Policy Change: An Advocacy Coalition Perspective
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Policy Change: An Advocacy Coalition Framework Perspective
Jonathan J. Pierce (Seattle University), Holly L. Peterson (Oregon State University), and
Katherine C. Hicks (Seattle University)
Version 8.31.16
Abstract
One of the purposes of the advocacy coalition framework (ACF) is to explain what factors
facilitate policy change. There has been a great amount of theoretical development and
updating of the ACF to explain policy change, but there has not been a comprehensive review
of how the ACF has been applied to study policy change. Understanding how the ACF is used in
practice, provides greater understanding about the strengths, weaknesses, and possible future
directions for research. This paper analyzes 67 articles applying the ACF from 2007 – 2014
inclusive of 149 policy processes. It finds that a majority of applications utilize multiple
pathways to policy change and a minority use secondary components besides minority coalition
mobilization. It also finds that multiple concepts such as major and minor policy change are
rarely used, and that concepts such as dominant and minority coalitions may lack internal
validity. The paper argues that greater research should be conducted to explain non-policy
changes in order to provide recommendations to practitioners about the probability of policy
change.
Paper presented at the European Consortium for Political Research General Conference,
September 7-10, 2016 at Charles University, Prague, Czech Republic
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Introduction
A major purpose of policy process research is to better understand policy change and
stasis (deLeon, 1999). To better understand the processes that lead to policy change dozens of
theories and frameworks have been developed (e.g. Baumgartner & Jones, 1993; Kingdon,
1984; Schneider & Ingram, 1993). One of these frameworks is the advocacy coalition
framework (ACF) developed by Paul Sabatier and Hank Jenkins-Smith (1986; 1993). A question
posed by the ACF asks what factors explain the likelihood of policy change occurring (Jenkins-
Smith et al., 2014). There have been hundreds of studies applying the ACF since its inception in
1986, and dozens addressing this question (see Weible et al., 2009; Pierce et al., 2016). This
paper reviews 67 articles applying the ACF to policy change. The purpose is to better
understand how the ACF has been historically applied in order to provide knowledge to ACF and
policy process scholars about the framework’s strengths and weaknesses, as well as proposing
a future research agenda for ACF studies of policy change.
Overview of the ACF
The ACF allows for diverse examination of policy foci in a manner encouraging
comparability, replicability, and falsification. It has been developed to understand policy
processes in North America (e.g. Sabatier & Jenkins-Smith, 1993) as well as in Europe and Asia
(e.g., Sabatier, 1998; Jang, Weible & Park, 2016). It models public policy as a translation of
competing beliefs, especially regarding contested issues. For this reason, it is particularly useful
for examining conflicting goals and technical or scientific information in policy processes (Pierce
& Weible, 2016). Although the framework can support analysis of various theoretical foci, its
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primary logic describes and explains theories of advocacy coalitions, policy learning, and policy
change.
Figure 1 provides a general flow diagram of the framework’s logic, identifying major ACF
components and variables, as well as their relationships to each other over time. ACF logic
posits that coalitions seeking to translate their beliefs into policy compete with one another
within a policy subsystem by using strategies to influence government decision makers.
Coalitional beliefs and strategic behaviors eventually influence policy outputs and impacts. This
process of coalitional competition is affected by both long and short-term opportunities,
constraints, and resources, which are in turn affected by both relatively stable parameters and
external subsystem events. Policy impacts may feed back into the system at multiple levels (see
Jenkins et al., 2014 for a further description).
Figure 1. Flow Diagram of the Advocacy Coalition Framework.
Source: Jenkins et al., 2014.
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Assumptions
The ACF proposes that researchers employing the framework consider time periods of
at least a decade in order to observe the theoretical foci the framework highlights (Weible &
Norstedt, 2012). The primary ACF unit of analysis is the policy subsystem, which includes all
relevant actors trying to influence policy and politics regarding a specific policy issue, within
geographical boundaries (Jenkins-Smith et al., 2014). Subsystems may be nested, either
vertically through levels of government or horizontally across differing jurisdictions and policy
issues (Sabatier & Jenkins-Smith, 1999). Within the ACF policy actors are organized within policy
subsystems according to their participation within advocacy coalitions. Actor membership
within advocacy coalitions correspond with beliefs about normative or empirical assessments of
the public issue (policy core beliefs) and sometimes more specific, instrumental ways of
achieving goals (secondary beliefs) (Weible & Norhstedt, 2012). These policy actors are
boundedly rational, goal-oriented, make sense of the world in part through general causal
(deep core) beliefs (Jenkins-Smith et al. 2014), and rely on science and technical information in
debates and coalition mobilization (Weible & Nohrstedt, 2012).
Advocacy Coalitions
Advocacy coalition membership, although based on policy core beliefs, must also
include non-trivial activity and coordination with other coalition members aimed at influencing
the policy process (Pierce & Weible, 2016). Research describing and explaining advocacy
coalitions may identify coalitions, beliefs, collaboration, stability, and defections (Pierce et al.,
2016). There are multiple hypotheses associated with advocacy coalition research (see Sabatier
& Weible 2007, p. 220).
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Policy Learning
Policy learning refers to enduring changes in understandings or intentions by coalition
members regarding the precepts of policy beliefs (Jenkins-Smith & Sabatier, 1993, pp. 41-58).
Such alterations may concern policy problems, solutions, or strategies (Jenkins et al., 2014).
Research in this area may focus on identifying coalitional learning; the role of policy brokers
(facilitators of coalitional negotiations); or deep core, policy core, or secondary belief change
(Pierce et al., 2016). While any actor may play the role of policy broker, research finds that they
are often administrative agencies (Jang, Weible & Park, 2016). Additionally, researchers in this
area may also be interested in the role of institutional factors, level of conflict, information
type, and policy actor attributes on learning (Jang, Weible & Park, 2016). There are multiple
hypotheses frequently associated with policy learning research (see Jenkins et al., 2014, pp.
199-200).
Policy Change
Policy change reflects winning advocacy coalitions’ policy beliefs. This theoretical
conceptualization of policy change is well suited for investigation using belief systems (Pierce et
al., 2016), as major policy change is associated with alterations in policy core beliefs and minor
policy change is associated with alterations in secondary beliefs (Sabatier & Jenkins-Smith,
1999). There are four pathways associated with bottom-up policy change in the ACF: external
perturbations or events external to the subsystem, internal events to the subsystem, policy
learning, and negotiated agreements (Sabatier & Weible, 2007; Jenkins-Smith et al., 2014).
Research in this area may target a pathway of policy change, including whether or not
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alteration in governing coalitions or imposition by a superior authority affected change (Pierce
et al., 2016). The hypotheses most frequently associated with policy change research include:
Policy Change Hypothesis 1, Bottom-up Policy Change: Significant perturbations external to the subsystem, a significant perturbation internal to the subsystem, policy-oriented learning, negotiated agreement, or some combination thereof are necessary, but not sufficient, sources of change in the policy core attributes of a governmental program (Weible & Nohrstedt, 2012, p. 133). Policy Change Hypothesis 2, Top-down Policy Change: The policy core attributes of a government program in a specific jurisdiction will not be significantly revised as long as the subsystem advocacy coalition that instated the program remains in power within that jurisdiction—except when the change is imposed by a hierarchically superior jurisdiction (Jenkins et al., 2014, pp. 203-204).
Figure 2 provides a general flow diagram of the theory of policy change, identifying the
pathways to policy change along with major ACF components, as well as their relationships to
each other over time.
Figure 2. Flow Diagram of the Theory of Policy Change within the ACF.
