Tag Archives: Policy

Policy Analysis in 750 Words: Political feasibility and policy success

Policy studies and policy analysis guidebooks identify the importance of feasible policy solutions:

  • Technical feasibility: will this solution work as intended if implemented?
  • Political feasibility: will it be acceptable to enough powerful people?

For example, Kingdon treats feasibility as one of three conditions for major policy change during a ‘window of opportunity’: (1) there is high attention to the policy problem, (2) a feasible solution already exists, and (3) key policymakers have the motive and opportunity to select it.

Guidebooks relate this requirement initially to your policymaker client: what solutions will they rule out, to the extent that they are not even worth researching as options (at least for the short term)?

Further, this assessment relates to types of policy ‘tool’ or ‘instrument’: one simple calculation is that ‘redistributive’ measures are harder to sell than ‘distributive’, while both may be less attractive than regulation (although complex problems likely require a mix of instruments).

These insights connect to Lindblom’s classic vision of:

  1. Incremental analysis. It is better to research in-depth a small number of feasible options than spread your resources too thinly to consider all possibilities.
  2. Strategic analysis. The feasibility of a solution relates strongly to current policy. The more radical a departure from the current negotiated position, the harder it will be to sell.

As many posts in the Policy Analysis in 750 words series describe, this advice is not entirely  useful for actors who seek rapid and radical departures from the status quo. Lindblom’s response to such critics was to seek radical change via a series of non-radical steps (at least in political systems like the US), which (broadly speaking) represents one of two possible approaches.

While incrementalism is not as popular as it once was (as a description of, or prescription for, policymaking), it tapped into the enduring insight that policymaking systems produce huge amounts of minor change. Rapid and radical policy change is rare, and it is even rarer to be able to connect it to influential analysis and action (at least in the absence of a major event). This knowledge should not put people off trying, but rather help them understand the obstacles that they seek to overcome.

Relating feasible solutions and strategies to ‘policy success’

One way to incorporate this kind of advice is to consider how (especially elected) policymakers would describe their own policy success. The determination of success and failure is a highly contested and political process (not simply a technical exercise called ‘evaluation’), and policymakers may refer – often implicitly – to the following questions when seeking success:

  1. Political. Will this policy boost my government’s credibility and chances of re-election?
  2. Process. Will it be straightforward to legitimise and maintain support for this policy?
  3. Programmatic. Will it achieve its stated objectives and produce beneficial outcomes if implemented?

The benefit to analysts, in asking themselves these questions, is that they help to identify the potential solutions that are technically but not politically feasible (or vice versa).

The absence of clear technical feasibility does not necessarily rule out solutions with wider political benefits (for example, it can be beneficial to look like you are trying to do something good). Hence the popular phrase ‘good politics, bad policy’.

Nor does a politically unattractive option rule out a technically feasible solution (not all politicians flee the prospect of ‘good policy, bad politics’). However, it should prompt attention to hard choices about whose support to seek, how long to wait, or how hard to push, to seek policy change. You can see this kind of thinking as ‘entrepreneurial‘ or ‘systems thinking’ depending on how much faith you have in agency in highly-unequal political contexts.

Further reading

It is tempting to conclude that these obstacles to ‘good policy’ reflect the pathological nature of politics. However, if we want to make this argument, we should at least do it well:

1. You can find this kind of argument in fields such as public health and climate change studies, where researchers bemoan the gap between (a) their high-quality evidence on an urgent problem and (b) a disproportionately weak governmental response. To do it well, we need to separate analytically (or at least think about): (a) the motivation and energy of politicians (usually the source of most criticism of low ‘political will’), and (b) the policymaking systems that constrain even the most sincere and energetic policymakers. See the EBPM page for more.

2. Studies of Social Construction and Policy Design are useful to connect policymaking research with a normative agenda to address ‘degenerative’ policy design.

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Policy Analysis in 750 Words: Changing things from the inside

How should policy actors seek radical changes to policy and policymaking?

This question prompts two types of answer:

1. Be pragmatic, and change things from the inside

Pragmatism is at the heart of most of the policy analysis texts in this series. They focus on the needs and beliefs of clients (usually policymakers). Policymakers are time-pressed, so keep your analysis short and relevant. See the world through their eyes. Focus on solutions that are politically as well as technically feasible. Propose non-radical steps, which may add up to radical change over the long-term.

This approach will seem familiar to students of research ‘impact’ strategies which emphasise relationship-building, being available to policymakers, and responding to the agendas of governments to maximise the size of your interested audience.

It will also ring bells for advocates of radical reforms in policy sectors such as (public) health and intersectoral initiatives such as gender mainstreaming:

  • Health in All Policies is a strategy to encourage radical changes to policy and policymaking to improve population health.  Common advice includes to: identify to policymakers how HiAP fits into current policy agendas, seek win-win strategies with partners in other sectors, and go to great lengths to avoid the sense that you are interfering in their work (‘health imperialism’).
  • Gender mainstreaming is a strategy to consider gender in all aspect of policy and policymaking. An equivalent playbook involves steps to: clarify what gender equality is, and what steps may help achieve it; make sure that these ideas translate across all levels and types of policymaking; adopt tools to ensure that gender is a part of routine government business (such as budget processes); and, modify existing policies or procedures while increasing the representation of women in powerful positions.

In other words, the first approach is to pursue your radical agenda via non-radical means, using a playbook that is explicitly non-confrontational.  Use your insider status to exploit opportunities for policy change.

2. Be radical, and challenge things from the outside

Challenging the status quo, for the benefit of marginalised groups, is at the heart of critical policy analysis:

  • Reject the idea that policy analysis is a rationalist, technical, or evidence-based process. Rather, it involves the exercise of power to (a) depoliticise problems to reduce attention to current solutions, and (b) decide whose knowledge counts.
  • Identify and question the dominant social constructions of problems and populations, asking who decides how to portray these stories and who benefits from their outcomes.

This approach resonates with frequent criticisms of ‘impact’ advice, emphasising the importance of producing research independent of government interference, to challenge policies that further harm already-marginalised populations.

It will also rings bells among advocates of more confrontational strategies to seek radical changes to policy and policymaking. They include steps to: find more inclusive ways to generate and share knowledge, produce multiple perspectives on policy problems and potential solutions, focus explicitly on the impact of the status quo on marginalised populations, politicise issues continuously to ensure that they receive sufficient attention, and engage in outsider strategies to protest current policies and practices.

Does this dichotomy make sense?

It is tempting to say that this dichotomy is artificial and that we can pursue the best of both worlds, such as working from within when it works and resorting to outsider action and protest when it doesn’t.

However, the blandest versions of this conclusion tend to ignore or downplay the politics of policy analysis in favour of more technical fixes. Sometimes collaboration and consensus politics is a wonderful feat of human endeavour. Sometimes it is a cynical way to depoliticise issues, stifle debate, and marginalise unpopular positions.

This conclusion also suggests that it is possible to establish what strategies work, and when, without really saying how (or providing evidence for success that would appeal to audiences associated with both approaches). Indeed, a recurrent feature of research in these fields is that most attempts to produce radical change prove to be dispiriting struggles. Non-radical strategies tend to be co-opted by more powerful actors, to mainstream new ways of thinking without changing the old. Radical strategies are often too easy to dismiss or counter.

The latter point reminds us to avoid excessively optimistic overemphasis on the strategies of analysts and advocates at the expense of context and audience. The 500 and 1000 words series perhaps tip us too far in the other direction, but provide a useful way to separate (analytically) the reasons for often-minimal policy change. To challenge dominant forms of policy and policymaking requires us to separate the intentional sources of inertia from the systemic issues that would constrain even the most sincere and energetic reformer.

Further reading

This post forms one part of the Policy Analysis in 750 words series, including posts on the role of analysts and marginalised groups. It also relates to work with St Denny, Kippin, and Mitchell (drawing on this draft paper) and posts on ‘evidence based policymaking’.

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Call for papers for a JEPP Special Issue, ‘The politics of policy analysis: theoretical insights on real world problems’

Note: this call will appear shortly on the JEPP page. See also my 750 words series on policy analysis.

For a special edition of the Journal of European Public Policy, we invite proposals for papers that reflect on the theory and practice of policy analysis. This special issue will include state of the art articles on the politics of policy analysis, and empirical studies that use theoretical insights to analyse and address real world problems.

Policy analysis is not a rationalist, technocratic, centrally managed, or ‘evidence based’ process to solve policy problems. Rather, critical policy analysis and mainstream policy studies describe contemporary policy analysis as a highly contested (but unequal) process in which many policymakers, analysts, and influencers cooperate or compete across many centres of government. Further, governments are not in the problem solving business. Instead, they inherit policies that address some problems and create or exacerbate others, benefit some groups and marginalize others, or simply describe problems as too difficult to solve. The highest profile problems, such as global public health and climate change, require the kinds of (1) cooperation across many levels of government (and inside and outside of government), and (2) attention to issues of justice and equity, of which analysts could only dream.

This description of policymaking complexity presents a conundrum. On the one hand, there exist many five-step guides to analysis, accompanied by simple stage-based descriptions of policy processes, but they describe what policy actors would need or like to happen rather than policymaking reality. On the other, policy theory-informed studies are essential to explanation, but not yet essential reading for policy analysts. Policy theorists may be able to describe policy processes – and the role of policy analysts – more accurately than simple guides, but do not offer a clear way to guide action. Practitioner audiences are receptive to accurate descriptions of policymaking reality, but also want a take-home message that they can pick up and use in their work. Critical policy analysts may appreciate insights on the barriers to policy and policymaking change, but only if there is equal attention to how to overcome them.

We seek contributions that engage with this conundrum. We welcome papers which use theories, concepts and frameworks that are considered the policy studies mainstream, but also contributions from critical studies that use research to support marginalized populations as they analyse contemporary policy problems. We focus on Europe broadly defined, but welcome contributions with  direct lessons from any other region.

Potential themes include but are not limited to:

  • State of the art articles that use insights from policy theories and/ or critical policy analysis to guide the study and practice of policy analysis
  • Articles that situate the analysis of contemporary policy problems within a wider policymaking context, to replace wishful thinking with more feasible (but equally ambitious) analysis
  • Articles that engage critically with contemporary themes in policy analysis and design, such as how to encourage ‘entrepreneurial’ policy analysis, foster ‘co-production’ during policy analysis and design, or engage in ‘systems thinking’ without relying on jargon and gimmicks.
  • Articles that engage with the unrealistic idea of ‘evidence-based policymaking’ to produce more feasible (and less technocratic) images of evidence-informed policymaking.

Expressions of interest consisting of a title, author(s) names and affiliation, and a short abstract (no more than 300 words) should be sent to p.a.cairney@stir.ac.uk by Feb 28th 2022. Successful authors should have a full article draft for submission into the JEPP review process by August 30th 2022.

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Policy Analysis in 750 Words: Two approaches to policy learning and transfer

This post forms one part of the Policy Analysis in 750 words series. It draws on work for an in-progress book on learning to reduce inequalities. Some of the text will seem familiar if you have read other posts. Think of it as an adventure game in which the beginning is the same but you don’t know the end.

Policy learning is the use of new information to update policy-relevant knowledge. Policy transfer involves the use of knowledge about policy and policymaking in one government to inform policy and policymaking in another.

These processes may seem to relate primarily to research and expertise, but they require many kinds of political choices (explored in this series). They take place in complex policymaking systems over which no single government has full knowledge or control.

Therefore, while the agency of policy analysts and policymakers still matters, they engage with a policymaking context that constrains or facilitates their action.

Two approaches to policy learning: agency and context-driven stories

Policy analysis textbooks focus on learning and transfer as an agent-driven process with well-established  guidance (often with five main steps). They form part of a functionalist analysis where analysts identify the steps required to turn comparative analysis into policy solutions, or part of a toolkit to manage stages of the policy process.

Agency is less central to policy process research, which describes learning and transfer as contingent on context. Key factors include:

Analysts compete to define problems and determine the manner and sources of learning, in a multi-centric environment where different contexts will constrain and facilitate action in different ways. For example, varying structural factors – such as socioeconomic conditions – influence the feasibility of proposed policy change, and each centre’s institutions provide different rules for gathering, interpreting, and using evidence.

The result is a mixture of processes in which:

  1.  Learning from experts is one of many possibilities. For example, Dunlop and Radaelli also describe ‘reflexive learning’, ‘learning through bargaining’, and ‘learning in the shadow hierarchy’
  2.  Transfer takes many forms.

How should analysts respond?

Think of two different ways to respond to this description of the policy process with this lovely blue summary of concepts. One is your agency-centred strategic response. The other is me telling you why it won’t be straightforward.

An image of the policy process (see 5 images)

There are many policy makers and influencers spread across many policymaking ‘centres’

  1. Find out where the action is and tailor your analysis to different audiences.
  2. There is no straightforward way to influence policymaking if multiple venues contribute to policy change and you don’t know who does what.

Each centre has its own ‘institutions’

  1. Learn the rules of evidence gathering in each centre: who takes the lead, how do they understand the problem, and how do they use evidence?
  2. There is no straightforward way to foster policy learning between political systems if each is unaware of each other’s unwritten rules. Researchers could try to learn their rules to facilitate mutual learning, but with no guarantee of success.

Each centre has its own networks

  1. Form alliances with policymakers and influencers in each relevant venue.
  2. The pervasiveness of policy communities complicates policy learning because the boundary between formal power and informal influence is not clear.

Well-established ‘ideas’ tend to dominate discussion

  1. Learn which ideas are in good currency. Tailor your advice to your audience’s beliefs.
  2. The dominance of different ideas precludes many forms of policy learning or transfer. A popular solution in one context may be unthinkable in another.

Many policy conditions (historic-geographic, technological, social and economic factors) command the attention of policymakers and are out of their control. Routine events and non-routine crises prompt policymaker attention to lurch unpredictably.

  1. Learn from studies of leadership in complex systems or the policy entrepreneurs who find the right time to exploit events and windows of opportunity to propose solutions.
  2. The policy conditions may be so different in each system that policy learning is limited and transfer would be inappropriate. Events can prompt policymakers to pay disproportionately low or high attention to lessons from elsewhere, and this attention relates weakly to evidence from analysts.

