrans, perhaps. Each level of analysis has spurred a variety of general ideas about how narratives might work, and some tested hypotheses indicate emerging patterns of narrative dynamics (see Shanahan et al., 2018). Generally speaking, this framework and the ideas about how narrative dynamics work emerge from lessons from other policy process approaches, most notably the Advocacy Coalition Framework (see Shanahan et al., 2011).

Macro-Level Narrative Attention

      Narrative attention has focused on the NPF macro level of analysis, specifically at US macropolitical institutions (i.e., legislative branch, executive branch, and federal court system). This is because at the level of institutional analysis, persuasion is not an effective means for facilitating policy change since policymakers within these institutions are unlikely to alter their positions (Baumgartner and Jones 2010). Narrative attention, though, may be an effective means in the right circumstances: Baumgartner and Jones describe how policy images (i.e., succinct and popular beliefs about policy based on general feelings of support or opposition to the policy status quo) can “catch fire” and sweep through the public sphere, advancing upon macro-level policymakers in a demand for policy change (2010). This “fire” doesn’t change minds but it does inspire attention that can generate new attention and may also alter the ordering of previous preferences (e.g., Jones, 1994, Jones & Baumgartner, 2005). However, this exclusion of persuasion at the macro level does not justify an exclusion of attention at the micro level. In fact, Jones and Baumgartner describe how attention shifts may sweep through the public sphere causing individuals to reorder priorities or become interested in new issues as well (2005; Jones, 1994; see Peterson & Jones, 2016), indicating an active life for narrative policy attention within individuals (i.e., NPF micro level) as well as institutions (i.e., NPF macro level). If this is the case, then public “narrative salience,” or the degree to which people agree with particular narrative policy images (i.e., policy images with narrative form), may have important policy process dynamics, including supporting protective behavior in emergency situations, such as the COVID-19 pandemic.

Micro-Level Narrative Attention

      To date, no empirical research has been conducted that explores narrative attention at the micro level of analysis. In a few “think” pieces exploring the integration of policy process theory ideas, Peterson (2018) and Peterson and Jones (2016), based on the work of Jones (1994) and Jones and Baumgartner (2005), posit that narrative attention shifts are likely important for how individuals think about public policy and how they are influenced by policy narratives. Following the work of Jones (1994), Peterson (2018) argues that similar to the effect within institutions, policy narratives may influence individuals by gaining the attention of previously apathetic persons and/or inspiring people who are already interested in a policy problem to reorder their preferences based on a policy narrative.

      This micro-level attention explanation is similar to the causal pathway identified by Peterson (2018, 2019) for attention to influence policy change in macro institutions. Of course, macro institutions consist of people; in fact, they are “more human than human” (Yuenger et al., 1995) in that they amplify characteristics of human information processing (Jones, 1994). However, an important distinction exists in that macro institutions are often processing highly salient policy images—those images that have gained enough agreement, and therefore agenda status, in the public and/or subsystems that they demand macro-level policymaking. Additionally, and more importantly, less stability in preferences is expected at the micro level of analysis than at the macro level (e.g., Baumgartner & Jones, 2010). In emergency situations like the COVID-19 pandemic, it may be important to impact people’s attention and preferences in a way that facilitates individual and collective safety. Drawing on recent lessons about partisanship and macro-level narrative images, we ask if these narrative images, that reflect policy images (e.g., Peterson, 2019), are capable of impacting people in the highly salient case of COVID-19. Such narratives communicate a general orientation to a public policy issue, like COVID-19, and potentially inspire agreement with activity to address it, such as CDC safety guidelines, by focusing the attention of the public, or perhaps, even persuading them.

Partisan Stories of Hope & Fear

       Previous narrative attention research identified two story types that emerged from narrative content analysis of macro-environmental narratives in the U.S.: those focused on problems and those focused on solutions (Peterson, 2019). In analysis of environmental policy narratives embedded within State of the Union speeches, Peterson found an increased likelihood for Republicans to deliver environmental narratives focusing on solutions and Democrats to deliver environmental narratives focusing on problems (2019). This finding follows the rationale put forth by Jones and Baumgartner (2015), who find that governments feed on policy problems—growing to address problems they are presented with. Republicans, not wanting to increase government response and spending on environmental problems, emphasize the fixes to the problem, and Democrats, willing to invest in more comprehensive and potentially expensive responses to the problem, emphasize the problems to be addressed (e.g., Greenburg & Jonas, 2003). Additionally, this explanation dovetails with the findings of Jost et al. (2003), who investigate conservatism as a psychological belief system, and find that conservatives generally fear large policy changes, and hence, avoid policy pathways that promote new and large-scale actions. This may be especially relevant regarding pathways that challenge the status quo in a way that could lead to greater investment in areas they would like to keep status quo (e.g., Baumgartner & Jones, 2010; Greenburg & Jonas, 2003).

      This characterization also tracks with contemporary partisan public messaging regarding the COVID-19 pandemic (e.g., Gollust et al., 2020). At the end of April 2020, as the caseload in the United States was reaching record-highs, then President Trump, a Republican, said “but a lot of movement and a lot of progress has been made in a vaccine. But I think what happens is it’s going to go away. This is going to go away. And whether it comes back in a modified form in the fall, we’ll be able to handle it” (Bump, 2020), emphasizing the solution, and down-playing a need for further efforts. Alternatively, President Biden, a Democrat, adopted a substantially different tone when he said on January 20th, 2021, as the caseloads were finally dropping: “We are in a race against time, and absent additional government assistance, the economic and public health crises could worsen in the months ahead; schools will not be able to safely reopen; and vaccinations will remain far too slow” (White House, 2021), emphasizing the problem of the pandemic and the need for action to address it. Certainly, partisanship seems to matter a lot for U.S. COVID-19 policy, even more than it does for other countries (Mordecai & Connaughton, 2020) even when there is bipartisan recognition of a problem (Cochrane, 2021).

