The Intersection of Narrative Policy Analysis and Policy Change Theory

The Intersection of Narrative Policy Analysis and PolicyChange Theory

Mark K. McBeth, Elizabeth A. Shanahan, Ruth J. Arnell, and Paul L. Hathaway

Narrative policy analysis and policy change theory rarely intersect in the literature. This researchproposes an integration of these approaches through an empirical analysis of the narrative politicalstrategies of two interest groups involved in policy debate and change over an eight-year period in theGreater Yellowstone Area. Three research questions are explored: (i) Is it possible to reconcile theseseemingly disparate approaches? (ii) Do policy narrative strategies explain how interest groups expandor contain policy issues despite divergent core policy beliefs? (3) How does this new method of analysisadd to the literature? One hundred and five documents from the Greater Yellowstone Coalition and theBlue Ribbon Coalition were content analyzed for policy narrative strategies: identification of winnersand losers, diffusion or concentration of costs and benefits, and use of condensation symbols, policysurrogates, and science. Five of seven hypotheses were confirmed while controlling for presidentialadministration and technical expertise. The results indicate that interest groups do use distinctivenarrative strategies in the turbulent policy environment.

KEY WORDS: Advocacy Coalition Framework, Greater Yellowstone Area, interest groups, narrativepolicy analysis, policy change

Introduction

Researchers in the field of public policy theory seek to explain the divergentcharacteristics of policy change, namely equilibrium and radical change. Why doesthe public undergo alterations in how they understand policy problems and why dopolicy issues that remain static for many years suddenly become dynamic? Threetheories have dominated the literature over the past decade: Kingdon’s (1995)“policy streams,” Baumgartner and Jones’ (1993) “punctuated equilibrium,” and“Advocacy Coalition Framework” (ACF). These authors individually seek to build atheory of policy change that stands up to the rigor of empirical analyses. In thisstudy, we posit a methodological innovation in the area of policy change by intro-ducing an integration of narrative policy analysis (NPA) into the traditional policychange theory. This integration is accomplished through a systematic study of thestrategic nature of policy narratives. The results help to further explain policy changeand the role that various groups play in prompting policy change or maintenance ofthe status quo.

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During the last two decades, the work of social constructionists in the field ofNPA (e.g., Fischer & Forrester, 1993; Roe, 1994; Stone, 2002) has developed concur-rently with that of policy change theorists. NPA focuses on the centrality of narra-tives in understanding policy issues, problems, and definitions and does so withoutthe grand theoretical aspirations of the more traditional policy change works. One ofthe most developed works in the narrative genre is that of Deborah Stone (2002),whose Policy Paradox is an NPA gold mine of “mini-theories” about agenda setting,issue and problem definition, and policy dynamics. The centerpiece of Stone’s workis the use of literary devices such as characters, plots, colorful language, and meta-phors to analyze policy narratives. In particular, the storyteller’s political tactics arerevealed in how they construct who wins and who loses in a policy story (or whoreaps the benefits and pays the costs), how they characterize policy issues and theiropposition, and how they either entangle policies in larger cultural issues or alter-natively try to ground such issues in the certainty of scientifically deduced numbersand facts. Ultimately, Stone (p. 229) asserts that the goal of this strategic problemdefinition is to portray a political problem so that one’s favored course of actionappears to be in the broadest public interest.

With some exceptions (Baumgartner, 1989; Baumgartner & Jones, 1993, pp. 27–9;Hajer, 1993; Radaelli, 1999; Schneider & Ingram, 2005), NPA and the policy changeliterature rarely intersect. The exclusion of narratives from the grand theories ofpolicy change is grounded in the belief that narratives are value-based randomgarble. Sabatier (2000, p. 138), for example, argues that constructivists “have dem-onstrated very little concern with being sufficiently clear to be proven wrong” andthat their lack of clarity leads him to have “no interest in popularizing their posi-tion.” We argue that narratives are the lifeblood of politics. Narratives are both thevisible outcome of differences in policy beliefs (McBeth, Shanahan, & Jones, 2005)and the equally visible outcome of political strategizing. Both policy beliefs andpolitical strategies, as found in policy narratives, are not random occurrences. Policybeliefs are arguably stable, and political strategies are predictable.

NPA and Policy Change Theory

Sabatier and Jenkins-Smith (1993, p. 16) outline premises for their ACF, forwhich we assert that narrative theory can serve a methodological role. First, theyclaim that policy change must be analyzed over time, a decade or longer; narrativesare written words that can easily be documented and tracked through a temporalperspective. Second, they purport that policy change can be understood through theexamination of political subsystems (advocacy coalitions) that seek to influencegovernmental decisions. Other research (McBeth et al., 2005) has discovered that thenarratives generated by political subsystems in the polity at large, not just in thelegislative arena, also contain stable core policy beliefs and are a legitimate source ofpolicy change analysis.

The work of Baumgartner and Jones (1993) is also essential for a study ofnarratives and policy change. They point out that, at any particular time, an interestgroup is part of a winning policy monopoly or they are part of an out-of-power

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minority coalition. However, with “wicked problems” (Rittel & Webber, 1973),where rationality and science are minimized in importance, winning and losingis more of a perception than necessarily a reality. Wicked problems resist “resolu-tion by appeal to the facts” (Schon & Rein, 1994, p. 4) and beliefs are grounded incompeting cultural norms (Wood & Doan, 2003, p. 641). Jenkins-Smith and Sabatier(1993, p. 49) furthermore assert that when core beliefs are at stake, competing sideswill defend their own belief systems and attack the belief systems of the opposition.Yet, as later hypothesized by Sabatier and Jenkins-Smith (1999, p. 124), through thedevelopment of technical expertise, coalitions move toward policy learning. Becauseof the intense value-based conflict between competing groups, policy narratives arean important element of study for wicked problems and add to the ability of moretraditional policy change theories to understand the strategic representation ofvalues in framing the conflict.

Interest groups attempt to maintain, demonstrate, and increase their politicalpower as they seek to win a favorable policy. Furthermore, whether an interest groupperceives themselves as winning or losing on a policy issue greatly influences howthey play politics. According to Schattschneider (1960, p. 16), winning groups try torestrict participation (issue containment) in a policy issue by limiting the scope ofthe conflict whereas losing groups try to widen participation (issue expansion) ina policy issue. Such a conclusion is reinforced in a wide variety of literature (e.g.,Baumgartner, 1989; Cobb & Elder, 1983; Baumgartner & Jones, 1993). While Radaelli(1999, p. 674) concludes that narratives are understood within “broader politicaldynamics,” the unanswered questions are how do the policy narratives of interestgroups play into the equation of issue containment and issue expansion in wickedpolicy problems and how do these narratives play into the role of policy change (orlack of change, thus contributing to policy intractability)? Our methodology,drawing on the insights of NPA, Baumgartner, Jones, Sabatier and Jenkins-Smith,allows us to answer these questions.

Primary Beliefs and Political Strategies

We assert that interest group narratives possess both primary beliefs and politi-cal strategies. This differs slightly from Sabatier and Jenkins-Smith’s (1999, p. 122)view of policy beliefs. They contend that policy beliefs are composed of core beliefsthat remain stable and secondary beliefs that are more susceptible to change. Thesame principle of core beliefs and secondary beliefs can be applied to the study ofpolicy narratives. When we read a policy narrative regarding an environmentalissue, part of the narrative consists of underlying beliefs in such issues as federalism,science, and the relationship between humans and nature. These are primary policybeliefs held by interest groups, and narratives reveal that they tend to be stable overtime (McBeth et al., 2005).

