Policy Analysis in 750 Words: policy analysis for marginalized groups in racialized political systems

Note: this post forms one part of the Policy Analysis in 750 words series overview.

For me, this story begins with a tweet by Professor Jamila Michener, about a new essay by Dr Fabienne Doucet, ‘Centering the Margins: (Re)defining Useful Research Evidence Through Critical Perspectives’:

Incisive essay from @bailabomba on studying the use of research evidence through critical perspectives that center the marginalized. There is so too much good stuff in here to summarize via twitter (you should just read it). But let me point out a few things that resonated (1/n) https://t.co/nIahyIjwBo

— Jamila Michener (@povertyscholar) December 17, 2019

Research and policy analysis for marginalized groups

For Doucet (2019: 1), it begins by describing the William T. Grant Foundation’s focus on improving the ‘use of research evidence’ (URE), and the key questions that we should ask when improving URE:

1. For what purposes do policymakers find evidence useful?

Examples include to: inform a definition of problems and solutions, foster practitioner learning, support an existing political position, or impose programmes backed by evidence (compare with How much impact can you expect from your analysis?).

2.   Who decides what to use, and what is useful?

For example, usefulness could be defined by the researchers providing evidence, the policymakers using it, the stakeholders involved in coproduction, or the people affected by research and policy (compare with Bacchi, Stone and Who should be involved in the process of policy analysis?).

3. How do critical theories inform these questions? (compare with T. Smith)

First, they remind us that so-called ‘rational’ policy processes have incorporated research evidence to help:

‘maintain power hierarchies and accept social inequity as a given. Indeed, research has been historically and contemporaneously (mis)used to justify a range of social harms from enslavement, colonial conquest, and genocide, to high-stakes testing, disproportionality in child welfare services, and “broken windows” policing’ (Doucet, 2019: 2)

Second, they help us redefine usefulness in relation to:

‘how well research evidence communicates the lived experiences of marginalized groups so that the understanding of the problem and its response is more likely to be impactful to the community in the ways the community itself would want’ (Doucet, 2019: 3)

In that context, potential responses include to:

1. Recognise the ways in which research and policy combine to reproduce the subordination of social groups.

• General mechanisms include: the reproduction of the assumptions, norms, and rules that produce a disproportionate impact on social groups (compare with Social Construction and Policy Design).
• Specific mechanism include: judging marginalised groups harshly according to ‘Western, educated, industrialized, rich and democratic’ norms (‘WEIRD’)

2. Reject the idea that scientific research can be seen as objective or neutral (and that researchers are beyond reproach for their role in subordination).
3. Give proper recognition to ‘experiential knowledge’ and ‘transdiciplinary approaches’ to knowledge production, rather than privileging scientific knowledge.
4. Commit to social justice, to help ‘eliminate oppressions and to emancipate and empower marginalized groups’, such as by disrupting ‘the policies and practices that disproportionately harm marginalized groups’ (2019: 5-7)
5. Develop strategies to ‘center race’, ‘democratize’ research production, and ‘leverage’ transdisciplinary methods (including poetry, oral history and narrative, art, and discourse analysis – compare with Lorde) (2019: 10-22)

See also Doucet, F. (2021) ‘Identifying and Testing Strategies to Improve the Use of Antiracist Research Evidence through Critical Race Lenses‘

Policy analysis in a ‘racialized polity’

A key way to understand these processes is to use, and improve, policy theories to explain the dynamics and impacts of a racialized political system. For example, ‘policy feedback theory’ (PFT) draws on elements from historical institutionalism and SCPD to identify the rules, norms, and practices that reinforce subordination.

In particular, Michener’s (2019: 424) ‘Policy Feedback in a Racialized Polity’ develops a ‘racialized feedback framework (RFF)’ to help explain the ‘unrelenting force with which racism and White supremacy have pervaded social, economic, and political institutions in the United States’. Key mechanisms include (2019: 424-6):

1. ‘Channelling resources’, in which the rules, to distribute government resources, benefit some social groups and punish others.

• Examples include: privileging White populations in social security schemes and the design/ provision of education, and punishing Black populations disproportionately in prisons (2019: 428-32).
• These rules also influence the motivation of social groups to engage in politics to influence policy (some citizens are emboldened, others alienated).

2. ‘Generating interests’, in which ‘racial stratification’ is a key factor in the power of interest groups (and balance of power in them).
3. ‘Shaping interpretive schema’, in which race is a lens through which actors understand, interpret, and seek to solve policy problems.
4. The ways in which centralization (making policy at the federal level) or decentralization influence policy design.

• For example, the ‘historical record’ suggests that decentralization is more likely to ‘be a force of inequality than an incubator of power for people of color’ (2019: 433).

Insufficient attention to race and racism: what are the implications for policy analysis?

One potential consequence of this lack of attention to race, and the inequalities caused by racism in policy, is that we place too much faith in the vague idea of ‘pragmatic’ policy analysis.

Throughout the 750 words series, you will see me refer generally to the benefits of pragmatism:

• How much impact can you expect from your analysis? focuses on making sure that your client-oriented advice builds on your client’s timetable and definition of the problem.
• What you need as an analyst versus policymaking reality focuses on managing your expectations about your ability to change policy (and influence policymaking).
• What can you realistically expect policymakers to do? describes how the pragmatic policy analysis texts (e.g. Bardach and Dunn) emphasise the value of this approach (albeit while inviting you to compare their accounts with the questioning, storytelling, and decolonizing approaches by Bacchi, Stone, and T. Smith).

In that context, pragmatism relates to the idea that policy analysis consists of ‘art and craft’, in which analysts assess what is politically feasible if taking a low-risk client-oriented approach.

In this context, pragmatism may be read as a euphemism for conservatism and status quo protection.

In other words, other posts in the series warn against too-high expectations for entrepreneurial and systems thinking approaches to major policy change, but they should not be read as an excuse to reject ambitious plans for much-needed changes to policy and policy analysis (compare with Meltzer and Schwartz, who engage with this dilemma in client-oriented advice).

Connections to blog themes

This post connects well to:

• policy analysis posts on T. Smith, Bacchi, Stone, and Who should be involved in the process of policy analysis?
• 500/1000 Words posts on Power and Knowledge, Feminist Institutionalism, Social Construction and Policy Design, and Feminism, Postcolonialism, and Critical Policy Studies, and
• the ‘politics of evidence based policymaking’ series.

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).

2. 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.

3. 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.

Bristol powerpoint: Paul Cairney Bristol EBPM January 2020

Policy Analysis in 750 Words: Who should be involved in the process of policy analysis?

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

Think of two visions for policy analysis. It should be primarily:

• ‘evidence based’, or
• ‘co-produced’.

These choices are not mutually exclusive, but there are key tensions between them that should not be ignored, such as when we ask:

• how many people should be involved in policy analysis?
• whose knowledge counts?
• who should control policy design?

Perhaps we can only produce a sensible combination of the two if we clarify their often very different implications for policy analysis. Let’s begin with one story for each and see where they take us.

A story of ‘evidence-based policymaking’

One story of ‘evidence based’ policy analysis is that it should be based on the best available evidence of ‘what works’.

Often, the description of the ‘best’ evidence relates to the idea that there is a notional hierarchy of evidence according to the research methods used.

At the top would be the systematic review of randomised control trials, and nearer the bottom would be expertise, practitioner knowledge, and stakeholder feedback.

This kind of hierarchy has major implications for policy learning and transfer, such as when importing policy interventions from abroad or ‘scaling up’ domestic projects.

Put simply, the experimental method is designed to identify the causal effect of a very narrowly defined policy intervention. Its importation or scaling up would be akin to the description of medicine, in which the evidence suggests the causal effect of a specific active ingredient to be administered with the correct dosage. A very strong commitment to a uniform model precludes the processes we might associate with co-production, in which many voices contribute to a policy design to suit a specific context (see also: the intersection between evidence and policy transfer).

A story of co-production in policymaking

One story of ‘co-produced’ policy analysis is that it should be ‘reflexive’ and based on respectful conversations between a wide range of policymakers and citizens.

Often, the description is of the diversity of valuable policy relevant information, with scientific evidence considered alongside community voices and normative values.

This rejection of a hierarchy of evidence also has major implications for policy learning and transfer. Put simply, a co-production method is designed to identify the positive effect – widespread ‘ownership’ of the problem and commitment to a commonly-agreed solution – of a well-discussed intervention, often in the absence of central government control.

Its use would be akin to a collaborative governance mechanism, in which the causal mechanism is perhaps the process used to foster agreement (including to produce the rules of collective action and the evaluation of success) rather than the intervention itself. A very strong commitment to this process precludes the adoption of a uniform model that we might associate with narrowly-defined stories of evidence based policymaking.

Where can you find these stories in the 750-words series?

1. Texts focusing on policy analysis as evidence-based/ informed practice (albeit subject to limits) include: Weimer and Vining, Meltzer and Schwartz, Brans, Geva-May, and Howlett (compare with Mintrom, Dunn)
2. Texts on being careful while gathering and analysing evidence include: Spiegelhalter
3. Texts that challenge the ‘evidence based’ story include: Bacchi, T. Smith, Hindess, Stone

 

How can you read further?

See the EBPM page and special series ‘The politics of evidence-based policymaking: maximising the use of evidence in policy’

There are 101 approaches to co-production, but let’s see if we can get away with two categories:

4. Co-producing policy (policymakers, analysts, stakeholders). Some key principles can be found in Ostrom’s work and studies of collaborative governance.
5. Co-producing research to help make it more policy-relevant (academics, stakeholders). See the Social Policy and Administration special issue ‘Inside Co-production’ and Oliver et al’s ‘The dark side of coproduction’ to get started.

To compare ‘epistemic’ and ‘reflexive’ forms of learning, see Dunlop and Radaelli’s ‘The lessons of policy learning: types, triggers, hindrances and pathologies’

My interest has been to understand how governments juggle competing demands, such as to (a) centralise and localise policymaking, (b) encourage uniform and tailored solutions, and (c) embrace and reject a hierarchy of evidence. What could possibly go wrong when they entertain contradictory objectives? For example:

• Paul Cairney (2019) “The myth of ‘evidence based policymaking’ in a decentred state”, forthcoming in Public Policy and Administration(Special Issue, The Decentred State) (accepted version)
• Paul Cairney (2019) ‘The UK government’s imaginative use of evidence to make policy’, British Politics, 14, 1, 1-22 Open AccessPDF

• Paul Cairney and Kathryn Oliver (2017) ‘Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy?’ Health Research Policy and Systems (HARPS), DOI: 10.1186/s12961-017-0192-x PDF
• Paul Cairney (2017) “Evidence-based best practice is more political than it looks: a case study of the ‘Scottish Approach’”, Evidence and Policy, 13, 3, 499-515 PDF

 

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.

William Dunn (2017) Public Policy Analysis 6^th 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 problems … Its 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)

2. 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).

2. 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).

3. 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)

4. 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).

5. 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.

 

 

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.

2. 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.

 

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)

‘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.

2. 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).

2. 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).

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

 

Understanding Public Policy 2nd edition

All going well, it will be out in November 2019. We are now at the proofing stage.

I have included below the summaries of the chapters (and each chapter should also have its own entry (or multiple entries) in the 1000 Words and 500 Words series).

 

Evidence-informed policymaking: context is everything

I thank James Georgalakis for inviting me to speak at the inaugural event of IDS’ new Evidence into Policy and Practice Series, and the audience for giving extra meaning to my story about the politics of ‘evidence-based based policymaking’. The talk (using powerpoint) and Q&A is here:

 

James invited me to respond to some of the challenges raised to my talk – in his summary of the event – so here it is.

I’m working on a ‘show, don’t tell’ approach, leaving some of the story open to interpretation. As a result, much of the meaning of this story – and, in particular, the focus on limiting participation – depends on the audience.

For example, consider the impact of the same story on audiences primarily focused on (a) scientific evidence and policy, or (b) participation and power.

Normally, when I talk about evidence and policy, my audience is mostly people with scientific or public health backgrounds asking why do policymakers ignore scientific evidence? I am usually invited to ruffle feathers, mostly by challenging a – remarkably prevalent – narrative that goes like this:

• We know what the best evidence is, since we have produced it with the best research methods (the ‘hierarchy of evidence’ argument).
• We have evidence on the nature of the problem and the most effective solutions (the ‘what works’ argument).
• Policymakers seems to be ignoring our evidence or failing to act proportionately (the ‘evidence-policy barriers’ argument).
• Or, they cherry-pick evidence to suit their agenda (the ‘policy based evidence’ argument).

