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.
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 firstname.lastname@example.org by Feb 28th 2022. Successful authors should have a full article draft for submission into the JEPP review process by August 30th 2022.
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.
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
‘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.
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).
This post is a shortened version of The Politics of Policy Analysis Annex A. It shows how to use insights from policy process research in policy analysis and policymaking coursework (much like the crossover between Scooby-Doo and Batman). It describes a range of exercises, including short presentations, policy analysis papers, blog posts, and essays. In each case, it explains the rationale for each exercise and the payoff to combining them.
If you prefer me to describe these insights less effectively, there is also a podcast:
One step to combining policy analysis and policy process research is to modify the former according to the insights of the latter. In other words, consider how a ‘new policy sciences’ inspired policy analysis differs from the analyses already provided by 5-step guides.
It could turn out that the effects of our new insights on a policy briefing could be so subtle that you might blink and miss them. Or, there are so many possibilities from which to choose that it is impossible to provide a blueprint for new policy science advice. Therefore, I encourage students to be creative in their policy analysis and reflective in their assessment of their analysis. Our aim is to think about the skills you need to analyse policy, from producing or synthesising evidence, to crafting an argument based on knowing your audience, and considering how your strategy might shift in line with a shifting context.
To encourgage creativity, I set a range of tasks so that students can express themselves in different ways, to different audiences, with different constraints. For example, we can learn how to be punchy and concise from a 3-minute presentation or 500-word blog, and use that skill to get to the point more quickly in policy analysis or clarify the research question in the essay.
The overall effect should be that students can take what they have learned from each exercise and use it for the others.
In each section below, I reproduce the ways in which I describe this mix of coursework to students then, in each box, note the underlying rationale.
1. A 3-minute spoken presentation to your peers in a seminar.
In 3 minutes, you need to identify a problem, describe one or more possible solutions, and end your presentation in a convincing way. For example, if you don’t make a firm recommendation, what can you say to avoid looking like you are copping out? Focus on being persuasive, to capture your audience’s imagination. Focus on the policy context, in which you want to present a problem as solvable (who will pay attention to an intractable problem?) but not make inflated claims about how one action can solve a major problem. Focus on providing a memorable take home message.
The presentation can be as creative as you wish, but it should not rely on powerpoint in the room. Imagine that none of the screens work or that you are making your pitch to a policymaker as you walk along the street: can you make this presentation engaging and memorable without any reference to someone else’s technology? Can you do it without just reading out your notes? Can you do it well in under 3 minutes? We will then devote 5 minutes to questions from the audience about your presentation. Being an active part of the audience – and providing peer review – is as important as doing a good presentation of your own.
BOX A1: Rationale for 3-minute presentation.
If students perform this task first (before the coursework is due), it gives them an initial opportunity to see how to present only the most relevant information, and to gauge how an audience responds to their ideas. Audience questions provide further peer-driven feedback. I also plan a long seminar to allow each student (in a group of 15-20 people) to present, then ask all students about which presentation they remember and why. This exercise helps students see that they are competing with each other for limited policymaker attention, and learn from their peers about what makes an effective pitch. Maybe you are wondering why I discourage powerpoint. It’s largely because it will cause each presenter to go way over time by cramming in too much information, and this problem outweighs the benefit of being able to present an impressive visualisation. I prefer to encourage students to only tell the audience what they will remember (by only presenting what they remember).
2. A policy analysis paper, and 3. A reflection on your analysis
Provide a policy analysis paper which has to make a substantive argument or recommendation in approximately two pages (1000 words), on the assumption that busy policymakers won’t read much else before deciding whether or not to pay attention to the problem and your solutions. Then provide a reflection paper (also approximately 1000 words) to reflect your theoretical understanding of the policy process. You can choose how to split the 2000 word length, between analysis and reflection. You can give each exercise 1000 each (roughly a 2-page analysis), provide a shorter analysis and more reflection, or widen the analysis and reject the need for conceptual reflection. The choice is yours to make, as long as you justify your choice in your reflection.
When writing policy analysis, I ask you to keep it super-short on the assumption that you have to make your case quickly to people with 99 other things to do. For example, what can you tell someone in one paragraph or a half-page to get them to read all 2 pages? It is tempting to try to tell someone everything you know, because everything is connected and to simplify is to describe a problem simplistically. Instead, be smart enough to know that such self-indulgence won’t impress your audience. In person, they might smile politely, but their eyes are looking at the elevator lights. In writing, they can skim your analysis or simply move on. So, use these three statements to help you focus less on your need to supply information and more on their demand:
Your aim is not to give a full account of a problem. It is to get powerful people to care about it.
Your aim is not to give a painstaking account of all possible solutions. It is to give a sense that at least one solution is feasible and worth pursuing.
Your guiding statement should be: policymakers will only pay attention to your problem if they think they can solve it, and without that solution being too costly.
Otherwise, I don’t like to give you too much advice because I want you to be creative about your presentation; to be confident enough to take chances and feel that you’ll see the reward of making a leap. At the very least, you have three key choices to make about how far you’ll go to make a point:
Who is your audience? Our discussion of the limits to centralised policymaking suggest that your most influential audience will not necessarily be an elected policymaker, but who else would it be?
