The Pennsylvania State University
The Graduate School
EXAMINING THE ROLE OF NARRATIVES IN POLICY AGENDA SETTING
AMID A “MANUFACTURED CRISIS”
A Dissertation in
Public Administration
by
Michael Smith
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
December 2020 ii
The dissertation of Michael Smith was reviewed and approved by the following:
Bing Ran Associate Professor of Public Administration, School of Public Affairs Professor-in-Charge, Doctor of Philosophy in Public Administration Professor-in-Charge, Master of Public Administration Professor-in-Charge, Juris Doctor and Master of Public Administration Professor-in-Charge, Certificate Program in Public Sector Human Resource Management Dissertation Advisor Chair of Committee
Beverly Cigler Distinguished Professor Emerita of Public Policy and Administration
Younhee Kim Associate Professor of Public Administration
Steven Peterson Professor Emeritus of Politics and Public Affairs
Elizabeth Tisdell Professor of Lifelong Learning and Adult Education, School of Behavioral Sciences and Education
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ABSTRACT
Narrative elements and strategies have long been recognized as significant inputs into policymaking, but little research has occurred to date linking the use of these tools directly to agenda setting. Using the Narrative Policy Framework and Multiple Streams Framework as theoretical foundations, this exploratory research examines through qualitative content analysis narratives policymakers employed amidst a partial shutdown of the United States government from late December 2018 through January 2019. Using a typological map of news media accuracy and bias from Ad Fontes Media, a sample of 100 articles from seven programs on four different broadcast networks were coded using a coding worksheet inspired by that used in Shanahan et al.’s study of a Cape Cod, MA, wind farm project. The worksheet tracked codes developed a priori based on narrative elements and strategies as conceptualized in the Narrative Policy Framework, as well as emergent codes identified in a grounded-theory-like manner. Interview transcripts were coded on two separate occasions and coefficients of agreement were calculated for each code. Only those codes demonstrating agreement were analyzed. The data reveal new story lines that expand the Narrative Policy Framework’s plot element and reinforce existing literature on the importance of group cohesion, suggesting shared messaging among political actors and coalitions is important for determining a narrative’s efficacy at advancing or blocking an issue from the decision agenda. The research also suggests narratives play an important role in constructing the legitimacy of problem definitions, focusing events, the political environment, and policy alternatives. The paper offers a new model for conceptualizing the role of narratives in agenda setting as theorized through the Multiple Streams Framework, including the introduction of four legitimacy checkpoints at which evaluations are made and are theorized to influence an issue’s prospects of reaching the decision agenda. iv
TABLE OF CONTENTS
LIST OF FIGURES ...... vi
LIST OF TABLES ...... viii
ACKNOWLEDGEMENTS ...... x
Chapter 1 Introduction ...... 1
Statement of the problem ...... 3 Research questions ...... 5 Significance of the study ...... 7
Chapter 2 Literature Review ...... 11
Crises and organizational change ...... 12 Framing ...... 16 Agenda setting ...... 22 Narrative policy framework ...... 28 Integrating theoretical perspectives ...... 31 Problem definitions and focusing events ...... 33 Narrative cognition and information processing ...... 35 Narratives as framing tools ...... 36 Building discourse coalitions...... 37 Modeling narratives in the Multiple Streams Framework ...... 38 Critiques of NPF and MSF ...... 40 Power ...... 45
Chapter 3 Research Design and Methods ...... 47
Philosophical assumptions and role of the researcher ...... 48 Theoretical framework ...... 51 Research design ...... 52 Jansick’s three-stage model ...... 52 Use of content analysis ...... 55 A qualitative approach ...... 57 Sample selection ...... 59 Sample selection criteria ...... 59 Sample size and breakdown ...... 65 Data collection ...... 67 Coding ...... 68 Data analysis ...... 71 Validity, reliability and trustworthiness strategies ...... 74 v
Chapter 4 Findings ...... 78
Round 1 vs. round 2 with coefficients of agreement ...... 79 Republicans vs. Democrats ...... 86 Codes by week...... 93 Interview counts ...... 93 Narrative elements and strategies ...... 95 Emergent codes ...... 106
Chapter 5 Contextual Data Analysis ...... 111
Setting the stage...... 112 Presidential controversies ...... 112 November 2018 election consequences ...... 114 The Oval Office meeting ...... 115 The narratives: elements and strategies ...... 120 Heroes ...... 120 Villains...... 123 Victims...... 130 Plot ...... 133 Morals of the story ...... 138 Narrative strategies ...... 144 Source cues ...... 164
Chapter 6 Limitations, Theoretical Contributions, and Research Recommendations ... 167
Limitations...... 168 Conceptual shortcomings ...... 169 Contextual understanding ...... 169 Potential biases ...... 170 Theoretical contributions ...... 171 New plot lines ...... 172 Message consistency...... 173 Legitimacy ...... 177 Marrying NPF with MSF: A revised model ...... 181 Future studies ...... 187 Conclusion ...... 189
References ...... 194
Appendix A: Code Book ...... 219
Appendix B: Coding Worksheet ...... 222
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LIST OF FIGURES
Figure 2-1: Multiple Streams Framework Model ...... 39
Figure 3-1: Ad Fontes Media News Outlet Typology ...... 61
Figure 4-1: Number of Interviews Coded by Week...... 94
Figure 4-2: Heroes by Week: Mean Count ...... 95
Figure 4-3: Heroes by Week: Ratio Count ...... 96
Figure 4-4: Villains by Week: Mean Count ...... 97
Figure 4-5: Villains by Week: Ratio Count ...... 97
Figure 4-6: Victims by Week: Mean Count ...... 98
Figure 4-7: Victims by Week: Ratio Count ...... 98
Figure 4-8: Plot by Week: Mean Count ...... 99
Figure 4-9: Plot by Week: Ratio Count ...... 99
Figure 4-10: Moral of the Story by Week: Mean Count ...... 101
Figure 4-11: Moral of the Story by Week: Ratio Count ...... 101
Figure 4-12: Angel-/Devil-shift and Scope of Conflict by Week: Mean Count ...... 102
Figure 4-13: Angel-/Devil-shift and Scope of Conflict by Week: Ratio Count ...... 103
Figure 4-14: Problem Definition by Week: Mean Count ...... 104
Figure 4-15: Problem Definition by Week: Ratio Count ...... 104
Figure 4-16: Numbers and Costs by Week: Mean Count ...... 105
Figure 4-17: Numbers and Costs by Week: Ratio Count ...... 106
Figure 5-1: Select Republican Problem Definition Code Ratios by Week ...... 148
Figure 5-2: Democratic Problem Definition Code Ratios by Week ...... 149 vii
Figure 5-3: Costs of Preferred Solutions by Party by Week ...... 160
Figure 5-4: Costs of Opposed Solutions by Party by Week ...... 160
Figure 5-5: Costs of Preferred Solutions Code Ratios by Party by Week ...... 161
Figure 5-6: Costs of Opposed Solutions Code Ratios by Party by Week ...... 161
Figure 6-1: Narratives in Agenda Setting Model with Legitimacy Checkpoints ...... 185
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LIST OF TABLES
Table 3-1: Count of Interviews by Branch of Government and Party Affiliation ...... 66
Table 3-2: Count of Interviews by Network and Program ...... 67
Table 4-1: Hero Code Counts and Coefficients of Agreement ...... 80
Table 4-2: Villain Code Counts and Coefficients of Agreement ...... 80
Table 4-3: Victim Code Counts and Coefficients of Agreement ...... 81
Table 4-4: Plot Code Counts and Coefficients of Agreement ...... 81
Table 4-5: Moral of the Story Code Counts and Coefficients of Agreement ...... 82
Table 4-6: Narrative Strategy, Angel- and Devil-Shifts, Scope of Conflict Code Counts and Coefficients of Agreement ...... 83
Table 4-7: Problem Definition Code Counts and Coefficients of Agreement ...... 84
Table 4-8: Causal Mechanism Code Counts and Coefficients of Agreement ...... 84
Table 4-9: Numbers, Costs and Benefits Code Counts and Coefficients of Agreement . 85
Table 4-10: Stance Code Counts and Coefficients of Agreement ...... 85
Table 4-11: Legitimacy Code Counts and Coefficients of Agreement ...... 85
Table 4-12: Hero Code Counts and Percentages by Party Affiliation ...... 87
Table 4-13: Villain Code Counts and Percentages by Party Affiliation ...... 87
Table 4-14: Victim Code Counts and Percentages by Party Affiliation ...... 88
Table 4-15: Plot Code Counts and Percentages by Party Affiliation...... 88
Table 4-16: Moral of the Story Code Counts and Percentages by Party Affiliation ...... 89
Table 4-17: Narrative Strategy, Angel- and Devil-shift, Scope of Conflict Code Counts and Percentages by Party Affiliation ...... 90 ix
Table 4-18: Problem Definition Code Counts and Percentages by Party Affiliation ...... 91
Table 4-19: Causal Mechanism Code Counts and Percentages by Party Affiliation ...... 91
Table 4-20: Numbers, Cost and Benefit Code Counts and Percentages by Party
Affiliation ...... 92
Table 4-21: Stance Code Counts and Percentages by Party Affiliation ...... 92
Table 4-22: Legitimacy Code Counts and Percentages by Party Affiliation ...... 93
Table 5-1: Emergent Legitimacy Code Counts and Coefficients of Agreement ...... 163
Table 5-2: Consolidated Legitimacy Code Counts and Percentages by Party Affiliation
...... 163
x
ACKNOWLEDGEMENTS
To the members of my committee, thank you for your guidance and your contributions throughout this process. Whether around the table discussing this dissertation or in the classroom, you each have challenged me and spurred my intellectual curiosity. In doing so, you have made my journey far more rewarding.
To my family, this pursuit would not have been possible without your encouragement and your sacrifices.
Staci, you are a kind, patient and endlessly supportive soul. I am lucky to be your husband, and I am grateful every year you say “Yes” to our 60-day renewal notice.
Carter and Braden, thank you for your understanding when I missed out on games, practices, playing in the pool, or trips away from home. We have a lot of fun times to make up.
Finally, to my parents: thank you for the opportunities you afforded me.
Mom, you have taught me strength and to remember what is most important in life.
Dad, thank you for teaching me never to rest on my laurels and to turn adversity into something positive. I miss you.
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Chapter 1
Introduction
In any democratic society, there are countless conditions vying for attention among policymakers. Some problems represent legitimate crises that merit action, while others are “just another day at the office” (Hilgartner & Bosk, 1988, p. 57). An issue must be framed in a strategically conducive manner in order to proceed from the broad systemic agenda of persistent conditions to the institutional agenda of problems considered for active debate (Birkland, 1998; Cobb & Elder, 1971) to the decision agenda where, in the legislative context, an issue is brought forth for a vote (Kingdon, 2003).
Narratives play an important role in framing issues, converting complex ideas into more readily comprehensible forms of information because stories resonate more effectively than the nuances and oftentimes complex minutiae of public policy. Despite their significance, many theories of public policymaking give little attention to narratives as a strategic input into the process (Shanahan et al., 2013).
The qualitative research presented here investigates how government actors use narratives in the public policymaking process. Specifically, this research examines through qualitative content analysis the narratives policymakers used during a partial shutdown of the United States federal government from late December 2018 through
January 2019. Congressional opposition to President Donald Trump’s request for more than $5 billion to build a physical barrier along the U.S.-Mexico border catalyzed the shutdown. Opponents of the president’s plan instead proposed appropriating fewer dollars for non-structural security measures, including technological improvements and additional personnel to patrol the border. 2
The debate situated the issue of a border wall within the institutional agenda throughout the course of the impasse. Ultimately, Trump’s proposal did not reach the decision agenda as it was not put to a vote of Congress. Rather, a conference committee of lawmakers reached agreement on a smaller funding package that omitted appropriations for a wall, per se, but did include approximately $1.4 billion for fencing.
While the president signed the legislation, he exercised executive authority the following day by declaring a national emergency, allowing him to re-allocate other previously appropriated dollars to construct a barrier along portions of the border (Macias & Schoen,
2019).
Legal challenges to Trump’s actions ensued, but the matter has been all but settled as of this writing. The U.S. Supreme Court ruled construction may proceed while cases progress through lower courts. In Trump vs. Sierra Club, a U.S. District Court judge issued an injunction in May 2019 against the Trump administration’s reallocation of monies otherwise appropriated for national defense. The U.S. Court of Appeals for the
Ninth Circuit refused to stay the order on appeal. The U.S. Supreme Court considered the case and overruled the appellate court’s decision, thereby allowing construction to begin
(Liptak, 2019). In late July 2020, the Supreme Court reinforced their position when justices refused to stay their earlier order following an appeal by the Sierra Club and others (Savage, 2020).
Throughout the course of the debate from late-December 2018 through mid-
February 2019, Congressional Democrats accused Trump of manufacturing a crisis, while
Trump accused his opponents of being unwilling to acknowledge the humanitarian crisis taking place along the border and being antagonists to the idea of erecting a wall many 3 supported prior to him taking office (Farrington, 2019). Both factions had resources at their disposal, which they utilized either to promote the border wall funding or to prevent the issue from being considered on the decision agenda, keeping it relegated to the institutional agenda.
To clarify the use of these terms, Cobb and Elder (1971) define “institutional agenda” as those issues that are granted attention by a decision-making body. Issues reach the institutional agenda from the systemic agenda, which consists of all possible problems governmental bodies may address. Of those issues that reach the institutional agenda, only a smaller subset will proceed to the decision agenda, which consists of issues brought forward for action, such as a legislative vote.
Statement of the problem
Narrative elements and strategies have long been recognized as significant inputs into policymaking, but little research has occurred to date linking the use of these tools directly to agenda setting. Of the literature reviewed for this essay, only two scholarly works explicitly draw connections between the agenda-setting stage of policymaking and narratives as conceptualized through the Narrative Policy Framework: Peterson and
Jones’ 2016 contribution to Zahariadis’ edited volume, Handbook of Public Policy
Agenda Setting, and a 2018 article by McBeth and Lybecker in Policy Studies Journal.
The relative dearth of research on narratives’ role in agenda setting is striking because scholars acknowledge that narratives help make sense of the world around us. As
Peterson and Jones (2016) write, “Much of the agenda setting literature argues that the agenda is significantly impacted by how people make sense of, define, process, and pay attention to policy problems” (p. 107), and narratives “serve as cognitive and 4 communicative shorthand that help people make sense of complex environments” (p.
106). Reinforcing this point, Shanahan et al. (2013) write that “none of the dominant theories and frameworks used to study public policy processes effectively account for this potentially critical meaning-making process” (p. 455).
While scholars have devoted little attention to narratives’ influence on agenda setting, the use of stories and linguistic elements understood to be part of narratives have long been at the heart of research in the policy sciences. The components of narratives as conceptualized in the Narrative Policy Framework mirror aspects of other policymaking theories, such as policy entrepreneurs using narratives to manipulate public opinion
(Multiple Streams Theory); narratives’ representation of policy beliefs (Advocacy
Coalition Framework); and the use of narratives to expand scope of conflict as explained by Schattschneider (McBeth & Lybecker, 2018). Deborah Stone (1989, 2002) has looked at the use of metaphors, symbols, numbers and other elements to craft stories in public policy. Schneider and Ingram (1993) use many of these same elements—symbolic language, metaphors and stories—to explain how groups are socially constructed and deemed worthy of public policy’s benefits and burdens. Symbols also can be used to represent problems already in the public’s mind, but not yet on government’s radar, according to Kingdon (2003), who writes, “Symbols catch on and have important focusing effects because they capture in a nutshell some sort of reality that people already sense in a vaguer, more diffuse way” (pp. 97-98). More recently, narratives as defined in the Narrative Policy Framework have emerged as an analytical tool to study change in different policy domains, including campaign finance reform (Jorgensen et al., 2017), renewable energy development (Shanahan et al., 2013), air quality issues and climatic 5 impacts in India (Weible et al., 2016), traffic congestion in London (Dudley, 2013), and gun control (Merry, 2016), among others.
Despite this extensive treatment, the specific use of narratives to transport an issue to policymaker’s decision agenda remains little studied. Recognizing this gap, Gray and Jones (2016) argue more research is needed on the strategic use of narratives in agenda setting, and they assert that qualitative methods are appropriate for such inquiries.
Research questions
This qualitative research undertaking is guided by a series of research questions.
Research questions represent the initial stage in constructing a qualitative research design. Research questions guide methodological decisions in any study (Jansick, 1998).
This study seeks to answer the following central research questions, along with the respective sub-questions:
1. How are narratives used in the public policymaking process to characterize
the worthiness of an issue for public debate as part of the decision agenda?
a. What narrative elements are employed by actors seeking to promote or
prevent an issue’s access to the decision agenda?
b. What narrative strategies do actors use to promote and prevent an
issue’s access to the decision agenda?
c. How do actors frame, or socially construct, a policy problem and
proposed alternatives as solutions through narratives? What narrative
strategies are employed to this end? 6
2. In cases such as the federal government shutdown studied here, where the
crisis is deemed to have been created by governmental action or inaction, how
are narratives used to legitimize or delegitimize that crisis?
Definitional and conceptual inconsistencies have limited the public policy field’s understanding of narratives’ use in policymaking (Weible et al., 2016). This study relies on the definition of policy narratives by Shanahan et al. (2011): “a policy narrative has a setting, a plot, characters (hero, villain, and victim), and is disseminated toward a preferred policy outcome (the moral of the story)” (p. 539, emphasis in original). Weible et al. (2016) write that narratives must offer readers a policy setting or a context in which basic facts are presented and a setting in which a policy debate takes place. Additionally, a policy narrative must include a plot that links characters to the setting of the policy debate; gives each character a role in the story; and assigns blame or responsibility for causing a public problem.
The constituent parts of a narrative under this definition are termed “narrative elements” in the literature. Specifically, elements include characters, story types or plot lines, and morals of the story. Additionally, “narrative strategies” are defined as tactics storytellers employ in policy debates to gain an advantage or undermine a problem definition or proposed solution (Shanahan et al., 2013). Examples include causal mechanisms, assigning blame for a problem, using numbers to reinforce or refute arguments, and articulating the allocation of burdens and benefits. Researchers assessing narrative strategies often rely on narrative elements to structure and categorize their data
(Maxwell & Chmiel, 2014). 7
The terms and conceptualizations used throughout this paper emanate from the literature on narratives and public policymaking. While the conceptual foundation for the definition of narratives is grounded in the work of Shanahan, Jones, McBeth and others who have contributed toward developing the Narrative Policy Framework, the work of other scholars in the public policy field is influential and informative here. Chapter 2 presents a literature review of work performed in this area, including the significant body of research related to agenda setting in public policy.
Significance of the study
Research into agenda setting offers considerable value because doing so helps us to understand the contextual, institutional, temporal and cultural factors of political and social environments at a moment in time. Studying agenda setting is important for the policy sciences because it helps identify social values; identifies the differences between governments and the governed; categorizes the political power of target populations and interest groups; creates a road map of how policymakers arrive at a final decision; and assigns meaning and significance to events (Zahariadis, 2016).
Scholars recognize narratives are valuable to the study of public policy for a number of reasons. Narratives make sense of complex environments. With much of public policy being rooted in values and belief systems, narratives provide a glimpse into the meaning individuals ascribe to issues in the short term and, as a story becomes more pervasive, demonstrate how those views change among institutions over the long term
(Peterson & Jones, 2016).
Developing a better understanding of narratives in agenda setting is important for the policy sciences field because narratives play a critical role in establishing the meaning 8 behind issues, particularly with respect to the social construction of an issue (Shanahan et al., 2013). Narratives also play crucial roles in defining problems (Oxley et al., 2014;
Peterson & Jones, 2016), establishing policy images (Smith & Larimer, 2017; True et al.,
2007), facilitating or restricting issue expansion (Baumgartner & Jones, 2009; Shanahan et al., 2011), and shaping public opinion (Ertas, 2015).
Assessing narratives’ influence upon these factors contributes toward a better understanding of how policy problems progress from the broader systemic pool of all public conditions to the small subset of problems considered among the decision agenda.
Trump’s preferred policy solution—building a wall—was on the institutional agenda, but the shutdown was evidence Congressional Democrats, primarily, who controlled the
House, were not willing to move it as proposed to the decision agenda for a vote.
The qualitative study performed here inductively assesses data gathered through a content analysis of interview transcripts by policymakers—specifically, members of
Congress, the president, and representatives of the Trump administration—to explore and better understand the phenomenon of the federal government shutdown. Specifically, this study looks at how actors define and frame a problem, promote or protest proposed solutions, and structure narrative strategies to those ends. Readers may find the results of this exploratory study beneficial in guiding future research, such as building empirical hypotheses to test the significance of narratives at the micro, meso and, over time, macro levels.
This study builds upon the body of theory regarding governmental policymaking, particularly under crisis circumstances. Specifically, this study contributes a better 9 understanding of how policy actors use narratives to legitimize or delegitimize such crises, and how groups share policy beliefs and related stories.
A number of existing theories and frameworks in the field acknowledge the role of sudden shocks to highly static systems and how those shocks alter public policy.
Punctuated Equilibrium Theory emphasizes the role of exogenous events that help to erode policy monopolies and prompt change; Multiple Streams Theory recognizes crises—either man-made or natural—as focusing events that create windows of opportunity for policy change; and Thomas Kuhn gave us the notion of Kuhnian shifts in policy paradigms.
I further consider theories beyond policymaking to understand why governments, particularly in the context of American democratic governance systems, are fertile ground for relying on crises to facilitate major policy change. Herbert Simon’s (1997) research demonstrates how resource constraints and human cognitive limitations create bottlenecks of thought, prevent a thorough examination of policy alternatives, and limit the number of issues considered at any one time, leaving others for another day.
Lindblom (1959) discusses how decisionmakers—amid unspecified objectives and competing public preferences—muddle through by enacting only incremental changes, and Pierson (2000) describes how path dependence and the law of increasing returns make it difficult for decisionmakers to deviate from an existing course of action, creating a sense of inertia that reinforces existing equilibria and resists change. Finally, Bai and
Lagunoff (2011) look at the implications of Faustian trade-offs in political policymaking decisions, arguing dominant political rulers who enact preferred policies do so at the expense of future political power. Instead, by delaying the pursuit of their policy goals or 10 tempering their policy choices to more modest objectives, they extend their time in power, thereby intentionally achieving only minor adjustments to the status quo.
American history is replete with examples of policy actors leveraging crises to enact policy change. Examples include the United States’ 2003 invasion of Iraq on the since debunked claim the latter nation possessed weapons of mass destruction; the 2013 partial federal government shutdown as the Obama administration and Congressional
Republicans clashed over the debt ceiling and budget cuts; and the USS Maine’s sinking in February 1898, which contributed toward the Spanish-American War. Amid persistently fractured and partisan governments today, arriving at any significant policy achievements seems to be increasingly difficult, forcing officials who wish to advance their agendas to resort to more extreme tactics, such as threatening government shutdowns. This study offers a preliminary examination of how government leaders react to such conditions through public narratives in the course of advancing or hindering certain policy alternatives. Its findings are instructive for understanding the political landscape of policy decision making in the face of crises.
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Chapter 2
Literature Review
While there has been a lack of exploratory and empirical research on the role of narratives in public policy agenda setting, an extensive body of work exists assessing these two areas independently. This chapter begins with a literature review from the field of organizational theory focused on change and crises. The discussion focuses on the influence of time pressures and risk, as well as the degree to which geographic and policy proximity affect the response of actors within subsystems to a crisis.
Next, I present literature related to framing. This section relies heavily on the field of communications theory. The ways in which issues, problems and solutions are depicted confront the pre-conceived beliefs of audience members. The congruence of frames and existing mental schemas influence support for or opposition to an issue. The literature reviewed here on framing contributes to subsequent sections related to narratives in public policymaking.
From there, the chapter proceeds to a review of public policy literature—in particular agenda setting and the Multiple Streams Framework—although I acknowledge other influential and well-regarded theoretical contributions to the field, such as the
Advocacy Coalition Framework and Punctuated Equilibrium Theory. Next, the paper addresses the conceptualization and use of narratives in public policymaking as theorized in the Narrative Policy Framework (NPF).
With these conceptual foundations in place, the paper then addresses ways in which these areas of scholarship intersect and will be of value in this research. Scholars contend that in order for NPF to continue its development and gain broader acceptance in 12 the field, it must evolve by reaching in other theoretical directions and demonstrating its applicability in democratic governance systems. I offer a preliminary model to contemplate NPF’s inter-theoretical potential.
Next, the chapter presents critiques of the Narrative Policy Framework and
Multiple Streams Framework. The paper emphasizes these two frameworks because both serve jointly as the theoretical foundation on which this study is built. This section, in particular, draws attention to the scholarly debate over the theoretical perspectives and research traditions that underlie the NPF. Critics question the framework’s attempt to meld quantitative and qualitative methods because of fundamental differences between the ontological and epistemological paradigms upon which these approaches are built.
Finally, Chapter 2 briefly reviews literature on power. While the concept is pervasive in public administration, public management, governance and public policy, it is little studied and insufficiently conceptualized. That said, power’s role as a tool of influence and control merits attention here.
The aforementioned areas of scholarship—organizational theory in the context of crisis situations, policymaking and agenda setting, framing, narratives and power—are relevant to this research on the federal government shutdown. The 35-day shutdown was deemed a crisis given its disruption of American institutions and systems of governance.
Moreover, it exposed partisan and policy differences between Democrats and
Republicans, and the United States’ legislative and executive branches of government.
Crises and organizational change
Change in organizations can be viewed as a slow process (Lindblom, 1959), but may occur quickly under crisis conditions. Crises emerge and threaten organizations by 13 posing a disruptive force that undermines confidence in and the legitimacy of organizations. In public institutions, when a crisis occurs, public officials either respond or attempt to distance themselves from any responsibility for the situation. The way in which leaders respond to a crisis is often the result of the event’s construction.
Sometimes responses are only symbolic due to information processing limitations and the availability of resources (Nohrstedt & Weible, 2010).
Rosenthal et al. (1991) define crises as “serious threats to basic social, institutional and organizational interests and structures. Moreover, fundamental values and norms can also be threatened” (p. 212). Boin and ‘t Hart (2003) stress that researchers must disabuse themselves of the notion that crises are clearly defined in time and space: “Instead, we need to treat crises as extended periods of high threat, high uncertainty, and high politics that disrupt a wide range of social, political, and organizational processes” (p. 545).
Some disruptions create a high degree of uncertainty and a sense of urgency, however the sense of the situation is subjective in that it is a matter of perceived urgency, which differs among involved actors. Likewise, the need for corrective action in response is a matter of perception. Generally, the longer the duration of the crisis, the stronger the perceived need for radical change that alters established norms and policy equilibria
(Rosenthal, Boin, & Comfort, 2001).
Rosenthal and Kouzmin (1997) observe that it is difficult to develop a typology of crises based on the practical implications of such events. They offer a dichotomous typology based on distinctions among two variables: the actual threat and the perceived solutions. With respect to the first variable, crises differ according to the object of the 14 threat (e.g., societal norms and values, organizations, political systems, etc.), the geographic location of the threat, and whether the threat originates from within or beyond the affected system.
The degree to which a crisis is rooted in exogenous events or willfully manufactured affects the perceived need for strong leadership and paradigm-shifting responses (‘t Hart et al., 1993). Nohrstedt and Weible (2010) assert research to date has not ascertained the degree to which internal or external shocks influence policy change.
Part of the problem, they argue, is an insufficient conceptualization of what constitutes an internal versus external shock. Rosenthal, Boin and Comfort (2001) observe that the
“distinction between ‘man-made disasters’ and acts of God has become obsolete” (p. 6).
Looking at Rosenthal and Kouzmin’s (1997) second variable—perceived solutions—actors and organizations face questions of whether and how to respond to a crisis. Decisionmakers face competing values and the trade-offs of action or inaction.
Institutional participants may fear one course of action over another given the risk of unsettling extant power dynamics. Information overload and pressures that accompany crises may promote inaction over action. Such overwhelming circumstances may paralyze policymakers, allowing events to transpire without intervention. In other cases, policymakers may practice strategic evasion, or the art of passing the buck in colloquial terms. When leaders or organizations feel incapable of correcting a crisis or fear there is little chance for success, decision makers distance themselves from any responsibility for the situation and instead assign the task for managing the crises to others (‘t Hart,
Rosenthal, & Kouzmin, 1993). 15
In the public policy context, a decisionmaker’s orientation with respect to the crisis’ location affects their response, as does the collective response of those within a policy subsystem. The new-institutional belief assumes actors within a system cannot facilitate change because existing policies are rooted firmly in the status quo, and those actors are embedded within existing equilibrium.
Viewing policy communities as organizations, Rozbicka and Spohr (2016) argue tightly knit communities acting as advocacy coalitions tend to exhibit great stability, but act when faced with an external shock to their entrenched system. These policy communities seek to minimize the perceived consequences of the crisis if potential responses deviate too greatly from established belief systems, or they may select certain ambiguous elements of the situation to reinforce existing beliefs.
Policy proximity and geographic proximity are hypothesized to characterize a threat’s policy subsystem impact. Crises that are close to or within the subsystem
(geographic proximity) will have greater impacts on subsystems that share a direct relation to certain policy design components (policy proximity). For example, Hurricane
Katrina had a greater impact on Louisiana’s emergency management subsystem than it did on climate change (Nohrstedt & Weible, 2010).
Crises create pressures and uncertainty for organizations. In the public sector, constituents typically demand swift action and scrutinize decisions, but governments— like all organizations—face limitations on their ability to process information thoroughly and rationally (Rosenthal, ‘t Hart, & Kouzmin, 1991). The influence of time pressure and risk are prominent themes in decision-making literature. For governmental institutions, 16 the pressure for timely decisions in urgent situations runs counter to norms of deliberate decision-making in structured, bureaucratic processes.
Time pressures and risk prompt decisionmakers to resort to availability heuristics and make trade-offs when evaluating risks and the potential for gains and losses (Tversky
& Kahneman, 1974). Prospect theory is informative when seeking to understand such situations (Kahneman & Tversky, 1984; Tversky & Kahneman, 1992). Research shows high pressure situations lead to risk avoidance strategies, although other studies find the relationship is not so clear. For instance, in cases of greater risk, the relationship between time pressure and risk preference is positively correlated so long as the expected value of the intended outcome is positive, but greater risk aversion exists when the expected values are negative (Young et al., 2012).
Framing
How threats are perceived is a byproduct of political framing by policy entrepreneurs, special interests, elite groups and media organizations. Threats like social problems are socially constructed (Meyer, 2009), thus the symbolic representation of political events warrants further examination of framing and its theoretical underpinnings. The idea of explaining issues in a manner that resonates with an audience has implications for social institutions, from the news media and public opinion to governmental agendas and democracy.
Framing connects two issues with a common thread understood to be compatible between issues. Entman (2007) calls framing “the process of culling a few elements of perceived reality and assembling a narrative that highlights connections among them to promote a particular interpretation” (p. 164). Claes deVreese (2012) provides two helpful 17 definitions citing, first, Gamson and Modigliani (1989), who write that a frame is a
“’central organizing idea or story line that provides meaning to an unfolding strip of events, weaving a connection among them. The frame suggests what the controversy is about, the essence of the issue’ (p. 143);” and, second, Entman (1993) who writes that in order to frame, one must “select some aspects of a perceived reality and make them more salient in a communicating context’” (p. 366).
Framing is often complicated by and confused with other functions of communications, particularly priming and agenda setting. The latter is defined in the communications field as a correlation between media coverage of an issue and the public’s perceived importance of that issue in relation to all others. Separately, priming explains how people are conditioned to make political evaluations based on their prevailing standards. “Priming occurs when news content suggests to audiences that they ought to use specific issues as benchmarks for evaluating the performance of leaders and governments. It is often understood as an extension of agenda setting” (Scheufele &
Tewksbury, 2007, p. 11).
Both priming and agenda setting are memory-based, or accessibility, effects of information processing, assuming people form attitudes and beliefs based on the most readily available information. Framing demands a higher level of cognition, requiring those who frame and those who receive the frame to make moral evaluations, causal connections, and recommendations for corrective actions (Scheufele & Tewksbury, 2007;
Weaver, 2007). The efficacy of communication frames in altering or reinforcing the frames and beliefs of an audience is referred to as a framing effect. 18
Chong and Druckman (2007) offer three processes to determine a frame’s effectiveness. First, a frame must be available for retrieval by an individual. If an individual fails to understand a frame’s underlying concept, the communicator’s intended connection will be lost on the receiver. Second, if the concept is available to an individual, it also must be readily accessible; it must be top of mind so the receiver can quickly organize and make the frame’s intended connection. Accessibility is a product of repetition and recency. The more often and/or more recently an individual is exposed to a concept, the faster he or she will be able to recall it. Third, the concept must be applicable to the situation at hand. This requires a measure of conscious evaluation on the part of the receiver, which depends on motivation and the degree to which an idea challenges preconceived beliefs or opinions. A motivated individual will commit the time and mental effort necessary to evaluate a suggested frame, and that motivation often stems from conflict between the presented information and their existing views. When this occurs, they seek to reconcile conflicting information and judge whether their views are reinforced or require adjustment (Chong & Druckman, 2007, 2007a).
Fully developed frames serve four functions: problem definition, causal analysis, moral judgment, and remedy promotion (Entman, 2007). Not all frames perform the latter two roles and different types of frames exist. For example, advocacy frames champion policy solutions. Journalistic frames in news coverage reinforce, neglect or contrast advocacy frames. There also exists a distinction between issue-specific and generic frames. The former is relevant only to specific issues, while the latter spans topics, time horizons, and cultural differences (deVreese, 2012). 19
The superiority of one frame over another is not a reflection of accuracy or superior morality. Effective frames may be premised on fears, prejudices, exaggerations and outright lies. Strong and effective frames rely on symbols, endorsements, preexisting beliefs, and heuristics to lessen the cognitive information processing demands on their audience (Tversky & Kahneman, 1981). Moderating variables such as culture and values, knowledge, credibility of the frame’s source, and competition influence the framing effect’s direction and magnitude (Chong & Druckman, 2007a).