      Although there was an initial bipartisan push to support COVID-19 legislation early in 2020 (Cochrane, 2020), by winter it was clear that much like the issue of environmental policy, Democrats supported more government action and spending than did Republicans (Peterson, 2020). This partisan split regarding COVID-19 policy was reflected in the public as well (e.g., Grossman et al., 2021), with conservatives less likely to see COVID-19 as a problem (Nowlan & Zane, 2020), to practice recommended transmission prevention procedures (Cankandar et al., 2020) including social distancing (Wu & Huber, 2021), and more likely to share misinformation about transmission prevention guidelines (Havey, 2020). Partisan avoidance of social distancing guidelines has been linked to worse health outcomes (e.g., Gollwitzer et al., 2020). However, despite conservative skepticism and reluctance toward COVID-19 safety policies, they do remain likely to agree that precautions are effective (Cankandar et al., 2020), indicating the great salience of this policy problem and suggesting a pathway for narratives to impact perceptions about transmission guidelines.

      Based upon this discussion, we develop the following hypotheses regarding stories of hope and fear, narrative salience, and agreement with CDC COVID-19 safety guidelines:

H1) Stories of fear are more salient for Democrats

Since Democratic presidential administrations are more likely to tell stories of fear in the case of environmental policy (Peterson, 2019), and Democrats may prefer a relatively larger policy response to COVID-19 as they do regarding environmental issues (Peterson, 2020), and stories of fear may infer a greater policy response (e.g., Baumgartner & Jones, 2015), and public partisans are thought to follow elite policy thinking about COVID-19 guidelines (Grossman et al., 2020), we expect that Democrats will be more likely to agree with stories of fear than Republicans.

H2) Stories of hope are more salient for Republicans

Since Republican presidential administrations are more likely to tell stories of hope in the case of environmental policy (Peterson, 2019), and Republicans may prefer a relatively smaller policy response to COVID-19 as they do regarding environmental issues (Peterson, 2020), and stories of hope may infer a smaller policy response (e.g., Baumgartner & Jones, 2015), and public partisans are thought to follow elite policy thinking about COVID-19 guidelines (Grossman et al., 2020), we expect that Republicans will be more likely to agree with stories of hope than Democrats.

H3) Stories of Fear will be more salient than Stories of Hope

Since the problem of COVID-19 is potentially the most expansive policy problem in recent times, devastating US public health and the economy, impacting virtually all Americans, and costing an estimated 16 trillion dollars in the United States alone (Cuttler & Summers, 2020), the scope of the issue is so large that we expect that comparatively more people will agree with stories of fear, which emphasize the problem and infer the need for more intensive policymaking (Peterson, 2019) than stories of hope, which emphasize the solution and infer a smaller policy response (e.g., Baumgartner & Jones, 2015).

H4) Stories of fear salience will be related to higher levels of agreement with CDC guidelines

Since we expect stories of fear to be more salient generally (H3), and for these stories to be salient with individuals who prefer a more robust response to the pandemic (H2), we expect that stories of fear will be associated with relatively higher agreement with CDC guidelines than stories of hope.

H5) Stories of hope salience will be related to lower levels of agreement with CDC guidelines

Since we expect stories of hope to be generally less salient (H3), and for these stories to be salient with individuals who prefer a less robust response to the pandemic (H1), we expect stories of hope to be associated with lower levels of agreement with the CDC guidelines than stories of fear.

H6) Narratives will increase agreement with CDC guidelines

Since narratives are thought to have the power to persuade (Jones & McBeth, 2010), reorder, and/or engage policy preferences (Peterson, 2019), we expect that respondents who read narratives will be more likely to agree with the CDC guidelines than those who do not.

H7) Stories of fear will impact agreement with CDC guidelines more than stories of hope

If stories of fear are more salient as compared to stories of hope (H3) and narratives impact agreement with CDC guidelines (H5), we expect that stories of fear will be more impactful, since they represent a greater call for change than stories of hope (Peterson, 2019).

To test these hypotheses, we conduct a narrative experiment with corresponding survey data, experimental design, and analysis procedure described in the following section.

Data and Methods

Data

      We apply data generated from an internet survey of the US public (n=1000) administered November 17 – 20, 2020 by YouGov, a survey research firm, to a sample of respondents 18 years old or older. Respondents were selected via stratified sampling by gender, age, race, education, and region to match target population statistics in the 2018 American Community Survey. It is important to note that this survey used purposive internet-based sampling methods and therefore lacks the representativeness of a probability-based sample. However, similar surveys using internet-based samples have been applied in previous NPF research as it allows for testing within and across narrative treatment effects on samples of the same approximate size and composition in post-test only control group experimental designs (Zanocco et al., 2018). Question items applied in this study are part of a larger survey conducted by the University of Massachusetts Lowell that asked the US public about a variety of topics related to the COVID-19 pandemic, and we provide a detailed description of the measures applied in our analysis in Table 1. We next describe the implementation of the narrative experiment within the survey design.

Narrative Experiment Design

      In this research, we employ a narrative experimental treatment design in an online survey setting. Similar to prior NPF research (e.g., Zanocco et al., 2018), we utilized both narrative treatment groups and a control group in the design of the experiment. Unlike previous NPF research, our narrative treatments are much shorter—one sentence long—compared to other NPF narrative treatments that are a paragraph of text or more. Similar to previous experimental surveys, these statements conform to the definition utilized within NPF research because they do include both a character and policy referent (e.g., Shanahan et al., 2018); however, these narratives were specifically developed to reflect the story of hope and fear narratives policy images from previous work, which captured generalized policy images of salient macro-level issues (Peterson, 2019). For this reason, the narratives employed focus primarily on either problems or solutions, as opposed to a variety of other elements and content that may be explored with NPF narratives (e.g., Pierce et al., 2014; Shanahan et al., 2018). To administer the different treatment arms, the survey sample was sorted into three groups each comprising approximately one-third of the total sample: fear narrative treatment group (n=341), hope narrative treatment group (n=340), and a control group that did not receive a narrative treatment (n=319). The fear and hope narrative treatment groups each received a narrative in the form of a question prompt, displayed below, while the control group did not receive a question prompt. Respondents in the narrative treatment groups were asked to indicate their agreement or disagreement with the following narratives, with response categories situated on a scale from 1= “Strongly Disagree” to 6= “Strongly Agree.”