In that context, I suggest that there are many claims to policy-relevant knowledge, policymakers have to ignore most information before making choices, and they are not in control of the policy process for which they are ostensibly in charge.

Limiting participation as a strategic aim

Then, I say to my audience that – if they are truly committed to maximising the use of scientific evidence in policy – they will need to consider how far they will go to get what they want. I use the metaphor of an ethical ladder in which each rung offers more influence in exchange for dirtier hands: tell stories and wait for opportunities, or demonise your opponents, limit participation, and humour politicians when they cherry-pick to reinforce emotional choices.

It’s ‘show don’t tell’ but I hope that the take-home point for most of the audience is that they shouldn’t focus so much on one aim – maximising the use of scientific evidence – to the detriment of other important aims, such as wider participation in politics beyond a reliance on a small number of experts. I say ‘keep your eyes on the prize’ but invite the audience to reflect on which prizes they should seek, and the trade-offs between them.

Limited participation – and ‘windows of opportunity’ – as an empirical finding

I did suggest that most policymaking happens away from the sphere of ‘exciting’ and ‘unruly’ politics. Put simply, people have to ignore almost every issue almost all of the time. Each time they focus their attention on one major issue, they must – by necessity – ignore almost all of the others.

For me, the political science story is largely about the pervasiveness of policy communities and policymaking out of the public spotlight.

The logic is as follows. Elected policymakers can only pay attention to a tiny proportion of their responsibilities. They delegate the rest to bureaucrats at lower levels of government. Bureaucrats lack specialist knowledge, and rely on other actors for information and advice. Those actors trade information for access. In many cases, they develop effective relationships based on trust and a shared understanding of the policy problem.

Trust often comes from a sense that everyone has proven to be reliable. For example, they follow norms or the ‘rules of the game’. One classic rule is to contain disputes within the policy community when actors don’t get what they want: if you complain in public, you draw external attention and internal disapproval; if not, you are more likely to get what you want next time.

For me, this is key context in which to describe common strategic concerns:

• Should you wait for a ‘window of opportunity’ for policy change? Maybe. Or, maybe it will never come because policymaking is largely insulated from view and very few issues reach the top of the policy agenda.
• Should you juggle insider and outsider strategies? Yes, some groups seem to do it well and it is possible for governments and groups to be in a major standoff in one field but close contact in another. However, each group must consider why they would do so, and the trade-offs between each strategy. For example, groups excluded from one venue may engage (perhaps successfully) in ‘venue shopping’ to get attention from another. Or, they become discredited within many venues if seen as too zealous and unwilling to compromise. Insider/outsider may seem like a false dichotomy to experienced and well-resourced groups, who engage continuously, and are able to experiment with many approaches and use trial-and-error learning. It is a more pressing choice for actors who may have only one chance to get it right and do not know what to expect.

Where is the power analysis in all of this?

I rarely use the word power directly, partly because – like ‘politics’ or ‘democracy’ – it is an ambiguous term with many interpretations (see Box 3.1). People often use it without agreeing its meaning and, if it means everything, maybe it means nothing.

However, you can find many aspects of power within our discussion. For example, insider and outsider strategies relate closely to Schattschneider’s classic discussion in which powerful groups try to ‘privatise’ issues and less powerful groups try to ‘socialise’ them. Agenda setting is about using resources to make sure issues do, or do not, reach the top of the policy agenda, and most do not.

These aspects of power sometimes play out in public, when:

• Actors engage in politics to turn their beliefs into policy. They form coalitions with actors who share their beliefs, and often romanticise their own cause and demonise their opponents.
• Actors mobilise their resources to encourage policymakers to prioritise some forms of knowledge or evidence over others (such as by valuing scientific evidence over experiential knowledge).
• They compete to identify the issues most worthy of our attention, telling stories to frame or define policy problems in ways that generate demand for their evidence.

However, they are no less important when they play out routinely:

• Governments have standard operating procedures – or institutions – to prioritise some forms of evidence and some issues routinely.
• Many policy networks operate routinely with few active members.
• Certain ideas, or ways of understanding the world and the nature of policy problems within it, becomes so dominant that they are unspoken and taken for granted as deeply held beliefs. Still, they constrain or facilitate the success of new ‘evidence based’ policy solutions.

In other words, the word ‘power’ is often hidden because the most profound forms of power often seem to be hidden.

In the context of our discussion, power comes from the ability to define some evidence as essential and other evidence as low quality or irrelevant, and therefore define some people as essential or irrelevant. It comes from defining some issues as exciting and worthy of our attention, or humdrum, specialist and only relevant to experts. It is about the subtle, unseen, and sometimes thoughtless ways in which we exercise power to harness people’s existing beliefs and dominate their attention as much as the transparent ways in which we mobilise resources to publicise issues. Therefore, to ‘maximise the use of evidence’ sounds like an innocuous collective endeavour, but it is a highly political and often hidden use of power.

See also:

I discussed these issues at a storytelling workshop organised by the OSF:

See also:

Policy in 500 Words: Power and Knowledge

The politics of evidence-based policymaking

Palgrave Communications: The politics of evidence-based policymaking

Using evidence to influence policy: Oxfam’s experience

The UK government’s imaginative use of evidence to make policy

 

Evidence-based policymaking and the ‘new policy sciences’

[I wasn’t happy with the first version, so this is version 2, to be enjoyed with the ppt & MP3 ]

In the ‘new policy sciences’, Chris Weible and I advocate:

• a return to Lasswell’s vision of combining policy analysis (to recommend policy change) and policy theory (to explain policy change), but
• focusing on a far larger collection of actors (beyond a small group at the centre),
• recognising new developments in studies of the psychology of policymaker choice, and
• building into policy analysis the recognition that any policy solution is introduced in a complex policymaking environment over which no-one has control.

However, there is a lot of policy theory out there, and we can’t put policy theory together like Lego to produce consistent insights to inform policy analysis.

Rather, each concept in my image of the policy process represents its own literature: see these short explainers on the psychology of policymaking, actors spread across multi-level governance, institutions, networks, ideas, and socioeconomic factors/ events.

What the explainers don’t really project is the sense of debate within the literature about how best to conceptualise each concept. You can pick up their meaning in a few minutes but would need a few years to appreciate the detail and often-fundamental debate.

Ideally, we would put all of the concepts together to help explain policymaker choice within a complex policymaking environment (how else could I put up the image and present is as one source of accumulated wisdom from policy studies?). Peter John describes such accounts as ‘synthetic’. I have also co-authored work with Tanya Heikkila – in 2014 and 2017 to compare the different ways in which ‘synthetic’ theories conceptualise the policy process.

However, note the difficulty of putting together a large collection of separate and diverse literatures into one simple model (e.g. while doing a PhD).

On that basis, I’d encourage you to think of these attempts to synthesise as stories. I tell these stories a lot, but someone else could describe theory very differently (perhaps by relying on fewer male authors or US-derived theories in which there is a very specific reference points and positivism is represented well).

The example of EBPM

I have given a series of talks to explain why we should think of ‘evidence-based policymaking’ as a myth or political slogan, not an ideal scenario or something to expect from policymaking in the real world. They usually involve encouraging framing and storytelling rather than expecting evidence to speak for itself, and rejecting the value of simple models like the policy cycle. I then put up an image of my own and encourage people to think about the implications of each concept:

I describe the advice as simple-sounding and feasible at first glance, but actually a series of Herculean* tasks:

• There are many policymakers and influencers spread across government, so find out where the action is, or the key venues in which people are making authoritative decisions.
• Each venue has its own ‘institutions’ – the formal and written, or informal and unwritten rules of policymaking – so learn the rules of each venue in which you engage.
• Each venue is guided by a fundamental set of ideas – as paradigms, core beliefs, monopolies of understanding – so learn that language.
• Each venue has its own networks – the relationships between policy makers and influencers – so build trust and form alliances within networks.
• Policymaking attention is often driven by changes in socioeconomic factors, or routine/ non-routine events, so be prepared to exploit the ‘windows of opportunity’ to present your solution during heightened attention to a policy problem.

Further, policy theories/ studies help us understand the context in which people make such choices. For example, consider the story that Kathryn Oliver and I tell about the role of evidence in policymaking environments:

If there are so many potential authoritative venues, devote considerable energy to finding where the ‘action’ is (and someone specific to talk to). Even if you find the right venue, you will not know the unwritten rules unless you study them intensely. Some networks are close-knit and difficult to access because bureaucracies have operating procedures that favour some sources of evidence. Research advocates can be privileged insiders in some venues and excluded completely in others. If your evidence challenges an existing paradigm, you need a persuasion strategy good enough to prompt a shift of attention to a policy problem and a willingness to understand that problem in a new way. You can try to find the right time to use evidence to exploit a crisis leading to major policy change, but the opportunities are few and chances of success low.  In that context, policy studies recommend investing your time over the long term – to build up alliances, trust in the messenger, knowledge of the system, and to seek ‘windows of opportunity’ for policy change – but offer no assurances that any of this investment will ever pay off

As described, this focus on the new policy sciences and synthesising insights helps explain why ‘the politics of evidence-based policymaking’ is equally important to civil servants (my occasional audience) as researchers (my usual audience).

To engage in skilled policy analysis, and give good advice, is to recognise the ways in which policymakers combine cognition/emotion to engage with evidence, and must navigate a complex policymaking environment when designing or selecting technically and politically feasible solutions. To give good advice is to recognise what you want policymakers to do, but also that they are not in control of the consequences.

From one story to many?

However, I tell these stories without my audience having the time to look further into each theory and its individual insights. If they do have a little more time, I go into the possible contribution of individual insights to debate.

For example, they adapt insights from psychology in different ways …

• PET shows the overall effect of policymaker psychology on policy change: they combine cognition and emotion to pay disproportionate attention to a small number of issues (contributing to major change) and ignore the rest (contributing to ‘hyperincremental’ change).
• The IAD focuses partly on the rules and practices that actors develop to build up trust in each other.
• The ACF describes actors going into politics to turn their beliefs into policy, forming coalitions with people who share their beliefs, then often romanticising their own cause and demonising their opponents.
• The NPF describes the relative impact of stories on audiences who use cognitive shortcuts to (for example) identify with a hero and draw a simple moral.
• SCPD describes policymakers drawing on gut feeling to identify good and bad target populations.
• Policy learning involves using cognition and emotion to acquire new knowledge and skills.

… even though the pace of change in psychological research often seems faster than the ways in which policy studies can incorporate new and reliable insights.

They also present different conceptions of the policymaking environment in which actors make choices. See this post for more on this discussion in relation to EBPM.

My not-brilliant conclusion is that:

1. Policy theory/ policy studies has a lot to offer other disciplines and professions, particularly in field like EBPM in which we need to account for politics and, more importantly, policymaking systems, but
2. Beware any policy theory story that presents the source literature as coherent and consistent.
3. Rather, any story of the field involves a series of choices about what counts as a good theory and good insight.
4. In other words, the exhortation to think more about what counts as ‘good evidence’ applies just as much to political science as any other.

Postscript: well, that is the last of the posts for my ANZOG talks. If I’ve done this properly, there should now be a loop of talks. It should be possible to go back to the first one and see it as a sequel to this one!

Or, for more on theory-informed policy analysis – in other words, where the ‘new policy sciences’ article is taking us – here is how I describe it to students doing a policy analysis paper (often for the first time).

Or, have a look at the earlier discussion of images of the policy process. You may have noticed that there is a different image in this post (knocked up in my shed at the weekend). It’s because I am experimenting with shapes. Does the image with circles look more relaxing? Does the hexagonal structure look complicated even though it is designed to simplify? Does it matter? I think so. People engage emotionally with images. They share them. They remember them. So, I need an image more memorable than the policy cycle.

Paul Cairney Brisbane EBPM New Policy Sciences 25.10.18

*I welcome suggestions on another word to describe almost-impossibly-hard

Evidence-based policymaking and the ‘new policy sciences’

I have given a series of talks to explain why we should think of ‘evidence-based policymaking’ as a myth or political slogan, not an ideal scenario or something to expect from policymaking in the real world. They usually involve encouraging framing and storytelling rather than expecting evidence to speak for itself, and rejecting the value of simple models like the policy cycle. I then put up an image of my own and encourage people to think about the implications of each concept:

I describe the advice as simple-sounding and feasible at first glance, but actually a series of Herculean* tasks:

• There are many policymakers and influencers spread across government, so find out where the action is, or the key venues in which people are making authoritative decisions.
• Each venue has its own ‘institutions’ – the formal and written, or informal and unwritten rules of policymaking – so learn the rules of each venue in which you engage.
• Each venue is guided by a fundamental set of ideas – as paradigms, core beliefs, monopolies of understanding – so learn that language.
• Each venue has its own networks – the relationships between policy makers and influencers – so build trust and form alliances within networks.
• Policymaking attention is often driven by changes in socioeconomic factors, or routine/ non-routine events, so be prepared to exploit the ‘windows of opportunity’ to present your solution during heightened attention to a policy problem.