Lecheler and deVreese (2012) add belief content and belief importance to the list of moderating variables. Belief importance “refers to framing as ‘altering the weight of particular considerations’ in the individual’s mind” while belief content “refers to the addition of new beliefs to an individual’s set and alludes to one of the most established mechanisms in media effects research—the persuasive effect” (p. 187, emphasis in original). Lecheler and deVreese found that belief importance is a significant influence upon how people think about new information. Likewise, belief content has a measurable persuasive influence, but more so with more knowledgeable individuals.
The media is an effective conduit through which actors seek to propagate their preferred issue frame. Skilled entrepreneurs use the media to construct a spectacle that rallies supporters to their cause, promotes certain leaders, identifies enemies, and builds opposition against undesirable solutions. Media adopt these frames and incorporate prevailing constructions into news coverage in order to organize events and make situations coherent to audiences. This is a deliberate choice, just as is the decision to ignore other frames and constructions. These choices reflect the institutionalized 20 practices of media organizations, as well as the political, economic, social and cultural systems in which the organizations operate (Soesilo & Wasburn, 1994).
Framing, agenda setting and priming influence media coverage, and media influence the public’s access to information by featuring certain issues more prominently or more often, which in turn affects judgments and choices. Cohen (1963) contends that
“the media may not be successful much of the time in telling people what to think, but is stunningly successful in telling its readers what to think about” (p. 13). Entman (2007) questions this logic, saying that if the media is effective in influencing what people think about, they must surely be effective in influencing what people think, and as such, they possess considerable power in shaping thought on political matters.
Recall that journalists present reports using journalistic frames. These frames reinforce, neglect or contrast procedural frames that reflect political strategies, and journalists tend to present matters in a way that emphasize conflict, which is an inherent byproduct of the journalistic profession’s obligation to report every side of an issue
(deVreese, 2012).
Further, research finds that journalists report issues using the frames presented to them. Seldom do they question the veracity of claims made in those frames and seldom do they suggest or report alternate frames. Competing frames are not always given equal treatment in news coverage (Matthes, 2012). Such biased reporting is referred to as slant, and when news outlets regularly slant their coverage, content biases emerge. Pervasive content bias is akin to Schattschneider’s notion of mobilization bias in which actors prevent competing ideas from emerging over preferred outcomes (Entman, 2007). 21
The pervasiveness of media choices and their influence on the public is a major issue in the legitimacy and efficacy of democratic government institutions. The elite seek to preserve the status quo, while disadvantaged groups seek to re-orient power dynamics and/or reallocate resources in their favor. A skilled tactician or policy entrepreneur can marshal the leadership needed to link crisis events to significant policy change (Nohrstedt
& Weible, 2010)—or frame the issue in a way that resonates with the public and those in power, compelling action. Chong and Druckman (2007a) caution, “If opinions can be arbitrarily manipulated by how issues are framed, there can be no legitimate representation of public interests” (p. 104).
Just as policy advocates operating outside of governmental bodies seek to influence public officials, politicians, appointed officials and bureaucrats seek to build support for preferred policies, and thus structure their communications to encourage favorable reactions. Schneider and Ingram (2019) have more recently expanded their social construction theory to account for anticipatory feedback in policymaking. The theory assumes elected officials design policy solutions in a manner that anticipates feedback from their core constituencies. Elected leaders rely on “their base” of voters for reelection, thus maintaining the support of this bloc of voters is important to officials’ political longevity. The social construction theory of anticipatory feedback takes into account the messages embedded in policy designs, rationales, and tactics that will resonate with targeted groups.
Evidence of this effect can be found in research that shows public opinion survey results produce drastically different responses when the context of a question—or how the problem is framed—is altered. A frequent example is public attitudes toward 22
“welfare” versus “assistance for the poor.” A study by Igartua et al., (2011, as cited in
Kühne et al., 2015) found vastly different views on immigration when the issue was framed as either an economic development opportunity or as a contributor to crime.
Ultimately, such examples demonstrate the challenge in understanding public policy preferences in an environment subject to variation depending on the applied frame and the presupposed feedback of constituents and interest groups.
Agenda setting
Much of the research into public policy agenda setting emanates from a number of fields, including, but not limited to, power and persuasion, human cognition, organizational and systems theories, as well as rational, social and political choice theories (Baumgartner & Jones, 2009; Cairney & Zahariadis, 2016). Agenda-setting research contributes to the field’s understanding of contextual, institutional, temporal and cultural factors of political and social environments at a moment in time. Studying agenda setting is important for the policy sciences because it helps identify social values; identifies the differences between governments and the governed; categorizes the political power of target populations and interest groups; creates a road map of how policymakers arrived at a final decision; and assigns meaning and significance to events
(Zahariadis, 2016).
One of the preeminent theories of agenda setting is John Kingdon’s (2003)
Multiple Streams Framework, or MSF. The framework views agenda setting as a process of stages. An issue must first make its way from the systemic agenda onto policymakers’ institutional agenda before any change in public policy can take place. Achieving this task is no small feat. The systemic agenda consists of countless issues vying for 23 government attention. Policy-making bodies will consider only a select few of these issues as part of the institutional agenda, and even fewer will make it onto the decision- making agenda for final vote or enactment (Kingdon, 2003; Rochefort & Cobb, 1993).
MSF posits that issues reach the decision agenda when three separate streams converge: the problem stream, the policy stream, and the politics stream. The problem stream consists of issues in need of attention, problem indicators, focusing events, and feedback from within and outside of government. The policy stream is composed of specialists and advocacy groups who operate in a policy community and seek to advance policy solutions that swirl around in a “primeval soup.” These competing ideas promote fragmentation in divided policy communities, although some communities are tightly knit with little internal dissent. Policy entrepreneurs act within the policy stream to advance preferred solutions, investing resources in pursuit of a positive return.
The politics stream is defined as the institutional and cultural context in which policy debates take place. It is conceptualized as reflecting the national mood, which represents “the fertile ground” (Kingdon, 2003, p. 147) from which policies grow. It is also populated by organized political interests jockeying for position on the agenda, as well as government agencies and personnel that likewise jockey for position to defend or expand their turf amidst overlapping and competing jurisdictional boundaries.
Each stream is assumed to operate independently, but when certain events or crises occur—regardless of the reason—those streams converge. The convergence is often prompted by signals that cross “a threshold, at which time [problems] cannot be ignored” (Jones & Baumgartner, 2005, p. 8). These signals (i.e., focusing events) play an important role in agenda setting, not only because they often present the catalyst for 24 policy change, but because they also play an instrumental role in framing and defining a problem and influencing who engages in the policy debate (Zahariadis, 2016).
Focusing events are not necessarily objectively defined shocks to the system; they are often strategically constructed issues portrayed as demanding attention. McBeth et al.
(2013) observe that “policy entrepreneurs must ‘socially construct’ such events and convince the public that the event they have created demonstrates the need for the specific solution” (p. 147). This process begins by establishing a problem’s underlying nature, which requires evaluation of and tradeoffs between ethical, political and economic considerations (Rochefort & Cobb, 1993).
How a problem is defined and constructed are important parts of policy change, influencing not only the alternatives considered, but also who engages in the debate, and who has an interest in its resolution (Herweg et al., 2018). Focusing events open policy windows, thereby creating opportunities for policy entrepreneurs to apply their preferred policy as a viable solution. Policy advocates who fail to capitalize on a policy window miss a golden opportunity as Dudley (2013) found in his study of the London congestion charge. A window that opened in 2003 to enact a traffic congestion charge on one roadway had closed by the time then-Mayor Ken Livingstone tried to replicate the program for another roadway in 2007.
It is incumbent on policy entrepreneurs to seize the moment created by a policy window. To do so successfully depends on three critical factors: the availability of resources, such as time and money; access to critical decision makers; and the right mix of effective strategies (Jones et al., 2016). 25
By defining public problems, focusing events also serve to mobilize others.
Groups that anticipate being affected by a problem will be compelled to engage on the matter and, as needed, invite others to join their cause to build support (Birkland, 1998).
Advocates partner with like-minded groups to form advocacy coalitions, and these coalitions endeavor to expand their membership to magnify their voice and influence
(Sabatier, 1988; Baumgartner & Jones, 2009; Jenkins-Smith et al., 2018).
Establishing a successful advocacy bloc depends on the nature of the issue, the congruence of views and interests among separate independent groups, and the group’s willingness to come together in pursuit of commonly shared goals (Peterson, 1995).
Typically, well-structured and endowed coalitions marshal their resources to capitalize on focusing events more effectively than less well off or structured groups (Cairney &
Zahariadis, 2016), and they are able to create policy images that more effectively inform how an issue is understood (Zahariadis, 2015).
The often-unexpected nature of a focusing event can be an opportunity for less advantaged groups in some instances, however. Well-resourced, politically elite coalitions may not have time to mobilize in defense of their interests or frame the issue in their preferred way. Engaged and attentive groups with fewer resources that lack ready access to decisionmakers can leverage these events to draw attention to their issues and preferred solutions (Birkland, 1998). This competition raises the issue of power and influence in agenda setting, which will be discussed later in this chapter.
While not an agenda-setting theory per se, Punctuated Equilibrium Theory acknowledges these dynamics. The theory contends that American political systems exhibit a preference for the status quo, making it a challenge for new ideas to emerge or 26 break through existing policy monopolies. When prevailing policy images are challenged, the monopoly deteriorates, thereby creating an opportunity for an agenda conducive to policy change. The policy image of an issue is an important consideration in problem definition, the degree to which groups are mobilized to action, and the capacity of engaged institutions to process information (Baumgartner et al., 2018).
McBeth et al. (2013) write that for focusing events to be effective, a human face must portray a victim or fallen hero. Birkland and Warnement (2013) observe that the symbolism of focusing events is not enough to compel action many times; it must be considered in tandem with the actual event and its construction must conform with the public’s pre-conceived notions of worthiness.
Information about problems and potential solutions can be overwhelming, though, requiring choices that are seldom informed by perfect information due to acquisition and analysis costs, as well as cognitive limitations. In this way, agenda setting does not always follow a rational model. Policymakers face ambiguity, make boundedly rational decisions, satisfice, or rely on heuristics (Cairney & Zahariadis, 2016; Simon, 1997), evaluating a smaller set of information and alternatives against their established values and prior experiences. Lindblom (1959) writes that to attempt to evaluate complete information is futile:
It assumes intellectual capacities and sources of information that men
simply do not possess, and it is even more absurd as an approach to policy
when the time and money that can be allocated to a policy problem is
limited. (p. 80) 27
Decision-making capacity is further challenged when the volume of issues, or the
“load” on the system, is high (Jones et al., 2016). Congested agendas consume available resources and may lead decisionmakers to ignore or miss problem signals, leading some problems to go unaddressed, which prevent access to the decision agenda. (Jones &
Baumgartner, 2005; Zahariadis, 2016). This competition for attention compels issue advocates and policy entrepreneurs to find opportunities that raise the profile of their issue. Actors in the policy process manipulate issues, problems and signals, artificially boosting the prospects of consideration by policymakers.
Dahl (1961) notes for political actors, the choice of what issues to address is often intended to expand an official’s base of support, particularly in an electoral context.
Issues reach the decision-making agenda only if policymakers believe they can have an impact acceptable to the public. For a solution to be acceptable, it must be politically viable. Viability follows a process of “softening up” wherein policy alternatives are evaluated and modified to make them more palatable to a larger number of stakeholders.
This process filters issues untenable to elected officials and the public (Herweg et al.,
2018).
The choice of what alternatives are suitable for consideration is informed by societal values that structure political environments. Jones and Baumgartner (2005) write that people tend to over- or under-react to certain problems based on the context of the moment and their interpretations of the situation. Further, they argue, it is difficult to draw direct correlations between indicators and responses because problems do not occur in a vacuum. Pre-existing belief systems, political ideologies, and predilections for the status quo further complicate problem identification and definition. 28
The preceding discussion outlined factors that influence an issue’s access to the agenda and how policymakers respond. To distill these factors down further, Zahariadias
(2015) offers four fundamental elements of agenda setting, which he terms “the four P’s”
(p. 7). First is power, which he identifies as the most important factor. “Actionable government priorities reflect the power of some groups or individuals over others in making their voices heard” (p. 7). The second is perception, which affects what issues warrant attention and justifies why. The third is potency, defined as an issue’s intensity or the perceived severity of consequences that could ensue absent corrective action. Fourth, access to the agenda is influenced by the proximity of a problem to people’s lives. If people perceive an issue as being likely to affect them, they are more likely to take an interest in it, thereby raising the demand for action, which in turn, prompts greater interest on the part of policymakers.
Narrative policy framework
Narratives in public policymaking serve to convert complex ideas into more readily understood information because stories resonate more effectively than the complexities of public policy. Scholars have coined this the knowledge deficit approach
(Crow & Jones, 2018).
The components of a story and its role in policymaking can be found throughout the literature. Deborah Stone (2002) studies the use of metaphors, symbols, numbers and particular plot lines to advance or obstruct proposed actions. Schneider and Ingram
(1993) rely on symbolic language, metaphors, and stories to explain the allocation of benefits and burdens through public policy based on groups’ cultural characterizations 29 and social constructions. Punctuated Equilibrium Theory identifies stories as strategic tools for policy monopolies to reinforce prevailing policy images. For groups outside the monopoly, stories help to undermine the dominant position and create a path for a new image and a new equilibrium (Baumgartner, Jones, & Mortensen, 2018). The Advocacy
Coalition Framework holds that narratives build support for preferred courses of action when external shocks disrupt a policy subsystem (Jenkins-Smith et al., 2018).
The Narrative Policy Framework, or NPF, is a more recent development. It provides a new lens through which to study stories’ roles in policymaking. The NPF asserts that policy narratives consist of two parts: narrative elements and narrative strategies. Narrative elements include a setting that grounds the story temporally, geographically, legally, politically and socioeconomically; a plot that defines the arc of action; characters, including heroes, villains and victims; as well as a moral of the story, which is depicted as a preferred policy solution (Shanahan et al., 2011). Narrative strategies affect the portrayal of narrative elements and are used to limit or expand the scope of conflict in a policy debate (Shanahan et al., 2013).
Stone’s work (1989, 2002) offers common narrative strategies, such as stories of control, decline and stymied progress, as well as causal mechanisms, which are fundamentally about assigning blame for a problem. She identifies four types of causal mechanisms. Mechanical causes are the result of man-made devices through which the manufacturer’s will is carried out in an intentional manner. Accidental causes are the result of unguided action and unintended occurrences, such as natural events and disasters. Intentional causes result from purposeful actions on the part of humans in order to produce an intended consequence. And inadvertent causes produce unintended 30 consequences as a result of willful actions. Inadvertent causes produce problems such as poverty and disease through careless or reckless behavior.
Another common narrative strategy is issue expansion. Groups strategically attempt to expand or contain issues and the number of actors engaged in those issues when attempting to control the agenda. In particular, active groups seek to engage apathetic groups, thereby expanding the scope of conflict, drawing greater attention to an issue, and making it more salient to more interests (Peterson & Jones, 2016).
Specific strategies that play a role in issue expansion include the angel- and devil- shift and James Q. Wilson’s typology of political competition. Narrators use angel- and devil-shifts to portray one party as capable of solving a problem or, respectively, to convince the audience an opposing side possesses more power than is actually the case.
The goal of this strategy is to rally supporters or ward off potential opponents.
Wilson’s typology is used to explain the allocation of costs and benefits as a tool to advance or thwart certain policy alternatives. Shanahan et al. (2013) explain the application of this strategy as follows: “When discussing the opposing policy alternative, the strongest narrative strategy is to diffuse the costs and concentrate the benefits; when discussing the preferred policy alternative, the strongest narrative strategy is to concentrate costs and diffuse benefits” (p. 460).
NPF further theorizes that groups adopt a stance in their narratives as a means of controlling issue expansion. When groups perceive themselves as losing, they will attempt to expand a conflict to invite supportive groups into the debate. Conversely, when perceived as winning, groups try to maintain the status quo and prevent potential opponents from engaging (Shanahan et al., 2018). 31
For resource-rich and well-organized interest groups, expanding or containing scopes of conflict can be rather easy. These organizations have money, staff, advocates in the field, and access to decision-makers that help them convey their messages widely. For disadvantaged groups, however, strongly structured narratives serve as a disruptive force to existing policy monopolies dominated by the political elite. This disruption creates new opportunities for disadvantaged groups to access the policymaking realm and establish their preferred narrative as the new prevailing issue definition (Baumgartner &
Jones, 2009).
Another element of persuasion that cannot go unacknowledged is the role of power, which represents one key area in need of further research in the policy studies field. While the concept of influence is acknowledged widely in the literature, further specification is needed.
Integrating theoretical perspectives
The advent of the Narrative Policy Framework provides a new lens through which to research the Multiple Streams Framework. This section seeks to explain the conceptual overlap of these two frameworks as the theoretical basis of the study presented here.
McBeth and Lybecker (2018) argue that in order for the NPF to gain further acceptance by the scholarly community, it must more fully integrate itself into other theories and demonstrate its utility in democratic governance systems. They specifically seek to demonstrate its application to the MSF through the advocacy work of policy entrepreneurs, but also note components of the NPF align closely with aspects of other highly regarded theories and ideas in the field, such as narratives’ representation of policy 32 beliefs in the Advocacy Coalition Framework and the use of narratives to expand scope of conflict as theorized by E. E. Schattschneider.
Despite the improved conceptualization of narratives under the Narrative Policy
Framework, little research has occurred to date linking the framework to agenda setting.
Of the literature reviewed for this paper, only two scholarly works explicitly draw connections between the agenda-setting stage of policymaking and NPF (Peterson &
Jones, 2016; McBeth & Lybecker, 2018). The following section demonstrates the congruence between elements of agenda setting and narratives as defined in the NPF, thereby illustrating ample opportunities for further research.
McBeth and Lybecker (2018) hypothesize the NPF is useful in understanding the
MSF in five key regards:
• First, policy entrepreneurs are expected to deploy narratives in a way that
construct problems as demanding attention and preferred alternatives as viable
and preferable solutions. Narratives also play a role in making focusing events
more prominent so as to present an advantageous policy image and generate
attention for an issue through the media.
• Second, narratives are expected to be void of significant evidence at the
systemic agenda phase, but more prominently depicted within the institutional
agenda. When used, evidence is expected to be incorporated within narrative
elements, such as the plot and moral of the story.
• Third, narratives are held to be important considerations for coupling the three
policy streams and creating policy windows, however, they suggest the shelf
life for narratives is limited, thus timing is a significant factor. 33
• Fourth, because narratives are expected to be effective for only a limited
period of time, policy entrepreneurs are expected to champion their preferred
story vigorously.
• Fifth, narratives are not unique to any one agenda. The idea of narrative
translation suggests narratives may move back and forth between policy
agendas.
The following looks at some of these roles more closely, beginning with problem definitions and focusing events. The section then reviews how narratives inform and influence cognition and information processing functions. The ways message recipients understand and respond to information is subject to the manner in which stories are presented when referenced against existing knowledge and belief structures. I discuss the significance of framing in narrative construction to address these considerations, after which I touch on the development of discourse coalitions, or networks of actors sharing narratives. Finally, the section concludes with a model to illustrate the assumed intersection of NPF and MSF.
Problem definitions and focusing events
An issue must first be recognized and defined as a problem prior to agenda access. Problem recognition depends on a “strategic representation of situations” (Stone,
2002). Oxley et al. (2014) write that “policy problem recognition occurs when new persuasive information is presented…that is different from the mental representation of the social and/or physical environment in the mind of the individual and when the consequences of the information are assessed to have a negative affective valence” (p.
253). 34
Beyond presenting vivid descriptions of the problem, Rochefort and Cobb (1993) suggest the more a problem is depicted as impacting people, the more likely the underlying issue will make its way onto an agenda. Cairney and Zahariadis (2016) introduce the issue of salience in agenda setting. Salience relates to three of Zahariadis’
(2016) “Four Ps” of agenda setting, in particular perception, potency and proximity.
Rochefort and Cobb (1993) stress the importance of salience, saying problems are recognized based on their severity, incidence (the scope of people affected), novelty
(expressed by decision-maker’s appetite for action), proximity to those potentially affected, and the problem’s perception as being a crisis.
Birkland (1998) reinforces this point, saying a focusing event’s utility is related to the story of how it harms people. This ties back to the discussion of issue expansion and mobilization: the degree to which interests mobilize corresponds with an issue’s salience. When interest is high, mobilization is extensive and rapid, as is the subsequent counter-mobilization by competing groups; when interest is low, there may be very little activity on either side. Salience is often gauged as a measure of public opinion, so to the extent interest in an issue is high, so too are its prospects for placement on the agenda
(Gray & Jones, 2016). Under the Narrative Policy Framework, narratives shape and measure public opinion at the micro, or individual, level.
Just as with problem definitions, narratives are used to construct focusing events in ways that propel issues onto agendas for action and define potential solutions.
Narratives add meaning to focusing events. Such events are not objectively defined, but rather are social constructions developed to serve a strategic purpose. McBeth et al.
(2013) and Kingdon (2003) argue that focusing events, alone, are not sufficient to open a 35 policy window and add an issue to the agenda. Kingdon (2003), in particular, observes,
“Crises, disasters, symbols, and other focusing events only rarely carry a subject to policy prominence by themselves. They need to be accompanied by something else” (p. 98).
That something else is a powerful narrative.
Narrative cognition and information processing
A narrative is powerful though only to the extent it influences the ways in which actors and groups think about an issue and compels them to action. Narratives are useful for introducing new information and understanding cognitive processes. Peterson and
Jones (2016) assert narrative cognition “helps people process new information, recall information, and communicate with and persuade others” (p. 112). This function makes narrative cognition a cornerstone of the Narrative Policy Framework’s information processing model. This dovetails with research on agenda setting given the reliance on bounded rationality and heuristics to make quick decisions, and the acknowledgement that people respond to information differently based on whether it comports with their pre-conceived notions, beliefs and values.
Messages are persuasive only to the extent opinions change, which is complicated by confirmation bias. People tend to dismiss information incongruent with their beliefs and self-select their information sources, making it more difficult to reach audiences with opposing viewpoints.
Previous research identifies two forms of cognition. In the first form, System 1, thought processes occur unconsciously and instead rely on emotional assessments based on previously held beliefs. In the System 2 form, people engage and process stimuli using rational thought (Kahneman, 2011, as cited in Crow & Jones, 2018). System 1 is a 36 voluntary practice, whereas System 2 requires greater mental exertion. Additionally,
System 1 can inform System 2, and narratives can be a useful heuristic tool in this process (Shanahan et al., 2018)
Research shows individuals who are well versed on a matter are more likely to alter their opinions of an existing condition when information is negative, when some aspect of the message pierces the barriers erected by pre-conceived notions, and when the information is conveyed by a trusted source, improving credibility (Oxley et al., 2014).
The task of altering opinions is less demanding on those who are uninformed (Ertas,
2015). Fischer and Forester argue that the content of a story—from language to forms of argumentation—are more important in making policy than rational tools such as cost- benefit analysis (1993 as cited in Lejano, Ingram & Ingram, 2018). In short, for a narrative to be persuasive, it need not be detailed—it simply needs to tell a good story
(Smith & Larimer, 2017).
Narratives as framing tools
Narratives help to frame issues, and how an issue is framed not only shapes public opinion and proposed policy remedies, it also influences political choice and information processing. Merry (2016) argues the Narrative Policy Framework is effective for studying the impacts of framing because “narratives (or stories) are primary mechanisms by which individuals process complex information and communicate about events and issues” (p.
375). Thus, narratives should reflect the prevailing issue frame, but intuitively, as narratives change, so should the frame.
An issue’s framing is a strategic construction by actors, or policy entrepreneurs, on behalf of special interests and policy communities. McBeth et al. (2013) illustrate 37 narrative’s impact on issue framing in their study of changing views on tobacco use and its public health impact. When tobacco use was first accused of being detrimental to public health, the industry countered that farmers, who enjoyed a favorable social construction, would suffer economic harms if tobacco consumption declined. Over time, however, thanks to events, science, court decisions, and government documents that substantiated the charges, tobacco opponents reframed the issue as a health threat. The introduction of new information created a new narrative with a new characterization of the tobacco industry as villains.
Returning to the discussion of focusing events, these signals can be as explicit as specific phenomena, or they can be represented implicitly through symbols. Symbols can represent a problem in need of a solution. When used in narratives, symbols capture the attention of policymakers if they correspond to a perceived reality already in the public’s consciousness or unconsciousness. The speed with which decision makers absorb and act upon symbols is tied to the strength of their association to a problem in public discourse
(Kingdon, 2003).
Building discourse coalitions
Policy entrepreneurs “sell” their preferred solutions to public problems in the primordial soup of policy and issue networks, but to gain traction, policy narratives must be actively narrated by a community of advocates. As narratives form, in order for them to survive, they must be adopted by a narrative community, or a discourse coalition that shares a common story line (Lejano et al., 2018). This notion is akin to the shared beliefs of groups in the Advocacy Coalition Framework. 38
Beyond advancing policies along a common storyline, discourse coalitions, or narrative networks, serve as a means of building group cohesion. For example, in the case of the devil-shift strategy, groups with similar interests vilify a common enemy, reinforcing the bond between the like-minded groups. By maintaining common enemies, groups effectively reinforce their membership and their cause (Merry, 2019).
Narrative networks allow groups some measure of flexibility so that the overarching story is the same, but it can be tailored to a groups’ respective audience. This plurivocity allows for a wider variety of participants in the network and introduces more diverse sources of knowledge. The network is considered intact to the extent narratives share emplotment, or a sequence of events or causal mechanisms that guide readers from the problem to a preferred solution, and alterity, or shared outsider groups that contrast the desired ends of narrative network group members (Lejano et al., 2018).
Modeling narratives in the Multiple Streams Framework
Figure 2-1 is a graphical depiction of the assumed relationship between NPF and
MSF in an attempt to visualize the general research question posed here asking how narratives advance issues to the decision agenda in public policy. 39
Figure 2-1: Multiple Streams Framework Model
On the left, three triangles depict each stream conceptualized in Kingdon’s framework. Briefly put, the top triangle represents the problem stream. It is divided into two parts to illustrate the progression of issues from the systemic agenda of all persistent conditions to the institutional agenda. The middle triangle represents the policy stream, or the collection of potential policy solutions in search of a problem. The bottom triangle represents the politics stream, which consists of the national mood and the collection of organized groups vying to position and advance their interests on the agenda.
The vertical blue line at left contains narrative elements within the Narrative
Policy Framework that are expected to influence and exist within the three streams.
Characters include policymakers, policy entrepreneurs, and interest groups acting within the politics stream. Plots represent the characterization of problems and issues that populate the problem stream, and morals of the story include the policy alternatives decisionmakers debate as solutions to those social problems in the policy stream. Finally, narrative strategies include the tactics storytellers employ to build support for their 40 preferred solutions, expand or contain the debate’s scope of conflict, or undermine opposed alternatives. These strategies are executed within each of the three streams.
The magnifying glass icon signifies focusing events or crises. These events draw attention to problems, define problems, and help the three streams converge. Focusing events catalyze policy change by merging the three streams to create a window of opportunity to advance an issue to the decision agenda. These events, however, take place amid a complex cloud of factors that influence public attitudes and perceptions of problems, policy actors’ efforts, reactions to streams, and the allocation of resources.
Throughout this entire process and based on the literature, narratives are understood to play a number of important roles. These roles are noted in the bottom arrow spanning the MSF process. This paper seeks to address at which points along the process—and in what ways—narratives are applied.
Critiques of NPF and MSF
Both the Narrative Policy Framework and the Multiple Streams Framework have been criticized on a number of fronts. NPF remains a relatively new addition to the policy sciences literature, thus the debate over its critiques remains unsettled. The MSF has endured for decades with scholars refining it to add clarity and broaden its application to policymaking stages beyond agenda setting.
NPF developed in response to criticisms by positivists who held that qualitative studies of narratives did not provide testable hypotheses subject to falsification, were too value laden, and did not produce generalizable findings (Jones & McBeth, 2010). To overcome these charges, NPF was conceived as a means of studying narratives in a 41 quantifiable way that allowed for hypothesis testing and application across multiple cases and policy domains (Shanahan et al., 2011).
Perhaps the most contentious debate surrounding the Narrative Policy Framework is its attempt to merge quantitative and qualitative modes of analysis in order to provide a robust means of studying stories in public policymaking. In terms of theoretical perspective, the framework roots itself in postpositivism as a tradition of inquiry and with an “objective epistemology applied to understand a subjective ontology” (Jones &
Radaelli, 2015, p. 339).
While NPF largely advocates positivistic methods of objective empirical observation, others have sought to extend the framework to qualitative studies (Jones &
Radaelli, 2015). Skeptics criticize attempts at marrying quantitative and qualitative methods, as well as ontological and epistemological perspectives, that are viewed as incompatible through NPF. Dodge (2015) questions NPF’s attempts to mix research paradigms, saying a mismatch exists between the framework’s underlying ontological and epistemological perspectives. She writes that, “Jones and Radaelli conflate ontology with epistemology and theoretical perspective,” (p. 362) saying NPF assumes a social constructivist view of the world. Dodge (2015) argues social constructivism is an epistemological position—not a means of discovering truth as she alleges Jones and
Radaelli attempt to do. As such, NPF’s attempts to adopt an ontology that acknowledges multiple truths “is inconsistent with an objectivist epistemology that would seek to discover the Truth in the form of generalizable patterns across contexts” (Dodge, 2015, p.
363). 42
Miller (2015) criticizes NPF’s efforts to bring objectivity to the social sciences.
He takes issue with NPF’s attempt to understand social constructivism, arguing the framework’s subjective interpretation of knowledge gives “no indication they fully embrace the social constructionist idea that meaning, reality and knowledge are manufactured things” (Miller, 2015, p. 358). He further calls NPF an “epistemological mishmash of scientism combined with a low-fidelity iteration of social constructivism,”
(Miller, 2015, p. 356), and adds that the NPF wishes to conform to the empirical research standards of reliability, validity, falsification, replicability and causality. Doing so simply serves to reinforce dogmas of objective scientism that it purports to overcome by claiming to bridge positivist and subjectivist theoretical perspectives.
These criticisms, according to Jones and Radaelli (2015), are valid only insofar as critics continue to hold onto the positivist-postpositivist dualism. While they acknowledge research requires conforming to this dichotomy and adhering to one perspective based on the nature of any given study, they suggest researchers willing to open their eyes find more intriguing connections between the two sides. Returning to their earlier cited characterization of NPF as an “objective epistemology applied to understand a subjective ontology,” (p. 339), they assert that a material world exists, but actors operating within it ascribe their own individual meanings to it, creating a subjective social construction of the world. Thus, for interpretivists—looking at interpretivism as a variant of postpositivism—who are not inclined to seek generalizable findings, the NPF holds the potential to identify narrative mechanisms that shape meaning and influence policy in a way that is applied to multiple cases. Generalizability, they further state, does not require statistical measures so often sought by positivists. 43
Lejano (2015) reinforces this potential marriage of interpretivism and the standards of empiricism. While he warns, “Objectifying narrative can be like trying to turn a good story into a formula…[but] a formula can spoil a story,” (2015, p. 369)
Lejano says interpretivists and many “other policy scholars study causality in policy- making outside of a positivist framework” (p. 370). They simply need to understand the meaning actors ascribe to people, places and events before they can explain policy outcomes.
Another criticism of NPF is that the study of narratives at the macro, or institutional, level is underdeveloped. Much of the research to date centers on impacts to individuals and organizations/groups at the micro and meso levels, respectively. While this criticism is valid, one must keep in mind that the NPF remains a nascent framework—appearing in the literature only within the last decade or so. As such, and because institutions are shaped over the period of decades or generations, too little time has elapsed for sufficient study.
Shifting to Multiple Streams, Kingdon’s model is the target of multiple conceptualization and operational criticisms, namely that it is both underdeveloped and under-applied. Barzelay (2006, as cited in Howlett et al., 2015) argues the framework, which was designed to apply to the agenda-setting stage of policy formation only, crosses over into subsequent stages of the process. Howlett et al. (2015, 2016) concur that
Kingdon’s framework can be applied to subsequent stages of the policy process, and they do so while adding a “process” stream at the policy formulation stage and a “program” stream during implementation. This revised model represents a promising evolution of
Kingdon’s 30-year-old work and one worthy of further study. 44
Kingdon’s original model also is criticized for failing to operationalize key elements sufficiently, specifically policy entrepreneurs, the national mood, as well as the politics and policy streams. These individual shortcomings, combined, make operationalization of the entire framework problematic (Cairney & Zahariadis, 2016).
While this is a valid criticism, it is not fatal. Multiple Streams is praised for its flexibility in the face of ambiguous contexts (Cairney & Jones, 2016). The framework’s flexibility makes it beneficial to the field—an opinion reinforced by its endurance over the past three decades.
Birkland and Warnement (2013) question whether the historical tendency of researchers to assess focusing events in the context of one particular policy at one moment in time is adequate. Because of bounded rationality, “Decisions maker [sic] cannot know every outcome of every policy choice and determine the ‘optimal path’ for policy” (p. 9, emphasis in original). Thus, if we wish to understand the impacts of decisions on public policy, a longitudinal study of decisions over time in a policy domain or regime is necessary.