Feel free to choose one or both forms of advice. One is useful for people who see analysts and researchers as essential to major policy change. The other is useful if it serves as a source of cautionary tales rather than fatalistic responses.

See also:

Policy Concepts in 1000 Words: Policy Transfer and Learning

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

Policy Concepts in 1000 Words: the intersection between evidence and policy transfer

Policy learning to reduce inequalities: a practical framework

Three ways to encourage policy learning

Epistemic versus bargaining-driven policy learning

The ‘evidence-based policymaking’ page explores these issues in more depth

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Policy Analysis in 750 Words: How to deal with ambiguity

This post forms one part of the Policy Analysis in 750 words series. It draws on this 500 Words post, then my interpretation of co-authored work with Drs Emily St Denny and John Boswell (which I would be delighted to share if it gets published). It trails off at the end.

In policy studies, ambiguity describes the ability to entertain more than one interpretation of a policy problem. There are many ways to frame issues as problems. However, only some frames receive high policymaker attention, and policy change relates strongly to that attention. Resolving ambiguity in your favour is the prize.

Policy studies focus on different aspects of this dynamic, including:

  1. The exercise of power, such as of the narrator to tell stories and the audience to engage with or ignore them.
  2. Policy learning, in which people collaborate (and compete) to assign concrete meaning to abstract aims.
  3. A complex process in which many policymakers and influencers are cooperating/ competing to define problems in many policymaking centres.

They suggest that resolving ambiguity affects policy in different ways, to influence the:

The latter descriptions, reflecting multi-centric policymaking, seem particularly relevant to major contemporary policy problems – such as global public health and climate crises – in which cooperation across (and outside of) many levels and types of government is essential.

Resolving ambiguity in policy analysis texts

This context helps us to interpret common (Step 1) advice in policy analysis textbooks: define a policy problem for your client, using your skills of research and persuasion but tailoring your advice to your client’s interests and beliefs. Yet, gone are the mythical days of elite analysts communicating to a single core executive in charge of formulating and implementing all policy instruments. Many analysts engage with many centres producing (or co-producing) many instruments. Resolving ambiguity in one centre does not guarantee the delivery of your aims across many.

Two ways to resolve ambiguity in policy analysis

Classic debates would highlight two different responses:

  • ‘Top down’ accounts see this issue through the lens of a single central government, examining how to reassert central control by minimising implementation gaps.

Policy analysis may focus on (a) defining the policy problem, and (b) ensuring the implementation of its solution.

  • ‘Bottom up’ accounts identify the inevitability (and legitimacy) of policy influence in multiple centres. Policy analysis may focus on how to define the problem in cooperation with other centres, or to set a strategic direction and encourage other centres to make sense of it in their context.

This terminology went out of fashion, but note the existence of each tendency in two ideal-type approaches to contemporary policy problems:

1. Centralised and formalised approaches.

Seek clarity and order to address urgent policy problems. Define the policy problem clearly, translate that definition into strategies for each centre, and develop a common set of effective ‘tools’ to ensure cooperation and delivery.

Policy analysis may focus on technical aspects, such as how to create a fine-detail blueprint for action, backed by performance management and accountability measures that tie actors to specific commitments.

The tagline may be: ambiguity is a problem to be solved, to direct policy actors towards a common goal.

2. Decentralised, informal, collaborative approaches.

Seek collaboration to make sense of, and address, problems. Reject a single definition of the problem, encourage actors in each centre (or in concert) to deliberate to make sense of problems together, and co-create the rules to guide a continuous process of collective behaviour.

Policy analysis may focus on how to contribute to a collaborative process of sense-making and rule-making.

The tagline may be: ambiguity presents an opportunity to energise policy actors, to harness the potential for innovation arising from deliberation.

Pick one approach and stick with it?

Describing these approaches in such binary terms makes the situation – and choice between approaches – look relatively straightforward. However, note the following issues:

  • Many policy sectors (and intersectoral agendas) are characterised by intense disagreement on which choice to make. These disagreements intersect with others (such as when people seek not only transformative policy change to solve global problems, but also equitable process and outcomes).
  • Some sectors seem to involve actors seeking the best of both worlds (centralise and localise, formalise and deliberate) without recognising the trade-offs and dilemmas that arise.
  • I have described these options as choices, but did not establish if anyone is in the position to make or contribute to that choice.

In that context, resolving ambiguity in your favour may still be the prize, but where would you even begin?

Further reading

Well, that was an unsatisfying end to the post, eh? Maybe I’ll write a better one when some things are published. In the meantime, some of these papers and posts explore some of these issues:

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Policy in 500 Words: Trust

This post summarises ‘COVID-19: effective policymaking depends on trust in experts, politicians, and the public’ by Adam Wellstead and me.

The meaning of trust

We define trust as ‘a belief in the reliability of other people, organizations, or processes’, but it is one of those terms – like ‘policy’ – that defies a single comprehensive definition. The term ‘distrust’ complicates things further, since it does not simply mean the absence of trust.

Its treatment in social science also varies, which makes our statement – ‘Trust is necessary for cooperation, coordination, social order, and to reduce the need for coercive state imposition’ – one of many ways to understand its role.

A summary of key concepts

Social science accounts of trust relate it to:

1. Individual choice

I may trust someone to do something if I value their integrity (if they say they will do it, I believe them), credibility (I believe their claim is accurate and feasible), and competence (I believe they have the ability).

This perception of reliability depends on:

  • The psychology of the truster. The truster assesses the risk of relying on others, while combining cognition and emotion to relate that risk of making themselves vulnerable to the benefit of collective action, while drawing on an expectation of reciprocity.
  • The behaviour of the trustee. They demonstrate their trustworthiness in relation to past performance, which demonstrates their competence and reliability and perhaps their selflessness in favour of collective action.
  • Common reference points. The trustee and truster may use shortcuts to collective action, such as a reference to something they have in common (e.g. their beliefs or social background), their past interactions, or the authority of the trustee.

2. Social and political rules (aka institutions).

Perhaps ideally, we would learn who to trust via our experiences of working together, but we also need to trust people we have never met, and put equivalent trust in organisations and ‘systems’.

In that context, approaches such as the Institutional Analysis and Development (IAD) identify the role of many different kinds of rules in relation to trust:

  • Rules can be formal, written, and widely understood (e.g. to help assign authority regardless of levels of interaction) or informal, unwritten, and only understood by some (e.g. resulting from interactions in some contexts).
  • Rules can represent low levels of trust and a focus on deterring breaches (e.g. creating and enforcing contracts) or high levels of trust (e.g. to formalize ‘effective practices built on reciprocity, emotional bonds, and/or positive expectations’).

3. Societal necessity and interdependence.

Trust is a functional requirement. We need to trust people because we cannot maintain a functional society or political system without working together. Trust-building underpins the study of collaboration (or cooperation and bargaining), such as in the Ecology of Games approach (which draws on the IAD).

  • In that context, trust is a resource (to develop) that is crucial to a required outcome.

Is trust good and distrust bad?

We describe trust as ‘necessary for cooperation’ and distrust as a ‘potent motivator’ that may prompt people to ignore advice or defy cooperation or instruction. Yet, neither is necessarily good or bad. Too much trust may be a function of: (1) the abdication of our responsibility to engage critically with leaders in political systems, (2) vulnerability to manipulation, and/ or (3) excessive tribalism, prompting people to romanticise their own cause and demonise others, each of which could lead us to accept uncritically the cynical choices of policymakers.

Further reading

Trust is a slippery concept, and academics often make it slippier by assuming rather than providing a definition. In that context, why not read all of the 500 Words series and ask yourself where trust/ distrust fit in?

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Policy Analysis in 750 Words: power and knowledge

This post adapts Policy in 500 Words: Power and Knowledge (the body of this post) to inform the Policy Analysis in 750 words series (the top and tails).

One take home message from the 750 Words series is to avoid seeing policy analysis simply as a technical (and ‘evidence-based’) exercise. Mainstream policy analysis texts break down the process into technical-looking steps, but also show how each step relates to a wider political context. Critical policy analysis texts focus more intensely on the role of politics in the everyday choices that we might otherwise take for granted or consider to be innocuous. The latter connect strongly to wider studies of the links between power and knowledge.

Power and ideas

Classic studies suggest that the most profound and worrying kinds of power are the hardest to observe. We often witness highly visible political battles and can use pluralist methods to identify who has material resources, how they use them, and who wins. However, key forms of power ensure that many such battles do not take place. Actors often use their resources to reinforce social attitudes and policymakers’ beliefs, to establish which issues are policy problems worthy of attention and which populations deserve government support or punishment. Key battles may not arise because not enough people think they are worthy of debate. Attention and support for debate may rise, only to be crowded out of a political agenda in which policymakers can only debate a small number of issues.

Studies of power relate these processes to the manipulation of ideas or shared beliefs under conditions of bounded rationality (see for example the NPF). Manipulation might describe some people getting other people to do things they would not otherwise do. They exploit the beliefs of people who do not know enough about the world, or themselves, to know how to identify and pursue their best interests. Or, they encourage social norms – in which we describe some behaviour as acceptable and some as deviant – which are enforced by (1) the state (for example, via criminal justice and mental health policy), (2) social groups, and (3) individuals who govern their own behaviour with reference to what they feel is expected of them (and the consequences of not living up to expectations).

Such beliefs, norms, and rules are profoundly important because they often remain unspoken and taken for granted. Indeed, some studies equate them with the social structures that appear to close off some action. If so, we may not need to identify manipulation to find unequal power relationships: strong and enduring social practices help some people win at the expense of others, by luck or design.

Relating power to policy analysis: whose knowledge matters?

The concept of‘epistemic violence’ is one way todescribe the act of dismissing an individual, social group, or population by undermining the value of their knowledge or claim to knowledge. Specific discussions include: (a) the colonial West’s subjugation of colonized populations, diminishing the voice of the subaltern; (b) privileging scientific knowledge and dismissing knowledge claims via personal or shared experience; and (c) erasing the voices of women of colour from the history of women’s activism and intellectual history.

It is in this context that we can understand ‘critical’ research designed to ‘produce social change that will empower, enlighten, and emancipate’ (p51). Powerlessness can relate to the visible lack of economic material resources and factors such as the lack of opportunity to mobilise and be heard.

750 Words posts examining this link between power and knowledge

Some posts focus on the role of power in research and/ or policy analysis:

These posts ask questions such as: who decides what evidence will be policy-relevant, whose knowledge matters, and who benefits from this selective use of evidence? They help to (1) identify the exercise of power to maintain evidential hierarchies (or prioritise scientific methods over other forms of knowledge gathering and sharing), and (2) situate this action within a wider context (such as when focusing on colonisation and minoritization). They reflect on how (and why) analysts should respect a wider range of knowledge sources, and how to produce more ethical research with an explicit emancipatory role. As such, they challenge the – naïve or cynical – argument that science and scientists are objective and that science-informed analysis is simply a technical exercise (see also Separating facts from values).

Many posts incorporate these discussions into many policy analysis themes.

See also

Policy Concepts in 1000 Words: Power and Ideas

Education equity policy: ‘equity for all’ as a distraction from race, minoritization, and marginalization. It discusses studies of education policy (many draw on critical policy analysis)

There are also many EBPM posts that slip this discussion of power and politics into discussions of evidence and policy. They don’t always use the word ‘power’ though (see Evidence-informed policymaking: context is everything)

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Policy Analysis in 750 Words: Separating facts from values

This post begins by reproducing Can you separate the facts from your beliefs when making policy?(based on the 1st edition of Understanding Public Policy) …

A key argument in policy studies is that it is impossible to separate facts and values when making policy. We often treat our beliefs as facts, or describe certain facts as objective, but perhaps only to simplify our lives or support a political strategy (a ‘self-evident’ fact is very handy for an argument). People make empirical claims infused with their values and often fail to realise just how their values or assumptions underpin their claims.

This is not an easy argument to explain. One strategy is to use extreme examples to make the point. For example, Herbert Simon points to Hitler’s Mein Kampf as the ultimate example of value-based claims masquerading as facts. We can also identify historic academic research which asserts that men are more intelligent than women and some races are superior to others. In such cases, we would point out, for example, that the design of the research helped produce such conclusions: our values underpin our (a) assumptions about how to measure intelligence or other measures of superiority, and (b) interpretations of the results.

‘Wait a minute, though’ (you might say). “What about simple examples in which you can state facts with relative certainty – such as the statement ‘there are X number of words in this post’”. ‘Fair enough’, I’d say (you will have to speak with a philosopher to get a better debate about the meaning of your X words claim; I would simply say that it is trivially true). But this statement doesn’t take you far in policy terms. Instead, you’d want to say that there are too many or too few words, before you decided what to do about it.

In that sense, we have the most practical explanation of the unclear fact/ value distinction: the use of facts in policy is to underpin evaluations (assessments based on values). For example, we might point to the routine uses of data to argue that a public service is in ‘crisis’ or that there is a public health related epidemic (note: I wrote the post before COVID-19; it referred to crises of ‘non-communicable diseases’). We might argue that people only talk about ‘policy problems’ when they think we have a duty to solve them.

Or, facts and values often seem the hardest to separate when we evaluate the success and failure of policy solutions, since the measures used for evaluation are as political as any other part of the policy process. The gathering and presentation of facts is inherently a political exercise, and our use of facts to encourage a policy response is inseparable from our beliefs about how the world should work.

It continues with an edited excerpt from p59 of Understanding Public Policy, which explores the implications of bounded rationality for contemporary accounts of ‘evidence-based policymaking’:

‘Modern science remains value-laden … even when so many people employ so many systematic methods to increase the replicability of research and reduce the reliance of evidence on individual scientists. The role of values is fundamental. Anyone engaging in research uses professional and personal values and beliefs to decide which research methods are the best; generate research questions, concepts and measures; evaluate the impact and policy relevance of the results; decide which issues are important problems; and assess the relative weight of ‘the evidence’ on policy effectiveness. We cannot simply focus on ‘what works’ to solve a problem without considering how we used our values to identify a problem in the first place. It is also impossible in practice to separate two choices: (1) how to gather the best evidence and (2) whether to centralize or localize policymaking. Most importantly, the assertion that ‘my knowledge claim is superior to yours’ symbolizes one of the most worrying exercises of power. We may decide to favour some forms of evidence over others, but the choice is value-laden and political rather than objective and innocuous’.

