Tag Archives: policy analysts

Policy Analysis in 750 words: Classic 5-step advice

Policy analysis’ describes the identification of a policy problem and possible solutions.

Classic models of policy analysis are client-oriented. Most texts identify the steps that a policy analysis should follow, from identifying a problem and potential solutions, to finding ways to predict and evaluate the impact of each solution. Each text describes this process in different ways, as outlined in Boxes 1-5. However, for the most part, they follow the same five steps:

  1. Define a policy problem identified by your client.
  2. Identify technically and politically feasible solutions.
  3. Use value-based criteria and political goals to compare solutions.
  4. Predict the outcome of each feasible solution.
  5. Make a recommendation to your client.

Further, they share the sense that analysts need to adapt pragmatically to a political environment. Assume that your audience is not an experienced policy analyst. Assume a political environment in which there is limited attention or time to consider problems, and some policy solutions will be politically infeasible. Describe the policy problem for your audience: to help them see it as something worthy of their energy. Discuss a small number of possible solutions, the differences between them, and their respective costs and benefits. Keep it short with the aid of visual techniques that sum up the issue concisely, to minimise cognitive load and make the problem seem solvable.

Box 1. Bardach (2012) A Practical Guide for Policy Analysis

  1. ‘Define the problem’. Provide a diagnosis of a policy problem, using rhetoric and eye-catching data to generate attention.
  2. ‘Assemble some evidence’. Gather relevant data efficiently.
  3. ‘Construct the alternatives’. Identify the relevant and feasible policy solutions that your audience might consider.
  4. ‘Select the criteria’. Typical value judgements relate to efficiency, equity and fairness, the trade-off between individual freedom and collective action, and the extent to which a policy process involves citizens in deliberation.
  5. ‘Project the outcomes’. Focus on the outcomes that key actors care about (such as value for money), and quantify and visualise your predictions if possible.
  6. ‘Confront the trade-offs’. Compare the pros and cons of each solution, such as how much of a bad service policymakers will accept to cut costs.
  7. ‘Decide’. Examine your case through the eyes of a policymaker.
  8. ‘Tell your story’. Identify your target audience and tailor your case. Weigh up the benefits of oral versus written presentation. Provide an executive summary. Focus on coherence and clarity. Keep it simple and concise. Avoid jargon.

Box 2. Dunn (2017) Public Policy Analysis

  1. What is the policy problem to be solved? Identify its severity, urgency, cause, and our ability to solve it. Don’t define the wrong problem, such as by oversimplifying.
  2. What effect will each potential policy solution have? ‘Forecasting’ methods can help provide ‘plausible’ predictions about the future effects of current/ alternative policies.
  3. Which solutions should we choose, and why? Normative assessments are based on values such as ‘equality, efficiency, security, democracy, enlightenment’ and beliefs about the preferable balance between state, communal, and market/ individual solutions (2017: 6; 205).
  4. What were the policy outcomes? ‘Monitoring is crucial because it is difficult to predict policy success, and unintended consequences are inevitable (2017: 250).
  5. Did the policy solution work as intended? Did it improve policy outcomes? Try to measure the outcomes your solution, while noting that evaluations are contested (2017: 332-41).

Box 3. Meltzer and Schwartz (2019) Policy Analysis as Problem Solving

  1. ‘Define the problem’. Problem definition is a political act of framing, as part of a narrative to evaluate the nature, cause, size, and urgency of an issue.
  2. ‘Identify potential policy options (alternatives) to address the problem’. Identify many possible solutions, then select the ‘most promising’ for further analysis (2019: 65).
  3. Specify the objectives to be attained in addressing the problem and the criteria  to  evaluate  the  attainment  of  these  objectives  as  well as  the  satisfaction  of  other  key  considerations  (e.g.,  equity,  cost, equity, feasibility)’.
  4. ‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.
  5. ‘Arrive at a recommendation’. Make a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups (2019: 212).

Box 4. Mintrom (2012) Contemporary Policy Analysis

  1. ‘Engage in problem definition’. Define the nature of a policy problem, and the role of government in solving it, while engaging with many stakeholders (2012: 3; 58-60).
  2. ‘Propose alternative responses to the problem’. Identify how governments have addressed comparable problems, and a previous policy’s impact (2012: 21).
  3. ‘Choose criteria for evaluating each alternative policy response’. ‘Effectiveness, efficiency, fairness, and administrative efficiency’ are common (2012: 21).
  4. ‘Project the outcomes of pursuing each policy alternative’. Estimate the cost of a new policy, in comparison with current policy, and in relation to factors such as savings to society or benefits to certain populations.
  5. ‘Identify and analyse trade-offs among alternatives’. Use your criteria and projections to compare each alternative in relation to their likely costs and benefits.
  6. ‘Report findings and make an argument for the most appropriate response’. Client-oriented advisors identify the beliefs of policymakers and tailor accordingly (2012: 22).

Box 5 Weimer and Vining (2017) Policy Analysis: Concepts and Practice

  1. ‘Write to Your Client’. Having a client such as an elected policymaker requires you to address the question they ask, by their deadline, in a clear and concise way that they can understand (and communicate to others) quickly (2017: 23; 370-4).
  2. ‘Understand the Policy Problem’. First, ‘diagnose the undesirable condition’. Second, frame it as ‘a market or government failure (or maybe both)’.
  3. ‘Be Explicit About Values’ (and goals). Identify (a) the values to prioritise, such as ‘efficiency’, ‘equity’, and ‘human dignity’, and (b) ‘instrumental goals’, such as ‘sustainable public finance or political feasibility’, to generate support for solutions.
  4. ‘Specify Concrete Policy Alternatives’. Explain potential solutions in sufficient detail to predict the costs and benefits of each ‘alternative’ (including current policy).
  5. ‘Predict and Value Impacts’. Short deadlines dictate that you use ‘logic and theory, rather than systematic empirical evidence’ to make predictions efficiently (2017: 27)
  6. ‘Consider the Trade-Offs’. Each alternatives will fulfil certain goals more than others. Produce a summary table to make value-based choices about trade-offs (2017: 356-8).
  7. ‘Make a Recommendation’. ‘Unless your client asks you not to do so, you should explicitly recommend one policy’ (2017: 28).

This is an excerpt from The Politics of Policy Analysis, found here: https://paulcairney.wordpress.com/policy-analysis-in-750-words/

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Policy Analysis in 750 Words: how much impact can you expect from your analysis?

This post forms one part of the Policy Analysis in 750 words series overview.

Throughout this series you may notice three different conceptions about the scope of policy analysis:

  1. ‘Ex ante’ (before the event) policy analysis. Focused primarily on defining a problem, and predicting the effect of solutions, to inform current choice (as described by Meltzer and Schwartz and Thissen and Walker).
  2. ‘Ex post’ (after the event) policy analysis. Focused primarily on monitoring and evaluating that choice, perhaps to inform future choice (as described famously by Weiss).
  3. Some combination of both, to treat policy analysis as a continuous (never-ending) process (as described by Dunn).

As usual, these are not hard-and-fast distinctions, but they help us clarify expectations in relation to different scenarios.

  1. The impact of old-school ex ante policy analysis

Radin provides a valuable historical discussion of policymaking with the following elements:

  • a small number of analysts, generally inside government (such as senior bureaucrats, scientific experts, and – in particular- economists),
  • giving technical or factual advice,
  • about policy formulation,
  • to policymakers at the heart of government,
  • on the assumption that policy problems would be solved via analysis and action.

This kind of image signals an expectation for high impact: policy analysts face low competition, enjoy a clearly defined and powerful audience, and their analysis is expected to feed directly into choice.

Radin goes on to describe a much different, modern policy environment: more competition, more analysts spread across and outside government, with a less obvious audience, and – even if there is a client – high uncertainty about where the analysis fits into the bigger picture.

Yet, the impetus to seek high and direct impact remains.

This combination of shifting conditions but unshifting hopes/ expectations helps explain a lot of the pragmatic forms of policy analysis you will see in this series, including:

  • Keep it catchy, gather data efficiently, tailor your solutions to your audience, and tell a good story (Bardach)
  • Speak with an audience in mind, highlight a well-defined problem and purpose, project authority, use the right form of communication, and focus on clarity, precision, conciseness, and credibility ( Smith)
  • Address your client’s question, by their chosen deadline, in a clear and concise way that they can understand (and communicate to others) quickly (Weimer and Vining)
  • Client-oriented advisors identify the beliefs of policymakers and anticipate the options worth researching (Mintrom)
  • Identify your client’s resources and motivation, such as how they seek to use your analysis, the format of analysis they favour (make it ‘concise’ and ‘digestible’), their deadline, and their ability to make or influence the policies you might suggest (Meltzer and Schwartz).
  • ‘Advise strategically’, to help a policymaker choose an effective solution within their political context (Thissen and Walker).
  • Focus on producing ‘policy-relevant knowledge’ by adapting to the evidence-demands of policymakers and rejecting a naïve attachment to ‘facts speaking for themselves’ or ‘knowledge for its own sake’ (Dunn).
  1. The impact of research and policy evaluation

Many of these recommendations are familiar to scientists and researchers, but generally in the context of far lower expectations about their likely impact, particularly if those expectations are informed by policy studies (compare Oliver & Cairney with Cairney & Oliver).