Policy change is a theory within the broader framework of the ACF. The theory states
that there are four pathways to policy change that are bottom-up and a fifth pathway that is
top-down. Outside of the policy subsystem there are relatively stable parameters (i.e. basic
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attributes of the problem area and distribution of natural resources, fundamental sociocultural
values and social structure, and basic constitutional structure) as well as external subsystem
events (i.e. changes in socioeconomic conditions, changes in public opinion, change in systemic
governing coalition, and changes in other policy subsystems) (Jenkins-Smith et al., 2014). These
lists of relatively stable parameters and external events are not exhaustive. These parameters
influence external events as well as long-term opportunity structures (i.e. degree of consensus
needed for a major policy change, openness of political system, and overlapping societal
cleavages), and the opportunity structures also influence external events. Long-term
opportunity structures and external events are all mediated through short-term constraints and
resources that represent the short-term opportunities for coalitions to exploit (Jenkins-Smith et
al., 2014). These short-term constraints and resources are similar to windows of opportunity as
identified by Kingdon (1984). At this point there is the opportunity for a superior jurisdiction to
impose a policy change on the subsystem. Multiple studies of policy change have identified an
association between external events and superior jurisdictions (e.g. Feindt, 2010; Miller, 2011;
Jang, Kim, & Han, 2010). The superior jurisdiction may or may not act to change policy, but their
actions are in part a function of relatively stable parameters, long-term opportunity structures,
external events, and short-term constraints. All of these components operate externally to and
influence the policy subsystem.
Within the policy subsystem there are often hundreds of actors that are simplified by
grouping them into coalitions as well as institutions. There may be one or more coalitions that
possess the following various attributes: hierarchical beliefs, resources, strategies, and
coordination (Jenkins-Smith et al., 2014). Within the subsystem events may occur such as
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crises, policy failures or fiascos, scandals, among others (Sabatier & Weible, 2007). Such events
provide an opportunity for the attributes of coalitions to change, such as the strategy of venue
shopping or confirming beliefs of a minority coalition about the failure of a policy (Jenkins-
Smith et al., 2014). Internal events, like external events, are subsystem-wide and represent
opportunities for changes in the attributes of coalitions.
Learning and negotiation are two other pathways to policy change. Negotiations may
occur between two or more coalitions that may lead to learning and/or policy change.
Negotiations may occur when coalitions recognize the existence of a hurting stalemate and may
be initiated by a policy broker (Jenkins-Smith et al., 2014). For greater discussion about
negotiation and the ACF see Weible and Sabatier (2007, pp. 205-207). Learning is a more
transitory pathway to policy change. Learning may occur across coalitions (e.g. Weber et al.,
2013) or within a coalition (e.g. Han et al., 2014), and either form may lead to policy change.
Multiple pathways may simultaneously occur or may occur in sequence, leading to a policy
change. Finally, a decision is made by a government authority within the subsystem to change a
policy. The decision whether to maintain the status quo or to change a policy will then lead to
new politics and another policy process both within the policy subsystem as well as becoming
an external event for another policy subsystem.
Methods
The first step in our review process was to produce a list of peer-reviewed journal
articles that would allow for an assessment of how the ACF is applied to study policy change.
We utilized the Web of Science database to create a list of peer-reviewed journal articles in
English that cite at least one of the following six ACF theoretical documents: Paul Sabatier
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Journal of Public Policy (1986); Paul Sabatier Policy Sciences (1988); Paul Sabatier and Hank
Jenkins-Smith (eds.) Policy Change and Learning: An Advocacy Coalition Approach (1993); Paul
Sabatier Journal of European Public Policy (1998); Paul Sabatier and Hank Jenkins-Smith “An
Advocacy Coalition Framework: An Assessment” in Theories of the Policy Process (1999); Paul
Sabatier and Christopher Weible “The Advocacy Coalition Framework: Innovations and
Clarifications” in Theories of the Policy Process, Second Edition (2007). These six documents
were utilized because they establish the theoretical basis and development of the ACF. Our
search criteria included only English language peer-reviewed journal articles between 2007 and
2014. This sampling frame was selected because of various time and language limitations, and
due to the existence of a previous systematic review of the entire ACF from 1987 to 2006 by
Weible et al. (2009). This initial search resulted in a total of 1,067 peer-reviewed articles.
Unpublished manuscripts, conference papers, dissertations, published reports, books, or book
chapters were not included.
Content analysis was conducted on the articles in two rounds. First, five coders recorded
the bibliographic information of each article. This included 10 identification codes such as title,
author, journal name, etc. Four codes were utilized to differentiate between applications and
those articles that only cited one of the six theoretical foundational documents. These codes
included: the frequency with which the keywords “coalition,” “learn,” or “advocacy” were used
in the title and abstract, and the frequency with which the six theoretical foundational
documents were cited. Articles were included for additional screening if they met two criteria:
(1) any combination of the keywords occurred a minimum of two times, and (2) the article’s
text contained at least two theoretical foundational citations. Using this combination of
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keyword and citation frequency counts we eliminated about half of the articles, leaving 512
potential applications.
Next, the coders read the 512 titles, abstracts, introduction, and literature review
sections to determine if they were complete applications or mere citations of the ACF. To help
coders distinguish between applications and citations of the ACF, applications are described as
having the following characteristics: 1. Data and/or a case study, 2. Utilize in the analysis
concepts of the ACF such as coalitions, policy change, and/or learning and 3. should not focus
on implementation or policy analysis. Inter-coder reliability assessments for this coding were
acceptable with more than 50% of a random sample of articles being reviewed by an inter-
coder.1 This sample is sufficient to determine inter-coder reliability given the population and a
95% level of probability and a confidence interval of 5% (Lacy and Riffe, 1996). Five coders had
an inter-coder reliability rate of greater than 80%. Inter-coder reliability at or above the 80%
threshold is considered reliable data (Lacy & Riffe, 1996; Lombard, Snyder-Duch, & Campanella
Bracken, 2002; Riffe et al., 2005). Utilizing this process, 161 articles were identified as ACF
applications.
In the second round of coding, three coders were utilized. The coders applied a detailed
codebook to analyze how the ACF is applied to policy change (see Appendix). This round of
coding analyzed the articles for presence of policy change, pathways, components, and other
theory-based codes. Overall, the codebook includes 15 codes in the first round, and 20 codes in
the second round for a total of 35 codes (as well as notes). It resulted in identifying 67 articles
1 A total of 256 articles were randomly selected for inter-coder reliability during this first round.
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that applied the ACF to policy change. These 67 articles are all cited in this paper and listed in
the references.
Three methodological design aspects ensured reliable results: the codebook used
specific wording to minimize interpretation, binary coding for presence was utilized, and the
data were analyzed using inter-coder reliability. To determine inter-coder reliability, a random
number generator was utilized and content analysis was conducted by two coders on 39/67
articles (58%). This sample is sufficient to determine inter-coder reliability given the population,
a 95% level of probability, and a confidence interval of 5% (Lacy and Riffe, 1996). Only codes
that utilized content analysis were tested for inter-coder reliability. Codes 11-15 and 17-35
listed in the Appendix were subject to inter-coder reliability. In total, 24 codes were analyzed
using inter-coder reliability among the 67 applications. All codes showed greater than or equal
to 80% agreement, which is considered reliable (e.g., Lacy & Riffe, 1996). Additionally, a
Cohen’s Kappa was run on the 24 codes that were nominal, producing a score of 0.40 or greater
for each code considered a moderate level of agreement (Landis and Koch, 1977). Therefore,
based on both percentage agreement and Cohen’s Kappa, these codes achieve acceptable
levels of inter-coder reliability. This two-step process of inter-rater reliability is important for
increasing reliability of both the sample and the data based on the content analysis.
Results
The results discuss the findings of the content analysis. These are organized based on
separating the instances of policy change from non-policy change, and then discussing a single
pathway, multiple pathways, and the secondary components (i.e. change in dominant coalition,
policy broker, etc.).