Implications for policy analysis

Many highly-intelligent and otherwise-sensible people seem to get very bothered with this kind of argument. For example, it gets in the way of (a) simplistic stories of heroic-objective-fact-based-scientists speaking truth to villainous-stupid-corrupt-emotional-politicians, (b) the ill-considered political slogan that you can’t argue with facts (or ‘science’), (c) the notion that some people draw on facts while others only follow their feelings, and (d) the idea that you can divide populations into super-facty versus post-truthy people.

A more sensible approach is to (1) recognise that all people combine cognition and emotion when assessing information, (2) treat politics and political systems as valuable and essential processes (rather than obstacles to technocratic policymaking), and (3) find ways to communicate evidence-informed analyses in that context. This article and 750 post explore how to reflect on this kind of communication.

Most relevant posts in the 750 series

Linda Tuhiwai Smith (2012) Decolonizing Methodologies 

Carol Bacchi (2009) Analysing Policy: What’s the problem represented to be? 

Deborah Stone (2012) Policy Paradox

Who should be involved in the process of policy analysis?

William Riker (1986) The Art of Political Manipulation

Using Statistics and Explaining Risk (David Spiegelhalter and Gerd Gigerenzer)

Barry Hindess (1977) Philosophy and Methodology in the Social Sciences

See also

To think further about the relevance of this discussion, see this post on policy evaluation, this page on the use of evidence in policymaking, this book by Douglas, and this short commentary on ‘honest brokers’ by Jasanoff.

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Policy Analysis in 750 Words: How to communicate effectively with policymakers

This post forms one part of the Policy Analysis in 750 words series overview. The title comes from this article by Cairney and Kwiatkowski on ‘psychology based policy studies’.

One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts. How might we combine insights to think about effective communication?

1. Insights from policy analysis texts

Most texts in this series relate communication to understanding your audience (or client) and the political context. Your audience has limited attention or time to consider problems. They may have a good antennae for the political feasibility of any solution, but less knowledge of (or interest in) the technical details. In that context, your aim is to help them treat the problem as worthy of their energy (e.g. as urgent and important) and the solution as doable. Examples include:

  • Bardach: communicating with a client requires coherence, clarity, brevity, and minimal jargon.
  • Dunn: argumentation involves defining the size and urgency of a problem, assessing the claims made for each solution, synthesising information from many sources into a concise and coherent summary, and tailoring reports to your audience.
  • Smith: your audience makes a quick judgement on whether or not to read your analysis. Ask yourself questions including: how do I frame the problem to make it relevant, what should my audience learn, and how does each solution relate to what has been done before? Maximise interest by keeping communication concise, polite, and tailored to a policymaker’s values and interests.

2. Insights from studies of policymaker psychology

These insights emerged from the study of bounded rationality: policymakers do not have the time, resources, or cognitive ability to consider all information, possibilities, solutions, or consequences of their actions. They use two types of informational shortcut associated with concepts such as cognition and emotion, thinking ‘fast and slow’, ‘fast and frugal heuristics’, or, if you like more provocative terms:

  • ‘Rational’ shortcuts. Goal-oriented reasoning based on prioritizing trusted sources of information.
  • ‘Irrational’ shortcuts. Emotional thinking, or thought fuelled by gut feelings, deeply held beliefs, or habits.

We can use such distinctions to examine the role of evidence-informed communication, to reduce:

  • Uncertainty, or a lack of policy-relevant knowledge. Focus on generating ‘good’ evidence and concise communication as you collate and synthesise information.
  • Ambiguity, or the ability to entertain more than one interpretation of a policy problem. Focus on argumentation and framing as you try to maximise attention to (a) one way of defining a problem, and (b) your preferred solution.

Many policy theories describe the latter, in which actors: combine facts with emotional appeals, appeal to people who share their beliefs, tell stories to appeal to the biases of their audience, and exploit dominant ways of thinking or social stereotypes to generate attention and support. These possibilities produce ethical dilemmas for policy analysts.

3. Insights from studies of complex policymaking environments

None of this advice matters if it is untethered from reality.

Policy analysis texts focus on political reality to note that even a perfectly communicated solution is worthless if technically feasible but politically unfeasible.

Policy process texts focus on policymaking reality: showing that ideal-types such as the policy cycle do not guide real-world action, and describing more accurate ways to guide policy analysts.

For example, they help us rethink the ‘know your audience’ mantra by:

Identifying a tendency for most policy to be processed in policy communities or subsystems:

Showing that many policymaking ‘centres’ create the instruments that produce policy change

Gone are the mythical days of a small number of analysts communicating to a single core executive (and of the heroic researcher changing the world by speaking truth to power). Instead, we have many analysts engaging with many centres, creating a need to not only (a) tailor arguments to different audiences, but also (b) develop wider analytical skills (such as to foster collaboration and the use of ‘design principles’).

How to communicate effectively with policymakers

In that context, we argue that effective communication requires analysts to:

1. Understand your audience and tailor your response (using insights from psychology)

2. Identify ‘windows of opportunity’ for influence (while noting that these windows are outside of anyone’s control)

3. Engage with real world policymaking rather than waiting for a ‘rational’ and orderly process to appear (using insights from policy studies).

See also:

Why don’t policymakers listen to your evidence?

3. How to combine principles on ‘good evidence’, ‘good governance’, and ‘good practice’

Entrepreneurial policy analysis

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Policy in 500 Words: Peter Hall’s policy paradigms

Several 500 Word and 1000 Word (a, b, c) posts try to define and measure policy change.

Most studies agree that policymaking systems produce huge amounts of minor change and rare instances of radical change, but not how to explain these patterns. For example:

  • Debates on incrementalism questioned if radical change could be managed via non-radical steps.
  • Punctuated equilibrium theory describes policy change as a function of disproportionately low or high attention to problems, and akin to the frequency of earthquakes (a huge number of tiny changes, and more major changes than we would see in a ‘normal distribution’).

One of the most famous accounts of major policy change is by Peter Hall. ‘Policy paradigms’ help explain a tendency towards inertia, punctuated rarely by radical change (compare with discussions of path dependence and critical junctures).

A policy paradigm is a dominant and often taken-for-granted worldview (or collection of beliefs) about: policy goals, the nature of a policy problem, and the instruments to address it.

Paradigms can operate for long periods, subject to minimal challenge or defended successfully during events that call current policies into question. Adherence to a paradigm produces two ‘orders’ of change:

  • 1st order: frequent routine bureaucratic changes to instruments while maintaining policy goals.
  • 2nd order: less frequent, non-routine changes (or use of new instruments) while maintaining policy goals.

Radical and rare – 3rd order – policy change may only follow a crisis in which policymakers cannot solve a policy problem or explain why policy is failing. It prompts a reappraisal and rejection of the dominant paradigm, by a new government with new ways of thinking and/or a government rejecting current experts in favour of new ones. Hall’s example was of rapid paradigm shift in UK economic policy – from ‘Keynesianism’ to ‘Monetarism’ – within very few years.

Hall’s account prompted two different debates:

1. Some describe Hall’s case study as unusual.

Many scholars produced different phrases to describe a more likely pattern of (a) non-radical policy changes contributing to (b) long-term paradigm change and (c) institutional change, perhaps over decades. They include: ‘gradual change with transformative results’ and ‘punctuated evolution’ (see also 1000 Words: Evolution).

2. Some describe Hall’s case study as inaccurate.

This UK paradigm change did not actually happen. Instead, there was:

(a) A sudden and profound policy change that did not represent a paradigm shift (the UK experiment with Monetarism was short-lived).

(b) A series of less radical changes that produced paradigm change over decades: from Keynesianism to ‘neo-Keynesianism’, or from state intervention to neoliberalism (such as to foster economic growth via private rather than public borrowing and spending)

These debates connect strongly to issues in policy analysis, particularly if analysts seek transformative policy change to challenge unequal and unfair outcomes (such as in relation to racism or the climate crisis):

  1. Is paradigm change generally only possible over decades?
  2. How will we know if this transformation is actually taking place and here to stay (if even the best of us can be fooled by temporary developments)?

See also:

1. Beware the use of the word ‘evolution

2. This focus on the endurance of policy instrument change connects to studies of policy success (see Great Policy Successes).

3. Paul Cairney and Chris Weible (2015) ‘Comparing and Contrasting Peter Hall’s Paradigms and Ideas with the Advocacy Coalition Framework’ in (eds) M. Howlett and J. Hogan Policy Paradigms in Theory and Practice (Basingstoke: Palgrave) PDF

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Education equity policy: ‘equity for all’ as a distraction from race, minoritization, and marginalization

By Paul Cairney and Sean Kippin

This post summarizes a key section of our review of education equity policymaking [see the full article for references to the studies summarized here].

One of the main themes is that many governments present a misleading image of their education policies. There are many variations on this theme, in which policymakers:

  1. Describe the energetic pursuit of equity, and use the right language, as a way to hide limited progress.
  2. Pursue ‘equity for all’ initiatives that ignore or downplay the specific importance of marginalization and minoritization, such as in relation to race and racism, immigration, ethnic minorities, and indigenous populations.
  3. Pursue narrow definitions of equity in terms of access to schools, at the expense of definitions that pay attention to ‘out of school’ factors and social justice.

Minoritization is a strong theme in US studies in particular. US experiences help us categorise multiple modes of marginalisation in relation to race and migration, driven by witting and unwitting action and explicit and implicit bias:

  • The social construction of students and parents. Examples include: framing white students as ‘gifted’ and more deserving of merit-based education (or victims of equity initiatives); framing non-white students as less intelligent, more in need of special needs or remedial classes, and having cultural or other learning ‘deficits’ that undermine them and disrupt white students; and, describing migrant parents as unable to participate until they learn English.
  • Maintaining or failing to challenge inequitable policies. Examples include higher funding for schools and colleges with higher white populations, and tracking (segregating students according to perceived ability), which benefit white students disproportionately.
  • Ignoring social determinants or ‘out of school’ factors.
  • Creating the illusion of equity with measures that exacerbate inequalities. For example, promoting school choice policies while knowing that the rules restrict access to sought-after schools.
  • Promoting initiatives to ignore race, including so-called ‘color blind’ or ‘equity for all’ initiatives.
  • Prioritizing initiatives at the expense of racial or socio-economic equity, such as measures to boost overall national performance at the expense of targeted measures.
  • Game playing and policy subversion, including school and college selection rules to restrict access and improve metrics.

The wider international – primarily Global North – experience suggests that minoritization and marginalization in relation to race, ethnicity, and migration is a routine impediment to equity strategies, albeit with some uncertainty about which policies would have the most impact.

Other country studies describe the poor treatment of citizens in relation to immigration status or ethnicity, often while presenting the image of a more equitable system. Until recently, Finland’s global reputation for education equity built on universalism and comprehensive schools has contrasted with its historic ‘othering’ of immigrant populations. Japan’s reputation for containing a homogeneous population, allowing its governments to present an image of classless egalitarianism and harmonious society, contrasts with its discrimination against foreign students. Multiple studies of Canadian provinces provide the strongest accounts of the symbolic and cynical use of multiculturalism for political gains and economic ends:

As in the US, many countries use ‘special needs’ categories to segregate immigrant and ethnic minority populations. Mainstreaming versus special needs debates have a clear racial and ethnic dimension when (1) some groups are more likely to be categorised as having learning disabilities or behavioural disorders, and (2) language and cultural barriers are listed as disabilities in many countries. Further, ‘commonwealth’ country studies identify the marginalisation of indigenous populations in ways comparable to the US marginalisation of students of colour.

Overall, these studies generate the sense that the frequently used language of education equity policy can signal a range of possibilities, from (1) high energy and sincere commitment to social justice, to (2) the cynical use of rhetoric and symbolism to protect historic inequalities.

Examples:

  • Turner, E.O., and Spain, A.K., (2020) ‘The Multiple Meanings of (In)Equity: Remaking School District Tracking Policy in an Era of Budget Cuts and Accountability’, Urban Education, 55, 5, 783-812 https://doi.org/10.1177%2F0042085916674060
  • Thorius, K.A. and Maxcy, B.D. (2015) ‘Critical Practice Analysis of Special Education Policy: An RTI Example’, Remedial and Special Education, 36, 2, 116-124 https://doi.org/10.1177%2F0741932514550812
  • Felix, E.R. and Trinidad, A. (2020) ‘The decentralization of race: tracing the dilution of racial equity in educational policy’, International Journal of Qualitative Studies in Education, 33, 4, 465-490 https://doi.org/10.1080/09518398.2019.1681538
  • Alexiadou, N. (2019) ‘Framing education policies and transitions of Roma students in Europe’, Comparative Education, 55, 3,  https://doi.org/10.1080/03050068.2019.1619334

See also: https://paulcairney.wordpress.com/2017/09/09/policy-concepts-in-500-words-social-construction-and-policy-design/

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I am not Peter Matthews

Some notes for my guest appearance on @urbaneprofessor ‘s module

Peter’s description

Paul comes from a Political Science background and started off his project trying to understand why politicians don’t make good policy. He uses a lot of Political Science theory to understand the policy process (what MPP students have been learning) and theory from Public Policy about how to make the policy process better.

I come from a Social Policy background. I presume policy will be bad, and approach policy analysis from a normative position, analysing and criticising it from theoretical and critical perspectives.

Paul’s description

I specialize in the study of public policy and policymaking. I ‘synthesise’ and use policy concepts and theories to ask: how do policy processes work, and why?

Most theories and concepts – summarized in 1000 and 500 words – engage with that question in some way.

As such, I primarily seek to describe and explain policymaking, without spending much time thinking about making it better (unless asked to do so, or unless I feel very energetic).

In particular, I can give you a decent account of how all of these policy theories relate to each other, which is more important that it first seems.