In that context, Weiss’ work is a key reference point. It gives us a menu of ways in which policymakers might use policy evaluation (and research evidence more widely):

  • to inform solutions to a problem identified by policymakers
  • as one of many sources of information used by policymakers, alongside ‘stakeholder’ advice and professional and service user experience
  • as a resource used selectively by politicians, with entrenched positions, to bolster their case
  • as a tool of government, to show it is acting (by setting up a scientific study), or to measure how well policy is working
  • as a source of ‘enlightenment’, shaping how people think over the long term (compare with this discussion of ‘evidence based policy’ versus ‘policy based evidence’).

In other words, researchers may have a role, but they struggle (a) to navigate the politics of policy analysis, (b) find the right time to act, and (c) to secure attention, in competition with many other policy actors.

  1. The potential for a form of continuous impact

Dunn suggests that the idea of ‘ex ante’ policy analysis is misleading, since policymaking is continuous, and evaluations of past choices inform current choices. Think of each policy analysis steps as ‘interdependent’, in which new knowledge to inform one step also informs the other four. For example, routine monitoring helps identify compliance with regulations, if resources and services reach ‘target groups’, if money is spent correctly, and if we can make a causal link between the policy solutions and outcomes. Its impact is often better seen as background information with intermittent impact.

Key conclusions to bear in mind

  1. The demand for information from policy analysts may be disproportionately high when policymakers pay attention to a problem, and disproportionately low when they feel that they have addressed it.
  2. Common advice for policy analysts and researchers often looks very similar: keep it concise, tailor it to your audience, make evidence ‘policy relevant’, and give advice (don’t sit on the fence). However, unless researchers are prepared to act quickly, to gather data efficiently (not comprehensively), to meet a tight brief for a client, they are not really in the impact business described by most policy analysis texts.
  3. A lot of routine, continuous, impact tends to occur out of the public spotlight, based on rules and expectations that most policy actors take for granted.

Further reading

See the Policy Analysis in 750 words series overview to continue reading on policy analysis.

See the ‘evidence-based policymaking’ page to continue reading on research impact.

ebpm pic

Bristol powerpoint: Paul Cairney Bristol EBPM January 2020

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Policy Analysis in 750 Words: what you need as an analyst versus policymaking reality

This post forms one part of the Policy Analysis in 750 words series overview. Note for the eagle eyed: you are not about to experience déjà vu. I’m just using the same introduction.

When describing ‘the policy sciences’, Lasswell distinguishes between:

  1. ‘knowledge of the policy process’, to foster policy studies (the analysis of policy)
  2. ‘knowledge in the process’, to foster policy analysis (analysis for policy)

The lines between each approach are blurry, and each element makes less sense without the other. However, the distinction is crucial to help us overcome the major confusion associated with this question:

Does policymaking proceed through a series of stages?

The short answer is no.

The longer answer is that you can find about 40 blog posts (of 500 and 1000 words) which compare (a) a stage-based model called the policy cycle, and (b) the many, many policy concepts and theories that describe a far messier collection of policy processes.

cycle

In a nutshell, most policy theorists reject this image because it oversimplifies a complex policymaking system. The image provides a great way to introduce policy studies, and serves a political purpose, but it does more harm than good:

  1. Descriptively, it is profoundly inaccurate (unless you imagine thousands of policy cycles interacting with each other to produce less orderly behaviour and less predictable outputs).
  2. Prescriptively, it gives you rotten advice about the nature of your policymaking task (for more on these points, see this chapter, article, article, and series).

Why does the stages/ policy cycle image persist? Two relevant explanations

 

  1. It arose from a misunderstanding in policy studies

In another nutshell, Chris Weible and I argue (in a secret paper) that the stages approach represents a good idea gone wrong:

  • If you trace it back to its origins, you will find Lasswell’s description of decision functions: intelligence, recommendation, prescription, invocation, application, appraisal and termination.
  • These functions correspond reasonably well to a policy cycle’s stages: agenda setting, formulation, legitimation, implementation, evaluation, and maintenance, succession or termination.
  • However, Lasswell was imagining functional requirements, while the cycle seems to describe actual stages.

In other words, if you take Lasswell’s list of what policy analysts/ policymakers need to do, multiple it by the number of actors (spread across many organisations or venues) trying to do it, then you get the multi-centric policy processes described by modern theories. If, instead, you strip all that activity down into a single cycle, you get the wrong idea.

  1. It is a functional requirement of policy analysis

This description should seem familiar, because the classic policy analysis texts appear to describe a similar series of required steps, such as:

  1. define the problem
  2. identify potential solutions
  3. choose the criteria to compare them
  4. evaluate them in relation to their predicted outcomes
  5. recommend a solution
  6. monitor its effects
  7. evaluate past policy to inform current policy.

However, these texts also provide a heavy dose of caution about your ability to perform these steps (compare Bardach, Dunn, Meltzer and Schwartz, Mintrom, Thissen and Walker, Weimer and Vining)

In addition, studies of policy analysis in action suggest that:

  • an individual analyst’s need for simple steps, to turn policymaking complexity into useful heuristics and pragmatic strategies,

should not be confused with

What you need versus what you can expect

Overall, this discussion of policy studies and policy analysis reminds us of a major difference between:

  1. Functional requirements. What you need from policymaking systems, to (a) manage your task (the 5-8 step policy analysis) and (b) understand and engage in policy processes (the simple policy cycle).
  2. Actual processes and outcomes. What policy concepts and theories tell us about bounded rationality (which limit the comprehensiveness of your analysis) and policymaking complexity (which undermines your understanding and engagement in policy processes).

Of course, I am not about to provide you with a solution to these problems.

Still, this discussion should help you worry a little bit less about the circular arguments you will find in key texts: here are some simple policy analysis steps, but policymaking is not as ‘rational’ as the steps suggest, but (unless you can think of an alternative) there is still value in the steps, and so on.

See also:

The New Policy Sciences

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Policy Analysis in 750 Words: Defining policy problems and choosing solutions

This post forms one part of the Policy Analysis in 750 words series overview.

When describing ‘the policy sciences’, Lasswell distinguishes between:

  1. ‘knowledge of the policy process’, to foster policy studies (the analysis of policy)
  2. ‘knowledge in the process’, to foster policy analysis (analysis for policy)

The idea is that both elements are analytically separable but mutually informative: policy analysis is crucial to solving real policy problems, policy studies inform the feasibility of analysis, the study of policy analysts informs policy studies, and so on.

Both elements focus on similar questions – such as What is policy? – and explore their descriptive (what do policy actors do?) and prescriptive (what should they do?) implications.

  1. What is the policy problem?

Policy studies tend to describe problem definition in relation to framing, narrative, social construction, power, and agenda setting.

Actors exercise power to generate attention for their preferred interpretation, and minimise attention to alternative frames (to help foster or undermine policy change, or translate their beliefs into policy).

Policy studies incorporate insights from psychology to understand (a) how policymakers might combine cognition and emotion to understand problems, and therefore (b) how to communicate effectively when presenting policy analysis.

Policy studies focus on the power to reduce ambiguity rather than simply the provision of information to reduce uncertainty. In other words, the power to decide whose interpretation of policy problems counts, and therefore to decide what information is policy-relevant.

This (unequal) competition takes place within a policy process over which no actor has full knowledge or control.

The classic 5-8 step policy analysis texts focus on how to define policy problems well, but they vary somewhat in their definition of doing it well (see also C.Smith):

  • Bardach recommends using rhetoric and eye-catching data to generate attention
  • Weimer and Vining and Mintrom recommend beginning with your client’s ‘diagnosis’, placing it in a wider perspective to help analyse it critically, and asking yourself how else you might define it (see also Bacchi, Stone)
  • Meltzer and Schwartz and Dunn identify additional ways to contextualise your client’s definition, such as by generating a timeline to help ‘map’ causation or using ‘problem-structuring methods’ to compare definitions and avoid making too many assumptions on a problem’s cause.
  • Thissen and Walker compare ‘rational’ and ‘argumentative’ approaches, treating problem definition as something to be measured scientifically or established rhetorically (see also Riker).

These approaches compare with more critical accounts that emphasise the role of power and politics to determine whose knowledge is relevant (L.T.Smith) and whose problem definition counts (Bacchi, Stone). Indeed, Bacchi and Stone provide a crucial bridge between policy analysis and policy studies by reflecting on what policy analysts do and why.

  1. What is the policy solution?

In policy studies, it is common to identify counterintuitive or confusing aspects of policy processes, including:

  • Few studies suggest that policy responses actually solve problems (and many highlight their potential to exacerbate them). Rather, ‘policy solutions’ is shorthand for proposed or alleged solutions.
  • Problem definition often sets the agenda for the production of ‘solutions’, but note the phrase solutions chasing problems (when actors have their ‘pet’ solutions ready, and they seek opportunities to promote them).

Policy studies: problem definition informs the feasibility and success of solutions

Generally speaking, to define the problem is to influence assessments of the feasibility of solutions:

  • Technical feasibility. Will they work as intended, given the alleged severity and cause of the problem?
  • Political feasibility. Will they receive sufficient support, given the ways in which key policy actors weigh up the costs and benefits of action?