Fear treatment narrative: “The COVID-19 pandemic is a major problem that has hurt many people.”

Hope treatment narrative: “Working together, government officials, medical professionals, and community members will strengthen our economy and public health in response to the COVID-19 pandemic.”

      In the next section of the survey, all respondents, including the control group, were then asked the same question about the importance of following nationally established social distancing guidelines for reducing the spread of COVID-19. Respondents indicated the level of importance of following the guidelines on a scale[1] from 1= “Extremely unimportant” to 7= “Extremely important.” See below for this guidance text:

To reduce the transmission of COVID-19, the CDC recommends that Americans:

  • Avoid close contact with people who are sick, even inside your home. If possible, maintain 6 feet between the person who is sick and other household members.
  • Put distance between yourself and other people outside of your home.
  • Remember that some people without symptoms may be able to spread the virus.
  • Stay at least 6 feet (about 2 arms’ length) from other people.
  • Do not gather in groups.
  • Stay out of crowded places and avoid mass gatherings.
  • Keeping distance from others is especially important for people who are at higher risk of getting very sick.

We then used responses to the question on COVID-19 guidance importance to form our main outcome measure. We applied elements of this survey treatment/control design, survey responses to these narrative questions and outcome measures, and responses to the sociodemographic questions to analyze the effect of COVID-19 narrative elements described in the next section.

 

Table 1. Summary statistics for all measures included in analytical modeling


Variable name Description Summary statistics
Narrative measures
Hope narrative agreement Agreement with the hope narrative on a scale from 1= “Strongly disagree” to 6= “Strong agree” Mean=4.17

Std. dev.=1.75

Fear narrative agreement Agreement with the fear narrative on a scale from 1= “Strongly disagree” to 6= “Strong agree” Mean=4.95

Std. dev.=1.50

Outcome measure
COVID-19 guidance importance Importance of COVID-19 guidance on a scale from 1= “Extremely unimportant” to 7= “Extremely important” Mean=5.77

Std. dev.=1.72

Sociodemographic characteristics
Age Age of respondent in years Average = 49 years old

Std. dev.=18.5

Male (vs. other) Gender of the respondent 48.9% male
White (vs. nonwhite) Race/ethnicity of the respondent 69.8% white only
Income Household income of the respondent on a scale from 1= “Less than $10,000” to 16= “500,000 or more” Median income is 6= “$50,000 – $59,999”
Democrat (vs. Independent/Other) Democratic political affiliation of the respondent 35.4% Democrat
Republican (vs. Independent/Other) Republican political affiliation of the respondent 29.7% Republican

 Analytical Approach

      In our analysis, we assess interrelated narrative components by leveraging our survey data and study design. Using ordinary least squares regression, we (1) examine factors related to narrative salience (i.e., H1, H2, and H3), (2) consider the relationship between narrative salience and COVID-19 guidance importance (i.e., H4 and H5), and (3) test narrative treatment effects on COVID-19 guidance importance by applying treatment status (treatment vs. control) (i.e., H6 and H7). As independent variables in all of our regression models, we include the age of the respondent, gender (male vs. all other), white (vs. nonwhite), income, and Democrat (vs. Independent/other), and Republican (vs. Independent/other). Summary statistics for these measures can be found in Table 1.

      For testing narrative agreement, we use the response to the narrative-based question prompt, which asks respondents to rate their level of agreement with the narrative to indicate salience as a dependent variable. We then test narrative salience by running regressions on narrative group subsets with sociodemographic measures as independent variables. Next, to test if there is a difference in narrative salience across narrative treatment groups, we pool both narrative groups and use agreement with the narrative statement as a dependent variable, and as independent variables include an indicator for hope vs. fear narrative groups as well as sociodemographics. Then, we consider how the effect of narrative agreement influences COVID-19 guidance importance. To do so, we model COVID-19 guidance importance as a dependent variable and narrative agreement and sociodemographics as independent variables. Finally, we test for narrative treatment effects by again modeling COVID-19 guidance importance as a dependent variable but running analysis on a pooled sample containing a treatment group and control group. We then include an indicator for treatment vs. control as an independent variable alongside sociodemographic controls to test for narrative treatment effects.

Findings

      We first consider the factors related to narrative treatment agreement (Table 2). For those sample respondents that received the fear narrative, we find that males (b = -0.587; p < 0.01) and Republicans (b = -0.467; p < 0.05) are less likely to agree with this narrative, while Democrats are more likely (b = 0.504; p < 0.05) (Model A1). However, for the hope narrative, we find no relationship with sociodemographics (Model A2). Finally, when identifying which narrative has a more comparative agreement, we find that after controlling for sociodemographics, the hope narrative is less salient than the fear narrative (hope narrative vs. fear narrative); b = -0.766; p < 0.001) (Model A3).

 

Table 2. Modeling narrative agreement


Dependent variable: Narrative agreement Model A1 Sample: Fear narrative group Model A2 Sample: Hope narrative group Model A3 Sample: Hope & Fear narrative groups
Coef. Est. p-value Coef. Est. p-value Coef. Est. p-value
Age -0.002 0.673 0.007 0.234 0.003 0.428
Male (vs. other) -0.587** 0.001 -0.121 0.557 -0.346* 0.010
White (vs. nonwhite) -0.066 0.731 -0.116 0.621 -0.101 0.502
Income (scale) 0.11 0.633 -0.014 0.648 -0.002 0.909
Republican (vs. other/independent) -0.467* 0.025 0.9 0.731 -0.192 0.248
Democrat (vs. other/independent) 0.504* 0.012 0.406 0.106 0.437** 0.006
Hope narrative (vs. fear narrative) -0.766*** <0.001
Constant 5.298*** <0.001 3.92*** <0.001 4.982*** <0.001
R-squared 0.115 0.017 0.090
Analytical sample size 303 304 607

Significance levels: * p < 0.05; ** p < 0.01; ***p < 0.001

      Our next set of analyses explore the relationship between narrative agreement and COVID-19 guidance importance. For the fear narrative group, we find that males and Republicans are less likely to report that COVID-19 guidance is important; however, the factor with the strongest effect is an agreement with the fear narrative (b = 0.446; p < 0.001) (Model B1). A similar pattern emerges for the hope narrative group, where agreement with the hope narrative is strongly related to COVID-19 guidance importance (b = 0.367; p < 0.001) (Model B2), while males are less likely to find COVID-19 guidance important, with whites and Democrats being more likely to find this guidance important.