A story of complex government

This ‘synthesis’ relates to my story about key elements of policy theories, with a different context influencing how I tell it. For example, I tend to describe ‘The Policy Process’ in 500 or 1000 words with the ‘Westminster Model’ versus ‘policy communities’ stories in mind (and a US scholar might tell this story in a different way):

Bounded rationality (500, 1000):

  • Individual policymakers can only pay attention to and understand a tiny proportion of (a) available information (b) the policy problems of which they are ostensibly responsible
  • So, they find cognitive shortcuts to pay attention to some issues/ information and ignore the rest (goal setting, relying on trusted advisors, belief translation, gut instinct, etc.)
  • Governmental organisations have more capacity, but also develop ‘standard operating procedures’ to limit their attention, and rely on many other actors for information and advice

Complex Policymaking Environments consisting of:

  • Many actors in many venues
  • Institutions (formal and informal rules)
  • Networks (relationships between policymakers and influencers)
  • Ideas (dominant beliefs, influencing the interpretation of problems and solutions)
  • Socioeconomic context and events

As such, the story of, say, multi-centric policymaking (or MLG, or complexity theory) contrasts with the idea of highly centralized control in the UK government.

A story of ‘evidence based policymaking’

That story provides context for applications to the agendas taken forward by other disciplines or professions.

  • The most obvious example is ‘evidence based policymaking’: my role is to explain why it is little more than a political slogan, and why people should not expect (or indeed want) it to exist, not to lobby for its existence
  • Also working on similar stories in relation to policy learning and policy design: my role is to highlight dilemmas and cautionary tales, not be a policy designer.

The politics of policymaking research

Most of the theories I describe relate to theory-informed empirical projects, generally originating from the US, and generally described as ‘positivist’ in contrast to (say) ‘interpretive’ (or, say, ‘constructivist’).

However, there are some interesting qualifications:

  • Some argue that these distinctions are overcooked (or, I suppose, overboiled)
  • Some try to bring in postpositivist ideas to positivist networks (NPF)
  • Some emerged from ‘critical policy analysis’ (SCPD)

The politics of policy analysis

This context helps understand my most recent book: The Politics of Policy Analysis

The initial podcast tells a story about MPP development, in which I used to ask students to write policy analyses (1st semester) without explaining what policy analysis was, or how to do it. My excuse is that the punchline of the module was: your account of the policy theories/ policy context is more important than your actual analysis (see the Annex to the book).

Since then, I have produced a webpage – 750 – which:

  • summarises the stories of the most-used policy analysis texts (e.g. Bardach) which identify steps including: define the problem; identify solutions; use values to compare trade-offs between solutions; predict their effects; make a recommendation
  • relates those texts to policy theories, to identify how bounded rationality and complexity change that story (and the story of the policy cycle)
  • relates both to ‘critical’ policy analysis and social science texts (some engage directly – like Stone, like Bacchi – while some provide insights – such as on critical race theory – without necessarily describing ‘policy analysis’)

A description of ‘critical’ approaches is fairly broad, but I think they tend to have key elements in common:

  • a commitment to use research to improve policy for marginalized populations (described by Bacchi as siding with the powerless against the powerful, usually in relation to class, race, ethnicity, gender, sexuality, disability)
  • analysing policy to identify: who is portrayed positively/negatively; who benefits or suffers as a result
  • analysing policymaking to identify: whose knowledge counts (e.g. as high quality and policy relevant), who is included or excluded
  • identifying ways to challenge (a) dominant and damaging policy frames and (b) insulated/ exclusive versus participatory/ inclusive forms of policymaking

If so, I would see these three approaches as ways to understand and engage with policymaking that could be complementary or contradictory. In other words, I would warn against assuming one or the other.

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The COVID-19 exams fiasco across the UK: why did policymaking go so wrong?

This post first appeared on the LSE British Politics and Policy blog, and it summarises our new article: Sean Kippin and Paul Cairney (2021) ‘The COVID-19 exams fiasco across the UK: four nations and two windows of opportunity’, British Politics, PDF Annex. The focus on inequalities of attainment is part of the IMAJINE project on spatial justice and territorial inequalities.

In the summer of 2020, after cancelling exams, the UK and devolved governments sought teacher estimates on students’ grades, but supported an algorithm to standardise the results. When the results produced a public outcry over unfair consequences, they initially defended their decision but reverted quickly to teacher assessment. These experiences, argue Sean Kippin and Paul Cairney, highlight the confluence of events and choices in which an imperfect and rejected policy solution became a ‘lifeline’ for four beleaguered governments. 

In 2020, the UK and devolved governments performed a ‘U-turn’ on their COVID-19 school exams replacement policies. The experience was embarrassing for education ministers and damaging to students. There are significant differences between (and often within) the four nations in terms of the structure, timing, weight, and relationship between the different examinations. However, in general, the A-level (England, Northern Ireland, Wales) and Higher/ Advanced Higher (Scotland) examinations have similar policy implications, dictating entry to further and higher education, and influencing employment opportunities. The Priestley review, commissioned by the Scottish Government after their U-turn, described this as an ‘impossible task’.

Initially, each government defined the new policy problem in relation to the need to ‘credibly’ replicate the purpose of exams to allow students to progress to tertiary education or employment. All four quickly announced their intentions to allocate in some form grades to students, rather than replace the assessments with, for example, remote examinations. However, mindful of the long-term credibility of the examinations system and of ensuring fairness, each government opted to maintain the qualifications and seek a similar distribution of grades to previous years. A key consideration was that UK universities accept large numbers of students from across the UK.

One potential solution open to policymakers was to rely solely on teacher grading (CAG). CAGs are ‘based on a range of evidence including mock exams, non-exam assessment, homework assignments and any other record of student performance over the course of study’. Potential problems included the risk of high variation and discrepancies between different centres, the potential overload of the higher education system, and the tendency for teacher predicted grades to reward already privileged students and punish disabled, non-white, and economically deprived children.

A second option was to take CAGs as a starting point, then use an algorithm to produce ‘standardisation’, which was potentially attractive to each government as it allowed students to complete secondary education and to progress to the next level in similar ways to previous (and future) cohorts. Further, an emphasis on the technical nature of this standardisation, with qualifications agencies taking the lead in designing the process by which grades would be allocated, and opting not share the details of its algorithm were a key part of its (temporary) viability. Each government then made similar claims when defending the problem and selecting the solution. Yet this approach reduced both the debate on the unequal impact of this process on students, and the chance for other experts to examine if the algorithm would produce the desired effect. Policymakers in all four governments assured students that the grading would be accurate and fair, with teacher discretion playing a large role in the calculation of grades.

To these governments, it appeared at first that they had found a fair and efficient (or at least defendable) way to allocate grades, and public opinion did not respond negatively to its announcement. However, these appearances proved to be profoundly deceptive and vanished on each day of each exam result. The Scottish national mood shifted so intensely that, after a few days, pursuing standardisation no longer seemed politically feasible. The intense criticism centred on the unequal level of reductions of grades after standardisation, rather than the unequal overall rise in grade performance after teacher assessment and standardisation (which advantaged poorer students).

Despite some recognition that similar problems were afoot elsewhere, this shift of problem definition did not happen in the rest of the UK until (a) their published exam results highlighted similar problems regarding the role of previous school performance on standardised results, and (b) the Scottish Government had already changed course. Upon the release of grades outside Scotland, it became clear that downgrades were also concentrated in more deprived areas. For instance, in Wales, 42% of students saw their A-Level results lowered from their Centre Assessed Grades, with the figure close to a third for Northern Ireland.

Each government thus faced similar choices between defending the original system by challenging the emerging consensus around its apparent unfairness; modifying the system by changing the appeal system; or abandoning it altogether and reverting to solely teacher assessed grades. Ultimately, all three governments followed the same path. Initially, they opted to defend their original policy choice. However, by 17 August, the UK, Welsh, and Northern education secretaries announced (separately) that examination grades would be based solely on CAGs – unless the standardisation process had generated a higher grade (students would receive whichever was highest).

Scotland’s initial experience was instructive to the rest of the UK and its example provided the UK government with a blueprint to follow (eventually). It began with a new policy choice – reverting to teacher assessed grades – sold as fairer to victims of the standardisation process. Once this precedent had been set, a different course for policymakers at the UK level became difficult to resist, particularly when faced with a similar backlash. The UK’s government’s decision in turn influenced the Welsh and Northern Irish governments.

In short, we can see that the particular ordering of choices created a cascading effect across the four governments which created initially one policy solution, before triggering a U-turn. This focus on order and timing should not be lost during the inevitable inquiries and reports on the examinations systems. The take-home message is to not ignore the policy process when evaluating the long-term effect of these policies. Focus on why the standardisation processes went wrong is welcome, but we should also focus on why the policymaking process malfunctioned, to produce a wildly inconsistent approach to the same policy choice in such a short space of time. Examining both aspects of this fiasco will be crucial to the grading process in 2021, given that governments will be seeking an alternative to exams for a second year.

__________________________

Note: the above draws on the authors’ published work in British Politics.

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Policy Concepts in 1000 Words: Policy Change

Christopher M. Weible & Paul Cairney

Policy change is a central concern of policy research and practice. Some want to explain it. Some want to achieve it.

Explanation begins with the ‘what is policy?’ question, since we cannot observe something without defining it.  However, we soon find that: no single definition can capture all forms of policy change, the absence of policy change is often more important, and important changes can be found in the everyday application of rules and practices related to public policies.  Further, studies often focus on changes in public policies without a focus on societal outcomes or effects.

One pragmatic solution is to define public policies as decisions made by policymakers or policymaking venues such as legislatures, executives, regulatory agencies, courts, national and local governments (and, in some countries, citizen-led policy changes).  Focusing on this type of policy change, two major categories of insights unfold:

  1. Patterns of Policy Change: incrementalism, punctuations, and drift

A focus on decisions suggests that most policymaking venues contribute primarily to incremental policy change, or often show little change from year to year but with the occasional punctuation of major policymaking activity.  This pattern reflects a frequent story about governments doing too much or nothing at all. The logic is that policymaking attention is always limited, so a focus on one issue in any policymaking venue requires minimal focus on others.  Then, when attention shifts, we see instances of major policy change as attempts to compensate (or overcompensate) for what was ignored for too long.

An additional focus on institutions highlights factors such as policy drift, to describe slow and small changes to policies, or to aspects of their design, that accumulate eventually and can have huge impacts on outcomes and society.  These drifts often happen outside the public eye or are overlooked as being negative but trivial.  For example, rising economic inequality in the US resulted from the slow accumulation of policies – related to labor unions, tax structures, and corporate governance – as well as globalization and labor-saving technologies.

  1. Factors Associated with Policy Change

Many factors help us understand instances of policy change. We can separate them analytically (as below) but, in practice, they occur simultaneously or sequentially, and can reinforce or stifle each other.

Context

Context includes history, biophysical conditions, socio-economic conditions, culture, and basic institutional structures (such as a constitution).  For example, historical and geographic conditions are often viewed as funneling or constraining the type of policy decisions made by a government.

Events 

Policymaking venues are often described as being resistant to change or in a state of equilibrium of competing political forces.  As a result, one common explanation for change is a focusing event or shock.  Events by themselves don’t create policy change. Rather, they present an opportunity for people or coalitions to exploit.   Focusing events might include disasters or crises, tragic incidents, a terrorist attack, disruptive changes in technology, or more routine events such as elections. Events may have tangible qualities, but studies tend to highlight the ways in which people frame events to construct their meaning and implications for policy.

Public Opinion 

The relationship between public opinion and policy change is a difficult one to assess.  Some research shows that the preferences of the general public only matter when they coincide with the preferences of the elite or major interest groups.  Or, it matters only when the topic is salient and the public is paying attention. Little evidence suggests that public opinion matters when few are paying attention.  Others describe public opinion as setting the boundaries within which the government operates.

Learning

Learning is a process of updating understandings of the world in response to signals from the environment.  Learning is a political activity rather than simply a technical exercise in which people learn from teachers. Learning could involve becoming aware of the severity of a policy problem, evaluating outcomes to determine if a government intervention works, and learning to trust an opponent and reach compromise. For example, certain types of rules in a collaborative process can shape the ways in which individuals gain new knowledge and change their views about the scientific evidence informing a problem.

Diffusion of Ideas 

Sometimes governments learn from or transfer policies from other governments. For example, in collections of policymaking venues (such as US state governments or EU member states) it is common for one venue to adopt a policy and prompt this policy to spread across other venues in a process of diffusion.  There are many explanations for diffusion including learning, a response to competition, mimicking, and coercion. In each case, the explanation for policy change comes from an external impetus and an internal context.

Champions and Political Associations

All policy change is driven, to some extent, by individual or group agency.  Key players include public policy champions in the form of policy entrepreneurs or in groups of government and/or non-government entities in the form of coalitions, social movements, epistemic communities, and political parties.  In each case, individuals or organizations mobilize resources, capitalize on opportunities, and apply pressure to formulate and adopt public policies.

 

The presence of these factors does not always lead to policy change, and no single study can capture a full explanation of policy change. Instead, many quantitative studies focus on multiple instances of policy change and are often broad in geographic scope or spans of time, while many case study or qualitative studies focus intensely on a very particular instance of policy change. Both approaches are essential.

See also:

Policy in 500 Words: what is public policy and why does it matter?

Policy in 500 Words: how much does policy change?

Policy Concepts in 1000 Words: Policy change and measurement (podcast download)

Policy Concepts in 1000 Words: how do policy theories describe policy change?

 

 

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Who can you trust during the coronavirus crisis?

By Paul Cairney and Adam Wellstead, based on this paper and article: Paul Cairney and Adam Wellstead (2020) ‘COVID-19: effective policymaking depends on trust in experts, politicians, and the public’, Policy Design and Practice https://www.tandfonline.com/doi/full/10.1080/25741292.2020.1837466 (PDF)

Trust is essential during a crisis. It is necessary for cooperation. Cooperation helps people coordinate action, to reduce the need for imposition. It helps reduce uncertainty in a complex world. It facilitates social order and cohesiveness. In a crisis, almost-instant choices about who to trust or distrust make a difference between life and death.

Put simply, we need to trust: experts to help us understand and address the problem, governments to coordinate policy and make choices about levels of coercion, and each other to cooperate to minimise infection.

Yet, there are three unresolved problems with understanding trust in relation to coronavirus policy.