This recommendation holds merit. While policy choices are rooted in the context of a moment, social, cultural, political and other dimensions evolve to reflect changing values and priorities. As a result, a focusing event that prompts attention at one point may go unnoticed at another point. The field of policy studies requires a longer view of how narratives impact policy choices over time. Regimes perspective provides insight into institutional arrangements, among other considerations, and because these arrangements are established over time (May & Jochin, 2013), it makes sense to study focusing events over longer time horizons. 45
These issues hold important implications for understanding policymaking. The
Narrative Policy Framework captures the attention of scholars for its value in establishing the underlying meaning of policies and strategic constructions that are tools of influence.
Despite criticisms leveled against it, the growing appreciation for the Narrative Policy
Framework is evidenced by its inclusion in Weible and Sabatier’s (2018) fourth edition of Theories of the Policy Process. The Multiple Streams Framework provides researchers with significant insights into policymaking and dynamics that determine which problems governments act to correct, but there is room for improvement.
Power
Although agenda setting is largely about influence and influence is a form of power, there is a deficit of scholarship with respect to the role of explicit power theories in agenda setting, broadly, and the frameworks discussed here, specifically. Power is an unnamed undercurrent in much policy scholarship. Policy monopolies establish dominance over a particular domain by perpetuating and reinforcing preferred narratives.
The narratives of elite groups—which Mills (1956) terms the power elite and Domhoff
(1967) calls the ruling elite—become pervasive at the meso-level and filter down over time to the public at the micro-level where they indirectly manipulate or reinforce beliefs and shape public opinion.
Infiltration of these metanarratives throughout society calls to mind Gramsci’s writings on hegemonic power. In order for an idea or belief to achieve broad societal penetration, some measure of control must be present, which hegemonists attribute to the influence of elite powers. In the Marxist tradition, dominant groups use institutions to exert power over the masses (Williams, 1960), convincing individuals and social groups 46 to conform to social values, norms, and stories—even when those norms are exploitative and contrary to their interests (Lejano et al., 2018).
Separately, groups exercise power directly at times by either putting an issue on the agenda or barring its access. At other times, simply the mere perception of power is enough to prevent an issue from being debated, a concept referred to as the second face of power or the mobilization of bias (Bachrach & Baratz, 1962, 1963; Crenson, 1971).
The media is a case in point. It represents an institution that both exerts and endures pressures on and from multiple fronts. Interest groups avail themselves of whatever means available to disseminate policy beliefs, and people rely on media for news on government activities and focusing events. One can easily question the ramifications for this flow of information when elite groups control the media. Elite groups exercise direct power by sharing only preferred information and inhibit the dissemination of narratives that support unwanted change.
While journalism abides by codes of ethics and practice, media outlets control who sees the message—not only in terms of what they print, publish or broadcast, but also in terms of to whom they market their product. The media is, after all, produced for the purpose of consumption or sale. Media outlets tailor their content to specific audiences for a variety of factors, such as attracting a certain type of advertiser that is willing to pay a certain price for exposure (Richardson, 2007). The conscious or unconscious decision of what information to print or broadcast is an exercise of power, which affects policies governing society.
47
Chapter 3
Research Design and Methods
This study employs a qualitative research design. A qualitative design is appropriate because the intent is to identify underlying meanings in data and to explore the potential connections between those meanings and subsequent action on the part of government policymakers. Understanding values, motivations, and ways in which actors convey those factors makes a qualitative design appropriate as qualitative research is more cognizant of values, perspectives, and life experiences.
Creswell (2014) defines qualitative research as a “means for exploring and understanding the meaning individuals or groups ascribe to a social or human problem”
(p. 246). Merriam and Tisdell (2016) write that, “Qualitative research is based on the belief that knowledge is constructed by people in an ongoing fashion as they engage in and make meaning of an activity, experience, or phenomenon” (p. 23).
Researchers in the qualitative tradition tend to hold more subjective worldviews along the lines of interpretivism, constructivism or postmodernism, and they rely on open-ended questions and observations to gather contextual data. Denzin and Lincoln
(1998) write that qualitative research can be multimethod in focus—using, for example, semiotic, narrative, content or discourse analyses, among others—and such studies take an interpretive and naturalistic approach to the topic of inquiry.
This chapter begins with an examination of the philosophical role and assumptions of the researcher, followed by a discussion of the design presented to address the aforementioned research questions. The next section delves into data collection and analysis techniques. The chapter concludes with a review of techniques 48 common to qualitative research that are intended to ensure quality and rigor.
Philosophical assumptions and role of the researcher
This study utilizes a qualitative design to assess public documents as the unit of analysis. Given the role qualitative researchers play in data collection and interpretation, it is important to acknowledge the researcher’s theoretical perspective (Riccucci, 2010).
It is likewise important to acknowledge that qualitative research is an “interactive process shaped by [the researcher’s] personal history, biography, gender, social class, race and ethnicity” (Denzin & Lincoln, 1998, p. 4). These perspectives shape the narratives researchers tell within a study’s chosen research paradigm. This study is situated within a constructivist-interpretivist paradigm.
I hold a subjective worldview, believing individuals socially construct their own reality based on their individual life experiences—thus there is no single reality or objective truth—and that knowledge is understood through a mutual negotiation of meaning-making between researchers and their subjects. This aligns with Denzin and
Lincoln’s (1998) definition of the constructivist-interpretivist paradigm, which “assumes a relativist ontology (there are multiple realities), a subjectivist epistemology (knower and subject create understandings), and a naturalistic (in the natural world) set of methodological procedures” (p. 27). Under the naturalistic approach, researchers make sense of or interpret phenomena in terms of the meanings people possess (Denzin &
Lincoln, 2011).
Schwandt (1998) draws distinctions between constructivism and interpretivism.
While he writes that both constructivism and interpretivism emanate from the same methodological and philosophical families, they are distinct insofar as the intent of the 49 researcher. Interpretivism is informed by the intellectual contributions of hermeneutics, sociology, phenomenology, and criticisms of scientism and positivism.
Schwandt introduces Max Weber’s notion of verstehen, or the empathic attempts to understand social phenomena. Critics of the constructive-interpretive paradigm—i.e., proponents of positivism—charged it is impossible to be “get inside the head” of actors in any phenomena, so at best verstehen is a heuristic means of basic discovery, but it does not go so far as to fully explain or justify the phenomena. Proponents of verstehen—or the antipositivists—counter this approach to research is not so much about getting inside the head of an actor, but rather finding intersubjective meanings and the symbolism of phenomena in life.
Schwandt relies heavily on Alfred Schutz in legitimizing interpretivism by drawing a distinction between the social and physical sciences. The thought objects that form social reality are of little significance to the physical world. In other words, the thoughts and perceptions of humans do not “’mean’ anything to molecules, electrons, and atoms” (Schutz, 1967, p. 59 as cited in Schwandt, 1998). Further, the interpretations of interpretivists are, themselves, interpretations of their observed research subjects’ interpretations of the world (Taylor, 1987 as cited in Schwandt, 1998).
Constructivists, on the other hand, are less open to differences between the physical and social worlds. They firmly contend that “[k]nowledge and truth are created, not discovered by the mind” (Schwandt, 1998, p. 236) and that “contrary to common- sense, there is no unique ‘real world’ that preexists and is independent of human mental activity and human symbolic language” (Bruner, 1986, p. 95). Constructivists contend that all self-evident realities are the result of complicated discursive practices and socially 50 determined.
Using Schwandt’s writing as a guide, the study presented here follows the interpretivist paradigm as it is in more in keeping with my worldview and my appreciation for differences in understanding and generating knowledge of the real versus social worlds. The constructivist’s outright rejection of objective reality makes such a perspective untenable in my mind, even in this study of narratives in the social/political world of policymaking, rather than being focused on the natural world. The forefathers of interpretivism maintained that a material difference exists between the social sciences and the natural sciences. Whereas the latter’s goal is explanation, the former is concerned with understanding the meaning of social activity and behavior (Schwandt, 1998) making it the more appropriate choice here.
Holstein and Gubrium (2011) warn that constructivism can lose its conceptual bearings, thus researchers must regularly keep in mind that the approach is not intended to present readers with a single portrait of social actions, but rather it should be “better understood as a mosaic of research efforts with diverse (but also shared) philosophical, theoretical, methodological, and empirical underpinnings” (p. 341). Another charge against the most radical adherence to constructivism is that researchers following this paradigm only construct reality and meaning to the extent of their own personal horizons, their own cultures, and their own personal histories (Brinkmann, 2013).
Finally, before discussing the theoretical framework underlying this study, because this paper addresses political matters, namely policy disagreements between
Democrats and Republicans, it is important to disclose my own political orientation.
Transparency is key in qualitative research, a factor that will be discussed in greater 51 detail later in this chapter. To that end, I have been a registered Democrat since the age of
18. As the sole data gatherer and analyst in this research effort, I am cognizant of the need to be objective so as to not allow my own political ideology to taint data collection and/or interpretation.
Theoretical framework
The theoretical framework upon which this research is conducted, Multiple
Streams Framework and the Narrative Policy Framework, is highly malleable—suitable for both qualitative and quantitative researchers. To this point, the Narrative Policy
Framework is presented as a means of bridging the divide between positivists and post- positivists (Shanahan et al., 2013). Whereas post-positivists are viewed as dominating research on narratives, their methods are often dismissed as insufficiently rigorous.
Conversely, positivists fail to offer a viable means of objectively and quantifiably researching narratives and the contextual understanding those stories offer about a case
(Jones & McBeth, 2010).
In response, the Narrative Policy Framework seeks to “ameliorate historical tensions between these two groups by arguing that in addition to the dominant postpositivist approach, narratives can and should be studied using the standards set out by Sabatier” (Jones & McBeth, 2010, p. 331), who believes theories of the policy process must be conceptually clear with testable hypotheses subject to falsification. To that end, the Narrative Policy Framework is capable of identifying and measuring narrative elements and strategies quantitatively that can be tested against other cases and generalized, but at the same time, assessed for contextual meaning underlying stories, thereby appeasing both positivists and post-positivists. 52
While attempts to marry positivism and post-positivism have been criticized and the Narrative Policy Framework offers a means of empirically testing hypotheses, qualitative applications of the framework are increasing in number. Jones and Radaelli
(2015) point out that “the NPF neither requires nor insists upon statistical generalization”
(p. 348). Further, Gray and Jones (2016) promote the value of a qualitative-only approach to the study of narratives, particularly at the meso, or organizational, level of analysis.
They recommend preserving the framework’s fundamental tenets of narrative elements and strategies, but also practicing generally accepted standards of qualitative research, which will be addressed later in this paper.
Research design
The research design is a critically important consideration. The research design provides a roadmap for researchers to connect theoretical paradigms, strategies of inquiry, and methods for data collection and interpretation (Denzin & Lincoln, 1998).
Research designs stipulate how investigators will address research questions, represent data and findings, and act to ensure legitimacy.
Jansick’s three-stage model
Jansick (1998) provides a useful outline of three stages to guide qualitative research beginning with a “warm up” stage. Here, researchers first select the focus of their inquiry based on the phenomena of interest, the circumstances surrounding that phenomena, as well as the circumstances under which they wish to conduct the study.
They must decide who associated with the phenomena, or which organizations or social institutions they wish study. Concurrent with the first step, researchers must decide where to conduct the study and how to gain access in pursuit of honest and authentic data. 53
The second stage, which Jansick labels the “total workout,” requires decisions that may affect the research design. The researcher analyzes data as it is collected to ensure he/she feels the phenomena’s full picture is being captured. This analysis helps develop working models that offer a preliminary explanation of what he/she has observed and the significance of those observations in relation to the research questions.
In the third stage, the “cool-down,” a researcher contemplates how to communicate his/her observations accurately. In doing so, the researcher must be cognizant of their own place in the study, the perspective they bring to the research, and how those considerations may affect the final report.
Guided by the research questions posed in Chapter 1, this study presents a qualitative content analysis of public comments by federal government officials, including President Donald Trump, members of his administration, and members of
Congress circa the 35-day federal government shutdown that occurred between late-
December 2018 through most of January 2019. Specifically, I studied comments made between December 16, 2018—the beginning of the week in which the shutdown began
(December 22, 2018)—through February 12, 2019, three days before President Trump signed a compromise bill and announced he would declare a national emergency to reallocate previously appropriated funds to supplement $1.375 billion contained in the compromise legislation both chambers of Congress passed two days earlier. Trump’s announcement on February 15, 2019, effectively ended the case at the heart of this study.
My interest in this study was born of working for nearly 15 years in the executive branch of state government, mainly in communications-related fields, but also policy analysis and development, as well as executive management. The experiences gained in 54 public service over that tenure offer a first-person perspective on the intersection of communications strategy and media relations with public policy development and legislative affairs. Thus, the research undertaken and reported here aligns with my professional background and interest, allowing for a convenient “warm up” stage, to use
Jansick’s vernacular.
The decisions made subsequently represented the second, or “total workout,” stage, beginning with the selection of an appropriate case. In consultation with my dissertation committee, initial cases involving state-level issues were dismissed for fears of insufficient data to allow for thorough analysis. Ultimately, the committee and I settled on the 35-day federal government shutdown because of its prominent placement in media coverage, which yielded ample data from which to choose for analysis. The total workout stage also resulted in the decision to perform a qualitative content analysis, which represents an outgrowth of the professional experience and interests in government policymaking and communications contemplated in the “warm up” stage.
Through content analysis, I investigated the use of narrative elements and strategies contained in comments by Trump, members of his administration, as well as
Republican and Democratic U.S. Senators and U.S. Representatives. I evaluated comments for the presences of characters; character portrayals (heroes, villains and victims); plots and morals of the story; problem definitions and causal mechanisms; and the use of other narrative strategies to support or oppose policy proposals, expand or contain the debate’s scope of conflict, attribute costs and benefits to proposals; and assign blame and responsibility for the government shutdown.
Finally, this paper represents the “cool down” stage. As noted earlier and 55 consistent with qualitative research recommendations, the “cool down” stage asks researchers to understand their own place in their research and to be aware of how findings are presented. Full transparency demonstrates application of Jansick’s third stage, but it also is an important consideration in establishing trust in qualitative research.
I address the trustworthiness issue later in this chapter.
Use of content analysis
The use of content analysis in narrative studies is a well-established practice in the literature and is appropriate for this research. Some of the earliest applications of content analysis pursued a more robust understanding of public opinion, social stereotypes, and attitudes in newspaper content (Krippendorff, 2019).
The method is characterized as an effective means of simplifying voluminous data to facilitate analysis. Stemler (2001) calls content analysis “a systematic, replicable technique for compressing many words of text into fewer content categories based on explicit rules of coding” (n.p.). McBeth et al. (2007) call it an efficient and inexpensive means of conducting research that does not intrude upon subjects and, in cases with extended time-horizons, allows for longitudinal analyses. One critique of content analysis is that the coding process can be time-consuming and labor intensive for researchers
(McBeth et al., 2013).
Looking at content analysis as a means of studying narratives in public policymaking, it is first important to differentiate the method from narrative inquiry.
While narrative inquiry is focused generally on life experiences as lived by narrators, the method is used to understand the contextual relationship between narrators’ stories, the environment in which they live, and the setting in which the narration takes place (Chase, 56
2011). Content analysis, conversely, is not concerned with stories, but rather themes or aspects unique and shared among datasets. As such, some refer to the method as thematic coding (Schreier, 2014).
The research presented here is more in keeping with discourse analysis and rhetorical analysis, and to a lesser extent influenced by grounded theory. Discourse analysts tend to assess how phenomena are represented in texts, whereas rhetorical analysis focuses on the methods of message delivery, how audiences receive those messages, and to what effect (Krippedorff, 2019). While the present research effort does not go so far as to analyze acts and styles of speech and argumentation as is common in rhetorical analyses, it does consider the political impact of messaging on audiences.
One foundational aspect of content analysis is the belief that texts are inherently meaningless. It is the receiver of a message who ascribes meaning to communications.
Messages or texts can have multiple meanings to multiple people. In the case of political communications, officials thoughtfully structure their messages in ways that will resonate with supporters or aggravate opponents, which underscores the duality of messages. This reality poses a challenge for content analysts as, conceivably, two individuals coding identical samples can arrive at two different interpretations. Mitigating this challenge relies heavily on understanding the context in which the communication takes place
(Krippendorff, 2019).
Context is an important consideration in content analysis. Typically, content analysts must make predictions or inferences from phenomena they cannot observe at the time of the event. As such, a thorough understanding of a situation’s context influences a study’s underlying research questions and the inferences analysts must make in the 57 course of research. Complicating and underscoring the importance of context is the belief that there is no limit to the number of contexts that may be applied to a content analysis.
Researchers must be explicit in defining their contextual foundation because each reader may apply a different contextual perspective when reviewing findings, thereby risking misinterpretation (Krippendorff, 2019).
A qualitative approach
The need for better understanding of meanings behind texts or accounts of phenomena reinforce the need for qualitative study. Content analyses are performed in both quantitative and qualitative designs. Many studies use a quantitative approach to content analysis. McBeth et al. (2007) hypothesize that associations exist between narrative strategies and the stance narrators take in their narratives on environmental issues within the Greater Yellowstone Area. Shanahan et al. (2013) use measures of statistical significance and association, as well as descriptive statistics and measures of frequency, in their study of narratives used by proponents and opponents of an off-shore wind power project in Cape Cod, Massachusetts. Crow and Berggren (2014) apply NPF using a multi-case research design in which they empirically examine the policy issues of water and energy across multiple venues in the legislative and regulatory realms, while also assessing the winners and losers of those debates. Merry (2016) employs counts of frequency in her content analysis of Twitter posts by pro- and anti-gun rights organizations.
Krippendorf (2019) warns, “For analysts seeking specific political information, quantitative indicators are extremely insensitive and shallow” (p. 16). Instead, he offers the following observations: “Reading is fundamentally a qualitative process, even when it 58 results in numerical accounts” (p. 25) and although “quantification is important in many scientific endeavors, qualitative methods have proven successful as well, particularly in political analyses” (p. 25).
Thus, despite extensive quantitative research using NPF, the research presented here employs a qualitative design. There is a need to expand the Narrative Policy
Framework’s application to qualitative designs (Jones & Radaelli, 2015), just as there is a need for broader qualitative scholarship in public administration. Ospina et al. (2018) review qualitative studies published in six leading public administration journals—
Governance, International Public Management Journal, Journal of Public
Administration Research and Theory, Public Administration, Public Administration
Review, and Public Management Review—between 2010 and 2014 and find that less than
7.57 percent of more than 1,700 published articles use qualitative methodologies. Of those, the vast majority, 108 articles, or 83.7 percent, use case study as the method. This dearth of qualitative research and the limited methodological choices researchers make are detrimental to the field because it denies public administration the pluralism of perspectives expected of a mature field, but also because with fewer studies, the rigor expected of such research is diminished, they argue.
Lowery and Evans (2004) echo this point. In a review of qualitative articles published in Public Administration Review between 1996 and 2000, they find case studies dominated public administration scholarship, but researchers lacked a sophisticated understanding of the method. In their words, unlike other fields, “public administration scholars appear to be ill-prepared to undertake qualitative research projects” (p. 312). 59
Beyond methodological variety, the qualitative design employed here is appropriate for a number of other reasons. First, qualitative studies counterbalance the quantitatively inclined positivists by contributing research that adds the richly descriptive contextual understanding of cases for which qualitative studies are known (Merriam,
2002). Second, the narrative elements and strategies defined through the Narrative Policy
Framework offer a ready-made set of codes for this research. Third, the qualitative design and methodological approach used here align with my own worldview, namely that multiple realities exist in the social world based on individual experiences and perspectives, and these unique backgrounds influence means of understanding.
Sample selection
Elo et al. (2014) and Ospina et al. (2018) assert that many qualitative studies devote little attention to explaining their sampling methods, because generalization of research findings is not a recognized goal of qualitative research. Exploratory studies are based generally on nonprobabilistic samples (Guest et al., 2012).
When it comes to selecting cases for study, one recommendation in qualitative research is to select those that offer the greatest potential based on expected information content. The goal of this information-oriented approach is to “maximize the utility of information from small samples and single cases” (Flyvbjerg, 2006, p. 230, as quoted in
Brinkmann, 2013, p. 57). Researchers also should consider access to information and the cost of information acquisition.
Sample selection criteria
For the purpose of this study, there are numerous potential data sources. The proliferation of media outlets in print, broadcast and digital mediums offers thousands of 60 options. When researching policy- and politically-oriented messaging, however, this wealth of sources yields a confusing and complicated landscape given the partisan slant of many news organizations. Despite this challenge, I sought a balanced perspective through media outlets that convey both sides of the issue in a nonpartisan manner.
Purposive sampling is deemed a suitable strategy here as this approach is used commonly in content analysis (Elo et al., 2014; Guest et al., 2012).
In such cases, a research study’s sampling plan should assess the information quality of data sources. When all sampling units are likely to be equally informative, a probabilistic sampling strategy is valuable in order to avoid sampling bias, however when sampling units are unequally informative—such as is the case with this study given the slanted nature of many media organizations—researchers must make judgements based on what they know about the potential data sources (Krippendorff, 2019).
To that end, and in order to assess the bias of various news outlets, this study relies on a typological map from Ad Fontes Media as a first step in identifying potential media outlets from which to sample data. Ad Fontes Media compiles a map that categorizes major information sources according to political biases
(MediaBiasChart.com, 2017). This study uses Version 3.0 of the chart as it was the version available during the timeframe in which data sources were produced and broadcast. Version 3.0, shown in Figure 3-1, groups information sources along a horizontal dimension that assesses a prevailing political ideology from “Liberal Utter
Garbage/Conspiracy Theories” on the left to “Conservative Utter Garbage/Conspiracy
Theories” on the right. A news outlet’s position relative to the Y-axis indicates overall 61 journalistic quality as determined by whether outlets offer “Original Fact Reporting” at the top to “Contains Inaccurate/Fabricated Info” at the bottom.
Figure 3-1: Ad Fontes Media News Outlet Typology
In selecting outlets for this study, the first condition is whether the organization fits within the boundaries of the green box labeled “News.” These outlets are situated within the partisan bias range of either “Skews Liberal (but still reputable),” “Mainstream
(minimum partisan bias),” or “Skews Conservative (but still reputable).” In terms of overall quality, news outlets in the green box rely on “Original Fact Reporting” or “Fact
Reporting.”
Only those outlets that fall within the “Mainstream (minimum partisan bias)” category are considered for this study. As such, potential candidates are the Associated 62
Press, Reuters, Agence-France Presse, Bloomberg, NBC News, ABC News, CBS News,
NPR, USA Today, PBS, BBC, and the Christian Science Monitor.
Second, the news outlets must have a domestic focus rooted in the United States.
This criterion eliminates Agence-France Press and the BBC from consideration. Third, the media outlet must produce and disseminate long-form interviews presented in a largely unedited manner. Such content allows for greater insight into the thinking of interview subjects by refraining from selective editing, which may reflect bias on the part of the producing media outlet (Entman, 2007). Based on searches of each media outlet’s websites, this third criterion eliminates Associated Press, USA Today, and the Christian
Science Monitor.
Finally, the news outlet must provide via a publicly available website transcripts of long-form interviews conducted between December 16, 2018, and February 12, 2019.
Access to such transcripts eases the challenge and cost of accessing information, thereby allowing for more convenient analysis of the data. Further this type of format makes possible a review of the entirety of their comments, which not only provides a more detailed perspective of the narratives used, but also a better appreciation for the context in which the narrative was communicated. This final criterion eliminates Reuters,
Bloomberg, and PBS. While each of these outlets offer video content of long-form interviews, none of the three provide a ready-made written transcript via their respective websites.
As such, the targeted media outlets and programs chosen for this study based on criteria set forth here include National Public Radio’s All Things Considered, Morning
Edition, and Weekend Edition, and Sunday morning political news programs airing on 63 three major broadcast networks: ABC’s This Week with George Stephanopoulos, CBS’s
Face the Nation, and NBC’s Meet the Press. The fourth major broadcast network, Fox, does air a weekly Sunday news program, Fox News Sunday, which does make transcripts available online. The network’s news division, Fox News, produces the show, but because Ad Fontes Media’s Version 3 map characterizes the network’s partisan bias as
“Hyper-Partisan Conservative (expressly promotes views)” and its overall quality as being somewhere between “Unfair interpretations of the news” and “Nonsense damaging to public discourse,” it was excluded from this study.
As noted earlier, the study’s sample gathered long-form interviews conducted between December 16, 2018, and February 12, 2019. These two dates were selected because December 16 was the Sunday prior to the shutdown’s commencement on
Saturday, December 22, 2018, thus making weekly political news programs on the three major broadcast networks selected for this study (ABC, CBS and NBC) eligible for coding. Each of the Sunday news programs on those three networks devoted time to interviews that addressed the impending shutdown. I selected February 12, 2019, as the end date of this bounded case because President Trump indicated on this date that he would sign a Congressional conference committee’s compromise bill, and he announced his intention to declare a national emergency to reallocate previously appropriated funding to other programs, effectively terminating the prospect of another government shutdown.
In the interest of transparency, the initial attempt at identifying samples yielded a larger number of potential media outlets. When sources were first collected in March and
April of 2019, nine audio recordings of broadcasts by NPR’s Here and Now, and one 64 interview with U.S. Senator Elizabeth Warren as published in the Framingham Source, a
Massachusetts-based news organization, were gathered. At the time of coding, however, the transcripts of these 10 sources were no longer available and thus excluded from the analysis.
Additionally, while the final sample included only interviews of elected and appointed officials in the federal government, the original research proposal for this study considered including interviews with other stakeholders affected by the shutdown and pundits who offered commentary on the state of affairs at the time. This group included
27 interviews broadcast on various NPR programs with local officials such as mayors of towns along the U.S.-Mexico border; representatives of interest groups such as partisan advocacy organizations; labor unions; federal employees; and politically-oriented media personalities. In the end, however, each of these 27 interviews were ultimately excluded because the content offered no new insight when compared to other interviews ultimately selected for coding and analysis.
Finally, the original data sources gathered for this study proposed including 15 transcripts from Trump administration officials available through WhiteHouse.gov. This group included 12 transcripts of remarks by President Trump, two by Vice President
Mike Pence, and one by then-Secretary of Homeland Security Kirstjen Nielsen. After consulting with dissertation committee members, these sources were deemed unnecessary for the same reasons offered in the decision to exclude the 27 interviews available through NPR mentioned in the preceding paragraph. 65
Sample size and breakdown
A sample is considered to be sufficient in size for qualitative research studies when no new information is identified (Lincoln & Guba, 1985). The views contained across the breadth of sampled transcripts represent an extensive array of opinions, issue frames, problem constructions, and proposed solutions in this particular case. Further, as the selected media outlets are considered nonpartisan according to Ad Fontes Media, and thus provide equal time to the two major political parties, the data sample yielded a balanced reflection of competing perspectives in the debate.
For example, of the 100 transcripts analyzed in this study, 51 cover interviews of
Republicans and 49 cover interviews of Democrats. Sixteen transcripts cover remarks by the president, vice president, or members of the executive branch, and the remaining 84 data sources reflect remarks by members of Congress. Of those given by members of
Congress, 28 were Democratic members of the U.S. House of Representatives, 17 were from Republican members of the House, 20 reflect comments from Democratic U.S. senators, and the remaining 18 are from Republican senators. The one remaining source from congressional members is a January 8, 2019 interview transcript from NPR.org that reflects comments from Speaker of the House Nancy Pelosi and Senate Minority Leader
Chuck Schumer, both Democrats. A breakdown of interviewees by branch of government service and political party can be found in Table 3-1.
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Table 3-1: Count of Interviews by Branch of Government and Party Affiliation
Branch of government Republican Democrat Executive 16 Legislative House of Representatives 17 28 Senate 18 20 Joint House member and Senate member 1 appearance Totals 51 49
Included in the count of Republicans are comments by unelected appointees to the
Trump administration. For the purpose of this study, these interviewees were coded
“Republican” in the “Political Affiliation” category as they advocate for the political and policy views of the Republican Trump administration.
Of the news sources chosen for this study, the apportionment of sources and interviews by media outlet and program is as follows: 16 interviews from eight episodes of ABC’s This Week with George Stephanopoulos; 21 interviews from nine episodes of
CBS’s Face the Nation; 15 interviews from six episodes of NBC’s Meet the Press; and
46 interviews as aired on NPR from 20 episodes of All Things Considered, 13 episodes of
Morning Edition, two episodes of Weekend Edition Saturday, and two episodes of
Weekend Edition Sunday. Two additional transcripts from NPR were not attributed to any particular show, but rather made available only online via the network’s website. A count of transcripts by network and media program is found in Table 3-2.
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Table 3-2: Count of Interviews by Network and Program
Network Program Name Interviews in Sample ABC This Week with George Stephanopoulos 16 CBS Face the Nation 21 NBC Meet the Press 15 National Public Radio All Things Considered 28 National Public Radio Morning Edition 14 National Public Radio Weekend Edition Saturday 1 National Public Radio Weekend Edition Sunday 3 National Public Radio Special broadcasts/Web transcripts 2 100
All told the 100 interviews reviewed and coded for this research contained
282,006 words as counted in Microsoft Word by pasting transcript text into one document. The diversity and richness of data found within the documents yielded fertile ground for comparing and contrasting the range of views among participating actors.
Another important consideration in sample selection is its impact on the legitimacy of the research. Transparency in sample selection is important for gauging validity and trustworthiness. One recommended strategy for strengthening internal validity of qualitative designs is for the researcher to be engaged closely in data collection and analysis (Merriam & Tisdell, 2016). On this point, the case of the 35-day federal government shutdown is one with which I am quite familiar as an avid consumer of political news. Additionally, as the sole researcher responsible for data collection, coding and analysis, I was immersed thoroughly in the research. The rationale for this methodological decision is explained further later in the paper.
Data collection
This section of Chapter 3 details the study’s data collection techniques. This is an important aspect for qualitative research because the techniques applied have implications for the study’s validity, reliability and trustworthiness (Elo et al., 2014)— 68 elements of this proposal that will be detailed further in a subsequent section of this paper.
Coding
For the purpose of this study, codes were assigned based on narrative elements and strategies prescribed by the Narrative Policy Framework. Such a system of categorization has been used in previously cited works using content analysis. Gray and
Jones (2016) code content according to settings, plots, characters and proposed policy solutions in their study of campaign finance reform in the United States. McBeth et al.
(2013) code U.S. newspaper accounts of obesity according to the presence of heroes or villains, problem definitions, causal mechanisms, and preferred regulatory solutions.
Finally, three categories consisting of 12 different codes are used in Shanahan et al.’s
(2013) study of the Cape Cod wind farm project, which is a particularly beneficial study here because the researchers provided a copy of their codebook that served as a guide for the present dissertation.
The development of a codebook is an important first step for categorizing data in a meaningful way. Codes are developed a priori in qualitative content analysis based on prior knowledge or an established theoretical framework, but researchers also must be vigilant for emergent codes. A posteriori codes are developed in a concept- or data-driven way where new codes are added to the codebook when identified in the course of data collection. Codes are the method by which researchers assign meaning to content and describe the data, allowing for analysis within a document and between documents.
Each code should include a specific name to facilitate identification in the analysis phase, a description of the name’s meaning, and rules to assure proper labeling 69 when utilizing multiple coders. Code definitions can also include examples in the codebook to reduce ambiguity in studies with multiple coders. Another strategy for improving clarity in the coding scheme is to conduct a pilot test of the codes before applying the final frame to all content in the chosen sample (Schreier, 2014).
Once the data is coded, it is important to go through the exercise a second time to measure the coding frames’ reliability and the coder’s interpretation of the data. Using two separate coders is the preferred method, but in cases where only one coder is available, a single coder should code data twice separately. It is recommended between
10 to 14 days elapse between attempts. In the present research endeavor, the number of days between coding attempts ranged from 15 to 92 days, with an average of 35.91 days.
The process of double-coding is a way of assessing how well categories are defined. Consequently, a second round of coding allows for testing whether the frame offers a reliable description of the content (Schreier, 2014). Two forms of reliability testing are recognized in content analysis: intercoder reliability when more than one person codes data and intracoder reliability for instances when one person codes the data at least twice (Holsti et al., 1963). To test the agreement between coders or coding attempts by a single coder, studies have used percent agreement as a measure of reliability. Reliability percentages, or coefficients of agreement, of 80 percent or higher are considered acceptable rates of agreement between coders. Figures less than 80 percent indicate inconsistencies between coding attempts and may signal which codes were not clearly defined and understood by the coder(s) (Jones et al., 2016).
Using multiple coders is preferable in content analysis, but research designs and methods must be cognizant of available resources, such as time and money. Further, too 70 great an emphasis on intercoder reliability among multiple individuals may lead coders to code only that which they are sure others will agree, which risks overlooking potentially valuable codes that may be borderline cases from the sample (Holsti et al., 1963). They suggest that high reliability and low reliability data among multiple coding attempts be compared to judge the conservatism of coding, which may reflect bias.
Schreier (2014) advises that codes should apply to only one category and there should be no ambiguity as to the meaning of each code. This advice raises the question of whether data can or should be coded only one way or whether it can apply to multiple coding categories. Maxwell and Chmiel (2014) respond that some elements can have multiple meanings and can thus be categorized in multiple ways. Krippendorff (2019) finds that categories that are coded as “not applicable” or “none of the above” are useful fail-safes in instances where established or emergent categories are not well defined and typically offer little analytical value.