How ‘manipulative’ should you be? Our discussions of ‘bounded rationality’ and ‘evidence-based policymaking’ suggest that policymakers combine ‘rational’ and ‘irrational’ shortcuts to gather information and make choices. So, do you appeal to their desire to set goals and gather a lot of scientific information, make an emotional appeal, or rely on Riker-style heresthetics?
What is your role? Contemporary discussions of science advice to government highlight unresolved debates about the role of unelected advisors: should you simply lay out some possible solutions or advocate one solution strongly?
For our purposes, there are no wrong answers to these questions. Instead, I want you to make and defend your decisions. That is the aim of your policy paper ‘reflection’: to ‘show your work’. You still have some room to be creative in your reflection: tell me what you know about policy theory and how it informed your decisions. Here are some examples, but it is up to you to decide what to highlight:
Show how your understanding of policymaker psychology helped you decide how to present information on problems and solutions.
Extract insights from policy theories, such as from punctuated equilibrium theory on policymaker attention, multiple streams analysis on timing and feasibility, or the NPF on how to tell persuasive stories.
Explore the implications of the lack of ‘comprehensive rationality’ and absence of a ‘policy cycle’: feasibility is partly about identifying the extent to which a solution is ‘doable’ when central governments have limited powers. What ‘policy style’ or policy instruments would be appropriate for the solution you favour?
I use the following questions to guide the marking on the policy paper: Tailored properly to a clearly defined audience? Punchy and concise summary? Clearly defined problem? Good evidence or argument behind the solution? Clear recommendations backed by a sense that the solution is feasible? Evidence of substantial reading, accompanied by well explained further reading?
In my experience of marking, successful students gave a very clear and detailed account of the nature and size of the policy problem. The best reports used graphics and/ or statistics to describe the problem in several ways. Some identified a multi-faceted problem – such as in health outcomes, and health inequalities – without presenting confusing analysis. Some were able to present an image of urgency, to separate this problem from the many others that might grab policymaker attention. Successful students presented one or more solutions which seemed technically and/ or politically feasible. By technically feasible, I mean that there is a good chance that the policy will work as intended if implemented. For example, they provided evidence of its success in a comparable country (or in the past) or outlined models designed to predict the effects of specific policy instruments. By politically feasible, I mean that you consider how open your audience would be to the solution, and how likely the suggestion is to be acceptable to key policymakers. Some students added to a good discussion of feasibility by comparing the pros/ cons of different scenarios. In contrast, some relatively weak reports proposed solutions which were vague, untested, and/ or not likely to be acted upon.
BOX A2: Rationale for policy analysis and reflection
Students already have 5-step policy analysis texts at their disposal, and they give some solid advice about the task. However, I want to encourage students to think more about how their knowledge of the policy process will guide their analysis. First, what do you do if you think that one audience will buy your argument, and another reject it wholeheartedly? Just pretend to be an objective analyst and put the real world in the ‘too hard’ pile? Or, do you recognise that policy analysts are political actors and make your choices accordingly? For me, an appeal to objectivity combined with insufficient recognition of the ways in which people respond emotionally to information, is a total cop-out. I don’t want to contribute to a generation of policy analysts who provide long, rigorous, and meticulous reports that few people read and fewer people use. Instead, I want students to show me how to tell a convincing story with a clear moral, or frame policy analysis to grab their audience’s attention and generate enthusiasm to try to solve a problem. Then, I want them to reflect on how they draw the line between righteous persuasion and unethical manipulation.
Second, how do you account for policymaking complexity? You can’t assume that there is a cycle in which a policymaker selects a solution and it sets in train a series of stages towards successful implementation. Instead, you need to think about the delivery of your policy as much as the substance. Students have several choices. In some cases, they will describe how to deliver policy in a multi-level or multi-centric environment, in which, say, a central government actor will need to use persuasion or cooperation rather than command-and-control. Or, if they are feeling energetic, they might compare a top-down delivery option with support for Ostrom-style polycentric arrangements. Maybe they’ll recommend pilots and/ or trial and error, to monitor progress continuously instead of describing a one-shot solution. Maybe they’ll reflect on multiple streams analysis and think about how you can give dependable advice in a policy process containing some serendipity. Who knows? Policy process research is large and heterogeneous, which opens the possibility for some creative solutions that I won’t be able to anticipate in advance.
4. One kind of blog post (for the policy analysis)
Write a short and punchy blog post which recognises the need to make an argument succinctly and grab attention with the title and first sentence/ paragraph, on the assumption that your audience will be reading it on their phone and will move on to something else quickly. In this exercise, your blog post is connected to your policy analysis. Think, for example, about how you would make the same case for a policy solution to a wider ‘lay’ audience. Or, use the blog post to gauge the extent to which your client could sell your policy solution. If they would struggle, should you make this recommendation in the first place?
Your blog post audience is wider than your policy analysis audience. You are trying to make an argument that will capture the attention of a larger group of people who are interested in politics and policy, but without being specialists. They will likely access your post from Twitter/ Facebook or via a search engine. This constraint produces a new requirement, to: present a punchy title which sums up the whole argument in under 280 characters (a statement is often better than a vague question); to summarise the whole argument in approximately 100 words in the first paragraph (what is the problem and solution?); then, to provide more information up to a maximum of 500 words. The reader can then be invited to read the whole policy analysis.