We argue that narratives also possess political strategies and that these elementsare much more dynamic. In contrast to Sabatier and Jenkins-Smith (1993: 30–31),who define secondary beliefs as instrumental decisions of policy implementation, weassert that in a policy narrative, the secondary political strategies (not necessarily

McBeth et al: Narrative Policy Analysis and Policy Change Theory Intersection 89

beliefs) include rhetorical devices outlined by Deborah Stone (2002), among others.Political strategies are an important and perhaps underdeveloped element of theACF. As Brown and Stewart (1993, p. 101) argue, the study of policy change mustfocus on “tactics employed by policy advocates.” As found in narratives, these tacticsor strategies shift depending on whether or not the coalition perceives itself aswinning. Competing policy narratives incorporate strategies such as identification ofwinners and losers, framing who benefits and who sustains costs in the policyconflict, the use of condensation symbols, the wrapping of issues in larger policysurrogates, and the use of scientific uncertainty. In turn, the choice of narrativestrategy is driven by the group’s perception of whether it is winning or losing on thepolicy issue. The analysis of both primary beliefs as defined by Sabatier and Jenkins-Smith (1999, p. 122) and political strategies (as informed by Stone) is an unexploredarea in which the two fields intersect and strengthen each other. While traditionalpolicy change theory can show that groups act strategically, our methodology drawson NPA to show how groups act strategically through narratives.

Political Narrative Strategies

Five narrative strategies are defined in the succeeding discussion. These politicalstrategies are hypothesized to contain the issue if the group is winning or to expandthe issue if the group is losing.

1. Identifying Winners and Losers. As part of issue containment and expansion, inter-est groups will strategically include or exclude mention of specific winners andlosers. Interest groups that perceive themselves as winning on a policy issue aremore likely to identify specific winners in their policy narratives, whereas interestgroups that perceive that they are losing on a policy issue are more likely to identifyspecific losers. Winning strategies attempt to contain the issue by illustrating thatthe status quo is positive and no change is necessary. In Baumgartner and Jones’s(1993) terminology, these groups attempt to preserve the current image of a policyproblem simply because this policy frame has helped to achieve the status of a policymonopoly. The goal is to maintain a “minimum winning coalition” (Riker, 1962);expanding the coalition would necessarily entail compromises in policy beliefs andoutcomes, which, in turn, weakens the power of the members of the policymonopoly. On the other hand, losing groups identify losers in the policy conflict inthe hope of mobilizing opposition in order to change the status quo. Stone (2002,p. 228) argues that “both sides try to amass the most power” and that it is the loser“who seeks to bring in outside help.”

2. Construction of Benefits and Costs. Baumgartner and Jones (1993, p. 19) contendthat losing groups seek to redefine issues in ways that will mobilize indifferentcitizens and groups in the hope that this mobilization will destabilize policy equi-librium. This expansion of an issue to “heightened general attention” is pivotal inpolicy change (Jones & Baumgartner, 1993, p. 20). In terms of narrative theory, whena competing interest group is losing, they use their policy narratives to attempt to


reallocate attention and expand the issue by diffusing costs and concentrating ben-efits. This strategy makes it appear that only a few (if any) groups are benefiting fromthe status quo while many groups are paying the costs. This tactic attempts tomobilize the public and bring new players into a coalition. Conversely, when a groupis winning, they are much more likely to contain the issue by diffusing benefits andconcentrating costs on a small group. This narrative strategy seeks to maintain thestatus quo and to restrict a wide-scale mobilization.

3. The Use of Condensation Symbols. Jones and Baumgartner (1993, p. 26) argue that“every public policy problem is usually understood, even by the politically sophis-ticated, in simplified and symbolic terms.” Stone (2002, p. 137) asserts more directlythat “symbolic representation is the essence of problem definition in politics.” Thus,we can hypothesize that interest groups that are winning or losing on a policy issuewill use “condensation symbols” or a language that “reduce[s] complicated conceptsinto simple, manageable, or memorable forms” (Achter, 2004, p. 315). Interest groupswill use condensation symbols to define the policy issue and to characterize theiropponents. We argue that winning groups have fewer incentives to use condensationsymbols because doing so might invoke unintended consequences such as riling ofthe opposition. Losing groups, however, have tremendous incentives to negativelyportray both the issue and their opponents through the use of condensation symbols.Again, their goal is to both rally their troops and call in additional reinforcements byexpanding the scope of the conflict.

4. The Policy Surrogate. In his discussion of the many causes of wicked resource-based policy conflict, Nie (2003) suggests that a key cause of conflict is the “policysurrogate.” Nie (p. 314) argues that “relatively straightforward policy problems canbe turned wicked when they are used by political actors as a surrogate to debatelarger and more controversial problems.” For environmental policy issues in theWestern United States, this means that issues like bison management and snowmo-bile access are wrapped in larger persistent controversies of Western communities:concerns about federalism, the role of public lands, and the fear of outsiders, to namea few. Our argument is that losing groups strategically entangle policy issues inlarger, emotionally charged debates in an effort to gain a competitive advantage byexpanding the scope of the policy issue. In short, these policy surrogates are used toignite the larger controversies already simmering in the political culture and tomobilize opposition.

5. Scientific Certainty and Disagreement. Nie (2003, p. 323) argues that scientific dis-agreement is also a major cause of intractable natural resource-based political con-flict. Furthermore, Nie (p. 323) notes that environmental policies have increasinglybecome disputes over science and concludes that political actors “frame value andinterest based political conflict as scientific ones” and that they “escape responsibil-ity for making the tough choices required of them.” Thus, this driver, contradictoryto the policy surrogate driver, suggests that policy actors intentionally reduce thescope of policy issues, ignoring the larger normative and cultural issues that invari-


ably surround resource-based environmental conflict. We argue that groups that arewinning in a policy issue are likely to define the issue in terms of scientific certainty,thus ignoring the larger normative issues involved in the controversy. Such a cer-tainty attempts to bring closure to debates surrounding policy issues, maintains thestatus quo and the minimum winning coalition, and simultaneously hopes to demo-bilize the opposition. Conversely, losing groups attack scientific results and presenta scientific disagreement in an attempt to open up the issue for a continueddeliberation.

Research Questions

This study addresses three research questions. First, we attempt to methodologi-cally demonstrate the useful intersection of NPA and policy change theory. Can suchontologically opposing theories be legitimately brought together in the study ofpolicy change? Second, we operationalize NPA into measurable tools to examine howgroups expand or contain policy issues, not just that they do. Do policy narrativestrategies of interest groups explain how these groups expand or contain policyissues despite divergent core policy beliefs? Third, how does this new method ofanalysis add to the existing literature on policy change?