In most cases, we don’t have time to discuss a more fundamental issue (at least for researchers using policy theory and political science concepts):

From where did these concepts come, and how well do we know them?

To cut a long story short, each concept represents its own literature: see these short explainers on the psychology of policymaking, actors spread across multi-level governance, institutions, networks, ideas, and socioeconomic factors/ events. What the explainers don’t really project is the sense of debate within the literature about how best to conceptualise each concept. You can pick up their meaning in a few minutes but would need a few years to appreciate the detail and often-fundamental debate.

Ideally, we would put all of the concepts together to help explain policymaker choice within a complex policymaking environment (how else could I put up the image and present is as one source of accumulated wisdom from policy studies?). Peter John describes such accounts as ‘synthetic’. I have also co-authored work with Tanya Heikkila – in 2014 and 2017 to compare the different ways in which ‘synthetic’ theories conceptualise the policy process. However, note the difficulty of putting together a large collection of separate and diverse literatures into one simple model (e.g. while doing a PhD).

The new policy sciences

More recently, in the ‘new policy sciences’, Chris Weible and I present a more provocative story of these efforts, in which we advocate:

• a return to Lasswell’s vision of combining policy analysis (to recommend policy change) and policy theory (to explain policy change), but
• focusing on a far larger collection of actors (beyond a small group at the centre),
• recognising new developments in studies of the psychology of policymaker choice, and
• building into policy analysis the recognition that any policy solution is introduced in a complex policymaking environment over which no-one has control.

This focus on psychology is not new …

• PET shows the overall effect of policymaker psychology on policy change: they combine cognition and emotion to pay disproportionate attention to a small number of issues (contributing to major change) and ignore the rest (contributing to ‘hyperincremental’ change).
• The IAD focuses partly on the rules and practices that actors develop to build up trust in each other.
• The ACF describes actors going into politics to turn their beliefs into policy, forming coalitions with people who share their beliefs, then often romanticising their own cause and demonising their opponents.
• The NPF describes the relative impact of stories on audiences who use cognitive shortcuts to (for example) identify with a hero and draw a simple moral.
• SCPD describes policymakers drawing on gut feeling to identify good and bad target populations.
• Policy learning involves using cognition and emotion to acquire new knowledge and skills.

… but the pace of change in psychological research often seems faster than the ways in which policy studies can incorporate new and reliable insights.

Perhaps more importantly, policy studies help us understand the context in which people make such choices. For example, consider the story that Kathryn Oliver and I tell about the role of evidence in policymaking environments:

If there are so many potential authoritative venues, devote considerable energy to finding where the ‘action’ is (and someone specific to talk to). Even if you find the right venue, you will not know the unwritten rules unless you study them intensely. Some networks are close-knit and difficult to access because bureaucracies have operating procedures that favour some sources of evidence. Research advocates can be privileged insiders in some venues and excluded completely in others. If your evidence challenges an existing paradigm, you need a persuasion strategy good enough to prompt a shift of attention to a policy problem and a willingness to understand that problem in a new way. You can try to find the right time to use evidence to exploit a crisis leading to major policy change, but the opportunities are few and chances of success low.  In that context, policy studies recommend investing your time over the long term – to build up alliances, trust in the messenger, knowledge of the system, and to seek ‘windows of opportunity’ for policy change – but offer no assurances that any of this investment will ever pay off

Then, have a look at this discussion of ‘synthetic’ policy theories, designed to prompt people to consider how far they would go to get their evidence into policy.

Theory-driven policy analysis

As described, this focus on the new policy sciences helps explain why ‘the politics of evidence-based policymaking’ is equally important to civil servants (my occasional audience) as researchers (my usual audience).

To engage in skilled policy analysis, and give good advice, is to recognise the ways in which policymakers combine cognition/emotion to engage with evidence, and must navigate a complex policymaking environment when designing or selecting technically and politically feasible solutions. To give good advice is to recognise what you want policymakers to do, but also that they are not in control of the consequences.

Epilogue

Well, that is the last of the posts for my ANZOG talks. If I’ve done this properly, there should now be a loop of talks. It should be possible to go back to the first one in Auckland and see it as a sequel to this one in Brisbane!

Or, for more on theory-informed policy analysis – in other words, where the ‘new policy sciences’ article is taking us – here is how I describe it to students doing a policy analysis paper (often for the first time).

Or, have a look at the earlier discussion of images of the policy process. You may have noticed that there is a different image in this post (knocked up in my shed at the weekend). It’s because I am experimenting with shapes. Does the image with circles look more relaxing? Does the hexagonal structure look complicated even though it is designed to simplify? Does it matter? I think so. People engage emotionally with images. They share them. They remember them. So, I need an image more memorable than the policy cycle.

 

Paul Cairney Brisbane EBPM New Policy Sciences 25.10.18

 

 

*I welcome suggestions on another word to describe almost-impossibly-hard

Theory and Practice: How to Communicate Policy Research beyond the Academy

Notes (and audio) for my first talk at the University of Queensland, Wednesday 24th October, 12.30pm, Graduate Centre, room 402.

Here is the powerpoint that I tend to use to inform discussions with civil servants (CS). I first used it for discussion with CS in the Scottish and UK governments, followed by remarkably similar discussions in parts of New Zealand and Australian government. Partly, it provides a way into common explanations for gaps between the supply of, and demand for, research evidence. However, it also provides a wider context within which to compare abstract and concrete reasons for those gaps, which inform a discussion of possible responses at individual, organisational, and systemic levels. Some of the gap is caused by a lack of effective communication, but we should also discuss the wider context in which such communication takes place.

I begin by telling civil servants about the message I give to academics about why policymakers might ignore their evidence:

1. There are many claims to policy relevant knowledge.
2. Policymakers have to ignore most evidence.
3. There is no simple policy cycle in which we all know at what stage to provide what evidence.

In such talks, I go into different images of policymaking, comparing the simple policy cycle with images of ‘messy’ policymaking, then introducing my own image which describes the need to understand the psychology of choice within a complex policymaking environment.

Under those circumstances, key responses include:

• framing evidence in terms of the ways in which your audience understands policy problems
• engaging in networks to identify and exploit the right time to act, and
• venue shopping to find sympathetic audiences in different parts of political systems.

However, note the context of those discussions. I tend to be speaking with scientific researcher audiences to challenge some preconceptions about: what counts as good evidence, how much evidence we can reasonably expect policymakers to process, and how easy it is to work out where and when to present evidence. It’s generally a provocative talk, to identify the massive scale of the evidence-to-policy task, not a simple ‘how to do it’ guide.

In that context, I suggest to civil servants that many academics might be interested in more CS engagement, but might be put off by the overwhelming scale of their task, and – even if they remained undeterred – would face some practical obstacles:

1. They may not know where to start: who should they contact to start making connections with policymakers?
2. The incentives and rewards for engagement may not be clear. The UK’s ‘impact’ agenda has changed things, but not to the extent that any engagement is good engagement. Researchers need to tell a convincing story that they made an impact on policy/ policymakers with their published research, so there is a notional tipping point of engagement in which it reaches a scale that makes it worth doing.
3. The costs are clearer. For example, any time spent doing engagement is time away from writing grant proposals and journal articles (in other words, the things that still make careers).
4. The rewards and costs are not spread evenly. Put most simply, white male professors may have the most opportunities and face the fewest penalties for engagement in policymaking and social media. Or, the opportunities and rewards may vary markedly by discipline. In some, engagement is routine. In others, it is time away from core work.

In that context, I suggest that CS should:

• provide clarity on what they expect from academics, and when they need information
• describe what they can offer in return (which might be as simple as a written and signed acknowledgement of impact, or formal inclusion on an advisory committee).
• show some flexibility: you may have a tight deadline, but can you reasonably expect an academic to drop what they are doing at short notice?
• Engage routinely with academics, to help form networks and identify the right people you need at the right time

These introductory discussions provide a way into common descriptions of the gap between academic and policymaker:

• Technical languages/ jargon to describe their work
• Timescales to supply and demand information
• Professional incentives (such as to value scientific novelty in academia but evidential synthesis in government
• Comfort with uncertainty (often, scientists project relatively high uncertainty and don’t want to get ahead of the evidence; often policymakers need to project certainty and decisiveness)
• Assessments of the relative value of scientific evidence compared to other forms of policy-relevant information
• Assessments of the role of values and beliefs (some scientists want to draw the line between providing evidence and advice; some policymakers want them to go much further)

To discuss possible responses, I use the European Commission Joint Research Centre’s ‘knowledge management for policy’ project in which they identify the 8 core skills of organisations bringing together the suppliers and demanders of policy-relevant knowledge

However, I also use the following table to highlight some caution about the things we can achieve with general skills development and organisational reforms. Sometimes, the incentives to engage will remain low. Further, engagement is no guarantee of agreement.

In a nutshell, the table provides three very different models of ‘evidence-informed policymaking’ when we combine political choices about what counts as good evidence, and what counts as good policymaking (discussed at length in teaching evidence-based policy to fly). Discussion and clearer communication may help clarify our views on what makes a good model, but I doubt it will produce any agreement on what to do.

In the latter part of the talk, I go beyond that powerpoint into two broad examples of practical responses:

1. Storytelling

The Narrative Policy Framework describes the ‘science of stories’: we can identify stories with a 4-part structure (setting, characters, plot, moral) and measure their relative impact.  Jones/ Crow and Crow/Jones provide an accessible way into these studies. Also look at Davidson’s article on the ‘grey literature’ as a rich source of stories on stories.

On one hand, I think that storytelling is a great possibility for researchers: it helps them produce a core – and perhaps emotionally engaging – message that they can share with a wider audience. Indeed, I’d see it as an extension of the process that academics are used to: identifying an audience and framing an argument according to the ways in which that audience understands the world.

On the other hand, it is important to not get carried away by the possibilities:

• My reading of the NPF empirical work is that the most impactful stories are reinforcing the beliefs of the audience – to mobilise them to act – not changing their minds.
• Also look at the work of the Frameworks Institute which experiments with individual versus thematic stories because people react to them in very different ways. Some might empathise with an individual story; some might judge harshly. For example, they discusse stories about low income families and healthy eating, in which they use the theme of a maze to help people understand the lack of good choices available to people in areas with limited access to healthy food.

See: Storytelling for Policy Change: promise and problems

2. Evidence for advocacy

The article I co-authored with Oxfam staff helps identify the lengths to which we might think we have to go to maximise the impact of research evidence. Their strategies include:

1. Identifying the policy change they would like to see.
2. Identifying the powerful actors they need to influence.
3. A mixture of tactics: insider, outsider, and supporting others by, for example, boosting local civil society organisations.
4. A mix of ‘evidence types’ for each audience

5. Wider public campaigns to address the political environment in which policymakers consider choices
6. Engaging stakeholders in the research process (often called the ‘co-production of knowledge’)
7. Framing: personal stories, ‘killer facts’, visuals, credible messenger
8. Exploiting ‘windows of opportunity’
9. Monitoring, learning, trial and error

In other words, a source of success stories may provide a model for engagement or the sense that we need to work with others to engage effectively. Clear communication is one thing. Clear impact at a significant scale is another.

See: Using evidence to influence policy: Oxfam’s experience

 

 

 

 

 

 

 

Taking lessons from policy theory into practice: 3 examples

Notes for ANZSOG/ ANU Crawford School/ UNSW Canberra workshop. Powerpoint here. The recording of the lecture (skip to 2m30) and Q&A is here (right click to download mp3 or dropbox link):

The context for this workshop is the idea that policy theories could be more helpful to policymakers/ practitioners if we could all communicate more effectively with each other. Academics draw general and relatively abstract conclusions from multiple cases. Practitioners draw very similar conclusions from rich descriptions of direct experience in a smaller number of cases. How can we bring together their insights and use a language that we all understand? Or, more ambitiously, how can we use policy theory-based insights to inform the early career development training that civil servants and researchers receive?

The first step is to translate policy theories into a non-technical language by trying to speak with an audience beyond our immediate peers (see for example Practical Lessons from Policy Theories).

