Please see the Policy Analysis in 750 words series overview before reading the summary. Please note that this is an edited book and the full list of authors (PDF) is here. I’m using the previous sentence as today’s excuse for not sticking to 750 words.
Our premise is that there is no single, let alone ‘one best’, way of conducting policy analyses (Thissen and Walker, 2013: 2)
Thissen and Walker (2013: 2) begin by identifying the proliferation of (a) policy analysts inside and outside government, (b) the many approaches and methods that could count as policy analysis (see Radin), and therefore (c) a proliferation of concepts to describe it.
Like Vining and Weimar, they distinguish between:
- Policy analysis, as the advice given to clients before they make a choice. Thissen and Walker (2013: 4) describe analysts working with a potential range of clients, when employed directly by governments or organisations, or acting more as entrepreneurs with multiple audiences in mind (compare with Bardach, Weimer & Vining, Mintrom).
- Policy process research, as the study of such actors within policymaking systems (see 500 and 1000).
Policy theory: implications for policy analysis
Policy process research informs our understanding of policy analysis, identifying what analysts and their clients (a) can and cannot do, which informs (b) what they should do.
As Enserink et al (2012: 12-3) describe, policy analysis (analysis for policy) will differ profoundly if the policy process is ‘chaotic and messy’ rather than ‘neat and rational’.
The range of policy concepts and theories (analysis of policy) at our disposal helps add meaning to policy analysis as a practice. Like Radin, Enserink et al trace historic attempts to seek ‘rational’ policy analysis then conclude that modern theories – describing policymaking complexity – are ‘more in line with political reality’ (2012: 13-6).
As such, policy analysis shifts from:
(a) A centralised process with few actors inside government, to (b) a messy process including many policymakers and influencers, inside and outside government
(a) Translating science into policy, to (b) a competition to frame issues and assess policy-relevant knowledge
(a) An ‘optimal’ solution from one perspective, to (b) a negotiated solution based on many perspectives (in which optimality is contested)
(a) Analysing a policy problem/ solution with a common metric (such as cost benefit analysis), to (b) developing skills relating to: stakeholder analysis, network management, collaboration, mediation or conflict resolution based on sensitivity to the role of different beliefs, and the analysis of policymaking institutions to help resolve fragmentation (2013: 17-34).
Their Table 2.1 (2012: 35) outlines these potential differences (pop your reading glasses on …. now!):
In many cases, the role of an analyst remains uncertain. If we follow the ACF story, does an analyst appeal to one coalition or seek to mediate between them? If we follow MSA, do they wait for a ‘window of opportunity’ or seek to influence problem definition and motivation to adopt certain solutions?
Policy Analysis: implications for policy theory
In that context, rather than identify a 5-step plan for policy analysis, Mayer et al (2013: 43-50) suggest that policy analysts tend to perform one or more of six activities:
- ‘Research and analyze’, to collect information relevant to policy problems.
- ‘Design and recommend’, to produce a range of potential solutions.
- ‘Clarify values and arguments’, to identify potential conflicts and facilitate high quality debate.
- ‘Advise strategically’, to help a policymaker choose an effective solution within their political context.
- ‘Democratize’, to pursue a ‘normative and ethical objective: it should further equal access to, and influence on, the policy process for all stakeholders’ (2013: 47)
- ‘Mediate’, to foster many forms of cooperation between governments, stakeholders (including business), researchers, and/ or citizens.
Styles of policy analysis
Policy analysts do not perform these functions sequentially or with equal weight.
Rather, Mayer et al (2013: 50-5) describe ‘six styles of policy analysis’ that vary according to the analyst’s ‘assumptions about science (epistemology), democracy, learning, and change’ (and these assumptions may change during the process):
- Rational, based on the idea that we can conduct research in a straightforward way within a well-ordered policy process (or modify the analysis to reflect limits to research and order).
- Argumentative, based on a competition to define policy problems and solutions (see Stone).
- Client advice, based on the assumption that analysis is part of a ‘political game’, and analysts bring knowledge of political strategy and policymaking complexity.
- Participatory, to facilitate a more equal access to information and debate among citizens.
- Process, based on the idea that the faithful adherence to good procedures aids high quality analysis (and perhaps mitigates an ‘erratic and volatile’ policy process)
- Interactive, based on the idea that the rehearsal of many competing perspectives is useful to policymaker deliberations (compare with reflexive learning).