The Case Study

The systematic analysis of different interest groups’ narrative political strategiesis conducted as a case study in the turbulent policy arena of the Greater YellowstoneArea (GYA). The 19 million acres of the GYA, with Yellowstone National Park (YNP)comprising 2.2 million acres of the region, are not only a world famous area forgeysers, wildlife, and scenic wonders but a well documented hotbed of politicalconflict (e.g., Cawley & Freemuth, 1993; Tierney & Frasure, 1998; Wilson, 1997). Infact, environmental policymaking in the region is often intractable or wicked (Rittel& Webber, 1973). To use the terminology of Jenkins-Smith and Sabatier (1993, p. 49),the conflict is intense and highly political since core policy beliefs (e.g., federalism,the relationship between humans and nature, science) are disputed and competingsides ground their arguments in myth (Tierney & Frasure, 1998).

Environmental groups and scientists have sought to redefine Yellowstone’simage from that of an isolated national park with definitive boundaries to that of theonly intact ecosystem left in the continental United States (Clark & Minta, 1994). Touse the theory of Baumgartner and Jones (1993), environmental groups have soughtto redefine the image of the area from “Yellowstone as zoo” to “Yellowstone as anopen ecological system.” Such an effort at image redefinition has intensified thepolitical conflict in the past decade. Two interest groups have dominated efforts bycompeting advocacy coalitions to define the policy images of GYA. The Blue RibbonCoalition (BRC) represents motorized recreation users (wise use coalition) and isbased in Pocatello, Idaho.1 The Greater Yellowstone Coalition (GYC), based inBozeman, Montana, represents the environmental coalition.2 These two groups are“purposive” interest groups, for those who join pursue ideological and issue-


oriented goals without material rewards (Berry, 1989, p. 47). This is important giventhe Sabatier and Jenkins-Smith (1999, p. 134) hypothesis that purposive groups aremore constrained in their willingness to compromise beliefs and policy positions.

From 1997 through 2004, the politics of the GYA have been characterized bycontinuous changes in public policy, instability, and policy wickedness. Policymonopolies have collapsed for short periods of time only to find resurgence and anability to regain political power. During the years of the Clinton administration,environmental groups pushed large policy initiatives into effect. The policy issuesthat demonstrated newfound environmental power in the GYA included wolf rein-troduction in 1995, regulations that ended snowmobiling inside YNP in 2000, and anational roadless initiative in national forests in 1999.

There is one exception to the wave of Clinton GYA environmental policy success,where the wise use coalition retained their monopoly: the management of free-ranging bison. In the winter of 1996–97, over 1,100 bison were killed by the MontanaState Livestock Department with assistance from the National Park Service becauseof concern over the potential role of bison in brucellosis transmission to cattle. Effortsby the Clinton administration and environmentalists to end the killing failed as apowerful subsystem of ranchers, federal and state elected officials, and the U.S.Department of Agriculture Animal, Plant, Health, Inspection Services retained itshistoric hegemonic stance.

Yet again, with the exception of the bison controversy, the policy changes in the1990s were overwhelmingly in the direction of the GYA environmental advocacycoalition. The election of George W. Bush in 2000, however, led to a large-scaleresurgence of the wise use coalition in at least two policy areas. Bush’s first term sawthe dramatic reversal of the Clinton era snowmobile ban in YNP. The president’ssecond term saw the overturning of the roadless rule in favor of state control over theuse of national forests. It is in the context of this turbulent policy arena from 1997through 2004 that the BRC and the GYC both generated strategic political narratives.

Research Methodology

A content analysis was performed on one hundred five documents produced bythe GYC (52 documents) and the BRC (53 documents) over eight years (January 1,1997 through December 31, 2004). The documents address one of three policy issuesin the GYA: (i) bison and brucellosis (14 documents); (ii) snowmobile access in YNP(70 documents); and (iii) the roadless initiative (21 documents). Our choice of contentanalysis was straightforward. Content analysis is unobtrusive, allows for a reliabilityanalysis, permits a longitudinal analysis, and is efficient and inexpensive. The docu-ments analyzed were readily archived and complete, thereby avoiding some of thedisadvantages of using content analysis (Johnson & Reynolds, 2005, pp. 232–34).Based on NPA and policy change theory, we propose seven hypotheses predictingan association between use of a winning or losing narrative frame (independentvariable, see Table 1) and seven different narrative political strategies (dependentvariables, see Table 1).


Hypotheses

For the following seven alternative hypotheses, each has a null hypothesis thatasserts no association between winning or losing policy narrative frames and theattending narrative political strategy. Because these are nominal-level variables, nodirection is predicted.

Hypothesis 1a: There is an association between winning policy narrative framesand identification of winners in the narrative.Hypothesis 1b: There is an association between losing policy narrative frames andidentification of losers in the narrative.

Hypothesis 2: There is an association between winning policy narrative frames andthe diffusion of benefits in the narrative; similarly, there is an association betweenlosing policy narrative frames and concentration of benefits in the narrative.3

Hypothesis 3: There is an association between winning policy narrative frames andthe concentration of costs in the narrative; similarly, there is an association betweenlosing policy narrative frames and diffusion of costs in the narrative.

Hypothesis 4: There is an association between losing policy narrative frames anduse of condensation symbols.

Table 1. Operationalization of Dependent, Independent, and Control Variables

Variables Definition n

Dependent VariablesIdentification of winner Identify a winner of policy objective

0 = none identified (no); 1 = winner (yes)105

Identification of loser Identify a loser of policy objective0 = none identified (no); 1 = loser (yes)

105

Benefits Concentrate or diffuse benefits of policy objective0 = concentrated; 1 = diffused

61

Costs Concentrate or diffuse costs of policy objective0 = concentrated; 1 = diffused

85

Condensation symbol Reduces issue into loaded, dichotomous symbol0 = no use; 1 = used condensation symbol

105

Policy surrogate Wraps a specific issue in larger normative issues0 = no use; 1 = used policy surrogate

105

Science Use of scientific certainty or disagreement0 = scientific certainty; 1 = scientific disagreement

54

Independent VariableWinning–losing The narrative frame regarding policy objective

0 = losing; 1 = winning105

Control VariablesPresidential administration President at the time the narrative was written

0 = Clinton; 1 = Bush105

Use of science Whether the narrative used science or not0 = no science used; 1 = used science

105

Source: Greater Yellowstone Coalition and Blue Ribbon Coalition documents, 1997–2004.


Hypothesis 5: There is an association between losing policy narrative frames anduse of policy surrogates.

Hypothesis 6: There is an association between winning policy narrative frames andthe use of scientific certainty in the narrative; similarly, there is an associationbetween losing policy narrative frames and the use of scientific uncertainty in thenarrative.

Dependent Variables

Temporally, whether the policy narrative is winning or losing precedes thepolitical strategies used; thus, the dependent variables in this study are the politicalstrategies (see Table 1). Using content analysis, a series of questions was developedto operationalize the dependent variables for the seven hypotheses (see Appendix A,questions 1–8 on the codebook).

Independent Variable

Whether a policy narrative is winning or losing explains what political strategiesare employed. The problem of how to operationalize whether an interest group waswinning or losing invoked much discussion among research team members. At onepoint, an objective measure was going to be utilized. In other words, based onexecutive, judicial, and administrative decisions, the interest group would be deter-mined to be winning or not. Interestingly, this proved difficult because of the vola-tility of Yellowstone policy issues during the time period under study. No interestgroup could be said to enjoy a true policy monopoly throughout the period (thebison issue is the most likely exception) because governmental decisions on theseissues rarely achieved a permanent or stable status. Thus, the team decided that whatwas important in narrative terms was not objective winning or losing, but rather theperceptions of the interest group on whether they were winning on an issue (thegroup supported the status quo) or losing (the group felt that they were under attackin the policy environment). Question 9 of the code book in Appendix A measuresthis perception of winning and losing.