  1. What does trust really mean?

Trust is one of those words that could mean everything and nothing. We feel like we understand it intuitively, but would also struggle to define it well enough to explain how exactly it works. For example, in social science, there is some agreement on the need to describe individual motivation, social relationships, and some notion of the ‘public good’:

  • the production of trust helps boost the possibility of cooperation, partly by
  • reducing uncertainty (low information about a problem) and ambiguity (low agreement on how to understand it) when making choices, partly by
  • helping you manage the risk of making yourself vulnerable when relying on others, particularly when
  • people demonstrate trustworthiness by developing a reputation for competence, honesty, and/ or reliability, and
  • you combine cognition and emotion to produce a disposition to trust, and
  • social and political rules facilitate this process, from the formal and well-understood rules governing behaviour to the informal rules and norms shaping behaviour.

As such, trust describes your non-trivial belief in the reliability of other people, organisations, or processes. It facilitates the kinds of behaviour that are essential to an effective response to the coronavirus, in which we need to:

  1. Make judgements about the accuracy of information underpinning our choices to change behaviour (such as from scientific agencies).
  2. Assess the credibility of the people with whom we choose to cooperate or take advice (such as more or less trust in each country’s leadership).
  3. Measure the effectiveness of the governments or political systems to which we pledge our loyalty.

Crucially, in most cases, people need to put their trust in actions or outcomes caused by people they do not know, and the explanation for this kind of trust is very different to trusting people you know.

  1. What does trust look like in policymaking?

Think of trust as a mechanism to boost cooperation and coalition formation, help reduce uncertainty, and minimise the ‘transactions costs’ of cooperation (for example, monitoring behaviour, or producing or enforcing contracts). However, uncertainty is remarkably high because the policy process is not easy to understand. We can try to understand the ‘mechanisms’ of trust, to boost cooperation, with reference to these statements about trustees and the trusted:

  1. Individuals need to find ways to make choices about who to trust and distrust.
  2. However, they must act within a complex policymaking environment in which they have minimal knowledge of what will happen and who will make it happen.
  3. To respond effectively, people seek ways to cooperate with others systematically, such as by establishing formal and informal rules.

People seeking to make and influence policy must act despite uncertainty about the probability of success or risk of failure. In a crisis, it happens almost instantly. People generate beliefs about what they want to happen and how their reliance on others can help it happen. This calculation depends on:

  • Another person or organisation’s reputation for being trustworthy, allowing people the ability to increase certainty when they calculate the risk of engagement.
  • The psychology of trust and perceptions of another actor’s motives. To some extent, people gather information and use logic to determine someone’s competence. However, they also use gut feeling or emotion to help them decide to depend on someone else. They may also trust a particular source if the cognitive load is low, such as because (a) the source is familiar (e.g. a well-known politician or a celebrity, or oft-used source), or (b) the information is not challenging to remember or accept.

If so, facilitators of trust include:

  • People share the same characteristics, such as beliefs, norms, or expectations.
  • Some people have reputations for being reliable, predictable, honest, competent, and/ or relatively selfless.
  • Good experiences of previous behaviour, including repeated interactions that foster rewards and help predict future risk (with face to face contact often described as particularly helpful).
  • People may trust people in a position of authority (or the organisation or office), such as an expert or policymaker (although perhaps the threat of rule enforcement is better understood as a substitute for trust, and in practice it is difficult to spot the difference).

High levels of trust are apparent when effective practices – built on reciprocity, emotional bonds, and/ or positive expectations – become the norms or formalised and written down for all to see and agree. High levels of distrust indicate a need to deter the breach of agreements, by introducing expectations combined with sanctions for not behaving as expected.

  1. Who should you trust?

These concepts do not explain fully why people trust particular people more than others, or help us determine who you should trust during a crisis.

Rather, first, they help us reflect on the ways in which people have been describing their own thought processes (click here, and scroll to ‘Limiting the use of evidence’), such as trusting an expert source because they: (a) have a particular scientific background, (b) have proven to be honest and reliable in the past, (c) represent a wider scientific profession/ community, (d) are part of a systematic policymaking machinery, (e) can be held to account for their actions, (f) are open about the limits to their knowledge, and/or (g) engage critically with information to challenge simplistic rushes to judgement. Overall, note how much trust relates to our minimal knowledge about their research skills, prompting us to rely on an assessment of their character or status to judge their behaviour. In most cases, this is an informal process in which people may not state (or really know) why they trust or distrust someone so readily.

Then, we can reflect on who we trust, and why, and if we should change how we make such calculations during a crisis like the coronavirus. Examples include:

  • A strong identity with a left or right wing cause might prompt us only to trust people from one political party. This thought process may be efficient during elections and debates, but does it work so well during a crisis necessitating so high levels of cross-party cooperation?
  • People may be inclined to ignore advice because they do not trust their government, but maybe (a) high empathy for their vulnerable neighbours, and (b) low certainty about the impact of their actions, should prompt them to trust in government advice unless they have a tangible reason not to (while low empathy helps explain actions such as hoarding).
  • Government policy is based strongly on the extent to which policymakers trust people to do the right thing. Most debates in liberal democracies relate to the idea that (a) people can be trusted, so give advice and keep action voluntary, or cannot be trusted, so make them do the right thing, and that (b) citizens can trust their government. In other words, it must be a reciprocal relationship (see the Tweets in Step 3).

Finally, governments make policy based on limited knowledge and minimal control of the outcomes, and they often respond with trial-and-error strategies. The latter is fine if attention to policy is low and trust in government sufficiently high. However, in countries like the UK and US, each new choice prompts many people to question not only the competence of leaders but also their motivation. This is a worrying development for which everyone should take some responsibility.

See also:

Policy Concepts in 1000 Words: the Institutional Analysis and Development Framework (IAD) and Governing the Commons

The coronavirus and evidence-informed policy analysis (short version)

The coronavirus and evidence-informed policy analysis (long version)

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Filed under 1000 words, 750 word policy analysis, Public health, public policy

The coronavirus and evidence-informed policy analysis (short version)

  • Paul Cairney (2020) ‘The UK Government’s COVID-19 policy: assessing evidence-informed policy analysis in real time’, British Politics https://rdcu.be/b9zAk (PDF)

The coronavirus feels like a new policy problem that requires new policy analysis. The analysis should be informed by (a) good evidence, translated into (b) good policy. However, don’t be fooled into thinking that either of those things are straightforward. There are simple-looking steps to go from defining a problem to making a recommendation, but this simplicity masks the profoundly political process that must take place. Each step in analysis involves political choices to prioritise some problems and solutions over others, and therefore prioritise some people’s lives at the expense of others.

My article in British Politics takes us through those steps in the UK, and situates them in a wider political and policymaking context. This post is shorter, and only scratches the surface of analysis.

5 steps to policy analysis

  1. Define the problem.

Perhaps we can sum up the initial UK government approach as: (a) the impact of this virus and illness will be a level of death and illness that could overwhelm the population and exceed the capacity of public services, so (b) we need to contain the virus enough to make sure it spreads in the right way at the right time, so (c) we need to encourage and make people change their behaviour (primarily via hygiene and social distancing). However, there are many ways to frame this problem to emphasise the importance of some populations over others, and some impacts over others.

  1. Identify technically and politically feasible solutions.

Solutions are not really solutions: they are policy instruments that address one aspect of the problem, including taxation and spending, delivering public services, funding research, giving advice to the population, and regulating or encouraging changes to social behaviour. Each new instrument contributes an existing mix, with unpredictable and unintended consequences. Some instruments seem technically feasible (they will work as intended if implemented), but will not be adopted unless politically feasible (enough people support their introduction). Or vice versa. From the UK government’s perspective, this dual requirement rules out a lot of responses.

  1. Use values and goals to compare solutions.

Typical judgements combine: (a) broad descriptions of values such as efficiency, fairness, freedom, security, and human dignity, (b) instrumental goals, such as sustainable policymaking (can we do it, and for how long?), and political feasibility (will people agree to it, and will it make me more or less popular or trusted?), and (c) the process to make choices, such as the extent to which a policy process involves citizens or stakeholders (alongside experts) in deliberation. They combine to help policymakers come to high profile choices (such as the balance between individual freedom and state coercion), and low profile but profound choices (to influence the level of public service capacity, and level of state intervention, and therefore who and how many people will die).

  1. Predict the outcome of each feasible solution.

It is difficult to envisage a way for the UK Government to publicise all of the thinking behind its choices (Step 3) and predictions (Step 4) in a way that would encourage effective public deliberation. People often call for the UK Government to publicise its expert advice and operational logic, but I am not sure how they would separate it from their normative logic about who should live or die, or provide a frank account without unintended consequences for public trust or anxiety. If so, one aspect of government policy is to keep some choices implicit and avoid a lot of debate on trade-offs. Another is to make choices continuously without knowing what their impact will be (the most likely scenario right now).

  1. Make a choice, or recommendation to your client.

Your recommendation or choice would build on these four steps. Define the problem with one framing at the expense of the others. Romanticise some people and not others. Decide how to support some people, and coerce or punish others. Prioritise the lives of some people in the knowledge that others will suffer or die. Do it despite your lack of expertise and profoundly limited knowledge and information. Learn from experts, but don’t assume that only scientific experts have relevant knowledge (decolonise; coproduce). Recommend choices that, if damaging, could take decades to fix after you’ve gone. Consider if a policymaker is willing and able to act on your advice, and if your proposed action will work as intended. Consider if a government is willing and able to bear the economic and political costs. Protect your client’s popularity, and trust in your client, at the same time as protecting lives. Consider if your advice would change if the problem seemed to change. If you are writing your analysis, maybe keep it down to one sheet of paper (in other words, fewer words than in this post up to this point).

Policy analysis is not as simple as these steps suggest, and further analysis of the wider policymaking environment helps describe two profound limitations to simple analytical thought and action.

  1. Policymakers must ignore almost all evidence

The amount of policy relevant information is infinite, and capacity is finite. So, individuals and governments need ways to filter out almost all of it. Individuals combine cognition and emotion to help them make choices efficiently, and governments have equivalent rules to prioritise only some information. They include: define a problem and a feasible response, seek information that is available, understandable, and actionable, and identify credible sources of information and advice. In that context, the vague idea of trusting or not trusting experts is nonsense, and the larger post highlights the many flawed ways in which all people decide whose expertise counts.

  1. They do not control the policy process.

Policymakers engage in a messy and unpredictable world in which no single ‘centre’ has the power to turn a policy recommendation into an outcome.

  • There are many policymakers and influencers spread across a political system. For example, consider the extent to which each government department, devolved governments, and public and private organisations are making their own choices that help or hinder the UK government approach.
  • Most choices in government are made in ‘subsystems’, with their own rules and networks, over which ministers have limited knowledge and influence.
  • The social and economic context, and events, are largely out of their control.

The take home messages (if you accept this line of thinking)

  1. The coronavirus is an extreme example of a general situation: policymakers will always have very limited knowledge of policy problems and control over their policymaking environment. They make choices to frame problems narrowly enough to seem solvable, rule out most solutions as not feasible, make value judgements to try help some more than others, try to predict the results, and respond when the results do not match their hopes or expectations.
  2. This is not a message of doom and despair. Rather, it encourages us to think about how to influence government, and hold policymakers to account, in a thoughtful and systematic way that does not mislead the public or exacerbate the problem we are seeing. No one is helping their government solve the problem by saying stupid shit on the internet (OK, that last bit was a message of despair).

Further reading:

The article (PDF) sets out these arguments in much more detail, with some links to further thoughts and developments.

This series of ‘750 words’ posts summarises key texts in policy analysis and tries to situate policy analysis in a wider political and policymaking context. Note the focus on whose knowledge counts, which is not yet a big feature of this crisis.

These series of 500 words and 1000 words posts (with podcasts) summarise concepts and theories in policy studies.

This page on evidence-based policymaking (EBPM) uses those insights to demonstrate why EBPM is  a political slogan rather than a realistic expectation.

These recorded talks relate those insights to common questions asked by researchers: why do policymakers seem to ignore my evidence, and what can I do about it? I’m happy to record more (such as on the topic you just read about) but not entirely sure who would want to hear what.

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Filed under 750 word policy analysis, agenda setting, Evidence Based Policymaking (EBPM), Policy learning and transfer, POLU9UK, Prevention policy, Psychology Based Policy Studies, Public health, public policy, Social change, UK politics and policy

The coronavirus and evidence-informed policy analysis (long version)

Final update 2.11.20. Don’t read this post. It became too long and unwieldy. I turned it into:

A published article https://rdcu.be/b9zAk (PDF)

A 25000 word version with more discussion and links Cairney UK coronavirus policy 25000 14.7.20 

This is the long version. It is long. Too long to call a blog post. Let’s call it a ‘living document’ that I update and amend as new developments arise (then start turning into a more organised paper). In most cases, I am adding tweets, so the date of the update is embedded. If I add a new section, I will add a date. If you seek specific topics (like ‘herd immunity’), it might be worth doing a search. The short version is shorter.

The coronavirus feels like a new policy problem. Governments already have policies for public health crises, but the level of uncertainty about the spread and impact of this virus seems to be taking it to a new level of policy, media, and public attention. The UK Government’s Prime Minister calls it ‘the worst public health crisis for a generation’.

As such, there is no shortage of opinions on what to do, but there is a shortage of well-considered opinions, producing little consensus. Many people are rushing to judgement and expressing remarkably firm opinions about the best solutions, but their contributions add up to contradictory evaluations, in which:

  • the government is doing precisely the right thing or the completely wrong thing,
  • we should listen to this expert saying one thing or another expert saying the opposite.

Lots of otherwise-sensible people are doing what they bemoan in politicians: rushing to judgement, largely accepting or sharing evidence only if it reinforces that judgement, and/or using their interpretation of any new development to settle scores with their opponents.

Yet, anyone who feels, without uncertainty, that they have the best definition of, and solution to, this problem is a fool. If people are also sharing bad information and advice, they are dangerous fools. Further, as Professor Madley puts it (in the video below), ‘anyone who tells you they know what’s going to happen over the next six months is lying’.

In that context, how can we make sense of public policy to address the coronavirus in a more systematic way?

Studies of policy analysis and policymaking do not solve a policy problem, but they at least give us a language to think it through.