Policy studies highlight the inextricable connection between technical and political feasibility. Put simply, (a) a ‘technocratic’ choice about the ‘optimality’ of a solution is useless without considering who will support its adoption, and (b) some types of solution will always be a hard sell, no matter their alleged effectiveness (Box 2.3 below).

In that context, policy studies ask: what types of policy tools or instruments are actually used, and how does their use contribute to policy change? Measures include the size, substance, speed, and direction of policy change.

box 2.3 2nd ed UPP

In turn, problem definition informs: the ways in which actors will frame any evaluation of policy success, and the policy-relevance of the evidence to evaluate solutions. Simple examples include:

  • If you define tobacco in relation to: (a) its economic benefits, or (b) a global public health epidemic, evaluations relate to (a) export and taxation revenues, or (b) reductions in smoking in the population.
  • If you define ‘fracking’ in relation to: (a) seeking more benefits than costs, or (b) minimising environmental damage and climate change, evaluations relate to (a) factors such as revenue and effective regulation, or simply (b) how little it takes place.

Policy analysis: recognising and pushing boundaries

Policy analysis texts tend to accommodate these insights when giving advice:

  • Bardach recommends identifying solutions that your audience might consider, perhaps providing a range of options on a notional spectrum of acceptability.
  • Smith highlights the value of ‘precedent’, or relating potential solutions to previous strategies.
  • Weimer and Vining identify the importance of ‘a professional mind-set’ that may be more important than perfecting ‘technical skills’
  • Mintrom notes that some solutions are easier to sell than others
  • Meltzer and Schwartz describe the benefits of making a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups
  • Dunn warns against too-narrow forms of ‘evidence based’ analysis which undermine a researcher’s ability to adapt well to the evidence-demands of policymakers
  • Thissen and Walker relate solution feasibility to a wide range of policy analysis ‘styles’

Still, note the difference in emphasis.

Policy analysis education/ training may be about developing the technical skills to widen definitions and apply many criteria to compare solutions.

Policy studies suggest that problem definition and a search for solutions takes place in an environment where many actors apply a much narrower lens and are not interested in debates on many possibilities (particularly if they begin with a solution).

I have exaggerated this distinction between each element, but it is worth considering the repeated interaction between them in practice: politics and policymaking provide boundaries for policy analysis, analysis could change those boundaries, and policy studies help us reflect on the impact of analysts.

I’ll take a quick break, then discuss how this conclusion relates to the idea of ‘entrepreneurial’ policy analysis.

Further reading

Understanding Public Policy (2020: 28) describes the difference between governments paying for and actually using the ‘tools of policy formulation’. To explore this point, see ‘The use and non-use of policy appraisal tools in public policy making‘ and The Tools of Policy Formulation.

p28 upp 2nd ed policy tools

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Policy Analysis in 750 Words: What can you realistically expect policymakers to do?

This post forms one part of the Policy Analysis in 750 words series overview.

One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.

In this case, modern theories of the policy process help you identify your audience and their capacity to follow your advice. This simple insight may have a profound impact on the advice you give.

Policy analysis for an ideal-type world

For our purposes, an ideal-type is an abstract idea, which highlights hypothetical features of the world, to compare with ‘real world’ descriptions. It need not be an ideal to which we aspire. For example, comprehensive rationality describes the ideal type, and bounded rationality describes the ‘real world’ limitations to the ways in which humans and organisations process information.

 

Imagine writing policy analysis in the ideal-type world of a single powerful ‘comprehensively rational’ policymaker at the heart of government, making policy via an orderly policy cycle.

Your audience would be easy to identify, your analysis would be relatively simple, and you would not need to worry about what happens after you make a recommendation for policy change.

You could adopt a simple 5-8 step policy analysis method, use widely-used tools such as cost-benefit analysis to compare solutions, and know where the results would feed into the policy process.

I have perhaps over-egged this ideal-type pudding, but I think a lot of traditional policy analyses tapped into this basic idea and focused more on the science of analysis than the political and policymaking context in which it takes place (see Radin and Brans, Geva-May, and Howlett).

Policy analysis for the real world

Then imagine a far messier and less predictable world in which the nature of the policy issue is highly contestedresponsibility for policy is unclear, and no single ‘centre’ has the power to turn a recommendation into an outcome.

This image is a key feature of policy process theories, which describe:

  • Many policymakers and influencers spread across many levels and types of government (as the venues in which authoritative choice takes place). Consequently, it is not a straightforward task to identify and know your audience, particularly if the problem you seek to solve requires a combination of policy instruments controlled by different actors.
  • Each venue resembles an institution driven by formal and informal rules. Formal rules are written-down or widely-known. Informal rules are unwritten, difficult to understand, and may not even be understood in the same way by participants. Consequently, it is difficult to know if your solution will be a good fit with the standard operating procedures of organisations (and therefore if it is politically feasible or too challenging).
  • Policymakers and influencers operate in ‘subsystems’, forming networks built on resources such as trust or coalitions based on shared beliefs. Effective policy analysis may require you to engage with – or become part of – such networks, to allow you to understand the unwritten rules of the game and encourage your audience to trust the messenger. In some cases, the rules relate to your willingness to accept current losses for future gains, to accept the limited impact of your analysis now in the hope of acceptance at the next opportunity.
  • Actors relate their analysis to shared understandings of the world – how it is, and how it should be – which are often so well-established as to be taken for granted. Common terms include paradigms, hegemons, core beliefs, and monopolies of understandings. These dominant frames of reference give meaning to your policy solution. They prompt you to couch your solutions in terms of, for example, a strong attachment to evidence-based cases in public health, value for money in treasury departments, or with regard to core principles such as liberalism or socialism in different political systems.
  • Your solutions relate to socioeconomic context and the events that seem (a) impossible to ignore and (b) out of the control of policymakers. Such factors range from a political system’s geography, demography, social attitudes, and economy, while events can be routine elections or unexpected crises.

What would you recommend under these conditions? Rethinking 5-step analysis

There is a large gap between policymakers’ (a) formal responsibilities versus (b) actual control of policy processes and outcomes. Even the most sophisticated ‘evidence based’ analysis of a policy problem will fall flat if uninformed by such analyses of the policy process. Further, the terms of your cost-benefit analysis will be highly contested (at least until there is agreement on what the problem is, and how you would measure the success of a solution).

Modern policy analysis texts try to incorporate such insights from policy theories while maintaining a focus on 5-8 steps. For example:

  • Meltzer and Schwartz contrast their ‘flexible’ and ‘iterative’ approach with a too- rigid ‘rationalistic approach’.
  • Bardachand Dunn emphasise the value of political pragmatism and the ‘art and craft’ of policy analysis.
  • Weimer and Vininginvest 200 pages in economic analyses of markets and government, often highlighting a gap between (a) our ability to model and predict economic and social behaviour, and (b) what actually happens when governments intervene.
  • Mintrom invites you to see yourself as a policy entrepreneur, to highlight the value of of ‘positive thinking’, creativity, deliberation, and leadership, and perhaps seek ‘windows of opportunity’ to encourage new solutions. Alternatively, a general awareness of the unpredictability of events can prompt you to be modest in your claims, since the policymaking environment may be more important (than your solution) to outcomes.
  • Thissen and Walker focus more on a range of possible roles than a rigid 5-step process.

Beyond 5-step policy analysis

  1. Compare these pragmatic, client-orientated, and communicative models with the questioning, storytelling, and decolonizing approaches by Bacchi, Stone, and L.T. Smith.
  • The latter encourage us to examine more closely the politics of policy processes, including the importance of framing, narrative, and the social construction of target populations to problem definition and policy design.
  • Without this wider perspective, we are focusing on policy analysis as a process rather than considering the political context in which analysts use it.
  1. Additional posts on entrepreneurs and ‘systems thinking’ [to be added] encourage us to reflect on the limits to policy analysis in multi-centric policymaking systems.

 

 

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Policy Analysis in 750 Words: Reflecting on your role as a policy analyst

This post forms one part of the Policy Analysis in 750 words series overview.

One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.

If we take key insights from policy theories seriously, we can use them to identify (a) the constraints to policy analytical capacity, and (b) the ways in which analysts might address them. I use the idea of policy analyst archetypes to compare a variety of possible responses.

Key constraints to policy analytical capacity

Terms like ‘bounded rationality’ highlight major limits on the ability of humans and organisations to process information.

Terms like policymaking ‘context’, ‘environments’, and multi-centric policymaking suggest that the policy process is beyond the limits of policymaker understanding and control.

  • Policy actors need to find ways to act, with incomplete information about the problem they seek to solve and the likely impact of their ‘solution’.
  • They gather information to help reduce uncertainty, but problem definition is really about exercising power to reduce ambiguity: select one way to interpret a problem (at the expense of most others), and limit therefore limit the relevance and feasibility of solutions.
  • This context informs how actors might use the tools of policy analysis. Key texts in this series highlight the use of tools to establish technical feasibility (will it work as intended?), but policymakers also select tools for their political feasibility (who will support or oppose this measure?).

box 2.3 2nd ed UPP

How might policy analysts address these constraints ethically?