Codes in the proposed study are modeled a priori after narrative elements and strategies of the Narrative Policy Framework, but given the exploratory nature of this study, it is important to recognize emergent codes that may provide valuable insight to further develop existing frameworks or new theories. A posteriori codes, such as new narrative strategies or plot lines, were recorded and evaluated. In this respect, the coding for this dissertation was inspired partially by grounded theory. Secondly, documents from the sample were coded on two separate occasions in accordance with Schreier’s recommendation and both coding attempts were compared. A coefficient of agreement was calculated for each code between attempts to ensure reliability. Chapter 4 addresses 71 the findings of a priori codes, a posteriori codes, and the calculation of coefficients of agreement.
The codebook for this study presents the definitions of each coding category. The worksheet used in this research was similar to that used in the Cape Cod wind farm research in that I coded documents for the presence of characters, plot, problem definitions, causal mechanisms, morals of the story (i.e., proposed solutions), and narrative strategies such as angel- and devil-shifts, scope of conflict, numbers, allocation of costs and benefits, and stance. Each data source also was coded for whether the narrative adopted a particular view on the legitimacy of the shutdown as a negotiating tactic. A version of the codebook for this research is found in Appendix A of this proposal. Appendix B presents the coding worksheet for reference.
Data analysis
The Narrative Policy Framework allows for analysis at micro, meso and macro levels. Micro-level analysis looks at the influence of narratives on individuals and public opinion. Studies analyzing data at the micro level tend to rely on a survey-research-based methodology and experimental designs. Studies at the meso-level address how narratives are constructed and used toward strategic ends by actors in the policy process. These studies typically rely on a qualitative content analysis methodology. Finally, analysis at the macro-level looks at how narratives shape policy among institutions and society over longer periods of time (Shanahan et al., 2013). Because this study focuses on narratives actors use in policy debates, this research will look at narratives’ use at the meso level. I present aggregated public polling data in certain instances for context. 72
Data is evaluated in content analyses either inductively or deductively depending on the desired ends (Bengtsson, 2016). Guest et al. (2012) call inductive processes appropriate for exploratory research, while deductive approaches are used in confirmatory content analyses when the researcher’s aim is hypothesis-driven. In the former, codes emanate from the data from samples that are typically purposive.
Conversely, in deductive forms, codes are developed a priori from hypotheses that test statistically-minded samples.
This study employs both inductive and deductive processes. Given the exploratory aims of this research, the present study uses an inductive approach, but because the theoretical foundation of this research effort is an established theory, namely
NPF, it employs deductive processes, as well.
The practice of labeling studies as one approach over another is subject to debate.
Elo and Kyngäs (2008) write that a deductive approach is appropriate “when the structure of analysis is operationalized on the basis of previous knowledge” and a “deductive approach is based on an earlier theory or model, and therefore it moves from the general to the specific” (p. 109). This guidance calls into question the inductive label as the research is rooted in the Narrative Policy Framework, and the framework provides the basis of the coding schema used. In the course of analyzing data in this study, new narrative elements and strategies emerged inductively from the data.
Flick et al. (2014) tout the benefits of generating data from substantive theories such as the Narrative Policy Framework, saying that doing so has important implications for future studies, such as reinforcing the validity of coding categories and identifying new data to which analytic procedures can later be applied. Armat et al. (2018) question 73 the strict adherence to labeling qualitative content analyses as either inductive or deductive, as certain studies may find that their approaches require a blended mix of reasoning.
A third type of inference is central to content analysis: abductive. Abductive inferences are subject to distinct domains. The domain of interest reflects a study’s theoretical or analytical construct and research questions, and it informs the inferences made. As Krippendorff (2019) states, “An analytical construct accounts for what the content analyst knows, suspects, or assumes about the context of the text, and it operationalizes that presumption procedurally in order to produce inferences” (p 178).
These constructs, as a result, warrant or justify the inferences made so long as they are substantiated by knowledge and the context of the phenomena.
In consideration of the above, upon completion of coding using templates created for this research in Microsoft Word, data were entered into a Microsoft Excel spreadsheet and analyzed to count the frequency with which actors used the identified narrative elements and strategies. This portion of the analysis holds a quantitative aspect in that the use of narrative elements and strategies were counted and reported. Content analyses that do not use statistics beyond measures of description are considered qualitative (Jones et al., 2016; Schreier, 2014; Stemler, 2001). Data are reported through charts and tables in chapters 4 and 5.
Beyond measuring frequency counts, data were analyzed for the emergence of new narrative elements and strategies. Further, texts of interview transcripts were reviewed with an eye toward capturing explicit and implicit values conveyed through actors’ narratives, as well as ensuring this final report descriptively conveys the 74 contextual situation accurately. Quotes from transcripts are excerpted and cited in
Chapter 5 to substantiate ideas and the contextual meaning behind them.
In terms of data analysis, qualitative studies often rely on computers for assistance. Such technological tools can be of value to researchers, particularly in instances with voluminous datasets, but these programs have limitations, too. Programs such as atlas.ti and NVIVO are particularly useful in categorizing data and helping researchers visualize data through features such as word clouds (Sturges, 2014). Merriam and Tisdell (2016) advise, however, “Small-scale qualitative studies probably do not need the capacity of these programs. Further, the time it would take you to learn to operate the program could be spent analyzing your data” (p. 224). Further, while computer-aided analytical tools are valuable in managing large amounts of data, there is no replacement for the value of close engagement and personal analysis vis-à-vis a close reading of texts
(Grimmer & Stewart, 2013).
As such, with 100 transcripts included in the sample of this study, each individual document and text is not exceedingly voluminous, thus manually reading, coding and analyzing each transcript and tracking the collected data using Microsoft Word and Excel were not exceedingly difficult.
Validity, reliability and trustworthiness strategies
Earlier sections of this essay address issues related to credibility in qualitative research, but the importance of these matters warrants further discussion here. Because qualitative studies typically focus on lived experiences and phenomena involving people, the ethical treatment of research subjects is an important consideration. Scholarly associations have developed codes of ethics—many with shared principles, such as 75 obtaining the informed consent of those being studied, avoiding deception, ensuring the privacy and confidentiality of those who participate in the study, and striving for accuracy in data collection and the manner in which findings are reported (Christians,
2011).
Validity, reliability, rigor, trustworthiness are important for gauging the credibility of research, but scholars have debated whether these ideas are appropriate for qualitative studies. Opponents to the use of these terms argue the concepts are associated too closely with the positivist orientation of quantitative studies. Some have proposed alternative terms, such as credibility, dependability, conformability and transferability
(Elo et al., 2014). This study utilizes the more conventional terms, rather than the alternative concepts. The term “credibility” is used to refer generally to this category of ideas.
Thoroughly documenting research methods and being transparent about those methods are important factors for strengthening the credibility of qualitative research.
Shank and Villela (2004) say transparency in research provides the audience with a window into the research process. One recommendation to enhance qualitative research credibility is to establish a chain of logic through the data collection phase of research.
Documenting this chain through memos written over the course of gathering and analyzing data and referring to those memos in the final report can serve to provide readers with such a window (Bengtsson, 2016). Such a logic chain also could be thought of as an audit trail, which describes research methods thoroughly, including data collection methods, the construction of codes or categories, and a detailed explanation of how decisions are made throughout the process. Such a road map allows others to 76 replicate the study and, if the research was performed well, arrive at similar conclusions or findings (Merriam & Tisdell, 2016).
Triangulation is an important strategy for bolstering the internal credibility of a qualitative study. At its core, triangulation is about offering a diversity of perspectives in terms of data sources. The more often different sources yield similar data, the stronger the validity of the findings (Stake, 1998). Denzin (1978) identifies four different forms of triangulation: data triangulation, or the use of multiple data sources; investigator triangulation in which multiple researchers or evaluators examine data; theory triangulation where data are evaluated and interpreted through multiple perspectives; and methodological triangulation, or the use of multiple methods in a study. Jansick (1998) offers a fifth form of triangulation: interdisciplinary triangulation. In this form, researchers analyze data from the perspective of more than one field. This study relies on data triangulation and theory triangulation, as well as interdisciplinary triangulation to an extent.
With qualitative studies, confirmation through triangulation assures readers a study’s findings are not solely the result of a researcher’s pre-conceived notions or biases
(Stemler, 2001). Denzin and Lincoln (1998) caution that triangulation “is not a tool or a strategy of validation, but an alternative” (p. 4). While an objective reality can never be acquired through qualitative research, the use of multiple methods does provide readers with an assurance that research attempts to convey an in-depth understanding of the phenomena being studied, they write.
Another essential feature of all research is trust in the researcher—not only by subjects of and participants in the research, but also among readers of the findings, thus 77 an investigator’s ethics are an important consideration, as is their ability to demonstrate competence (Patton, 2015). In qualitative studies, where the researcher is integral to data collection and analysis, trust is particularly important.
Self-reflection on the part of the researcher is also at the heart of reflexivity in qualitative studies. Flick et al. (2014) defines reflexivity as “processes of self-awareness and self-criticism” (n.p.). Because data analysis in qualitative studies relies on the subjective interpretation of the researcher, it is important for those conducting research to reflect on their personal background and involvement in the case or study. Such disclosures can reveal researcher’s political and personal biases, which may influence their analytical capabilities (Bishop and Shepherd, 2011).
Finally, the ethical treatment of data to ensure confidentiality of participants must be top of mind for researchers. The disclosure of data and the connection of that data to individual research participants risks embarrassing the participant, diminishing their social standing, employment loss, or other hardships (Stake, 1998). For the purpose of this study, confidentiality is less of a concern as data were gathered from the public domain, specifically, interviews that were broadcast widely with transcripts of those interviews readily available online.
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Chapter 4
Findings
As discussed in Chapter 3, I coded 100 interviews on two separate occasions for this study. These interviews, conducted between December 16, 2018 and February 12,
2019, were found within 60 different broadcasts by ABC, CBS, NBC and NPR. The number of interviews does not equal the number of broadcasts as some broadcast transcripts included interviews with multiple elected and appointed officials according to the sample selection criteria set forth earlier.
This chapter begins by disclosing the coefficients of agreement between the two separate coding rounds, including the counts of each code per round, and the mean of both rounds. As stated in Chapter 3, codes are considered to be well defined and conceptualized when there is agreement of at least 80 percent between attempts. In keeping with this recommendation, two coefficients of agreement were calculated for each code—one comparing Round 1 to Round 2, and another by comparing Round 2 to
Round 1. I calculated coefficients by dividing the total number of times a particular code was identified as being present in Round 1 by the total number of times the same code was identified in Round 2 and vice versa.
For example, Trump was coded as a “Hero” in 28 transcripts during in Round 1, but only 27 times in Round 2. Thus, dividing 28 by 27 arrives at a coefficient of agreement of 103.7, while dividing 27 by 28 generates a coefficient of agreement of
96.43. Because both coefficients are within +/- 20 percentage points of 100 percent, the two coding attempts are considered to be consistent for the purpose of this research.
Codes within this range (+/- 20 percentage points) are referred to as “consistent” or as 79
“demonstrating agreement” throughout this chapter. Such codes are denoted with an asterisk in data reporting tables.
The second mode of analysis looks at data according to narrators’ political party.
The narrative elements and strategies Republicans employed are compared to those of
Democrats. The next section presents codes used by week. The federal government shutdown case defined for this research was bounded by a nine-week period, throughout which narratives and strategies shifted amid changing political and social dynamics. Data are described in text and supplemented with tables and figures throughout the chapter.
Finally, Chapter 4 concludes with a discussion on emergent codes identified in the course of research. This study relied on a priori codes as defined and conceptualized by the Narrative Policy Framework, however the two coding attempts generated codes that did not conform with the definitions set forth in the code book developed prior to undertaking this study. Emergent codes were noted on coding worksheets during both rounds of coding. The section on emergent codes yields new insight and contributions to the Narrative Policy Framework that invite subsequent research and analysis to expand the theory and its utility for evaluating policy and political communications.
Round 1 vs. round 2 with coefficients of agreement
Looking at narrative elements, in the “Hero” category, the two coding attempts found agreement within +/-20 percentage points of 100 percent for Trump (103.7 percent comparing Round 1 to Round 2, and 96.43 percent comparing Round 2 to Round 1),
American People (100 percent, 100 percent), and Others (89.19 percent, 112.12 percent), as shown in Table 4-1. The first round of coding found 28 instances where Trump and/or his administration were labeled Hero, and 27 instances in the second round (mean=27.5). 80
The American People were coded as heroes one time in both rounds (mean=1), while
Other actors were coded as such 33 times in the first round and 37 times in the second
(mean=35).
Table 4-1: Hero Code Counts and Coefficients of Agreement
Hero: Hero: Hero: Hero: Hero: Hero: Hero: Trump* CongRep CongDem Law Immigrant Americans* Other* Round 1 28 4 13 4 1 1 33 Round 2 27 8 24 5 0 1 37 Mean 27.5 6 18.5 4.5 0.5 1 35 R1/R2 103.70% 50.00% 54.17% 80.00% #DIV/0! 100.00% 89.19% R2/R1 96.43% 200.00% 184.62% 125.00% 0.00% 100.00% 112.12%
For “Villains,” coding attempts reported in Table 4-2 were consistent for Trump
(95 percent, 105.26 percent), Law Enforcement (100 percent, 100 percent), and Others
(102.86 percent, 97.22 percent). Officials within the Trump administration were coded as such 38 times in the first round and 40 times in the second (mean=39). Law enforcement were coded one time in each round as villains (mean=1), and Other actors were coded villains 36 and 35 times in the first and second rounds, respectively (mean=35.5).
Table 4-2: Villain Code Counts and Coefficients of Agreement
Villain: Villain: Villain: Villain: Villain: Villain: Villain: Trump* CongRep CongDem Law* Immigrant Americans Other* Round 1 38 7 18 1 3 0 36 Round 2 40 10 28 1 6 1 35 Mean 39 8.5 23 1 4.5 0.5 35.5 R1/R2 95.00% 70.00% 64.29% 100.00% 50.00% 0.00% 102.86% R2/R1 105.26% 142.86% 155.56% 100.00% 200.00% #DIV/0! 97.22%
The “Victim” code registered agreement between attempts among the
Congressional Republicans (100 percent, 100 percent), American People (111.11 percent,
90 percent), and Other (88.57 percent, 112.9 percent) codes. Congressional Republicans were coded as Victims one time in each round (mean=1). American people were coded as such 20 times in the first and 18 times in the second (mean=19), while Other individuals 81 were coded as Victims 31 times during the first round and 35 times in the second round
(mean=33), as shown in Table 4-3.
Table 4-3: Victim Code Counts and Coefficients of Agreement
Victim: Victim: Victim: Victim: Victim: Victim: Victim: Trump CongRep* CongDem Law Immigrant Americans* Other* Round 1 1 1 0 8 11 20 31 Round 2 0 1 0 6 16 18 35 Mean 0.5 1 0 7 13.5 19 33 R1/R2 #DIV/0! 100.00% #DIV/0! 133.33% 68.75% 111.11% 88.57% R2/R1 0.00% 100.00% #DIV/0! 75.00% 145.45% 90.00% 112.90%
A clearly defined “Plot” was coded consistently between rounds (95.79 percent,
104.4 percent). In terms of specific Plot codes, stories of Decline (100 percent, 100 percent), Conspiracy (100 percent, 100 percent), and Other (103.9 percent, 96.25 percent) each demonstrated consistency between coding attempts. Interviewees were coded as using at least some form of Plot 91 times in Round 1 and 95 times in Round 2
(mean=93). Codes for stories of Decline (five times) and Conspiracy (13 times) matched identically between attempts. Interview subjects used an Other plot 80 times and 77 times, respectively, in the first and second rounds (mean=78.5). Plot data can be viewed in Table 4-4.
Table 4-4: Plot Code Counts and Coefficients of Agreement
Plot* Stymie Decline* Change Control Conspir- Blame Plot: d Prog acy* Victim Other* Round 1 91 20 5 0 4 13 0 80 Round 2 95 30 5 0 8 13 1 77 Mean 93 25 5 0 6 13 0.5 78.5 R1/R2 95.79% 66.67% 100.00% #DIV/0! 50.00% 100.00% 0.00% 103.90% 104.40 150.00 R2/R1 % % 100.00% #DIV/0! 200.00% 100.00% #DIV/0! 96.25%
Presence of a preferred Moral of the Story was coded consistently between rounds
(98.92 percent, 101.09 percent). Each individual type of Moral with the exception of 82
Shutdown the Government demonstrated agreement: building a wall (105.88 percent,
94.44 percent), funding non-structural solutions (89.47 percent, 111.76 percent), funding a mix of structural and non-structural solutions (100 percent, 100 percent), reopen the government (85.71 percent, 116.67 percent), and Other (101.67 percent, 98.36 percent).
Interviewees identified at least one preferred policy alternative as a solution in 92 transcripts during Round 1 and 93 transcripts in Round 2 (mean=92.5). Referring to specific solutions between the first and second rounds, transcripts were coded in the following respective ways: building a Wall, 18 and 17 times (mean=17.5); funding Non-
Structural techniques 17 and 19 times (mean=18); using a Mix of structural and non- structural techniques 21 times each; Reopen the government 30 and 35 times
(mean=32.5); and an Other solution 61 and 60 times (mean=60.5). Moral of the Story data is presented in Table 4-5.
Table 4-5: Moral of the Story Code Counts and Coefficients of Agreement
Non- Moral: Moral* Wall* structural* Mix* Shutdown Reopen* Other* Round 1 92 18 17 21 1 30 61 Round 2 93 17 19 21 0 35 60 Mean 92.5 17.5 18 21 0.5 32.5 60.5 R1/R2 98.92% 105.88% 89.47% 100.00% #DIV/0! 85.71% 101.67% R2/R1 101.09% 94.44% 111.76% 100.00% 0.00% 116.67% 98.36%
Looking next at Narrative Strategies (Table 4-6), the two coding attempts were consistent in the use of at least one strategy (95 percent, 105.26 percent). Similarly, the use of the angel-shift (87.5 percent, 114.29 percent) and devil-shift (90.63 percent,
110.34 percent) were coded consistently. In terms of Scope of Conflict, only the use of a
Scope of Conflict strategy (84.38 percent, 118.52 percent) was consistent. There was no agreement in the use of either an expansion or containment strategy. The first coding 83 round identified the use of a Narrative Strategy 76 times in the first coding attempt and
80 times in the second (mean=78). In terms of specific strategies coded consistently between attempts, the Angel- and Devil-shifts were used respectively 49 and 58 times in the first round, and 56 and 64 times in the second. Consequently, the mean for Angel- shift was 52.5 times and 61 times for the Devil-shift. Scope of Conflict was coded 27 times in the first and 32 times in the second (mean=29.5).
Table 4-6: Narrative Strategy, Angel- and Devil-Shifts, Scope of Conflict Code Counts and Coefficients of Agreement
NarrStrategy Angel* Devil* Scope* ScopeExpan ScopeLimi * d t Round 1 76 49 58 27 25 2 Round 2 80 56 64 32 32 0 Mean 78 52.5 61 29.5 28.5 1 R1/R2 95.00% 87.50% 90.63% 84.38% 78.13% #DIV/0! R2/R1 105.26% 114.29% 110.34% 118.52% 128.00% 0.00%
The use of Problem Definition (Table 4-7) was coded consistently between rounds (84.21 percent, 118.75 percent). The specific definitions of Manufactured Crisis
(90.91 percent, 110 percent) and Ineffective Leadership (100 percent, 100 percent) demonstrated agreement. Sixty-four interviews were coded as defining a problem in the first round and 76 interviews were coded as such in the second attempt (mean=70). Of the two Problem Definition codes to register agreement between attempts, Manufactured
Crisis was counted 10 and 11 times between rounds (mean=10.5), while Ineffective
Leadership was coded in 16 transcripts each round.
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Table 4-7: Problem Definition Code Counts and Coefficients of Agreement
Proble Humani- Manufact Legal Past Security Ineffective Other m Def* tarian -ured* Fail admin fail Threat leadership * Round 1 64 11 10 7 0 7 16 34 Round 2 76 8 11 11 0 15 16 47 Mean 70 9.5 10.5 9 0 11 16 40.5 R1/R2 84.21% 137.50% 90.91% 63.64% #DIV/0! 46.67% 100.00% 72.34% 118.75 157.14 214.29 138.24 R2/R1 % 72.73% 110.00% % #DIV/0! % 100.00% %
When coding Causal Mechanisms (Table 4-8), there was agreement in the use of some form of causal mechanism (90.67 percent, 110.29 percent), as well as the Other code (94.12 percent, 106.25 percent). A Causal Mechanism was coded 68 and 75 times
(mean=71.5), respectively, in rounds 1 and 2, and the Other code was found 32 and 34 times in the first and second rounds (mean=33).
Table 4-8: Causal Mechanism Code Counts and Coefficients of Agreement
Causal* Accidental Inadvertent Intentional Mechanical CausalOther* Round 1 68 0 2 33 7 32 Round 2 75 0 3 47 9 34 Mean 71.5 0 2.5 40 8 33 R1/R2 90.67% #DIV/0! 66.67% 70.21% 77.78% 94.12% R2/R1 110.29% #DIV/0! 150.00% 142.42% 128.57% 106.25%
Of the remaining types of Narrative Strategies, Numbers (92.16 percent, 108.51 percent), the use of Costs for preferred solutions (114.29 percent, 87.5 percent) and opposed solutions (93.33 percent, 107.14 percent), each demonstrated agreement.
Benefits of Preferred and Opposed solutions were not coded consistently. Numbers were coded 47 times in the first round and 51 times in the second round (mean=49). Assigning
Costs to Preferred solutions was coded 16 and 14 times (mean=15) and to Opposed solutions 14 and 15 times (mean=14.5). Data for the Numbers and Costs categories are found in Table 4-9. 85
Table 4-9: Numbers, Costs and Benefits Code Counts and Coefficients of Agreement
Numbers* CostsPrefer* CostsOppose* BenefitsPrefer BenefitsOppose Round 1 47 16 14 25 0 Round 2 51 14 15 38 0 Mean 49 15 14.5 31.5 0 R1/R2 92.16% 114.29% 93.33% 65.79% #DIV/0! R2/R1 108.51% 87.50% 107.14% 152.00% #DIV/0!
In the Narrative Stance of interviewees, the two coding rounds found consistency in the use of the No Stance code (114.10 percent, 87.64 percent) as shown in Table 4-10.
The No Stance code was identified 89 times in the first round and 78 times in Round 2
(mean=83.5).
Table 4-10: Stance Code Counts and Coefficients of Agreement
StanceWin StanceLose StanceNo* StanceOther Round 1 2 6 89 1 Round 2 5 11 78 7 Mean 3.5 8.5 83.5 4 R1/R2 40.00% 54.55% 114.10% 14.29% R2/R1 250.00% 183.33% 87.64% 700.00%
Under the Legitimacy category, only the Legitimate code (100 percent, 100 percent) demonstrated agreement in terms of how interviewees felt about the shutdown.
The shutdown was coded as Legitimate five times in both coding rounds. Code counts for this category are found in Table 4-11.
Table 4-11: Legitimacy Code Counts and Coefficients of Agreement
Legitimate* Illegitimate Manufactured Unnecessary Unwanted Round 1 5 9 14 15 16 Round 2 5 16 18 10 13 Mean 5 12.5 16 12.5 14.5 R1/R2 100.00% 56.25% 77.78% 150.00% 123.08% R2/R1 100.00% 177.78% 128.57% 66.67% 81.25%
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Republicans vs. Democrats
Each transcript’s data coding worksheet logged the political party affiliation of interview subjects. Members of Congress were coded as being either Republicans or
Democrats based on their party registration. Trump and members of his administration were coded as Republicans. In this section, I contrast data as coded among Democrats and Republicans, comparing the percentage of interviews given by members of each party that included a code found to be consistent between rounds. As with the prior section, codes demonstrating agreement are designated with an asterisk in table column headers.
Generally, Narrative Elements and Narrative Strategies exhibited distinct differences in coded content of Republicans versus Democrats. The following percentages are calculated as a sum of code counts among both rounds of coding by party, divided by the sum of transcripts by party, which totaled 98 transcripts by
Democrats (49 interviews coded twice) and 102 transcripts by Republicans (51 interviews coded twice).
With respect to heroes, Republicans identified Trump and his administration as heroes in 52.94 percent of their interviews among both coding attempts, but only 1.02 percent of Democratic interview transcripts described the administration in heroic terms.
Republicans cited Americans as heroes in 1.96 percent of interviews, while Democrats did not code Americans as such in any of their 49 transcripts. For the Other code, 40.82 percent of Democrats used it as a Hero code as did 29.41 percent of Republicans (Table
4-12).
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Table 4-12: Hero Code Counts and Percentages by Party Affiliation
Hero: Hero: Hero: Hero: Hero: Hero: Hero: Trump* CongRep CongDem Law Immigrant Americans Other* * Democrat 1 3 36 0 1 0 40 Republican 54 9 1 9 0 2 30 Democrat % 1.02% 3.06% 36.73% 0.00% 1.02% 0.00% 40.82% Republican % 52.94% 8.82% 0.98% 8.82% 0.00% 1.96% 29.41%
Looking at villains (Table 4-13), Democrats labeled Trump and/or his administration as such in 78.57 percent of their interviews, while slightly less than 1 percent (0.98 percent) of Republican interviewees vilified the administration. The margin of differences was smaller for other consistent Villain codes; Law Enforcement registered similar percentages between Democrats and Republicans at 1.02 percent and 0.98 percent, respectively, while Other was coded on 42.16 percent of Republican interviews and 28.57 percent of Democratic interviews.
Table 4-13: Villain Code Counts and Percentages by Party Affiliation
Villain: Villain: Villain: Villain: Villain: Villain: Villain: Trump* CongRep CongDem Law* Immigrant Americans Other* Democrat 77 16 1 1 1 1 28 Republican 1 1 45 1 8 0 43 Democrat % 78.57% 16.33% 1.02% 1.02% 1.02% 1.02% 28.57% Republican % 0.98% 0.98% 44.12% 0.98% 7.84% 0.00% 42.16%
Under the category of Victim, 2.04 percent of Democrats and zero percent of
Republicans identified Congressional Republicans as victims. Democrats viewed the
American people as victims in nearly 24.5 percent of their interviews and Other in 40.82 percent of their transcripts. Meanwhile, Republicans viewed the American people as victims 13.73 percent of the time, and they cited Other as being victimized nearly 25.5 percent of the time (Table 4-14). 88
Table 4-14: Victim Code Counts and Percentages by Party Affiliation
Victim: Victim: Victim: Victim: Victim: Victim: Victim: Trump CongRep* CongDem Law Immigrant Americans* Other* Democrat 1 2 0 8 18 24 40 Republican 0 0 0 6 9 14 26 Democrat % 1.02% 2.04% 0.00% 8.16% 18.37% 24.49% 40.82% Republican % 0.00% 0.00% 0.00% 5.88% 8.82% 13.73% 25.49%
Moving to Plot lines, as shown in Table 4-15, approximately 93 percent of members from both parties used at least one clearly defined Plot in their narrative (92.86 percent of Democrats and 93.14 percent of Republicans). As for those specific plot categories that registered agreement between coding rounds, Democrats employed the
Story of Decline in 5.1 percent of their interviews, while Republicans used it in 4.9 percent of their interviews. Republicans used the Story of Conspiracy approximately four times as often as Democrats—20.59 percent to 5.1 percent, respectively. The difference between parties was less for the use of an Other plot. Democrats used it in 84.69 percent of their transcripts, while Republicans used it in 72.55 percent of their interviews.
Table 4-15: Plot Code Counts and Percentages by Party Affiliation
Plot* Sty- Decline* Change Con- Conspir- Blame Plot: mied trol acy* Victim Other* Prog Democrat 91 22 5 0 5 5 0 83 Republican 95 28 5 0 7 21 1 74 Democrat % 92.86% 22.45% 5.10% 0.00% 5.10% 5.10% 0.00% 84.69% Republican % 93.14% 27.45% 4.90% 0.00% 6.86% 20.59% 0.98% 72.55%
Democrats pointed to a clearly defined Moral of the Story 95.92 percent of the time, and Republicans expressed their preferred solution in 89.22 percent of interviews. 89
Republicans voiced support for the construction of a wall between Mexico and the U.S., as Trump advocated, in 34.31 percent of interviews, while no Democrats voiced support exclusively for this solution. Near opposite percentages applied to those who were proponents of only Non-structural solutions. Democrats advocated for the use of enhanced technology and more border patrol agents, among other tactics, in 34.69 percent of their interviews, but only 1.96 percent of Republicans did so. When it came to advocating for a Mix of structural and non-structural solutions as the Moral of the Story,
Democrats employed that narrative element in 10.2 percent of transcripts and
Republicans used it 31.37 percent of their interviews. Democrats preferred Reopening the government as a solution by a margin of approximately two-to-one over Republicans
(43.88 percent versus 21.57 percent, respectively). The closest similarity between the two parties was support for an Other Moral of the Story. More than 60 percent of Democratic transcripts incorporated this Moral and 60.78 percent of Republican transcripts did so.
Code data on Morals of the Story can be found in Table 4-16.
Table 4-16: Moral of the Story Code Counts and Percentages by Party Affiliation
Moral* Wall* Non- Mix* Shutdown Reopen* Moral: structural* Other* Democrat 94 0 34 10 0 43 59 Republican 91 35 2 32 1 22 62 Democrat % 95.92% 0.00% 34.69% 10.20% 0.00% 43.88% 60.20% Republican % 89.22% 34.31% 1.96% 31.37% 0.98% 21.57% 60.78%
Turning to narrative strategies, Democrats used some form of strategy more often than Republicans, with the former incorporating a strategy into their narrative 86.73 percent of the time and the latter at 69.61 percent. Democrats were coded as using the
Angel-shift 56.12 percent of the time and the Devil-shift in 71.43 percent of their 90 interviews. Conversely, Republicans used the Angel-shift 49.02 percent and the Devil- shift 50.98 percent of the time. Scope of Conflict was used almost equally between
Republicans and Democrats at 29.41 percent and 29.59 percent, respectively. There was not agreement between rounds of coding for the specific strategies of expanding or limiting the Scope of Conflict. See Table 4-17 for related data.
Table 4-17: Narrative Strategy, Angel- and Devil-shift, Scope of Conflict Code Counts and Percentages by Party Affiliation
NarrStrategy* Angel* Devil* Scope* ScopeExpand ScopeLimit Democrat 85 55 70 29 28 1 Republican 71 50 52 30 29 1 Democrat % 86.73% 56.12% 71.43% 29.59% 28.57% 1.02% Republican % 69.61% 49.02% 50.98% 29.41% 28.43% 0.98%
Under the category of Problem Definition (Table 4-18), 74.49 percent of
Democrats and 64.71 percent of Republicans clearly defined the problem in their narratives, but in terms of specific definitions, only Manufactured Crisis and Ineffective
Leadership were coded consistently. These codes, however, demonstrated differences by political party. Democrats viewed the situation as a Manufactured Crisis in 21.43 percent of interviews. No Republicans used the Manufactured code in their transcripts.
Democrats cited Ineffective Leadership 28.57 percent of interviews, while Republicans defined the underlying problem as such only 3.92 percent of the time.
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Table 4-18: Problem Definition Code Counts and Percentages by Party Affiliation
Proble Humani Manufact Legal Past Secur- Ineffective Other m Def* -tarian -ured* Fail admi ity leadership n fail Threat * Democrat 73 3 21 3 0 1 28 38 Republica n 66 16 0 15 0 21 4 43 Democrat 0.00 38.78 % 74.49% 3.06% 21.43% 3.06% % 1.02% 28.57% % Republica 14.71 0.00 20.59 42.16 n % 64.71% 15.69% 0.00% % % % 3.92% %
For the more general category of Causal Mechanism, only the use of this narrative strategy and the Other code were coded consistently. As shown in Table 4-19, 75.51 percent of Democrats and 67.65 percent of Republicans used a Causal Mechanism.
Democrats attributed the cause of the shutdown to some Other reason 31.63 percent of the time, and Republicans pointed to some Other mechanism in 34.31 percent of their transcripts.
Table 4-19: Causal Mechanism Code Counts and Percentages by Party Affiliation
Causal* Accidental Mechanical Inadvertent Intentional CausalOther* Democrat 74 0 3 28 26 31 Republican 69 0 7 21 16 35 Democrat % 75.51% 0.00% 3.06% 28.57% 26.53% 31.63% Republican % 67.65% 0.00% 6.86% 20.59% 15.69% 34.31%
Both parties used Numbers to support their arguments. Nearly half of both
Republicans (49.02 percent) and Democrats (48.98 percent) used Numbers in their narratives. Referencing Costs of Preferred and Opposed policy solutions were employed differently, however. Republicans were nearly four times more likely to cite the costs of their preferred policy solution—constructing a wall—than Democrats. Members of the
GOP cited these costs in 23.53 percent of their interviews, but only 6.12 percent of 92
Democrats did so. Democrats, however, cited the costs of their Opposed policy approach in 20.41 percent of their interviews, while Republicans did so only 8.82 percent of the time. Table 4-20 presents data on Numbers, Costs and Benefits.
Table 4-20: Numbers, Cost and Benefit Code Counts and Percentages by Party
Affiliation
Numbers* CostsPrefer* CostsOpposed* BenefitsPrefer BenefitsOpp Democrat 48 6 20 28 0 Republican 50 24 9 35 0 Democrat % 48.98% 6.12% 20.41% 28.57% 0.00% Republican % 49.02% 23.53% 8.82% 34.31% 0.00%
Because the vast majority of interviewees took No Stance on the shutdown—the only code in this category to register agreement between coding attempts—there is little value in comparing Republicans to Democrats here, but nevertheless, 84.69 percent of
Democrats took this stance compared to 82.35 percent of Republicans (Table 4-21).