  1. Let’s focus on the UK as an example, and use common steps in policy analysis, to help us think through the problem and how to try to manage it.
  • In each step, note how quickly it is possible to be overwhelmed by uncertainty and ambiguity, even when the issue seems so simple at first.
  • Note how difficult it is to move from Step 1, and to separate Step 1 from the others. It is difficult to define the problem without relating it to the solution (or to the ways in which we will evaluate each solution).
  1. Let’s relate that analysis to research on policymaking, to understand the wider context in which people pay attention to, and try to address, important problems that are largely out of their control.

Throughout, note that I am describing a thought process as simply as I can, not a full examination of relevant evidence. I am highlighting the problems that people face when ‘diagnosing’ policy problems, not trying to diagnose it myself. To do so, I draw initially on common advice from the key policy analysis texts (summaries of the texts that policy analysis students are most likely to read) that simplify the process a little too much. Still, the thought process that it encourages took me hours alone (spread over three days) to produce no real conclusion. Policymakers and advisers, in the thick of this problem, do not have that luxury of time or uncertainty.

See also: Boris Johnson’s address to the nation in full (23.3.20) and press conference transcripts

Step 1 Define the problem

Common advice in policy analysis texts:

  • Provide a diagnosis of a policy problem, using rhetoric and eye-catching data to generate attention.
  • Identify its severity, urgency, cause, and our ability to solve it. Don’t define the wrong problem, such as by oversimplifying.
  • Problem definition is a political act of framing, as part of a narrative to evaluate the nature, cause, size, and urgency of an issue.
  • Define the nature of a policy problem, and the role of government in solving it, while engaging with many stakeholders.
  • ‘Diagnose the undesirable condition’ and frame it as ‘a market or government failure (or maybe both)’.

Coronavirus as a physical problem is not the same as a coronavirus policy problem. To define the physical problem is to identify the nature, spread, and impact of a virus and illness on individuals and populations. To define a policy problem, we identify the physical problem and relate it (implicitly or explicitly) to what we think a government can, and should, do about it. Put more provocatively, it is only a policy problem if policymakers are willing and able to offer some kind of solution.

This point may seem semantic, but it raises a profound question about the capacity of any government to solve a problem like an epidemic, or for governments to cooperate to solve a pandemic. It is easy for an outsider to exhort a government to ‘do something!’ (or ‘ACT NOW!’) and express certainty about what would happen. However, policymakers inside government:

  1. Do not enjoy the same confidence that they know what is happening, or that their actions will have their intended consequences, and
  2. Will think twice about trying to regulate social behaviour under those circumstances, especially when they
  3. Know that any action or inaction will benefit some and punish others.

For example, can a government make people wash their hands? Or, if it restricts gatherings at large events, can it stop people gathering somewhere else, with worse impact? If it closes a school, can it stop children from going to their grandparents to be looked after until it reopens? There are 101 similar questions and, in each case, I reckon the answer is no. Maybe government action has some of the desired impact; maybe not. If you agree, then the question might be: what would it really take to force people to change their behaviour?

See also: Coronavirus has not suspended politics – it has revealed the nature of power (David Runciman)

The answer is: often too much for a government to consider (in a liberal democracy), particularly if policymakers are informed that it will not have the desired impact.

If so, the UK government’s definition of the policy problem will incorporate this implicit question: what can we do if we can influence, but not determine (or even predict well) how people behave?

Uncertainty about the coronavirus plus uncertainty about policy impact

Now, add that general uncertainty about the impact of government to this specific uncertainty about the likely nature and spread of the coronavirus:

A summary of this video suggests:

  • There will be an epidemic (a profound spread to many people in a short space of time), then the problem will be endemic (a long-term, regular feature of life) (see also UK policy on coronavirus COVID-19 assumes that the virus is here to stay).
  • In the absence of a vaccine, the only way to produce ‘herd immunity’ is for most people to be infected and recover

[Note: there is much debate on whether ‘herd immunity’ is or is not government policy. Much of it relates to interpretation, based on levels of trust/distrust in the UK Government, its Prime Minister, and the Prime Minister’s special adviser. I discuss this point below under ‘trial and error policymaking’. See also Who can you trust during the coronavirus crisis? ]

  • The ideal spread involves all well people sharing the virus first, while all vulnerable people (e.g. older, and/or with existing health problems that affect their immune systems) protected in one isolated space, but it won’t happen like that; so, we are trying to minimise damage in the real world.
  • We mainly track the spread via deaths, with data showing a major spike appearing one month later, so the problem may only seem real to most people when it is too late to change behaviour

See also: Coronavirus: Government expert defends not closing UK schools (BBC, Sir Patrick Vallance 13th March 2020)

  • The choice in theory is between a rapid epidemic with a high peak, or a slowed-down epidemic over a longer period, but ‘anyone who tells you they know what’s going to happen over the next six months is lying’.
  • Maybe this epidemic will be so memorable as to shift social behaviour, but so much depends on trying to predict (badly) if individuals will actually change (see also Spiegelhalter on communicating risk).

None of this account tells policymakers what to do, but at least it helps them clarify three key aspects of their policy problem:

  1. The impact of this virus and illness could overwhelm the population, to the extent that it causes mass deaths, causes a level of illness that exceeds the capacity of health services to treat, and contributes to an unpredictable amount of social and economic damage.
  2. We need to contain the virus enough to make sure it (a) spreads at the right speed and/or (b) peaks at the right time. The right speed seems to be: a level that allows most people to recover alone, while the most vulnerable are treated well in healthcare settings that have enough capacity. The right time seems to be the part of the year with the lowest demand on health services (e.g. summer is better than winter). In other words, (a) reduce the size of the peak by ‘flattening the curve’, and/or (b) find the right time of year to address the peak, while (c) anticipating more than one peak.

My impression is that the most frequently-expressed aim is (a) …

… while the UK Government’s Deputy Chief Medical Officer also seems to be describing (b):

  1. We need to encourage or coerce people to change their behaviour, to look after themselves (e.g. by handwashing) and forsake their individual preferences for the sake of public health (e.g. by self-isolating or avoiding vulnerable people). Perhaps we can foster social trust and empathy to encourage responsible individual action. Perhaps people will only protect others if obliged to do so (compare Stone; Ostrom; game theory).

See also: From across the Ditch: How Australia has to decide on the least worst option for COVID-19 (Prof Tony Blakely on three bad options: (1) the likelihood of ‘elimination’ of the virus before vaccination is low; (2) an 18-month lock-down will help ‘flatten the curve’; (3) ‘to prepare meticulously for allowing the pandemic to wash through society over a period of six or so months. To tool up the production of masks and medical supplies. To learn as quickly as possible which treatments of people sick with COVID-19 saves lives. To work out our strategies for protection of the elderly and those with a chronic condition (for whom the mortality from COVID-19 is much higher’).

From uncertainty to ambiguity

If you are still with me, I reckon you would have worded those aims slightly differently, right? There is some ambiguity about these broad intentions, partly because there is some uncertainty, and partly because policymakers need to set rather vague intentions to generate the highest possible support for them. However, vagueness is not our friend during a crisis involving such high anxiety. Further, they are only delaying the inevitable choices that people need to make to turn a complex multi-faceted problem into something simple enough to describe and manage. The problem may be complex, but our attention focuses only on a small number of aspects, at the expense of the rest. Examples that have arisen, so far, include to accentuate:

  1. The health of the whole population or people who would be affected disproportionately by the illness.
  • For example, the difference in emphasis affects the health advice for the relatively vulnerable (and the balance between exhortation and reassurance)
  1. Inequalities in relation to health, socio-economic status (e.g. income, gender, race, ethnicity), or the wider economy.
  • For example, restrictive measures may reduce the risk of harm to some, but increase the burden on people with no savings or reliable sources of income.
  • For example, some people are hoarding large quantities of home and medical supplies that (a) other people cannot afford, and (b) some people cannot access, despite having higher need.
  • For example, social distancing will limit the spread of the virus (see the nascent evidence), but also produce highly unequal forms of social isolation that increase the risk of domestic abuse (possibly exacerbated by school closures) and undermine wellbeing. Or, there will be major policy changes, such as to the rules to detain people under mental health legislation, regarding abortion, or in relation to asylum (note: some of these tweets are from the US, partly because I’m seeing more attention to race – and the consequence of systematic racism on the socioeconomic inequalities so important to COVID-19 mortality – than in the UK).

See also: COVID-19: how the UK’s economic model contributes towards a mismanagement of the crisis (Carolina Alves and Farwa Sial 30.3.20),

Economic downturn and wider NHS disruption likely to hit health hard – especially health of most vulnerable (Institute for Fiscal Studies 9.4.20),

Don’t be fooled: Britain’s coronavirus bailout will make the rich richer still (Christine Berry 13.4.20)

 

cc

https://twitter.com/boodleoops/status/1246717497308577792

https://twitter.com/boodleoops/status/1246717497308577792

https://twitter.com/MarioLuisSmall/status/1239879542094925825

  • For example, governments cannot ignore the impact of their actions on the economy, however much they emphasise mortality, health, and wellbeing. Most high-profile emphasis was initially on the fate of large and small businesses, and people with mortgages, but a long period of crisis will a tip the balance from low income to unsustainable poverty (even prompting Iain Duncan Smith to propose policy change), and why favour people who can afford a mortgage over people scraping the money together for rent?
  1. A need for more communication and exhortation, or for direct action to change behaviour.
  2. The short term (do everything possible now) or long term (manage behaviour over many months).
  1. How to maintain trust in the UK government when (a) people are more or less inclined to trust a the current part of government and general trust may be quite low, and (b) so many other governments are acting differently from the UK.
  • For example, note the visible presence of the Prime Minister, but also his unusually high deference to unelected experts such as (a) UK Government senior scientists providing direct advice to ministers and the public, and (b) scientists drawing on limited information to model behaviour and produce realistic scenarios (we can return to the idea of ‘evidence-based policymaking’ later). This approach is not uncommon with epidemics/ pandemics (LD was then the UK Government’s Chief Medical Officer):
  • For example, note how often people are second guessing and criticising the UK Government position (and questioning the motives of Conservative ministers).

See also: Coronavirus: meet the scientists who are now household names

  1. How policy in relation to the coronavirus relates to other priorities (e.g. Brexit, Scottish independence, trade, education, culture)

7. Who caused, or who is exacerbating, the problem? The answers to such questions helps determine which populations are most subject to policy intervention.

  • For example, people often try to lay blame for viruses on certain populations, based on their nationality, race, ethnicity, sexuality, or behaviour (e.g. with HIV).
  • For example, the (a) association between the coronavirus and China and Chinese people (e.g. restrict travel to/ from China; e.g. exacerbate racism), initially overshadowed (b) the general role of international travellers (e.g. place more general restrictions on behaviour), and (c) other ways to describe who might be responsible for exacerbating a crisis.

See also: ‘Othering the Virus‘ by Marius Meinhof

Under ‘normal’ policymaking circumstances, we would expect policymakers to resolve this ambiguity by exercising power to set the agenda and make choices that close off debate. Attention rises at first, a choice is made, and attention tends to move on to something else. With the coronavirus, attention to many different aspects of the problem has been lurching remarkably quickly. The definition of the policy problem often seems to be changing daily or hourly, and more quickly than the physical problem. It will also change many more times, particularly when attention to each personal story of illness or death prompts people to question government policy every hour. If the policy problem keeps changing in these ways, how could a government solve it?

Step 2 Identify technically and politically feasible solutions

Common advice in policy analysis texts:

  • Identify the relevant and feasible policy solutions that your audience/ client might consider.
  • Explain potential solutions in sufficient detail to predict the costs and benefits of each ‘alternative’ (including current policy).
  • Provide ‘plausible’ predictions about the future effects of current/ alternative policies.
  • Identify many possible solutions, then select the ‘most promising’ for further analysis.
  • Identify how governments have addressed comparable problems, and a previous policy’s impact.

Policy ‘solutions’ are better described as ‘tools’ or ‘instruments’, largely because (a) it is rare to expect them to solve a problem, and (b) governments use many instruments (in different ways, at different times) to make policy, including:

  1. Public expenditure (e.g. to boost spending for emergency care, crisis services, medical equipment)
  2. Economic incentives and disincentives (e.g. to reduce the cost of business or borrowing, or tax unhealthy products)
  3. Linking spending to entitlement or behaviour (e.g. social security benefits conditional on working or seeking work, perhaps with the rules modified during crises)
  4. Formal regulations versus voluntary agreements (e.g. making organisations close, or encouraging them to close)
  5. Public services: universal or targeted, free or with charges, delivered directly or via non-governmental organisations
  6. Legal sanctions (e.g. criminalising reckless behaviour)
  7. Public education or advertising (e.g. as paid adverts or via media and social media)
  8. Funding scientific research, and organisations to advise on policy
  9. Establishing or reforming policymaking units or departments
  10. Behavioural instruments, to ‘nudge’ behaviour (seemingly a big feature in the UK , such as on how to encourage handwashing).

As a result, what we call ‘policy’ is really a complex mix of instruments adopted by one or more governments. A truism in policy studies is that it is difficult to define or identify exactly what policy is because (a) each new instrument adds to a pile of existing measures (with often-unpredictable consequences), and (b) many instruments designed for individual sectors tend, in practice, to intersect in ways that we cannot always anticipate. When you think through any government response to the coronavirus, note how every measure is connected to many others.

Further, it is a truism in public policy that there is a gap between technical and political feasibility: the things that we think will be most likely to work as intended if implemented are often the things that would receive the least support or most opposition. For example:

  1. Redistributing income and wealth to reduce socio-economic inequalities (e.g. to allay fears about the impact of current events on low-income and poverty) seems to be less politically feasible than distributing public services to deal with the consequences of health inequalities.
  2. Providing information and exhortation seems more politically feasible than the direct regulation of behaviour. Indeed, compared to many other countries, the UK Government seems reluctant to introduce ‘quarantine’ style measures to restrict behaviour.

Under ‘normal’ circumstances, governments may be using these distinctions as simple heuristics to help them make modest policy changes while remaining sufficiently popular (or at least looking competent). If so, they are adding or modifying policy instruments during individual ‘windows of opportunity’ for specific action, or perhaps contributing to the sense of incremental change towards an ambitious goal.