However, translation is not enough. A second crucial step is to consider how policymakers and practitioners are likely to make sense of theoretical insights when they apply them to particular aims or responsibilities. For example:

1. Central government policymakers may accept the descriptive accuracy of policy theories emphasising limited central control, but not the recommendation that they should let go, share power, and describe their limits to the public.
2. Scientists may accept key limitations to ‘evidence based policymaking’ but reject the idea that they should respond by becoming better storytellers or more manipulative operators.
3. Researchers and practitioners struggle to resolve hard choices when combining evidence and ‘coproduction’ while ‘scaling up’ policy interventions. Evidence choice is political choice. Can we do more than merely encourage people to accept this point?

I discuss these examples below because they are closest to my heart (especially example 1). Note throughout that I am presenting one interpretation about: (1) the most promising insights, and (2) their implications for practice. Other interpretations of the literature and its implications are available. They are just a bit harder to find.

Example 1: the policy cycle endures despite its descriptive inaccuracy

The policy cycle does not describe and explain the policy process well:

• If we insist on keeping the cycle metaphor, it is more accurate to see the process as a huge set of policy cycles that connect with each other in messy and unpredictable ways.
• The cycle approach also links strongly to the idea of ‘comprehensive rationality’ in which a small group of policymakers and analysts are in full possession of the facts and full control of the policy process. They carry out their aims through a series of stages.

Policy theories provide more descriptive and explanatory usefulness. Their insights include:

• Limited choice. Policymakers inherit organisations, rules, and choices. Most ‘new’ choice is a revision of the old.
• Limited attention. Policymakers must ignore almost all of the policy problems for which they are formally responsible. They pay attention to some, and delegate most responsibility to civil servants. Bureaucrats rely on other actors for information and advice, and they build relationships on trust and information exchange.
• Limited central control. Policy may appear to be made at the ‘top’ or in the ‘centre’, but in practice policymaking responsibility is spread across many levels and types of government (many ‘centres’). ‘Street level’ actors make policy as they deliver. Policy outcomes appear to ‘emerge’ locally despite central government attempts to control their fate.
• Limited policy change. Most policy change is minor, made and influenced by actors who interpret new evidence through the lens of their beliefs. Well-established beliefs limit the opportunities of new solutions. Governments tend to rely on trial-and-error, based on previous agreements, rather than radical policy change based on a new agenda. New solutions succeed only during brief and infrequent windows of opportunity.

However, the cycle metaphor endures because:

• It provides a simple model of policymaking with stages that map onto important policymaking functions.
• It provides a way to project policymaking to the public. You know how we make policy, and that we are in charge, so you know who to hold to account.

In that context, we may want to be pragmatic about our advice:

1. One option is via complexity theory, in which scholars generally encourage policymakers to accept and describe their limits:

• Accept routine error, reduce short-term performance management, engage more in trial and error, and ‘let go’ to allow local actors the flexibility to adapt and respond to their context.
• However, would a government in the Westminster tradition really embrace this advice? No. They need to balance (a) pragmatic policymaking, and (b) an image of governing competence.

2. Another option is to try to help improve an existing approach.

• Koski and Workman build on PET to recommend ways to process information more effectively.
• Studies built on MSA tend to focus on ‘entrepreneurs’ having a technically and politically feasible solution ready for the next window of opportunity.
• Sanderson describes the skills required for ‘intelligent policymaking’.
• I sum up these approaches in ‘How Can Policy Theory Have an Impact on Policy Making?’ Teaching Public Administration, 33, 1, 22-39 

Further reading (blog posts):

The language of complexity does not mix well with the language of Westminster-style accountability

Making Sense of Policymaking: why it’s always someone else’s fault and nothing ever changes

Two stories of British politics: the Westminster model versus Complex Government

Example 2: how to deal with a lack of ‘evidence based policymaking’

I used to read many papers on tobacco policy, with the same basic message: we have the evidence of tobacco harm, and evidence of which solutions work, but there is an evidence-policy gap caused by too-powerful tobacco companies, low political will, and pathological policymaking. These accounts are not informed by theories of policymaking.

I then read Oliver et al’s paper on the lack of policy theory in health/ environmental scholarship on the ‘barriers’ to the use of evidence in policy. Very few articles rely on policy concepts, and most of the few rely on the policy cycle. This lack of policy theory is clear in their description of possible solutions – better communication, networking, timing, and more science literacy in government – which does not describe well the need to respond to policymaker psychology and a complex policymaking environment.

So, I wrote The Politics of Evidence-Based Policymaking and one zillion blog posts to help identify the ways in which policy theories could help explain the relationship between evidence and policy.

Since then, the highest demand to speak about the book has come from government/ public servant, NGO, and scientific audiences outside my discipline. The feedback is generally that: (a) the book’s description sums up their experience of engagement with the policy process, and (b) maybe it opens up discussion about how to engage more effectively.

But how exactly do we turn empirical descriptions of policymaking into practical advice?

For example, scientist/ researcher audiences want to know the answer to a question like: Why don’t policymakers listen to your evidence? and so I focus on three conversation starters:

1. they have a broader view on what counts as good evidence (see ANZSOG description)
2. they have to ignore almost all information (a nice way into bounded rationality and policymaker psychology)
3. they do not understand or control the process in which they seek to use evidence (a way into ‘the policy process’)

We can then consider many possible responses in the sequel What can you do when policymakers ignore your evidence?

Examples include:

• ‘How to do it’ advice. I compare tips for individuals (from experienced practitioners) with tips based on policy concepts. They are quite similar-looking tips – e.g. find out where the action is, learn the rules, tell good stories, engage allies, seek windows of opportunity – but I describe mine as 5 impossible tasks!
• Organisational reform. I describe work with the European Commission Joint Research Centre to identify 8 skills or functions of an organisation bringing together the supply/demand of knowledge.
• Ethical dilemmas. I use key policy theories to ask people how far they want to go to privilege evidence in policy. It’s fun to talk about these things with the type of scientist who sees any form of storytelling as manipulation.

Further reading:

Is Evidence-Based Policymaking the same as good policymaking?

A 5-step strategy to make evidence count

Political science improves our understanding of evidence-based policymaking, but does it produce better advice?

Principles of science advice to government: key problems and feasible solutions

Example 3: how to encourage realistic evidence-informed policy transfer

This focus on EBPM is useful context for discussions of ‘policy learning’ and ‘policy transfer’, and it was the focus of my ANZOG talk entitled (rather ambitiously) ‘teaching evidence-based policy to fly’.

I’ve taken a personal interest in this one because I’m part of a project – called IMAJINE – in which we have to combine academic theory and practical responses. We are trying to share policy solutions across Europe rather than explain why few people share them!

For me, the context is potentially overwhelming:

• Policy learning is a political process, and expert-driven learning is one of many possibilities, alongside dialogue, bargaining, and hierarchical models.
• Indeed, researchers often do badly because they don’t learn how to deal with the politics.
• Policy transfer can be maximal or minimal, and driven voluntarily or via coercion. A policy solution, when ‘imported’, can be mimicked, translated or transformed.
• When transfer is voluntary, receptivity to new solutions is more important than their supply.
• There are very different ‘evidence-based’ models on how to ‘scale up’ policy success.

So, when we start to focus on sharing lessons, we will have three things to discover:

1. What is the evidence for success, and from where does it come? Governments often project success without backing it up.
2. What story do policymakers tell about the problem they are trying to solve, the solutions they produced, and why? Two different governments may be framing and trying to solve the same problem in very different ways.
3. Was the policy introduced in a comparable policymaking system? People tend to focus on political system comparability (e.g. is it unitary or federal?), but I think the key is in policymaking system comparability (e.g. what are the rules and dominant ideas?).

To be honest, when one of our external assessors asked me how well I thought I would do, we both smiled because the answer may be ‘not very’. In other words, the most practical lesson may be the hardest to take, although I find it comforting: the literature suggests that policymakers might ignore you for 20 years then suddenly become very (but briefly) interested in your work.

 

The slides are a bit wonky because I combined my old ppt to the Scottish Government with a new one for UNSW Paul Cairney ANU Policy practical 22 October 2018

I wanted to compare how I describe things to (1) civil servants (2) practitioners/ researcher (3) me, but who has the time/ desire to listen to 3 powerpoints in one go? If the answer is you, let me know and we’ll set up a Zoom call.

Teaching evidence based policy to fly: how to deal with the politics of policy learning and transfer

This post provides (a generous amount of) background for my ANZSOG talk Teaching evidence based policy to fly: transferring sound policies across the world.

The event’s description sums up key conclusions in the literature on policy learning and policy transfer:

1. technology and ‘entrepreneurs’ help ideas spread internationally, and domestic policymakers can use them to be more informed about global policy innovation, but
2. there can be major unintended consequences to importing ideas, such as the adoption of policy solutions with poorly-evidenced success, or a broader sense of failed transportation caused by factors such as a poor fit between the aims of the exporter/importer.

In this post, I connect these conclusions to broader themes in policy studies, which suggest that:

3. policy learning and policy transfer are political processes, not ‘rational’ or technical searches for information
4. the use of evidence to spread policy innovation requires two interconnected choices: what counts as good evidence, and what role central governments should play.
5. the following ’11 question guide’ to evidence based policy transfer serves more as a way to reflect than a blueprint for action.

As usual, I suggest that we focus less on how we think we’d like to do it, and more on how people actually do it.

Policy transfer describes the use of evidence about policy in one political system to help develop policy in another. Taken at face value, it sounds like a great idea: why would a government try to reinvent the wheel when another government has shown how to do it?

Therefore, wouldn’t it be nice if I turned up to the lecture, equipped with a ‘blueprint’ for ‘evidence based’ policy transfer, and declared how to do it in a series of realistic and straightforward steps? Unfortunately, there are three main obstacles:

1. ‘Evidence based’ is a highly misleading description of the use of information in policy.
2. To transfer a policy blueprint completely, in this manner, would require all places and contexts to be the same, and for the policy process to be technocratic and apolitical.
3. There are general academic guides on how to learn lessons from others systematically – such as Richard Rose’s ‘practical guide’  – but most academic work on learning and transfer does not suggest that policymakers follow this kind of advice.

Instead, policy learning is a political process – involving the exercise of power to determine what and how to learn – and it is difficult to separate policy transfer from the wider use of evidence and ideas in policy processes.

Let’s take each of these points in turn, before reflecting on their implications for any X-step guide:

3 reasons why ‘evidence based’ does not describe policymaking

In a series of ANZSOG talks on ‘evidence based policymaking’ (EBPM), I describe three main factors, all of which are broadly relevant to transfer:

1. There are many forms of policy-relevant evidence and few policymakers adhere to a strict ‘hierarchy’ of knowledge.

Therefore, it is unclear how one government can, or should, generate evidence of another government’s policy success.

2. Policymakers must find ways to ignore most evidence – such as by combining ‘rational’ and ‘irrational’ cognitive shortcuts – to be able to act quickly.

The generation of policy transfer lessons is a highly political process in which actors adapt to this need to prioritise information while competing with each other. They exercise power to: prioritise some information and downplay the rest, define the nature of the policy problem, and evaluate the success of another government’s solutions. There is a strong possibility that policymakers will import policy solutions without knowing if, and why, they were successful.

3. They do not control the policy process in which they engage.

We should not treat ‘policy transfer’ as separate from the policy process in which policymakers and influencers engage. Rather, the evidence of international experience competes with many other sources of ideas and evidence within a complex policymaking system.

The literature on ‘policy learning’ tells a similar story

Studies of the use of evaluation evidence (perhaps to answer the question: was this policy successful?) have long described policymakers using the research process for many different purposes, from short term problem-solving and long-term enlightenment, to putting off decisions or using evidence cynically to support an existing policy.

We should therefore reject the temptation to (a) equate ‘policy learning’ with a simplistic process that we might associate with teachers transmitting facts to children, or (b) assume that adults simply change their beliefs when faced with new evidence. Rather, Dunlop and Radaelli describe policy learning as a political process in the following ways:

1.It is collective and rule-bound

Individuals combine cognition and emotion to process information, in organisations with rules that influence their motive and ability to learn, and in wider systems, in which many actors cooperate and compete to establish the rules of evidence gathering and analysis, or policymaking environments that constrain or facilitate their action.

2.’Evidence based’ is one of several types of policy learning

• Epistemic. Primarily by scientific experts transmitting knowledge to policymakers.
• Reflection. Open dialogue to incorporate diverse forms of knowledge and encourage cooperation.
• Bargaining. Actors learn how to cooperate and compete effectively.
• Hierarchy. Actors with authority learn how to impose their aims; others learn the limits to their discretion.