 

Table 3. Modeling the effect of narrative agreement on COVID-19 guidance importance


Dependent variable: COVID-19 guidance importance Model B1  Sample: Fear narrative group Model B2  Sample: Hope narrative group
Coef. Est. p-value Coef. Est. p-value
Age -0.002 0.739 -0.003 0.515
Male (vs. other) -0.384* 0.027 -0.451* 0.012
White (vs. nonwhite) -0.107 0.575 0.482* 0.018
Income (scale) -0.032 0.178 0 0.986
Republican (vs. other/independent) -0.513* 0.015 -0.366 0.107
Democrat (vs. other/independent) 0.134 0.508 0.755** 0.001
Fear narrative agreement 0.446*** <0.001
Hope narrative agreement 0.367*** <0.001
Constant 3.94*** <0.001 4.098*** <0.001
R-squared 0.267 0.253
Analytical sample size 304 305

Significance levels: *p < 0.05; **p < 0.01; ***p < 0.001

      We then test whether there is a treatment effect associated with receiving a narrative treatment by comparing respondents from the two treatment groups against the control group (Table 4). For both the fear and hope treatments, we do not observe a narrative treatment effect (Model C1 and C2). However, we do observe stable effects related to sociodemographics across these specifications, with males and Republicans being less likely to find COVID-19 guidance important while Democrats were more likely to find this guidance important.

 

Table 4. Modeling the effect of narrative treatments on COVID-19 guidance importance


Dependent variable: COVID-19 guidance importance Model C1:   Sample: Fear narrative group & Control group Model C2:  Sample: Hope narrative group & Control group
Coef. Est. p-value Coef. Est. p-value
Age -0.002 0.599 -0.002 0.596
Male (vs. other) -0.421** 0.002 -0.344* 0.014
White (vs. nonwhite) 0.047 0.764 0.35* 0.027
Income (scale) 0.028 0.151 0.008 0.703
Republican (vs. other/independent) -0.64*** <0.001 -0.458** 0.009
Democrat (vs. other/independent) 0.437** 0.007 0.697*** <0.001
Fear narrative group (vs. control group) 0.144 0.282
Hope narrative group (vs. control group) 0.013 0.926
Constant 5.893*** <0.001 5.624*** <0.001
R-squared 0.088 0.088
Analytical sample size 593 595

Significance levels: *p < 0.05; **p < 0.01; ***p <0.001

Discussion

      Our analyses supported several of our hypotheses about narrative salience, but none of those about narrative impacts: Hypothesis H1, H3, H4, and H5 were supported, but not H2, H6, or H7. We found that stories of fear were more salient for Democrats as compared to Republicans (H1) and were also more salient overall as compared to stories of hope (H3), where partisanship remained a significant factor, as expected. We also found that the narrative salience of fear and hope stories was significantly related to the agreement with CDC safety guidelines for social distancing (H4 and H5).

 Partisan & Gendered Salience

      Our results indicate an important role for partisanship regarding narrative policy images. These findings align with our expectations that Democrats will agree more with stories that imply greater policymaking and would support a greater policy response compared to Republicans. We did not find support for our expectation that Republicans would agree more with stories that inferred a more restrained policy response (i.e., stories of hope, H2), but we did find evidence that Republicans agreed less with the story of fear. This finding further supports our expectation that the problem-focused narratives appeal to Democrats, in this situation where they may prefer increased policymaking, but also points to an unexpected Republican dynamic.

      Since Republicans are less likely to agree with the problem-focused story, are not responsive to the solution-focused story, but overall do agree with the CDC guidelines, perhaps they simply distrust political narratives about science. Given the increased politicization of the Republican party under former President Trump, including sometimes ridiculous and outlandish rhetoric, the American public has likely become familiar with highly politicized policy narratives (e.g., Smith, 2020). In the context of partisan policy narratives, American trust for politicians is at a near all-time low, but trust for scientists remains high (Funk et al., 2020), especially regarding COVID-19 (Kreps & Kriner, 2020). Perhaps Republicans, accustomed to Trump’s rhetoric style and a highly partisan policymaking environment (e.g., Pew, 2019), see narratives as vehicles for value-based as opposed to science-based information and therefore distrust this method of communication for science or emergency information. Perhaps political distrust of narratives means they are more effective as reminders of previously held ideas in this context than movers of policy preferences. Indeed, one computational study estimated that Trump’s “narrative control” peaked in 2017, as he focused on attacking Hilary Clinton and the status quo, but then dwindled to little by 2020, as COVID-19 rose as a national issue of concern (Dodds et al., 2021). In this example, Trump was more effective when the topic was more about politics than public health.

      This notion may contradict previous NPF work arguing that narratives are always important for communicating issues, including controversial issues, like climate change (e.g., Jones, 2014;, Jones & Peterson, 2016). However, with the rapid changes American politics have been experiencing in the past 20 years, perhaps some of the public suffers from a “narrative fatigue,” feeling put-off from being overly-narratively-communicated to. Narrative fatigue could function similar to the boomerang effect in the framing literature, when respondents double down on their previous perceptions following a framing treatment due to suspicion of manipulation (De Vries, 2016). Future research should explore the dynamics of the changing narrative nature of the Republican party and the possibility of narrative fatigue in the public.