In turn, these styles prompt different questions to evaluate the activities associated with analysis (2013: 56):
In relation to the six policy analysis activities,
- the criteria for good policy analysis include: the quality of knowledge, usefulness of advice to clients and stakeholders, quality of argumentation, pragmatism of advice, transparency of processes, and ability to secure a mediated settlement (2013: 58).
- The positive role for analysts includes ‘independent scientist’ or expert, ‘ethicist’, ‘narrator’, ‘counsellor’, ‘entrepreneur’,’ democratic advocate’, or ‘facilitator’ (2013: 59).
Further, their – rather complicated – visualisations of these roles (e.g. p60; compare with the Appendix) project the (useful) sense that (a) individuals face a trade-off between roles (even if they seek to combine some), and (b) many people making many trade-offs adds up to a complex picture of activity.
Therefore, we should bear in mind that
(a) there exist some useful 5-step guides for budding analyst, but
(b) even if they adopt a simple strategy, analysts will also need to find ways to understand and engage with a complex policymaking systems containing a huge number of analysts, policymakers, and influencers.
Policy Analysis styles: implications for problem definition and policy design
Thissen (2013: 66-9) extends the focus on policymaking context and policy analysis styles to problem definition, including:
- A rational approach relies on research knowledge to diagnose problems (the world is knowable, use the best scientific methods to produce knowledge, and subject the results to scientific scrutiny).
- A ‘political game model’ emphasises key actors and their perspectives, value conflicts, trust, and interdependence (assess the potential to make deals and use skills of mediation and persuasion to secure them).
These different starting points influence they ways in which analysts might take steps to identify: how people perceive policy problems, if other definitions are more useful, how to identify a problem’s cause and effect, and the likely effect of a proposed solution, communicate uncertainty, and relate the results to a ‘policy arena’ with its own rules on resolving conflict and producing policy instruments (2013: 70-84; 93-4).
Similarly, Bots (2013: 114) suggests that these styles inform a process of policy design, constructed to change people’s minds during repeated interactions with clients (such as by appealing to scientific evidence or argumentation).
Bruijn et al (2013: 134-5) situate such activities in modern discussions of policy analysis:
- In multi-centric systems, with analysts focused less on ‘unilateral decisions using command and control’ and more on ‘consultation and negotiation among stakeholders’ in networks.
- The latter are necessary because there will always be contestation about what the available information tells us about the problem, often without a simple way to negotiate choices on solutions.
- In relation to categories of policy problems, including
- ‘tamed’ (high knowledge/ technically solvable, with no political conflict)
- ‘untamed ethical/political’ (technically solvable, with high moral and political conflict)
- ‘untamed scientific’ (high consensus but low scientific knowledge)
- ‘untamed’ problems (low consensus, low knowledge).
Put simply, ‘rational’ approaches may help address low knowledge, while other skills are required to manage processes such as conflict resolution and stakeholder engagement (2013: 136-40)
Policy Analysis styles: implications for models
Part 2 of the book relates such styles (and assumptions about how ‘rational’ and comprehensive our analyses can be) to models of policy analysis. For example,
- Walker and van Daalen (2013: 157-84) explore models designed to compare the status quo with a future state, often based on the (shaky) assumption that the world is knowable and we can predict with sufficient accuracy the impact of policy solutions.
- Hermans and Cunningham (2013: 185-213) describe models to trace agent behaviour in networks and systems, and create multiple possible scenarios, which could help explore the ‘implementability’ of policies.
- Walker et al (2013: 215-61) relate policy analysis styles to how analysts might deal with uncertainty.
- Some models may serve primarily to reduce ‘epistemic’ uncertainty associated with insufficient knowledge about the future (perhaps with a focus on methods and statistical analysis).
- Others may focus on resolving ambiguity, in which many actors may define/ interpret problems and feasible solutions in different ways.
Overall, this book contains one of the most extensive discussions of 101 different technical models for policy analysis, but the authors emphasise their lack of value without initial clarity about (a) our beliefs regarding the nature of policymaking and (b) the styles of analysis we should use to resolve policy problems. Few of these initial choices can be resolved simply with reference to scientific analysis or evidence.