The style of blog posts varies markedly, so you should consult many examples before attempting your own (for example, compare the LSE with The Conversation and newspaper blogs to get a sense of variations in style). When you read other posts, take note of their strengths and weaknesses. For example, many posts associated with newspapers introduce a personal or case study element to ground the discussion in an emotional appeal. Sometimes this works, but sometimes it causes the reader to scroll down quickly to find the main argument. Perhaps ironically, I recommend storytelling but I often skim past people’s stories. Many academic posts are too long (well beyond your 500 limit), take too long to get to the point, and do not make explicit recommendations, so you should not emulate them. You should aim to be better than the scholars whose longer work you read. You should not just chop down your policy analysis to 500 words; you need a new kind of communication.
Hopefully, by the end of this fourth task, you will appreciate the transferable life skills. I have generated some uncertainty about your task to reflect the sense among many actors that they don’t really know how to make a persuasive case and who to make it to. We can follow some basic Bardach-style guidance, but a lot of this kind of work relies on trial-and-error. I maintain a short word count to encourage you to get to the point, and I bang on about ‘stories’ in modules to encourage you to present a short and persuasive story to policymakers.
This process seems weird at first, but isn’t it also intuitive? For example, next time you’re in my seminar, measure how long it takes you to get bored and look forward to the weekend. Then imagine that policymakers have the same attention span as you. That’s how long you have to make your case! Policymakers are not magical beings with an infinite attention span. In fact, they are busier and under more pressure than us, so you need to make your pitch count.
BOX A3: Rationale for blog post 1
This exercise forces students to make their case in 500 words. It helps them understand the need to communicate in different ways to different audiences. It suggests that successful communication is largely about knowing how your audience consumes information, rather than telling people all you know. I gauge success according to questions such as: Punchy and eye grabbing title? Tailored to an intelligent ‘lay’ audience rather than a specific expert group? Clearly defined problem? Good evidence or argument behind the solution? Clear recommendations backed by a sense that the solution is feasible? Well embedded weblinks to further relevant reading?
5. Writing a theory-informed essay
I tend to set this simple-looking question for coursework in policy modules: what is policy, how much has it changed, and why? Students get to choose the policy issue, timeframe, political system, and relevant explanatory concepts.
On the face of it, it looks very straightforward. Give it a few more seconds, and you can see the difficulties:
We spend a lot of time in class agreeing that it seems almost impossible to define policy
There are many possible measures of policy change
There is an almost unmanageable number of models, concepts, and theories to use to explain policy dynamics.
I try to encourage some creativity when solving this problem, but also advise students to keep their discussion as simple and jargon-free as possible (often by stretching an analogy with competitive diving, in which a well-executed simple essay can score higher than a belly-flopped hard essay).
Choosing a format: the initial advice
Choose a policy area (such as health) or issue (such as alcohol policy).
Describe the nature of policy, and the extent of policy change, in a particular time period (such as in a particular era, after an event or constitutional change, or after a change in government).
Select one or more policy concepts or theory to help structure your discussion and help explain how and why policy has changed.
For example, a question might be: What is tobacco policy in the UK, how much has it changed since the 1980s, and why? I use this example because I try to answer that question myself, even though some of my work is too theory-packed to be a good model for a student essay (Cairney, 2007 is essentially a bad model for students).
Choosing a format: the cautionary advice
You may be surprised about how difficult it is to answer a simple question like ‘what is policy?’ and I will give you a lot of credit for considering how to define and measure it; by identifying, for example, the use of legislation/ regulation, funding, staff, and information sharing, and/ or by considering the difference between, say, policy as a statement of intent or a long term outcome. In turn, a good description and explanation of policy change is difficult. If you are feeling ambitious, you can go further, to compare, say, two issues (such as tobacco and alcohol) or places (such UK Government policy and the policy of another country), but sometimes a simple and narrow discussion can be more effective. Similarly, you can use many theories or concepts to aid explanation, but one theory may do. Note that (a) your description of your research question, and your essay structure, is more important than (b) your decision on what topic or concepts to use.
BOX A4: Rationale for the essay
The wider aim is to encourage students to think about the relationship between differentperspectives on policy theory and analysis. For example, in a blog and policy analysis paper they try to generate attention to a policy problem and advocate a solution. Then, they draw on policy theories and concepts to reflect on their papers, highlighting (say): the need to identify the most important audience; the importance of framing issues with a mixture of evidence and emotional appeals; and, the need to present ‘feasible’ solutions.
The reflection can provide a useful segue to the essay, since we’re already identifying important policy problems, advocating change, reflecting on how best to encourage it – such as by presenting modest objectives – and then, in the essay, trying to explain (say) why governments have not taken that advice in the past. Their interest in the policy issue can prompt interest in researching the issue further; their knowledge of the issue and the policy process can help them develop politically-aware policy analysis. All going well, it produces a virtuous circle.
BOX A5: Rationale for blog post 2
I get students to do the analysis/reflection/blog combination in the first module, and an essay/ blog combo in the second module. The second blog post has a different aim. Students use the 500 words to present a jargon-free analysis of policy change. The post represents a useful exercise in theory translation. Without it, students tend to describe a large amount of jargon because I am the audience and I understand it. By explaining the same thing to a lay audience, they are obliged to explain key developments in a plain language. This requirement should also help them present a clearer essay, because people (academics and students) often use jargon to cover the fact that they don’t really know what they are saying.