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Policy Change
In total, there are 67 articles identifying 149 different policy processes using the ACF.
Among those policy processes, 129 identify cases of policy change and 20 identify cases of non-
policy change. There are 28 articles that analyze more than one policy with a maximum of 11
policies (Fischer, 2014). Whether policy change occurs or not is based on the explicit statements
made by the author(s). In each case, the policy is specified by the author. These include a wide
range of policy processes such as Badger Culling in the UK (Lodge & Matus, 2014), an EU
satellite program (Bandelow & Kundolf, 2011), language used for street signs in the EU (Sloboda
et al., 2010), and German defense policy and the War in Afghanistan (Schroer, 2014). This
demonstrates that a large minority (42%) of articles applying the ACF analyze more than one
policy. Further analysis of this subset of the population reveals that 17 articles compare policies
across subsystems. For example, Dougherty et al., (2010) compares undocumented immigrants
and higher education policy in the U.S. states of Texas and Arizona. In contrast, 11 articles
compare multiple policies over time within a single subsystem. For example, Penning-Rowsell et
al. (2014) describe the development of UK flood insurance over six decades. Thus, there is an
active research agenda among ACF scholars conducting comparative policy change research
between subsystems geographically and temporally.
Major and minor policy changes are not frequently identified. Among the 129 cases of
policy change, major policy change is identified 25 times (e.g., Albright, 2011) and minor policy
change 17 times (e.g., Fischer, 2014). In total, only 15/67 articles identify either major or minor
policy changes.
Pathways to Policy Change
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The ACF posits that one of the following pathways to policy change are necessary but
not sufficient: (1) changes imposed by a superior jurisdiction, (2) external events, (3) internal
events, (4) learning, and (5) negotiated agreement. These pathways may occur in combination
with each other or in isolation. An examination of the 129 cases of policy change reveals that
eight do not identify any pathways to policy change. Among the 129 cases of policy change, 51
(40%) have only a single pathway, but 70 (54%) have multiple pathways to policy change. Table
1 (n=129) shows the total frequencies each pathway is identified, as well as the frequency each
pathway is identified in isolation or in concert with another pathway. This data is expressed
both as frequency counts as well as percentages of all policy changes.
Table 1. Frequency of Pathways to Policy Change (n=129).
Pathway to Policy Change
Single Path
Multiple Paths
Total Frequency
As Percentage of All Policy Changes
Superior jurisdiction 2 18 20 16%
External event 20 57 77 60%
Internal event 1 11 12 9%
Negotiation 13 28 41 32%
Learning 15 57 72 56%
The most frequently referenced pathways are external events (60%) (e.g., Hersperger et al.
2014; Mailand, 2010; Montefrio, 2014) and learning (56%) (e.g., Karapin, 2012; Parsell et al.,
2014), both of which occur in a majority of policy processes analyzed. Studying policy change as
a function of only internal events or superior jurisdiction is relatively rare. Instead, when these
two pathways to policy change are applied they tend to be in combination with other
pathways. Negotiation (e.g., Marfo & McKeown, 2013) as a single pathway is applied almost as
frequently as learning, but it is applied in conjunction with other pathways about half as
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frequently compared to external events and learning. A majority of policy changes are
explained utilizing multiple pathways rather than just a single pathway to policy change. This
demonstrates the flexibility and utilitarian nature of the ACF to explain policy changes that are
associated with a myriad of processes, rather than focusing only on a single process such as
external events or learning.
Further examination of the 70 policy changes that identify multiple pathways reveals
some associative patterns. Superior jurisdiction is identified 18 times with other pathways and
15/18 include external events (e.g., Kim, 2012). This demonstrates a possible strong
relationship between external events and superior jurisdictions. Internal events are also highly
associated with external events. Among the 11 internal events identified among the multiple
pathways, eight are associated with external events (e.g., Adshead, 2011). External events are
also associated with negotiation and learning. Among the 28 policy changes that identify
negotiation along with other pathways to policy change, 18 include external events (e.g., Diaz-
Kope et al., 2013). External events are even more common among policy changes that utilize
learning as a pathway. External events and learning are identified together in 46 policy changes,
representing a majority of all learning (72) and external event (77) pathways identified (e.g.,
Ness, 2010; Nohrstedt, 2013). Overall, external events are identified in a majority of policy
changes that include multiple pathways (57/70) and this total accounts for 44% of all policy
changes (e.g., Stich, 2008). In a majority of policy processes examined, external events are a
necessary pathway to policy change and often in combination with other pathways.
Learning occurs in combination with other pathways to policy change just as frequently
as external events (57/70). Learning is identified in combination with superior jurisdiction in 13
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policy changes (e.g., Ley & Weber, 2014), which is a majority of the total times superior
jurisdiction is identified (20). In contrast, learning is only identified three times with internal
events (e.g., Albright, 2011), which represents a clear minority of the times that internal events
are identified (12). Learning is most frequently identified with external events (46) (e.g.,
Nedergaard, 2008). The next most frequent pathway in association with learning is negotiation.
Negotiation and learning are identified together in 22 policy changes (e.g., Johnson et al.,
2012), representing a majority of the times negotiation is identified (41). In total, learning is
identified in 57 policy changes that have multiple pathways (e.g., Leifeld, 2013). This is the
same frequency as external events and represents a clear majority of the 70 policy changes that
have multiple pathways.
There are also several policy changes that include three or more pathways. In total there
are 28 policy changes that include three or more pathways, but no policy changes that include
all five pathways. The most frequent combinations are negotiation + external events + learning
(13) (e.g., Dressel, 2012), and superior jurisdiction + external events + learning (11) (e.g.,
Stensdal, 2014).
Secondary Components
The ACF also identifies several secondary components that are intermediate variables
that may lead to a policy change. These include: (1) new dominant coalition, (2) change in
distribution of resources, (3) opening or closing of venues, (4) minority coalition mobilization,
(5) change in beliefs among dominant coalition, (6) changes in beliefs among minority coalition,
(7) confirmation of beliefs among dominant coalition, (8) confirmation of beliefs among
minority coalition, (9) change in strategy by dominant coalition, (10) change in strategy by
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minority coalition, (11) a hurting stalemate between coalitions, and (12) presence of a policy
broker. Jenkins-Smith et al. (2014) identify all of these secondary components as intermediate
variables that may be associated with one or multiple pathways to policy change. Jenkins-Smith
et al. (2014) identify two other secondary components that are not included in this study: (1)
heightened public and political attention, and (2) changes in the government agenda. They
were excluded because by definition a policy change requires increases in government
attention and changes in the agenda. Therefore, these secondary components should always be
present in cases of policy change. In comparison, the other 12 intermediate variables varied
and were associated with various pathways to policy change.
The components are presented below with both the total frequency and percentage of
their presence among the 129 policy changes identified. The most common and only
component to be identified in a majority of policy changes is minority coalition mobilization
(62%) (e.g., Kettell & Cairney, 2010). This is expected as the ACF tends to focus on competing
coalitions attempting to translate their beliefs into public policy. The next two most frequent
components are belief confirmation (25%) (e.g., Pollak et al., 2011; Breton et al., 2008) and
changes in beliefs (22%) (e.g., Bauman & White, 2015) both among the dominant coalition.
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Table 2. Frequency of Secondary Components among Policy Changes (n=129).