      While gender emerged as an important factor in this analysis, we did not provide expectations for the role of gender. Gender has been identified as an important factor in determining the distribution of burdens in the COVID-19 pandemic (e.g., Raile et al., 2020). Gender has been identified as a determinant of policy preferences regarding climate change and previous NPF work has addressed its role thusly (Jones & Peterson, 2016); however, to date, there is limited work within the NPF to address the role of gender and narrative salience or impacts. The exposition of gender within the policy process literature itself is sparse. This suggests that future research should more directly investigate the role of gender in the NPF.

      The finding regarding a gendered and partisan agreement with stories of fear could also potentially be useful when communicating to audiences about risk. Focusing on problems may be useful to those who agree that a response is warranted, and to non-men specifically, but could be problematic for those who do not think a response is warranted, and to men. This strategy, the story of fear, may resonate with the policy image in the minds of some individuals and ease communication and increase attention, but put up barriers for others. For those who think the issue warrants less of a response, in this case, Republicans and men, focusing on problems does not resonate and may be off-putting to them. Future research in risk may want to consider the influences of a problem- or solution-focus when communicating emergency preparedness or response information to specific audiences.

Agreement with Safety Guidelines

      The narrative agreement for both problem and solution narratives were linked to an agreement with CDC guidelines, as expected. Although agreement with both narratives was linked to an agreement with the guidelines, and both parties reported high levels of agreement to guidelines, in both cases, the relationship was somewhat mitigated by partisanship for Republicans—they were less likely to agree with the guidelines. However, the existence of this relationship across both narrative types, when partisanship was not a significant predictor of agreement with the story of hope, indicates partisanship’s persistent relationship to agreement with CDC guidelines. This relationship overshadows the role of narratives in our analysis and is prominently evidenced in our final models, which predict narrative impacts on agreement with CDC guidelines.

      We did not find support that narratives influence agreement with social distancing guidelines (H6), or relatedly, that stories of fear influenced agreement with CDC guidelines more than stories of hope (H7). Since the narrative treatment had no impact on agreement with the CDC guidelines, we were unable to test H7, which would have compared the effectiveness of the two story types. However, we did find evidence that partisanship and gender did predict agreement with the guidelines in each of the narrative tests. We see more evidence of these relationships than in other analyses, because every partisan measure is significant in these models, suggesting a robust relationship to our dependent variable. While these measures all align with our primary rationale that Democrats are more likely to support the guidelines than Republicans, they also support observations of the politicization of the CDC through the pandemic (Aatresh, 2020). In both the fear and hope treatments, Democrats were more likely to support the guidelines, and Republicans were less likely to support the guidelines. This relationship may be too great for narratives to matter where they change minds or focus attention. Partisanship and gender are the consistent findings in these analyses, overriding even the salient story of fear. Given the highly salient and polarized nature of this topic and the CDC itself, narratives may have been more influential on a different dependent variable.

      Finally, this analysis employed attention narratives, those that pointed to policy images as conceptualized in Peterson 2018, 2019, and In Press. It is possible that these short narratives did not provide ample length to engage readers in narrative transportation (see Shanahan et al., 2019), which may have been more effective in persuading individuals or shifting their attention than simple stories, which may not allow space for respondents to have a robust emotional response. Additionally, since our goal was to discern the effects of story types instead of specific characters or other narrative elements within narratives, our narratives were not as comparable to each other as the longer form narratives used in previous NPF experimental designs, which may complicate the comparison of these findings with previous NPF experimental surveys. Future NPF research should continue to explore other narrative constructions that may be linked to persuasion or preference change. However, despite the many NPF cases to date (see Shanahan et al., 2018), few identify a narrative treatment effect (e.g., McMorris et al., 2018; Zanocco et al., 2018), suggesting the ability of exposure to a single narrative may change minds may be limited, and narratives may be most useful in influencing the feelings or behaviors of those who already agree, or disagree, with them.

      This research contributes to the NPF by testing novel story types (i.e., fear and hope), short attention-based narratives, and exploring the role of narrative saliency in a broadly impactful case with relevance across the globe. Our findings expand and point to several largely untapped veins of NPF exploration: narrative attention, narrative salience, and narrative fatigue. These concepts challenge the supremacy of persuasion as a focus of NPF study and ask what other narrative dynamics might have important implications in the policy process. Future research should explore demographic creation and reception of narratives, especially among gender, race, and wealth. As people’s lived experiences vary so greatly, so too may the narrative perspectives they inhabit and resonate with. Traditionally, NPF research has relied heavily on academics to choose what narratives likely matter for whom. Future research should expand upon the efforts of Shanahan et al. (2019) and ask policy targets what matters for them. Additionally, in the case of highly salient policy areas that have already been inundated with narrative-based information delivery and communication (e.g., COVID-19), future causal NPF research should innovate new research designs and narrative operationalizations that move beyond survey-based experiments.

Conclusion

      Our research tested the role of short, policy-image-like-narratives on agreement with CDC social distancing guidelines regarding the COVID-19 pandemic. The narratives were crafted based upon recent findings of macro-level narratives in the case of environmental policy. Environmental policy is similarly related to COVID-19 in that it has predictable partisan policy beliefs. Our hypotheses emerge from this comparison and we test three general relationships with seven specific hypotheses. First, we identify the determinants of narrative agreement with our narratives, the stories of fear and hope. Second, we test the impact of narrative agreement with the CDC guideline agreement. Third, we test the impact of the narratives themselves.

      Our findings indicate that there may be no room for narrative to maneuver in the highly salient and partisan (and gendered) case of CDC COVID-19 guidelines. Partisanship and gender are the persistent and robust factors in our analysis. We find that despite the existence of an agreement with our narratives, and the relationship of that agreement to the agreement with COVID-19 safety guidelines, the narratives themselves do not impact agreement with the guidelines, only partisanship and gender do.