Table 4-21: Stance Code Counts and Percentages by Party Affiliation
StanceWin StanceLose StanceNo* StanceOther Democrat 1 8 83 5 Republican 6 9 84 3 Democrat % 1.02% 8.16% 84.69% 5.10% Republican % 5.88% 8.82% 82.35% 2.94%
The views on justification for the shutdown, or the legitimacy of it, presented a more notable distinction, although not as great as other coding categories. Nearly one- tenth of Republicans (9.8 percent) viewed the shutdown as Legitimate among the two coding attempts, but no Democrats shared that view. Table 4-22 shows coding counts for interviewees’ views on the shutdown’s Legitimacy.
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Table 4-22: Legitimacy Code Counts and Percentages by Party Affiliation
Legitimate* Illegitimate Manufactured Unnecessary Unwanted Democrat 0 13 29 13 12 Republican 10 12 3 12 17 Democrat % 0.00% 13.27% 29.59% 13.27% 12.24% Republican % 9.80% 11.76% 2.94% 11.76% 16.67%
Codes by week
Over the course of the nine-week period of this study, interest in the government shutdown issue waxed and waned. This section begins by reviewing the number of interviews, by week, as a proxy for public interest in the shutdown and pressure on lawmakers to end the impasse. From there, data are analyzed for trends in narrative elements and strategies as the shutdown dragged on. Data for narrative elements and strategies are presented as the mean of totals counted during the first and second coding attempts. Further, to control for variation in transcript counts by week, I calculate a percentage to demonstrate the ratio of code use among all transcripts collected, by party, in a given week. The following sections present data only for those codes demonstrating agreement between coding rounds. Corresponding graphs are found throughout the section. Because only the Other code within the Causal Mechanism category and No
Stance code registered consistency, this section forgoes discussion on these codes.
Interview counts
As shown in Figure 4-1, the week prior to the shutdown’s commencement on
December 22, 2018, and the first week thereafter, interest in the matter remained relatively low insofar as the sample’s selected media outlets and programs produced content on the topic. The sample selection from the week prior to shutdown (December
16-22, 2018) found nine interviews broadcast by the selected media outlets that addressed 94 the shutdown. The number of interviews dipped the following week—the first in which the shutdown went into effect—to seven interviews, but increased to 10 interviews in
Week 3. As the shutdown endured, interest generally increased. Weeks four (January 6-
12, 2019) and six (January 20-26, 2019) produced the highest number of interviews at 18 each. In Week 5, the interview count dipped to 10, perhaps due to media interest in a New
York Times story published one week earlier reporting the U.S. Federal Bureau of
Investigation had opened a counter-intelligence investigation into Trump following his firing of former FBI director James Comey. The report suggested investigators sought to ascertain whether Trump was an agent of the Russian government.
Interview counts related to the shutdown generally decreased in weeks 7-9.
Toward the end of Week 6, specifically on January 25, 2019, Trump and Congressional leaders reached a compromise to fund and reopen shuttered government operations for three weeks, thereby allowing parties more time to negotiate budgets for the remainder of the fiscal year. The selected media outlets produced 14 interviews in Week 7, six interviews in Week 8, and eight interviews in Week 9.
20 18 16 14 12 10 8 6 4 2 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Figure 4-1: Number of Interviews Coded by Week 95
Narrative elements and strategies
Of the four codes registering agreement in the Hero category (Figure 4-2), Trump and his administration were labeled heroes most often. In terms of means, the administration was coded as a Hero more often as the shutdown endured, going from an average of 1.5 times in interviews conducted during Week 1 to between four and five times in weeks three through six. After the three-week, short-term compromise was signed, the average number of times the administration was cited as a Hero dropped to a range of between two and three times in the final three weeks.
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Hero: Trump Hero: Americans Hero: Other
Figure 4-2: Heroes by Week: Mean Count
Stated as percentages (Figure 4-3), Other was coded more often in weeks one and two at 50 percent and 57.1 percent, then dropped to between 25 percent and 40 percent in weeks three through nine. The Trump code was found in less than 20 percent of interviews in weeks one and two, after which it alternated between 45 percent and 22 percent in weeks three through 6. In weeks seven, eight and nine, Trump was coded as a 96
Hero in 14.28 percent, 50 percent, and 31.25 percent of interviews, respectively. Finally,
Americans were coded as heroes in only 5.56 percent of interviews during Week 4.
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Hero: Trump Hero: Americans Hero: Other
Figure 4-3: Heroes by Week: Ratio Count
Trump and his administration’s portrayal as villains escalated as the shutdown dragged on. While coded as such between an average of 3.5 and 4.5 times in weeks 1-3, the administration was coded as a Villain an average of 11 times by Week 6, after which the code was used less often, eventually being used only an average of one time in weeks
8 and 9. Here, too, Others were often recognized as a Villain with an upward trend in the average counts that reached its peak in the middle portion of the nine-week case. Mean counts for Villains are found in Figure 4-4, while codes under this category as percentages of interviews by week are found in Figure 4-5. Trends in the latter mirrored those in the former, with differences noted in the treatment of Trump and his administration between weeks three and five and in the Other code for weeks seven through nine. 97
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Villain: Trump Villain: Law Villain: Other
Figure 4-4: Villains by Week: Mean Count
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Villain: Trump Villain: Law Villain: Other
Figure 4-5: Villains by Week: Ratio Count
The American people and emergent codes of Other actors were the most often cited Victims in narratives by federal officials as shown in Figure 4-6. Americans were most often referred to as Victims in Week 4, having not registered any references in weeks 1 and 2, and 2.5 times in Week 3. After Week 4, the code was coded fewer times generally. Others were coded as Victims more often than the American people. The curve 98 for Other was not as pronounced between weeks four and six when evaluated on a percentage basis, but generally similar for American People and Congressional
Republicans as shown in Figure 4-7.
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Victim: CongRep Victim: Americans Victim: Other
Figure 4-6: Victims by Week: Mean Count
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Victim: CongRep Victim: Americans Victim: Other
Figure 4-7: Victims by Week: Ratio Count
Three Plot categories that demonstrated consistency between coding attempts were employed in the narratives to differing extents. Stories of Decline and Stories of 99
Conspiracy were the two consistent a priori codes found most often in the narratives of interviewees, but Other plots emerged in the course of coding that were, cumulatively found more often as shown in Figure 4-8. Here too, just as with Other code in the Victim category, the week-to-week difference for the same code under Plot lines when shown as a percentage of occurrences by week were not as pronounced as evidenced in Figure 4-9.
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Decline Conspiracy Plot: Other
Figure 4-8: Plot by Week: Mean Count
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Decline Conspiracy Plot: Other
Figure 4-9: Plot by Week: Ratio Count 100
With respect to Morals of the Story, four of the five predetermined codes demonstrated consistency in the findings. Three of those—building a Wall, funding Non- structural approaches to enhance border security, and a Mix of a physical barrier and non- structural techniques—were coded an average of between 1 and 5.5 times per week.
More variability was found in the coding of Re-opening the government. As the policy impasse prolonged the shutdown, calls for this solution intensified, having not been used in the first two weeks, to averages of three times in Week 3, 11 times in Week 4, six times in Week 5, and 11.5 times in Week 6. Following enactment of the three-week compromise, the presence of this code dropped to between 0 and 0.5 times. Mean counts for Morals of the Story are depicted graphically in Figure 4-10.
Figure 4-11 displays consistently coded Morals as percentages of use by week.
Comparing the two different methods for presenting data under this category, the ratio count shows more consistency in the preference for reopening the government between weeks four and six, suggesting the fewer number of transcripts in Week 5 as compared to weeks four and six affected the mean count for this code. Another noticeable variation is the curve depicting the Wall code in weeks six through nine. Whereas the mean-count graph depicts a more subtle increase in use of this code among interviewees, the same line under the ratio count graph indicates a more pronounced increase in preference for this policy solution in the waning weeks of the shutdown and three-week compromise period. 101
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Wall Non-structural Mix Reopen Moral: Other
Figure 4-10: Moral of the Story by Week: Mean Count
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Wall Non-structural Mix Reopen Moral: Other
Figure 4-11: Moral of the Story by Week: Ratio Count
Use of the Angel- and Devil-shift strategies generally increased through the first four weeks of the case when assessed using the mean count between both coding attempts. Both codes declined in Week 5—due to the smaller number of transcripts available this week—before each reaching new highs in Week 6 with the Angel-shift being tallied an average of 24 times and the Devil-shift being coded 23 times. A Scope of 102
Conflict strategy was coded less frequently, on average, than the Angel- and Devil-shifts throughout each of the nine weeks. The mean counts of Scope of Conflict codes generally increased through the first seven weeks, going from an average of four instances between both coding attempts in Week 1 to an average of 12 uses in Week 7. Use of this code then declined in weeks eight and nine (Figure 4-12).
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Angel Devil Scope
Figure 4-12: Angel-/Devil-shift and Scope of Conflict by Week: Mean Count
When analyzed as a ratio of uses among counts of interviews by week, each shift strategy exhibited a slightly different pattern of usage. The Devil-shift was used in approximately 55 percent of interviews in Week 1 to a high of 85 percent of interviews in
Week 3. Its use then generally declined through Week 8 (to approximately 46 percent) before rebounding to nearly 67 percent in Week 9. The Angel-shift went from being coded in approximately 39 percent of interviews in Week 1 to slightly more than 64 percent in Week 2. This code then dipped to 55 percent or below in weeks three and four before increasing to at or near 70 percent in weeks five and six. Use of the Angel-shift declined throughout the remaining two weeks. Again, the Scope of Conflict code was 103 used with less frequency when measured as a ratio. Ratio data for these three codes are found in Figure 4-13.
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8
Angel Devil Scope
Figure 4-13: Angel-/Devil-shift and Scope of Conflict by Week: Ratio Count
The two Problem Definition codes demonstrating agreement exhibited different patterns of use. Whereas Ineffective Leadership was coded more often in the early weeks of the shutdown, Manufactured Crisis steadily increased, plateauing from weeks four to five despite fewer interviews being produced in the sample during the latter, but ultimately culminating in an average count of 5.5 coded instances in Week 6, as shown in
Figure 4-14. Figure 4-15 illustrates similar patterns, but a broader range for Ineffective
Leadership when accounting for use of the code as a percentage of interviews coded by week, suggesting the characterization was more prevalent than shown as a mean count. 104
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Manufactured Ineffective leadership
Figure 4-14: Problem Definition by Week: Mean Count
0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Manufactured Ineffective leadership
Figure 4-15: Problem Definition by Week: Ratio Count
Of the remaining narrative strategies, Causal Mechanisms will be discussed in the discussion on emergent codes as none of the a priori codes demonstrated agreement between attempts. Under the categories of Numbers and Costs, each demonstrated a generally similar pattern with interviewees employing this strategy more so on the frontside of the shutdown as shown in Figure 4-16. Only Numbers and Costs are 105 discussed here based on consistency of coding. Numbers were coded more frequently than the assignment of costs (for either the preferred or opposed solutions), with the exception of Week 8. Each code was identified less often after the fourth week of the shutdown.
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Numbers CostsPrefer CostsOpposed
Figure 4-16: Numbers and Costs by Week: Mean Count
When analyzed as a percentage of transcripts analyzed by week (Figure 4-17),
Numbers were shown to be used more frequently in Week 1 at 72.2 percent and generally declining thereafter. Assigning costs to preferred and opposed policy alternatives also were found to be more frequently used strategies earlier in the nine-week period when analyzed in this way as opposed to the average count by week. Assignment of Costs exhibited a similar pattern of decline as the shutdown proceeded though. 106
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Figure 4-17: Numbers and Costs by Week: Ratio Count
Emergent codes
Throughout the course of data gathering and analysis, a number of codes emerged a posteriori. The coding worksheet used to gather data provided seven different text fields associated with each of the following general categories: Hero, Villain, Victim, Plot,
Moral of the Story, Problem Definition, and Causal Mechanism. The text fields provided a means to chronicle emergent codes for subsequent analysis. In many cases, these emergent codes were found repeatedly in the data.
When coding for heroes, characters emerged that were not among those identified a priori. On 19 instances throughout both coding attempts, an official identified him/herself as an actor capable of solving the problem. Characters in these instances could have been coded to other codes, such as a Democratic Senator being included in the category of Congressional Democrats, but because Congress consists of two chambers, and members have diverse constituencies and policy platforms, the decision was made to code self-heroic-identification as a separate code under Other. After January 25, 2019, 107
Congressional conference committee members assigned to negotiate a long-term funding agreement were identified as heroes 10 times in interview transcripts. The Senate, as an institution, and Congress as a single body were each identified seven times in the transcripts.
Individual Congressional leaders were among the top actors characterized as
Villains. Foremost among them was House Speaker Nancy Pelosi, who was portrayed as a Villain 26 times between the two coding attempts. The actor identified next most often under this category was Senate Minority Leader Chuck Schumer at 14 different times, followed by Senate Majority Leader Mitch McConnell on 13 different occasions.
When coding Victims, the most often cited emergent code was federal workers affected by the shutdown. They were mentioned on 55 different occasions. During the
35-day period, federal workers missed two paychecks, which was a frequently used justification for citing them as Victims.
Before moving beyond characters, it is worth noting non-human characters were identified as heroes, villains and victims. According to the literature, inanimate objects can be viewed as characters when exhibiting agency. Otherwise inanimate objects help to structure the narrative and its setting, serving as alter objects that receive the action of actors. Specifically, ego actors exhibit agency as heroes and villains. Alter characters can also receive this action when they serve as victims or beneficiaries (Lejano et al., 2018;
Weible et al., 2016).
In the sample, the border wall Trump sought to build; partisan advocates such as right- and left-wing organizations and media; and the nation were identified as common inanimate objects that received the action of other characters and/or helped to structure 108 the narrative. The wall was cited three times (once as a Hero and twice as a Villain); partisan advocates were cited seven times (each as a Villain); and the country was cited five times (as a Victim).
Turning to emergent Plot lines, optimism was a frequent recurring code (37 times), with interviewees expressing a Story of Hope that a compromise would be reached, thereby allowing the government to reopen. Compromise and negotiation were used 30 times as another story type. Officials often told a Story of Responsibility, either the practice or lack thereof, 32 times throughout the two coding rounds, and it was used in the context of an adherence to or an abandonment of duty, fiscal policies and stewardship of public funds.
The unprecedented duration of the shutdown prompted many officials to question when it might end. This was the basis for a Story of Uncertainty throughout the timeline of the case, as well as Pessimism and Skepticism over the prospects of a resolution. The
Story of Uncertainty was coded 18 different times and the Story of Pessimism/Skepticism a dozen times.
Two other emergent stories lines that were not found as frequently as others cited above, but were used often enough to warrant mention here were the Story of
Disingenuousness and the Story of Broken Promises. The former was used often by actors to question the opposing side’s commitment to brokering a resolution to the policy disagreement. It was found 10 times throughout the two coding trials. The Story of
Broken Promises was a regular Democratic refrain to remind audiences that despite
Trump’s pursuit of billions of dollars in funding for a border wall, he had promised previously that Mexico would pay for it. This story was coded as occurring eight times. 109
Under the Morals of the Story category, three new codes emerged. The first,
Compromise and Negotiate, mirrors a plot line that emerged in the course of research as discussed earlier. While assigning this code to two categories could be contested and questioned, its repeated use in the sample’s narratives justifies it standing as a Moral of the Story code. It was coded as a preferred policy solution 59 times.
The next most frequent emergent Moral code is broadly defined here as
Immigration Reform. As the shutdown extended from days to weeks, a number of officials coupled funding for a border wall with immigration reform. Often, the prospect of enacting immigration reforms, or a pathway to citizenship for immigrants, was used to expand the Scope of Conflict by both Republicans and Democrats. Immigration Reform was coded in the narratives 50 times.
Finally, as the shutdown extended into its sixth week and as a three-week compromise allowed the government to reopen, more policy makers—specifically those serving in Congress—broached the idea of preventing future shutdowns legislatively.
Popular ideas included an outright ban on shutdowns and penalizing the legislative and executive branches. This proposed solution was coded 24 times in the transcripts. Fifteen of those instances occurred in interviews during the sixth, seventh and eighth weeks of the case. There were six instances of this code in the week prior to the shutdown, as well, with officials expressing a desire to reach an agreement and avoid closing a portion of the federal government.
Emergent codes in the Causal Mechanism category largely mirrored those identified as Problem Definitions. Interview subjects attributed a political or partisan motivation as the reason for the shutdown on 36 occasions. 110
With this data and analysis in hand, Chapter 5 contemplates the practical applications of these findings in the study’s collected case of the 2018-2019 federal government shutdown. The chapter places the data into context by reviewing the political environment at the time and excerpting quotes taken from the sample transcripts.
Subsequently, Chapter 6 examines the theoretical contributions of the research findings while also discussing the study’s limitations and potential areas for future examination.
Data gathered in the course of this study offer promising insight into how scientists in the fields of public administration and policymaking can better understand the rhetorical tools actors employ to legitimize their policy preferences and actions. The findings offer promising new advancements in our understanding of how issues are advanced on the agenda when democratic institutions are unable to reach agreement on politically charged issues in a divided government.
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Chapter 5
Contextual Data Analysis
Narratives play important roles in the public policymaking process. Political actors use stories to focus attention on persistent conditions, thereby elevating on the agenda those conditions vying for attention. In doing so, stories define the problem and justify the need for governmental action; they simplify complex policy environments and imbue policy debates with meaning and values; and they frame issues in ways that resonate with or challenge pre-existing belief systems while building or undermining, respectively, public support for potential policy solutions.
In this chapter, I discuss the findings of this case study using content analysis as a means of analyzing narratives as conceptualized in the Narrative Policy Framework.
Before commencing with that analysis though, recognizing the importance of context in any content analysis, the chapter begins with a review of Trump’s presidency as of late
2018, considering events that had transpired in the first two years of his term in office, and how those events shaped his administration’s relationship with Congress, public opinion, and electoral performance of the executive and legislative branch institutions.
After setting the stage, so to speak, the chapter proceeds to a review of narratives actors in this case employed through the nine-week period from whence data was collected. Major and recurring narrative elements and strategies gathered from the data are extracted and elaborated upon. The chapter assesses the case through a theoretical lens. Specifically, the paper reviews how what transpired in late 2018 and early 2019 relates to theories of the policy process, organizational theory, and communications theory. 112
Setting the stage
On December 22, 2019, an impasse between Congress and the Trump administration over how to appropriate funds led to a partial shutdown of the federal government that would endure for 35 days—the longest shutdown in American history.
At the heart of the dispute was a disagreement over how much funding to dedicate to building a physical barrier between the United States and Mexico. Trump sought approximately $5 billion. Members of the 115th session of Congress, which concluded on
January 3, 2019, were unable to pass legislation to budget funding for the wall, and after
Democrats assumed the majority in the House, leadership of that conference were unwilling to acquiesce to Trump’s request.
Presidential controversies
At the time, the border wall conflict represented the latest dispute between Trump and Democratic Congressional members. Democrats had criticized a number of Trump’s policies. For example, Democrats spoke out against the president’s executive orders banning immigrants from certain majority-Muslim nations from entering the United
States (Newman, 2017) as he had promised to do in 2015 during the presidential campaign (Pilkington, 2015); against Trump’s proclamation that judges who ruled against his immigration policies were biased (Simendinger, 2017); and against the administration’s attempts to repeal the Affordable Care Act (Rovner, 2018).
Aside from policy matters, Trump’s conduct while in office has drawn intense scrutiny in addition to criticism. Democrats opposed Trump’s threats to fire Special
Counsel Robert Mueller (Breuninger, 2018) and his termination of U.S. Federal Bureau of Investigation Director James Comey (Detrow, 2017). Democrats also questioned 113
Trump’s relationship with Russia (Cheney, 2018), including his decision to disclose highly classified information to diplomats from that country, which prompted Republican leaders to ask questions, as well (Taylor, 2017).
The prevailing media discourse on Trump’s presidency during his first two years in office was that the administration was fraught with controversies given its undisciplined leadership. Following news of the Russian diplomat meeting, U.S. Senator
Bob Corker (R-TN) referring to the Trump White House said, “Obviously, they’re in a downward spiral right now and they’ve got to come to grips with all that’s happening”
(n.p.) to Reuters news service in an article that also wrote this of the president: “Since taking office in January, Trump has careened from controversy to controversy” (Mason
& Zengerle, 2017, n.p.).
Aside from issue-based controversies, media reports painted a picture of an administration in disarray as evidenced by personnel turnover in key positions. Within the first two years of Trump’s presidency, the president had dismissed Comey and two attorneys general—Linda Kelly, who served in an acting capacity, and Jeff Sessions—the former for failing to enforce Trump’s immigration ban and the latter reportedly for recusing himself from an investigation into Russian interference in the 2016 election.
Trump dismissed Secretary of State Rex Tillerson via Twitter in March 2018. The following month, National Security Adviser HR McMaster left the administration after serving in the post 13 months. McMaster had replaced Michael Flynn who served in the role only 23 days following reports of his contact with Russian Ambassador Sergei
Kislyak, about which he lied to the FBI. During the course of the federal government 114 shutdown, Defense Secretary James Mattis left his post, as did White House Chief of
Staff John Kelly—Trump’s second chief of staff in as many years (BBC, 2019).
November 2018 election consequences
The political landscape changed the month prior to the federal government shutdown. In November 2018, Democratic candidates won a net gain of 40 races— enough seats to retake the majority in the House of Representatives. It was the largest
Democratic gain in the House since 1974 following the Watergate scandal (Enten, 2018).
With Democrats retaking the House, Republicans no longer held majorities in both chambers of Congress while a Republican president resided in the White House, thereby providing a check on the executive branch.
In order to advance any legislation or policy priorities, compromise would be in order. Polling on public attitudes about the prospects of cooperation suggested low expectations. One-third of respondents to a November 2018 Gallup poll expected Trump to cooperate with Democrats in Congress, while an even smaller percentage, 28 percent, expected Democrats to work with Trump. Those surveyed were more likely to believe elected officials from the party with which they were registered would be more cooperative. Forty-two percent of Democratic respondents expected Democrats in
Congress to cooperate with Trump, but only 11 percent expected Trump to cooperate with Democrats. Conversely, 61 percent of Republicans expected the president to work across the aisle, but only 14 percent expected the same treatment from Democrats.
Gallup’s poll indicated lower expectations for cooperation than in 2006, which was the last time congressional Democrats held a majority with a Republican president (Jones,
2018). The divide suggested a more hyper-partisan environment in 2018 than in 2006. 115
The Oval Office meeting
A December 11, 2018, meeting in the Oval Office lent credence to that notion.
Trump invited Democratic leaders of each respective chamber, Senate Minority Leader
Chuck Schumer and then-House Minority Leader Nancy Pelosi, who was expected to become Speaker of the House when the 116th Congress convened in January.
Trump began the meeting by predicting a major criminal justice reform bill would soon pass the Senate on a bipartisan basis, thereby achieving a major policy victory sought by both parties for a number of years. He went on then to indicate the latest federal Farm Bill would also be moving through Congress, saying, “[W]e think the farm bill is in very good shape. A lot of good things are happening with it, and our farmers are well taken care of. And again, that will be quite bipartisan and it will happen pretty soon”
(Marketwatch, 2018, n.p.).
From there, the conversation became more contentious. Trump moved the discussion to his desire for a border wall, assuring the “wall will get built” (Marketwatch,
2018, n.p.), while also acknowledging the partisan divide on the issue. “We have great
Republican support. We don’t have Democrat support” (Marketwatch, 2018, n.p.). The president went on to say:
And one way or the other, it’s going to get built. I’d like not to see a
government closing, a shutdown. We will see what happens over the next
short period of time. But the wall is a very important thing to us.
I might put it a different way. Border security is extremely important, and
we have to take care of border security. When you look at what happened
with the caravans, with the people, with a lot of -- we shut it down; we had 116
no choice. We shut it down. But it could be a lot easier if we had real
border security. (Marketwatch, 2018, n.p.)
This statement reflects a number of aspects that define the debate. First, Trump’s statement illuminates two points: one that he viewed construction of the border wall as a priority, saying it “is a very important thing to us;” and two, he frames the issue as one of border security, referencing the “caravans” of migrants that were approaching the U.S.-
Mexico border at the time from Central America.
With respect to the first point, it is important to remember building a wall between the U.S. and Mexico was one of the initial campaign promises Trump made when he launched his bid for the White House on June 16, 2015:
I would build a great wall, and nobody builds walls better than me, believe
me, and I’ll build them very inexpensively, I will build a great, great wall
on our southern border. And I will have Mexico pay for that wall. (Trump,
2015, n.p.)
At the time, Trump argued a wall was necessary to prevent Mexico’s economic exploitation of the United States, saying, “They’re laughing at us, at our stupidity. And now they are beating us economically. They are not our friend, believe me. But they’re killing us economically” (Trump, 2015, n.p.). He also put forth a belief that Mexican immigrants were responsible for a number of social ills plaguing America:
When Mexico sends its people, they’re not sending their best. They’re not
sending you. They’re not sending you. They’re sending people that have
lots of problems, and they’re bringing those problems with us. They’re
bringing drugs. They’re bringing crime. They’re rapists. 117
But I speak to border guards and they tell us what we’re getting.
And it only makes common sense. It only makes common sense. They’re
sending us not the right people. (Trump, 2015, n.p.)
A poll shortly after Trump’s campaign kickoff speech suggested the idea of a wall was popular with voters, particularly Republicans. Rasmussen Reports conducted a national telephone survey of 1,000 likely voters between August 17-18, 2015, which found among all likely voters, 51 percent supported construction of the wall. Among
Republicans, 70 percent of likely voters agreed the U.S. should build a wall along the
Mexican border to curb illegal immigration, while only 17 percent disagreed, thus making it an important issue politically for Trump (Rasmussen, 2015). This likely explains Trump’s reasoning for not only advocating for funding, but also stressing during the Oval Office meeting that, “Tremendous amounts of wall have already been built,”
(Marketplace, 2018, n.p.). On the heels of a mid-term election in which Democrats gained 40 seats in the House, thereby retaking the majority, Trump sought to assure his electoral base that he was making good on a campaign promise heading into his 2020 re- election bid.
With respect to the second point—Trump’s framing of the issue as one of border security—the Oval Office meeting is instructive because it offers insight into the administration’s original narrative on the issue. He praised agents and officers of
Customs and Border Patrol along with Immigration and Customs Enforcement, as well as the military, casting them as heroes. Meanwhile, he implies migrants from Central
America are villains for wanting to enter the country illegally, saying, “We can’t let people come in that way.” With these characters established and the problem defined as 118 one of illegal immigration, he goes on to tout statistics on the efficacy of walls in areas of the U.S. where such structures are present already, namely San Diego, El Paso and
Yuma. For comparative purposes, he says a wall in Israel was 99.9 percent effective, and promises the wall he seeks to build “will be every bit as good as that, if not better”
(Marketplace, 2018, n.p.).
Pelosi and Schumer held different views. When Trump originally introduced the topic, Schumer responded that the issue was not the wall, but rather retorted, “It’s called
‘funding the government,’ Mr. President” (Marketwatch, 2018, n.p.). Later during the meeting, when Trump turned to Pelosi for comments, she began cordially by casting the meeting as an opportunity to “work together in a bipartisan way to meet the needs of the
American people,” and went on to say, “I think the American people recognize that we must keep government open, that a shutdown is not worth anything, and that you should not have a Trump shutdown” (Marketplace, 2018, n.p.).
Trump instead responded to Pelosi’s characterization of the shutdown as a “Pelosi shutdown,” to which she noted that, at the moment, Republicans controlled the executive branch and both chambers of Congress, thus the president “should pass it right now,” referring to the appropriations bills remaining to be enacted. Trump counters that he cannot pass the bills because Republicans lack the 60 votes necessary to avoid a filibuster in the Senate, at which point Pelosi notes Trump could ask the House to move the bills where there is no such procedural obstacle. Trump argues it is meaningless to initiate legislative action in the House when such efforts are sure to be stymied in the Senate. At that point, Pelosi expresses the belief that Trump has not pursued her advice because a sufficient number of votes did not exist among House Republicans for funding a border 119 wall. Said Pelosi, “But there are no votes in the House, a majority of votes, for a wall -- no matter where you [Trump] start,” and Schumer quickly adds, “That is exactly right.
You don’t have the votes in the House” (Marketplace, 2018, n.p.).
Later and at multiple points in the meeting, Schumer implores Trump not to shut down the government over this agreement, saying the president had stated on 20 different occasions that he would do so if his funding demands were not met. A relevant and important point for further discussion later can be found in the following excerpt of the transcript:
THE PRESIDENT: You know what I’ll say: Yes, if we don’t get what we
want, one way or the other -- whether it’s through you, through a military,
through anything you want to call -- I will shut down the government.
Absolutely.
SENATE MINORITY LEADER SCHUMER: Okay. Fair enough.
We disagree.
THE PRESIDENT: And I am proud -- and I’ll tell you what --
SENATE MINORITY LEADER SCHUMER: We disagree.
THE PRESIDENT: I am proud to shut down the government for
border security, Chuck, because the people of this country don’t want
criminals and people that have lots of problems and drugs pouring into our
country. So I will take the mantle. I will be the one to shut it down. I’m
not going to blame you for it. The last time you shut it down, it didn’t
work. I will take the mantle of shutting down. (Marketplace, 2018, n.p.) 120
Here, again, the exchange between Trump, Pelosi and Schumer is instructive because it provides a window into the narratives Democrats would rely upon in the case.
The Democratic leaders insist Trump would be responsible for the shutdown should it come to pass due to his intentional decision not to compromise, and they defined the problem as a matter of funding the government, not a border security crisis. By challenging his ability to rally support in Congress for border wall funding, the
Democrats implied Trump was an ineffective leader. They made clear a wall was not necessarily the proper policy solution without more thorough examination of data and alternative solutions.
The narratives: elements and strategies
Heroes
In the Hero category, three codes demonstrated agreement between rounds:
Trump and his administration, Americans, and Other emergent codes, of which self- identification, conferees, the U.S. Senate, and Congress as a legislative body were most often used. Despite consistent coding, Americans was not a frequently found code in the data, being identified only once in both rounds one and two of coding. As such, this section does not address the American code, nor does it the self-identification, Senate or
Congress codes. More often than not, self-references were made to recognize personal interest in resolving the shutdown or demonstrations of past legislative actions to signal commitment to resolving the border security issue at the heart of this research.
References to the Senate and Congress were typically made to demonstrate passage of legislation that passed a particular chamber previously that could serve as a basis for 121 compromise to the shutdown dilemma but had no application in the current case due to having taken place in the prior legislative sessions.
As noted in Chapter 4, there was considerable partisan differences in the references to Trump and his administration as heroes within the data. Nearly 53 percent of Republicans referred to Trump as a hero, or an actor capable of solving a problem, while only slightly more than 1 percent of Democrats did so.
Generally, Republicans portrayed Trump as someone genuinely concerned about the problems underlying the crisis, as they characterized it, and willing to negotiate in good faith in order to reach a compromise to fund broader border security measures. For example, in a January 3, 2019, interview broadcast on NPR’s Morning Edition, White
House Director of Strategic Communications Mercedes Schlapp said of the administration:
So we've been trying to negotiate this for months. The president, when—
in the last negotiation which we had, which—it was—Vice President Mike
Pence, as well as Jared Kushner and Mick Mulvaney, went over to the
Hill, presented the offer. It was a good faith offer, a reasonable offer to
increase border wall funding. (as quoted in Martin, 2019, n.p.)
As the shutdown dragged on and the issue of immigration reform became a more frequent discussion point, the administration often signaled the president’s willingness to engage in those negotiations in order to resolve the matter, which had plagued federal policymakers for years, and to counter a contrary opinion that the president was hostile to the interests of immigrants. To that end, then-White House Chief of Staff Mick Mulvaney told NBC’s Meet the Press host Chuck Todd, “The president is very interested in larger 122 immigration reform. He's said that publicly. He’s said that privately. He wants to solve immigration, okay?” (as quoted in Todd, 2019, n.p.). Mulvaney structured his last sentence as question in order to dispel the notion Trump was not committed to immigration reforms.
Republicans in Congress regularly defended the president against criticisms he was not open to compromise. Then-House Majority Leader Kevin McCarthy (R-CA) said on Face the Nation, “I know this President is focused on giving [sic] this government open. That's why the President is here” (as quoted in Brennan, 2019a, n.p.). In the same interview, McCarthy identifies himself as a hero for his commitment to remain in
Washington, D.C. in order to negotiate a settlement to the shutdown. He added, “I’m not in Puerto Rico. I’m here because I want to solve this problem” (as quoted in Brennan,
2019a, n.p.). The reference to Puerto Rico stemmed from a previously scheduled trip to the island territory organized by the Congressional Hispanic Caucus to observe recovery efforts following Hurricane Maria (Gamboa, 2019).
The emergent Conferees code under the Hero category demonstrated agreement, but its utility in understanding the agenda-setting role of narratives is not generally germane to this case. References to the committee largely recognized their work or expressed a degree of optimism or pessimism regarding their prospects for achieving compromise. Those references were of little use in understanding narratives’ roles in the issue reaching the decision agenda.
For example, Democratic House member David Price of North Carolina, told
NPR’s All Things Considered host Audie Cornish, “[W]e will do our very, very best to forge an agreement that will pass both houses” (as quoted in Cornish, 2019a, n.p.). 123
Fellow conference committee member Republican U.S. Representative Steven Palazzo
(R-MS) similarly expressed hope in an interview the following day with Cornish, saying,
“I hope we can provide, you know, a bipartisan compromise to securing our border and give it to the president and he will accept it and sign it into law and we can put this behind us” (as quoted in Cornish, 2019b, n.p.)