Most policy analysis texts (in this series) consider the role of professional ethics and values during the production of policy analysis. However, they also point out that there is not a clearly defined profession and associated code of conduct (e.g. see Adachi). In that context, let us begin with some questions about the purpose of policy analysis and your potential role:

  1. Is your primary role to serve individual clients or some notion of the ‘public good’?
  2. Should you maximise your role as an individual or play your part in a wider profession?
  3. What is the balance between the potential benefits of individual ‘entrepreneurship’ and collective ‘co-productive’ processes?
  4. Which policy analysis techniques should you prioritise?
  5. What forms of knowledge and evidence count in policy analysis?
  6. What does it mean to communicate policy analysis responsibly?
  7. Should you provide a clear recommendation or encourage reflection?

 

Policy analysis archetypes: pragmatists, entrepreneurs, manipulators, storytellers, and decolonisers

In that context, I have created a story of policy analysis archetypes to identify the elements that each text emphasises.

The pragmatic policy analyst

  • Bardach provides the classic simple, workable, 8-step system to present policy analysis to policymakers while subject to time and resource-pressed political conditions.
  • Dunn also uses Wildavsky’s famous phrase ‘art and craft’ to suggest that scientific and ‘rational’ methods can only take us so far.

The professional, clientoriented policy analyst

  • Weimer and Vining provide a similar 7-step client-focused system, but incorporating a greater focus on professional development and economic techniques (such as cost-benefit-analysis) to emphasise a particular form of professional analyst.
  • Meltzer and Schwartz also focus on advice to clients, but with a greater emphasis on a wide variety of methods or techniques (including service design) to encourage the co-design of policy analysis with clients.

The communicative policy analyst

  •  C. Smith focuses on how to write and communicate policy analysis to clients in a political context.
  • Compare with Spiegelhalter and Gigerenzer on how to communicate responsibly when describing uncertainty, probability, and risk.

The manipulative policy analyst.

  • Riker helps us understand the relationship between two aspects of agenda setting: the rules/ procedures to make choice, and the framing of policy problems and solutions.

The entrepreneurial policy analyst

  • Mintrom shows how to combine insights from studies of policy entrepreneurship and policy analysis, to emphasise the benefits of collaboration and creativity.

The questioning policy analyst

  • Bacchi  analyses the wider context in which people give and use such advice, to identify the emancipatory role of analysis and encourage policy analysts to challenge dominant social constructions of problems and populations.

The storytelling policy analyst

  • Stone identifies the ways in which people use storytelling and argumentation techniques to define problems and justify solutions. This process is about politics and power, not objectivity and optimal solutions.

The decolonizing policy analyst.

  • L.T. Smith does not describe policy analysis directly, but shows how the ‘decolonization of research methods’ can inform the generation and use of knowledge.
  • Compare with Hindess on the ways in which knowledge-based hierarchies rely on an untenable, circular logic.
  • Compare with Michener’s thread, discussing Doucet’s new essay on (a) the role of power and knowledge in limiting (b) the ways in which we gather evidence to analyse policy problems.

Using archetypes to define the problem of policy analysis

Studies of the field (e.g. Radin plus Brans, Geva-May, and Howlett) suggest that there are many ways to do policy analysis. Further, as Thissen and Walker describe, such roles are not mutually exclusive, your views on their relative value could change throughout the process of analysis, and you could perform many of these roles.

Further, each text describes multiple roles, and some seem clustered together:

  • pragmatic, client-orientated, and communicative could sum-up the traditional 5-8 step approaches, while
  • questioning, storytelling, and decolonizing could sum up an important (‘critical’) challenge to narrow ways of thinking about policy analysis and the use of information.

Still, the emphasis matters.

Each text is setting an agenda or defining the problem of policy analysis more-or-less in relation to these roles. Put simply, the more you are reading about economic theory and method, the less you are reading about dominance and manipulation.

How can you read further?

Michener’s ‘Policy Feedback in a Racialized Polity’ connects to studies of historical institutionalism, and reminds us to use insights from policy theories to identify the context for policy analysis.

I have co-authored a lot about uncertainty/ ambiguity in relation to ‘evidence based policymaking’, including:

See also The new policy sciences for a discussion of how these issues inform Lasswell’s original vision for the policy sciences (combining the analysis of and for policy).

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Policy Analysis in 750 words: Marleen Brans, Iris Geva-May, and Michael Howlett (2017) Routledge Handbook of Comparative Policy Analysis

Please see the Policy Analysis in 750 words series overview before reading the summary (and click here for the full list of authors). This post is a mere 500 words over budget (not including these words describing the number of words).

Brans et al 2017 cover

Marleen Brans, Iris Geva-May, and Michael Howlett (editors) (2017) Routledge Handbook of Comparative Policy Analysis (London: Routledge)

The Handbook … covers … the state of the art knowledge about the science, art and craft of policy analysis in different countries, at different levels of government and by all relevant actors in and outside government who contribute to the analysis of problems and the search for policy solutions’ (Brans et al, 2017: 1).

This book focuses on the interaction between (in Lasswell’s terms) ‘analysis for policy’ (policy analysis) and ‘analysis of policy’ (policy process research). In other words,

  • what can the study of policy analysis tell us about policymaking, and
  • what can studies of policymaking tell budding policy analysts about the nature of their task in relation to their policymaking environment?

Brans et al’s (2017: 1-6) opening discussion suggests that this task is rather unclear and complicated. They highlight the wide range of activity described by the term ‘policy analysis’:

  1. The scope of policy analysis is wide, and its meaning unclear

Analysts can be found in many levels and types of government, in bodies holding governments to account, and outside of government, including interest groups, think tanks, and specialist firms (such as global accountancy or management consultancy firms – Saint-Martin, 2017).

Further, ‘what counts’ as policy analysis can relate to the people that do it, the rules they follow, the processes in which they engage, the form of outputs, and the expectations of clients (Veselý, 2017: 103; Vining and Boardman, 2017: 264).

  1. The role of a policy analyst varies remarkably in relation to context

It varies over time, policy area, type of government (such as central, subnational, local), country, type of political system (e.g. majoritarian and consensus democracies), and ‘policy style’.

  1. Analysis involves ‘science, art and craft’ and the rules are written and unwritten

The process of policy analysis – such as to gather and analyse information, define problems, design and compare solutions, and give policy advice – includes ‘applied social and scientific research as well as more implicit forms of practical knowledge’, and ‘both formal and informal professional practices’ (see also studies of institutions and networks).

  1. The policy process is complex.

It is difficult to identify a straightforward process in which analysts are clearly engaged in multiple, well-defined ‘stages’ of policymaking.

  1. Key principles and practices can be institutionalised, contested, or non-existent.

The idea of policy analysis principles – ‘of transparency, effectiveness, efficiency and accountability through systematic and evidence-based analysis’ – may be entrenched in places like the US but not globally.

In some political systems (particularly in the ‘Anglo-Saxon family of nations’), the most-described forms of policy analysis (in the 750 words series) may be taken for granted (2017: 4):

Even so, the status of science and expertise is often contested, particularly in relation to salient and polarised issues, or more generally:

  • During ‘attempts by elected politicians to restore the primacy of political judgement in the policymaking process, at the expense of technical or scientific evidence’ (2017: 5).
  • When the ‘blending of expert policy analysis with public consultation and participation’ makes ‘advice more competitive and contested’ (2017: 5).
  • When evidence based really means evidence informed, given that there are many legitimate claims to knowledge, and evidence forms one part of a larger process of policy design (van Nispen and de Jong, 2017: 153).

In many political systems, there may be less criticism of the idea of ‘systematic and evidence-based analysis’ because there less capacity to process information. It is difficult to worry about excessively technocratic approaches if they do not exist (a point that CW made to me just before I read this book).

Implications for policy analysis

  1. It is difficult to think of policy analysis as a ‘profession’.

We may wonder if ‘policy analysis’ can ever be based on common skills and methods (such as described by Scott, 2017, and in Weimer and Vining), connected to ‘formal education and training’, a ‘a code of professional conduct’, and the ability of organisations to control membership (Adachi, 2017: 28; compare with Radin and Geva-May).

  1. Policy analysis is a loosely-defined collection of practices that vary according to context.

Policy analysis may, instead, be considered a collection of ‘styles’ (Hassenteufel and Zittoun, 2017), influenced by:

  • competing analytical approaches in different political systems (2017: 65)
  • bureaucratic capacity for analysis (Mendez and Dussauge-Laguna, 2017: 82)
  • a relative tendency to contract out analysis (Veselý, 2017: 113)
  • the types and remits of advisory bodies (e.g. are they tasked simply with offering expert advice, or also to encourage wider participation to generate knowledge?) (Crowley and Head, 2017)
  • the level of government in which analysts work, such as ‘subnational’ (Newman, 2017) or ‘local’ (Lundin and Öberg, 2017)
  • the type of activity, such as when (‘performance’) budgeting analysis is influenced heavily by economic methods and ‘new public management’ reforms (albeit with limited success, followed by attempts at reform) (van Nispen and de Jong, 2017: 143-52)

Policy analysis can also describe a remarkably wide range of activity, including:

  • Public inquiries (Marier, 2017)
  • Advice to MPs, parliaments, and their committees (Wolfs and De Winter, 2017)
  • The strategic analysis of public opinion or social media data (Rothmayr Allison, 2017; Kuo and Cheng, 2017)
  • A diverse set of activities associated with ‘think tanks’ (Stone and Ladi, 2017) and ‘political party think tanks’ (Pattyn et al, 2017)
  • Analysis for and by ‘business associations’ (Vining and Boardman, 2017), unions (Schulze and Schroeder, 2017), and voluntary/ non-profit organisations (Evans et al, 2017), all of whom juggle policy advice to government with keeping members on board.
  • The more-or-less policy relevant work of academic researchers (Blum and Brans, 2017; compare with Dunn and see the EBPM page).
  1. The analysis of and for policy is not so easy to separate in practice.

