Introduction Advocacy Coalition is a policy framework which arose out of a need to address limitations of the stages heuristic of the policy process. The Advocacy Coalition framework (ACF) is built on a set of assumptions and highlights policy change as a function of: the interaction of competing advocacy coalitions within a policy subsystem; changes external to the subsystem; the effects of relatively stable system parameters. This work is an application of ACF, as an analytical tool to the Avian Influenza disease control policy in Australia.
It traces the political context surrounding the emergence of stakeholder groups and identifies the impact of current policies on principles of equity and social justice. In closing alternative policy strategies and their benefits are discussed. Public Policy Development Selected Theoretical Framework: Advocacy Coalition Framework The Advocacy Coalition Framework (ACF) as a theoretical tool of the policy process provides a framework which explains how interested actors/ stakeholders interact to influence emergence of a public policy or policy change within a specific policy subsystem, over time.
The ACF was developed to simplify the complexity of interacting issues such as learning, belief, policy change and role of scientific and technical information, in policy making (Weiber, Sabatier & McQueen, 2009). In response to what was perceived as limitations in the policy process literature, Sabatier and Jenkins-Smith, created the ACF in the late 1980’s.
These limitations included the inadequacy of stages heuristic as causal theory of the policy process, the absence of and poignant need for system-based theories of policymaking and thirdly, a need to place scientific and technical information centrally in the policy process (Weiber, Sabatier & McQueen 2009). Within the ACF, policy communities are articulated in terms of beliefs; as such policies are the product of the belief systems of the actors concerned by a given policy subsystem.
The important structures – advocacy coalitions –are modeled as flowing from the bonds and relationships of actors who share similar values and beliefs. These coalitions, which may be tightly or loosely coupled, are comprised of government agencies and officials, interest groups, associations, think tanks, academics, academics, persons of the media, and prominent individuals who more or less share a global view and agree generally on policy solutions (Lindquist, 2001). Contextually, within the ACF policies emerge from numerous confrontations and negotiations between different coalitions of actors in the subsystem.
Articulating the ACF and the Avian Influenza Policy Subsystem The ACF frequently seeks to explain stakeholder behavior and policy outcomes in intense political conflicts over periods of a decade or more. According to Lindquist (2001), any truly political theory of the policy process must account for the fact that political actors engage in the policy process not only to respond to perceived social problems, but also to advance their own political interests and careers under the prevailing socio-political climate of the particular period.
If we couple the foregoing with the central assumption of the ACF that identifies beliefs as the causal generator of political behavior (Weiber, Sabatier & McQueen 2009), it becomes manifest that the ACF framing of policies proceeds within the following premise, amongst others, that there exists: (i) identifiable stakeholder groups; (ii) possibly current policy/ policies; and (iii) prevailing political context/ environment.
Australian Avian Influenza Policy Subsystem (i) Stakeholder groups: As rightly pointed out by Weible (2006) stakeholder analysis and identification helps policy makers conceptualize actors within and the dynamics of a policy subsystem. Weible (2006) instructs that to properly identify relevant stakeholders there was need to first delineate the most useful unit of analysis – the policy subsystem – as recognized by the ACF.
Further to this, supplying answers to such questions as: what interests and beliefs are held by visible actors within the subsystem; what critical resource (who controls such) are available within the subsystem; with whom do identified actors align to form coalitions; what strategies and venues do actors use to achieve their objectives? , clearly defines stakeholders within the policy subsystem being evaluated as modeled by the ACF.
A parliamentary research note aptly titled: Avian Influenza – is Australia a sitting Duck (Parliamentary Library, 2004) gives indication of stakeholders involved in the Australian Avian influenza policy subsystem and provides classical instance of the multiple stakeholders usually involved in an AC framework (Schlager & Blomquist, 1996) .
The document gives a global view of the implicated stakeholders as including the Commonwealth, states, territories and local government departments of Health; Industry, tourism & resources; Agriculture, fisheries and forestry, non governmental organizations (like Public Health Association of Australia, National Tourism Alliance, Australian Safety and Compensation Council), Airports and Border agencies, Poultry-, and Agric- businesses and industry, Animal Health Australia , as well as professional bodies (Australian Medical Council, Pharmaceutical Society of Australia and Australian Veterinary Council).
This agrees with Schlager & Blomquist (1996) opinion that: “a policy subsystem consists of actors from public and private organizations who are actively concerned with a policy problem”. In their analysis of contemporary Australian newspaper coverage of the threat of pandemic AI in humans, Stephenson & Jamieson (2009) suggested that newspaper accounts invokes a specific form of nation building; thereby implicating the media as stakeholders, also. ii) Existing Policies: Australia’s health policy regarding avian influenza is hinged on its (AI) lethality and significance from the perspective of production and trade of poultry and poultry products and possible transmission to humans (DAFF, 2008). AI is a highly contagious viral infection. According to the WHO (2010), the strain of avian influenza that has emerged from Asia and become widespread internationally since 2003 is classified as an H5N1 AI virus. H5N1 has become infamous globally due not only to its rapid and diverse international spread, but its ability to infect and cause fatalities in humans.