Control Variables

ACF controls of coalitional resources (i.e., presidential administration) and coa-litional policy learning (i.e., whether or not scientific evidence was used) were used.The ACF theory asserts that over time, policy change is, in part, a function ofchanging governing coalitions (affecting coalitional resources) and coalitional tech-nical expertise (impacting policy learning). To better assess the relationship betweenpolitical strategies and winning—losing narrative frames, each Chi-square test (insucceeding discussions) was subsequently controlled for presidential administrationand whether or not the narrative used science.


The content analysis was conducted by three coders. Ten documents were pre-tested using an initial codebook. The documents were coded independently by thecoders who then met periodically after coding every 25–35 documents. At theirmeetings, the coders discussed their results, redefined and narrowed rules, andultimately reconciled their disagreements. The reliability of the three coders wasevaluated by comparing them in three pairs on their coding of all questions. Thereliability ratings range from a low of 78 percent to a high of 93 percent, with anaverage of agreeing 84 percent of the time (see Appendix B), thus reasonably estab-lishing intercoder reliability.

Given that the narrative strategies were operationalized as nominal-level vari-ables, a Chi-square test of significance was conducted for each hypothesis to in-vestigate the statistical difference between the occurrence of narrative frame(winning–losing) and that of political narrative strategy or if the strategies utilizedare attributed to chance alone. A continuity correction was applied with the occur-rence of small cells (n � 5); a Fisher’s Exact Test was used to determine the statis-tical significance (Ramsey & Schafer, 1997, pp. 547–51). The magnitude of theChi-square results was assessed with a Cramér’s V, the preferred Chi-squaremeasure of association (McClendon, 2004, p. 455). Odds ratios (ORs) were calcu-lated as a cross-product ratio (Knoke, Bohrnstedt, & Mee, 2002, p. 161) and wereused to indicate the odds of a specific political strategy occurring with a winningor losing narrative.

Research Results

Table 2 provides descriptive statistics of the one hundred five narratives coded.Note that of the winning and losing narratives, 71 of the 105 documents (68 percent)were coded as losing narratives, whereas only 34 (32 percent) were coded aswinning. There are at least two reasons for this. First, groups may well be more likelyto articulate and distribute a policy narrative when they are losing as their goal is tochange the status quo, and their narrative is a form of both political defense andattack. Second, as discussed earlier, there were no clear policy monopolies in thistime period. Instead, both interest groups experienced back-and-forth short-termwins and losses characteristic of wicked problems. Thus, both interest groups feltunder attack consistently from nonfriendly forces. This is evidenced by the fact thatboth groups produced more losing policy narratives than winning across all threepolicy issues regardless of presidential administration.

Hypotheses 1a and b: Identification of Winners and Losers

Table 3 indicates statistically significant associations between winning narrativeframes and the identification of a specific winner (c2[d.f. = 1] = 13.049, p < 0.001) andlosing narrative frames and identification of a specific loser (c2[d.f. = 1] = 23.134,p < 0.001). In winning narrative frames, a specific winner was identified 82.4 percentof the time (fo = 28; fe = 19.4), compared with that of losing narrative frames identi-fying a winner 45.1 percent of the time (fo = 32; fe = 40.6). The odds ratio of a winning


Table 2. Descriptive Statistics of Interest Group Narratives by Winning or Losing Frame, Policy Issue,and Presidential Administration

InterestGroup

TotalDocuments

WinningNarratives

LosingNarratives

Policy Issue PresidentialAdministration

GYC 52 (100%) 14 (27%) 38 (73%) Bison ClintonWinning 3 (30%) Winning 4 (31%)Losing 7 (70%) Losing 9 (69%)Total 10 (100%) Total 13 (100%)

Snowmobiles BushWinning 7 (22%) Winning 10 (26%)Losing 25 (78%) Losing 29 (74%)Total 32 (100%) Total 39 (100%)

RoadlessWinning 4 (40%)Losing 6 (60%)Total 10 (100%)

BRC 53 (100%) 20 (38%) 33 (62%) Bison ClintonWinning 0 (0%) Winning 6 (26%)Losing 4 (100%) Losing 17 (74%)Total 4 (100%) Total 23 (100%)

Snowmobiles BushWinning 18 (47%) Winning 14 (47%)Losing 20 (53%) Losing 16 (53%)Total 38 (100%) Total 30 (100%)

RoadlessWinning 2 (18%)Losing 6 (82%)Total 8 (100%)

Total 105 (100%) 34 (32%) 71 (68%)

Source: GYC and BRC documents, 1997–2004.GYC, Greater Yellowstone Coalition; BRC, Blue Ribbon Coalition.

Table 3. Chi-Square Results for Identification of Winners and Losers by Narrative Frame

Losing Narrative Winning Narrative Total

Identification of winner Yes 45.1%(32)

82.4%(28)

60

No 54.9%(39)

17.6%(6)

45

Total 100.0%(71)

100.0%(34)

105

c2(d.f. = 1) = 13.049, p < 0.001; Cramér’s V = 0.353, p < 0.001; ORWW = 5.69Identification of loser Yes 95.8%

(68)58.8%

(20)88

No 4.2%(3)

41.2%(14)

17

Total 100.0%(71)

100.0%(34)

105

c2(d.f. = 1) = 23.134, p < 0.001; Cramér’s V = 0.469, p < 0.001; ORLL = 15.87

Source: Greater Yellowstone Coalition and Blue Ribbon Coalition documents, 1997–2004.OR, odds ratio; ORWW, odds ratio of a winning frame; ORLL, odds ratio of a losing narrative frame.


frame identifying a specific winner is 5.69; thus, winning policy narratives are fivetimes more likely than losing narrative frames to identify a winner. Losing narrativeframes contained a specific loser 95.8 percent of the time (fo = 68; fe = 59.5), comparedwith winning narrative frames identifying a loser 58.8 percent of the time (fo = 20;fe = 28.5). The odds ratio of a losing narrative frame identifying a specific loser is15.87; thus, losing narrative frames are fifteen times more likely than winning framesto identify a loser. The magnitude of these relationships is strong, with Cramér’sV = 0.353 (p < 0.001) and 0.469 (p < 0.001) for winning and losing policy frames,respectively. We can accept hypotheses 1a and b.

The strategy of winning narratives is to maintain the status quo; the BRC oftencites local communities and small business owners as winners while the GYC citesYellowstone visitors. Interestingly, the BRC and the GYC see the maintenance of thestatus quo in the hands of local vs. national constituencies, respectively. Yet theirstrategy is keenly predictable here. Losing narratives identify losers in an attempt togrow a coalition and change the status quo. The BRC invokes a wide coalition ofpotential losers in trying to debunk what they view as environmental propaganda:the public, visitors to YNP, snowmobile riders, and the snowmobile industry(Eggers, 1999). Similarly, in arguing for snowmobile regulation, the GYC identifieswildlife, park employees, public safety, American families, and the taxpayer as losersin the status quo of YNP snowmobile use (Catton & Buffington, 2002). The politicalstrategy is the same despite divergent policy beliefs.