Not all members of Congress were as optimistic or hopeful about the outcome of negotiations by the committee. U.S. Representative Mark Meadows (R-NC), who has since gone on to become Trump’s chief of staff, adopted a skeptical and pessimistic tone on February 10, 2019, saying to Face the Nation host Margaret Brennan:
I don’t know that they’re real serious about reaching a compromise. I
mean, they’ve met twice in—in almost two weeks now. … Border patrol
came in to brief the conference. They gave their top three priorities and the
conferees have said zero money for those top three priorities. How can
you be serious about securing our border if the very people that are experts
on securing it say these are our top three priorities, we need money. And
yet they’re saying zero dollars for that. (as quoted in Brennan, 2019d, n.p.)
Villains
Codes demonstrating agreement in the Villain category included Trump, Law
Enforcement, and Other. Then-House Minority Leader Pelosi, Senate Minority Leader
Schumer, Senate Majority Leader Mitch McConnell, and partisan influences (e.g. media, organizations and pundits) were the most common emergent codes in the transcripts.
Because Law Enforcement was coded only once in each round, it is not deemed a significant finding and, thus, is not examined in detail here. 124
As with the Hero category, references to Trump as a Villain exhibited distinct partisan differences. More than 78.5 percent of Democratic transcripts referred to Trump as a Villain, while fewer than 1 percent of Republican transcripts contained this code.
Generally, Democrats portrayed Trump as intransigent, undisciplined and inconsistent in his demands, creating obstacles to reaching agreement. Further, they referred to him as callous and indifferent to hardships they believed he created.
In terms of characterizing Trump as uncaring and indifferent to people’s well- being, before the shutdown began, Democratic U.S. Senator Amy Klobuchar (D-MN), who at the time was seeking her party’s nomination for president in 2020, accused Trump of “playing games with people’s lives” on Face the Nation in December 2018 before the shutdown began (as quoted in Brennan, 2018, n.p.). More than a month later, another
U.S. Senator and one-time candidate for the 2020 Democratic presidential nomination,
Kirsten Gillibrand of New York, said the following on ABC’s This Week:
What he’s doing, again, he has no compassion for anyone. He has no
empathy for the struggles and the hardships that he’s placing on people,
whether it’s the government workers who aren’t getting paid or the
dreamers who are contributing to our country in amazing ways. He doesn’t
care about anyone but himself. It’s just about him. (as quoted in Raddatz,
2019, n.p.)
Along similar lines, Democratic narratives often accused Trump of hostage taking, imperiling the health, safety and livelihoods of Americans for the sake of securing wall funding. Senator Chris Van Hollen (D-MD) paired an allegation of indifference to federal workers’ livelihoods with the hostage metaphor when he told NPR’s Ari Shapiro 125
January 9, 2019, “The president doesn't know what it's like to have to skip his mortgage payment. If he did, he wouldn't be holding the country and 800,000 federal employees hostage in the way he is” (as quoted in Shapiro, 2019, n.p.). U.S. Representative Hakeem
Jeffries (D-NY) said on ABC’s This Week, “[W]e are not willing to pay $2.5 billion or $5 billion and wasting taxpayer dollars on a ransom note because Donald Trump decided that he was going to shutdown the government and hold the American people hostage”
(as quoted in Raddatz, 2018, n.p.).
Referring back to the December 11, 2018, Oval Office meeting between Trump,
Pence, Pelosi and Schumer, Democrats often used their narratives a remind audiences that Trump stated he would gladly shut down the government in order to secure his desired border wall funding. On December 21, U.S. Senator Mark Warner (D-VA) said during an NPR interview, “A week ago, the president met with Democratic leaders and said he was then proud to have a government shutdown” (as quoted in Kelly, 2018, n.p.).
Democratic U.S. Senator Chris Coons of Delaware echoed that reminder, saying it was
Trump who, paraphrasing the president, said, “I will cheer on a government shutdown, I will champion a government shutdown, I will take responsibility for a shutdown” (as quoted in Brennan, 2018a, n.p.).
Schumer accused Trump of not possessing a temperament conducive to reaching a deal and seeking only to placate his base of political supporters. “[H]e shouldn’t use innocent workers as hostage [sic] for his temper tantrum to sort of throw a bone to his base” (as quoted in Todd, 2018, n.p.). Using the same characterization, in a joint appearance with then-Speaker Pelosi on January 8, 2019, Schumer repeated the point:
“We don’t govern by temper tantrum. No president should pound the table and demand 126 he gets his way or else the government shuts down, hurting millions of Americans who are treated as leverage” (Pelosi & Schumer, 2019, n.p.). Democrats repeatedly relied on use of the word “tantrum” in their villainous characterizations of Trump. U.S.
Representatives Katherine Clark, David Price and Bennie Thompson, and U.S. Senator
Tim Kaine, each made use of the word.
In terms of charging Trump with making inconsistent demands, on December 30,
2018, Jon Tester, a Democratic U.S. senator from Montana, said Trump originally requested $1.6 billion for border wall funding, but, “The President moved the goalposts and said, ‘No, now I want five billion’” (as quoted in Brennan, 2018b, n.p.). Senator Dick
Durbin (D-IL) said, “[T]he president’s word didn’t stand up” (as quoted in Todd, 2018a, n.p.). Even as late as January 30, 2019, after the three-week compromise package had been enacted, Congressman Henry Cuellar (D-TX), a member of the conference committee, reminded All Thing Considered’s audience that “back in December, we thought we had a deal. … But then it changed. And all the sudden, [Trump] said, no, I want $5.7 billion, and I'm not going to change my mind. And it's still on that” (as quoted in Kelly, 2019a, n.p.).
Some Democratic officials attributed Trump’s inconsistent policy demands to pressure from conservative-leaning organizations, pundits and voters. Congressman
Adam Smith (D-WA) accused Trump during an interview with George Stephanopoulos on This Week of shutting down the government so as to not break his campaign promise to build a wall (Stephanopoulos, 2019). Representative Adam Schiff (D-CA) said on
January 27, 2019, “The only thing that got in the way [of a compromise in December] was the president was frightened off by Ann Coulter and Rush Limbaugh” (as quoted in 127
Stephanopoulos, 2019b, n.p.), who are two prominent Republican media commentators.
U.S. Senator Mark Warner (D-VA) made a similar observation, saying, “[A]s is typical of Mr. Trump, he gets a little pushback from the far-right, and he changes his position,” adding later in the interview with NPR that “it's hard to know what this White House will actually accept because the White House's willingness to stick to a deal or the president's willingness to keep his word, nobody has a lot of trust in that” (as quoted in Kelly, 2018, n.p.).
Aside from Trump and his administration, Democrats also identified Senate
Majority Leader McConnell (R-KY) as a Villain, characterizing him either as being the president’s accomplice in perpetrating the shutdown or subservient to the chief executive.
During the third week of the shutdown, Representative Hakeem Jeffries said in an NPR interview, “[H]opefully Mitch McConnell and his colleagues in the Senate will realize that they should not function as wholly-owned subsidiaries of the Trump administration”
(as quoted in Cornish, 2019, n.p.). Senator Gillibrand questioned McConnell’s conduct and obstructionism when she said, “Mitch McConnell’s not letting us vote on the things that could pass. I don’t know why he’s not standing up to President Trump and doing what’s right” (as quoted in Raddatz, 2019, n.p.).
Many Republicans’ Villain characterizations mirrored those of Democrats. Most notable is the reference to hostage-taking, although not all references by Republicans to hostage-taking were directed toward officials from the opposing party. For example, in explaining why he broke ranks from the GOP to vote for a Democratic-sponsored resolution to reopen the government, U.S. Representative Adam Kinzinger (R-IL) said: 128
[I]f I can vote to reopen parts of government, frankly, that don't have
anything to do with this wall argument, I'm going to vote for it because, in
essence, we're holding hostages. And the more we can release those
hostages in this negotiation, the better. (as quoted in Shapiro, 2019a, n.p)
Prominent Republicans did, however, accuse Democrats of taking hostages through the shutdown. Senator Ted Cruz (R-TX) lobbed such an accusation in his interview with Meet the Press host Chuck Todd (2019a). Senator Lindsey Graham (R-
SC) was more pointed in his interview with Margaret Brennan (2019) on Face the
Nation, saying Democrats with extremist views were the ones holding federal workers hostage. In this way, Republicans vilified radical elements of the political left just as
Democrats vilified the radical right. In his interview, Graham summarized his assessment of these extremist Democratic views:
We’re having to negotiate with people who see the border patrol agents
gassing children, rather than defending our borders as professional law
enforcement officers and we’re negotiating with people who will give us
one dollar for the wall, even though, it’s immoral and accuse all of us who
support a wall as part of border security as racist. As long as the radical
left is in charge we’re never going to get anywhere. (as quoted in Brennan,
2019, n.p.)
Members of Trump’s administration labeled left-leaning actors as Villains before the shutdown began. Referencing a 2017 decision by U.S. District Court Judge Ann
Donnelly overturning Trump’s earlier executive order banning immigration from certain nations (Associated Press, 2017), Stephen Miller, a senior policy advisor to the president, 129 said on Face the Nation on December 16, 2018, “a left wing, activist judge issued a reckless nationwide injunction on the President’s order putting thousands of lives at risk and further enriching these grotesque…[interrupted]…—heinous, smuggle organizations” (as quoted in Brennan, 2018, n.p.).
Republicans regularly ascribed the Villain label to Democratic leaders Nancy
Pelosi and Chuck Schumer. Leveling the “hostage” charge, then-White House Chief of
Staff Mick Mulvaney pointed to Pelosi as the impediment to negotiations, saying, “And if
I were sitting here talking to Nancy Pelosi, I’d say, ‘Nancy, why are you holding border security, why are you holding every—all of these 800,000 workers—…[interrupted]…— hostage to a border security’” (as quoted in Todd, 2019, n.p.). Trump criticized Pelosi after the three-week compromise took effect, effectively ending the shutdown, saying:
[S]he’s costing the country hundreds of billions of dollars because what’s
happening is when you have a porous border, and when you have drugs
pouring in, and when you have people dying all over the country because
of people like Nancy Pelosi who don’t want to give proper border security
for political reasons, she’s doing a terrible disservice to our country. (as
quoted in Brennan, 2019c, n.p.)
Prior to the 116th Congress convening in January 2019, Republicans such as
Kevin Hassett, who served as chairman of the White House Council of Economic
Advisors, accused Schumer of stalling until Democrats took control of the House, thereby making Pelosi the new Speaker (Inskeep, 2018). Republicans accused the two
Democratic leaders of not negotiating in good faith, prolonging the stalemate, as U.S.
Representative Steve Scalise (R-LA) did to ABC’s This Week: “Not one single time, 130
George, has Nancy Pelosi or Chuck Schumer put a counter-offer on the table except a dollar. Nancy Pelosi said a dollar. That’s not serious, we all know that” (as quoted in
Stephanopoulos, 2019a, n.p.).
Victims
Americans and the emergent codes of Federal Workers and the Nation registered agreement between coding attempts. Congressional Republicans similarly demonstrated agreement, but only due to a single instance being identified in each round, thus it is excluded from discussion here as being insignificant.
Although Democrats cited Americans more frequently as Victims than
Republicans, both parties used the character code. The manner of victimization, though, differed among parties.
In the early weeks of the shutdown, Democrats labeled the nation and its people as financial victims of Trump’s insistence on building a wall given the expense to taxpayers. U.S. Senator Jon Tester (D-MT) built on the plot lines of inconsistent leadership and broken promises, which will be discussed later, saying on Face the
Nation:
[T]he President wants to continue to take a campaign promise that he
made, which was to have Mexico pay for a wall and say no, the rules have
changed now we’re still going to build a wall but we’re going to have the
American taxpayer pay for it we’re going to use the American taxpayer
like an ATM machine. (as quoted in Brennan, 2018b, n.p.)
As the shutdown dragged on, Democrats increasingly focused on the loss of governmental services and the impact of the shutdown on Federal Workers, their lives 131 and livelihoods, which was among the emergent codes identified in the Victim category.
Said Senator Durbin, “Think about the hundreds of thousands of people who will be entitled to income tax refund checks who won’t receive them because the Treasury
Department has been shut down” (as quoted in Brennan, 2019, n.p.). With respect to
Federal Workers, he added:
Look at those at the airport who were carefully—going through—the
passengers to make sure that they’re safe on airplanes. As of next Friday,
they’ll miss a payday that may mean some problems for mortgage
payments, problems and balancing the budget of their own families and
households. (as quoted in Brennan, 2019, n.p.)
Democrats frequently cited the prospect of Federal Workers missing mortgage payments in their narratives, as seen in remarks by Pelosi and Schumer on January 8,
2019, and Senator Van Hollen on January 9, 2019 (Shapiro, 2019), for example.
Representative Donna Shalala (D-FL) spoke about the hardships on her constituents who either work for federal government agencies or who rely on government assistance programs:
[S]o many of my constituents—both the husband and the wife work for
the government. So it’s even a double-whammy for those families. Air
traffic controllers, people that get food stamps—a quarter of the people in
my district get food stamps. They are scared to death. (as quoted in
Shapiro, 2019b, n.p.)
Among Democrats, the shutdown and its resulting hardships were the source of victimization, but for Republicans—at least early on during the shutdown—it was the 132 threat of illegal immigrants who victimized the American People. Congressman Ted
Yoho (R-FL), characterized Americans as being under threat of violence by undocumented immigrants being harbored in so-called sanctuary cities. Yoho referenced the case of Kate Steinle, a medical device sales representative who was struck and killed by an errant bullet fired by Jose Ines Garcia Zarate. Garcia Zarate was an undocumented immigrant and repeat felon who had been deported from the United States on multiple occasions prior to the Steinle shooting (Maxouris & Watts, 2020). Said Yoho, “How many more times are we going to see a Kate Steinle or the officer that just got shot by the guy that had been coming—came into this country illegally twice and was in a sanctuary city” (as quoted in Kelly, 2019, n.p.)?
When addressing the nation on January 8, 2019, President Trump cited a number of ways in which undocumented immigrants threatened the American people. In his remarks, Trump asserted, “all Americans are hurt by uncontrolled, illegal migration,” citing 300 weekly American deaths from heroin that originated from south of the U.S.-
Mexico border and an unspecified case where an “illegal alien was charged with murder for killing, beheading, and dismembering his neighbor” (Trump, 2019, n.p.).
Within two days of Trump’s remarks, Republicans began acknowledging the hardships on Americans and federal workers imposed by the shutdown. White House
Communications Director Mercedes Schlapp said on January 10, 2019, “It is very unfortunate that we have federal workers caught in the middle of this” (as quoted in
Martin, 2019a, n.p.). Three days later, House Republican leader Kevin McCarthy said, “It is unacceptable that eight hundred thousand U.S. employees are not being paid” (as 133 quoted in Brennan, 2019a, n.p.). Later on, Republican Senator Rob Portman of Ohio pointed to the cost of the shutdown on Americans and on federal employees:
Actually, the taxpayer, in my experience, having lived through about six
of these shutdowns now, end up paying more because, again, you come
back after the fact and pay people, often paying people for services that
were not provided. So it’s a hardship for federal employees and their
families. It’s a hardship for a lot of small businesses that can’t get paid for
the government work. And it’s a hardship for a lot of taxpayers who aren’t
getting their services. (as quoted in Martin, 2019, n.p.)
It is notable, as mentioned earlier, that Democrats generally cited Victims more often than Republicans throughout the nine-week period, although Republicans reference to an Other type of Victim surpassed Democrats in weeks four and five. This trend coincides with the fact that in Week 4, federal workers missed their first paycheck because of the shutdown. Federal employees would miss a second paycheck on January
26, 2019, during the sixth week of the time period this case covers. It was after this milestone that officials reached the short-term, three-week compromise. Consequently, the use of Victim codes began to drop among both parties.
Plot
A number of Plot codes were coded consistently between both rounds. Among the codes developed a priori, the Story of Decline and Story of Conspiracy registered agreement. The most frequent codes identified a posteriori included the stories of
Optimism/Hope, Compromise/Negotiation, Responsibility, Uncertainty,
Pessimism/Skepticism, Disingenuousness, and Broken Promises. 134
Beginning with the a priori codes, first recall that stories of decline apply when a narrative suggests a problem will worsen if an opposed policy solution is enacted
(Shanahan et al., 2013). Stories of conspiracy are a variation of the Story of Helplessness and Control. Stone (2002) defines the Story of Conspiracy as a plot that “claims to show that all along control has been in the hands of a few who have used it to their benefit and concealed it from the rest of us” (p. 143). In the case of the federal government shutdown studied here, Republicans and Democrats differed in how they articulated their preferred
Plot lines using these narrative strategies.
Republicans, particularly in the early weeks of the case—leading to and during the shutdown—told a Story of Decline that foretold the consequences of Democrats preserving the status quo and failing to support Trump’s border wall funding. White
House advisor Stephen Miller laid out this Story of Decline during his December 16,
2018, appearance on Face the Nation: “the Democrat Party wants to go down the road of continuing to preserve a model that enriches smuggling organizations, that spreads misery on both sides of the border, that kills three hundred Americans a week through heroin overdoses alone” (as quoted in Brennan, 2018, n.p.).
Democrats, who used the code most often in Week 4, painted a picture of a declining state of American governance. Senator Schumer in his joint appearance with
Speaker Pelosi, said Trump’s agency in shutting down the government reflected behavior inconsistent with the practice of democracy:
[T]he President of the United States—having failed to get Mexico to pay
for his ineffective, unnecessary border wall, and unable to convince the 135
Congress or the American people to foot the bill—has shut down the
government.
American democracy doesn’t work that way. We don’t govern by
temper tantrum. No president should pound the table and demand he gets
his way or else the government shuts down, hurting millions of Americans
who are treated as leverage. (Pelosi & Schumer, 2019, n.p.)
Democrats also used the Story of Decline to assert that an already challenging situation was deteriorating further due to Trump’s policies. Congressman Bennie
Thompson (D-MS) expressed the view on January 9, 2019, that Trump’s “zero-tolerance policy [toward migrants from Latin countries] that was implemented without any policies and procedures has exacerbated the problem” (as quoted in Inskeep, 2019, n.p.). Later, on
January 20, 2019, Senator Gillibrand told ABC’s This Week guest host Martha Raddatz that she had spoken with border officials who, paraphrasing, said:
[T]he way President Trump is conducting his immigration enforcement is
destroying their ability to actually do anti-terrorism. I have a letter from 19
ICE agents written to the secretary of Homeland Security, saying that what
President Trump has done under ICE is making it impossible to do their
jobs. (as quoted in Raddatz, 2019, n.p.)
Republicans used the Conspiracy code far more frequently than Democrats, particularly in weeks four through eight. During Week 5, in particular, half of all
Republican transcripts contained this code. For the GOP, the nature of the Conspiracy plot revolved around Democratic support for border wall funding and construction prior to Trump taking office. With a Republican in the White House, however, GOP officials 136 alleged Democrats were using their power to benefit themselves politically by denying
Trump a policy victory.
Senator James Lankford (R-OK) noted on NPR that:
The Secure Fences Act passed 10 years ago. That had Senator Obama.
Senator Clinton voted for it. That built 650 miles of fencing at that time
across our southern border. So apparently the word fencing wasn’t
offensive, but the word wall has become offensive. (as quoted in Kelly,
2018, n.p.)
Trump repeated this charge when he addressed the country on January 8, 2019, saying that Schumer and many other Democrats had supported funding for a physical border barrier in the past, but, “They changed their mind only after I was elected
President” (Trump, 2019, n.p.). Other Republicans including, Kevin McCarthy, Ted
Cruz, and Mick Mulvaney would repeat this refrain throughout the remainder of January.
Among Democrats, the use of the Conspiracy plot line was not as common, nor as clearly defined. Briefly put, Democrats’ use of the Plot code peaked in Week 3 when one-quarter of the interviews given by members of their party contained the code—due to one interview by Dick Durbin on NPR’s All Things Considered. There, Durbin alleged
Trump was using his power as the nation’s chief executive to shut down the government in order to compel support for his border wall funding request.
Turning to the new Plot codes that emerged in the research, a number of interesting stories presented themselves in the narratives. The most frequently coded emergent Plot was the Story of Responsibility. It was coded 32 times between both 137 rounds, and it was used at least once in every week other than Week 8, with the heaviest concentration in weeks three and four at seven and nine instances, respectively.
This code took many forms. Both parties used it to ascribe culpability for the shutdown to the opposing party, but Democrats used it most frequently to refer to their view that Trump’s conduct and management of federal funds was fiscally irresponsible.
For example, a number of Democrats observed that the Trump administration—at the time of this case—had yet to expend previously appropriated dollars for border security fully. Oregon Senator Jeff Merkley emphasized this point on This Week when he said, referring to Trump:
He’s sitting on over $1 billion, 94 percent of what we sent him last year
for border security he hasn’t bothered to spend. If you’re not going to
spend nine out of 10 dollars on an issue, you obviously don’t care about it
that much. This is politics, not policy. (as quoted in Karl, 2018, n.p.)
The Story of Broken Promises was another common refrain among Democrats, particularly in weeks four through six with half of the mentions coded in Week 6. It was not coded among Republicans. Democrats used this Plot line to reinforce Trump’s responsibility for the shutdown in order to fulfill his campaign promise to build a wall, although that pledge included a commitment that Mexico would finance construction costs. Said Democratic Representative Jamie Raskin of Maryland on NPR’s January 19,
2019, broadcast of All Things Considered, “We know the president had a campaign chant which was, build the wall. Mexico will pay for it. Well, that promise is out the window because Mexico hasn’t paid for it” (as quoted in Block, 2019, n.p.). 138
Both parties were coded as using the Story of Disingenuousness. Of the 10 instances in which this code was identified, 80 percent were found in weeks five and six.
Democratic Representative Adam Schiff said on Face the Nation, “[T]he vice president and the President know that what the President announced yesterday was not going to go anywhere. It wasn’t really intended to. It was a—I think an effort to prop up the
President's sagging poll numbers” (as quoted in Brennan, 2019b, n.p.). Conversely, GOP
Representative Steve Scalise used the code in the context of portraying Democrats as unwilling to negotiate in good faith.
If they say they’re for border security, which they say, but they’re yet to
be willing to put a dollar offer on the table for what it’s going to cost to
secure the border. We all know there’s a cost to this. They’ve got to put a
counteroffer on the table. (as quoted in Stephanopoulos, 2019a, n.p.)
As noted earlier, other codes emerged that demonstrated agreement between coding rounds, namely stories of Optimism/Hope, Compromise/Negotiation, Uncertainty, and Pessimism/Skepticism. Each of these were used by members of both parties, at different times, to express their respective outlook on negotiations and convey a desire to end the stalemate. Because of this generally equal application of the code, little insight of significant or theoretical value was gained through this analysis, thus these codes are deemed not worthy of discussion here.
Morals of the story
Four of the five codes developed prior to data collection demonstrated agreement between coding rounds, including building a Wall, funding Non-structural improvements to border security, funding a Mix of structural and non-structural improvements, and 139
Reopening the government. The most common emergent codes discussed here include
Compromise/Negotiate, Immigration Reform, and Prevent Future Shutdowns.
Only Republicans advocated for building a wall exclusively. Because the nature of that policy solution is self-explanatory, it will not be discussed in detail here. Over time, however, as the president adjusted his position and expressed publicly a willingness to pursue non-structural alternatives—as Democrats advocated—Republicans more frequently called for a mix of structural and nonstructural solutions. GOP officials referred to this code, in both real count and percentage of Republican interviews by week, most often in Week 4—the week in which Trump offered a compromise that included funding for non-structural tactics. Republican officials were coded as using this code 11 times in Week 4, or 55 percent of the interviews within the sample from that week.
Calls for a Mix of solutions dropped among Republicans following Week 4, while support among Democrats remained generally level. Calls exclusively for non-structural alternatives by Democrats began to fall following Week 5. Calls for Other solutions began to increase among Democrats beginning in Week 4 and the same code saw an increase among Republicans from Week 6 to Week 7.
Among the Other category, Compromise/Negotiate was the most frequently identified code, being found 59 times between rounds one and two. Calls for reaching some form of agreement generally rose after Week 2, reaching a high of 18 coded instances in Week 6. After this point, when the short-term compromise packaged had been enacted, calls for negotiation and compromise began to fall.
The next most often used emergent code identified was to enact some form of immigration reform legislation. Such calls peaked at 14 instances in Week 4, dropped to 140 four in Week 5, but rebounded to nine instances in both weeks six and seven. After this point, during the two remaining weeks of the case’s timeframe, calls dropped to only two instances in Week 8 and no instances in Week 9—due likely to the realization achieving such comprehensive legislation was unrealistic during the three weeks conferees had to negotiate border wall funding.
Finally, the third common emergent code was to avoid shutdowns in the future.
Week 1, before the shutdown went into effect, saw six instances of this code being used.
Frequency of use remained relatively low until Week 7—the first week in which the compromise three-week funding agreement had been implemented. Officials employed this code 12 times as they expressed a desire not to repeat the turmoil of the preceding five weeks.
In terms of how officials characterized these Morals of the Story, Democrats regularly referred to the wall as an antiquated solution to a modern problem, instead highlighting technological tools. Senator Mark Warner told NPR, “we ought to use 21st- century technology—drones, electronics, other sensors—rather than 14th century technology of a wall” (as quoted in Kelly, 2018, n.p.). Representative Jeffries coupled the
Plot of fiscal responsibility while denigrating the wall as a border security measure:
“[S]pending billions on a medieval border wall that would be ineffective would be a waste of taxpayer dollars. That’s a fifth-century solution to a 21st century problem” (as quoted in Todd, 2019b, n.p.).
On the Republican side, by late-December 2018 and January 2019, the administration and its supporters were touting the virtues of a structural/non-structural mix of solutions, making clear such a Mix was contained in their appropriation request of 141
Congress. In his December 30, 2018, appearance on This Week, head of the U.S. Customs and Border Patrol, Kevin McAleenan explained that “what we’re talking about is not just a dumb barrier, we’re talking about sensors, cameras, lighting, access roads for our agents, a system that helps us secure that area of the border. That’s what we were asking
Congress” (as quoted in Raddatz, 2018, n.p.).
Reopening the government was cited as a Moral of the Story frequently by members of both parties. Although not a policy solution to the problem at the heart of the debate, namely improving border security, it was coded a posteriori as a Moral because officials repeatedly referenced it as a solution to the shutdown that would pave the way for negotiations between Congress and the White House.
For Democrats, reopening the government was a prerequisite for negotiating with
Trump. Senator Van Hollen asserted on January 9, 2019, that it was not only Democrats who held that view, but members of the GOP, as well:
[M]any of them have said in the last several days that they agree with us
that the first order of business is to reopen the government. About four to
five Republican senators are on the record now saying that’s what we
should do. …This is a dispute over whether or not paying tens of billions
of dollars ultimately for a 2,000-mile wall is the best way to do that. It’s
not. And that’s what the experts say. But let’s reopen the government, and
then we can have that discussion. (as quoted in Shapiro, 2019, n.p.)
Other Democrats, like Representative Sharice Davids of Kansas, saw the shutdown as impeding congressional work on more pressing priorities. “We can come together to get the federal government back up and running so that everyone who is 142 sitting in Congress can come forward with the things that they know their constituents want them to be working on,” she said on NPR (as quoted in Fadel, 2019, n.p.).
Conversely, Trump insisted in his Oval Office remarks on January 8, 2019, that reopening was contingent on securing border wall funding: “[T]he only solution is for
Democrats to pass a spending bill that defends our borders and re-opens the government.”
Other Republicans, such as the moderate senator from Maine, Susan Collins, instead viewed opening the government as important for the sake of paying federal workers. She also stressed that the shutdown should not apply to those agencies that were not involved in border security matters. “We could reopen much of government where there’s no dispute over issues involving certain departments like Ag, Transportation,
Housing, and Interior. Let’s get those reopened while the negotiations continue,” said
Collins on Meet the Press on January 6, 2019 (as quoted in Todd, 2019, n.p.).
Following enactment of the three-week compromise agreement that reopened the federal government, calls to avoid future shutdowns were found regularly in the data.
Congresswoman Donna Shalala (D-FL) implied the shutdown over border wall funding reflected childish behavior on the part of federal officials. When All Things Considered host Ari Shapiro asked Shalala how to prevent another shutdown, she replied, “[E]lect grownups to Congress and to the presidency” (as quoted in Shapiro, 2019b, n.p.).
Congressman Kevin McCarthy (R-CA) suggested an effective motivator for the legislative branch is to target members’ pocketbooks, saying, “You want to know how you’ll never have a shutdown again? Let’s not pay the members of Congress and the
Senate” (as quoted in Todd, 2019b, n.p.). 143
Members on both sides of the aisle introduced legislation to avoid shutdowns.
Virginia Democratic Senator Mark Warner introduced the Stop the STUPIDITY Act, which in the event of an impasse, would extend funding at existing levels, but leave the
Congress and White House unfunded (Rascoe, 2019). GOP Senator Rob Portman explained how he has repeatedly tried to introduce legislation to this end, telling Morning
Edition host Rachel Martin (2019b) that he had introduced legislation, called the End
Government Shutdowns Act, five different times to no avail.
As mentioned earlier, Immigration Reform was the second most common emergent code to be identified in the research. Democratic and Republican actors advocated for reform in order to expand the scope of conflict, but also to couple a long- unresolved policy problem with a separate, but related issue—border security—in the hope of achieving policy change. Achieving this change would break the policy equilibrium that had endured in this domain, but was viewed by many as being dysfunctional.
Among the interviews coded in this research, Senator Schumer was the first to call for immigration reform—a call he issued on December 16, 2018, six days before the shutdown commenced—during an appearance on Meet the Press. Said Schumer, “We
Democrats—we’re for a path to citizenship…So we want, we want to create a path to citizenship for those [in the United States] illegally” (as quoted in Todd, 2018, n.p.).
Republicans typically justified pursuit of immigration reform on the argument the existing system is broken. While not calling for immigration reform explicitly, White
House aide Stephen Miller characterized the state of America’s immigration policies as a failure of the legal system: “One of the great tragedies that is going on in our country 144 today is the loopholes in our immigration laws and the deficiencies in our immigration laws” (as quoted in Brennan, 2018, n.p.). Chief of Staff Mulvaney signaled the White
House would be open to a discussion on immigration reform, while underscoring the broken nature of America’s immigration laws a week later on This Week:
If you try to enter the country illegally, the law works to your advantage.
That needs to be fixed as well in order to solve this issue of the difficulties
on the southern border. We are more than willing to talk about all of those
things. (as quoted in Stephanopoulos, 2018a, n.p.)
Senator Lindsey Graham on January 6, 2019, characterized the situation as follows:
[T]he goal is not to open up the government. The goal is to fix a broken
immigration system to bring reality to this table. That ICE is not the
problem, it’s the solution. The goal is to repair a damaged, broken
immigration system. (as quoted in Brennan, 2019, n.p.)
Narrative strategies
In the category of Narrative Strategies, although the Angel- and Devil-shifts demonstrated coding agreement, the Narrative Policy Framework theorizes that angels and devils align with heroes and villains, respectively. The earlier discussion on these codes negates the need for further discussion, but the following section addresses the theoretical application of these codes for strategic purposes, namely the expansion and limitation of Scope of Conflict, which also is addressed here. Otherwise, this section focuses primarily on the Problem Definitions, Numbers, and Costs of preferred and opposed policy solutions. 145
Angel- and Devil-Shifts, Scope of Conflict, and Stance. The Narrative Policy
Framework conceptualizes these three strategies as being interrelated. Angel- and devil- shifts align closely with the narrative elements of heroes and villains. Merry (2019) writes, “the devil shift and angel shift are typically operationalized as the prominence of villains or heroes, respectively, in policy narratives” (p. 883). The angel-shift is present when groups self-identify as heroes capable of solving a problem, while the devil-shift is utilized when actors exaggerate the power and maliciousness of their opponents.
The use of one type of shift is theorized to reflect the prospects of success or vulnerability by any one side in a debate. The angel-shift is presumed to accompany a sense of optimism, believing a problem will be solved in a way favorable to a group’s preferred policy outcome. Conversely, use of the devil-shift is expected when one side feels threatened or pessimistic about an outcome (Shanahan et al., 2013).
In this way, research into the angel- and devil-shift identifies similar strategic purposes as scope of conflict. Actors employing the use of an angel-shift strategy are expected to perceive themselves as winning a debate and, therefore, seeking to contain the breadth—or scope—of an argument in a way that preserves the status quo. The devil- shift, on the other hand, is used when groups fearful of being in a losing position, villainize the other side and emphasize burdens of the status quo. In the process, the devil-shift is postulated to expand the scope of conflict and draw in other parties to expand a coalition. In this regard, angel- and devil-shifts are viewed as tools for maintaining group cohesion or drawing other groups into a debate, respectively (Merry,
2019). 146
The data coded through this research did not draw connections between the two types of shifts and the Hero and Villain characters as expected under the NPF, nor was the strategic purpose of those shifts as evident in terms of expanding/containing the scope of conflict or asserting a clearly defined stance with respect to a group’s position in the border wall funding debate. This perhaps provides reason why the coding categories of expanding the Scope of Conflict, limiting the Scope of Conflict, a winning Stance, and a losing Stance did not register agreement between coding attempts in this research. When comparing coding rounds, only the presence of a Scope of Conflict strategy and No
Stance registered agreement.
These findings call into question the association between Angel- and Devil-shifts, and the Scope of Conflict and Stance narrative strategies. The data substantiates calls in the literature to re-examine these strategies. Specifically, Merry (2019) calls for the
Angel- and Devil-shift strategies to be reconceptualized, while McBeth and Lybecker
(2018) suggest the role of Scope of Conflict be further studied in the context of democratic governance, agenda setting, and transferability between policy agendas.