As such the existing Australian AI policy targets to eradicate the disease in the shortest possible period, while limiting the risk of human infection and minimizing economic impact, by implementing the following strategies: • Stamping out: destruction of all birds on infected premises (IPs) where there is clinical disease or evidence of active infection with HPAI virus, and the sanitary disposal of destroyed birds and contaminated avian products to remove the source of infection; • Pre-emptive slaughter: of birds possibly on other premises, depending on information derived from tracing, surveillance and study of the behaviour of he disease; • Quarantine and movement: controls on birds, avian products and associated items in declared areas to prevent spread of infection • Decontamination: of facilities, products and associated items to eliminate the virus on IPs and to prevent spread in declared areas; • Tracing and surveillance: to determine the source and extent of infection, and to establish proof of freedom from the disease; • Enhanced biosecurity: at poultry establishments and premises holding cage or zoo birds; • Zoning and compartmentalization: to define infected and disease-free areas; • Public awareness campaign: to communicate risk and promote cooperation from industry, zoos, cage bird owners and the community; and • Protection of public health: by requiring that personnel engaged in eradication activities be vaccinated (with the currently available human vaccine), be treated with antivirals (if appropriate) and wear protective clothing. Vaccination: may be considered if an outbreak of HPAI is likely to spread or has spread out of control. (iii) Political context: Breton et al (2008) explain that the ACF as a model allows coalitions to mobilize and put (especially political) resources to use in order to dominate their environment. The 2004 Liberal election victory returned incumbent John Howard as prime minister and saw continuation of Tony Abbott as health minister. Both political actors viewed decisive action on the AI threat (to public health and economy) as an opportunity to demonstrate the government’s capacity to offer security and safety; thus proving its competence and as justification for its recent reelection (Stephenson & Jamieson, 2009).
This contemporary political posture of the government at the time and its focus on Australia’s increased role and visibility in Asia- Pacific’s regional economic and health issues provides the political backdrop that framed the extensive AI policy development and the financial commitment to its management (Stephenson & Jamieson, 2009; Kelton, 2006). Review of Current AI Policy The Australian AI policy situation has seen a coordination of efforts by the Commonwealth, states and territories and a response approach that can be largely considered from six fronts (DAFF, 2008). Firstly is Border security: Australian animal health and quarantine services continue to monitor and strengthen biosecurity at airports and seaports; next is the
Whole-of-Government approach: through implementation of the National Action Plan for Human Influenza Pandemic and a concerted collaboration of government agencies (Department of Agriculture, Fisheries and Forestry, Food, Food Standards Australia New Zealand, Foreign Affairs and Trade, AusAID, Environment) there is an assured preparedness in the event of any human-to-human transmission of AI; Government-Industry liaison: through Animal Health Australia (AHA) the not-for-profit public company established by the Australian, state and territory governments and major national livestock industry organizations was formed to reduce the risk of entry and spread of emergency animal diseases; simulation exercises: a national simulation (Exercise Eleusis ’05) was held in December 2005 to evaluate national capability (industry and government) to manage an outbreak of AI; finally is Emergency communication arrangements, Education and Awareness: this three-pronged information based response directs adequate, relevant and accurate resources to the poultry industry and also the Australia public while Effectiveness of Policies Evaluation of effectiveness of AI control policy calls for revisit to some of ACF’s hypothetical assumptions and concepts. ACF highlights multiple major actors, and that the policy change process normally occurs over a period of a decade or more. Sabatier, (1988) opines that this is to lend credence and bring variety to roles in the generation, dissemination and evaluation of policy ideas.
In this light, the AI control policy development in Australia transcended actors within administrative agencies and legislative committees to include journalists, researchers, business concerns, farmers, policy analysts and interest groups and; continued from 1993 through 2003 (Asian pandemic), with technical changes being made till date. Another primary assumption of the ACF regarding an effective policy change is that stakeholders should primarily be motivated to convert their (policy-core) beliefs into actual policy (Schlager and Blomquist 1996). The primacy of core issues and beliefs of stakeholders in the AI subsystem is appreciated from the stance that agriculture (and by involvement poultry) is a major industry in Australia and that the nation is dependent on products, revenue, and employment in this sector (Ungerera & Rogersa, 2006).
An interesting postulation of the ACF is that more often than not policy or policy change is affected by external perturbations (Weiber, Sabatier & McQueen 2009; Schlager and Blomquist 1996; Sabatier, 1988). Employing the interaction of the subsystem with external perturbations (in this case changes to governing coalitions) as parameter for analyzing effectiveness (), it is found that the 2004 election of a liberal government which placed in office a health minister who was ready to champion wider latitude AI policy changes and press parliament for drastic and expedited action (Stephenson & Jamieson, 2009; Kelton, 2006; Parliamentary Library, 2004).