Secondary Component Frequency Percentage
New Dominant Coalition 19 15%
Change Distribution of Resources 27 21%
Opening or Closing of Venues 29 22%
Minority Coalition Mobilization 80 62%
Belief Change Dominant Coalition 28 22%
Belief Change Minority Coalition 12 9%
Belief Confirmation Dominant Coalition 32 25%
Belief Confirmation Minority Coalition 25 19%
Strategy Change Dominant Coalition 18 14%
Strategy Change Minority Coalition 25 19%
Hurting stalemate 14 11%
Policy broker 23 18%
Further analysis of the secondary components reveals patterns of association with various
pathways to policy change. Table 3 presents the results of the frequency of each secondary
component in conjunction with each pathway. The percentage represents the frequency that
the secondary component occurs among the frequency count of pathways. For example,
superior jurisdiction is identified as a pathway to policy change 20 times, and among those a
new dominant coalition is identified eight times, or 40% of the time (e.g., Beverwijk et al.,
2008).
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Table 3. Frequency of Secondary Components Among Pathways to Policy Change2
Secondary Component
Superior Jurisdiction
(n=20)
External Event (n=77)
Internal Event (n=12)
Negotiation (n=41)
Learning (n=72)
Total (n=222)
New Dominant Coalition
8 (40%) 17 (22%) 3 (25%) 5 (12%) 10 (14%) 43 (19%)
Change Distribution of
Resources
7 (35%) 20 (26%) 6 (50%) 8 (20%) 16 (22%) 57 (26%)
Opening or Closing of
Venues
9 (45%) 20 (26%) 6 (50%) 6 (15%) 21 (29%) 62 (28%)
Minority Coalition
Mobilization
13 (65%) 44 (57%) 9 (75%) 31 (76%) 43 (60%) 140 (63%)
Belief Change Dominant Coalition
5 (25%) 16 (21%) 5 (42%) 10 (24%) 23 (32%) 59 (27%)
Belief Change Minority Coalition
1 (5%) 4 (5%) 0 (0%) 9 (22%) 11 (15%) 25 (11%)
Belief Confirmation
Dominant Coalition
5 (25%) 13 (17%) 1 (8%) 8 (20%) 14 (19%) 41 (18%)
Belief Confirmation
Minority Coalition
3 (15%) 10 (13%) 2 (17%) 8 (20%) 10 (14%) 33 (15%)
Strategy Change Dominant Coalition
3 (15%) 12 (16%) 1 (8%) 7 (17%) 11 (15%) 34 (15%)
Strategy Change Minority Coalition
3 (15%) 12 (16%) 5 (42%) 7 (17%) 18 (25%) 49 (22%)
Hurting stalemate
1 (5%) 7 (9%) 1 (8%) 12 (29%) 12 (17%) 33 (15%)
Policy broker 7 (35%) 14 (18%) 2 (17%) 12 (29%) 18 (25%) 53 (24%)
2 Note the number of secondary components is per pathway identified, and not just per policy change. In this case, as 70 policy changes identify multiple pathways there are a total of 222 pathways among the 129 policy changes.
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Only minority coalition mobilization (63%) (e.g., Blatter, 2008), occurs a majority of the time
among the aggregate pathways. The next two most frequent secondary components among all
of the pathways are opening or closing venues (28%) (e.g., Frasha et al., 2014) and belief
change among the dominant coalition (27%) (e.g., Kuebler, 2007). The least frequent secondary
component is belief change among the minority coalition (11%) (e.g., Schilling & Keyes, 2008).
On the other hand, when examining each individual pathway there are some associative
patterns between pathway and secondary component. The most frequent secondary
components associated with superior jurisdiction are minority coalition mobilization (65%)
(e.g., Li, 2012), opening or closing of venues (45%) (e.g., Stensdal, 2014), and a new dominant
coalition (40%) (e.g., Miller, 2011). Among external events the most frequent secondary
components are minority coalition mobilization (57%) (e.g., Winkel & Sotirov, 2011), opening or
closing of venues (26%) (e.g., Parrish, 2008), and changes in the distribution of resources (26%)
(e.g., Quaglia, 2012). Internal events, which are the rarest pathway (12), also exhibit the highest
frequency of secondary components. Three secondary components occur during at least half of
the incidences of internal events. These are: minority coalition mobilization, 75% (e.g.,
Heinmiller, 2013); change distribution resources, 50% (e.g., Kwon, 2007); and opening or
closing of venues, 50% (e.g., Bukowski, 2007). Among the negotiation pathway, the most
frequent secondary components are minority coalition mobilization (76%) (e.g., Van den Bulck
& Donders, 2014), hurting stalemate (29%) (e.g., Heikkila et al., 2014) and policy broker (29%)
(e.g., Ingold, 2011). The most frequent secondary components associated with the pathway of
learning are minority coalition mobilization (60%) (e.g., Hirsch et al., 2010), change in beliefs
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among dominant coalition (32%) (e.g., Olsson, 2009), and opening or closing of venues (29%)
(e.g., Kingiri, 2011).
No Policy Change
In addition to the 129 cases of policy change identified among the 67 articles, there are
20 cases of no policy change among 14 articles (e.g., Dougherty et al., 2013). While this is a
small total, these cases reveal some important observations. These non-policy changes also
identify pathways to policy change as well as secondary components. In total, 14 non-policy
changes also identify at least one pathway to policy change. Seven non-policy changes identify
a single pathway and seven identify multiple pathways that did not lead to a policy change.
Superior jurisdiction changing a policy is not examined in this section because by definition it
leads to a policy change. Internal events (e.g., Rossegger & Ramin, 2013) and negotiation (e.g.,
Smith, 2009) are both identified twice in association with no policy change. External events are
identified 11 times (e.g., Nohrstedt, 2011) and learning 8 times (e.g., Neville, 2012) in terms of
not being associated with policy change. This research supports the hypotheses that such
pathways are necessary, but not sufficient to bring about policy change (Jenkins-Smith et al.,
2014).
Examination of the secondary components in relation to the non-policy changes (n=20)
reveals some potential associations. Confirmation of beliefs among the dominant coalition is
identified 10 times (e.g. Babon et al., 2014) among the 20 non-policy changes. This may lead
one to judge that belief confirmation by the dominant coalition is an impediment to policy
change, but after the mobilization of a minority coalition it was the most frequent secondary
component among all policy changes. Other secondary components that occurred a notable
Policy Change: An Advocacy Coalition Perspective
21
amount of times include minority coalition mobilization (8 times) (e.g., Weber et al., 2013),
opening or closing of venues (6 times) (e.g., Weible, 2007), confirmation beliefs of minority
coalition (6 times) (e.g., Babon et al., 2014), and change distribution of resources (4 times) (e.g.
Van Gossum et al., 2008). The remaining secondary components (new dominant coalition,
belief change of dominant or minority coalition, belief confirmation of dominant coalition,
strategy change of dominant or minority coalition, hurting stalemate, and policy broker) are
only identified two times or fewer in association with no policy change.
Two relationships of note between the pathways and secondary components in relation
to no policy change are (1) confirmation of beliefs among the dominant coalition and (2)
external events and learning. Among the 20 non-policy changes, 11 identify the external event
pathway. Of those 11, nine also identify no belief change among the dominant coalition (e.g.,
Weible, 2007). Therefore, in almost all cases where there is an external event and no policy
change, there is also no change among the beliefs of the dominant coalition. Learning also has a
similar pattern. Learning as a pathway does not lead to policy change in eight cases (e.g., Smith,
2009). Of these eight cases, six include the confirmation of beliefs among the dominant
coalition (75%) (e.g., Bukowski, 2007). In comparison, of those cases of successful policy change
where learning occurred (72), there are 10 that identify confirmation of beliefs among the
dominant coalition (14%) (e.g., Adshead, 2011). Thus, the confirmation of beliefs among the
dominant coalition in association with external events or learning may be an obstacle to policy
change, but does not prevent such changes.
