Advocacy Coalition Framework, Higher Education
Controversy is a common feature in any organization and governing system where there exist divergent value orientations. A number of policy issues in higher education illustrate such controversies, including debates over securing and allocating funds, affordability, and rights to free speech. One approach for understanding these controversies is the Advocacy Coalition Framework (ACF, Jenkins-Smith et al. 2014).
Through 2016, there have been over 300 applications of the ACF. Most of these applications have focused on North American and Europe, with an increasing number of studies in other parts of the world. Just over half the applications of the ACF have been in environmental policy, followed by health and economic policy. There have been a handful of applications of the ACF to higher education policy issues, including Beverwijk et al. (2008), Shakespeare (2008), Dougherty et al. (2010), Ness (2010), and Balbachevsky (2015).
The scope of the ACF centers around three main areas: advocacy coalitions, policy-oriented learning, and policy change. Questions regarding advocacy coalitions typically ask: How do actors build and maintain coalitions? How are coalitions used to achieve policy objectives? What are the characteristics of coalitions? How do actors interact within and between coalitions? In terms of policy-oriented learning, common questions include: How do actors learn within and between coalitions? What impact does learning have on coalition stability? How do actors use information, and what types of information are used, in learning processes? Finally, standard questions about policy change and, conversely, policy stasis ask: What explains the likelihood of policy change? What types of policy change might occur over time? What are the conditions that lead to policy change? How does policy change impact stakeholders and the public?
There are several assumptions upon which the framework is built. First, the ACF aims to explain policy processes within a “subsystem.” The subsystem is the basic unit of analysis and bounds the realm of interpretation and application. A policy subsystem is defined by a policy topic, a geographical scope, and the actors who attempt to influence the processes within. Three characteristics bound a subsystem. First, within a subsystem there may be innumerable components that interact intentionally toward outputs and outcomes on a given policy topic. These components could be at the individual level, such as belief systems or resources, or they could be at the subsystem level, such as institutional (rules and norms), socioeconomic, and biophysical conditions. It is the intention of the framework to identify and clarify subsystem components to illuminate the policy process. Second, subsystems involve actors who are intentionally involved in the given policy topic; subsystems are not comprised of any and all actors who might be interested or even affected by a policy decision. Finally, the framework recognizes that subsystems are often nested and overlap and are not wholly independent.
The second assumption of the ACF is that individuals are boundedly rational and are motivated by their belief systems. The actors involved in the subsystem, who are by definition those regularly attempting to influence policy, are limited in their ability to process information. Consequently, they rely on their belief systems to guide decision-making. The three-tiered belief system is a central component of the framework. The first tier is comprised of “deep core beliefs,” stable normative and ontological foundations that are not policy specific. Rarely are there changes to deep core beliefs. The next tier of beliefs is “policy core beliefs,” which are bound to the policy subsystem. Policy core beliefs can be normative or may be more empirical. Normative policy core beliefs might consider value priorities and welfare of stakeholders within the subsystem. Empirical policy core beliefs might refer to the attributes of the severity of the problem and its causes or assess the impacts of the preferred solutions. The final tier of belief systems in the ACF are termed “secondary beliefs,” which usually refer to the individuals’ preferred instrumental means for achieving the goals of their policy core beliefs.
The next assumption of the ACF combines individuals within subsystems to form coalitions. Actors aggregate into one or more coalitions with the intent to influence subsystem affairs. There are numerous ways through which actors could form coalitions, but the ACF assumes that actors join together based on shared beliefs and coordination strategies. Ultimately, policy is conceptualized as a translation of the belief systems of the winning coalition(s). Importantly, policy is not static – it can shift over time as coalitional power waxes and wanes. This is related to another original assumption of the ACF: In order to fully understand policy processes, researchers should consider a long-term time perspective (10 years or more).
Finally, the ACF assumes that information, particularly scientific and technical information, is vital to understanding coalitions, coalitional behavior, and subsystem affairs. The ACF does not assume that individuals completely disregard evidence and rely on values when making decisions under cognitive constraints. The framework recognizes that individuals incorporate scientific and technical information into their belief systems. This information can be used to shape understandings of causes, problem attributes, and preferred solutions.
Groups of regularly participating actors who share beliefs and intentionally coordinate their actions to influence a policy subsystem comprise an advocacy coalition. There are five hypotheses within this theoretical emphasis.