Hypothesis 2: Concentration or Diffusion of Benefits

Table 4 also reveals a statistically significant association between the occurrenceof concentration or diffusion of benefits in policy narrative frames(c2[d.f. = 1] = 6.959, p < 0.01), with an indication of a strong measure of association:Cramér’s V = 0.338 (p < 0.01). Losing narratives diffuse benefits 18.2 percent of the

Table 4. Chi-Square Results for Benefits and Costs by Narrative Frame


Benefits Concentrated benefits 81.8%(27)

50.0%(14)

41

Diffuse benefits 18.2%(6)

50.0%(14)

20

Total 100.0%(33)

100.0%(28)

61

c2(d.f. = 1) = 6.959, p < 0.01; Cramér’s V = 0.338, p < 0.01; OR = 4.5Costs Concentrated costs 16.4%

(11)55.6%

(10)21

Diffuse costs 83.6%(56)

44.4%(8)

64

Total 100.0%(67)

100.0%(18)

85

c2(d.f. = 1) = 11.683, p < 0.001; Cramér’s V = 0.371, p < 0.001; OR = 6.36

Source: Greater Yellowstone Coalition and Blue Ribbon Coalition documents, 1997–2004.OR, odds ratio.


time (fo = 6; fe = 10.8), compared with winning narratives that do so 50 percent of thetime (fo = 14; fe = 9.2). Losing narratives concentrate benefits 81.8% of the time(fo = 27; fe = 22.2), compared with winning narratives that do so 50 percent of the time(fo = 14; fe = 18.8). Losing narratives are 4.5 times more likely to concentrate benefits,whereas winning narratives are 4.5 times more likely to diffuse benefits (OR = 4.5).We can accept hypothesis 2.

The association between (i) winning narratives and diffusing benefits and (ii)losing narratives and concentrating benefits is a political strategy used by interestgroups to influence policy outcome. For example, the GYC applauded the success ofthe Clinton era snowmobile reductions by citing the improved National Park Serviceemployees’ health as well as that of all visitors (Scott, 2004); thus, they diffused thebenefits of the ban to many people. Similarly, the BRC presented the diffuse distri-bution of the benefits of snowmobile use to local economies, residents, and snow-mobile riders (Collins, 1998). Examples of concentrating benefits when losing arefound as a political strategy in both the BRC and the GYC narratives. In a time whensnowmobiling was under attack in the courts, the BRC contended that the onlybeneficiary from snowmobile regulation was the environmental group Fund forAnimals (Cook, 1997). Similarly, the GYC concentrated benefits by claiming thatPresident Bush was ignoring larger national interests and instead was “bowing tointense lobbying by the snowmobile industry and the park’s border towns” (GYC,2002). Concentrating or diffusing the benefits of a policy proposal is a politicalnarrative strategy employed to influence policy outcome.

Hypothesis 3: Concentration and Diffusion of Costs

Table 4 also indicates a statistically significant association between the concen-tration and diffusion of costs of the narrative frame (c2[d.f. = 1] = 11.683, p < 0.001).The measure of association is strong, with Cramér’s V = 0.371, p < 0.001. Winningnarrative frames concentrate costs 55.6 percent of the time (fo = 10; fe = 4.4) comparedto 16.4 percent for groups with losing frames (fo = 11; fe = 16.6). As hypothesized,losing narrative frames diffuse costs 83.6 percent of the time (fo = 56; fe = 50.4) com-pared to 44.4 percent of the time for winning frames (fo = 8; fe = 13.6). Losing narra-tives are six times more likely to concentrate costs, whereas winning narratives aresix times more likely to diffuse costs (OR = 6.36). We can accept hypothesis 3.

Losing narratives are thought to diffuse costs of the proposed policy as a wayto expand the issue, whereas winning narratives contain the issue by concentratingthe costs on a few. When losing, the BRC tended to diffuse costs by focusing onhow snowmobile riders and the snowmobile community would pay the costs intime, enjoyment, and recreational access, which would negatively impact tourismand gateway communities. Similarly, the GYC diffused costs over stressed wildlife,human health, visitor enjoyment, deteriorating ecosystems, nonmotorized recre-ationists, and public safety. Finally, when concentrating costs, winning narrativesconstruct narrow entities to endure costs, such as “commercial logging” (GYC,2001a) or narrow special interest groups (Welch, 2000).


Hypothesis 4: Use of Condensation Symbols

There is a statistically significant association between the use of condensationsymbols and narrative frames (c2[d.f. = 1] = 3.490, p < 0.10); this is asserted with theacceptance of a higher risk of making a Type I error, with p < 0.10 (see Table 5). Themeasure of association is weak, with Cramér’s V = 0.182, p < 0.10. As hypothesized,losing narrative frames use condensation symbols more frequently than winningnarratives, that of 42.3 percent of the time (fo = 30; fe = 25.7) compared to 23.5 percentof the time (fo = 8; fe = 12.3), respectively. Losing narratives are approximately2.4 times more likely to use condensation symbols (ORLCS = 2.39). We can accepthypothesis 4.

The effect of condensation symbols is to heighten emotions and create a Hob-son’s choice in policy preference. Interestingly, the BRC was more likely to usecharacterization symbols (n = 9 for the BRC or 17 percent of the time; n = 4 for theGYC or 7.7 percent of the time), whereas the GYC was much more likely to use issuesymbols (n = 19 for GYC or 36.5 percent of the time; n = 9 for the BRC or 17 percentof the time). For example, while on the losing end of policy disputes, the BRCcharacterized their opponents as “school yard bullies” with “hit lists” and “hatemail” (Collins, 1998) and “out in left field” (Eggers, 1999), while the GYC refers to aYellowstone with snowmobiling as a “noisy speedway” (GYC, 2001b).

Hypothesis 5: Use of Surrogates

Table 5 reveals a statistically significant association between use of policy sur-rogates and narrative frames (c2[d.f. = 1] = 5.122, p < 0.05), with a Cramér’s Vmeasure of association of 0.221 (p < 0.05). Policy surrogates are used by losing nar-ratives 32.4 percent of the time (fo = 23; fe = 18.3), whereas they are used by winning

Table 5. Chi-Square Results for Condensation Symbols and Policy Surrogates by Narrative Frame


Condensation symbols Yes 42.3%(30)

23.5%(8)

38

No 57.7%(41)

76.5%(26)

67

Total 100.0%(71)

100.0%(34)

105

c2(d.f. = 1) = 3.490, p < 0.10; Cramér’s V = 0.182, p < 0.10; ORLCS = 2.39Policy surrogate Yes 32.4%

(23)11.8%

(4)27

No 67.6%(48)

88.2%(30)

78

Total 100.0%(71)

100.0%(34)

105

c2(d.f. = 1) = 5.122, p < 0.05; Cramér’s V = 0.221, p < 0.05; ORLPS = 3.59

Source: Greater Yellowstone Coalition and Blue Ribbon Coalition documents, 1997–2004.ORLCS, odds ratio of losing narratives’ use of condensation symbols.ORLPS, odds ratio of losing narratives’ use of policy surrogates.


narratives only 11.8 percent of the time (fo = 4; fe = 8.7). Losing narratives are morethan three times more likely to use a policy surrogate than a winning narrative(ORLPS = 3.59). We can accept hypothesis 5.