When defining policy analysis largely as a collection of highly-variable practices, in complex policymaking systems, we can see the symbiotic relationship between policy analysis and policy research. Studying policy analysis allows us to generate knowledge of policy processes. Policy process research demonstrates that the policymaking context influences how we think about policy analysis.

  1. Policy analysis education and training is incomplete without policy process research

Put simply, we should not assume that graduates in ‘policy analysis’ will enter a central government with high capacity, coherent expectations, and a clear demand for the same basic skills. Yet, Fukuyama argues that US University programmes largely teach students:

a battery of quantitative methods … applied econometrics, cost-benefit analysis, decision analysis, and, most recently, use of randomized experiments for program evaluation’ that ‘will tell you what the optimal policy should be’, but not ‘how to achieve that outcome. The world is littered with optimal policies that don’t have a snowball’s chance in hell of being adopted’.

In that context, additional necessary skills include: stakeholder mapping, to identify who is crucial to policy success, defining policy problems in a way that stakeholders and policymakers can support, and including those actors continuously during a process of policy design and delivery. These skills are described at more length by Radin and Geva May, while Botha et al (2017) suggest that the policy analysis programmes (across North American and European Universities) offer a more diverse range of skills (and support for experiential learning) than Fukuyama describes.

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Policy Analysis in 750 words: William Dunn (2017) Public Policy Analysis

Please see the Policy Analysis in 750 words series overview before reading the summary. This book is a whopper, with almost 500 pages and 101 (excellent) discussions of methods, so 800 words over budget seems OK to me. If you disagree, just read every second word.  By the time you reach the cat hanging in there baby you are about 300 (150) words away from the end.

Dunn 2017 cover

William Dunn (2017) Public Policy Analysis 6th Ed. (Routledge)

Policy analysis is a process of multidisciplinary inquiry aiming at the creation, critical assessment, and communication of policy-relevant knowledge … to solve practical problemsIts practitioners are free to choose among a range of scientific methods, qualitative as well as quantitative, and philosophies of science, so long as these yield reliable knowledge’ (Dunn, 2017: 2-3).

Dunn (2017: 4) describes policy analysis as pragmatic and eclectic. It involves synthesising policy relevant (‘usable’) knowledge, and combining it with experience and ‘practical wisdom’, to help solve problems with analysis that people can trust.

This exercise is ‘descriptive’, to define problems, and ‘normative’, to decide how the world should be and how solutions get us there (as opposed to policy studies/ research seeking primarily to explain what happens).

Dunn contrasts the ‘art and craft’ of policy analysts with other practices, including:

  1. The idea of ‘best practice’ characterised by 5-step plans.
  • In practice, analysis is influenced by: the cognitive shortcuts that analysts use to gather information; the role they perform in an organisation; the time constraints and incentive structures in organisations and political systems; the expectations and standards of their profession; and, the need to work with teams consisting of many professions/ disciplines (2017: 15-6)
  • The cost (in terms of time and resources) of conducting multiple research and analytical methods is high, and highly constrained in political environments (2017: 17-8; compare with Lindblom)
  1. The too-narrow idea of evidence-based policymaking
  • The naïve attachment to ‘facts speak for themselves’ or ‘knowledge for its own sake’ undermines a researcher’s ability to adapt well to the evidence-demands of policymakers (2017: 68; 4 compare with Why don’t policymakers listen to your evidence?).

To produce ‘policy-relevant knowledge’ requires us to ask five questions before (Qs1-3) and after (Qs4-5) policy intervention (2017: 5-7; 54-6):

  1. What is the policy problem to be solved?
  • For example, identify its severity, urgency, cause, and our ability to solve it.
  • Don’t define the wrong problem, such as by oversimplifying or defining it with insufficient knowledge.
  • Key aspects of problems including ‘interdependency’ (each problem is inseparable from a host of others, and all problems may be greater than the sum of their parts), ‘subjectivity’ and ‘artificiality’ (people define problems), ‘instability’ (problems change rather than being solved), and ‘hierarchy’ (which level or type of government is responsible) (2017: 70; 75).
  • Problems vary in terms of how many relevant policymakers are involved, how many solutions are on the agenda, the level of value conflict, and the unpredictability of outcomes (high levels suggest ‘wicked’ problems, and low levels ‘tame’) (2017: 75)
  • ‘Problem-structuring methods’ are crucial, to: compare ways to define or interpret a problem, and ward against making too many assumptions about its nature and cause; produce models of cause-and-effect; and make a problem seem solve-able, such as by placing boundaries on its coverage. These methods foster creativity, which is useful when issues seem new and ambiguous, or new solutions are in demand (2017: 54; 69; 77; 81-107).
  • Problem definition draws on evidence, but is primarily the exercise of power to reduce ambiguity through argumentation, such as when defining poverty as the fault of the poor, the elite, the government, or social structures (2017: 79; see Stone).
  1. What effect will each potential policy solution have?
  • Many ‘forecasting’ methods can help provide ‘plausible’ predictions about the future effects of current/ alternative policies (Chapter 4 contains a huge number of methods).
  • ‘Creativity, insight, and the use of tacit knowledge’ may also be helpful (2017: 55).
  • However, even the most-effective expert/ theory-based methods to extrapolate from the past are flawed, and it is important to communicate levels of uncertainty (2017: 118-23; see Spiegelhalter).
  1. Which solutions should we choose, and why?
  • ‘Prescription’ methods help provide a consistent way to compare each potential solution, in terms of its feasibility and predicted outcome, rather than decide too quickly that one is superior (2017: 55; 190-2; 220-42).
  • They help to combine (a) an estimate of each policy alternative’s outcome with (b) a normative assessment.
  • Normative assessments are based on values such as ‘equality, efficiency, security, democracy, enlightenment’ and beliefs about the preferable balance between state, communal, and market/ individual solutions (2017: 6; 205 see Weimer & Vining, Meltzer & Schwartz, and Stone on the meaning of these values).
  • For example, cost benefit analysis (CBA) is an established – but problematic – economics method based on finding one metric – such as a $ value – to predict and compare outcomes (2017: 209-17; compare Weimer & Vining, Meltzer & Schwartz, and Stone)
  • Cost effectiveness analysis uses a $ value for costs, but compared with other units of measurement for benefits (such as outputs per $) (2017: 217-9)
  • Although such methods help us combine information and values to compare choices, note the inescapable role of power to decide whose values (and which outcomes, affecting whom) matter (2017: 204)
  1. What were the policy outcomes?
  • ‘Monitoring’ methods help identify (say): levels of compliance with regulations, if resources and services reach ‘target groups’, if money is spent correctly (such as on clearly defined ‘inputs’ such as public sector wages), and if we can make a causal link between the policy inputs/ activities/ outputs and outcomes (2017: 56; 251-5)
  • Monitoring is crucial because it is so difficult to predict policy success, and unintended consequences are almost inevitable (2017: 250).
  • However, the data gathered are usually no more than proxy indicators of outcomes. Further, the choice of indicators reflect what is available, ‘particular social values’, and ‘the political biases of analysts’ (2017: 262)
  • The idea of ‘evidence based policy’ is linked strongly to the use of experiments and systematic review to identify causality (2017: 273-6; compare with trial-and-error learning in Gigerenzer, complexity theory, and Lindblom).
  1. Did the policy solution work as intended? Did it improve policy outcomes?
  • Although we frame policy interventions as ‘solutions’, few problems are ‘solved’. Instead, try to measure the outcomes and the contribution of your solution, and note that evaluations of success and ‘improvement’ are contested (2017: 57; 332-41).  
  • Policy evaluation is not an objective process in which we can separate facts from values.
  • Rather, values and beliefs are part of the criteria we use to gauge success (and even their meaning is contested – 2017: 322-32).
  • We can gather facts about the policy process, and the impacts of policy on people, but this information has little meaning until we decide whose experiences matter.

Overall, the idea of ‘ex ante’ (forecasting) policy analysis is a little misleading, since policymaking is continuous, and evaluations of past choices inform current choices.

Policy analysis methods are ‘interdependent’, and ‘knowledge transformations’ describes the impact of knowledge regarding one question on the other four (2017: 7-13; contrast with Meltzer & Schwartz, Thissen & Walker).

Developing arguments and communicating effectively

Dunn (2017: 19-21; 348-54; 392) argues that ‘policy argumentation’ and the ‘communication of policy-relevant knowledge’ are central to policymaking’ (See Chapter 9 and Appendices 1-4 for advice on how to write briefs, memos, and executive summaries and prepare oral testimony).