 The narrative effect appears to be lost in translation—with partisan beliefs and gender overwhelming our stories emphasizing problems and solutions. We believe this indicates three main future considerations for the NPF, and especially for narrative risk studies moving forward: the important and under-researched role of gender in narrative studies, the relationship between narrative and partisanship, and the persuasive and attention impacts of narratives. Exploring the latter provides perhaps has the most potential for yielding interesting narrative insights, with a greater understanding of the attention impacts presenting a way to circumvent these translation barriers when they are observed. In this respect, our research suggests that NPF scholars should pay more attention to the impacts of narrative attention in highly salient and potentially contentious policy environments.

 

Acknowledgments: The authors would like to thank the Center for Public Opinion at the University of Massachusetts Lowell for their generous support in conducting this research.

 

References

 

Aatresh, A. 2020. Science, society, and Security: Politicization in the age of COVID-19. Retrieved March 30, 2021, from https://harvardpolitics.com/science-society-security/

Ang, Joshua Ping, Fang Dong, and Jason Patalinghug. 2021. “COVID-19: Effectiveness of Socioeconomic Factors in Containing the Spread and Mortality.” International Review of Applied Economics 35 (2): 164–87. https://doi.org/10.1080/02692171.2020.1853078.

Angulo, Frederick J., Lyn Finelli, and David L. Swerdlow. 2021. “Estimation of US SARS-CoV-2 Infections, Symptomatic Infections, Hospitalizations, and Deaths Using Seroprevalence Surveys.” JAMA Network Open 4 (1): e2033706. https://doi.org/10.1001/jamanetworkopen.2020.33706.

Baumgartner, Frank R., and Bryan D. Jones. 2010. Agendas and Instability in American Politics. University of Chicago Press.

———. 2015. The Politics of Information: Problem Definition and the Course of Public Policy in America. University of Chicago Press.

Bump, Philip. n.d. “Analysis | Trump Leans into His ‘180,000 Deaths Is a Statistic’ Reelection Strategy.” Washington Post. Accessed March 27, 2021. https://www.washingtonpost.com/politics/2020/08/31/trump-leans-into-his-180000-deaths-is-statistic-reelection-strategy/.

Cakanlar, Aylin, Remi Trudel, and Katherine White. 2020. “Political Ideology and the Perceived Impact of Coronavirus Prevention Behaviors for the Self and Others.” The Association of Consumer Research Early View. https://doi.org/10.1086/711834.

Carson, Mike. 2020. “Kay Ivey Seeks Pre-Landfall FEMA Declaration on Sally.” Al. September 14, 2020. https://www.al.com/news/2020/09/gov-kay-ivey-issues-state-of-emergency-for-tropical-storm-sally.html.

Cason, Mike. 2020. “Gov. Kay Ivey Issues Stay-at-Home Order Effective Saturday.” Al, April 3, 2020, sec. News. https://www.al.com/news/2020/04/gov-kay-ivey-to-give-update-on-coronavirus-response.html.

Cochrane, Emily. 2020. “House Passes $8.3 Billion Emergency Coronavirus Response Bill.” The New York Times, March 4, 2020, sec. U.S. https://www.nytimes.com/2020/03/04/us/politics/coronavirus-emergency-aid-congress.html.

———. 2021. “‘From Crisis to Crisis’: The Moments That Defined a Historic Congress.” The New York Times, January 2, 2021, sec. U.S. https://www.nytimes.com/2021/01/02/us/politics/congress-impeachment-coronavirus-trump.html.

Cutler, David M., and Lawrence H. Summers. 2020. “The COVID-19 Pandemic and the $16 Trillion Virus.” JAMA 324 (15): 1495. https://doi.org/10.1001/jama.2020.19759.

Czeisler, Mark É. 2020. “Public Attitudes, Behaviors, and Beliefs Related to COVID-19, Stay-at-Home Orders, Nonessential Business Closures, and Public Health Guidance — United States, New York City, and Los Angeles, May 5–12, 2020.” MMWR. Morbidity and Mortality Weekly Report 69. https://doi.org/10.15585/mmwr.mm6924e1.

De Vries, G. 2016. How positive framing may fuel opposition to low-carbon technologies. Journal of Language and Social Psychology, 36(1), 28-44. doi:10.1177/0261927×16663590

Dodds, Peter Sheridan, Joshua R. Minot, Michael V. Arnold, Thayer Alshaabi, Jane Lydia Adams, Andrew J. Reagan, and Christopher M. Danforth. 2020. “Computational Timeline Reconstruction of the Stories Surrounding Trump: Story Turbulence, Narrative Control, and Collective Chronopathy.” ArXiv Preprint ArXiv:2008.07301.

Funk, C., Kennedy, B., & Johnson, C. (2020, December 17). Trust in medical scientists has grown in U.S., but mainly Among Democrats. Retrieved March 29, 2021, from https://www.pewresearch.org/science/2020/05/21/trust-in-medical-scientists-has-grown-in-u-s-but-mainly-among-democrats/

Gollust, Sarah E., Rebekah H. Nagler, and Erika Franklin Fowler. 2020. “The Emergence of COVID-19 in the US: A Public Health and Political Communication Crisis.” Journal of Health Politics, Policy and Law 45 (6): 967–81. https://doi.org/10.1215/03616878-8641506.

Gollwitzer, Anton, Cameron Martel, William J. Brady, Philip Pärnamets, Isaac G. Freedman, Eric D. Knowles, and Jay J. Van Bavel. 2020. “Partisan Differences in Physical Distancing Are Linked to Health Outcomes during the COVID-19 Pandemic.” Nature Human Behaviour 4 (11): 1186–97. https://doi.org/10.1038/s41562-020-00977-7.

Gore, Leada. 2020a. “No Mandated Masks Statewide; Gov. Ivey ‘Reserves Right to Reverse Course’ Back to Restrictions.” Al, June 30, 2020, sec. News. https://www.al.com/news/2020/06/no-mandated-masks-statewide-gov-ivey-reserves-right-to-reverse-course-back-to-restrictions.html.