This post summarises new research on the health equity strategy Health in All Policies. As our previous post suggests, it is common to hope that a major event will create a ‘window of opportunity’ for such strategies to flourish, but the current COVID-19 experience suggests otherwise. If so, what do HIAP studies tell us about how to respond, and do they offer any hope for future strategies? The full report is on Open Research Europe, accompanied by a brief interview on its contribution to the Horizon 2020 project – IMAJINE – on spatial justice.
COVID-19 should have prompted governments to treat health improvement as fundamental to public policy
Many had made strong rhetorical commitments to public health strategies focused on preventing a pandemic of non-communicable diseases (NCDs). To do so, they would address the ‘social determinants’ of health and health inequalities, defined by the WHO as ‘the unfair and avoidable differences in health status’ that are ‘shaped by the distribution of money, power and resources’ and ‘the conditions in which people are born, grow, live, work and age’.
COVID-19 reinforces the impact of the social determinants of health. Health inequalities result from factors such as income and social and environmental conditions, which influence people’s ability to protect and improve their health. COVID-19 had a visibly disproportionate impact on people with (a) underlying health conditions associated with NCDs, and (b) less ability to live and work safely.
Yet, the opposite happened. The COVID-19 response side-lined health improvement
This experience shows that the evidence does not speak for itself
The evidence on social determinants is clear to public health specialists, but the idea of social determinants is less well known or convincing to policymakers.
It also challenges the idea that the logic of health improvement is irresistible
Health in All Policies (HIAP) is the main vehicle for health improvement policymaking, underpinned by: a commitment to health equity by addressing the social determinants of health; the recognition that the most useful health policies are not controlled by health departments; the need for collaboration across (and outside) government; and, the search for high level political commitment to health improvement.
Its logic is undeniable to HIAP advocates, but not policymakers. A government’s public commitment to HIAP does not lead inevitably to the roll-out of a fully-formed HIAP model. There is a major gap between the idea of HIAP and its implementation. It is difficult to generate HIAP momentum, and it can be lost at any time.
Instead, we need to generate more realistic lessons from health improvement and promotion policy
However, most HIAP research does not provide these lessons. Most HIAP research combines:
functional logic (here is what we need)
programme logic (here is what we think we need to do to achieve it), and
Policy theory-informed empirical studies of policymaking could help produce a more realistic agenda, but very few HIAP studies seem to exploit their insights.
To that end, this review identifies lessons from studies of HIAP and policymaking
It summarises a systematic qualitative review of HIAP research. It includes 113 articles (2011-2020) that refer to policymaking theories or concepts while discussing HIAP.
We produced these conclusions from pre-COVID-19 studies of HIAP and policymaking, but our new policymaking context – and its ironic impact on HIAP – is impossible to ignore.
It suggests that HIAP advocates produced a 7-point playbook for the wrong game
The seven most common pieces of advice add up to a plausible but incomplete strategy:
adopt a HIAP model and toolkit
raise HIAP awareness and support in government
seek win-win solutions with partners
avoid the perception of ‘health imperialism’ when fostering intersectoral action
find HIAP policy champions and entrepreneurs
use HIAP to support the use of health impact assessments (HIAs)
challenge the traditional cost-benefit analysis approach to valuing HIAP.
Yet, two emerging pieces of advice highlight the limits to the current playbook and the search for its replacement:
treat HIAP as a continuous commitment to collaboration and health equity, not a uniform model; and,
address the contradictions between HIAP aims.
As a result, most country studies report a major, unexpected, and disappointing gap between HIAP commitment and actual outcomes
These general findings are apparent in almost all relevant studies. They stand out in the ‘best case’ examples where: (a) there is high political commitment and strategic action (such as South Australia), or (b) political and economic conditions are conducive to HIAP (such as Nordic countries).
These studies show that the HIAP playbook has unanticipated results, such as when the win-win strategy leads to HIAP advocates giving ground but receiving little in return.
HIAP strategies to challenge the status quo are also overshadowed by more important factors, including (a) a far higher commitment to existing healthcare policies and the core business of government, and (b) state retrenchment. Additional studies of decentralised HIAP models find major gaps between (a) national strategic commitment (backed by national legislation) and (b) municipal government progress.
Some studies acknowledge the need to use policymaking research to produce new ways to encourage and evaluate HIAP success
Studies of South Australia situate HIAP in a complex policymaking system in which the link between policy activity and outcomes is not linear.
Studies of Nordic HIAP show that a commitment to municipal responsibility and stakeholder collaboration rules out the adoption of a national uniform HIAP model.
However, most studies do not use policymaking research effectively or appropriately
Almost all HIAP studies only scratch the surface of policymaking research (while some try to synthesise its insights, but at the cost of clarity).
Most HIAP studies use policy theories to:
produce practical advice (such as to learn from ‘policy entrepreneurs’), or
supplement their programme logic (to describe what they think causes policy change and better health outcomes).
Most policy theories were not designed for this purpose.
Policymaking research helps primarily to explain the HIAP ‘implementation gap’
Its main lesson is that policy outcomes are beyond the control of policymakers and HIAP advocates. This explanation does not show how to close implementation gaps.