He identifies seven elements of a ‘policy argument’ (2017: 19-21; 348-54), including:

  • The claim itself, such as a description (size, cause) or evaluation (importance, urgency) of a problem, and prescription of a solution
  • The things that support it (including reasoning, knowledge, authority)
  • Incorporating the things that could undermine it (including any ‘qualifier’, the communication of uncertainty about current knowledge, and counter-arguments).

The key stages of communication (2017: 392-7; 405; 432) include:

  1. ‘Analysis’, focusing on ‘technical quality’ (of the information and methods used to gather it), meeting client expectations, challenging the ‘status quo’, albeit while dealing with ‘political and organizational constraints’ and suggesting something that can actually be done.
  2. ‘Documentation’, focusing on synthesising information from many sources, organising it into a coherent argument, translating from jargon or a technical language, simplifying, summarising, and producing user-friendly visuals.
  3. ‘Utilization’, by making sure that (a) communications are tailored to the audience (its size, existing knowledge of policy and methods, attitude to analysts, and openness to challenge), and (b) the process is ‘interactive’ to help analysts and their audiences learn from each other.

 

hang-in-there-baby

 

Policy analysis and policy theory: systems thinking, evidence based policymaking, and policy cycles

Dunn (2017: 31-40) situates this discussion within a brief history of policy analysis, which culminated in new ways to express old ambitions, such as to:

  1. Use ‘systems thinking’, to understand the interdependence between many elements in complex policymaking systems (see also socio-technical and socio-ecological systems).
  • Note the huge difference between (a) policy analysis discussions of ‘systems thinking’ built on the hope that if we can understand them we can direct them, and (b) policy theory discussions that emphasise ‘emergence’ in the absence of central control (and presence of multi-centric policymaking).
  • Also note that Dunn (2017: 73) describes policy problems – rather than policymaking – as complex systems. I’ll write another post (short, I promise) on the many different (and confusing) ways to use the language of complexity.
  1. Promote ‘evidence based policy, as the new way to describe an old desire for ‘technocratic’ policymaking that accentuates scientific evidence and downplays politics and values (see also 2017: 60-4).

In that context, see Dunn’s (47-52) discussion of comprehensive versus bounded rationality:

  • Note the idea of ‘erotetic rationality’ in which people deal with their lack of knowledge of a complex world by giving up on the idea of certainty (accepting their ‘ignorance’), in favour of a continuous process of ‘questioning and answering’.
  • This approach is a pragmatic response to the lack of order and predictability of policymaking systems, which limits the effectiveness of a rigid attachment to ‘rational’ 5 step policy analyses (compare with Meltzer & Schwartz).

Dunn (2017: 41-7) also provides an unusually useful discussion of the policy cycle. Rather than seeing it as a mythical series of orderly stages, Dunn highlights:

  1. Lasswell’s original discussion of policymaking functions (or functional requirements of policy analysis, not actual stages to observe), including: ‘intelligence’ (gathering knowledge), ‘promotion’ (persuasion and argumentation while defining problems), ‘prescription’, ‘invocation’ and ‘application’ (to use authority to make sure that policy is made and carried out), and ‘appraisal’ (2017: 42-3).
  2. The constant interaction between all notional ‘stages’ rather than a linear process: attention to a policy problem fluctuates, actors propose and adopt solutions continuously, actors are making policy (and feeding back on its success) as they implement, evaluation (of policy success) is not a single-shot document, and previous policies set the agenda for new policy (2017: 44-5).

In that context, it is no surprise that the impact of a single policy analyst is usually minimal (2017: 57). Sorry to break it to you. Hang in there, baby.

hang-in-there-baby

 

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Policy Analysis in 750 words: Wil Thissen and Warren Walker (2013) Public Policy Analysis

Thissen Walker 2013 cover

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.

Wil Thissen and Warren Walker (editors) (2013) Public Policy Analysis: New Developments (Springer)

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:

  1. 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).
  2. 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!):

Enserink et al 2012 page 35

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:

  1. ‘Research and analyze’, to collect information relevant to policy problems.
  2. ‘Design and recommend’, to produce a range of potential solutions.
  3. ‘Clarify values and arguments’, to identify potential conflicts and facilitate high quality debate.
  4. ‘Advise strategically’, to help a policymaker choose an effective solution within their political context.
  5. ‘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)
  6. ‘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):

  1. 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).
  2. Argumentative, based on a competition to define policy problems and solutions (see Stone).
  3. Client advice, based on the assumption that analysis is part of a ‘political game’, and analysts bring knowledge of political strategy and policymaking complexity.
  4. Participatory, to facilitate a more equal access to information and debate among citizens.
  5. 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)
  6. 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):

p56 Mayer et al

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:

  1. 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).
  2. 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:

  1. 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.
  1. 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,

  1. 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.
  2. 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.
  3. 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.

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Policy Analysis in 750 words: Rachel Meltzer and Alex Schwartz (2019) Policy Analysis as Problem Solving

Please see the Policy Analysis in 750 words series overview before reading the summary. This post might well represent the largest breach of the ‘750 words’ limit, so please get comfortable. I have inserted a picture of a cat hanging in there baby after the main (*coughs*) 1400-word summary. The rest is bonus material, reflecting on the links between this book and the others in the series.

Meltzer Schwartz 2019 cover

Rachel Meltzer and Alex Schwartz (2019) Policy Analysis as Problem Solving (Routledge)

We define policy analysis as evidence-based advice giving, as the process by which one arrives at a policy recommendation to address a problem of public concern. Policy analysis almost always involves advice for a client’ (Meltzer and Schwartz, 2019: 15).

Meltzer and Schwartz (2019: 231-2) describe policy analysis as applied research, drawing on many sources of evidence, quickly, with limited time, access to scientific research, or funding to conduct a lot of new research (2019: 231-2). It requires:

  • careful analysis of a wide range of policy-relevant documents (including the ‘grey’ literature often produced by governments, NGOs, and think tanks) and available datasets
  • perhaps combined with expert interviews, focus groups, site visits, or an online survey (see 2019: 232-64 on methods).

Meltzer and Schwartz (2019: 21) outline a ‘five-step framework’ for client-oriented policy analysis. During each step, they contrast their ‘flexible’ and ‘iterative’ approach with a too- rigid ‘rationalistic approach’ (to reflect bounded, not comprehensive, rationality):

  1. ‘Define the problem’.

Problem definition is a political act of framing, not an exercise in objectivity (2019: 52-3). It is part of a narrative to evaluate the nature, cause, size, and urgency of an issue (see Stone), or perhaps to attach to an existing solution (2019: 38-40; compare with Mintrom).

In that context, ask yourself ‘Who is defining the problem? And for whom?’ and do enough research to be able to define it clearly and avoid misunderstanding among you and your client (2019: 37-8; 279-82):

  • Identify your client’s resources and motivation, such as how they seek to use your analysis, the format of analysis they favour, their deadline, and their ability to make or influence the policies you might suggest (2019: 49; compare with Weimer and Vining).
  • Tailor your narrative to your audience, albeit while recognising the need to learn from ‘multiple perspectives’ (2019: 40-5).
  • Make it ‘concise’ and ‘digestible’, not too narrowly defined, and not in a way that already closes off discussion by implying a clear cause and solution (2019: 51-2).

In doing so:

  • Ask yourself if you can generate a timeline, identify key stakeholders, and place a ‘boundary’ on the problem.
  • Establish if the problem is urgent, who cares about it, and who else might care (or not) (2019 : 46).
  • Focus on the ‘central’ problem that your solution will address, rather than the ‘related’ and ‘underlying’ problems that are ‘too large and endemic to be solved by the current analysis’ (2019: 47).
  • Avoid misdiagnosing a problem with reference to one cause. Instead, ‘map’ causation with reference to (say) individual and structural causes, intended and unintended consequences, simple and complex causation, market or government failure, and/ or the ability to blame an individual or organisation (2019: 48-9).
  • Combine quantitative and qualitative data to frame problems in relation to: severity, trends in severity, novelty, proximity to your audience, and urgency or crisis (2019: 53-4).

During this process, interrogate your own biases or assumptions and how they might affect your analysis (2019: 50).

2. ‘Identify potential policy options (alternatives) to address the problem’.

Common sources of ideas include incremental changes from current policy, ‘client suggestions’, comparable solutions (from another time, place, or policy area), reference to common policy instruments, and ‘brainstorming’ or ‘design thinking’ (2019: 67-9; see box 2.3 and 7.1, below, from Understanding Public Policy).

box 2.3 2nd ed UPP

Identify a ‘wide range’ of possible solutions, then select the (usually 3-5) ‘most promising’ for further analysis (2019: 65). In doing so:

  • be careful not to frame alternatives negatively (e.g. ‘death tax’ – 2019: 66)
  • compare alternatives in ‘good faith’ rather than keeping some ‘off the table’ to ensure that your preferred solution looks good (2019: 66)
  • beware ‘ best practice’ ideas that are limited in terms of (a) applicability (if made at a smaller scale, or in a very different jurisdiction), and (b) evidence of success (2019: 70; see studies of policy learning and transfer)
  • think about how to modify existing policies according to scale or geographical coverage, who to include (and based on what criteria), for how long, using voluntary versus mandatory provisions, and ensuring oversight (2019: 71-3)
  • consider combinations of common policy instruments, such as regulations and economic penalties/ subsidies (2019: 73-7)
  • consider established ways to ‘brainstorm’ ideas (2019: 77-8)
  • note the rise of instruments derived from the study of psychology and behavioural public policy (2019: 79-90)
  • learn from design principles, including ‘empathy’, ‘co-creating’ policy with service users or people affected, ‘prototyping’ (2019: 90-1)

box 7.1

3. ‘Specify the objectives to be attained in addressing the problem and the criteria to  evaluate  the  attainment  of  these  objectives  as  well as  the  satisfaction  of  other  key  considerations  (e.g.,  equity,  cost, equity, feasibility)’.