Discussion
Policy Change: An Advocacy Coalition Perspective
22
This paper analyzes how the ACF is used to study policy change. It examines 67 articles
and 149 policy processes. The policy process is the unit of analysis to better understand the
pathways and components that authors associate with policy changes (n=129) and non-policy
changes (n=20). The ACF would greatly benefit from more studies that examine non-policy
changes, whether in comparison across policy subsystems or identifying failures of policy
change chronologically that may eventually lead to a policy change. Eleven articles identify
multiple policy processes over time, often including multiple policy changes and at times non-
policy changes.
According to Sabatier and Jenkins-Smith (1999), a major strength of the ACF is a “clear-
cut criterion” for distinguishing between major and minor policy changes (p. 147). This
distinction has been perpetuated in theoretical works about the ACF, such as Jenkins-Smith et
al. (2014). Yet in practice, the vast majority of articles (78%) do not distinguish a major and/or
minor policy change. This may be due to a variety of reasons, such as the purpose of the
application, the utility of the concept, and limitations in measuring policy changes. In practice,
distinguishing between major and minor policy changes is a weakness of the ACF.
Based on this study it is clear that most policy changes and even most non-policy
changes include multiple pathways. This reinforces the hypotheses of the ACF (e.g. Jenkins-
Smith et al., 2014) that in cases of policy change, these pathways work together and not
separately. A major limitation for identifying these pathways is the permeability of boundaries
and transformative features of policy subsystems, in particular those at the national level (e.g.,
Kwon, 2007; Li, 2012; Diaz-Kope et al. 2013). As about half of all ACF applications since 2007 are
at the national level (see Pierce et al., 2016) this is a major issue. Subsystems at the national
Policy Change: An Advocacy Coalition Perspective
23
level raise questions about the exclusion of policy actors and institutions, differentiating
between external and internal events, as well as identifying a superior jurisdiction. This is a
possible weakness of the ACF and theory of policy change in relation to national level
subsystems.
A major limitation of this content analysis and many of the policy processes examined is
the absence of timing and sequencing of pathways and secondary components. Using content
analysis leads to cross-sectional data removing the variables of time and sequence. To better
capture the process of timing and sequence, this paper first employed process tracing, but
found that comparison across over 100 policy processes was too difficult. Crisp set qualitative
comparative analysis was also attempted, but the lack of enough negative cases of non-policy
change combined with having too many variables when the 12 secondary components were
included as well as the five pathways led to the abandonment of this method. The ACF would
greatly benefit from greater theoretical development and empirical investigation into the
timing and sequencing of the pathways and secondary components that lead to various policy
processes. The utilization of event history analysis, process tracing and other methods that
include time and sequence as a variable should become methods applied by ACF scholars to
understand policy change.
Beyond the pathways to policy change, secondary components are also included in this
study. There are several limitations in terms of the utility and possibly validity of some of these
components. Overall, many of these secondary components are difficult to categorize. The
opening and closing of venues is identified as a secondary component but also may be a
superior jurisdiction. For example, the role of the courts could represent both a venue and a
Policy Change: An Advocacy Coalition Perspective
24
superior jurisdiction (e.g., Miller, 2011). Another component that resists categorization is public
opinion, which could be treated as an external event (e.g., Bukowski, 2007), a resource for
advocacy coalitions (e.g., Babon et al., 2014), or even a venue (e.g., Blatter, 2009). Another
major issue is the identification of dominant and minority coalitions. These labels are
contextual in both time and space. As a policy changes over time through various processes the
minority coalition may become dominant. Coalitions may venue shop strategically to avoid
engaging dominant coalitions (e.g. Pralle, 2003). Therefore, what would otherwise be
considered a minority coalition may become a dominant coalition at an alternative venue. This
raises two important questions: do policy subsystems have multiple venues and, if so, can a
coalition be both a dominant and a minority coalition within the same subsystem at the same
time? Considering that policy processes occur in nested subsystems, have various venues, and
develop over time, raises many questions about the internal validity of a dominant coalition’s
definition as possessing superior resources and political authority (Jenkins-Smith et al., 2014).
The ACF has an obstacle when it comes to theory development about policy change.
This is because ACF studies need to identify a subsystem and advocacy coalition(s) first. This
primary step relies on the ACF theory of advocacy coalitions such as the primacy of policy core
beliefs and coordination among policy actors. For an academic article to sufficiently identify a
subsystem and advocacy coalitions takes a great amount of analysis, article space, and
generally the author’s time. One resolution is the division of labor. ACF scholars should seek to
separate analysis of theories of advocacy coalitions from policy change. Developing first articles
establishing advocacy coalitions, and then using citations of this to focus on policy change
would help to clarify the phenomena investigated in each and allow greater development and
Policy Change: An Advocacy Coalition Perspective
25
analysis of each theory. Previous examples of this include Ellison and Newmark (2010) building
on the findings of Ellison (1998), or Nohrstedt’s work on nuclear energy in Sweden (2008,
2010). Another option is finding other long-format outlets such as books for more holistic
studies of multiple theories within the ACF.
Two areas of research focusing on pathways to policy change that are understudied are
(1) superior jurisdictions and (2) the combination of negotiation and learning. An understudied
phenomenon is how a superior jurisdiction changes policy in a manner that matches the policy
core belief(s) of a minority coalition (who is already operating within the subsystem) after an
external or internal event (e.g. Ansell et al., 2009; Hirschi & Widmer, 2010). Another is the
combination of learning and negotiation. The two are identified together often, in 22 policy
processes (e.g., Adshead, 2011; Karapin, 2012), accounting for more than half of all negotiation
pathways that lead to policy change. This raises important questions about how learning and
negotiation, as well as the other pathways, interact and reinforce each other as well as in what
sequence do they occur? These are possible strengths of the ACF that need further research.
Conclusion
This paper examines how the ACF is being applied to policy change. Content analysis is
conducted on 149 policy processes among 67 articles examining 129 policy changes and 20
non-policy changes. It identifies the frequency and associations between the pathways to policy
change as predicted by the ACF (superior jurisdiction, external events, internal events,
negotiation, and learning) as well as the frequency and associations of 12 secondary
components (e.g. opening or closing of venues, policy brokers, etc.). The paper finds that the
most frequent pathways to policy change are external events and learning. It also finds that a
Policy Change: An Advocacy Coalition Perspective
26
majority of applications both explaining policy change and non-policy change identify multiple
pathways. There are frequent associations among the pathways such as between superior
jurisdiction and external events, and negotiation and learning that require future research. The
only secondary component identified by a majority of policy processes is the mobilization of a
minority coalition. Secondary components are infrequently identified among these policy
processes, and some such as dominant and minority coalition and public opinion may have
internal validity problems.
There are two main limitations to this research. First, it does not capture every
application of the ACF from 2007 – 2014. Only applications that are in English and in peer-
reviewed journals are included. Therefore, non-English applications, such as applications in
Swedish, German, Korean, or in Spanish are not included. Applications that are books, book
chapters, and other mediums are also not included. Also, the population is limited to those
applications that cite one of the theoretical contributions by Paul Sabatier. It is technically
possible that authors could apply the ACF without citing any of the selected theoretical
foundation texts. In addition, our identification of applications in comparison to citations of the
ACF is subjective. By utilizing presence of citations, keywords, data/case study and application
of ACF concepts we attempt to mitigate that subjectivity, but Type 1 and Type 2 errors probably
did occur. Second, content analysis was conducted on these 67 articles utilizing multiple coders.
The level of interpretation by these coders was mitigated by having codes focusing on presence
rather than frequency or strength. Also, multiple forms of inter-coder reliability were tested
and found acceptable. While systemic issues may have been mitigated there will be outliers and
limitations when utilizing approximately 2,000 pages of material as the sources of data.