Its practical lessons come from critical reflection on dilemmas and politics, not the reinvention of a playbook
It prompts advocates to:
Treat HIAP as a political project, not a technical exercise or puzzle to be solved.
Re-examine the likely impact of a focus on intersectoral action and collaboration, to recognise the impact of imbalances of power and the logic of policy specialisation.
Revisit the meaning-in-practice of the vague aims that they take for granted without explaining, such as co-production, policy learning, and organisational learning.
Engage with key trade-offs, such as between a desire for uniform outcomes (to produce health equity) but acceptance of major variations in HIAP policy and policymaking.
Avoid reinventing phrases or strategies when facing obstacles to health improvement.
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.
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.
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 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.
This post first appeared on LSE British Politics and Policy (27.11.20) and is based on this article in British Politics.
Paul Cairneyassesses government policy in the first half of 2020. He identifies the intense criticism of its response so far, encouraging more systematic assessments grounded in policy research.
In March 2020, COVID-19 prompted policy change in the UK at a speed and scale only seen during wartime. According to the UK government, policy was informed heavily by science advice. Prime Minister Boris Johnson argued that, ‘At all stages, we have been guided by the science, and we will do the right thing at the right time’. Further, key scientific advisers such as Sir Patrick Vallance emphasised the need to gather evidence continuously to model the epidemic and identify key points at which to intervene, to reduce the size of the peak of population illness initially, then manage the spread of the virus over the longer term.
Both ministers and advisors emphasised the need for individual behavioural change, supplemented by government action, in a liberal democracy in which direct imposition is unusual and unsustainable. However, for its critics, the government experience has quickly become an exemplar of policy failure.
Initial criticisms include that ministers did not take COVID-19 seriously enough in relation to existing evidence, when its devastating effect was apparent in China in January and Italy from February; act as quickly as other countries to test for infection to limit its spread; or introduce swift-enough measures to close schools, businesses, and major social events. Subsequent criticisms highlight problems in securing personal protective equipment (PPE), testing capacity, and an effective test-trace-and-isolate system. Some suggest that the UK government was responding to the ‘wrong pandemic’, assuming that COVID-19 could be treated like influenza. Others blame ministers for not pursuing an elimination strategy to minimise its spread until a vaccine could be developed. Some criticise their over-reliance on models which underestimated the R (rate of transmission) and ‘doubling time’ of cases and contributed to a 2-week delay of lockdown. Many describe these problems and delays as the contributors to the UK’s internationally high number of excess deaths.
How can we hold ministers to account in a meaningful way?
I argue that these debates are often fruitless and too narrow because they do not involve systematic policy analysis, take into account what policymakers can actually do, or widen debate to consider whose lives matter to policymakers. Drawing on three policy analysis perspectives, I explore the questions that we should ask to hold ministers to account in a way that encourages meaningful learning from early experience.
These questions include:
Was the government’s definition of the problem appropriate? Much analysis of UK government competence relates to specific deficiencies in preparation (such as shortages in PPE), immediate action (such as to discharge people from hospitals to care homes without testing them for COVID-19), and implementation (such as an imperfect test-trace-and-isolate system). The broader issue relates to its focus on intervening in late March to protect healthcare capacity during a peak of infection, rather than taking a quicker and more precautionary approach. This judgment relates largely to its definition of the policy problem which underpins every subsequent policy intervention.
Did the government select the right policy mix at the right time? Who benefits most from its choices?
Most debates focus on the ‘lock down or not?’ question without exploring fully the unequal impact of any action. The government initially relied on exhortation, based on voluntarism and an appeal to social responsibility. Initial policy inaction had unequal consequences on social groups, including people with underlying health conditions, black and ethnic minority populations more susceptible to mortality at work or discrimination by public services, care home residents, disabled people unable to receive services, non-UK citizens obliged to pay more to live and work while less able to access public funds, and populations (such as prisoners and drug users) that receive minimal public sympathy. Then, in March, its ‘stay at home’ requirement initiated a major new policy and different unequal impacts in relation to the income, employment, and wellbeing of different groups. These inequalities are list in more general discussions of impacts on the whole population.
Did the UK government make the right choices on the trade-offs between values, and what impacts could the government have reasonably predicted?
Initially, the most high-profile value judgment related to freedom from state coercion to reduce infection versus freedom from the harm of infection caused by others. Then, values underpinned choices on the equitable distribution of measures to mitigate the economic and wellbeing consequences of lockdown. A tendency for the UK government to project centralised and ‘guided by the science’ policymaking has undermined public deliberation on these trade-offs between policies. The latter will be crucial to ongoing debates on the trade-offs associated with national and regional lockdowns.
Did the UK government combine good policy with good policymaking?
A problem like COVID-19 requires trial-and-error policymaking on a scale that seems incomparable to previous experiences. It requires further reflection on how to foster transparent and adaptive policymaking and widespread public ownership for unprecedented policy measures, in a political system characterised by (a) accountability focused incorrectly on strong central government control and (b) adversarial politics that is not conducive to consensus seeking and cooperation.
These additional perspectives and questions show that too-narrow questions – such as was the UK government ‘following the science’ – do not help us understand the longer term development and wider consequences of UK COVID-19 policy. Indeed, such a narrow focus on science marginalises wider discussions of values and the populations that are most disadvantaged by government policy.