Your objectives relate to your problem definition and aims: what is the problem, what do you want to happen when you address it, and why?

  • For example, questions to your client may include: what is your organization’s ‘mission’, what is feasible (in terms of resources and politics), which stakeholders to you want to include, and how will you define success (2019: 105; 108-12)?

In that values-based context, your criteria relate to ways to evaluate each policy’s likely impact (2019: 106-7). They should ensure:

  • Comprehensiveness. E.g. how many people, and how much of their behaviour, can you influence while minimizing the ‘burden’ on people, businesses, or government? (2019: 113-4)
  • Mutual Exclusiveness. In other words, don’t have two objectives doing the same thing (2019: 114).

Common criteria include (2019: 116):

  1. Effectiveness. The size of its intended impact on the problem (2019: 117).
  2. Equity (fairness). The impact in terms of ‘vertical equity’ (e.g. the better off should pay more), ‘horizontal equity’ (e.g. you should not pay more if unmarried), fair process, fair outcomes, and ‘intergenerational’ equity (e.g. don’t impose higher costs on future populations) (2019: 118-19).
  3. Feasibility (administrative, political, and technical). The likelihood of this policy being adopted and implemented well (2019: 119-21)
  4. Cost (or financial feasibility). Who would bear the cost, and their willingness and ability to pay (2019: 122).
  5. Efficiency. To maximise the benefit while minimizing costs (2019: 122-3).

 

4. ‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.

When explaining objectives and criteria,

  • ‘label’ your criteria in relation to your policy objectives (e.g. to ‘maximize debt reduction’) rather than using generic terms (2019: 123-7)
  • produce a table – with alternatives in rows, and criteria in columns – to compare each option
  • quantify your policies’ likely outcomes, such as in relation to numbers of people affected and levels of income transfer, or a percentage drop in the size of the problem, but also
  • communicate the degree of uncertainty related to your estimates (2019: 128-32; see Spiegelhalter)

Consider using cost-benefit analysis to identify (a) the financial and opportunity cost of your plans (what would you achieve if you spent the money elsewhere?), compared to (b) the positive impact of your funded policy (2019: 141-55).

  • The principle of CBA may be intuitive, but a thorough CBA process is resource-intensive, vulnerable to bias and error, and no substitute for choice. It requires you to make a collection of assumptions about human behaviour and likely costs and benefits, decide whose costs and benefits should count, turn all costs and benefits into a single measure, and imagine how to maximise winners and compensate losers (2019: 155-81; compare Weimer and Vining with Stone).
  • One alternative is cost-effectiveness analysis, which quantifies costs and relates them to outputs (e.g. number of people affected, and how) without trying to translate them into a single measure of benefit (2019: 181-3).
  • These measures can be combined with other thought processes, such as with reference to ‘moral imperatives’, a ‘precautionary approach’, and ethical questions on power/ powerlessness (2019: 183-4).

 

5. ‘Arrive at a recommendation’.

Predict the most likely outcomes of each alternative, while recognising high uncertainty (2019: 189-92). If possible,

  • draw on existing, comparable, programmes to predict the effectiveness of yours (2019: 192-4)
  • combine such analysis with relevant theories to predict human behaviour (e.g. consider price ‘elasticity’ if you seek to raise the price of a good to discourage its use) (2019: 193-4)
  • apply statistical methods to calculate the probability of each outcome (2019: 195-6), and modify your assumptions to produce a range of possibilities, but
  • note Spiegelhalter’s cautionary tales and anticipate the inevitable ‘unintended consequences’ (when people do not respond to policy in the way you would like) (2019: 201-2)
  • use these estimates to inform a discussion on your criteria (equity, efficiency, feasibility) (2019: 196-200)
  • present the results visually – such as in a ‘matrix’ – to encourage debate on the trade-offs between options
  • simplify choices by omitting irrelevant criteria and options that do not compete well with others (2019: 203-10)
  • make sure that your recommendation (a) flows from the analysis, and (b) is in the form expected by your client (2019: 211-12)
  • consider making a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups (2019: 212).

 

hang-in-there-baby

 

Policy analysis in a wider context

Meltzer and Schwartz’s approach makes extra sense if you have already read some of the other texts in the series, including:

  1. Weimer and Vining, which represents an exemplar of an X-step approach informed heavily by the study of economics and application of economic models such as cost-benefit-analysis (compare with Radin’s checklist).
  2. Geva-May on the existence of a policy analysis profession with common skills, heuristics, and (perhaps) ethics (compare with Meltzer and Schwartz, 2019: 282-93)
  3. Radin, on:
  • the proliferation of analysts across multiple levels of government, NGOs, and the private sector (compare with Meltzer and Schwartz, 2019: 269-77)
  • the historic shift of analysis from formulation to all notional stages (contrast with Meltzer and Schwartz, 2019: 16-7 on policy analysis not including implementation or evaluation)
  • the difficulty in distinguishing between policy analysis and advocacy in practice (compare with Meltzer and Schwartz, 2019: 276-8, who suggest that actors can choose to perform these different roles)
  • the emerging sense that it is difficult to identify a single client in a multi-centric policymaking system. Put another way, we might be working for a specific client but accept that their individual influence is low.
  1. Stone’s challenge to
  • a historic tendency for economics to dominate policy analysis,
  • the applicability of economic assumptions (focusing primarily on individualist behaviour and markets), and
  • the pervasiveness of ‘rationalist’ policy analysis built on X-steps.

Meltzer and Schwartz (2019: 1-3) agree that economic models are too dominant (identifying the value of insights from ‘other disciplines – including design, psychology, political science, and sociology’).

However, they argue that critiques of rational models exaggerate their limitations (2019: 23-6). For example:

  • these models need not rely solely on economic techniques or quantification, a narrow discussion or definition of the problem, or the sense that policy analysis should be comprehensive, and
  • it is not problematic for analyses to reflect their client’s values or for analysts to present ambiguous solutions to maintain wide support, partly because
  • we would expect the policy analysis to form only one part of a client’s information or strategy.

Further, they suggest that these critiques provide no useful alternative, to help guide new policy analysts. Yet, these guides are essential:

to be persuasive, and credible, analysts must situate the problem, defend their evaluative criteria, and be able to demonstrate that their policy recommendation is superior, on balance, to other alternative options in addressing the problem, as defined by the analyst. At a minimum, the analyst needs to present a clear and defensible ranking of options to guide the decisions of the policy makers’ (Meltzer and Schwartz, 2019: 4).

Meltzer and Schwartz (2019: 27-8) then explore ways to improve a 5-step model with insights from approaches such as ‘design thinking’, in which actors use a similar process – ‘empathize, define the problem, ideate, prototype, test and get feedback from others’ – to experiment with policy solutions without providing a narrow view on problem definition or how to evaluate responses.

Policy analysis and policy theory

One benefit to Meltzer and Schwartz’s approach is that it seeks to incorporate insights from policy theories and respond with pragmatism and hope. However, I think you also need to read the source material to get a better sense of those theories, key debates, and their implications. For example:

  1. Meltzer and Schwartz (2019: 32) note correctly that ‘incremental’ does not sum up policy change well. Indeed, Punctuated Equilibrium Theory shows that policy change is characterised by a huge number of small and a small number of huge changes.
  • However, the direct implications of PET are not as clear as they suggest. Baumgartner and Jones have both noted that they can measure these outcomes and identify the same basic distribution across a political system, but not explain or predict why particular policies change dramatically.
  • It is useful to recommend to policy analysts that they invest some hope in major policy change, but also sensible to note that – in the vast majority of cases – it does not happen.
  • On his point, see Mintrom on policy analysis for the long term, Weiss on the ‘enlightenment’ function of research and analysis, and Box 6.3 (from Understanding Public Policy), on the sense that (a) we can give advice to ‘budding policy entrepreneurs’ on how to be effective analysts, but (b) should note that all their efforts could be for nothing.

box 6.3

  1. Meltzer and Schwartz (2019: 32-3) tap briefly into the old debate on whether it is preferable to seek radical or incremental change. For more on that debate, see chapter 5 in the 1st ed of Understanding Public Policy in which Lindblom notes that proposals for radical/ incremental changes are not mutually exclusive.
  2. Perhaps explore the possible tension between Meltzer and Schwartz’s (2019: 33-4) recommendation that (a) policy analysis should be ‘evidence-based advice giving’, and (b) ‘flexible and open-ended’.
  • I think that Stone’s response would be that phrases such as ‘evidence based’ are not ‘flexible and open-ended’. Rather, they tend to symbolise a narrow view of what counts as evidence (see also Smith, and Hindess).
  • Further, note that the phrase ‘evidence based policymaking’ is a remarkably vague term (see the EBPM page), perhaps better seen as a political slogan than a useful description or prescription of policymaking.