———. 2020b. “Gov. Ivey Issues Statewide Mask Order.” Al. July 15, 2020. https://www.al.com/news/2020/07/gov-ivey-covid-update-today-press-conference-planned-for-11-am-watch-live.html.

Greenberg, Jeff, and Eva Jonas. 2003. “Psychological Motives and Political Orientation–The Left, the Right, and the Rigid: Comment on Jost et al. (2003).” Psychological Bulletin 129 (3): 376–82. https://doi.org/10.1037/0033-2909.129.3.376.

Grossman, Guy, Soojong Kim, Jonah M. Rexer, and Harsha Thirumurthy. 2020. “Political Partisanship Influences Behavioral Responses to Governors’ Recommendations for COVID-19 Prevention in the United States.” Proceedings of the National Academy of Sciences 117 (39): 24144–53. https://doi.org/10.1073/pnas.2007835117.

Guenther, Sara K., and Elizabeth A. Shanahan. 2020. “Communicating Risk in Human-Wildlife Interactions: How Stories and Images Move Minds.” PLoS One 15 (12): e0244440.

Havey, Nicholas Francis. 2020. “Partisan Public Health: How Does Political Ideology Influence Support for COVID-19 Related Misinformation?” Journal of Computational Social Science 3 (2): 319–42. https://doi.org/10.1007/s42001-020-00089-2.

Horowitz, Juliana Menasce, Anna Brown, and Rachel Minkin. 2021. “The COVID-19 Pandemic’s Long-Term Financial Impact.” Pew Research Center’s Social & Demographic Trends Project (blog). March 5, 2021. https://www.pewresearch.org/social-trends/2021/03/05/a-year-into-the-pandemic-long-term-financial-impact-weighs-heavily-on-many-americans/.

Jones, Bryan D. 1994. Reconceiving Decision-Making in Democratic Politics: Attention, Choice, and Public Policy. University of Chicago Press.

Jones, Bryan D., and Frank R. Baumgartner. 2005. The Politics of Attention: How Government Prioritizes Problems. University of Chicago Press.

Jones, Jeffery. 2021. “Roundup of Gallup COVID-19 Coverage.” Gallup.Com (blog). March 12, 2021. https://news.gallup.com/opinion/gallup/308126/roundup-gallup-covid-coverage.aspx.

Jones, Michael D., and Mark K. McBeth. 2010. “A Narrative Policy Framework: Clear Enough to Be Wrong?” Policy Studies Journal 38 (2): 329–53. https://doi.org/10.1111/j.1541-0072.2010.00364.x.

Jones, Michael D., Mark K. McBeth, and Elizabeth A. Shanahan. 2014. “Introducing the Narrative Policy Framework” in The Science of Stories: Applications of the Narrative Policy Framework in Public Policy Analysis. New York, NY: Palgrave Macmillan.

Jones, Michael D., and Holly Peterson. 2017. “Narrative Persuasion and Storytelling as Climate Communication Strategies.” In Oxford Research Encyclopedia of Climate Science.

Jones, Michael D., and Geoboo Song. 2014. “Making Sense of Climate Change: How Story Frames Shape Cognition.” Political Psychology 35 (4): 447–76.

Jost, John T., Jack Glaser, Arie W. Kruglanski, and Frank J. Sulloway. 2003. “Political Conservatism as Motivated Social Cognition.” Psychological Bulletin 129 (3): 339–75. https://doi.org/10.1037/0033-2909.129.3.339.

Kincaid, John, and J. Wesley Leckrone. 2021. “Partisan Fractures in U.S. Federalism’s COVID-19 Policy Responses.” State and Local Government Review, February, 0160323X20986842. https://doi.org/10.1177/0160323X20986842.

Koplowitz, Howard. 2021. “Ainsworth Urged Ivey to Lift ‘Big Brother-Style’ Mask Mandate.” AL.Com, March 3, 2021, sec. Politics. https://www.al.com/politics/2021/03/ainsworth-again-calls-on-ivey-to-lift-big-brother-style-mask-mandate.html.

Kreps, Sarah, and Douglas Kriner. 2020. Model uncertainty, Political contestation, and Public Trust in Science: Evidence from the COVID-19 pandemic. Retrieved March 30, 2021, from https://advances.sciencemag.org/content/6/43/eabd4563

McMorris, Claire, Chad Zanocco, and Michael Jones. 2018. “Policy Narratives and Policy Outcomes: An NPF Examination of Oregon’s Ballot Measure 97.” Policy Studies Journal 46 (4): 771–97.

Mervosh, Sarah, Denise Lu, and Vanessa Swales. 2020. “See Which States and Cities Have Told Residents to Stay at Home.” The New York Times, March 31, sec. U.S. https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html.

Mordecai, Mara, and Aidan Connaughton. 2020. “Public Opinion about Coronavirus Is More Politically Divided in U.S. than in Other Advanced Economies.” Pew Research Center (blog). October 28, 2020. https://www.pewresearch.org/fact-tank/2020/10/28/public-opinion-about-coronavirus-is-more-politically-divided-in-u-s-than-in-other-advanced-economies/.

Nowlan, Luke, and Daniel M. Zane. 2020. “Getting Conservatives and Liberals to Agree on the COVID-19 Threat.” Journal of the Association for Consumer Research Early View. https://doi.org/10.1086/711838.

Peterson, Holly L. 2018. “Political Information Has Bright Colors: Narrative Attention Theory.” Policy Studies Journal 46 (4): 828–42.

———. 2019. “Macro Stories: Policy Process Dynamics of Presidential Environmental Ideas.”

———. In Press. ““Narrative Policy Images: Intersecting Narrative & Attention in Presidential Stories about the Environment.” Policy Studies Journal.

Peterson, Holly L., and Michael D. Jones. 2016. “Making Sense of Complexity: The Narrative Policy Framework and Agenda Setting.” In Handbook of Public Policy Agenda Setting. Edward Elgar Publishing.