Paul Cairney (2020) ‘The UK Government’s COVID-19 policy: assessing evidence-informed policy analysis in real time’, British Politicshttps://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
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.
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.
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).
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).
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.
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.
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)
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.
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).
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 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.
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).
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.
In our latest guest blog, Jonny Pearson-Stuttard, RSPH Trustee and Public Health Doctor @imperialcollege sets out what we know about the spread of coronavirus to date, and why the Government has taken the measures it hashttps://t.co/XM7zKKjwtE
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:
Do not enjoy the same confidence that they know what is happening, or that their actions will have their intended consequences, and
Will think twice about trying to regulate social behaviour under those circumstances, especially when they
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?
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
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:
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.
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):
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’).
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:
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)
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).
COVID-19 has brought new focus to women’s continued inequality. Without a gendered response to both the health and economic crises, gender inequality will be further cemented. Read more on the blog: https://t.co/zYxSFpUTNE
Available evidence (though injuriously limited) shows that Black people are being infected & dying of #coronavirus at higher rates. Disproportionate Black suffering is what many of us have suspected and feared because it is consistent with the entirety of American history. https://t.co/qzmXvGCGvV
“I believe that the actions and omissions of world leaders in charge of fighting the #COVID19 pandemic will reveal historical and current impacts of colonial violence and continued health inequities” https://t.co/nUuBIKfrVL
BBC news reports on the disproportionate deaths of African Americans & minorities in the US from #COVID19, but silence on similar issues in the UK. Why? Where is the reporting? Where is the accountability? https://t.co/DkGPjfnWG1
What the coronavirus bill will do: https://t.co/qoBdKKr64H Mental Health Act – detention implemented using just one doctor’s opinion (not 2) & AMHP, & temporarily allow extension or removal of time limits to allow for greater flexibility where services are less able to respond
Abortion services for women from Northern Ireland remain available free of charge in England. This provision will continue until services are available to meet these needs in Northern Ireland. For more information, visit: https://t.co/YYjop5lSgUpic.twitter.com/M8k95aIisM
BREAKING NEWS!!!! The Home Office have confirmed that ALL evictions and terminations of asylum support have been paused for 3 months. Find out more and read the letter from Home Office Minister Chris Philp confirming this on our website at: https://t.co/KDlVr4PHyP
In relation to Prison Rule Changes – these would only ever be used as an absolute last resort, in order to protect staff & those in our care. I can confirm that emergency changes to showering have not been implemented in any establishment.
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?
A need for more communication and exhortation, or for direct action to change behaviour.
The short term (do everything possible now) or long term (manage behaviour over many months).
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).
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.
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
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:
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.
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.
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
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.
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:
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.
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.
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.
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).
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
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.
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.
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.
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.
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.
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:
(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.
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.
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:
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.
To deal with uncertainty and change, encourage trial-and-error projects, or pilots, that can provide lessons, or be adopted or rejected, relatively quickly.
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).
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.
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.
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):
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 ittheheadline “How ‘herd immunity’ can help fight coronavirus” as 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’).
[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):
The take home messages
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.
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.
‘Policy analysis’ describes the identification of a policy problem and possible solutions.
Classic models of policy analysis are client-oriented. Most texts identify the steps that a policy analysis should follow, from identifying a problem and potential solutions, to finding ways to predict and evaluate the impact of each solution. Each text describes this process in different ways, as outlined in Boxes 1-5. However, for the most part, they follow the same five steps:
Define a policy problem identified by your client.
Identify technically and politically feasible solutions.
Use value-based criteria and political goals to compare solutions.
Predict the outcome of each feasible solution.
Make a recommendation to your client.
Further, they share the sense that analysts need to adapt pragmatically to a political environment. Assume that your audience is not an experienced policy analyst. Assume a political environment in which there is limited attention or time to consider problems, and some policy solutions will be politically infeasible. Describe the policy problem for your audience: to help them see it as something worthy of their energy. Discuss a small number of possible solutions, the differences between them, and their respective costs and benefits. Keep it short with the aid of visual techniques that sum up the issue concisely, to minimise cognitive load and make the problem seem solvable.
Box 1. Bardach (2012) A Practical Guide for Policy Analysis
‘Define the problem’. Provide a diagnosis of a policy problem, using rhetoric and eye-catching data to generate attention.
‘Assemble some evidence’. Gather relevant data efficiently.
‘Construct the alternatives’. Identify the relevant and feasible policy solutions that your audience might consider.
‘Select the criteria’. Typical value judgements relate to efficiency, equity and fairness, the trade-off between individual freedom and collective action, and the extent to which a policy process involves citizens in deliberation.
‘Project the outcomes’. Focus on the outcomes that key actors care about (such as value for money), and quantify and visualise your predictions if possible.
‘Confront the trade-offs’. Compare the pros and cons of each solution, such as how much of a bad service policymakers will accept to cut costs.
‘Decide’. Examine your case through the eyes of a policymaker.
‘Tell your story’. Identify your target audience and tailor your case. Weigh up the benefits of oral versus written presentation. Provide an executive summary. Focus on coherence and clarity. Keep it simple and concise. Avoid jargon.