In political narratives, losing groups are more likely to strategically wrap theissue in the larger contentious cultural context by using policy surrogates. This useof a policy surrogate is again consistent with Baumgartner’s and Jones (1993) theoryof issue expansion when a group is losing and with the research of Nie (2003) onenvironmental policy conflict. The BRC’s policy surrogates tend to focus on eitherfederalism or environmental elitism, arguing, “we can’t rely on the federal govern-ment to represent the public’s interest” (Cook, 1997). Furthermore, the BRC arguedthat policy was needed to “see our natural resources protected FOR the peopleinstead of FROM the people” (Eggers, 1999). The GYC almost exclusively usedsurrogates when they were losing, only using a surrogate once when they werewinning on an issue. Their surrogates focused on corruption by special interests, asexemplified in this statement from one of their articles: “National interest is beingsacrificed to the special interest of the snowmobile industry in of all places, Ameri-ca’s first national park” (Sieck, 2002).

Hypothesis 6: Scientific Certainty or Uncertainty

As revealed in Table 6, there is no statistical association between winning–losingnarrative frames and how science is used, either to show certainty or uncertainty. Wereject hypothesis 6. Approximately 50 percent of both winning and losing narrativesuse science in their narratives; of those, both narrative frames used scientific cer-tainty at high rates, 89.5 and 85.7 percent, respectively.

When both interest groups used science regardless of whether they werewinning or losing, they tended to use it in terms of scientific certainty to back uptheir policy preference. Nie (2003, p. 323) concludes that competing groups in envi-ronmental policy controversies use science to “forward their preferred policy objec-tives.” The focus of science used in the two groups’ narratives is different; the GYCuses a conservation biology approach whereas the BRC uses a more technologicalapproach (McBeth et al., 2005, p. 422). In general, the conflict over science betweencompeting interest groups is usually a battle over the stable policy core beliefsembedded in the science rather than part of a dynamic narrative political strategy.

Table 6. Chi-Square Results for Science by Narrative Frame


Science Certainty 85.7%(30)

89.5%(17)

47

Uncertainty 14.3%(5)

10.5%(2)

7

Total 100.0%(35)

100.0%(19)

54

c2(d.f. = 1) = 0.154, ns; Cramér’s V = 0.053, ns

Source: Greater Yellowstone Coalition and Blue Ribbon Coalition documents, 1997–2004.


Controlling for Presidential Administration and Use of Science

The ACF theory asserts that changes in governing coalitions affect policy changein that coalitional resources expand or contract, depending on whether the admin-istration aligns itself with a group’s core beliefs or not. For example, the Bushadministration’s shared policy beliefs added resources (power) to the BRC. Notsurprisingly, controlling for presidential administration resulted in additive relation-ships among all six statistically significant political strategies. The relationshipbetween political strategies and narrative frame persisted in direction and variedonly somewhat in each control table. Thus, in understanding policy change, changesin governing coalitions and political strategies are critical.

Additionally, the ACF theory differentiates between policy learning within abelief system and across belief systems (Jenkins-Smith & Sabatier, 1993, p. 48). In theformer, science is used to bolster a group’s core beliefs; in the latter, scientificevidence and coalitional technical expertise can alter core beliefs over time. Given thewicked-problem nature of the GYA, when groups use science, it is used within abelief system to reify a group’s policy beliefs. In controlling for those narratives thatused science, five of the six political strategies remained virtually unchanged; thus,use of science is not related to winning or losing strategies. However, controlling foruse of science led to the evaporation of any relationship between condensationsymbols and narrative frame, thus weakening the interpretation of the use of con-densation symbols as a narrative political strategy.

Discussion

In this study, we seek to present a new methodological approach to the under-standing of the policy change process by integrating NPA and policy change theorywhile upholding the standards of traditional social science research. Our firstresearch question—whether or not NPA can be used appropriately within thecontext of traditional policy change theory—is answered affirmatively. In this study,issue expansion and containment in the turbulent GYA policy arena is empiricallytested through coding interest group narratives. We systematically test whether ornot winning narrative frames attempt to contain the issue with predictable narrativestrategies (identification of winners, diffusion of benefits and concentration of costsof policy success, and use of scientific certainty) and whether or not losing narrativeframes attempt to expand the issue with predictable narrative strategies (identifica-tion of losers, concentration of benefits and diffusion of costs of policy failure, use ofcondensation symbols and policy surrogates, and use of scientific uncertainty).While advocacy coalitions embed stable policy core beliefs in narratives, they alsouse those narratives to further dynamic political strategies.

Our second research question—whether or not operationalized narrative strat-egies reflect how groups attempt to contain or expand the policy issue—is alsoanswered affirmatively. When using the ACF controls, five of the seven hypothesesare supported. The data provide evidence for the notion that interest group narra-tives are indicators of a group’s political strategies and tactics and are tied to whether


a group is winning (and trying to contain an issue) or losing (and trying to expandan issue). Importantly, these strategies are not tied to core beliefs, nor are theyideologically based or reflective of writing ability or style. These strategies cut acrossideological lines, are used by both sides in the policy dispute, and are connected tohow a group perceives its position in the policy battle. Thus, narratives as a source ofstudy are strategic, predictable, and testable and are an appropriate unit of analysisfor scholars interested in studying policy change.

Finally, our third research question explores the additions to the literature. Thismethod of analysis integrates NPA with policy change theory and adds to theexisting literature. The contribution here addresses Brown and Stewart’s (1993,p. 101) criticism of the ACF. We argue that narratives as political strategies are avaluable source of study for researchers. The activity in the GYA occurred in periodsof alternating victories and losses. Although several external subsystem events (e.g.,court opinions, well-publicized media events, changes in presidential administra-tions) could have swung the policy battles toward one group or another by produc-ing shifts in coalitional resources, the two interest groups consistently perceivedthemselves as losing 67.6 percent of the time. Losing narratives, as we have seen, aremore confrontational and seek to expand conflict to additional parties. In wickedpolicy problems, interest group narratives only reinforce and exacerbate policyintractability. Short-term wins are quickly replaced by the perception of losing andthe need to retaliate. The effect is that the narratives almost continually expand thescope of the conflict, thus drawing in more groups to the policy dispute. As seen inthe eight-year course of this study, the result is long periods of protracted conflict.The GYA policymaking meets the conditions of what Sabatier and Jenkins-Smith(1999, p. 132) call the “devil shift” or the situation where opposing coalitions“remember losses more than victories” and inflate the evilness and power of oppos-ing groups. In addition, this research involved two purposive interest groups, andthese groups, as hypothesized by Sabatier and Jenkins-Smith (1999, p. 134), maintaina tight script and thus resist alterations to their scripts that would move dialoguestoward policy learning.

In policy environments where there is both a clear policy monopoly and a clearout-of-power coalition, we would assume that the minimal coalition of a policymonopoly would rarely perceive that they are losing and that their narratives wouldconsistently reflect the theory of issue containment. Research on narratives in stablepolicy environments might provide initial signs for policy researchers that the policyequilibrium had been punctuated.