3.The process can be ‘dysfunctional’: driven by groupthink, limited analysis, and learning how to dominate policymaking, not improve policy.

Their analysis can produce relevant take-home points such as:

• Experts will be ineffective if they assume that policy learning is epistemic. The assumption will leave them ill-prepared to deal with bargaining.
• There is more than one legitimate way to learn, such as via deliberative processes that incorporate more perspectives and forms of knowledge.

What does the literature on transfer tell us?

‘Policy transfer’ can describe a spectrum of activity:

• driven voluntarily, by a desire to learn from the story of another government’s policy’s success. In such cases, importers use shortcuts to learning, such as by restricting their search to systems with which they have something in common (such as geography or ideology), learning via intermediaries such as ‘entrepreneurs’, or limiting their searches for evidence of success.
• driven by various forms of pressure, including encouragement by central (or supranational) governments, international norms or agreements, ‘spillover’ effects causing one system to respond to innovation by another, or demands by businesses to minimise the cost of doing business.

In that context, some of the literature focuses on warning against unsuccessful policy transfer caused by factors such as:

• Failing to generate or use enough evidence on what made the initial policy successful
• Failing to adapt that policy to local circumstances
• Failing to back policy change with sufficient resources

However, other studies highlight some major qualifications:

• If the process is about using ideas about one system to inform another, our attention may shift from ‘transfer’ to ‘translation’ or ‘transformation’, and the idea of ‘successful transfer’ makes less sense
• Transfer success is not the same as implementation success, which depends on a wider range of factors
• Nor is it the same as ‘policy success’, which can be assessed by a mix of questions to reflect political reality: did it make the government more re-electable, was the process of change relatively manageable, and did it produce intended outcomes?

The use of evidence to spread policy innovation requires a combination of profound political and governance choices

When encouraging policy diffusion within a political system, choices about: (a) what counts as ‘good’ evidence of policy success have a major connection to (b) what counts as good governance.

For example, consider these ideal-types or models in table 1:

In one scenario, we begin by relying primarily on RCT evidence (multiple international trials) and import a relatively fixed model, to ensure ‘fidelity’ to a proven intervention and allow us to measure its effect in a new context. This choice of good evidence limits the ability of subnational policymakers to adapt policy to local contexts.

In another scenario, we begin by relying primary on governance principles, such as to respect local discretion as well as incorporate practitioner and user experience as important knowledge claims. The choice of governance model relates closely to a less narrow sense of what counts as good evidence, but also a more limited ability to evaluate policy success scientifically.

In other words, the political choice to privilege some forms of evidence is difficult to separate from another political choice to privilege the role of one form of government.

Telling a policy transfer story: 11 questions to encourage successful evidence based policy transfer  

In that context, these steps to evidence-informed policy transfer serve more to encourage reflection than provide a blueprint for action. I accept that 11 is less catchy than 10.

1. What problem did policymakers say they were trying to solve, and why?
2. What solution(s) did they produce?
3. Why?

Points 1-3 represent the classic and necessary questions from policy studies: (1) ‘what is policy?’ (2)  ‘how much did policy change?’ and (3) why? Until we have a good answer, we do not know how to draw comparable lessons. Learning from another government’s policy choices is no substitute for learning from more meaningful policy change.

4. Was the project introduced in a country or region which is sufficiently comparable? Comparability can relate to the size and type of country, the nature of the problem, the aims of the borrowing/ lending government and their measures of success.

5. Was it introduced nationwide, or in a region which is sufficiently representative of the national experience (it is not an outlier)?

6. How do we account for the role of scale, and the different cultures and expectations in each policy field?

Points 4-6 inform initial background discussions of case study reports. We need to focus on comparability when describing the context in which the original policy developed. It is not enough to state that two political systems are different. We need to identify the relevance and implications of the differences, from another government’s definition of the problem to the logistics of their task.

7. Has the project been evaluated independently, subject to peer review and/ or using measures deemed acceptable to the government?

8. Has the evaluation been of a sufficient period in proportion to the expected outcomes?

9. Are we confident that this project has been evaluated the most favourably – i.e. that our search for relevant lessons has been systematic, based on recognisable criteria (rather than reputations)?

10. Are we identifying ‘Good practice’ based on positive experience, ‘Promising approaches’ based on positive but unsystematic findings, ‘Research–based’ or based on ‘sound theory informed by a growing body of empirical research’, or ‘Evidence–based’, when ‘the programme or practice has been rigorously evaluated and has consistently been shown to work’?

Points 7-10 raise issues about the relationships between (a) what evidence we should use to evaluate success or potential, and (b) how long we should wait to declare success.

11. What will be the relationship between evidence and governance?

Should we identify the same basic model and transfer it uniformly, tell a qualitative story about the model and invite people to adapt it, or focus pragmatically on an eclectic range of evidential sources and focus on the training of the actors who will implement policy?

In conclusion

Information technology has allowed us to gather a huge amount of policy-relevant information across the globe. However, it has not solved the limitations we face in defining policy problems clearly, gathering evidence on policy solutions systematically, and generating international lessons that we can use to inform domestic policy processes.

This rise in available evidence is not a substitute for policy analysis and political choice. These choices range from how to adjudicate between competing policy preference, to how to define good evidence and good government. A lack of attention to these wider questions helps explain why – at least from some perspectives – policy transfer seems to fail.

Paul Cairney Auckland Policy Transfer 12.10.18

 

 

The Politics of Evidence-Based Policymaking: ANZSOG talks

This post introduces a series of related talks on ‘the politics of evidence-based policymaking’ (EBPM) that I’m giving as part of larger series of talks during this ANZOG-funded/organised trip.

The EBPM talks begin with a discussion of the same three points: what counts as evidence, why we must ignore most of it (and how), and the policy process in which policymakers use some of it. However, the framing of these points, and the ways in which we discuss the implications, varies markedly by audience. So, in this post, I provide a short discussion of the three points, then show how the audience matters (referring to the city as a shorthand for each talk).

The overall take-home points are highly practical, in the same way that critical thinking has many practical applications (in other words, I’m not offering a map, toolbox, or blueprint):

• If you begin with (a) the question ‘why don’t policymakers use my evidence?’ I like to think you will end with (b) the question ‘why did I ever think they would?’.
• If you begin by taking the latter as (a) a criticism of politics and policymakers, I hope you will end by taking it as (b) a statement of the inevitability of the trade-offs that must accompany political choice.
• We may address these issues by improving the supply and use of evidence. However, it is more important to maintain the legitimacy of the politicians and political systems in which policymakers choose to ignore evidence. Technocracy is no substitute for democracy.

3 ways to describe the use of evidence in policymaking

1. Discussions of the use of evidence in policy often begin as a valence issue: who wouldn’t want to use good evidence when making policy?

However, it only remains a valence issue when we refuse to define evidence and justify what counts as good evidence. After that, you soon see the political choices emerge. A reference to evidence is often a shorthand for scientific research evidence, and good often refers to specific research methods (such as randomised control trials). Or, you find people arguing very strongly in the almost-opposite direction, criticising this shorthand as exclusionary and questioning the ability of scientists to justify claims to superior knowledge. Somewhere in the middle, we find that a focus on evidence is a good way to think about the many forms of information or knowledge on which we might make decisions, including: a wider range of research methods and analyses, knowledge from experience, and data relating to the local context with which policy would interact.

So, what begins as a valence issue becomes a gateway to many discussions about how to understand profound political choices regarding: how we make knowledge claims, how to ‘co-produce’ knowledge via dialogue among many groups, and the relationship between choices about evidence and governance.

2. It is impossible to pay attention to all policy relevant evidence.

There is far more information about the world than we are able to process. A focus on evidence gaps often gives way to the recognition that we need to find effective ways to ignore most evidence.

There are many ways to describe how individuals combine cognition and emotion to limit their attention enough to make choices, and policy studies (to all intents and purposes) describe equivalent processes – described, for example, as ‘institutions’ or rules – in organisations and systems.

One shortcut between information and choice is to set aims and priorities; to focus evidence gathering on a small number of problems or one way to define a problem, and identify the most reliable or trustworthy sources of evidence (often via evidence ‘synthesis’). Another is to make decisions quickly by relying on emotion, gut instinct, habit, and existing knowledge or familiarity with evidence.

Either way, agenda setting and problem definition are political processes that address uncertainty and ambiguity. We gather evidence to reduce uncertainty, but first we must reduce ambiguity by exercising power to define the problem we seek to solve.

3. It is impossible to control the policy process in which people use evidence.

Policy textbooks (well, my textbook at least!) provide a contrast between:

• The model of a ‘policy cycle’ that sums up straightforward policymaking, through a series of stages, over which policymakers have clear control. At each stage, you know where evidence fits in: to help define the problem, generate solutions, and evaluate the results to set the agenda for the next cycle.
• A more complex ‘policy process’, or policymaking environment, of which policymakers have limited knowledge and even less control. In this environment, it is difficult to know with whom engage, the rules of engagement, or the likely impact of evidence.

Overall, policy theories have much to offer people with an interest in evidence-use in policy, but primarily as a way to (a) manage expectations, to (b) produce more realistic strategies and less dispiriting conclusions. It is useful to frame our aim as to analyse the role of evidence within a policy process that (a) we don’t quite understand, rather than (b) we would like to exist.

The events themselves

Below, you will find a short discussion of the variations of audience and topic. I’ll update and reflect on this discussion (in a revised version of this post) after taking part in the events.

Social science and policy studies: knowledge claims, bounded rationality, and policy theory

For Auckland and Wellington A, I’m aiming for an audience containing a high proportion of people with a background in social science and policy studies. I describe the discussion as ‘meta’ because I am talking about how I talk about EBPM to other audiences, then inviting discussion on key parts of that talk, such as how to conceptualise the policy process and present conceptual insights to people who have no intention of deep dives into policy theory.

I often use the phrase ‘I’ve read it, so you don’t have to’ partly as a joke, but also to stress the importance of disciplinary synthesis when we engage in interdisciplinary (and inter-professional) discussion. If so, it is important to discuss how to produce such ‘synthetic’ accounts.

I tend to describe key components of a policymaking environment quickly: many policy makers and influencers spread across many levels and types of government, institutions, networks, socioeconomic factors and events, and ideas. However, each of these terms represents a shorthand to describe a large and diverse literature. For example, I can describe an ‘institution’ in a few sentences, but the study of institutions contains a variety of approaches.

Background post: I know my audience, but does my other audience know I know my audience?

Academic-practitioner discussions: improving the use of research evidence in policy

For Wellington B and Melbourne, the audience is an academic-practitioner mix. We discuss ways in which we can encourage the greater use of research evidence in policy, perhaps via closer collaboration between suppliers and users.

Discussions with scientists: why do policymakers ignore my evidence?

Sydney UNSW focuses more on researchers in scientific fields (often not in social science).  I frame the question in a way that often seems central to scientific researcher interest: why do policymakers seem to ignore my evidence, and what can I do about it?

Then, I tend to push back on the idea that the fault lies with politics and policymakers, to encourage researchers to think more about the policy process and how to engage effectively in it. If I’m trying to be annoying, I’ll suggest to a scientific audience that they see themselves as ‘rational’ and politicians as ‘irrational’. However, the more substantive discussion involves comparing (a) ‘how to make an impact’ advice drawn from the personal accounts of experienced individuals, giving advice to individuals, and (b) the sort of advice you might draw from policy theories which focus more on systems.

Background post: What can you do when policymakers ignore your evidence?

Early career researchers: the need to build ‘impact’ into career development

Canberra UNSW is more focused on early career researchers. I think this is the most difficult talk because I don’t rely on the same joke about my role: to turn up at the end of research projects to explain why they failed to have a non-academic impact.  Instead, my aim is to encourage intelligent discussion about situating the ‘how to’ advice for individual researchers into a wider discussion of policymaking systems.

Similarly, Brisbane A and B are about how to engage with practitioners, and communicate well to non-academic audiences, when most of your work and training is about something else entirely (such as learning about research methods and how to engage with the technical language of research).