Policy Change: An Advocacy Coalition Perspective
27
Jenkins-Smith et al. (2014) ask to what end does ACF research serve? More specifically,
they cite Weible et al. (2012) in asking if and how the logic of the ACF can help people
strategically influence the policy process. One clear way this can be accomplished is by
predicting the probability that a policy change will occur. In order to better understand and
possibly even predict the probability of policy change, scholars will need to further expand the
study of non-policy changes. This could mean in comparison across policy subsystems,
longitudinally to explain the timing of failed changes, as well as isolated case studies of non-
policy change. By accumulating these non-policy changes along with cases of policy change, ACF
scholars can begin to analyze the probability that the presence and characteristics of the
pathways and secondary components are associated with policy change. By researching how
these variables relate to non-policy change, we can better understand the probability that
policy change will occur.
Policy Change: An Advocacy Coalition Perspective
28
References
Adshead, M. 2011. “An Advocacy Coalition Framework Approach to the Rise and Fall of Social Partnership.” Irish Political Studies, 26(1), 73-93. Albright, E. A. 2011. “Policy Change and Learning in Response to Extreme Flood Events in Hungary: An Advocacy Coalition Approach.” Policy Studies Journal, 39(3), 485-511. Ansell, C., Reckhow, S., & Kelly, A. 2009. “How to Reform a Reform Coalition: Outreach, Agenda Expansion, and Brokerage in Urban School Reform.” Policy Studies Journal, 37(4), 717-743. Babon, A., McIntyre, D., Gowae, G. Y., Gallemore, C., Carmenta, R., Di Gregorio, M., & Brockhaus, M. 2014. “Advocacy coalitions, REDD+, and forest governance in Papua New Guinea: how likely is transformational change?” Ecology and Society, 19(3), 16. Bandelow, N.C. and Kundolf, S. 2011. "Belief Systems and the emergence of advocacy coalitions in nascent subsystems: A case study of the European GNSS program Galileo.” German Policy Studies, 7(2): 113. Baumann, C., & White, S. 2015. “Collaborative Stakeholder Dialogue: A Catalyst for Better Transport Policy Choices.” International Journal of Sustainable Transportation, 9(1), 30-38. Baumgartner, F. R., & Jones, B. D. 1993. Agendas and Instability in American Politics. University of Chicago Press. Beverwijk, J., Goedegebuure, L., & Huisman, J. 2008. “Policy change in nascent subsystems: Mozambican higher education policy 1993-2003.” Policy Sciences, 41(4), 357-377. Blatter, J. 2009. “Performing Symbolic Politics and International Environmental Regulation: Tracing and Theorizing a Causal Mechanism beyond Regime Theory.” Global Environmental Politics, 9(4), 81-110. Breton, E., Richard, L., Gagnon, F., Jacques, M., & Bergeron, P. 2008. “Health promotion research and practice require sound policy analysis models: The case of Quebec's Tobacco Act.” Social Science & Medicine, 67(11), 1679-1689. Bukowski, J. 2007. “Spanish water policy and the national hydrological plan: An advocacy coalition approach to policy change.” South European Society and Politics, 12(1), 39-57. Cairney, P. 2007. “A ‘Multiple Lenses’ Approach to Policy Change: The Case of Tobacco Policy in the UK.” British Politics, 2(1): 45-68. deLeon, Peter. 1999. "The Stages to the Policy Process." In Theories of the Policy Process, Edited by P.A. Sabatier. Boulder, CO: Westview Press, 19-32.
Policy Change: An Advocacy Coalition Perspective
29
Diaz-Kope Luisa M. John R. Lombard, Katrina Miller-Stevens. 2013. "A Shift in Federal Policy Regulation of the Automobile Industry: Policy Brokers and the ACF" Politics & Policy, 41(4):563-587. Dougherty, K. J., Nienhusser, H. K., & Vega, B. E. 2010. “Undocumented Immigrants and State Higher Education Policy: The Politics of In-State Tuition Eligibility in Texas and Arizona.” Review of Higher Education, 34(1), 123-173. Dougherty, K. J., Natow, R. S., Bork, R. H., Jones, S. M., & Vega, B. E. 2013. “Accounting for Higher Education Accountability: Political Origins of State Performance Funding for Higher Education.” Teachers College Record, 115(1), 50. Dressel, B. 2012. “Targeting the Public Purse: Advocacy Coalitions and Public Finance in the Philippines.” Administration & Society, 44(6), 65S-84S. Ellison, B.A. 1998. “The Advocacy Coalition Framework and Implementation of the Endangered Species Act: A Case Study in Western Water Politics.” Policy Studies Journal 26: 11–29. Ellison, B. A., & Newmark, A. J. 2010. “Building the Reservoir to Nowhere: The Role of Agencies in Advocacy Coalitions.” Policy Studies Journal, 38(4), 653-678. Feindt, P. H. 2010. “Policy-Learning and Environmental Policy Integration in the Common Agricultural Policy, 1973-2003.” Public Administration, 88 (2): 296-314. Fischer, M. 2014. “Coalition Structures and Policy Change in a Consensus Democracy.” Policy Studies Journal, 42(3), 344-366. Frahsa, A., Rutten, A., Roeger, U., Abu-Omar, K., & Schow, D. 2014. “Enabling the powerful? Participatory action research with local policymakers and professionals for physical activity promotion with women in difficult life situations.” Health Promotion International, 29(1), 171-184. Han, H. J., Swedlow, B., & Unger, D. 2014. “Policy Advocacy Coalitions as Causes of Policy Change in China? Analyzing Evidence from Contemporary Environmental Politics.” Journal of Comparative Policy Analysis, 16(4), 313-334. Heikkila, T., Pierce, J. J., Gallaher, S., Kagan, J., Crow, D. A., & Weible, C. M. 2014. “Understanding a Period of Policy Change: The Case of Hydraulic Fracturing Disclosure Policy in Colorado.” Review of Policy Research, 31(2), 65-87. Heinmiller, T.B. 2013 "Advocacy Coalitions and the Alberta Water Act." Canadian Journal of Political Science, 46(3): 525-547.
Policy Change: An Advocacy Coalition Perspective
30
Hersperger, A. M., Franscini, M. P. G., & Kubler, D. 2014. “Actors, Decisions and Policy Changes in Local Urbanization.” European Planning Studies, 22(6), 1301-1319. Hirsch, R, Baxter, J, & Brown, C. 2010. “The importance of skillful community leaders: understanding municipal pesticide policy change in Calgary and Halifax.” Journal of Environmental Planning and Management, 53(6), 743 – 757. Hirschi, C., & Widmer, T. 2010. “Policy Change and Policy Stasis: Comparing Swiss Foreign Policy toward South Africa (1968-94) and Iraq (1990-91).” Policy Studies Journal, 38(3), 537-563. Ingold, K. 2011. “Network Structures within Policy Processes: Coalitions, Power, and Brokerage in Swiss Climate Policy.” Policy Studies Journal, 39(3), 435-459. Jang, J., Kim, S., & Han, C. 2010. “Advocacy Coalitions in Regulating Big Business in South Korea: Change of Chaebol's Holding Company Policy.” Korea Observer, 41(2), 161-188. Jang, S., Weible, C.M., and Park, K. 2016. "Policy processes in South Korea through the lens of the Advocacy Coalition Framework." Journal of Asian Public Policy: 1-17. DOI: 10.1080/17516234.2016.1201877 Jenkins-Smith, H., & Sabatier, P.A. 1993. “The Dynamics of Policy-Oriented Learning.” In Policy Change and Learning: An advocacy coalition framework, edited by P.A. Sabatier and H. Jenkins-Smith. Boulder, CO: Westview Press, 41-58. Jenkins-Smith, H.C., Nohrstedt, D., Weible, C.M., & Sabatier, P.A. 2014. “The Advocacy Coalition Framework: Foundations, Evolution, and Ongoing Research.” In Theories of the Policy Process, Third Edition edited by Paul A. Sabatier and Christopher M. Weible. Boulder, CO: Westview Press, 183-223. Johnson, D. B., Payne, E. C., McNeese, M. A., & Allen, D. 2012. “Menu-Labeling Policy in King County, Washington.” American Journal of Preventive Medicine, 43(3), S130-S135. Karapin, R. 2012. “Explaining Success and Failure in Climate Policies Developing Theory through German Case Studies.” Comparative Politics, 45(1), 46-68. Kettell, S., & Cairney, P. 2010. “Taking the power of ideas seriously - the case of the United Kingdom's 2008 Human Fertilisation and Embryology Bill.” Policy Studies, 31(3), 301-317. Kim, P. S. 2012. “Advocacy Coalitions and Policy Change: The Case of South Korea's Saemangeum Project.” Administration & Society, 44(6), 85S-103S. Kingiri, A. N. 2011. “Conflicting advocacy coalitions in an evolving modern biotechnology regulatory subsystem: policy learning and influencing Kenya's regulatory policy process.” Science and Public Policy, 38(3), 199-211.