Overall the Australian AI control policy was largely effective as evidenced by success of the partnership underlying the technical response embodied in the AUSVETPALN (AUSVETPALN, 2008) Policy Impact on Public Health Principles In Australia as in other parts of the world, public policy regarding AI has had serious implications for those who are economically poor, of socially subordinate class or directly involved in poultry farming. Uscher-Pines et al (2007) are of the view that pandemics such as AI have serious potential to exacerbate existing social and economic inequalities and there is need to consider a pandemic not only as a pressing public health issue, but also as an urgent matter of social justice.
The policy while having addressed special needs of disadvantaged groups with respect to public health communications through culturally appropriate communications in a variety of formats, including the translation of messages into multiple languages did not provide for social interventions to counteract possible prejudicial social stereotyping or stigmatization of at risk populations such as poultry farmers, recent migrants, indigenous people and healthcare workers (Stephenson & Jamieson, 2009; Uscher- Pines et al, 2007). Another sphere of the AI policy, with serious economic implications is the ‘global public good response’ aspect with reference to mass culling of chickens. Notwithstanding compensation, the possible catastrophic impact of this intervention remains and will largely depend on the alternative sources of income, if any, of the farmers (Scoones & Forster, 2008). Alternative Strategies Discuss: Alliances and Tensions The area of tension within the AI policy subsystem was relatively insignificant as stakeholders were more inclined to forge alliances to realize their objectives.
In quiescent subsystems there may only be a single coalition; never exceeding four coalitions – the number being limited by all the factors which push actors to coalesce if they are to form effective alliances/ coalitions and avoid defeat (Sabatier, 1988). Animal Health Australia (AHA) is the embodiment of stakeholder alliance efforts in the AI policy process. As an innovative partnership involving Commonwealth, state and territory governments, major livestock industries and other stakeholders it was instrumental in the development and channeling of initiatives culminating in the policy and disease strategy for the control and eradication of avian influenza (AUSVETPALN, 2008). Key to alliance formation is that stakeholders identify and utilize available resources (Weible, 2006).
Resources available and employed by AHA, include 1) Information (scientific and technical)- which it utilized to buttress its policy view and convince policy making sovereigns to support its position 2) Financial resources- it was able to bankroll sympathetic candidates, thereby gaining inside access to legislators and political appointees. It was also able to finance research and think tanks to generate information to influence the policy process 3) Skillful leadership- through skillful leadership it was able to articulate a coherent belief system strengthening resolve and focus while navigating the coalition toward policy victory (AHA, 2010). Policy Alternatives: Benefits and Potential Impact on Public Health Principles: Epidemiological Dynamics The international and Australian AI policy response has tended to assume an outbreak emergency/crisis response- focusing on diseased organism/area with disease control/eradication measures (Scoones & Forster, 2008).
These are fairly standardized, universal responses of plans, programmes, strategies backed up by protocols, manuals and regulations, and implemented globally by a technically-equipped and well resourced, international system. This modeling assumes that diseases spread in concentric circles, that borders of countries and districts do not matter and, if localized and eradicated/contained at source, a global pandemic can be prevented (Longini et al, 2006). Often, it is not always that simple. Complex disease dynamics imply that the what, when, pattern and impact of disease outbreaks is highly context specific requiring a deeper understanding of changing ecologies, demographies and socio-economic contexts – in particular, their interactions and dynamics in specific places (Scoones & Forster, 2008).
As noted by Scoones & Forster (2008 pp27) this field level understanding of dynamic contexts is what is startlingly absent in much of the work on avian influenza. There has been remarkably little detailed socio-ecological investigation of the dynamics of change; surely, understanding the underlying drivers of disease change – and the socio-ecological dynamics of emergence – must and ought to be part of any international and Australian policy response. Such a policy perspective, would cast the agenda wider; and as it focuses on socio-cultural-livelihood interactions would translate into gains for economically disadvantaged and socially stereotyped typed populations (Hewlett & Hewlett, 2007).
Usefulness of Theoretical Frameworks Theoretical frameworks, especially as it relates to Advocacy Coalition Framework provides a systematic depiction of a political context, for stakeholders involved in a policy conflict and/ or process. Through careful evaluation of a subsystem, involved stakeholders are able to identify the different categorizations of theirs and other’s policy (core) beliefs, alliances, usable resources, and accessible venues. Furthermore, use of theoretical frameworks as analytical tool helps to identify possible policy -preferences, and –problems and, the elucidation of approaches necessary to realizing objectives. Limitations of Theoretical Frameworks
In articulating the limitations of theoretical frameworks it is instructive the explanation by Maddison & Dennis (2009 pp100) that theoretical frameworks provides us with causal explanation of the complexities of how and why policies are or what relationships between variables drive the policy process. From this it is understood that theoretical frameworks, in general, are not able to provide us with an artificial construct of ideal-type situations to aid exploration of problems and forces that shape social processes. Insight into the possible limitations regarding ACF specifically is obtained from Weiber, Sabatier & McQueen (2009). These may include: bias toward pluralistic, democratic political system- with limited application in authoritarian regimes; no clear-cut definitive way of demonstrating link between external perturbation(s) and policy (change) as well as learning to policy change. Paul Forster References
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