Conclusion

This work has used a case study of environmental policy making in the GYA toexamine the interest group use of narrative political strategies in defending existingpolicies or advocating new policies. Grounded in the theories of Sabatier, Jenkins-Smith, Baumgartner, Jones, Schattschneider, Stone, and others, the methodologicalmodel is generalizable to any policy subsystem in such policy areas as economic


development, energy, crime, and foreign policy. The intersection of policy changetheory and NPA prompts theory building.

In determining the extent to which our work contributes to this theory building,we turn to Sabatier (1999, pp. 266–70), who argues that there are seven guidelines fortheory development. First, our analysis is empirical with testable hypotheses.Second, our method allows for testing of our hypotheses in a variety of policysettings. Third, we found a causal relationship between perception of winning andlosing and policy narrative strategies and have accounted for some ACF controls.Fourth, our study suggests that individuals are political, seek to win, and intention-ally and strategically use narratives to either contain or diffuse a policy issue. Fifth,we have shown a consistency among five of our seven hypotheses. Sixth, our aim isto build a long-term research agenda and invite others to build upon our method-ology. Finally, our research uses principles from the ACF, punctuated equilibrium,and three streams of policy change and enhances these works with NPA. We con-clude that narrative political strategies are a vital source for analyzing policy changein a complex political environment.

Mark K. McBeth is a professor of political science at Idaho State University.Elizabeth A. Shanahan is an assistant professor of political science at Montana StateUniversity.Ruth J. Arnell is a doctoral student in political science at Idaho State University.Paul L. Hathaway is a doctoral student in political science at Idaho State University.

Notes

A different version of this paper was presented at the 2005 Western Political Science Conference inAlbuquerque, New Mexico. The authors wish to thank Teri Peterson for her statistical consultations.

1. The BRC is part of a larger advocacy coalition (the wise use coalition) that includes ranchers, localbusiness elites, snowmobile, ATV, and motorcycle manufacturers, elected officials, and scientists.

2. The GYC is part of a larger advocacy coalition (the environmental coalition) that includes nationalenvironmental groups, local business elites, elected officials, and scientists.

3. The identification of benefits as diffuse or concentrated resulted in mutually exclusive coded responses;in other words, when benefits were coded, they were either concentrated or diffused. Hence, they areincluded in the same hypothesis. The same is true for concentrated—diffuse costs (hypothesis 3) anduncertainty—certainty in use of science (hypothesis 6).

References

Achter, Paul J. 2004. “TV Technology, and Mccarthyism: Crafting the Democratic Renaissance in an Age ofFear.” Quarterly Journal of Speech 90: 307–26.

Baumgartner, Frank R. 1989. Conflict and Rhetoric in French Policy Making. Pittsburgh, PA: University ofPittsburgh Press.

Baumgartner, Frank R., and Bryan D. Jones. 1993. Agendas and Instability in American Politics. Chicago, IL:University of Chicago Press.

Berry, Jeffrey M. 1989. The Interest Group Society, 2nd ed. New York: Harper Collins Publishers.

Brown, Anthony, and Joseph Stewart Jr. 1993. “Competing Advocacy Coalitions, Policy Evolution, andAirline Deregulation.” In Policy Change and Learning: An Advocacy Coalition Approach, ed. Paul A.Sabatier, and Hank C. Jenkins-Smith. Boulder, CO: Westview Press, 83–103.


Catton, John, and Betsy Buffington. 2002. “Snowmobiling Abuses in Yellowstone Continue Despite CostlyNew Mitigation Efforts.” http://www.greateryellowstone.org/snowmobiles_exhaust_nr.htmlAccessed August 20, 2002.

Cawley, McGreggor R., and John Freemuth. 1993. “Tree Farms, Mother Earth, and Other Dilemmas: ThePolitics of Ecosystem Management in Greater Yellowstone.” Society and Natural Resources 6: 41–53.

Clark, Timothy W., and Steven C. Minta. 1994. Greater Yellowstone’s Future. Moose, WY: HomesteadPublishing.

Cobb, Roger W., and Charles D. Elder. 1983. Participation in American Politics: The Dynamics of AgendaBuilding. Baltimore, MD: Johns Hopkins University Press.

Collins, Clark. 1998. “Help Save Snowmobiling on Our Public Lands.” Blue Ribbon Magazine (July).http://www.sharetrails.org/mag/jul98. Accessed August 17, 2002.

Cook, Adena. 1997. “Yellowstone Suit Settlement Denounced: Hanky Panky Uncovered.” Blue RibbonMagazine (November). http://www.sharetrails.org/mag/nov97/hanky. Accessed August 17, 2002.

Eggers, Viki. 1999. “Earth Island Petition Debunked: Enviro ‘Facts’ Are Fantasy.” Blue Ribbon Magazine(March). http://www.sharetrails.org/mag/Mar99/Story4.htm. Accessed February 21, 2003.

Fischer, Frank, and John Forrester. 1993. The Argumentative Turn in Policy Analysis and Planning. Durham,NC: Duke University Press.

Greater Yellowstone Coalition (GYC). 2001a. “A Nationwide Plea is Heard: Barring Political Interference,America’s Roadless Forests Will Be Protected.” Greater Yellowstone Report Newsletter (Spring 2001).http://www.greateryellowstone.org/roadless_sp01nl.html. Accessed December 13, 2003.

———. 2001b. “Yellowstone’s Road to Recovery.” Greater Yellowstone Report (Early Summer): 9–11.

———. 2002. “Park Plan Stinks, No Matter How You Cut It.” http://www.greateryellowstone.org/snowmobiles_gftrib_ed02.html. Accessed December 13, 2003.

Hajer, Maarten. 1993. “Discourse Coalitions and the Institutionalization of Practice: The Case of Acid Rainin Great Britain.” In The Argumentative Turn in Policy Analysis and Planning, ed. Frank Fischer, andJohn Forester. Durham, NC: Duke University Press, 43–76.

Jenkins-Smith, Hank C., and Paul A. Sabatier. 1993. “The Dynamics of Policy-Oriented Learning.” In PolicyChange and Learning: An Advocacy Coalition Approach, ed. Paul A. Sabatier, and Hank C. Jenkins-Smith.Boulder, CO: Westview Press, 41–58.

Johnson, Janet Buttolph, and H. T. Reynolds. 2005. Political Science Research Methods. 5th ed. Washington,DC: CQ Press.

Kingdon, John W. 1995. Agendas, Alternatives, and Public Policies. New York: Longman.

Knoke, David, George W. Bohrnstedt, and Alisa Potter Mee. 2002. Statistics for Social Science Data Analysis,4th ed. Itasca, IL. F.E. Peacock Publishers.

McBeth, Mark K., Elizabeth A. Shanahan, and Michael D. Jones. 2005. “The Science of Storytelling:Measuring Policy Beliefs in Greater Yellowstone.” Society and Natural Resources 18: 413–29.

McClendon, McKee J. 2004. Statistical Analysis in the Social Sciences. Belmont, CA: Thomson.

Nie, Martin A. 2003. “Drivers of Natural Resource-Based Political Conflict.” Policy Sciences 36: 307–41.

Radaelli, Claudio M. 1999. “Harmful Tax Competition in the EU: Policy Narratives and Advocacy Coali-tions.” Journal of Market Studies 37: 661–82.