Right now, we may be pushing the boundaries of what seems possible, since crises – and the need to address public anxiety – tend to change what seems politically feasible. However, many options that seem politically feasible may not be possible (e.g. to buy a lot of extra medical/ technology capacity quickly), or may not work as intended (e.g. to restrict the movement of people). Think of technical and political feasibility as necessary but insufficient on their own, which is a requirement that rules out a lot of responses.

Step 3 Use value-based criteria and political goals to compare solutions

Common advice in policy analysis texts:

  • Typical value judgements relate to efficiency, equity and fairness, the trade-off between individual freedom and collective action, and the extent to which a policy process involves citizens in deliberation.
  • 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
  • ‘Specify the objectives to be attained in addressing the problem and the criteria  to  evaluate  the  attainment  of  these  objectives  as  well as  the  satisfaction  of  other  key  considerations  (e.g.,  equity,  cost, equity, feasibility)’.
  • ‘Effectiveness, efficiency, fairness, and administrative efficiency’ are common.
  • Identify (a) the values to prioritise, such as ‘efficiency’, ‘equity’, and ‘human dignity’, and (b) ‘instrumental goals’, such as ‘sustainable public finance or political feasibility’, to generate support for solutions.
  • Instrumental questions may include: Will this intervention produce the intended outcomes? Is it easy to get agreement and maintain support? Will it make me popular, or diminish trust in me even further?

Step 3 is the most simple-looking but difficult task. Remember that it is a political, not technical, process. It is also a political process that most people would like to avoid doing (at least publicly) because it involves making explicit the ways in which we prioritise some people over others. Public policy is the choice to help some people and punish or refuse to help others (and includes the choice to do nothing).

Policy analysis texts describe a relatively simple procedure of identifying criteria and producing a table (with a solution in each row, and criteria in each column) to compare the trade-offs between each solution. However, these criteria are notoriously difficult to define, and people resolve that problem by exercising power to decide what each term means, and whose interests should be served when they resolve trade-offs. For example, see Stone on whose needs come first, who benefits from each definition of fairness, and how technical-looking processes such as ‘cost benefit analysis’ mask political choices.

Right now, the most obvious and visible trade-off, accentuated in the UK, is between individual freedom and collective action, or the balance between state, communal, and market/ individual solutions. In comparison with many countries (and China and Italy in particular), the UK Government seems to be favouring individual action over state quarantine measures. However, most trade-offs are difficult to categorise

  1. What should be the balance between efforts to minimise the deaths of some (generally in older populations) and maximise the wellbeing of others? This is partly about human dignity during crisis, how we treat different people fairly, and the balance of freedom and coercion.
  2. How much should a government spend to keep people alive using intensive case or expensive medicines, when the money could be spent improving the lives of far more people? This is partly about human dignity, the relative efficiency of policy measures, and fairness.

If you are like me, you don’t really want to answer such questions (indeed, even writing them looks callous). If so, one way to resolve them is to elect policymakers to make such choices on our behalf (perhaps aided by experts in moral philosophy, or with access to deliberative forums). To endure, this unusually high level of deference to elected ministers requires some kind of reciprocal act:

https://twitter.com/devisridhar/status/1240648925998178304

See also: We must all do everything in our power to protect lives (UK Secretary of State for Health and Social Care)

Still, I doubt that governments are making reportable daily choices with reference to a clear and explicit view of what the trade-offs and priorities should be, because their choices are about who will die, and their ability to predict outcomes is limited.

See also: Media experts despair at Boris Johnson’s coronavirus campaign (Sonia Sodha)

Step 4 Predict the outcome of each feasible solution.

Common advice in policy analysis texts:

  • Focus on the outcomes that key actors care about (such as value for money), and quantify and visualise your predictions if possible. Compare the pros and cons of each solution, such as how much of a bad service policymakers will accept to cut costs.
  • ‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.
  • Estimate the cost of a new policy, in comparison with current policy, and in relation to factors such as savings to society or benefits to certain populations. Use your criteria and projections to compare each alternative in relation to their likely costs and benefits.
  • Explain potential solutions in sufficient detail to predict the costs and benefits of each ‘alternative’ (including current policy).
  • Short deadlines dictate that you use ‘logic and theory, rather than systematic empirical evidence’ to make predictions efficiently.
  • Monitoring is crucial because it is difficult to predict policy success, and unintended consequences are inevitable. Try to measure the outcomes of your solution, while noting that evaluations are contested.

It is difficult to envisage a way for the UK Government to publicise the thinking behind its choices (Step 3) and predictions (Step 4) in a way that would encourage effective public deliberation, rather than a highly technical debate between a small number of academics:

Further, people often call for the UK Government to publicise its expert advice and operational logic, but I am not sure how they would separate it from their normative logic, or provide a frank account without unintended consequences for public trust or anxiety. If so, government policy involves (a) to keep some choices implicit to avoid a lot of debate on trade-offs, and (b) to make general statements about choices when they do not know what their impact will be.

Step 5 Make a recommendation to your client

Common advice in policy analysis texts:

  • Examine your case through the eyes of a policymaker. Keep it simple and concise.
  • Make a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups
  • Client-oriented advisors identify the beliefs of policymakers and tailor accordingly.
  • ‘Unless your client asks you not to do so, you should explicitly recommend one policy’

I now invite you to make a recommendation (step 5) based on our discussion so far (steps 1-4). Define the problem with one framing at the expense of the others. Romanticise some people and not others. Decide how to support some people, and coerce or punish others. Prioritise the lives of some people in the knowledge that others will suffer or die. Do it despite your lack of expertise and profoundly limited knowledge and information. Learn from experts, but don’t assume that only scientific experts have relevant knowledge (decolonise; coproduce). Recommend choices that, if damaging, could take decades to fix after you’ve gone. Consider if a policymaker is willing and able to act on your advice, and if your proposed action will work as intended. Consider if a government is willing and able to bear the economic and political costs. Protect your client’s popularity, and trust in your client, at the same time as protecting lives. Consider if your advice would change if the problem would seem to change. If you are writing your analysis, maybe keep it down to one sheet of paper (and certainly far fewer words than in this post). Better you than me.

Please now watch this video before I suggest that things are not so simple.

Would that policy analysis were so simple

Imagine writing policy analysis in an imaginary world, in which there is a single powerful ‘rational’ policymaker at the heart of government, making policy via an orderly series of stages.

cycle and cycle spirograph 18.2.20

Your audience would be easy to identify at each stage, your analysis would be relatively simple, and you would not need to worry about what happens after you make a recommendation for policy change (since the selection of a solution would lead to implementation).  You could adopt a simple 5 step policy analysis method, use widely-used tools such as cost-benefit analysis to compare solutions, and know where the results would feed into the policy process.

Studies of policy analysts describe how unrealistic this expectation tends to be (Radin, Brans, Thissen).

Table for coronavirus 750

For example, there are many policymakers, analysts, influencers, and experts spread across political systems, and engaging with 101 policy problems simultaneously, which suggests that it is not even clear how everyone fits together and interacts in what we call (for the sake of simplicity) ‘the policy process’.

Instead, we can describe real world policymaking with reference to two factors.

The wider policymaking environment: 1. Limiting the use of evidence

First, policymakers face ‘bounded rationality’, in which they only have the ability to pay attention to a tiny proportion of available facts, are unable to separate those facts from their values (since we use our beliefs to evaluate the meaning of facts), struggle to make clear and consistent choices, and do not know what impact they will have. The consequences can include:

  • Limited attention, and lurches of attention. Policymakers can only pay attention to a tiny proportion of their responsibilities, and policymaking organizations struggle to process all policy-relevant information. They prioritize some issues and information and ignore the rest.
  • Power and ideas. Some ways of understanding and describing the world dominate policy debate, helping some actors and marginalizing others.
  • Beliefs and coalitions. Policymakers see the world through the lens of their beliefs. They engage in politics to turn their beliefs into policy, form coalitions with people who share them, and compete with coalitions who don’t.
  • Dealing with complexity. They engage in ‘trial-and-error strategies’ to deal with uncertain and dynamic environments (see the new section on trial-and-error- at the end).
  • Framing and narratives. Policy audiences are vulnerable to manipulation when they rely on other actors to help them understand the world. People tell simple stories to persuade their audience to see a policy problem and its solution in a particular way.
  • The social construction of populations. Policymakers draw on quick emotional judgements, and social stereotypes, to propose benefits to some target populations and punishments for others.
  • Rules and norms. Institutions are the formal rules and informal understandings that represent a way to narrow information searches efficiently to make choices quickly.
  • Learning. Policy learning is a political process in which actors engage selectively with information, not a rational search for truth.

Evidence-based or expert-informed policymaking

Put simply, policymakers cannot oversee a simple process of ‘evidence-based policymaking’. Rather, to all intents and purposes:

  1. They need to find ways to ignore most evidence so that they can focus disproportionately on some. Otherwise, they will be unable to focus well enough to make choices. The cognitive and organisational shortcuts, described above, help them do it almost instantly.
  2. They also use their experience to help them decide – often very quickly – what evidence is policy-relevant under the circumstances. Relevance can include:
  • How it relates to the policy problem as they define it (Step 1).
  • If it relates to a feasible solution (Step 2).
  • If it is timely, available, understandable, and actionable.
  • If it seems credible, such as from groups representing wider populations, or from people they trust.
  1. They use a specific shortcut: relying on expertise.

However, the vague idea of trusting or not trusting experts is a nonsense, largely because it is virtually impossible to set a clear boundary between relevant/irrelevant experts and find a huge consensus on (exactly) what is happening and what to do. Instead, in political systems, we define the policy problem or find other ways to identify the most relevant expertise and exclude other sources of knowledge.

In the UK Government’s case, it appears to be relying primarily on expertise from its own general scientific advisers, medical and public health advisers, and – perhaps more controversially – advisers on behavioural public policy.

box 7.1

Right now, it is difficult to tell exactly how and why it relies on each expert (at least when the expert is not in a clearly defined role, in which case it would be irresponsible not to consider their advice). Further, there are regular calls on Twitter for ministers to be more open about their decisions.

See also: Coronavirus: do governments ever truly listen to ‘the science’?

However, don’t underestimate the problems of identifying why we make choices, then justifying one expert or another (while avoiding pointless arguments), or prioritising one form of advice over another. Look, for example, at the kind of short-cuts that intelligent people use, which seem sensible enough, but would receive much more intense scrutiny if presented in this way by governments:

  • Sophisticated speculation by experts in a particular field, shared widely (look at the RTs), but questioned by other experts in another field:
  • Experts in one field trusting certain experts in another field based on personal or professional interaction:
  • Experts in one field not trusting a government’s approach based on its use of one (of many) sources of advice:
  • Experts representing a community of experts, criticising another expert (Prof John Ashton), for misrepresenting the amount of expert scepticism of government experts (yes, I am trying to confuse you):
  • Expert debate on how well policymakers are making policy based on expert advice
  • Finding quite-sensible ways to trust certain experts over others, such as because they can be held to account in some way (and may be relatively worried about saying any old shit on the internet):

There are many more examples in which the shortcut to expertise is fine, but not particularly better than another shortcut (and likely to include a disproportionately high number of white men with STEM backgrounds).

Update: of course, they are better than the volume trumps expertise approach:

See also:

Further, in each case, we may be receiving this expert advice via many other people, and by the time it gets to us the meaning is lost or reversed (or there is some really sophisticated expert analysis of something rumoured – not demonstrated – to be true):

For what it’s worth, I tend to favour experts who:

(a) establish the boundaries of their knowledge, (b) admit to high uncertainty about the overall problem:

(c) (in this case) make it clear that they are working on scenarios, not simple prediction

(d) examine critically the too-simple ideas that float around, such as the idea that the UK Government should emulate ‘what works’ somewhere else

(e) situate their own position (in Prof Sridhar’s case, for mass testing) within a broader debate

See also:

See also: Prof Sir John Bell (4.3.20) on why an accurate antibody test is at least one month away and these exchanges on the problems with test ‘accuracy’:

(f) use their expertise on governance to highlight problems with thoughtless criticism

However, note that most of these experts are from a very narrow social background, and from very narrow scientific fields (first in modelling, then likely in testing), despite the policy problem being largely about (a) who, and how many people, a government should try to save, and (b) how far a government should go to change behaviour to do it (Update 2.4.20: I wrote that paragraph before adding so many people to the list). It is understandable to defer in this way during a crisis, but it also contributes to a form of ‘depoliticisation’ that masks profound choices that benefit some people and leave others vulnerable to harm.

See also: COVID-19: a living systematic map of the evidence

See also: To what extent does evidence support decision making during infectious disease outbreaks? A scoping literature review

See also: Covid-19: why is the UK government ignoring WHO’s advice? (British Medical Journal editorial)

See also: Coronavirus: just 2,000 NHS frontline workers tested so far

See also: ‘What’s important is social distancing’ coronavirus testing ‘is a side issue’, says Deputy Chief Medical Officer [Professor Jonathan Van-Tam talks about the important distinction between a currently available test to see if someone has contracted the virus (an antigen test) and a forthcoming test to see if someone has had and recovered from COVID-19 (an antibody test)]. The full interview is here (please feel free to ignore the editorialising of the uploader):

See also: Why is Germany able to test for coronavirus so much more than the UK? (which is mostly a focus on Germany’s innovation and partly on the UK (Public Health England) focus on making sure its test is reliable, in the context of ‘coronavirus tests produced at great speed which have later proven to be inaccurate’ (such as one with a below-30% accuracy rate, which is worse than not testing at all). Compare with The Coronavirus Hit Germany And The UK Just Days Apart But The Countries Have Responded Differently. Here’s How and the Opinion piece ‘A public inquiry into the UK’s coronavirus response would find a litany of failures

See also: Rights and responsibilities in the Coronavirus pandemic

See also: UK police warned against ‘overreach’ in use of virus lockdown powers (although note that there is no UK police force and that Scotland has its own legal system) and Coronavirus: extra police powers risk undermining public trust (Alex Oaten and Chris Allen)

See also (Calderwood resigned as CMO that night):

See also: Social Licensing of Privacy-Encroaching Policies to Address the COVID-19 Pandemic (U.K.) (research on public opinion)

The wider policymaking environment: 2. Limited control

Second, policymakers engage in a messy and unpredictable world in which no single ‘centre’ has the power to turn a policy recommendation into an outcome. I normally use the following figure to think through the nature of a complex and unwieldy policymaking environment of which no ‘centre’ of government has full knowledge or control.

image policy process round 2 25.10.18

It helps us identify (further) the ways in which we can reject the idea that the UK Prime Minister and colleagues can fully understand and solve policy problems:

Actors. The environment contains many policymakers and influencers spread across many levels and types of government (‘venues’).