Box 2. Dunn (2017) Public Policy Analysis
What is the policy problem to be solved? Identify its severity, urgency, cause, and our ability to solve it. Don’t define the wrong problem, such as by oversimplifying.
What effect will each potential policy solution have? ‘Forecasting’ methods can help provide ‘plausible’ predictions about the future effects of current/ alternative policies.
Which solutions should we choose, and why? Normative assessments are based on values such as ‘equality, efficiency, security, democracy, enlightenment’ and beliefs about the preferable balance between state, communal, and market/ individual solutions (2017: 6; 205).
What were the policy outcomes? ‘Monitoring is crucial because it is difficult to predict policy success, and unintended consequences are inevitable (2017: 250).
Did the policy solution work as intended? Did it improve policy outcomes? Try to measure the outcomes your solution, while noting that evaluations are contested (2017: 332-41).
Box 3. Meltzer and Schwartz (2019) Policy Analysis as Problem Solving
‘Define the problem’. Problem definition is a political act of framing, as part of a narrative to evaluate the nature, cause, size, and urgency of an issue.
‘Identify potential policy options (alternatives) to address the problem’. Identify many possible solutions, then select the ‘most promising’ for further analysis (2019: 65).
‘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)’.
‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.
‘Arrive at a recommendation’. Make a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups (2019: 212).
‘Engage in problem definition’. Define the nature of a policy problem, and the role of government in solving it, while engaging with many stakeholders (2012: 3; 58-60).
‘Propose alternative responses to the problem’. Identify how governments have addressed comparable problems, and a previous policy’s impact (2012: 21).
‘Choose criteria for evaluating each alternative policy response’. ‘Effectiveness, efficiency, fairness, and administrative efficiency’ are common (2012: 21).
‘Project the outcomes of pursuing each policy alternative’. Estimate the cost of a new policy, in comparison with current policy, and in relation to factors such as savings to society or benefits to certain populations.
‘Identify and analyse trade-offs among alternatives’. Use your criteria and projections to compare each alternative in relation to their likely costs and benefits.
‘Report findings and make an argument for the most appropriate response’. Client-oriented advisors identify the beliefs of policymakers and tailor accordingly (2012: 22).
Box 5 Weimer and Vining (2017) Policy Analysis: Concepts and Practice
‘Write to Your Client’. Having a client such as an elected policymaker requires you to address the question they ask, by their deadline, in a clear and concise way that they can understand (and communicate to others) quickly (2017: 23; 370-4).
‘Understand the Policy Problem’. First, ‘diagnose the undesirable condition’. Second, frame it as ‘a market or government failure (or maybe both)’.
‘Be Explicit About Values’ (and goals). Identify (a) the values to prioritise, such as ‘efficiency’, ‘equity’, and ‘human dignity’, and (b) ‘instrumental goals’, such as ‘sustainable public finance or political feasibility’, to generate support for solutions.
‘Specify Concrete Policy Alternatives’. Explain potential solutions in sufficient detail to predict the costs and benefits of each ‘alternative’ (including current policy).
‘Predict and Value Impacts’. Short deadlines dictate that you use ‘logic and theory, rather than systematic empirical evidence’ to make predictions efficiently (2017: 27)
‘Consider the Trade-Offs’. Each alternatives will fulfil certain goals more than others. Produce a summary table to make value-based choices about trade-offs (2017: 356-8).
‘Make a Recommendation’. ‘Unless your client asks you not to do so, you should explicitly recommend one policy’ (2017: 28).
Incisive essay from @bailabomba on studying the use of research evidence through critical perspectives that center the marginalized. There is so too much good stuff in here to summarize via twitter (you should just read it). But let me point out a few things that resonated (1/n) https://t.co/nIahyIjwBo
‘maintain power hierarchies and accept social inequity as a given. Indeed, research has been historically and contemporaneously (mis)used to justify a range of social harms from enslavement, colonial conquest, and genocide, to high-stakes testing, disproportionality in child welfare services, and “broken windows” policing’ (Doucet, 2019: 2)
Second, they help us redefine usefulness in relation to:
‘how well research evidence communicates the lived experiences of marginalized groups so that the understanding of the problem and its response is more likely to be impactful to the community in the ways the community itself would want’ (Doucet, 2019: 3)
In that context, potential responses include to:
Recognise the ways in which research and policy combine to reproduce the subordination of social groups.
Commit to social justice, to help ‘eliminate oppressions and to emancipate and empower marginalized groups’, such as by disrupting ‘the policies and practices that disproportionately harm marginalized groups’ (2019: 5-7)
Develop strategies to ‘center race’, ‘democratize’ research production, and ‘leverage’ transdisciplinary methods (including poetry, oral history and narrative, art, and discourse analysis – compare with Lorde) (2019: 10-22)
A key way to understand these processes is to use, and improve, policy theories to explain the dynamics and impacts of a racialized political system. For example, ‘policy feedback theory’ (PFT) draws on elements from historical institutionalism and SCPD to identify the rules, norms, and practices that reinforce subordination.
In particular, Michener’s (2019: 424) ‘Policy Feedback in a Racialized Polity’ develops a ‘racialized feedback framework (RFF)’ to help explain the ‘unrelenting force with which racism and White supremacy have pervaded social, economic, and political institutions in the United States’. Key mechanisms include (2019: 424-6):
‘Channelling resources’, in which the rules, to distribute government resources, benefit some social groups and punish others.