Background posts:

What can you do when policymakers ignore your evidence? Tips from the ‘how to’ literature from the science community

What can you do when policymakers ignore your evidence? Encourage ‘knowledge management for policy’

See also:

1. A similar talk at LSHTM (powerpoint and audio)

Now available as a live recording (of the powerpoint and sound; the hand waving exists only in our memories and our hearts) https://t.co/UcK5IdVL0R https://t.co/tKFt0OcSYC

— Professor Paul Cairney (@CairneyPaul) November 21, 2018

2. European Health Forum Gastein 2018 ‘Policy in Evidence’ (from 6 minutes)

https://webcasting.streamdis.eu/Mediasite/Play/8143157d976146b4afd297897c68be5e1d?catalog=62e4886848394f339ff678a494afd77f21&playFrom=126439&autoStart=true

 

See also:

Evidence-based policymaking and the new policy sciences

 

Managing expectations about the use of evidence in policy

Notes for the #transformURE event hosted by Nuffield, 25^th September 2018

I like to think that I can talk with authority on two topics that, much like a bottle of Pepsi and a pack of Mentos, you should generally keep separate:

1. When talking at events on the use of evidence in policy, I say that you need to understand the nature of policy and policymaking to understand the role of evidence in it.
2. When talking with students, we begin with the classic questions ‘what is policy?’ and ‘what is the policy process’, and I declare that we don’t know the answer. We define policy to show the problems with all definitions of policy, and we discuss many models and theories that only capture one part of the process. There is no ‘general theory’ of policymaking.

The problem, when you put together those statements, is that you need to understand the role of evidence within a policy process that we don’t really understand.

It’s an OK conclusion if you just want to declare that the world is complicated, but not if you seek ways to change it or operate more effectively within it.

Put less gloomily:

• We have ways to understand key parts of the policy process. They are not ready-made to help us understand evidence use, but we can use them intelligently.
• Most policy theories exist to explain policy dynamics, not to help us adapt effectively to them, but we can derive general lessons with often-profound implications.

Put even less gloomily, it is not too difficult to extract/ synthesise key insights from policy theories, explain their relevance, and use them to inform discussions about how to promote your preferred form of evidence use.

The only remaining problem is that, although the resultant advice looks quite straightforward, it is far easier said than done. The proposed actions are more akin to the Labours of Hercules than [PAC: insert reference to something easier].

They include:

1. Find out where the ‘action’ is, so that you can find the right audience for your evidence. Why? There are many policymakers and influencers spread across many levels and types of government.
2. Learn and follow the ‘rules of the game’. Why? Each policymaking venue has its own rules of engagement and evidence gathering, and the rules are often informal and unwritten.
3. Gain access to ‘policy networks’. Why? Most policy is processed at a low level of government, beyond the public spotlight, between relatively small groups of policymakers and influencers. They build up trust as they work together, learning who is reliable and authoritative, and converging on how to use evidence to understand the nature and solution to policy problems.
4. Learn the language. Why? Each venue has its own language to reflect dominant ideas, beliefs, or ways to understand a policy problem. In some arenas, there is a strong respect for a ‘hierarchy’ of evidence. In others, they key reference point may be value for money. In some cases, the language reflects the closing-off of some policy solutions (such as redistributing resources from one activity to another).
5. Exploit windows of opportunity. Why? Events, and changes in socioeconomic conditions, often prompt shifts of attention to policy issues. ‘Policy entrepreneurs’ lie in wait for the right time to exploit a shift in the motive and opportunity of a policymaker to pay attention to and try to solve a problem.

So far so good, until you consider the effort it would take to achieve any of these things: you may need to devote the best part of your career to these tasks with no guarantee of success.

Put more positively, it is better to be equipped with these insights, and to appreciate the limits to our actions, than to think we can use top tips to achieve ‘research impact’ in a more straightforward way.

Kathryn Oliver and I describe these ‘how to’ tips in this post and, in this article in Political Studies Review, use a wider focus on policymaking environments to produce a more realistic sense of what individual researchers – and research-producing organisations – could achieve.

There is some sensible-enough advice out there for individuals – produce good evidence, communicate it well, form relationships with policymakers, be available, and so on – but I would exercise caution when it begins to recommend being ‘entrepreneurial’. The opportunities to be entrepreneurial are not shared equally, most entrepreneurs fail, and we can likely better explain their success with reference to their environment than their skill.

Evidence-based policymaking: political strategies for scientists living in the real world

Note: I wrote the following discussion (last year) to be a Nature Comment but it was not to be!

Nature articles on evidence-based policymaking often present what scientists would like to see: rules to minimise bias caused by the cognitive limits of policymakers, and a simple policy process in which we know how and when to present the best evidence.[1]  What if neither requirement is ever met? Scientists will despair of policymaking while their competitors engage pragmatically and more effectively.[2]

Alternatively, if scientists learned from successful interest groups, or by using insights from policy studies, they could develop three ‘take home messages’: understand and engage with policymaking in the real world; learn how and when evidence ‘wins the day’; and, decide how far you should go to maximise the use of scientific evidence. Political science helps explain this process[3], and new systematic and thematic reviews add new insights.[4] [5] [6] [7]

Understand and engage with policymaking in the real world

Scientists are drawn to the ‘policy cycle’, because it offers a simple – but misleading – model for engagement with policymaking.[3] It identifies a core group of policymakers at the ‘centre’ of government, perhaps giving the impression that scientists should identify the correct ‘stages’ in which to engage (such as ‘agenda setting’ and ‘policy formulation’) to ensure the best use of evidence at the point of authoritative choice. This is certainly the image generated most frequently by health and environmental scientists when they seek insights from policy studies.[8]

Yet, this model does not describe reality. Many policymakers, in many levels and types of government, adopt and implement many measures at different times. For simplicity, we call the result ‘policy’ but almost no modern policy theory retains the linear policy cycle concept. In fact, it is more common to describe counterintuitive processes in which, for example, by the time policymaker attention rises to a policy problem at the ‘agenda setting’ stage, it is too late to formulate a solution. Instead, ‘policy entrepreneurs’ develop technically and politically feasible solutions then wait for attention to rise and for policymakers to have the motive and opportunity to act.[9]

Experienced government science advisors recognise this inability of the policy cycle image to describe real world policymaking. For example, Sir Peter Gluckman presents an amended version of this model, in which there are many interacting cycles in a kaleidoscope of activity, defying attempts to produce simple flow charts or decision trees. He describes the ‘art and craft’ of policy engagement, using simple heuristics to deal with a complex and ‘messy’ policy system.[10]

Policy studies help us identify two such heuristics or simple strategies.

First, respond to policymaker psychology by adapting to the short cuts they use to gather enough information quickly: ‘rational’, via trusted sources of oral and written evidence, and ‘irrational’, via their beliefs, emotions, and habits. Policy theories describe many interest group or ‘advocacy coalition’ strategies, including a tendency to combine evidence with emotional appeals, romanticise their own cause and demonise their opponents, or tell simple emotional stories with a hero and moral to exploit the biases of their audience.[11]

Second, adapt to complex ‘policy environments’ including: many policymakers at many levels and types of government, each with their own rules of evidence gathering, network formation, and ways of understanding policy problems and relevant socioeconomic conditions.[2] For example, advocates of international treaties often find that the evidence-based arguments their international audience takes for granted become hotly contested at national or subnational levels (even if the national government is a signatory), while the same interest groups presenting the same evidence of a problem can be key insiders in one government department but ignored in another.[3]

Learn the conditions under which evidence ‘wins the day’ in policymaking

Consequently, the availability and supply of scientific evidence, on the nature of problems and effectiveness of solutions, is a necessary but insufficient condition for evidence-informed policy. Three others must be met: actors use scientific evidence to persuade policymakers to pay attention to, and shift their understanding of, policy problems; the policy environment becomes broadly conducive to policy change; and, actors exploit attention to a problem, the availability of a feasible solution, and the motivation of policymakers, during a ‘window of opportunity’ to adopt specific policy instruments.10

Tobacco control represents a ‘best case’ example (box 1) from which we can draw key lessons for ecological and environmental policies, giving us a sense of perspective by highlighting the long term potential for major evidence-informed policy change. However, unlike their colleagues in public health, environmental scientists have not developed a clear sense of how to produce policy instruments that are technically and politically feasible, so the delivery of comparable policy change is not inevitable.[12]

Box 1: Tobacco policy as a best case and cautionary tale of evidence-based policymaking

Tobacco policy is a key example – and useful comparator for ecological and environmental policies – since it represents a best case scenario and cautionary tale.[13] On the one hand, the scientific evidence on the links between smoking, mortality, and preventable death forms the basis for modern tobacco control policy. Leading countries – and the World Health Organisation, which oversees the Framework Convention on Tobacco Control (FCTC) – frame tobacco use as a public health ‘epidemic’ and allow their health departments to take the policy lead. Health departments foster networks with public health and medical groups at the expense of the tobacco industry, and emphasise the socioeconomic conditions – reductions in (a) smoking prevalence, (b) opposition to tobacco control, and (c) economic benefits to tobacco – most supportive of tobacco control. This framing, and conducive policymaking environment, helps give policymakers the motive and opportunity to choose policy instruments, such as bans on smoking in public places, which would otherwise seem politically infeasible.

On the other hand, even in a small handful of leading countries such as the UK, it took twenty to thirty years to go from the supply of the evidence to a proportionate government response: from the early evidence on smoking in the 1950s prompting major changes from the 1980s, to the evidence on passive smoking in the 1980s prompting public bans from the 2000s onwards. In most countries, the production of a ‘comprehensive’ set of policy measures is not yet complete, even though most signed the FCTC.

Decide how far you’ll go to maximise the use of scientific evidence in policymaking

These insights help challenge the naïve position that, if policymaking can change to become less dysfunctional[1], scientists can be ‘honest brokers’[14] and expect policymakers to use their evidence quickly, routinely, and sincerely. Even in the best case scenario, evidence-informed change takes hard work, persistence, and decades to achieve.

Since policymaking will always appear ‘irrational’ and complex’[3], scientists need to think harder about their role, then choose to engage more effectively or accept their lack of influence.

To deal with ‘irrational’ policymakers, they should combine evidence with persuasion, simple stories, and emotional appeals, and frame their evidence to make the implications consistent with policymakers’ beliefs.

To deal with complex environments, they should engage for the long term to work out how to form alliances with influencers who share their beliefs, understand in which ‘venues’ authoritative decisions are made and carried out, the rules of information processing in those venues, and the ‘currency’ used by policymakers when they describe policy problems and feasible solutions.[2] In other words, develop skills that do not come with scientific training, avoid waiting for others to share your scientific mindset or respect for scientific evidence, and plan for the likely eventuality that policymaking will never become ‘evidence based’.

This approach may be taken for granted in policy studies[15], but it raises uncomfortable dilemmas regarding how far scientists should go, to maximise the use of scientific evidence in policy, using persuasion and coalition-building.

These dilemmas are too frequently overshadowed by claims – more comforting to scientists – that politicians are to blame because they do not understand how to generate, analyse, and use the best evidence. Scientists may only become effective in politics if they apply the same critical analysis to themselves.

[1] Sutherland, W.J. & Burgman, M. Nature 526, 317–318 (2015).

[2] Cairney, P. et al. Public Administration Review 76, 3, 399-402 (2016)

[3] Cairney, P. The Politics of Evidence-Based Policy Making (Palgrave Springer, 2016).

[4] Langer, L. et al. The Science of Using Science (EPPI, 2016)

[5] Breckon, J. & Dodson, J. Using Evidence. What Works? (Alliance for Useful Evidence, 2016)

[6] Palgrave Communications series The politics of evidence-based policymaking (ed. Cairney, P.)

[7] Practical lessons from policy theories (eds. Weible, C & Cairney, P.) Policy and Politics April 2018

[8] Oliver, K. et al. Health Research Policy and Systems, 12, 34 (2016)

[9] Kingdon, J. Agendas, Alternatives and Public Policies (Harper Collins, 1984)

[10] Gluckmann, P. Understanding the challenges and opportunities at the science-policy interface

[11] Cairney, P. & Kwiatkowski, R. Palgrave Communications.

[12] Biesbroek et al. Nature Climate Change, 5, 6, 493–494 (2015)

[13] Cairney, P. & Yamazaki, M. Journal of Comparative Policy Analysis

[14] Pielke Jr, R. originated the specific term The honest broker (Cambridge University Press, 2007) but this role is described more loosely by other commentators.

[15] Cairney, P. & Oliver, K. Health Research Policy and Systems 15:35 (2017)

Why don’t policymakers listen to your evidence?

Since 2016, my most common academic presentation to interdisciplinary scientist/ researcher audiences is a variant of the question, ‘why don’t policymakers listen to your evidence?’

I tend to provide three main answers.

1. Many policymakers have many different ideas about what counts as good evidence

Few policymakers know or care about the criteria developed by some scientists to describe a hierarchy of scientific evidence. For some scientists, at the top of this hierarchy is the randomised control trial (RCT) and the systematic review of RCTs, with expertise much further down the list, followed by practitioner experience and service user feedback near the bottom.