Policy Change: An Advocacy Coalition Perspective
31
Kuebler, D. 2007. “Understanding the recent expansion of Swiss family policy: An idea-centred approach.” Journal of Social Policy, 36, 217-237. Kwon, H. J. 2007. “Advocacy coalitions and health politics in Korea.” Social Policy & Administration, 41(2), 148-161. Lacy, S., and Riffe, D. 1996. "Sampling Error and Selecting Intercoder Reliability Samples for Nominal Content Categories." Journalism & Mass Communication Quarterly, 73 (4): 963–73. Landis, J. R., & Koch, G. G. 1977. “The Measurement of Observer Agreement for Categorical Data.” Biometrics, 33(1), 159–174. Leifeld, P. 2013. “Reconceptualizing Major Policy Change in the Advocacy Coalition Framework: A Discourse Network Analysis of German Pension Politics.” Policy Studies Journal, 41(1), 169-198. Ley, A. J., & Weber, E. 2014. “Policy Change and Venue Choices: Field Burning in Idaho and Washington.” Society & Natural Resources, 27(6), 645-655. Li, W. X. 2012. “Advocating Environmental Interests in China.” Administration & Society, 44(6), 26S-42S. Lodge, M., & Matus, K. 2014. “Science, Badgers, Politics: Advocacy Coalitions and Policy Change in Bovine Tuberculosis Policy in Britain.” Policy Studies Journal, 42(3), 367-390. Lombard, M., Snyder-Duch, J., and Bracken, C.C. 2002. "Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability." Human Communication Research, 28 (4): 587–604. Mailand, M. 2010. “The common European flexicurity principles: How a fragile consensus was reached.” European Journal of Industrial Relations, 16(3), 241-257. Marfo, E., & McKeown, J. P. 2013. “Negotiating the supply of legal timber to the domestic market in Ghana: Explaining policy change intent using the Advocacy Coalition Framework.” Forest Policy and Economics, 32, 23-31. Miller, E. A. 2011. “Repealing Federal Oversight of State Health Policy: Lessons from the Boren Amendment.” Review of Policy Research, 28(1), 5-23. Montefrio, M. J. F. 2014. “State versus Indigenous Peoples' Rights: Comparative Analysis of Stable System Parameters, Policy Constraints and the Process of Delegitimation.” Journal of Comparative Policy Analysis, 16(4), 335-355.
Policy Change: An Advocacy Coalition Perspective
32
Nedergaard, P. 2008. “The Reform of the 2004 Common Agricultural Policy: An Advocacy Coalition Explanation.” Policy Studies, 29(2): 179-195. Ness, E. C. 2010. “The Politics of Determining Merit Aid Eligibility Criteria: An Analysis of the Policy Process.” Journal of Higher Education, 81(1), 33-60. Neville, J. 2012. “Explaining Local Authority Choices on Public Hospital Provision in the 1930s: A Public Policy Hypothesis.” Medical History, 56(1), 48-71. Nohrstedt, D. 2008. “The politics of crisis policymaking: Chernobyl and Swedish nuclear energy policy.” Policy Studies Journal, 36(2), 257-278. Nohrstedt, D. 2010. “Do Advocacy Coalitions Matter? Crisis and Change in Swedish Nuclear Energy Policy.” Journal of Public Administration Research and Theory, 20(2), 309-333. Nohrstedt, D. 2011. “Shifting Resources and Venues Producing Policy Change in Contested Subsystems: A Case Study of Swedish Signals Intelligence Policy.” Policy Studies Journal, 39(3), 461-484. Nohrstedt, D. 2013. “Advocacy Coalitions in Crisis Resolution: Understanding Policy Dispute in The European Volcanic Ash Cloud Crisis.” Public Administration, 91(4), 964-979. Olsson, J. 2009. “The power of the inside activist: Understanding policy change by empowering the Advocacy Coalition Framework (ACF).” Planning Theory and Practice. 10(2): 167-187. Parrish, R. 2008. "Access to Major Events on Television under European Law." Journal of Consumer Policy, 31(1): 79-98. Parsell, C., Fitzpatrick, S., & Busch-Geertsema, V. 2014. “Common Ground in Australia: An Object Lesson in Evidence Hierarchies and Policy Transfer.” Housing Studies, 29(1), 69-87. Penning-Rowsell, E. C., Priest, S., & Johnson, C. 2014. “The evolution of UK flood insurance: incremental change over six decades.” International Journal of Water Resources Development, 30(4), 694-713. Pierce, J.J., and C.M. Weible. 2016. “Advocacy Coalition Framework”. In American Governance, edited by Stephen L. Schechter, Thomas S. Vontz, Thomas A. Birkland, Mark A. Graber, and John J. Patric. Farmington Hills, MI: Gale, Cengage Learning, 22-23. Pierce, J.J., Peterson, H.L., Jones, M.D., Garrard, S., and Vu, T. 2016. “There and Back Again: A Tale of the Advocacy Coalition Framework.” Paper Presented at the Annual Midwest Political Science Association Conference.
Policy Change: An Advocacy Coalition Perspective
33
Pollak, M., Phillips, S. J., & Vajjhala, S. 2011. “Carbon capture and storage policy in the United States: A new coalition endeavors to change existing policy.” Global Environmental Change-Human and Policy Dimensions, 21(2), 313-323. Pralle, S.B. (2003). “Venue Shopping, Political Strategy, and Policy Change: The Internationalization of Canadian Forest Advocacy.” Journal of Public Policy, 23(3), 233-260. Quaglia, L. 2012. “The 'Old' and 'New' Politics of Financial Services Regulation in the European Union.” New Political Economy, 17(4), 515-535. Riffe, D., Lacy, S., and Fico, F. 2005. Analyzing Media Messages: Using Quantitative Content Analysis in Research. New York: Routledge. Rossegger, U., & Ramin, R. 2013. “Explaining the ending of Sweden's nuclear phase-out policy: a new approach by referring to the advocacy coalition framework theory.” Innovation-the European Journal of Social Science Research, 26(4), 323-343. Sabatier, P.A. 1986. "Top-down and bottom-up approaches to implementation research: a critical analysis and suggested synthesis." Journal of Public Policy, 6 (1): 21-48. Sabatier, P.A. 1988. "An advocacy coalition framework of policy change and the role of policy-oriented learning therein." Policy sciences 21 (2-3): 129-168. Sabatier, P. A. 1998. “The advocacy coalition framework: revisions and relevance for Europe.” Journal of European public policy, 5(1), 98-130. Sabatier, P.A., and Jenkins-Smith, H. (eds). 1993. Policy Change and Learning: An advocacy coalition framework. Boulder, CO: Westview Press. Sabatier, P.A., and Jenkins-Smith, H. 1999. "The Advocacy Coalition Framework: An Assessment." In Theories of the Policy Process, Edited by Paul A. Sabatier, 117-166. Boulder, CO: Westview Press. Sabatier, P.A., and Weible, C.M. 2007. “The Advocacy Coalition Framework: Innovations and Clarifications.” In Theories of the Policy Process, 2nd edition, edited by P.A. Sabatier. Boulder, CO: Westview Press, 189–222. Schilling, J. and Keyes, S.D. 2008. “The Promise of Wisconsin's 1999 Comprehensive Planning Law: Land-Use Policy Reforms to Support Active Living.” Journal Politics, Policy and Law, 33(3): 455-496. Schneider, A., & Ingram, H. 1993. “Social construction of target populations: Implications for politics and policy.” American political science review, 87(2), 334-347.