Problem Definition and Causal Mechanism. Although not defined in the
Narrative Policy Framework as a particular strategy, the different ways in which actors defined the problem underlying the federal government shutdown—as identified in the initial coding round—warranted further examination and discussion here. Trump initially defined the problem as one of border security. Others, like his aide, Stephen Miller, called it a crisis brought about by a failure of the legal system. When defining the problem this way, Trump sought to protect Americans from the economic and criminal effects of illegal immigration. 147
Over time, the nature of the president’s problem definition evolved to being a humanitarian crisis. The presence of so-called “caravans” of migrants approaching the
U.S. border from Central America created a window for him to redefine the problem. In doing so, he could emphasize the threat this migration posed to women and children, specifically, relying on a more favorable social construction of these groups to aid his argument and build support. For example, in his January 8, 2019, address to the nation,
Trump defined the problem and victims as such, although preserving his assertion that the legal system was to blame:
This is a humanitarian crisis—a crisis of the heart and a crisis of the soul.
Last month, 20,000 migrant children were illegally brought into the United
States—a dramatic increase. These children are used as human pawns by
vicious coyotes and ruthless gangs. One in three women are sexually
assaulted on the dangerous trek up through Mexico. Women and children
are the biggest victims, by far, of our broken system. (Trump, 2019, n.p.)
Although the Humanitarian Crisis, Legal Failure, and Security Threat codes did not register agreement between coding attempts, Figure 5-1 depicts the number of instances between coding attempts when Republicans used these codes in their narratives by week. More than a third of Republicans defined the problems as being a failure of the legal system during weeks one and two. Use of this code dropped through Week 5, before rebounding though Week 8. From weeks two through four, use of the Humanitarian
Crisis problem definition code increased among GOP interviewees. Throughout this change in messaging strategy, the party continued to underscore the security threat facing 148 the country, as evidenced by the increase in weeks two through five and the higher or equal frequency of use in weeks six through eight.
0.45 0.4
0.35 0.3 0.25
0.2 0.15 0.1
0.05 0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Humanitarian Legal Fail Security Threat
Figure 5-1: Select Republican Problem Definition Code Ratios by Week
Democrats generally saw the problem as a manufactured crisis or a failure of leadership—both on the part of Trump and Republicans in Congress. As Figure 5-2 shows, at least 25 percent of Democratic interview transcripts contained the Ineffective
Leadership code in weeks one through five, with 100 percent of Democratic transcripts coded to contain this Problem Definition in Week 2. Use of the Manufactured Crisis code increased steadily from Week 3 through Week 6, afterward it declined following the three-week compromise plan that reopened the government. 149
1.2
1
0.8
0.6
0.4
0.2
0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Manufactured Ineffective leadersip
Figure 5-2: Democratic Problem Definition Code Ratios by Week
When considered in conjunction with the devil-shift strategy, focusing their attention and villainous characterizations on the other political party reinforced group cohesion among Democrats. At the same time, by depicting Republicans in a negative light, Democrats sought to undermine GOP proposals to build the border wall.
There is some evidence this strategy was effective. According to a Gallup survey released January 15, 2019—25 days after the shutdown began—Trump’s approval rating dipped to only 37 percent, which was the lowest the firm had registered since late-
February/early March 2018. Prior to the funding impasse, Trump had registered a 43 percent approval rating in Gallup’s poll. Between a poll conducted December 17-22,
2018 and the poll released January 15, which was conducted between January 2-10, 2019,
Trump lost support across the political spectrum. Support dropped eight percentage points among Independents, two points among Democrats, and one point among
Republicans (Jones, 2019). 150
Comparing public opinion of Congress over the same time period, favorability ratings among Independents and Republicans declined five and three percentage points, respectively. Democrats saw a 13-point increase (Jones, 2019). While the available data through Gallup is not sufficient to establish causation—even correlation—it is reasonable to assume the shutdown and public messaging by elected officials influenced public opinion.
Turning to the Causal Mechanism code, while none of the a priori codes demonstrated agreement between coding rounds, the Other category was coded consistently. As noted in Chapter 4, interview transcripts were coded as attributing a political or partisan motivation as the reason for the shutdown on 36 different occasions.
While a case could be made that partisan motivations constitute an Intentional form of
Causal Mechanism, because that particular code did not register agreement and the explicit nature of the language interviewees used to reference political influences as a cause of the shutdown, Partisanship merits consideration as a new code or type of Causal
Mechanism.
A separate Gallup poll of polarization among American adults released on
January 16, 2019, provides further justification of the changing nature of American governance and politics. Following a nearly year-long survey of people aged 18 and older living in all 50 states and the District of Columbia, the sample of nearly 73,000 people found the “average 79-percentage-point difference between Republicans’ and Democrats’ job approval ratings of President Donald Trump during his second year in office is the largest Gallup has measured in any presidential year to date” (Jones, 2019a, n.p.). The 151 survey report went on to say, “Extreme partisan views of presidents are the new norm in politics” (Jones, 2019a, n.p.).
Numbers. Nowhere in the body of literature on Narrative Policy Framework are numbers identified as a narrative strategy. The framework recognizes the utility of citing and quantifying costs and benefits, which entails a quantitative aspect. Stone (2002) writes that numbers when used to measure are a common way of defining a problem, saying, “The fundamental issues of any policy conflict are always contained in the question of how to count the problem” (p. 164). With these considerations in mind, numbers were included as a code a priori data collection.
This research found actors regularly employed numbers to quantify the problem, substantiate their policy preferences, and bolster their arguments. Interviewees generally used numbers with less frequency as the shutdown dragged on when measured weekly as a ratio of code count-to-interview count by party. Further, numbers were used as a narrative strategy in all but one of the case’s nine-week timeframe (see Figures 4-14 and
4-15 in Chapter 4).
Republicans used numbers more frequently than Democrats. At least two reasons may account for this disparity. First, given the way a problem is defined influences the solutions available to solve it, members of the GOP likely saw value in stating and repeatedly reinforcing numbers that illustrated the security threat they perceived to exist along the southern border. Second, the shared use of numbers among actors or groups helps to build group cohesion and mobilize politically like-minded groups and individuals. Recall that majority control of the U.S. House of Representatives changed hands from Republicans to Democrats during the course of the shutdown. Additionally, at 152
53 members, Republicans held an insufficient number of seats in the Senate to thwart a filibuster by invoking the chamber’s cloture rule. If Republicans were to deliver on this priority agenda item of a president from their party, they could not afford any defectors from their conference.
Achieving group cohesion was a tall order given public opinions of the shutdown.
Quinnipiac University released a national poll on January 14, 2019, that American voters supported Democrats’ plan to reopen parts the government unrelated to border security while continuing negotiations on border wall funding by a margin of 63-30. Voters by a similar margin (63-32) opposed shutting down the government to compel funding for the barrier. Further, 56 percent of voters held Trump and Republicans in Congress responsible for the shutdown (as opposed to 36 percent who held Democrats responsible) and 55 percent opposed building a wall along the southern border (Quinnipiac University
Poll, 2019).
The numbers Republicans used are categorized according to the following five themes: counts of immigrants crossing the border; crime statistics; economic costs;
Democratic hypocrisy; and severity of the shutdown. Under the first thematic category, members of the Trump administration, such as Kevin McAleenan of Customs and Border
Patrol (Raddatz, 2018) and Chief of Staff Mick Mulvaney (Todd, 2019) each claimed that
60,000 immigrants entered the United States illegally each month. McAleenan also claimed 65 percent of those crossing the border were families with children. White House aide Stephen Miller said “last year”—presumably a reference to 2017—100,000 immigrant children entered the U.S (Brennan, 2018). 153
In the category of crime statistics, Republicans cited numbers that underscored the threat of illegal immigration to Americans. Trump relied heavily on this strategy during his January 8, 2019, speech to the nation. In those remarks, Trump claimed that 300
Americans were killed every week by heroin, 90 percent of which entered the U.S. through the southern border. Further, he stated that Immigration Customs and
Enforcement agents arrested 266,000 aliens with criminal records over the past two years, including those who had either been convicted of or charged with, collectively, 100,000 assaults, 30,000 sex crimes, and 4,000 “violent” killings. Trump emphasized the violent nature of those and other homicides, saying, “Over the years, thousands of Americans have been brutally killed by those who illegally entered our country, and thousands more lives will be lost if we don't act right now” (Trump, 2019, n.p.).
Also, as mentioned in this chapter’s discussion of Victims, Trump mentioned threats to migrant women and children making their way to the U.S., attempting to capitalize on the more favorable social construction of those populations. He noted
20,000 migrant children were illegally brought to the U.S. in December 2018, and one third of women making the “dangerous trek up through Mexico” were sexually assaulted.
Said Trump, “Women and children are the biggest victims, by far, of our broken system”
(Trump, 2019, n.p.).
The third Numbers code Republicans used in their narratives associated immigration’s social ills to financial costs imposed on U.S. taxpayers and the economy.
Stephen Miller claimed heroin costs the United States $230 billion each year without specifying the nature of that expense (Brennan, 2018). Trump (2019) claimed the cost of all illegal drugs in the U.S. exceeds $500 billion annually. 154
Republicans used numbers to undermine the position of their Democratic colleagues. In the fourth commonly used code, Republicans substantiated their Story of
Conspiracy plot with numbers. Recall from earlier in this chapter that GOP interviewees frequently emphasized that Democrats who opposed additional funding for a border wall in this case had voted for similar appropriations prior to Trump taking office and even after he had occupied the Oval Office.
Senator Susan Collins of Maine suggested a compromise was attainable prior to the shutdown taking effect. She noted that 46 of 49 Democratic senators voted earlier in
2018 to appropriate $2.5 billion a year over the next 10 years for border security
(Stephanopoulos, 2018). That Democrats were unwilling to heed Trump’s calls for border wall funding at even that level, which would have been approximately 44 percent of the president’s budget request in late 2018, suggests a measure of partisanship or political motivations (a frequently cited Causal Mechanism), particularly when considered in concert with November 2018’s election results and just weeks prior to Democrats taking control of the House of Representatives. Texas GOP Senator Ted Cruz recalled that every
Democrat in the Senate in 2013 voted for 350 miles of fencing, but in 2018, were unwilling to support funding for approximately 230 miles of new barriers (Todd, 2019a).
Finally, Republicans used numbers to downplay the significance of the shutdown often when taking a defensive stance in response to questions. For example, Florida
Representative Ted Yoho was asked on January 3, 2019, why a Democratic proposal to reopen the government on a short-term basis in order to allow more time for negotiations was a bad idea. In his exchange with All Things Considered host Mary Louise Kelly
(2019), which follows, he seeks to minimize the significance of the shutdown and 155 number of affected workers, and instead, tries to shift the conversation back to border security, which is consistent with how members of the GOP sought to frame this issue:
KELLY: So Democrats control the House now, as you well know. So let’s
start with what they have put on the table—a spending bill that would fund
most of the government through September, a separate measure that would
fund the Department of Homeland Security till next month—so basically
buying a few weeks for you in Congress and the president to keep
debating the border wall but meanwhile allowing the government to
reopen. Why is that not a good idea?
YOHO: Well, you know, right now, close to 75 percent of the
government's already open and funded. This is a small portion, 24 percent.
In that is DHS.
KELLY: That's thousands and thousands of people not getting a
paycheck and not doing their jobs, though.
YOHO: Well, it’s—they have over 50,000 people, but only 6,000 I
think or 9,000 haven’t—they've been deemed nonessential. The goal is to
get the government open and running. But this goes back to the previous
discussion that the president had and we had on border security. This is
something that the American people want—is border security. You know,
too much emphasis is being put on the wall versus border security. And
we need to focus on—what we’re trying to do is keep this country safe.
(n.p.) 156
House Republican Minority Leader Kevin McCarthy tried minimizing the scope of the shutdown when responding to a question of whether Trump should declare a national emergency, thereby allowing him to re-allocate funding from other appropriations to border wall construction. While briefly defending Trump’s right to pursue such measures, he shifts to emphasizing that the matter of funding for a border wall would be better resolved legislatively. Given the fractional nature of the disagreement in the scope of the entire federal budget, he believed compromise should be attainable: “It’s one tenth of one percent of the federal budget. If we cannot do this together, what else can we not do in the future? This is not that big of a problem” (as quoted in Brennan, 2019a, n.p.).
Among Democrats, numbers were a tool largely to either undermine the administration’s rationale for a border wall, thereby casting the proposal as irresponsible; reinforce the problem definition of Trump’s ineffective leadership; or put into context the personal toll the shutdown had on federal workers. With respect to the first purpose,
Democrats quantified the nature of drug-smuggling across the southern border and the weaknesses in border patrol agents’ surveillance systems. For example, Senator Dick
Durbin said of all the drugs coming into the United States through Mexico, 80 percent of narcotics enter through legal ports of entry and fewer than one in five vehicles are scanned before entering the country (Stephanopolous, 2018). Congressman Pete Aguilar
(D-CA) expanded on this point and reinforced the argument that border security investments need to be smart and address the real shortcomings of the system by saying only 1 percent of personal vehicles and 17 percent of commercial vehicles are scanned at the border crossing (Inskeep, 2019a). As such, unsecured sections of the border without 157 physical barriers were not the problem in the eyes of Democrats—it was the legal ports of entry that did not have proper tools to conduct surveillance effectively.
To substantiate the ineffective leadership charges, Democrats often cited the uncommitted or unspent funds the administration had at its disposal from prior border security appropriations. To repeat one example cited earlier, Oregon Democratic Senator
Merkley said 94 percent of funding, or more than $1 billion, appropriated the prior year for border security remained unspent, calling into question the legitimacy of the president’s position and arguments.
Finally, Democrats often cited the shutdown’s human toll. Democrats Kirsten
Gillibrand, Steny Hoyer, Nancy Pelosi, Jamie Raskin, Bennie Thompson, Chris Van
Hollen, and Mark Warner each cited the 800,000 federal employees who were either working without pay or furloughed from their jobs because of the shutdown. Merkley, in particular, put the shutdown into context for Americans who receive federally subsidized benefits, while undermining the efficacy of a border wall and the fiscal prudence of the president’s proposal, too. Using $5 billion as an approximation of Trump’s demand, he said:
The American people want us to spend money in a smart way. $5 billion is
a lot of money. That’s 650,000 children attending head start. It’s 2 million
meals a day for a year—for a year for—for seniors. And to spend it on a
4th century strategy rather than on stuff that actually improves border
security is something we’re just not going to do. (as quoted in Karl, 2019,
n.p.) 158
Costs. The Narrative Policy Framework conceptualizes costs in a way that aligns closely with a narrators’ Stance and Scope of Conflict strategies. The framework theorizes that groups perceiving themselves as winning attempt to diffuse benefits and concentrate costs through their stories, while those perceived as losing a debate seek the opposite—to concentrate the benefits and diffuse the costs (Jones & McBeth 2010). In this way, the allocation of costs and benefits corresponds with the Stance narrators communicate. Groups who seek to preserve the status quo are expected to take a winning stance, whereas losing groups seek to disrupt the existing policy equilibrium. Further, given this theoretical expectation, losing groups seek to expand the Scope of Conflict, bringing new groups into the debate, whereas winning groups try to contain the conflict, limiting those who are engaged (Shanahan et al., 2011).
Returning to the case at hand, only the Costs of Preferred Solutions and Costs of
Opposed Solutions registered agreement; neither Benefits category coded consistently.
Discussions of costs focused on the expense associated with an appropriation to fund construction of the border wall. The costs were not disputed. Instead, the dispute centered around the necessity of appropriating at least $5 billion as Trump demanded.
In terms of who was characterized as bearing those costs when referencing either the preferred or opposed policy solutions, as viewed respectively by the narrator’s individual support for or opposition to the border wall’s construction, members of both parties overwhelmingly cited the American people, economy and/or taxpayers. At times, this attribution was explicit; at other times, it was implied. Democratic Senator Chris
Coons specifically mentioned taxpayers in his interview with CBS Face the Nation host
Margaret Brennan, weaving the attribution into a Story of Broken Promises: “There is 159 frankly no path towards his getting five billion dollars in American taxpayer money to meet his campaign promise of a big beautiful wall with Mexico” (as quoted in Brennan,
2018a, n.p.). During the same program, Republican Senator Rand Paul of Kentucky objected to burdening taxpayers with such a price tag for the border wall unless cost savings could be found elsewhere to offset the expense: “We promised to spend less money and so I won’t vote for it. Well, I would if we were to offset it with cuts somewhere” (as quoted in Brennan, 2018a, n.p.). Republicans who supported funding the wall without condition justified that position on the belief Americans were willing to bear the costs because it provides greater security, which they want. GOP Congressman Ted
Yoho said:
You know, the American people want border security. When we poll
people, across the board, Republicans and Democrats, one of —the
biggest issue that comes up is security for their families. And so this is
something we want to make sure we do… (as quoted in Kelly, 2019, n.p.)
Looking at the data as coded by party and by week, Republicans more regularly used the Costs of Preferred solutions code as shown in Figure 5-3. Their use of the narrative strategy increased between weeks two through four, then dropped in Week 5 after which use remained relatively low. Figure 5-4 shows that Democrats relied on citing the Costs of Opposed solutions more often during the first four weeks of the case, after which both parties employed the strategy no more than twice per week. 160
8
7
6
5 4
3 2 1
0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Democrat Republican
Figure 5-3: Costs of Preferred Solutions by Party by Week
7
6
5
4
3
2
1
0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Democrat Republican
Figure 5-4: Costs of Opposed Solutions by Party by Week
Adjusting the data to account for number of transcripts by party by week (Figure
5-5), Republicans used the Costs of Preferred code most often in Week 3 in half of their interviews, while Democrats used the code in a third of their Week 2 interviews and then only again in one-eighth of their Week 4 interviews. Democratic interviews contained the
Costs of Opposed Solution code most often in Week 2 (66.67 percent), while 161
Republicans used it in no more than one-fifth of their interviews during Week 5 as shown in Figure 5-6.
0.6
0.5
0.4
0.3
0.2
0.1
0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Democrat Republican
Figure 5-5: Costs of Preferred Solutions Code Ratios by Party by Week
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Democrat Republican
Figure 5-6: Costs of Opposed Solutions Code Ratios by Party by Week
In assigning the cost burden to Americans, each party viewed the public and taxpayers differently. For Republicans, Americans were viewed as a diffuse class, seemingly arguing that spreading out the costs among the nation’s taxpayers was a 162 necessary and justifiable expense in the name of national security. GOP members tended to ignore Trump’s prior promise that Mexico would finance construction of a border wall.
Democrats, on the other hand, viewed American taxpayers as a concentrated group bearing an unreasonable cost for an ineffective policy solution.
Based on the literature, this data does not seem to conform with theoretical assumptions of the Narrative Policy Framework, generally, thus it does further question the conceptualization of the Stance strategy. A complete assessment of the framework’s conceptualization, though, is difficult given the coding inconsistency of the Benefits strategies. The framework’s assumption that winning stances require concentrated costs and diffuse benefits and losing stances require diffuse costs and concentrated benefits requires data on both codes.
The conceptualization of Stance is further questioned because both parties sought to couple the border wall funding question with the prospect of enacting immigration reform. Based on this code, alone, one could argue both sides viewed themselves as losing the debate because both tried to expand the conflict’s scope.
Zahariadis (2015) writes that emphasizing costs and benefits may be futile or of little value as sometimes using information that capitalizes or reflects the national mood is more important. May and Jochin’s research (2013) suggests the ambiguous allocation of costs and benefits may indicate weak policy designs and/or a relative lack of institutional support for implementing the policy. In such cases, weak policy regimes fail to generate positive feedback for their preferred designs, further eroding their support and making enactment of the preferred solution more challenging. 163
Legitimacy. An a posteriori code that emerged following Round 1 of coding was
Legitimacy. Support for considering Legitimacy as a new narrative strategy is found in the perfect coding agreement between Round 1 to Round 2, as shown earlier in Table 4-
11.
Looking at the other Legitimacy a posteriori codes developed through this research—Illegitimate, Manufactured, Unnecessary and Unwanted—although neither registered agreement between coding attempts, this may be attributed to poor conceptualization. If counts of these four codes are aggregated into one new code defined as “Illegitimate” the aggregate figures would have demonstrated coding consistency between rounds as shown in Table 5-1.
Table 5-1: Emergent Legitimacy Code Counts and Coefficients of Agreement
Illegitimate Manufactured Unnecessary Unwanted Aggregate Illegitimate* Round 1 9 14 15 16 54 Round 2 16 18 10 13 57 Mean 12.5 16 12.5 14.5 55.5 R1/R2 56.25% 77.78% 150.00% 123.08% 94.74% R2/R1 177.78% 128.57% 66.67% 81.25% 105.56%
Using this new binary coding system for Legitimacy, Republicans coded as believing the shutdown was Legitimate in 10 interviews, while no Democratic interviews were so coded. Conversely, Democrats were one-and-a-half times more likely than
Republicans to view the shutdown as “Illegitimate” as illustrated in Table 5-2.
Table 5-2: Consolidated Legitimacy Code Counts and Percentages by Party Affiliation
Legiti- Illegiti- Manufact- Unnecessary Unwanted Aggregate mate* mate ured Illegitimate* Democrat 0 13 29 13 12 67 Republican 10 12 3 12 17 44 Democrat % 0.00% 13.27% 29.59% 13.27% 12.24% 60.36% Republican % 9.80% 11.76% 2.94% 11.76% 16.67% 39.64%
164
As discussed earlier under the “Problem Definition and Causal Mechanisms” section, the shutdown’s characterization as Legitimate or Illegitimate is, in effect, an opinion of federal officials on how the problem is defined and the underlying causes of the shutdown. This view should prompt further research into the application and attribution of Legitimacy claims, which will be discussed in Chapter 6 when addressing potential areas for further research.
Source cues
Although outside of this study’s focus, source cues by interviewers were an often- noticed feature of transcripts. Source cues are recognized as ways in which media outlets and vehicles adopt and communicate the social construction of a policy issue (McBeth et al., 2013). The interview transcripts coded for this research found a number of instances where the interviewer frames a question in a manner that reflects a certain narrative element or strategy under study here, or a particular framing strategy that challenges the story of the interviewee.
For example, in her January 2, 2019, interview with Senator Dick Durbin,
National Public Radio All Things Considered host Audie Cornish (2019) quotes the senator’s previous use of the word “hostage” to assign responsibility for the shutdown to
Trump. Her NPR colleague Rachel Martin used the same word on January 10, 2019, when responding to a comment from White House Communications Director Mercedes
Schlapp that the White House is eager to negotiate. Said Martin on the network’s
Morning Edition show: “But people point out it doesn’t seem fair to hold the government hostage while tens of thousands of federal workers don’t get a paycheck” (Martin, 2019a, n.p.). Martin’s comment implies to listeners that Trump is responsible for the shutdown 165 and given the context in which she made the statement, that Trump had not demonstrated a willingness to negotiate or compromise to that point.
Later in the same month, another NPR host, Steve Inskeep (2019a), on Morning
Edition said during his introduction of an interview with Representative Pete Aguilar,
“Democrats and, privately, many Republicans have scorned the president’s insistence on building a border wall. Yesterday, the president said the committee is wasting its time unless they’re discussing that wall” (n.p.). This source cue suggests to listeners
Republicans have divisions within their rank and that Trump is unwilling to compromise.
When Aguilar says appropriators are focused on funding effective solutions and not helping Trump fulfill a campaign promise, Inskeep replies, “Or not, I guess, since he said
Mexico would pay for the wall” (n.p.). Again, this suggests dishonesty on Trump’s part and paints him in an unfavorable light, reinforcing Democratic narratives.
Republican arguments were similarly the basis of source cues in the coded interviews. During an interview with Democratic Congressman Bennie Thompson, This
Week host Martha Raddatz (2019) echoes Republican claims that Democrats were not willing to compromise, asking, “[I]f Democrats are unwilling to budge at all on the funding for the border wall, don’t you own some of the blame for the continued stalemate” (n.p.)? Later in the program, Raddatz (2019) refers to the president defining the problem as a humanitarian crisis, and references video taken from the U.S.-Mexico border that shows, in her words, “the dire conditions that we saw firsthand down there”
(n.p.).
How media portrays an issue is an important part of framing in communications and agenda setting (Entman, 1993). The frames media embrace and subsequently 166 communicate influence how an issue is understood by its audience. The likelihood of an audience adopting the frame, however, is subject to the attention viewers, readers and listeners devote to the messaging, the frequency to which they are exposed to it, and their underlying knowledge of the core issue, which influences their response to the cue and the degree of information processing in which they are willing to engage (Chong &
Druckman, 2007; Scheufele & Tewksbury, 2007).
That interviewers adopted and relayed certain narrative elements and strategies in their questions likely speaks to the frequency with which those items were found in the data. As discussed earlier in this paper, federal workers were cited regularly as victims; stories of Responsibility, Compromise/Negotiate, and Broken Promises were among the more frequent emergent Plot codes; and Politics/Partisanship was a frequent refrain when it came to defining the problem and attributing a cause for the shutdown. Thus, source cues interviewers incorporated into their questions and statements indicate these codes effectively gained traction in framing the debate.
The magnitude of influence these narrative elements and strategies had in priming individual viewers and collective public opinion is unknown however based on data collected and available here. These are important questions and the literature on the memory-based accessibility and applicability effects of information remains unsettled
(Scheufele & Tewksbury, 2007).
167
Chapter 6
Limitations, Theoretical Contributions, and Research Recommendations
Research into the case of the 2018-2019 federal government shutdown is an exercise in understanding the role of narratives in advancing public policy issues.
Trump’s calls for funding construction of additional physical barriers along the U.S.-
Mexico border were situated squarely in the institutional agenda of issues garnering attention among policymakers in Congress and the presidential administration. The
Democrat-controlled U.S. House of Representatives’ opposition to Trump’s preferred policy alternative effectively blocked the matter from reaching the legislative decision agenda, although the president’s use of executive powers to reallocate funding from previously appropriated defense programs could be argued to have effectively accomplished the same end goal.
The research conducted through this study, however, assesses only the act of policymaking through the traditional legislative process and the narratives communicated in the course thereof. In that process, narratives played a seemingly coordinated role among both sides debating border wall funding.
The purpose of this exploratory study is not to demonstrate empirically the efficacy of narratives in advancing an issue onto the agenda of government institutions.
Instead, it offers an initial exploration into how narratives are used to that end. This research offers important insight into application of the Narrative Policy Framework in public policy agenda setting and yields potentially meaningful additions to the field’s theoretical understanding and conceptualizations. 168
This chapter begins with a discussion of factors limiting the research I have conducted, followed by an examination of the study’s contributions to the field of public policy agenda setting. The research offers three findings, including the identification of new plots that are of particular value in understanding how problems are defined, and issues and solutions are framed in the context of a crisis; the importance of message consistency, or repetition, in information processing and influencing decision making, and most significantly, the paper theorizes that problems, solutions, and the political environment must be perceived as legitimate in order for an issue to proceed from the systemic agenda to the decision agenda. Finally, the chapter suggests a number of areas for future research to further validate these findings and continue building the body of knowledge on narratives in agenda setting.
Limitations
This paper seeks to present a balanced depiction of narratives used during the
2018-2019 U.S. government shutdown by studying the interviews of federal officials in unbiased news organizations as identified by Ad Fontes Media. That said, this research— like any research—faced limitations that may affect its findings.
The following section addresses three areas that limited the research. Those areas are as follows: insufficient theoretical conceptualization of key narrative strategies in the literature that may have contributed to the lack of code agreement between coding rounds; a lack of secondary public opinion survey data sources to provide a fuller contextual understanding of the political environment and national mood at the time the case took place; and potential sources of biases, including unconscious personal bias and sampling bias. 169
Conceptual shortcomings
As noted throughout this paper, a number of codes failed to demonstrate consistency between two rounds of coding. I attribute this partly to insufficient conceptualization of these narrative strategies. Because these codes did not register agreement, they were omitted from analysis and thus, the potential for meaningful insight into the roles of these narrative components in agenda setting is lost.
I echo Merry’s (2019) call to better conceptualize the angel- and devil-shifts.
These shifts did not align in this study with Scope of Conflict and Stance strategies as theorized in the literature. Groups seeking to expand the scope of conflict are expected to exaggerate the malicious intent of villains through the devil-shift, while winning groups that hope to contain the scope of conflict are expected to emphasize the heroic qualities of certain actors.
Similarly, the Stance narrative strategy must be revisited. The Narrative Policy
Framework theorizes that groups conveying a winning Stance narrative will employ an angel-shift strategy to emphasize their virtues and ability to solve problems. At the same time, these groups are likely to limit the scope of conflict so as to preserve the status quo.
As discussed in Chapter 5, such connections between these three coding categories were not evident in the data.
Contextual understanding
Public polling data cited previously—and later in this chapter—from Quinnipiac and Gallup help to illustrate the national mood, which is an important element in understanding the context in which the shutdown took place. Chapter 3 notes the 170 significance of context in content analysis, specifically, and qualitative research more generally.
The polling data also offer some insight into public perceptions of characters,
Morals of the Story, and Problem Definitions/Causal Mechanisms communicated through the narratives studied here. I was unable, though, to find survey data on public opinions toward victims as a general category. Understanding public views on those affected by the shutdown or the problem, however it is defined, could be useful in interpreting the motivations of heroes and villains, as well as reactions to characters’ preferred policy solutions. For example, it may be assumed that the plight of 800,000 federal workers going without pay for five weeks elicited sympathy from the public, which in turn, negatively influenced their views of the shutdown’s propriety and may have affected the preferred alternatives of whomever or whichever side of the argument was viewed as being responsible for the shutdown.
Potential biases
Chapter 3 addresses the importance of transparency in research, especially in qualitative research given the subjectivity of interpretations and the researcher’s role in data collection and analysis. It was there that I disclosed my own political registration with the Democratic Party. In the course of this research, while I made conscious attempts to avoid having my political views influence interpretation of the data, I must acknowledge this may have occurred subconsciously or unintentionally.
Additionally, while this research design sought to sample news content with minimal partisan bias, it is important to recognize other perspectives exist in media 171 sources prone to slant, and those competing views influence the political and public environment beyond news organizations and broadcast shows studied here.
The proliferation of partisan news organizations offers diametrically opposed views of political activities. These competing perspectives allow the public to self-select news sources that reinforce their existing beliefs and perceptions, contributing toward confirmation biases. This polarization of journalism and mass communications influences the actions and discourse of government officials because their constituents and base of electoral support follow these partisan news organizations and engage in networks that rely on them to disseminate their messages. Consequently, the news content selected here for analysis undoubtedly exhibits a degree of sampling bias in that it ignores narratives contained within more partisan news coverage that likely influenced the selected case and actors therein in unknown ways.
Individually and collectively, these limitations call for further research into the role of narratives in public policy agenda setting. Those opportunities will be discussed later in this chapter after addressing the theoretical contributions of this research.
Theoretical contributions
The research presented here makes three contributions to the body of knowledge with respect to narratives’ role in public policy agenda setting. The content analysis of interviews pertaining to the federal government shutdown over border wall funding identifies new plot lines that broaden those commonly associated with the Narrative
Policy Framework and found elsewhere in the literature; it suggests message consistency is an important factor in narratives’ ability to influence public opinion and actions of policymakers; and it introduces the concept of legitimacy as an important factor at 172 multiple stages of influencing an issue’s progression to the decision agenda. These three contributions are discussed next in order of increasing significance to the field.
New plot lines
As explained in Chapter 4, while a Plot was coded in approximately 93 percent of interviews by Democrats and Republicans (92.86 percent and 93.14 percent, respectively), many of the a priori Plot codes were little used in narratives of each party.
These codes, such as the stories of Stymied Progress, Change, Control and Blame the
Victim, were based on the influential and enduring work of Deborah Stone (2002). As data collected here suggest, though, these stories seem to have little utility in the context of this case.
Instead, what emerged from the data are new stories that speak to the divisiveness of American politics and the proclivity of politicians to point fingers and assign blame.
At the same time, though, officials often sought to articulate the potential for members of political parties to come together, negotiate in good faith, and achieve compromise.
Plots serve to either introduce an audience to a problem or explain how the problem came to be (Crow & Jones, 2018). In this way, plots are very much about problem definition and causality, thus plots are context dependent. The context of the federal government shutdown case is one in which America’s governance system proved incapable of achieving compromise in the face of competing views on the nature and cause of the problem. One side viewed the problem as one of a broken immigration system that created downstream challenges to the economy and national security, and the other side viewed the issue as a manufactured, or artificial, crisis brought about by ineffective leadership. 173
As such, this case identifies stories of Hope, Compromise/Negotiate, Uncertainty,
Pessimism/Skepticism, Disingenuousness, Broken Promises, and Responsibility. Federal policymakers introduced these plots to advance their conceptualizations of the situation.
These stories could represent meaningful contributions to the field’s understanding of narratives by broadening the catalog of plot lines political actors employ in the course of policymaking, generally, and agenda setting, in particular—as least in the context of a policy impasse crisis.
Connections between these emergent stories and other factors in policymaking are conceivable. For instance, the Story of Compromise/Negotiate could influence the politics and policy stream as conceptualized under the Multiple Streams Framework, and the Story of Disingenuousness and Responsibility could influence the problem stream, including the problem definition, as well as the perceptions of certain actors and the policy solutions they advocate.
Message consistency
As noted in Chapter 5, the Trump administration initially defined the problem as one of porous borders undermining national security. Republicans portrayed immigrants as a drain on the nation’s economy and as a competitive workforce depriving American citizens of jobs—a situation created by failures of the extant immigration legal system.