Yet, most policymakers – and many academics – prefer a wider range of sources of information, combining their own experience with information ranging from peer reviewed scientific evidence and the ‘grey’ literature, to public opinion and feedback from consultation.

While it may be possible to persuade some central government departments or agencies to privilege scientific evidence, they also pursue other key principles, such as to foster consensus driven policymaking or a shift from centralist to localist practices.

Consequently, they often only recommend interventions rather than impose one uniform evidence-based position. If local actors favour a different policy solution, we may find that the same type of evidence may have more or less effect in different parts of government.

2. Policymakers have to ignore almost all evidence and almost every decision taken in their name

Many scientists articulate the idea that policymakers and scientists should cooperate to use the best evidence to determine ‘what works’ in policy (in forums such as INGSA, European Commission, OECD). Their language is often reminiscent of 1950s discussions of the pursuit of ‘comprehensive rationality’ in policymaking.

The key difference is that EBPM is often described as an ideal by scientists, to be compared with the more disappointing processes they find when they engage in politics. In contrast, ‘comprehensive rationality’ is an ideal-type, used to describe what cannot happen, and the practical implications of that impossibility.

The ideal-type involves a core group of elected policymakers at the ‘top’, identifying their values or the problems they seek to solve, and translating their policies into action to maximise benefits to society, aided by neutral organisations gathering all the facts necessary to produce policy solutions. Yet, in practice, they are unable to: separate values from facts in any meaningful way; rank policy aims in a logical and consistent manner; gather information comprehensively, or possess the cognitive ability to process it.

Instead, Simon famously described policymakers addressing ‘bounded rationality’ by using ‘rules of thumb’ to limit their analysis and produce ‘good enough’ decisions. More recently, punctuated equilibrium theory uses bounded rationality to show that policymakers can only pay attention to a tiny proportion of their responsibilities, which limits their control of the many decisions made in their name.

More recent discussions focus on the ‘rational’ short cuts that policymakers use to identify good enough sources of information, combined with the ‘irrational’ ways in which they use their beliefs, emotions, habits, and familiarity with issues to identify policy problems and solutions (see this post on the meaning of ‘irrational’). Or, they explore how individuals communicate their narrow expertise within a system of which they have almost no knowledge. In each case, ‘most members of the system are not paying attention to most issues most of the time’.

This scarcity of attention helps explain, for example, why policymakers ignore most issues in the absence of a focusing event, policymaking organisations make searches for information which miss key elements routinely, and organisations fail to respond to events or changing circumstances proportionately.

In that context, attempts to describe a policy agenda focusing merely on ‘what works’ are based on misleading expectations. Rather, we can describe key parts of the policymaking environment – such as institutions, policy communities/ networks, or paradigms – as a reflection of the ways in which policymakers deal with their bounded rationality and lack of control of the policy process.

3. Policymakers do not control the policy process (in the way that a policy cycle suggests)

Scientists often appear to be drawn to the idea of a linear and orderly policy cycle with discrete stages – such as agenda setting, policy formulation, legitimation, implementation, evaluation, policy maintenance/ succession/ termination – because it offers a simple and appealing model which gives clear advice on how to engage.

Indeed, the stages approach began partly as a proposal to make the policy process more scientific and based on systematic policy analysis. It offers an idea of how policy should be made: elected policymakers in central government, aided by expert policy analysts, make and legitimise choices; skilful public servants carry them out; and, policy analysts assess the results with the aid of scientific evidence.

Yet, few policy theories describe this cycle as useful, while most – including the advocacy coalition framework , and the multiple streams approach – are based on a rejection of the explanatory value of orderly stages.

Policy theories also suggest that the cycle provides misleading practical advice: you will generally not find an orderly process with a clearly defined debate on problem definition, a single moment of authoritative choice, and a clear chance to use scientific evidence to evaluate policy before deciding whether or not to continue. Instead, the cycle exists as a story for policymakers to tell about their work, partly because it is consistent with the idea of elected policymakers being in charge and accountable.

Some scholars also question the appropriateness of a stages ideal, since it suggests that there should be a core group of policymakers making policy from the ‘top down’ and obliging others to carry out their aims, which does not leave room for, for example, the diffusion of power in multi-level systems, or the use of ‘localism’ to tailor policy to local needs and desires.

Now go to:

What can you do when policymakers ignore your evidence?

Further Reading

The politics of evidence-based policymaking

The politics of evidence-based policymaking: maximising the use of evidence in policy

Images of the policy process

How to communicate effectively with policymakers

Special issue in Policy and Politics called ‘Practical lessons from policy theories’, which includes how to be a ‘policy entrepreneur’.

See also the 750 Words series to explore the implications for policy analysis

Policy in 500 words: uncertainty versus ambiguity

In policy studies, there is a profound difference between uncertainty and ambiguity:

• Uncertainty describes a lack of knowledge or a worrying lack of confidence in one’s knowledge.
• Ambiguity describes the ability to entertain more than one interpretation of a policy problem.

Both concepts relate to ‘bounded rationality’: policymakers do not have the ability to process all information relevant to policy problems. Instead, they employ two kinds of shortcut:

• ‘Rational’. Pursuing clear goals and prioritizing certain sources of information.
• ‘Irrational’. Drawing on emotions, gut feelings, deeply held beliefs, and habits.

I make an artificially binary distinction, uncertain versus ambiguous, and relate it to another binary, rational versus irrational, to point out the pitfalls of focusing too much on one aspect of the policy process:

1. Policy actors seek to resolve uncertainty by generating more information or drawing greater attention to the available information.

Actors can try to solve uncertainty by: (a) improving the quality of evidence, and (b) making sure that there are no major gaps between the supply of and demand for evidence. Relevant debates include: what counts as good evidence?, focusing on the criteria to define scientific evidence and their relationship with other forms of knowledge (such as practitioner experience and service user feedback), and what are the barriers between supply and demand?, focusing on the need for better ways to communicate.

2. Policy actors seek to resolve ambiguity by focusing on one interpretation of a policy problem at the expense of another.

Actors try to solve ambiguity by exercising power to increase attention to, and support for, their favoured interpretation of a policy problem. You will find many examples of such activity spread across the 500 and 1000 words series:

• Actors combine facts with emotional appeals to draw attention to their issues, at the expense of attention to other issues in a crowded political environment.
• Actors form coalitions with people who share their beliefs. They romanticise their own cause and demonise their opponents.
• Actors tell simple stories with a hero and moral to appeal to the emotional and ideological biases of policymakers.
• Policy entrepreneurs use skills of persuasion to frame problems, make sure their favoured solutions are feasible, and exploit times when policymakers have the motive and opportunity to act.
• Dominant ways of thinking act as a lens through which we process information.
• Policymakers exploit stereotypes of target populations.

A focus on reducing uncertainty gives the impression that policymaking is a technical process in which people need to produce the best evidence and deliver it to the right people at the right time.

In contrast, a focus on reducing ambiguity gives the impression of a more complicated and political process in which actors are exercising power to compete for attention and dominance of the policy agenda. Uncertainty matters, but primarily to describe the role of a complex policymaking system in which no actor truly understands where they are or how they should exercise power to maximise their success.

Further reading:

For a longer discussion, see Fostering Evidence-informed Policy Making: Uncertainty Versus Ambiguity (PDF)

Or, if you fancy it in French: Favoriser l’élaboration de politiques publiques fondées sur des données probantes : incertitude versus ambiguïté (PDF)

Framing

The politics of evidence-based policymaking

To Bridge the Divide between Evidence and Policy: Reduce Ambiguity as Much as Uncertainty

How to communicate effectively with policymakers: combine insights from psychology and policy studies

Here is the relevant opening section in UPP:

The UK government’s imaginative use of evidence to make policy

This post describes a new article published in British Politics (Open Access). Please find:

(1) A super-exciting video/audio powerpoint I use for a talk based on the article

(2) The audio alone (link)

(3) The powerpoint to download, so that the weblinks work (link) or the ppsx/ presentation file in case you are having a party (link)

(4) A written/ tweeted discussion of the main points

May I have your attention as I describe to you my new article, 'The UK government’s imaginative use of evidence to make policy'? https://t.co/AD4BZBRsUf pic.twitter.com/c4P1NHJgNF

— Professor Paul Cairney (@CairneyPaul) January 8, 2018

In retrospect, I think the title was too subtle and clever-clever. I wanted to convey two meanings: imaginative as a euphemism for ridiculous/ often cynical and to argue that a government has to be imaginative with evidence. The latter has two meanings: imaginative (1) in the presentation and framing of evidence-informed agenda, and (2) when facing pressure to go beyond the evidence and envisage policy outcomes.

So I describe two cases in which its evidence-use seems cynical, when:

1. Declaring complete success in turning around the lives of ‘troubled families’
2. Exploiting vivid neuroscientific images to support ‘early intervention’

Then I describe more difficult cases in which supportive evidence is not clear:

1. Family intervention project evaluations are of limited value and only tentatively positive
2. Successful projects like FNP and Incredible Years have limited applicability or ‘scalability’

As scientists, we can shrug our shoulders about the uncertainty, but elected policymakers in government have to do something. So what do they do?

At this point of the article it will look like I have become an apologist for David Cameron’s government. Instead, I’m trying to demonstrate the value of comparing sympathetic/ unsympathetic interpretations and highlight the policy problem from a policymaker’s perspective:

I suggest that they use evidence in a mix of ways to: describe an urgent problem, present an image of success and governing competence, and provide cover for more evidence-informed long term action.

The result is the appearance of top-down ‘muscular’ government and ‘a tendency for policy to change as is implemented, such as when mediated by local authority choices and social workers maintaining a commitment to their professional values when delivering policy’

I conclude by arguing that ‘evidence-based policy’ and ‘policy-based evidence’ are political slogans with minimal academic value. The binary divide between EBP/ PBE distracts us from more useful categories which show us the trade-offs policymakers have to make when faced with the need to act despite uncertainty.

As such, it forms part of a far wider body of work …

The article is part of (1) a larger project on ‘the politics of evidence-based policymaking’https://t.co/0haxkUcdba pic.twitter.com/jIPjBfqleW

— Professor Paul Cairney (@CairneyPaul) January 8, 2018

The article is also part of (2) a larger project on politics, prevention, and early intervention in the lives of children and adults, with @EmilyStDenny https://t.co/rUTi1UIyGI pic.twitter.com/blIbLbkjbd

— Professor Paul Cairney (@CairneyPaul) January 8, 2018

In both cases, the common theme is that, although (1) the world of top-down central government gets most attention, (2) central governments don’t even know what problem they are trying to solve, far less (3) how to control policymaking and outcomes.

In that wider context, it is worth comparing this talk with the one I gave at the IDS (which, I reckon is a good primer for – or prequel to – the UK talk):

@CairneyPaul came to @IDS_UK and challenged our assumptions about evidence use – are we just generating evidence with people who already agree with us? Watch https://t.co/EwvySbqLQs @TransformUre @JordanTchil @mleach_ids @JohnGaventa

— James Georgalakis (@Bloggs74) January 17, 2019

See also:

Early intervention policy, from ‘troubled families’ to ‘named persons’: problems with evidence and framing ‘valence’ issues

Why doesn’t evidence win the day in policy and policymaking?

(found by searching for early intervention)

See also:

Here’s why there is always an expectations gap in prevention policy

Social investment, prevention and early intervention: a ‘window of opportunity’ for new ideas?

(found by searching for prevention)

Powerpoint for guest lecture: Paul Cairney UK Government Evidence Policy

The Politics of Evidence revisited

This is a guest post by Dr Justin Parkhurst, responding to a review of our books by Dr Joshua Newman, and my reply to that review.

I really like that Joshua Newman has done this synthesis of 3 recent books covering aspects of evidence use in policy. Too many book reviews these days just describe the content, so some critical comments are welcome, as is the comparative perspective.

I’m also honoured that my book was included in the shortlist (it is available here, free as an ebook: bit.ly/2gGSn0n for interested readers) and I’d like to follow on from Paul to add some discussion points to the debate here – with replies to both Joshua and Paul (hoping first names are acceptable).

Have we heard all this before?

Firstly, I agree with Paul that saying ‘we’ve heard this all before’ risks speaking about a small community of active researchers who study these issues, and not the wider community. But I’d also add that what we’ve heard before is a starting point to many of these books, not where they end up.