Policy Change: An Advocacy Coalition Perspective
34
Schroer, A. 2014. “Lessons Learned? German Security Policy and the War in Afghanistan.” German Politics, 23(1-2), 78-102. Sloboda, M., Szabo-Gilinger, E., Vigers, D., Simicic, L. 2010. "Carrying out a language policy change: Advocacy coalitions and the management of the Linguistic Landscape." Current Issues in Language Planning. 11(2): 95-113. Smith, M. P. 2009. “Finding Common Ground: How Advocacy Coalitions Succeed in Protecting Environmental Flows.” Journal of the American Water Resources Association, 45(5), 1100-1115. Stensdal, I. 2014. “Chinese Climate-Change Policy, 1988-2013: Moving On Up.” Asian Perspective, 38(1), 111-135. Stich, B. 2008. “Using the Advocacy Coalition Framework to Understand Freight Transportation Policy Change.” Public Works Management & Policy, 13(1): 62-74. Van den Bulck, H., & Donders, K. 2014. “Of discourses, stakeholders and advocacy coalitions in media policy: Tracing negotiations towards the new management contract of Flemish public broadcaster VRT.” European Journal of Communication, 29(1), 83-99. Van Gossum, P., Ledene, L., Arts, B., De Vreese, R., & Verheyen, K. 2008. “Implementation failure of the forest expansion policy in Flanders (Northern Belgium) and the policy learning potential.” Forest Policy and Economics, 10(7-8), 515-522. Weber, M., Driessen, P. P. J., Schueler, B. J., & Runhaar, H. A. C. 2013. “Variation and stability in Dutch noise policy: an analysis of dominant advocacy coalitions.” Journal of Environmental Planning and Management, 56(7), 953-981. Weible, C.M. 2007. “An Advocacy Coalition Framework Approach to Stakeholder Analysis: Understanding the Political Context of California Marine Protected Area Policy.” Journal of Public Administration Research and Theory 17(1): 95-117. Weible, C. M., Sabatier, P. A., & McQueen, K. 2009. “Themes and variations: Taking stock of the advocacy coalition framework.” Policy Studies Journal, 37(1), 121-140. Weible, C.M., Heikkila, T., deLeon, P., and Sabatier, P.A. 2012. “Understanding and Influencing the Policy Process.” Policy Sciences, 45: 1-21. Weible, C.M., and Nohrstedt, D. 2012. “The Advocacy Coalition Framework: Coalitions, Learning, and Policy Change.” In Handbook of Public Policy, edited by E. Araral, S. Fritzen, M. Howlett, M. Ramesh, and X. Wu. New York: Routledge, 125–137.
Policy Change: An Advocacy Coalition Perspective
35
Winkel, G., & Sotirov, M. 2011. “An obituary for national forest programmes? Analyzing and learning from the strategic use of "new modes of governance" in Germany and Bulgaria.” Forest Policy and Economics, 13(2), 143-154.
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Appendix. ACF Policy Change Codebook
Coding Item Examples and Coding Instructions
Round 1 Coding
1. Coder Initials Initials JP
2. Inter-coder initials Initials JP
3. Doc ID # 001, 002, 957
4. Full Citation APA format citation
5. First Author last name and first name initial Nohrstedt, D ; Nicholson-Crotty, S.
6. University or Organization of first author Seattle University
7. Country of first author US; Australia
8. Total Number of authors 3
9. Year of Publication 2007
10. Journal Policy Studies Journal
11. Does the title and abstract use one or more of the following key words a total of two or more times: “coalition” “learn” OR “advocacy”
If 1 or fewer = 0, If 2 or more = 1
12. Number of citations of foundation documents found in the TEXT including footnotes (Sabatier 1986, Sabatier 1988 (Policy Sciences), Sabatier 1998, Sabatier and Jenkins-Smith 1993, Sabatier and Jenkins-Smith 1999, OR Sabatier and Weible 2007)
If 1 or fewer = 0, If 2 or more = 1
13. Total combination of codes 11+12 0, 1, 2
14. If 2 = Application, If 1 “questionable” read further Application = 1, Not = 0
15. Give your opinion, was the article an application of the ACF? An application should have data and/or case study, be about a topic, utilize the concepts of the ACF such as coalitions, policy change, and or learning, may cite Jenkins-Smith or Weible but not Mazmanian, should not be about implementation
0 = No, 1 = Yes
Round 2 Coding
16. Multiple ID # If there are multiple cases of policy change or no change, then each of these should be coded independently.
17. Does the case describe/explain a policy change? 0 = No, 1 = Yes
18. Does the case describe/explain major policy change? Must explicitly state as “major”
0 = No, 1 = Yes
19. Does the article describe/explain minor policy change? Must explicitly state as “minor”
0 = No, 1 = Yes
20. Superior Jurisdiction (SJ). Was policy change imposed by a superior jurisdiction?
0 = No, 1 = Yes
21. External events (EE). Were significant perturbations 0 = No, 1 = Yes
Policy Change: An Advocacy Coalition Perspective
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external to the policy subsystem necessary for policy change? External shocks include events outside of the subsystem and out of the control of subsystem actors. They involve changes in socioeconomic conditions, elections or regime change, outputs from other subsystems, and extreme events such as crises.
22. Internal events (IE). Were significant perturbations internal to the policy subsystem necessary for policy change? Internal events occur internal to the subsystem and are likely affected by policy actors. Types include policy failure, crises, scandals, etc.
0 = No, 1 = Yes
23. Negotiation (N). Was a negotiated agreement between coalitions necessary for policy change?
0 = No, 1 = Yes
24. Learning (L). These are alterations in the concepts and/or assumptions about policies or problems by subsystem actors. Generally, this is the result of new information such as a policy analysis being presented. Make sure that learning is explicitly stated as a necessary function of policy change.
0 = No, 1 = Yes
25. New dominant coalition (NDC). Explicit and necessary
0 = No, 1 = Yes
26. Redistribution of resources among or between coalitions (R). Explicit and necessary
0 = No, 1 = Yes
27. Opening and/or closing of policy venues (V). Explicit and necessary
0 = No, 1 = Yes
28. Minority coalition mobilization (MM). Explicit and necessary
0 = No, 1 = Yes
29. Belief change among dominant coalition (BDC). Explicit and necessary
0 = No, 1 = Yes
30. Belief change among minority coalition (BMC). Explicit and necessary
0 = No, 1 = Yes
31. Belief confirmation among dominant coalition (CDC). Explicit and necessary
0 = No, 1 = Yes
32. Belief confirmation among minority coalition (CMC). Explicit and necessary
0 = No, 1 = Yes
33. Changes in strategies among dominant coalition (SDC). Explicit and necessary
0 = No, 1 = Yes
34. Changes in strategies among minority coalition (SMC). Explicit and necessary
0 = No, 1 = Yes
35. Hurting stalemate between coalitions (HS). Explicit and necessary
0 = No, 1 = Yes
36. Notes