These social constructions are in keeping with the narrative hegemony that has long existed within American institutions and has been found to be especially pervasive within the discourse of conservative-leaning politicians (Brown, 2016; Newton, 2005; Seybold,
2013). 174
Later in the course of this case, as so-called “caravans” of Central American migrants began traversing Mexico and arriving at the United States’ southern border
(BBC, 2018; Cuffe, 2019), the administration shifted its messaging to define the situation as a humanitarian crisis. Under this new problem definition, Trump emphasized the risk to women and children making the trek among the caravans, conceivably to capitalize on the more favorable social construction of these migrants among sympathetic Americans.
Even so, he and members of his administration did not fully abandon their original rationale for protecting national security and American workers. In the previous chapter,
Figure 5-1 demonstrates Republicans’ evolving Problem Definition strategy as expressed by a ratio of code counts to the number of interviews given by week.
Democrats demonstrated greater consistency in adhering to two problem definitions in their messaging, which contributed to the consistent coding of
Manufactured Crisis and Ineffective Leadership as shown in Figure 5-2 of Chapter 5. I contend Democrats’ consistency in messaging served to undermine the position of Trump and Republicans, who characterized the problem more inconsistently.
Republicans’ Problem Definition message was partially rooted in fear and in politics. Trump’s reluctance to abandon his original definition may have stemmed from his past success with this narrative strategy, namely defining immigration and immigrants as threats to public safety, because that approach partly carried him to the presidency in
2016. Zahariadis (2015) writes that when “politicians in the short term rouse fear in support of major policies, the same ingredient of short-term success constrains long-term flexibility, prompting policy makers to continue pursuing the same policy even in the face of mounting losses” (p. 471-472). Thus, Trump’s messaging and earlier 175 commitments to building a wall bound him to that policy solution even as polling data showed voters disapproved of the proposed solution by a margin of 55-43 percent
(Quinnipiac University Poll, 2019). Further, a majority of voters—56 percent—in the same poll held Trump and congressional Republicans responsible for the shutdown, thus
Democrats’ efforts to paint Trump and the GOP as culpable for the impasse resonated with the public. Trump’s prospects for his preferred approach may have improved had he embraced the Humanitarian Crisis problem definition earlier. The Quinnipiac poll (2019) found 68 percent of voters adopted the humanitarian crisis view compared to 54 percent who viewed the border situation as a security threat.
Democrats’ message consistency further benefited from being aligned with the public’s view that Trump and congressional Republicans were villains in this debate.
Recall that nearly 79 percent of Democratic interviews coded Trump as a Villain. This comports with Quinnipiac’s findings, as stated above, that nearly 60 percent of voters held the president and GOP responsible for the shutdown. Democrats most often cited
Trump as a Villain. Republicans meanwhile, by smaller percentages, coded almost evenly
Congressional Democrats (44.12 percent) and Other (42.16 percent) as Villains, which included House Speaker Pelosi, Senate Minority Leader Schumer, as well as everything from activist judges, left-leaning partisan organizations, and human traffickers.
When expressing a preferred policy alternative to the situation at the border, again, Democrats were unified in their opposition to a wall as the exclusive solution and largely uniform in their calls for more technology-based, non-structural solutions, although some did advocate for a mix of structural and non-structural approaches. Zero
Democratic interviews called for a wall in the coding data. Nearly 35 percent supported 176 non-structural solutions and 10.2 percent supported some physical barriers in addition to non-structural tactics.
Similar to the Problem Definition category and Narrative Element codes,
Republicans suffered from a lack of uniformity when expressing their preferred policy solution. Only 34.3 percent specifically supported a wall in their interview transcripts, and 31.37 percent supported a mix of physical and non-structural approaches. Even though Trump eventually amended his proposal to include additional border personnel and technology, the emphasis on a border wall was never abandoned. The changing nature of Trump’s proposal and the lack of public support for a wall likely hampered
Republicans’ willingness to embrace the proposal and advocate for it vigorously.
Quinnipiac’s mid-January 2019 poll found 59 percent of voters did not think funding a wall along the Mexico border was a good use of taxpayer money, including 15 percent of
Republicans and 63 percent of Independent voters. Meanwhile, 61 percent of voters said they would support legislation that funded new border security measures, but not a wall.
Included in that 61 percent were 36 percent of Republicans and 66 percent of
Independents.
Quinnipiac’s (2019) findings bolstered Democrats’ position as voters by a slim margin of 49 percent to 44 percent trusted their party on border security more than
Trump. Again, this poll—when interpreted in concert with data collected through the content analysis performed here and other polling data referenced earlier—suggests
Trump and the GOP faced considerable obstacles in convincing federal policymakers and the public to support the president’s border wall funding request. The narratives Trump and his supporters articulated were ineffective in swaying opinions. The changing 177 elements of Trump’s proposal may have led voters to question the president’s allegiance to his initial policy position; if he was so convinced a wall was necessary, why later accept the need for non-structural alternatives unless it was a political calculation to
“save face,” so to speak?
Trump and his allies did attempt in their narratives to portray this position change in a way that presented him as someone willing to compromise, which honored the calls to compromise and negotiate a settlement to the stalemate (a frequently coded emergent code under the Plot and Moral of the Story categories), but this about face may have occurred too late to influence the president’s preferred policy prospects.
Legitimacy
The second research question of this study asked how narratives are used to legitimize or delegitimize a crisis. The data collected and analyzed here suggests legitimacy is an important consideration in agenda setting and that narratives could influence perceptions of legitimacy with respect to each factor of the agenda-setting process as theorized in the Multiple Streams Framework. Introducing the concept of legitimacy to the body of research on agenda setting is an important finding of this research and a potentially meaningful contribution to the field of public policymaking.
This contribution first requires conceptualization. Simply put, the concept of legitimacy describes how an amalgamation of other narrative elements and strategies intersect and are subsequently perceived by decision makers in the policy realm and the public. Taken a step further, this concept could be applied to specific stages of the agenda-setting process, such as problem definition, or any of the three streams and focusing events as defined by the Multiple Streams Framework. In short, the findings of 178 this research suggest there exists a legitimacy test that must be passed before an issue, problem definition, or policy alternative proceeds to different stages of the agenda.
Gauging legitimacy is ultimately a psychological and cognitive process through which individuals judge how issues, actions or directives are situated within established belief systems and social norms. The extent to which ideas and actions conform with individual and institutional expectations determines legitimacy. In many ways, establishing legitimacy is an exercise in power and authority of individuals and groups over others. Authorities strive to establish their deservedness to impose or effectuate desired outcomes (Tyler, 2006).
Political legitimacy is a prominent feature in the literature on political science and democratic theory. Rothstein (2009) challenges the notion that political legitimacy is born of inputs into democratic systems. Instead, he argues, political leaders establish legitimacy through adherence to the rule of law and impartial treatment of people. In the present case of the U.S. federal government shutdown, one could argue Republicans’ social construction of immigrants and preferred immigration policies deviated from established American norms of accepting people from around the globe. These deviations translated into a sense of illegitimacy by the public.
Levi et al. (2009) write that in addition to adhering to the rule of law, which they term “procedural justice,” governments must also exhibit trustworthiness in order to achieve legitimacy. Trump was handicapped by voters’ views of him and his administration. Quinnipiac’s poll found nearly half (49 percent) of viewers believed the president’s January 8, 2019, address to the nation was “mostly misleading,” while fewer than a third (32 percent) said it was “mostly accurate.” Further, even before the 179 shutdown, the public already questioned the ethical principles of Trump and his top officials. Gallup found a majority of Americans held Trump’s ethical standards to be lower than each of the six elected presidents who preceded him, and 57 percent of surveyed adults rated his administration officials’ ethics as “poor” or “not good,” while only 38 percent responded “good” or “excellent” (Brenan, 2018). Returning to the importance of message consistency, the consistency of Democratic narratives painting
Trump as an ineffective leader who breaks promises matched voters’ views, likely benefiting the party’s efforts to thwart Trump’s policy goal.
These public views could explain why only slightly more than half (52.94 percent) of Republican interview transcripts coded Trump as a Hero—keeping in mind 16 of the 51 Republican interviews sampled (31.37 percent) were given by members of his administration. A lack of widespread support for the president from those within Trump’s own party likely undermined his policy goal.
Returning to the importance of trustworthiness, the polling data on political polarization in America discussed in Chapter 5 suggests the nation’s population lacks faith in their government’s ability to solve problems. Scholars characterize governmental capacity according to four dimensions: coordination capacity, analytical capacity, regulation capacity, and delivery capacity. For the purpose of this research, coordination capacity and delivery capacity are most relevant. Coordination capacity pertains to government’s ability to marshal its resources toward joint action, while delivery capacity speaks to a government’s ability to exercise power and provide public services
(Christensen et al., 2016). The partial U.S. government shutdown at the heart of this case 180 demonstrates a failing on both fronts, which in turn contributes toward the public’s eroded faith in the institution’s legitimacy and ability to respond to a crisis.
The data suggests that in order to overcome these challenges, actors within political and governmental systems must strive to construct a perception of legitimacy.
This requires they justify their actions and positions. Patriotta et al. (2010) offer three means for doing so:
in developing their justifications within the public arena, actors have to
provide rationales consistent with socially accepted definitions of the
common good. Second, to do so, actors may have to actively engage with
competing definitions of the common good held by different social
groups. Third, the development of effective justifications in such contexts
requires specific competencies with regard to the construction of
convincing accounts and arguments. (p. 1805)
I contend Trump and Republicans failed the legitimacy test on a number of fronts—at least insofar as the test applies to the legislative process. Democrats ultimately failed to delegitimize the border wall alternative to the point where it would have been politically impractical or detrimental for Trump to enact this goal via emergency declaration and executive order.
First, looking at Trump and the GOP, Democrats consistently preached narratives that called into question the need for a government shutdown from the first week studied through this case study. Democrats noted that even when Republicans held the majority in the House of Representatives, Trump was unable to secure the votes necessary to construct additional miles of border wall. This reality lent itself to a belief the shutdown 181 was the result of a political failing, which consequently challenged Trump’s assertion that a border wall was essential to national security. The GOP’s narratives were insufficient to portray the situation as a legitimate problem and once the shutdown commenced, Trump and his allies were incapable of capitalizing on the impasse as a focusing event to open a window of opportunity for their solution.
Marrying NPF with MSF: A revised model
In this section, I present the study’s new theorizing that message consistency and legitimacy are important factors in agenda setting and couple these contributions with the existing literature from the policy studies and communications fields. Aside from the recommendations for establishing legitimacy discussed earlier, I submit that consistency in messaging is likewise an important element for reinforcing preferred policy approaches. As the old adage goes, repetition is the mother of learning, thus repeatedly reinforcing a preferred message is an important consideration in information processing and policy learning.
Given the inconsistency of Republican narratives, it is possible insufficient repetition weakened the GOP’s preferred framing, hindering its widespread adoption as expected under framing theory (Chong & Druckman, 2007). Another possible explanation is that Trump’s perception as a dishonest and unethical public official also worked to the detriment of his preferred frame. In order for messaging to affect change in opinions, it must prompt sufficient information processing to reinforce or challenge beliefs, it must be from a creditable source, it must be considered valid, and it must be accessible (Oxley et al., 2014). 182
Second, public opinion, or the national mood proved an obstacle for Trump. As
Quinnipiac’s poll reported, only slightly more than half (54 percent) of American adults believed a security crisis existed along the U.S.-Mexico border. Although Republicans’ attempted to frame the problem through narrative as one of security by connecting undocumented immigrants—who can be found in states across the country—to public threats, the poll’s findings suggest the issue was not salient to almost half of the country.
Recall that the issue of salience is prominent in the literature on agenda setting and narratives (Cairney & Zahariadis, 2016; Zahariadis, 2016, Rochefort & Cobb, 1993).
Gray and Jones (2016) equate salience with public opinion.
Democrats undercut the GOP’s inconsistent attempts to rationalize the border wall by stating consistently that 1) Republicans failed to act on border funding when they controlled both chambers of Congress and the White House; 2) that the majority of drugs entering the U.S. do so through legal ports of entry; and 3) that the border wall was an archaic solution when compared to the efficacy of technology and added personnel.
Officials and policy entrepreneurs who wish to advance any issue on an agenda must overcome challenges to public perceptions of legitimacy in order to make a matter salient to those whom it may affect. Weaver (2007) writes that:
Framing works to shape and alter audience members’ interpretations and
preferences through priming. That is, frames introduce or raise the
salience or apparent importance of certain ideas, activating schemas that
encourage target audiences to think, feel, and decide in a particular way.
(p. 164) 183
The narratives from Trump and GOP members who supported the president’s policy agenda failed to fulfill this role.
Third, each side recognized the importance of legitimizing their preferred policies and delegitimizing the options they opposed. On numerous occasions in the coded data, officials cited consultations with border security personnel and experts within the
Department of Homeland Security to validate their favored alternatives. Democrats claimed border officials told them a wall was unnecessary and that more personnel, scanners and drones were needed. Republicans said border officials and the Department of Homeland Security included funding for additional barriers among their top budgetary priorities. Each party sought validation from experts to legitimize their preferred policy claims.
Potentially another reason for opposition to Trump’s preferred policy is that it had not been sufficiently “softened up,” or as referenced in Chapter 2’s review of agenda setting literature, subjected to the process by which ideas come to be deemed politically acceptable and viable. Recall that policy alternatives are often altered to attract political support through a process that discards those unacceptable to elected officials and the public (Herweg et al., 2018). Despite Trump’s advocacy since announcing his presidential candidacy, the evidence presented here suggests his narratives were ineffective in making construction of a border wall politically palatable.
Based on these observations, I propose a revision to the model depicted in Figure
2-1 that illustrates the role of narratives in agenda setting using the Narrative Policy
Framework and Multiple Streams Approach as theoretical foundations. This revised 184 model, presented in Figure 6-1, incorporates the elements of message consistency and legitimacy.
Inserted within the model are four legitimacy checkpoints. These checkpoints indicate critical evaluative stages in the process of problem definition; narrative cognition; policy construction and political viability testing; and interpretation of focusing events or crises during which audiences, or message recipients, must judge the legitimacy of the information presented to them, as well as conditions within the system.
If at any these four points actions or inputs are deemed illegitimate, the outcome of the preferred solution is complicated, rendering its fate uncertain. Those actions and inputs that are deemed legitimate are more likely to proceed toward a preferred outcome.
The model also takes into account the consistency of messaging. Actors sharing narratives form discourse coalitions, which in turn strengthen group cohesion. Sharing narratives among discourse coalitions is akin to groups sharing beliefs under the
Advocacy Coalition Framework (Sabatier, 1988). As detailed earlier, Democrats in the federal government shutdown case demonstrated far more consistency in their narrative construction when compared to Republicans. Democrats exhibited greater group cohesion in their shared problem definitions, which subsequently informed their preferred policy approach. In this way, Democrats demonstrated the emplotment role of narratives.
Additionally, their widely held construction of Trump as the villain established alterity in their views of the president as the “outsider,” leveraging his unpopularity among the public to favorably reinforce their views of him. These elements are important factors in bonding groups to a shared message (Lejano et al., 2018) 185
Figure 6-1: Narratives in Agenda Setting Model with Legitimacy Checkpoints
According to the revised model, as political actors and the public contemplate each step in the system, they must determine the legitimacy of that step. First, the legitimacy of the problem definition must be judged, which requires a decision on whether the adopted issue frame corresponds with existing beliefs or sufficiently alters previously held views. Second, audiences are faced with the choice of how to respond to narratives and judge their legitimacy. 186
At the third checkpoint, the legitimacy of the three streams are evaluated. Given the evaluation made at the first checkpoint, if favorable, one can reasonably assume the problem stream has already established its legitimacy. At the same time, actors and audiences must concurrently decide on the policy alternative, itself, and the political environment. With regards to the former, do government officials and the public believe the policy will be an effective response to the problem as defined? In terms of the former, do the politics of the time favor that particular approach? This requires calculating the cost/benefit allocation, position of stakeholders, electoral consequences, practicality and reasonability of enactment at the administrative level, risk of loss versus opportunity for gains, and the national mood. Policy alternatives that have undergone the “softening up” process are more likely to be judged favorably at this stage.
Finally, the focusing event that couples these streams to create a window of opportunity for a policy faces the fourth checkpoint. Focusing events can either be the constructions of policy entrepreneurs or the result of shocks from outside of or within a system. In the federal government shutdown case, neither the shutdown or the situation at the border were deemed legitimate focusing events as evidenced by Democratic narratives and public opinion polls.
This revised model offers tremendous utility for the study of public policy narratives and their role in agenda setting because of its transferability; it can be applied to preferred solutions and opposed solutions. In other words, groups seeking to advance an issue to the decision agenda are expected to achieve that goal more likely when 1) their problem definition, narratives, policy solutions, political environments, and focusing events are deemed legitimate by decisionmakers and the public, and 2) their narratives 187 are consistent. Groups that question the existence of a certain problem or oppose a certain policy are expected to prevent access to the decision agenda to the extent they either achieve or undermine the same measures of legitimacy and message consistency.
Future studies
The findings of this research represent potentially significant advances in our understanding of narratives in the policy process, specifically agenda setting, but more research is in order. Future researchers should consider applying the findings of this exploratory study to guide future research, both qualitatively to substantiate the existence of the new stories identified here, but also empirically to hypothesize and test the role of narrative consistency and perceptions of legitimacy in agenda setting. Opportunities exist for such research at the micro, meso and, over time, macro levels of analysis.
Taking the findings presented here, researchers could survey perceptions of legitimacy to demonstrate empirically the influence on agendas and seek correlations between favorable legitimacy views with agenda access. The same should be done by assessing message consistency.
The earlier discussion on the role of shared messages and group cohesion is also a promising area for subsequent investigation. Studies should seek to better understand in the context of policymaking how message consistency affects information processing and cognition, as well as message receptibility and adoption. Understanding the ways in which messages spread through networks may provide valuable insight into decision making and political choice.
As noted earlier, more research also is needed to refine the conceptualizations of key narrative strategies—specifically those that did not demonstrate coding agreement 188 between rounds. This recommendation applies to the Expand and Limit scopes of conflict. Aside from not registering agreement, the data on these codes did not find the association with Angel- and Devil-shifts expected given the Narrative Policy
Framework’s theorizing.
In a similar vein, the Stance narrative strategy outlined in the NPF demands revisiting. While interviewees cited heroes and villains regularly, or attempted to alter the scope of conflict, those narratives did not communicate a sense of winning or losing the policy debate as NPF posits. Perhaps this observation is unique to the particularities of the case under study here, but the deviation from NPF’s theoretical assumptions bears further consideration.
Likewise, the inconsistent coding of Benefits during this research may be unique to the present study, but additional research to confirm this finding is warranted. Both sides in the border wall debate cited groups who would benefit from preferred policies.
Among other arguments, Republicans claimed the American people would benefit from a safer country through greater controls of undocumented immigration and drug trafficking. Democrats argued Americans would benefit from improved security and economic prosperity by not wasting billions of dollars on an ineffective border wall, preferring instead technology and added personnel, which they believed capitalized on improved capabilities available in the 21st century.
Further, as stated in Chapter 2, more research on the role of power in policy making is needed. Power is a prominently acknowledged in policy scholarship, but little studied. Groups exercise power to move issues onto the agenda or prevent their access, 189 while elite actors deploy their resources and reputations to influence public discourse, social structures, and the allocation of benefits and burdens.
Furthermore, the linkage between power and legitimacy is additional justification for additional research into the role of power in agenda setting. Recall that Tyler (2006) writes that determining legitimacy is an exercise in power and authority as groups convey ideas as either conforming to or violating social norms. In doing so, actors use their power to establish deservedness and validate desired outcomes.
Future research is needed to better conceptualize and operationalize these contributions in the field of public policy. While a large body of scholarship exists to some degree on each issue in the policy sciences, the field would benefit from opening its aperture to enlist knowledge from other fields, such as psychology. Integrating prior research from those other fields will improve our understanding of legitimacy and communication strategies on an inter-disciplinary basis, potentially yielding significant new insights for policy theory.
Conclusion
The exploratory study presented here sheds light onto the role of narratives in agenda setting, thereby filling a conspicuous gap in the literature. In doing so, this research further builds on the growing body of work seeking to develop and refine the
Narrative Policy Framework, broadening its theoretical applications.
Using a widely referenced typology of news outlets’ accuracy and political bias to determine the research sample, I collected data through a content analysis of 100 interviews given by federal officials in the executive and legislative branches between
December 16, 2018 and February 12, 2019. Only those outlets deemed to exercise 190 minimal partisan bias and as being reputable were considered among the population of news media sources. Additionally, the population was narrowed further according to media programs from those news organizations that offered publicly accessible transcripts of full-length interviews. The final sample included interviews of 51
Republican and 49 Democratic federal government officials from seven programs on four different broadcast networks: ABC, CBS, NBC and National Public Radio.
Interviews were coded on two separate occasions with a coding worksheet influenced greatly by Shanahan, Jones, McBeth and Lane’s (2013) study of a wind farm project in Cape Code, MA. The worksheet used here (Appendix B) included codes developed a priori based on narrative elements and strategies prescribed by the Narrative
Policy Framework. Additional fields offered the opportunity to gather codes that emerged a posteriori through data gathering and analysis in a grounded theory-like fashion.
Following the data collection stage, I calculated coefficients of agreement between the two coding rounds. Codes demonstrating agreement within +/- 20 percentage points of 100 percent were deemed consistent, or as demonstrating agreement. Only those codes demonstrating agreement were analyzed subsequently. I argue those that did not demonstrate agreement require additional research to improve their conceptualization for improved operationalization. This call for additional studies applies to the narrative strategies of Angel- and Devil-shifts, Scope of Conflict, allocation of Benefits, and the
Stance narrators convey through their messaging.
The collected data reveal narratives to be ubiquitous in the case of the 35-day federal government shutdown. President Trump and Republican members of the U.S.
Senate and U.S. House of Representatives attempted to secure more than $5 billion in 191 federal funding for construction of additional physical barriers along the United States-
Mexico border. Democrats objected to this appropriations request, instead voicing support for more modest investments in technology resources and additional personnel to enhance border security.
In the course of this debate, Democrats demonstrated greater levels of group cohesion through their consistent use of shared narratives. Republicans did not demonstrate the same level of consistency or group cohesion. Throughout the course of the nine-week-bounded case, GOP members and the Trump administration adjusted their problem definition, message framing, and preferred Moral of the Story, otherwise known as their preferred policy solution according to the Narrative Policy Framework.
I contend based on my analysis that these adjustments undermined Republicans’ attempts to achieve their policy goals, while the contrasting consistency on the part of
Democrats served effectively to achieve their goal, namely preventing Trump’s border wall funding proposal from reaching the Congressional decision agenda as he proposed and facing a vote. Substantiating this assessment is Trump’s decision to re-allocate previously appropriated funds from other sources toward border wall construction via an emergency declaration. In short, because Trump’s preferred policy solution did not reach the Congressional decision agenda, he shifted the decision-making venue to the executive branch, thereby allowing him to achieve his end goal.
The data gathered in the course of this research suggests three new and important contributions to the understanding of narratives and agenda-setting in public policy. First,
I argue that beyond roles narratives are theorized to play in agenda-setting, they must go further and establish the legitimacy of problems, solutions, politics and focusing events. 192
If the legitimacy of these policy process and system inputs are delegitimized at any point, the pathway to a preferred policy outcome is complicated, becoming more uncertain.
Second, I contend that consistency in messaging is important. The literature on narratives and discourse already acknowledge the significance of shared communications in building group cohesion and reinforcing or altering pre-existing beliefs. These aspects must be incorporated into the theoretical foundations of policymaking narratives, although further empirical study is needed to measure the influence of repetition in messaging.
Third, I identify new stories that build the repertoire of Plots available for analysis through the Narrative Policy Framework. These stories of Hope, Compromise/Negotiate,
Uncertainty, Pessimism/Skepticism, Disingenuousness, Broken Promises, and
Responsibility emerged a posteriori in the course of data analysis. Each may be of value in subsequent research or may be unique to the contextual crisis of the case studied here.
In closing, this research suggests narratives do play a role in agenda setting. The exploratory findings indicate their use—in and of themselves—are insufficient to carry an issue from the systemic and institutional agendas to the point of decision making.
Scholars should develop new research designs to revisit the research questions posed here using qualitative and quantitative methods.
The Narrative Policy Framework represents an exciting and relatively new contribution to the policy studies field, but it must extend its theoretical application, be open to the integration of other concepts and theories, and demonstrate its utility in a measurable way. To again quote Shanahan et al. (2011): “[F]or the NPF to fully realize its potential as a viable framework of the policy process, researchers must find ways to 193 measure how policy narratives actually influence policy outcomes” (p. 555). While measuring such influence is not the intent of this paper, the findings presented here do set the table for future research that may benefit from the new concepts I have introduced.
194
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Appendix A: Code Book
Narrative Policy Framework in Agenda Setting Federal Government Shutdown: December 2018 – January 2019 Italicized text represents deviations from/additions to code book in Shanahan, Jones, McBeth & Lane (2013)
1.0 Narrative Elements 1.1 Characters The participants in a policy narrative. 1.1.1 Victim The entity hurt by a specified condition. 1.1.2 Villain The entity responsible for the damage done to the victim. 1.1.3 Hero The entity designated as fixing or being able to fix the specified problem. 1.2 Plot A story device linking the characters, evidence (setting), causal mechanism, and moral of the story (policy solution). 1.2.1 Story of “a tale that things will get worse if the opposing solution is decline enacted” (Shanahan, Jones, McBeth, & Lane, 2013, p. 458) 1.2.2 Story of Characterizes a situation as bad, but subsequently improved stymied progress thanks to the intervention of someone or something, but another actor/party is interfering with improvements to the point where things will get worse again. (Stone, 2002) 1.2.3 Change is People thought things were getting better, but they were only an illusion wrong and here is proof. (Stone, 2002) 1.2.4 Helplessness “The situation is bad. We have always believed that the and control situation was out of our control, something we had to accept but could not influence. Now, however, let me show you that in fact we can control things” (Stone, 2002, p. 142) 1.2.5 Conspiracy “Its plot moves us from the realm of fate to the realm of control, but it claims to show that all along control has been in the hands of a few who have used it to their benefit and concealed it from the rest of us” (Stone, 2002, p. 143). 1.2.6 Blame the The person suffering from a problem is responsible for the victim situation in which they find themselves. 1.3 Moral of the story A policy solution offered that is intended to solve the specified problem. 2.0 Narrative Strategies 2.1 Devil shift A policy story exaggerating the power of an opponent while understating the power of the narrating group or coalition. 2.2 Angel shift A policy story that emphasizes a group or coalition’s ability and/or commitment to solving a problem, while de- emphasizing the villain. 220
2.3 Scope of conflict: Attempts to expand or contain interest in a policy issue, by either introducing, referencing or demonstrating potential impacts to and among other groups, or minimizing or ignoring the implications of an issue outside one particular category or group of interests. 2.3.1 Expansion A policy story depicting concentrated benefits and diffuse costs that is intended to draw in more participants and expand the scope of conflict. 2.3.2 Containment A policy story depicting diffused benefits and concentrated costs that is intended to dissuade new participants and maintain the status quo. 2.4 Causal mechanism A theoretical relationship denoting a cause and effect relationship between one or more independent variables and a dependent variable. 2.4.1 Intentional “an action was willfully taken by human beings in order to bring about the consequences that actually happened “(Stone, 1989, p. 285) 2.4.2 Mechanical “things that have no will of their own but are designed, programmed, or trained by humans to produce certain consequences” (Stone, 1989, p. 286) 2.4.3 Inadvertent “the unintended consequences of willed human action” (Stone, 1989, p. 285) 2.4.4 Accidental “These phenomena are devoid of purpose, either in their actions or consequences” (Stone, 1989, p. 284) 2.5 Numbers Numerical representations denoting value or scale (e.g. dollars, statistics, counts of people or incidents, etc.) 2.6 Costs Financial burdens imposed on certain groups as the result of a policy action and/or inaction. 2.7 Benefits: Positive treatment of some value (e.g., pecuniary, security, equity, etc.) accruing to certain group(s) as a result of a policy action. 2.8 Stance 2.8.1 Winning “A ‘winner’s tale’ constructs a story that seeks to preserve the status quo”; “winning groups try to restrict participation (issue containment) by limiting the scope of the conflict”; “winning narratives diffuse benefits and concentrate costs” (Shanahan, Jones & McBeth, 2011, p. 544) 2.8.2 Losing “a ‘loser’s tale’ seeks policy change”; “losing groups try to widen participation (issue expansion) in a policy issue”; “losing narratives concentrate benefits and diffuse costs” (Shanahan, Jones & McBeth, 2011, p. 544) 2.9 Legitimacy The way in which the shutdown is characterized (e.g., necessary, unnecessary, a manufactured crisis, or some other manner) 3. General Observations 221
3.1. Notes For noting aspects such as whether the narrator defines the problem; provides evidence to support the problem; whether the interviewer provides source cues; or other observations of the coder that may prove of value in the final analysis
222
Appendix B: Coding Worksheet
Broadcast Network: Program Name:
Broadcast Date: Interviewee Name and Title:
Date of Coding: Political Party Affiliation:
NARRATIVE ELEMENTS
1. Hero: Who is/are the direct or implied hero(oes) identified in the narrative? a. President Donald Trump/Trump administration: b. Congressional Republicans: c. Congressional Democrats: d. Law enforcement: e. Immigrants: f. American people: g. Other: 2. Villain: Who is/are the direct or implied villain(s) identified in the narrative? a. President Donald Trump/Trump administration: b. Congressional Republicans: c. Congressional Democrats: d. Law enforcement: e. Immigrants: f. American people: g. Other: 3. Victim: Who is/are the direct or implied victim(s) identified in the narrative? a. President Donald Trump/Trump administration: b. Congressional Republicans: c. Congressional Democrats: d. Law enforcement: e. Immigrants: f. American people: g. Other: 223
4. Plot: Does the narrative have an established plot (circle one)? a. If yes, identify: Stymied progress: Story of decline: Change is only an illusion: Helplessness and control: Conspiracy: Blame the victim: Other 5. Moral of the story: Does the narrative have direct or implied moral(s) of the story (aka a preferred policy solution? Fund construction of a border wall Invest in non-structural techniques (e.g. border enforcement officers, technology, drones, etc.) Mix of structural and non-structural techniques Shutdown the government Reopen the government Other:
NARRATIVE STRATEGIES 6. Narrative strategy(ies): Does the narrative use a narrative strategy(ies)? Angel-shift: Devil-shift: Scope of Conflict a. Does the narrative attempt to expand or limit the scope of conflict? Expand Limit 7. Problem Definition: Is the problem in need of a solution explicitly mentioned?
a. If yes, how is the problem defined or framed? Humanitarian crisis: Manufactured crisis: Failure of legal system: Failure of past administrations: 224
Threat to national security: Ineffective/incapable leadership: Other: 8. Causal Mechanisms. a. If yes, what kind? Accidental Inadvertence Intentionality Mechanical Other: 9. Numbers: Does the narrative use numbers to justify/substantiate a position? a. If yes, what numbers? b. Are numbers used to support or refute an argument? c. Whose argument: 10. Costs: a. Does the narrative assign costs to their preferred policy solution? i. If yes, who/what entities bear the cost(s)? ii. Are the costs concentrated or diffused? b. Does the narrative assign costs to their opposed policy solution? i. If yes, who/what entities bear the cost(s)? ii. Are the costs concentrated or diffused? 11. Benefits: a. Does the narrative assign benefits to their preferred policy solution? i. If yes, who/what entities receive the benefit(s)? ii. Are the benefits concentrated or diffused? b. Does the narrative assign benefits to their opposed policy solution? i. If yes, who/what entities receive the benefits(s)? ii. Are the benefits concentrated or diffused? 12. Stance: On the whole, what type of policy stance does the narrator construct? Winning stance Losing stance No stance Other: 225
13. Legitimacy: Is the government shutdown characterized as being necessary, unnecessary, as a manufactured crisis, or by some other means?
GENERAL OBSERVATIONS 14. General observations of the narrative:
VITA Michael F. Smith Academic: [email protected] | Personal: [email protected] | Phone: 724-388-5979
EDUCATION
• Doctor of Philosophy, Pennsylvania State University Harrisburg (2020) • Master of Arts in Public Affairs, Indiana University of Pennsylvania (2005) • Bachelor of Science in Marketing, Indiana University of Pennsylvania (2002)
PROFESSIONAL EXPERIENCE
Director of Commonwealth Relations November 2018 – Present University of Pennsylvania Executive Deputy Secretary February 2015 – November 2018 PA Department of Agriculture Deputy Chief of Staff/Director of Communications January 2011 – February 2015 PA Treasury Deputy Director of Communications September 2008 – January 2011 Office of the Governor Communications Director April 2007 – September 2008 PA Department of Environmental Protection Communications Manager January 2006 – April 2007 Office of the Governor Communications Assistant April 2005 – January 2006 PA Department of Agriculture Advertising Representative July 2002 – January 2004 Johnstown Tribune-Democrat
PROFESSIONAL AND COMMUNITY INVOLVEMENT
• Pennsylvania Gaming Control Board (designee), 2015-2018 • Governor’s Pipeline Infrastructure Task Force, Member; Agriculture workgroup, Chair, 2015 • Pennsylvania Environmental Quality Board (alternate), 2015-2018 • Team PA Foundation Board of Directors (alternate), 2016-2018 • State Planning Board (alternate), 2016-2018 • Auditor General-Elect DePasquale Transition Committee, Member, 2012 • Penn State Public Administration Doctoral Student Organization, Vice President, 2013-2015 • Lower Paxton Township Greenways Advisory Committee, Member, 2010-2013 • Lower Paxton Township Community Engagement Committee, Member, 2012-2013 • Pennsylvania Public Relations Society, Member, 2008-2013