In terms of where we start: I’m sure many of us who work in this field are somewhat frustrated at meetings when we hear people making statements that are well established in the literature. Some examples include:

• “There can be many types of evidence, not just scientific research…”
• “In the legal field, ‘evidence’ means something different…”
• “We need evidence-based policy, not policy-based evidence…”
• “We need to know ‘what works’ to get evidence into policy…”

Thus, I do think there is still a need to cement the foundations of the field more strongly – in essence, to establish a disciplinary baseline that people weighing in on a subject should be expected to know about before providing additional opinions. One way to help do this is for scholars to continue to lay out the basic starting points in our books – typically in the first chapter or two.

Of course, other specialist fields and disciplines have managed to establish their expertise to a point that individuals with opinions on a subject typically have some awareness that there is a field of study out there which they don’t necessarily know about. This is most obvious in the natural sciences (and perhaps in economics). E.g. most people (current presidents of some large North American countries aside?) are aware that don’t know a lot about engineering, medicine, or quantum physics – so they won’t offer speculative or instinctive opinions about why airplanes stay in the air, how to do bypass surgery, or what was wrong with the ‘Ant-Man’ film. Or when individuals do offer views, they are typically expected to know the basics of the subject.

For the topic of evidence and policy, I often point people to Huw Davies, Isabel Walter, and Sandra Nutley’s book Using Evidence, which is a great introduction to much of this field, as well as Carol Weiss’ insights from the late 70s on the many meanings of research utilisation. I also routinely point people to read The Honest Broker by Roger Pielke Jr. (which I, myself, failed to read before writing my book and, as such, end up repeating many of his points – I’ve apologised to him personally).

So yes, I think there is space for work like ours to continue to establish a baseline, even if some of us know this, because the expertise of the field is not yet widely recognised or established. Yet I think is it not accurate for Joshua to argue we end up repeating what is known, considering our books diverge in key ways after laying out some of the core foundations.

Where do we go from there?

More interesting for this discussion, then, is to reflect on what our various books try to do beyond simply laying out the basics of what we know about evidence use and policy. It is here where I would disagree with Joshua’s point claiming we don’t give a clear picture about the ‘problem’ that ‘evidence-based policy’ (his term – one I reject) is meant to address. Speaking only for my own book, I lay out the problem of bias in evidence use as the key motivation driving both advocates of greater evidence use as well as policy scholars critical of (oversimplified) knowledge translation efforts. But I distinguish between two forms of bias: technical bias – whereby evidence is used in ways that do not adhere to scientific best practice and thus produce sub-optimal social outcomes; and issue bias – whereby pieces of evidence, or mechanisms of evidence use, can obscure the important political choices in decision making, skewing policy choices towards those things that have been measured, or are conducive to measurement. Both of these forms of bias are violations of widely held social values – values of scientific fidelity on the one hand, and of democratic representation on the other. As such, for me, these are the problems that I try to consider in my book, exploring the political and cognitive origins of both, in order to inform thinking on how to address them.

That said, I think Joshua is right in some of the distinctions he makes between our works in how we try to take this field forward, or move beyond current challenges in differing ways. Paul takes the position that researchers need to do something, and one thing they can do is better understand politics and policy making. I think Paul’s writings about policy studies for students is superb (see his book and blog posts about policy concepts). But in terms of applying these insights to evidence use, this is where we most often diverge. I feel that keeping the focus on researchers puts too much emphasis on achieving ‘uptake’ of researcher’s own findings. In my view, I would point to three potential (overlapping) problems with this.

• First – I do not think it is the role or responsibility of researchers to do this, but rather a failure to establish the right system of evidence provision;
• Second – I feel it leaves unstated the important but oft ignored normative question of how ‘should’ evidence be used to inform policy;
• Third – I believe these calls rest on often unstated assumptions about the answer to the second point which we may wish to challenge.

In terms of the first point: I’m more of an institutionalist (as Joshua points out). My view is that the problems around non-use or misuse of evidence can be seen as resulting from a failure to establish appropriate systems that govern the use of evidence in policy processes. As such, the solution would have to lie with institutional development and changes (my final chapter advocates for this) that establish systems which serve to achieve the good governance of evidence.

Paul’s response to Joshua says that researchers are demanding action, so he speaks to them. He wants researchers to develop “useful knowledge of the policy process in which they might want to engage” (as he says above).  Yet while some researchers may wish to engage with policy processes, I think it needs to be clear that doing so is inherently a political act – and can take on a role of issue advocacy by promoting those things you researched or measured over other possible policy considerations (points made well by Roger Pielke Jr. in The Honest Broker). The alternative I point towards is to consider what good systems of evidence use would look like. This is the difference between arguing for more uptake of research, vs. arguing for systems through which all policy relevant evidence can be seen and considered in appropriate ways – regardless of the political savvy, networking, or activism of any given researcher (in my book I have chapters reflecting on what appropriate evidence for policy might be, and what a good process for its use might be, based on particular widely shared values).

In terms of the second and third points – my book might be the most explicit in its discussion of the normative values guiding efforts to improve evidence, and I am more critical than some about the assumption that getting researchers work ‘used’ by policymakers is a de-facto good thing. This is why I disagree with Joshua’s conclusion that my work frames the problem as ‘bridging the gap’. Rather I’d say I frame the problem as asking the question of ‘what does a better system of evidence use look like from a political perspective?’ My ‘good governance of evidence’ discussion presents an explicitly normative framework based the two sets of values mentioned above – those around democratic accountability and around fidelity to scientific good practice – both of which have been raised as important in discussions about evidence use in political processes.

Is the onus on researchers?

Finally, I also would argue against Joshua’s conclusion that my work places the burden of resolving the problems on researchers. Paul argues above that he does this but with good reason. I try not to do this. This is again because my book is not making an argument for more evidence to be ‘used’ per se. (and I don’t expect policy makers to just want to use it either). Rather I focus on identifying principles by which we can judge systems of evidence use, calling for guided incremental changes within national systems.

While I think academics can play an important role in establishing ‘best practice’ ideas, I explicitly argue that the mandate to establish, build, or incrementally change evidence advisory systems lies with the representatives of the people. Indeed, I include ‘stewardship’ as a core principle of my good governance of evidence framework to show that it should be those individuals who are accountable to the public that build these systems in different countries. Thus, the burden lies not with academics, but rather with our representatives – and, indirectly with all of us through the demands we make on them – to improve systems of evidence use.

 

Debating the politics of evidence-based policy

My new essay on evidence-based policy is now out in the current issue of Public Administration: https://t.co/6qabMyvYa6. Nothing personal, @CairneyPaul @justinparkhurst @ProfStoker @MarkEvansACT… hope to continue the debate!

— Joshua Newman (@_joshuanewman) December 20, 2017

Joshua Newman has provided an interesting review of three recent books on evidence/ policy (click here). One of those books is mine: The Politics of Evidence-Based Policy Making (which you can access here).

His review is very polite, for which I thank him. I hope my brief response can be seen in a similarly positive light (well, I had hoped to make it brief). Maybe we disagree on one or two things, but often these discussions are about the things we emphasize and the way we describe similar points.

There are 5 points to which I respond because I have 5 digits on my right hand. I’d like you to think of me counting them out on my fingers. In doing so, I’ll use ‘Newman’ throughout, because that’s the academic convention, but I’d also like to you imagine me reading my points aloud and whispering ‘Joshua’ before each ‘Newman’.

1. Do we really need to ‘take the debate forward’ so often?

I use this phrase myself, knowingly, to keep a discussion catchy, but I think it’s often misleading. I suggest not to get your hopes up too high when Newman raises the possibility of taking the debate forward with his concluding questions. We won’t resolve the relationship between evidence, politics & policy by pretending to reframe the same collection of questions about the prospect of political reform that people have been asking for centuries. It is useful to envisage better political systems (the subject of Newman’s concluding remarks) but I don’t think we should pretend that this is a new concern or that it will get us very far.

Indeed, my usual argument is that researchers need to do something (such as improve how we engage in the policy process) while we wait for political system reforms to happen (while doubting if they will ever happen).

Further, Newman does not produce any political reforms to address the problems he raises. Rather, for example, he draws attention to Trump to describe modern democracies as ‘not pluralist utopias’ and to identify examples in which policymakers draw primarily on beliefs, not evidence. By restating these problems, he does not solve them. So, what are researchers supposed to do after they grow tired of complaining that the world does not meet their hopes or expectations?

In other words, for me, (a) promoting political change and (b) acting during its absence are two sides of the same coin. We go round and round more often than we take things forward.

2. What debate are we renaming?

Newman’s ‘we’ve heard it before’ argument seems more useful, but there is a lot to hear and relatively few people have heard it. I’d warn against the assumption that ‘I’ve heard this before’ can ever equal ‘we’ve heard it before’ (unless ‘we’ refers to a tiny group of specialists talking only to each other).

Rather, one of the most important things we can do as academics is to tell the same story to each other (to check if we understand the same story, in the same way, and if it remains useful) and to wider audiences (in a way that they can pick up and use without dedicating their career to our discipline).

Some of our most important insights endure for decades and they sometimes improve in the retelling. We apply them to new eras, and often come to the same basic conclusions, but it seems unhelpful to criticise a lack of complete novelty in individual texts (particularly when they are often designed to be syntheses). Why not use them to occasionally take a step back to discuss and clarify what we know?

Perhaps more importantly, I don’t think Newman is correct when he says that each book retells the story of the ‘research utilization’ literature. I’m retelling the story of policy theory, which describes how policymakers deal with bounded rationality in a complex policymaking environment. Policy theory’s intellectual histories often provide very different perspectives – of the policymaker trying to make good enough decisions, rather than the researcher trying to improve the uptake of their research – than the agenda inspired by Weiss et al (see for example The New Policy Sciences).

3. Don’t just ‘get political’; understand the policy process

I draw on policy theory because it helps people understand policymaking. It would be a mistake to conclude from my book that I simply want researchers to ‘get political’. Rather, I want them to develop useful knowledge of the policy process in which they might want to engage. This knowledge is not freely available; it takes time to understand the discipline and reflect on policy dynamics.

Yet, the payoff can be profound, if only because it helps people think about the difference between two analytically separate causes of a notional ‘evidence policy gap’: (a) individuals making choices based on their beliefs and limited information (which is relatively easy to understand but also to caricature), and (b) systemic or ‘environmental’ causes (which are far more difficult to conceptualise and explain, but often more useful to understand).

4. Don’t throw out the ‘two communities’ phrase without explaining why

Newman criticises the phrase ‘two communities’ as a description of silos in policymaking versus research, partly because (a) many policymakers use research frequently, and (b) the real divide is often between users/ non-users of research within policymaking organisations. In short, there are more than two communities.

I’d back up his published research with my anecdotal experience of giving talks to government audiences: researchers and analysts within government are often very similar in outlook to academics and they often talk in the same way as academics about the disconnect between their (original or synthetic) research and its use by their ‘operational’ colleagues.

Still, I’m not sure why Newman concludes that the ‘two communities’ phrase is ‘deeply flawed and probably counter-productive’. Yes, the world is more nuanced and less binary than ‘two communities’ suggests. Yes, the real divide may be harder to spot. Still, as Newman et al suggest: ‘Policy makers and academics should focus on bridging instruments that can bring their worlds closer together’. This bullet point from their article seems, to me, to be the point of using the phrase ‘two communities’. Maybe Caplan used the phrase differently in 1979, but to assert its historic meaning then reject the phrase’s use in modern discussion seems less useful than simply clarifying the argument in ways such as:

• There is no simple policymaker/ academic divide but, … note the major difference in requirements between (a) people who produce or distribute research without taking action, which allows them (for example) to be more comfortable with uncertainty, and (b) people who need to make choices despite having incomplete information to hand.
• You might find a more receptive audience in one part of government (e.g. research/ analytical) than another (e.g. operational), so be careful about generalising from singular experiences.

5. Should researchers engage in the policy process?

Newman says that each book, ‘unfairly places the burden of resolving the problem in the hands of an ill-equipped group of academics, operating outside the political system’.

I agree with Newman when he says that many researchers do not possess the skills to engage effectively in the policy process. Scientific training does not equip us with political skills. Indeed, I think you could read a few of my blog posts and conclude, reasonably, that you would like nothing more to do with the policy process because you’d be more effective by focusing on research.

The reason I put the onus back on researchers is because I am engaging with arguments like the one expressed by Newman (in other words, part of the meaning comes from the audience). Many people conclude their evidence policy discussions by identifying (or ‘reframing’) the problem primarily as the need for political reform. For me, the focus on other people changing to suit your preferences seems unrealistic and misplaced. In that context, I present the counter-argument that it may be better to adapt effectively to the policy process that exists, not the one you’d like to see. Sometimes it’s more useful to wear a coat than complain about the weather.

See also:  The Politics of Evidence 

The Politics of Evidence revisited