Coalition Hypothesis 1
On major controversies within a policy subsystem when policy core beliefs are in dispute, the lineup of allies and opponents tends to be rather stable over periods of a decade or so.
Empirical work has largely confirmed Coalition Hypothesis 1. Coalitional stability does not demand that membership is static over time; it often changes. However, the basic composition of the coalition remains stable, as it is formed around shared beliefs, not individuals. Further work on Coalition Hypothesis 1 should explore the determinants of coalition defection and, in cases when coalition breakdown has occurred, determine why and how that disintegration happened and how the subsystem was impacted as a result.
Coalition Hypothesis 2
Actors within an advocacy coalition will show substantial consensus on issues pertaining to the policy core, although less so on secondary aspects.
Coalition Hypothesis 3
An actor (or coalition) will give up secondary aspects of her (its) belief system before acknowledging weaknesses in the policy core.
Actors need not share deep core beliefs, but they should at least agree on policy core beliefs. Empirical work on belief systems has considered the extent to which policy core and secondary beliefs must be shared to encourage the formation of an advocacy coalition. Coalition Hypotheses 2 and 3 have found only partial support thus far in empirical applications. This could be because the three-tiered belief system has not yet been standardized and thus differing approaches to operationalization and measurement have led to differing results. Since belief systems are central to coalition formation, it is essential to develop commonly understood concepts of the tripartite belief system.
Coalition Hypothesis 4
Within a coalition, administrative agencies will usually advocate more moderate positions than their interest group allies.
Coalition Hypothesis 5
Actors within purposive groups are more constrained in their expression of beliefs and policy positions than actors from material groups.
Coalition Hypotheses 4 and 5 have not been tested empirically to a great extent. In the applications that have tested Coalition Hypothesis 4, there has been mixed confirmation; to date, Coalition Hypothesis 5 has been tested only a limited number of times with promising indications of confirmation. However, much more work remains to be done on these hypotheses before confirmation or even partial support can be claimed.
An informal but increasingly influential hypothesis within the advocacy coalition emphasis tests the assumption that coalitions form on the basis of shared beliefs. The “Belief Homophily Hypothesis” has been largely confirmed, but researchers have found that coalition stability is contingent not only upon shared beliefs but also on other factors such as perceived influence of the actors within the coalition, coalition resources, and trust. Overall, empirical support for advocacy coalitions is strong for Coalition Hypothesis 1 and the Belief Homophily Hypothesis, mixed for Coalition Hypotheses 2 and 3, and underdeveloped for Coalition Hypotheses 4 and 5. This could reflect difficulties in testing these hypotheses, or it may be due to the lack of standardized definitions of belief systems.
There are four additional concepts that are influential in the study of advocacy coalitions. First, not all subsystems are characterized by coalitions that necessarily share power equally. There can be dominant and minority coalitions. A subsystem may be dominated by one coalition that retains the majority of control, while a minority coalition contests that dominance and searches for opportunities for influence. Second, coalition formation is contingent upon overcoming threats to collective action. Beyond shared beliefs, actors must intentionally choose to coordinate their actions into an advocacy coalition. How and why actors coordinate remains underdeveloped in the framework. Third, not all actors play equal roles within a coalition. Some actors are recognized as principle actors that may be more influential or active, and other actors serve as auxiliary actors who are perhaps not as regularly engaged in coalition activities. Finally, resources play an important role in determining the capacity of coalitions to engage in subsystem politics. Resources can constrain or enable advocacy coalitions to participate and influence subsystem affairs.
In higher education policy, Dougherty et al. (2010) applied the ACF and identified the structure and beliefs of coalitions to better understand the politics surrounding in-state tuition eligibility for undocumented students. Similarly, Balbachevsky (2015) used the ACF to understand fissures and points of compromise in the political landscape of Brazilian higher education by identifying key stakeholders and coalitional alliances.
Learning as a concept is important in policy processes; however, the concept is difficult to study, which has been reflected in the relatively limited number of empirical applications focusing on policy-oriented learning. Policy-oriented learning refers to changes that eventually become permanent in belief systems. Of interest to the ACF is how learning is associated with changes in belief systems and what information, specifically scientific and/or technical, is used to promote learning.