Ramsey, Fred L., and Daniel W. Schafer. 1997. The Statistical Sleuth: A Course in Methods of Data Analysis.Belmont, CA: Wadsworth Publishing Company.

Riker, William H. 1962. A Theory of Winning Coalitions. New Haven, CT: Yale University Press.

Rittel, Horst W. J., and Melvin M. Webber. 1973. “Dilemmas in a General Theory of Planning.” PolicySciences 4: 155–69.

Roe, Emery. 1994. Narrative Policy Analysis. Durham, NC: Duke University Press.

Sabatier, Paul A. 1999. “Fostering the Development of Policy Theory.” In Theories of the Policy Process, chap.10, ed. Paul A. Sabatier. Boulder, CO: Westview Press, 261–76.

———. 2000. “Clear Enough to Be Wrong.” Journal of European Public Policy 7: 134–40.


http://www.greateryellowstone.org/snowmobiles_exhaust_nr.html

http://www.sharetrails.org/mag/jul98

http://www.sharetrails.org/mag/nov97/hanky

http://www.sharetrails.org/mag/Mar99/Story4.htm

http://www.greateryellowstone.org/roadless_sp01nl.html

http://www.greateryellowstone.org

Sabatier, Paul A., and Hank C. Jenkins-Smith, eds. 1993. Policy Change and Learning: An Advocacy CoalitionApproach. Boulder, CO: Westview Press.

———. 1999. “The Advocacy Coalition Framework: An Assessment.” In Theories of the Policy Process, ed.Paul A. Sabatier. Boulder, CO: Westview Press, 117–66.

Schattschneider, E. E. 1960. The Semi-Sovereign People. New York: Holt, Rinehart, & Winston.

Schneider, Anne L., and Helen M. Ingram. 2005. Deserving and Entitled: Social Constructions and PublicPolicy. Albany: State University of New York Press.

Schon, Donald, and Martin Rein. 1994. Frame Reflection: Toward the Resolution of Intractable Policy Contro-versies. New York: Basic Books.

Scott, Michael. 2004. “Rangers No Longer Getting Sick in Yellowstone.” News Release (February). http://www.news.greateryellowstone.bridgeband.net/article. Accessed July 7, 2004.

Sieck, Hope. 2002. “Yellowstone’s Winter in Question.” Greater Yellowstone Reports (Summer): 6–7.

Stone, Deborah. 2002. Policy Paradox: The Art of Political Decision Making, revised ed. New York: W.W.Norton.

Tierney, John, and William Frasure. 1998. “Culture Wars on the Frontier: Interests, Values, and PolicyNarratives in Public Lands Politics.” In Interest Group Politics, ed. Allan J. Cigler, and Burdett Loomis.Washington, DC: CQ Press, 303–26.

Welch, Jack. 2000. “Blue Ribbon Delivers 10,170 Comment Letters to National Park Service on Winter UsePlan for Yellowstone.” Blue Ribbon Magazine (January). http://www.sharetrails.org/mag/Jan2000/story6.htm. Accessed August 17, 2002.

Wilson, Matthew A. 1997. “The Wolf in Yellowstone: Science, Symbol, or Politics? Deconstructing theConflict between Environmentalism and Wise Use.” Society and Natural Resources 10: 453–68.

Wood, B. Dan, and Alesha Doan. 2003. “The Politics of Problem Definition: Applying and Testing Thresh-old Models.” American Journal of Political Science 47 (4): 640–53.

Appendix A: Abbreviated Code Book

1. Does the narrative identify a specific winner (entity that benefits) of a policydecision or potential decision? For example, “anti-recreationists will rejoice overthis policy decision” or “the snowmobile industry is clearly rooting for thislawsuit to be thrown out of court.”

A-Yes (go to question #2) B-No (skip to question #3)

2. What best describes how the narrative constructs the benefits of the policydecision?

A-The narrative is constructed as providing concentrated benefits (a fewgain). For example, “the wealthy environmentalists will have YNP astheir personal playground” or “this decision benefits the snowmobileindustry.”Paragraph number(s) and group:B-The narrative is constructed as providing diffused benefits (many gain).For example, “the American people will benefit from the closing of YNP tosnowmobiles” or “snowmobile enthusiasts from throughout the countryapplauded this decision.”Paragraph number(s) and group:

3. Does the narrative identify a specific loser (entity that pays the costs) of a policydecision? For example, “the American people are the losers when industrycontrols government” or “local businesses are hurt by these actions of the NPS.”


http://www.news.greateryellowstone.bridgeband.net/article

http://www.news.greateryellowstone.bridgeband.net/article

http://www.sharetrails.org/mag/Jan2000

A-Yes (go to question #4) B-No (skip to question #5)

4. What best describes how the narrative constructs the costs of the policy decision?A-The narrative is constructed as providing concentrated costs (a few pay).For example, “This regulation will harm only a small number of greedybusiness owners who fail to adapt to changing times” or “The throwing outof this policy will only harm the sensibilities of a few extremists.”Paragraph number(s) and group:B-The narrative is constructed as providing diffused costs (the many pay).For example, “this plan protects bison while projecting costs over manydiffering groups” or “this plan protects snowmobiling with only minoradjustments required of business owners who must now be licensed guidesand use cleaner machines.”Paragraph number(s) and group:

5. Does the narrative contain at least one condensation symbol? The definition of acondensation symbol is a word or phrase that “shrinks and reduces complicatedconcepts into simple, manageable, or memorable forms.”

A-Yes, list and identify paragraph(s) B-No

6. Does this narrative use a policy surrogate? For example, policy surrogate =“greedy snowmobile corporations exploit Yellowstone for their own purposeswhile the pollution gets worse and worse” or “this issue is all about people inWashington, DC telling people in our small towns about how to live their lives.”

A-Yes, list and identify paragraph(s) B-No

7. Does the narrative use science to define a problem, counter a problem definition,or justify a policy approach?

A-Yes. (go to question #8) B-No (go to question #9)

8. Is the mention of science used in the context of:A-Disputing science B-Establishing scientific certainty

9. What is the stance of the narrative towards the policy being discussed?

A. Winning (supports the policy environment and actions discussed in thenarrative)

B. Losing (the group is under attack even if they are partially winning)


Appendix B: Intercoder Reliability

Question Agreement (%) Disagreement (%) Total Codings

1 243 (78%) 72 (22%) 3152 23 (93%) 9 (7%) 1323 275 (87%) 40 (13%) 3154 210 (89%) 25 (11%) 2355 268 (85%) 47 (85%) 3156 259 (82%) 56 (18%) 3157 156 (84%) 30 (16%) 1868 156 (96%) 6 (4%) 1629 251 (80%) 64 (20%) 315

TOTAL 1,941 (85%) 349 (15%) 2,290 (100%)

Note. Questions 1, 3, 5, 6, and 9 are paired codings comparing the three coders to each other. All coderscoded this screening questions. These questions all sum to 315 (105 documents ¥ 3 coders). Questions 2and 4 are also paired codings but have smaller numbers because of screenings. The first 75 documents forquestion #7 were coded by only two coders. Because there were only 2 coders there was only 1 pairedcoding instead of 3 on this question. Thus the total number of codings for question 7 equals only 186.


Education

The Intersection of Narrative Policy Analysis and Policy Change Theory