 

Finally, if you read enough of these policy analysis texts, you get a sense that many are bunched together even if they describe their approach as new or distinctive.

  • Indeed, Meltzer and Schwarz (2019: 22-3) provide a table (containing Bardach and Patashnik, Patton et al, Stokey and Zeckhauser, Hammond et al, and Weimer & Vining) of ‘quite similar’ X-step approaches.
  • Weimer and Vining also discuss the implications of policy theories and present the sense that X-step policy analysis should be flexible and adaptive.
  • Many texts – including Radin, and Smith (2016) – focus on the value of case studies to think through policy analysis in particular contexts, rather than suggesting that we can produce a universal blueprint.

However, as Geva-May might suggest, this is not a bad thing if our aim is to generate the sense that policy analysis is a profession with its own practices and heuristics.

 

 

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Policy Analysis in 750 words: Beryl Radin, B (2019) Policy Analysis in the Twenty-First Century

Please see the Policy Analysis in 750 words series overview before reading the summary. As usual, the 750-word description is more for branding than accuracy.

Beryl Radin (2019) Policy Analysis in the Twenty-First Century (Routledge)

Radin cover 2019

The basic relationship between a decision-maker (the client) and an analyst has moved from a two-person encounter to an extremely complex and diverse set of interactions’ (Radin, 2019: 2).

Many texts in this series continue to highlight the client-oriented nature of policy analysis (Weimer and Vining), but within a changing policy process that has altered the nature of that relationship profoundly.

This new policymaking environment requires new policy analysis skills and training (see Mintrom), and limits the applicability of classic 8-step (or 5-step) policy analysis techniques (2019: 82).

We can use Radin’s work to present two main stories of policy analysis:

  1. The old ways of making policy resembled a club, or reflected a clear government hierarchy, involving:
  • a small number of analysts, generally inside government (such as senior bureaucrats, scientific experts, and – in particular- economists),
  • giving technical or factual advice,
  • about policy formulation,
  • to policymakers at the heart of government,
  • on the assumption that policy problems would be solved via analysis and action.
  1. Modern policy analysis is characterised by a more open and politicised process in which:
  • many analysts, inside and outside government,
  • compete to interpret facts, and give advice,
  • about setting the agenda, and making, delivering, and evaluating policy,
  • across many policymaking venues,
  • often on the assumption that governments have a limited ability to understand and solve complex policy problems.

As a result, the client-analyst relationship is increasingly fluid:

In previous eras, the analyst’s client was a senior policymaker, the main focus was on the analyst-client relationship, and ‘both analysts and clients did not spend much time or energy thinking about the dimensions of the policy environment in which they worked’ (2019: 59). Now, in a multi-centric policymaking environment:

  1. It is tricky to identify the client.
  • We could imagine the client to be someone paying for the analysis, someone affected by its recommendations, or all policy actors with the ability to act on the advice (2019: 10).
  • If there is ‘shared authority’ for policymaking within one political system, a ‘client’ (or audience) may be a collection of policymakers and influencers spread across a network containing multiple types of government, non-governmental actors, and actors responsible for policy delivery (2019: 33).
  • The growth in international cooperation also complicates the idea of a single client for policy advice (2019: 33-4)
  • This shift may limit the ‘face-to-face encounters’ that would otherwise provide information for – and perhaps trust in – the analyst (2019: 2-3).
  1. It is tricky to identify the analyst
  • Radin (2019: 9-25) traces, from the post-war period in the US, a major expansion of policy analysts, from the notional centre of policymaking in federal government towards analysts spread across many venues, inside government (across multiple levels, ‘policy units’, and government agencies) and congressional committees, and outside government (such as in influential think tanks).
  • Policy analysts can also be specialist external companies contracted by organisations to provide advice (2019: 37-8).
  • This expansion shifted the image of many analysts, from a small number of trusted insiders towards many being treated as akin to interest groups selling their pet policies (2019: 25-6).
  • The nature – and impact – of policy analysis has always been a little vague, but now it seems more common to suggest that ‘policy analysts’ may really be ‘policy advocates’ (2019: 44-6).
  • As such, they may now have to work harder to demonstrate their usefulness (2019: 80-1) and accept that their analysis will have a limited impact (2019: 82, drawing on Weiss’ discussion of ‘enlightenment’).

Consequently, the necessary skills of policy analysis have changed:

Although many people value systematic policy analysis (and many rely on economists), an effective analyst does not simply apply economic or scientific techniques to analyse a problem or solution, or rely on one source of expertise or method, as if it were possible to provide ‘neutral information’ (2019: 26).

Indeed, Radin (2019: 31; 48) compares the old ‘acceptance that analysts would be governed by the norms of neutrality and objectivity’ with

(a) increasing calls to acknowledge that policy analysis is part of a political project to foster some notion of public good or ‘public interest’, and

(b)  Stone’s suggestion that the projection of reason and neutrality is a political strategy.

In other words, the fictional divide between political policymakers and neutral analysts is difficult to maintain.

Rather, think of analysts as developing wider skills to operate in a highly political environment in which the nature of the policy issue is contested, responsibility for a policy problem is unclear, and it is not clear how to resolve major debates on values and priorities:

  • Some analysts will be expected to see the problem from the perspective of a specific client with a particular agenda.
  • Other analysts may be valued for their flexibility and pragmatism, such as when they acknowledge the role of their own values, maintain or operate within networks, communicate by many means, and supplement ‘quantitative data’ with ‘hunches’ when required (2019: 2-3; 28-9).

Radin (2019: 21) emphasises a shift in skills and status

The idea of (a) producing new and relatively abstract ideas, based on high control over available information, at the top of a hierarchical organisation, makes way for (b) developing the ability to:

  • generate a wider understanding of organisational and policy processes, reflecting the diffusion of power across multiple policymaking venues
  • identify a map of stakeholders,
  • manage networks of policymakers and influencers,
  • incorporate ‘multiple and often conflicting perspectives’,
  • make and deliver more concrete proposals (2019: 59-74), while recognising
  • the contested nature of information, and the practices sued to gather it, even during multiple attempts to establish the superiority of scientific evidence (2019: 89-103),
  • the limits to a government’s ability to understand and solve problems (2019: 95-6),
  • the inescapable conflict over trade-offs between values and goals, which are difficult to resolve simply by weighting each goal (2019: 105-8; see Stone), and
  • do so flexibly, to recognise major variations in problem definition, attention and networks across different policy sectors and notional ‘stages’ of policymaking (2019: 75-9; 84).

Radin’s (2019: 48) overall list of relevant skills include:

  1. ‘Case study methods, Cost- benefit analysis, Ethical analysis, Evaluation, Futures analysis, Historical analysis, Implementation analysis, Interviewing, Legal analysis, Microeconomics, Negotiation, mediation, Operations research, Organizational analysis, Political feasibility analysis, Public speaking, Small- group facilitation, Specific program knowledge, Statistics, Survey research methods, Systems analysis’

They develop alongside analytical experience and status, from the early career analyst trying to secure or keep a job, to the experienced operator looking forward to retirement (2019: 54-5)

A checklist for policy analysts

Based on these skills requirements, the contested nature of evidence, and the complexity of the policymaking environment, Radin (2019: 128-31) produces a 4-page checklist of – 91! – questions for policy analysts.

For me, it serves two main functions:

  1. It is a major contrast to the idea that we can break policy analysis into a mere 5-8 steps (rather, think of these small numbers as marketing for policy analysis students, akin to 7-minute abs)
  2. It presents policy analysis as an overwhelming task with absolutely no guarantee of policy impact.

To me, this cautious, eyes-wide-open, approach is preferable to the sense that policy analysts can change the world if they just get the evidence and the steps right.

Further Reading:

  1. Iris Geva-May (2005) ‘Thinking Like a Policy Analyst. Policy Analysis as a Clinical Profession’, in Geva-May (ed) Thinking Like a Policy Analyst. Policy Analysis as a Clinical Profession (Basingstoke: Palgrave)

Although the idea of policy analysis may be changing, Geva-May (2005: 15) argues that it remains a profession with its own set of practices and ways of thinking. As with other professions (like medicine), it would be unwise to practice policy analysis without education and training or otherwise learning the ‘craft’ shared by a policy analysis community (2005: 16-17). For example, while not engaging in clinical diagnosis, policy analysts can draw on 5-step process to diagnose a policy problem and potential solutions (2005: 18-21). Analysts may also combine these steps with heuristics to determine the technical and political feasibility of their proposals (2005: 22-5), as they address inevitable uncertainty and their own bounded rationality (2005: 26-34; see Gigerenzer on heuristics). As with medicine, some aspects of the role – such as research methods – can be taught in graduate programmes, while others may be better suited to on the job learning (2005: 36-40). If so, it opens up the possibility that there are many policy analysis professions to reflect different cultures in each political system (and perhaps the venues within each system).

  1. Vining and Weimar’s take on the distinction between policy analysis and policy process research

 

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