There are four explanations associated with learning. First, the attributes of the arenas or venues in which actors participate in the policy process occurs dictate the opportunities for learning. Attributes can be shaped by coalition opportunity structures, such as the openness of venues, or by short-term constraints and resources of actors. The second explanation for learning is associated with the level of conflict between coalitions. It has been theorized that there is an indirect relationship between the level of conflict between coalitions within a subsystem and the potential for cross-coalition learning. The higher the level of conflict within a subsystem, the lower the potential for cross-coalition learning. Third, the quality of information available within a given subsystem is directly related to the potential for policy-oriented learning. With higher levels of quality data comes increased potential for cross-coalition learning. Finally, some attributes of actors regularly involved in subsystem activities have been identified to have an impact on the potential for policy-oriented learning. When actors display extreme beliefs, it appears to be less likely that cross-coalition learning may occur. It has also been identified that an individual actor in the coalition may play a role as a policy broker who works to encourage learning and agreements among coalitions. These four explanations can be found within the five hypotheses related to policy-oriented learning in the ACF.
Learning Hypothesis 1
Policy-oriented learning across belief systems is most likely when there is an intermediate level of informed conflict between the two coalitions. This requires that (1) each have the technical resources to engage in the debate and (2) the conflict be between secondary aspects of one belief system and core elements of the other or, alternatively, between important secondary aspects of the two belief systems.
Learning Hypothesis 2
Policy-oriented learning across belief systems is most likely when there exists a forum that is (1) prestigious enough to force professionals from different coalitions to participate and (2) dominated by professional norms.
Learning Hypothesis 3
Problems for which accepted quantitative data and theory exist are more conducive to policy-oriented learning across belief systems than those in which data and theory are generally qualitative, quite subjective, or altogether lacking.
Learning Hypothesis 4
Problems involving natural systems are more conducive to policy-oriented learning across belief systems than those involving purely social or political systems because, in the former, many of the critical variables are not themselves active strategists and because controlled experimentation is more feasible.
Learning Hypothesis 5
Even when accumulation of technical information does not change the views of the opposing coalition, it can have important impacts on policy – at least in the short run – by altering the views of policy brokers.
In a study of merit aid eligibility in Georgia, Ness (2010) found learning to be a factor in shaping beliefs and policy; more specifically, people learned about policy issues from other subsystems. Similarly, Shakespeare (2008) found that different information patterns affect belief systems in New York’s higher education system.
The final theoretical emphasis within the ACF focuses on policy change and pathways to policy change.
Policy Change Hypothesis 1
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.
Policy change can be either major or minor. The ACF theorizes that policy change can occur through an external event to the subsystem, such as a change in socioeconomic conditions or extreme events such as a crisis or disaster. Internal events to the subsystem can also lead to policy change by influencing beliefs and drawing attention to ongoing government policies. Policy-oriented learning has mostly been found to affect incremental or minor policy change. Negotiated agreements can emerge in various ways but largely dependent upon a hurting stalemate, when both sides of an issue no longer view the status quo as acceptable and have no other option but negotiation, and supported by institutions that encourage collaboration and cross-coalitional interactions. There has been significant empirical support for Policy Change Hypothesis 1.
There is a second hypothesis related to policy change. Policy Change Hypothesis 2 brings together coalition influence on the subsystem with propensity for major policy change as mitigated or encouraged by the hierarchical nature of jurisdictions. This hypothesis has found partial to somewhat strong support within empirical research applications, but the number of tests has been relatively few. Beverwijk et al. (2008) found that external system-wide changes in the political system affected policy change in reallocating authority to govern higher education in Mozambique.
Policy Change Hypothesis 2
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.
There is still great potential to improve knowledge about coalitions, learning, and policy change and to develop a better understanding of controversies in the area of higher education policy. In doing so, researchers can embrace a variety of methods, but these must be supported by clear and transparent definitions and measurements. Otherwise, the best lessons from these applications will not accumulate into a cohesive and cumulative body of knowledge.
- Balbachevsky, E. 2015. The role of internal and external stakeholders in Brazilian higher education. In Higher education in BRICS countries, 193–214. Dordrecht: Springer.Google Scholar
- Jenkins-Smith, H.C., D. Nohrstedt, C.M. Weible, and P.A. Sabatier. 2014. The advocacy coalition framework: Foundations, evolution, and ongoing research. In Theories of the policy process, ed. P.A. Sabatier and C.M. Weible, 3rd ed., 183–223. Boulder: Westview Press.Google Scholar