For example, consider how many key decisions that (a) have been made by organisations not in the UK central government, and (b) are more or less consistent with its advice, including:

  • Devolved governments announcing their own healthcare and public health responses (although the level of UK coordination seems more significant than the level of autonomy).
  • Public sector employers initiating or encouraging at-home working (and many Universities moving quickly from in-person to online teaching)
  • Private organisations cancelling cultural and sporting events.

Context and events. Policy solutions relate to socioeconomic context and events which can be impossible to ignore and out of the control of policymakers. The coronavirus, and its impact on so many aspects on population health and wellbeing, is an extreme example of this problem.

Networks, Institutions, and Ideas. Policymakers and influencers operate in subsystems (specialist parts of political systems). They form networks or coalitions built on the exchange of resources or facilitated by trust underpinned by shared beliefs or previous cooperation. Many different parts of government have practices driven by their own formal and informal rules. Formal rules are often written down or known widely. Informal rules are the unwritten rules, norms and practices that are difficult to understand, and may not even be understood in the same way by participants. Political actors relate their analysis to shared understandings of the world – how it is, and how it should be – which are often so established as to be taken for granted. These dominant frames of reference establish the boundaries of the political feasibility of policy solutions.  These kinds of insights suggest that most policy decisions are considered, made, and delivered in the name of – but not in the full knowledge of – government ministers.

Trial and error policymaking in complex policymaking systems (17.3.20)

There are many ways to conceptualise this policymaking environment, but few theories provide specific advice on what to do, or how to engage effectively in it. One notable exception is the general advice that comes from complexity theory, including:

  • Law-like behaviour is difficult to identify – so a policy that was successful in one context may not have the same effect in another.
  • Policymaking systems are difficult to control; policy makers should not be surprised when their policy interventions do not have the desired effect.
  • Policy makers in the UK have been too driven by the idea of order, maintaining rigid hierarchies and producing top-down, centrally driven policy strategies.  An attachment to performance indicators, to monitor and control local actors, may simply result in policy failure and demoralised policymakers.
  • Policymaking systems or their environments change quickly. Therefore, organisations must adapt quickly and not rely on a single policy strategy.

On this basis, there is a tendency in the literature to encourage the delegation of decision-making to local actors:

  1. Rely less on central government driven targets, in favour of giving local organisations more freedom to learn from their experience and adapt to their rapidly-changing environment.
  2. To deal with uncertainty and change, encourage trial-and-error projects, or pilots, that can provide lessons, or be adopted or rejected, relatively quickly.
  3. Encourage better ways to deal with alleged failure by treating ‘errors’ as sources of learning (rather than a means to punish organisations) or setting more realistic parameters for success/ failure (although see this example and this comment).
  4. Encourage a greater understanding, within the public sector, of the implications of complex systems and terms such as ‘emergence’ or ‘feedback loops’.

In other words, this literature, when applied to policymaking, tends to encourage a movement from centrally driven targets and performance indicators towards a more flexible understanding of rules and targets by local actors who are more able to understand and adapt to rapidly-changing local circumstances.

[See also: Complex systems and systems thinking]

Now, just imagine the UK Government taking that advice right now. I think it is fair to say that it would be condemned continuously (even more so than right now). Maybe that is because it is the wrong way to make policy in times of crisis. Maybe it is because too few people are willing and able to accept that the role of a small group of people at the centre of government is necessarily limited, and that effective policymaking requires trial-and-error rather than a single, fixed, grand strategy to be communicated to the public. The former highlights policy that changes with new information and perspective. The latter highlights errors of judgement, incompetence, and U-turns. In either case, the advice is changing as estimates of the coronavirus’ impact change:

I think this tension, in the way that we understand UK government, helps explain some of the criticism that it faces when changing its advice to reflect changes in its data or advice. This criticism becomes intense when people also question the competence or motives of ministers (and even people reporting the news) more generally, leading to criticism that ranges from mild to outrageous:

For me, this casual reference to a government policy to ‘cull the heard of the weak’ is outrageous, but you can find much worse on Twitter. It reflects wider debate on whether ‘herd immunity’ is or is not government policy. Much of it relates to interpretation of government statements, based on levels of trust/distrust in the UK Government, its Prime Minister and Secretaries of State, and the Prime Minister’s special adviser

However, I think that some of it is also about:

1. Wilful misinterpretation (particularly on Twitter). For example, in the early development and communication of policy, Boris Johnson was accused (in an irresponsibly misleading way) of advocating for herd immunity rather than restrictive measures.

See: Here is the transcript of what Boris Johnson said on This Morning about the new coronavirus (Full Fact)

full fact coronavirus

Below is one of the most misleading videos of its type. Look at how it cuts each segment into a narrative not provided by ministers or their advisors (see also this stinker):

See also:

2. The accentuation of a message not being emphasised by government spokespeople.

See for example this interview, described by Sky News (13.3.20) as: The government’s chief scientific adviser Sir Patrick Vallance has told Sky News that about 60% of people will need to become infected with coronavirus in order for the UK to enjoy “herd immunity”. You might be forgiven for thinking that he was on Sky extolling the virtues of a strategy to that end (and expressing sincere concerns on that basis). This was certainly the write-up in respected papers like the FT (UK’s chief scientific adviser defends ‘herd immunity’ strategy for coronavirus). Yet, he was saying nothing of the sort. Rather, when prompted, he discussed herd immunity in relation to the belief that COVID-19 will endure long enough to become as common as seasonal flu.

The same goes for Vallance’s interview on the same day (13.3.20) during Radio 4’s Today programme (transcribed by the Spectator, which calls Vallance the author, and gives it the headlineHow ‘herd immunity’ can help fight coronavirusas if it is his main message). The Today Programme also tweeted only 30 seconds to single out that brief exchange:

Yet, clearly his overall message – in this and other interviews – was that some interventions (e.g. staying at home; self-isolating with symptoms) would have bigger effects than others (e.g. school closures; prohibiting mass gatherings) during the ‘flattening of the peak’ strategy (‘What we don’t want is everybody to end up getting it in a short period of time so that we swamp and overwhelm NHS services’). Rather than describing ‘herd immunity’ as a strategy, he is really describing how to deal with its inevitability (‘Well, I think that we will end up with a number of people getting it’).

See also: British government wants UK to acquire coronavirus ‘herd immunity’, writes Robert Peston (12.3.20) and live debates (and reports grasping at straws) on whether or not ‘herd immunity’ was the goal of the UK government:

See also: Why weren’t we ready? (Harry Lambert) which is a good exemplar of the ‘U turn’ argument, and compare with the evidence to the Health and Social Care Committee (CMO Whitty, DCMO Harries) that it describes.

A more careful forensic analysis (such as this one) will try to relate each government choice to the ways in which key advisory bodies (such as the New and Emerging Respiratory Virus Threats Advisory Group, NERVTAG) received and described evidence on the current nature of the problem:

See also: Special Report: Johnson listened to his scientists about coronavirus – but they were slow to sound the alarm (Reuters)

Some aspects may also be clearer when there is systematic qualitative interview data on which to draw. Right now, there are bits and pieces of interviews sandwiched between whopping great editorial discussions (e.g. FT Alphaville Imperial’s Neil Ferguson: “We don’t have a clear exit strategy”; compare with the more useful Let’s flatten the coronavirus confusion curve) or confused accounts by people speaking to someone who has spoken to someone else (e.g. Buzzfeed Even The US Is Doing More Coronavirus Tests Than The UK. Here Are The Reasons Why).

See also: other rabbit holes are available

[OK, that proved to be a big departure from the trial-and-error discussion. Here we are, back again]

In some cases, maybe people are making the argument that trial-and-error is the best way to respond quickly, and adapt quickly, in a crisis but that the UK Government version is not what, say, the WHO thinks of as good kind of adaptive response. It is not possible to tell, at least from the general ways in which they justify acting quickly.

See also the BBC’s provocative question (which I expect to be replaced soon):

Compare with:

The take home messages

  1. The coronavirus is an extreme example of a general situation: policymakers will always have very limited knowledge of policy problems and control over their policymaking environment. They make choices to frame problems narrowly enough to seem solvable, rule out most solutions as not feasible, make value judgements to try help some more than others, try to predict the results, and respond when the results to not match their hopes or expectations.
  2. This is not a message of doom and despair. Rather, it encourages us to think about how to influence government, and hold policymakers to account, in a thoughtful and systematic way that does not mislead the public or exacerbate the problem we are seeing.

Further reading, until I can think of a better conclusion:

This series of ‘750 words’ posts summarises key texts in policy analysis and tries to situate policy analysis in a wider political and policymaking context. Note the focus on whose knowledge counts, which is not yet a big feature of this crisis.

These series of 500 words and 1000 words posts (with podcasts) summarise concepts and theories in policy studies.

This page on evidence-based policymaking (EBPM) uses those insights to demonstrate why EBPM is  a political slogan rather than a realistic expectation.

These recorded talks relate those insights to common questions asked by researchers: why do policymakers seem to ignore my evidence, and what can I do about it? I’m happy to record more (such as on the topic you just read about) but not entirely sure who would want to hear what.

See also: Advisers, Governments and why blunders happen? (Colin Talbot)

See also: Why we might disagree about … Covid-19 (Ruth Dixon and Christopher Hood)

See also: Pandemic Science and Politics (Daniel Sarewitz)

See also: We knew this would happen. So why weren’t we ready? (Steve Bloomfield)

See also: Europe’s coronavirus lockdown measures compared (Politico)

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Filed under 750 word policy analysis, agenda setting, Evidence Based Policymaking (EBPM), Policy learning and transfer, POLU9UK, Prevention policy, Psychology Based Policy Studies, Public health, public policy, Social change, UK politics and policy

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

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

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

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

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

Does policymaking proceed through a series of stages?

The short answer is no.

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

cycle

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

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

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

 

  1. It arose from a misunderstanding in policy studies

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

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

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

  1. It is a functional requirement of policy analysis

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

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

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

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

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

should not be confused with

What you need versus what you can expect

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

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

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

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

See also:

The New Policy Sciences

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Filed under 750 word policy analysis, agenda setting, public policy

Can A Government Really Take Control Of Public Policy?

This post first appeared on the MIHE blog to help sell my book.

During elections, many future leaders give the impression that they will take control of public policy. They promise major policy change and give little indication that anything might stand in their way.

This image has been a major feature of Donald Trump’s rhetoric on his US Presidency. It has also been a feature of campaigns for the UK withdrawal from the European Union (‘Brexit’) to allow its leaders to take back control of policy and policymaking. According to this narrative, Brexit would allow (a) the UK government to make profound changes to immigration and spending, and (b) Parliament and the public to hold the UK government directly to account, in contrast to a distant EU policy process less subject to direct British scrutiny.

Such promises are built on the false image of a single ‘centre’ of government, in which a small number of elected policymakers take responsibility for policy outcomes. This way of thinking is rejected continuously in the modern literature. Instead, policymaking is ‘multi-centric’: responsibility for policy outcomes is spread across many levels and types of government (‘centres’), and shared with organisations outside of government, to the extent that it is not possible to simply know who is in charge and to blame. This arrangement helps explain why leaders promise major policy change but most outcomes represent a minor departure from the status quo.

Some studies of politics relate this arrangement to the choice to share power across many centres. In the US, a written constitution ensures power sharing across different branches (executive, legislative, judicial) and between federal and state or local jurisdictions. In the UK, central government has long shared power with EU, devolved, and local policymaking organisations.

However, policy theories show that most aspects of multi-centric governance are necessary. The public policy literature provides many ways to describe such policy processes, but two are particularly useful.

The first approach is to explain the diffusion of power with reference to an enduring logic of policymaking, as follows:

  • The size and scope of the state is so large that it is always in danger of becoming unmanageable. Policymakers manage complexity by breaking the state’s component parts into policy sectors and sub-sectors, with power spread across many parts of government.
  • Elected policymakers can only pay attention to a tiny proportion of issues for which they are responsible. They pay attention to a small number and ignore the rest. They delegate policymaking responsibility to other actors such as bureaucrats, often at low levels of government.
  • At this level of government and specialisation, bureaucrats rely on specialist organisations for information and advice. Those organisations trade that information/advice and other resources for access to, and influence within, the government.
  • Most public policy is conducted primarily through small and specialist ‘policy communities’ that process issues at a level of government not particularly visible to the public, and with minimal senior policymaker involvement.

This description suggests that senior elected politicians are less important than people think, their impact on policy is questionable, and elections may not provide major changes in policy. Most decisions are taken in their name but without their intervention.

A second, more general, approach is to show that elected politicians deal with such limitations by combining cognition and emotion to make choices quickly. Although such action allows them to be decisive, they occur within a policymaking environment over which governments have limited control. Government bureaucracies only have the coordinative capacity to direct policy outcomes in a small number of high priority areas. In most other cases, policymaking is spread across many venues, each with their own rules, networks, ways of seeing the world, and ways of responding to socio-economic factors and events.

In that context, we should always be sceptical when election candidates and referendum campaigners (or, in many cases, leaders of authoritarian governments) make such promises about political leadership and government control.

A more sophisticated knowledge of policy processes allows us to identify the limits to the actions of elected policymakers, and develop a healthier sense of pragmatism about the likely impact of government policy. The question of our age is not: how can governments take back control? Rather, it is: how can we hold policymakers to account in a complex system over which they have limited knowledge and even less control?

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

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

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

Radin cover 2019

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

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

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

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

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

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

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

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

Consequently, the necessary skills of policy analysis have changed:

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

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

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

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

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

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

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

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

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

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

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

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

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

A checklist for policy analysts

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

For me, it serves two main functions:

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

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

Further Reading:

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

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

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

 

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