Peterson, Kristina. 2020. “Lawmakers Look to Short-Term Funding Measure as Spending Talks Continue.” Wall Street Journal, December 7, 2020, sec. Politics. https://www.wsj.com/articles/lawmakers-look-to-short-term-funding-measure-as-spending-talks-continue-11607293308.

Pew Research Center, June, 2019, “Public Highly Critical of State of Political Discourse in the U.S.” https://www.pewresearch.org/fact-tank/2020/11/13/america-is-exceptional-in-the-nature-of-its-political-divide/.

Pierce, Jonathan J., Aaron Smith-Walter, and Holly L. Peterson. 2014. “Research Design and the Narrative Policy Framework.” In The Science of Stories: Applications of the Narrative Policy Framework in Public Policy Analysis. New York, NY: Palgrave Macmillan. 27–44.

“President Biden Announces American Rescue Plan.” 2021. The White House. January 20, 2021. https://www.whitehouse.gov/briefing-room/legislation/2021/01/20/president-biden-announces-american-rescue-plan/.

Raile, Amber NW, Eric D. Raile, David CW Parker, Elizabeth A. Shanahan, and Pavielle Haines. 2020. “Women and the Weight of a Pandemic: A Survey of Four Western US States Early in the Coronavirus Outbreak.” Gender, Work & Organization.

Schaeffer, Katherine. n.d. “Far More Americans See ‘Very Strong’ Partisan Conflicts Now than in the Last Two Presidential Election Years.” Pew Research Center (blog). Accessed March 27, 2021. https://www.pewresearch.org/fact-tank/2020/03/04/far-more-americans-see-very-strong-partisan-conflicts-now-than-in-the-last-two-presidential-election-years/.

Shanahan, Elizabeth A., Michael D. Jones, and Mark K. McBeth. 2011. “Policy Narratives and Policy Processes.” Policy Studies Journal 39 (3): 535–61.

Shanahan, Elizabeth A., Michael D. Jones, Mark K. McBeth, and Claudio M. Radaelli. 2018. “The Narrative Policy Framework.” Theories of the Policy Process, 173–213.

Shanahan, Elizabeth A., Ann Marie Reinhold, Eric D. Raile, Geoffrey C. Poole, Richard C. Ready, Clemente Izurieta, Jamie McEvoy, Nicolas T. Bergmann, and Henry King. 2019. “Characters Matter: How Narratives Shape Affective Responses to Risk Communication.” PloS One 14 (12): e0225968.

Sharp, John. 2021. “Will Kay Ivey Follow Other Republican Governors in Lifting Mask Orders?” Al, March 3, sec. News. https://www.al.com/news/2021/03/alabama-crossroads-will-kay-ivey-follow-other-republican-governors-in-lifting-mask-orders.html.

Smith, Matthew. 2021. “Global Study: How Many People Know Someone Who Has Died from COVID-19? | YouGov.” https://today.yougov.com/topics/health/articles-reports/2021/01/27/global-study-how-many-people-know-someone-who-has-.

Smith, David. n.d. “Trump Has Changed America by Making Everything about Politics, and Politics All about Himself.” The Conversation. Accessed May 18, 2021. http://theconversation.com/trump-has-changed-america-by-making-everything-about-politics-and-politics-all-about-himself-146839.

Taylor, Steven, and Gordon J. G. Asmundson. 2021. “Negative Attitudes about Facemasks during the COVID-19 Pandemic: The Dual Importance of Perceived Ineffectiveness and Psychological Reactance.” PLOS ONE 16 (2): e0246317. https://doi.org/10.1371/journal.pone.0246317.

Times, The New York. 2020. “Coronavirus in the U.S.: Latest Map and Case Count.” The New York Times, July 20, 2020, sec. U.S. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

Wicke, Philipp, and Marianna M. Bolognesi. 2020. “Framing COVID-19: How We Conceptualize and Discuss the Pandemic on Twitter.” PLOS ONE 15 (9): e0240010. https://doi.org/10.1371/journal.pone.0240010.

Wu, Jennifer D., and Gregory A. Huber. n.d. “Partisan Differences in Social Distancing May Originate in Norms and Beliefs: Results from Novel Data.” Social Science Quarterly n/a (n/a). Accessed March 27, 2021. https://doi.org/10.1111/ssqu.12947.

Xu, H. Daniel, and Rashmita Basu. 2020. “How the United States Flunked the COVID-19 Test: Some Observations and Several Lessons.” The American Review of Public Administration 50 (6–7): 568–76. https://doi.org/10.1177/0275074020941701.

Yamamoto, Nao, Bohan Jiang, and Haiyan Wang. 2021. “Quantifying Compliance with COVID-19 Mitigation Policies in the US: A Mathematical Modeling Study.” Infectious Disease Modelling 6 (January): 503–13. https://doi.org/10.1016/j.idm.2021.02.004.

Yuenger, Jay Noel, Rob Zombie, Rob Wolfgang Zombie, and Shauna Yseult Renyolds. 1995. More Human than Human.

Zanocco, Chad, Geoboo Song, and Michael Jones. 2018. “Fracking Bad Guys: The Role of Narrative Character Affect in Shaping Hydraulic Fracturing Policy Preferences.” Policy Studies Journal 46 (4): 978–99.

Zanocco, C., Flora, J., Rajagopal, R., & Boudet, H. 2021. “Exploring the effects of California’s COVID-19 shelter-in-place order on household energy practices and intention to adopt smart home technologies.” Renewable and Sustainable Energy Reviews, 110578.

 

[1] The original measure was presented on a scale from 1=” Extremely important” to 7=”Extremely unimportant” which we have recoded for ease in interpretability.

 

License

Icon for the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Narratives and the Policy Process: Applications of the Narrative Policy Framework by Michael D. Jones; Mark K. McBeth; and Elizabeth Shanahan is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, except where otherwise noted.

Digital Object Identifier (DOI)

https://doi.org/doi.org/10.15788/npf5

Share This Book