Examples include: privileging White populations in social security schemes and the design/ provision of education, and punishing Black populations disproportionately in prisons (2019: 428-32).
In that context, pragmatism relates to the idea that policy analysis consists of ‘art and craft’, in which analysts assess what is politically feasible if taking a low-risk client-oriented approach.
In this context, pragmatism may be read as a euphemism for conservatism and status quo protection.
In other words, other posts in the series warn against too-high expectations for entrepreneurial and systems thinking approaches to major policy change, but they should not be read as an excuse to reject ambitious plans for much-needed changes to policy and policy analysis (compare with Meltzer and Schwartz, who engage with this dilemma in client-oriented advice).
This post forms one part of the Policy Analysis in 750 words series overview and connects to previous posts on complexity.The first 750 words tick along nicely, then there is a picture of a cat hanging in there baby to signal where it can all go wrong. I updated it (22.6.20) to add category 11 then again (30.9.20) when I realised that the former category 11 was a lot like 6.
There are a million-and-one ways to describe systems and systems thinking. These terms are incredibly useful, but also at risk of meaning everything and therefore nothing (compare with planning and consultation).
Policy outcomes seem to ‘emerge’ from policymaking systems in the absence of central government control. As such, we should rely less on central government driven targets (in favour of local discretion to adapt to environments), encourage trial-and-error learning, and rethink the ways in which we think about government ‘failure’ (see, for example, Hallsworth on ‘system stewardship’, the OECD on ‘Systemic Thinking for Policy Making‘, and this thread)
Systems thinking is about learning and adapting to the limits to policymaker control.
Dunn (2017: 73) describes the interdependent nature of problems:
“Subjectively experienced problems – crime, poverty, unemployment, inflation, energy, pollution, health, security – cannot be decomposed into independent subsets without running the risk of producing an approximately right solution to the wrong problem. A key characteristic of systems of problems is that the whole is greater – that is, qualitatively different – than the simple sum of its parts” (contrast with Meltzer and Schwartz on creating a ‘boundary’ to make problems seem solveable).
Systems thinking is about addressing policy problems holistically.
Used to explain how and why policy actors might cooperate to manage finite resources.
Systems thinking is about identifying the conditions under which actors develop layers of rules to foster trust and cooperation.
Performing the metaphor of systems
Governments often use the language of complex systems – rather loosely – to indicate an awareness of the interconnectedness of things. They often perform systems thinking to give the impression that they are thinking and acting differently, but without backing up their words with tangible changes to policy instruments.
Systems thinking is about projecting the sense that (a) policy and policymaking is complicated, but (b) governments can still look like they are in control.
Four more meanings of systems thinking
Now, let’s compare these storylines with a small sample of wider conceptions of systems thinking:
Systems thinking was about the human ability to turn potential chaos into well-managed systems (such as ‘large technical systems’ to distribute energy)
The new way of accepting complexity but seeking to make an impact
Based on the idea that we can identify ‘leverage points’, or the places that help us ‘intervene in a system’ (see Meadows then compare with Arnold and Wade).
Systems thinking is about the human ability to use a small shift in a system to produce profound changes in that system.
A way to rethink cause-and-effect
Based on the idea that current research methods are too narrowly focused on linearity rather than the emergent properties of systems of behaviour (for example, Rutter et al on how to analyse the cumulative effect of public health interventions, and Greenhalgh on responding more effectively to pandemics).
Systems thinking is about rethinking the ways in which governments, funders, or professions conduct policy-relevant research on social behaviour.
How can we clarify systems thinking and use it effectively in policy analysis?
Now, imagine you are in a room of self-styled systems thinkers, and that no-one has yet suggested a brief conversation to establish what you all mean by systems thinking. I reckon you can make a quick visual distinction by seeing who looks optimistic.
I’ll be the morose-looking guy sitting in the corner, waiting to complain about ambiguity, so you would probably be better off sitting next to Luke Craven who still ‘believes in the power of systems thinking’.
If you can imagine some amalgam of these pessimistic/ optimistic positions, perhaps the conversation would go like this:
Reasons to expect some useful collaboration.
Some of these 10 discussions seem to complement each other. For example:
We can use 3 and 9 to reject one narrow idea of ‘evidence-based policymaking’, in which the focus is on (a) using experimental methods to establish cause and effect in relation to one policy instrument, without showing (b) the overall impact on policy and outcomes (e.g. compare FNP with more general ‘families’ policy).
1-3 and 10 might be about the need for policy analysts to show humility when seeking to understand and influence complex policy problems, solutions, and policymaking systems.
In other words, you could define systems thinking in relation to the need to rethink the ways in which we understand – and try to address – policy problems. If so, you can stop here and move on to the next post. There is no benefit to completing this post.
Reasons to expect the same old frustrating discussions based on no-one defining terms well enough (collectively) to collaborate effectively (beyond using the same buzzwords).
Although all of these approaches use the language of complex systems and systems thinking, note some profound differences:
Holding on versus letting go.
Some are about intervening to take control of systems or, at least, make a disproportionate difference from a small change.
Some are about accepting our inability to understand, far less manage, these systems.
Talking about different systems.
Some are about managing policymaking systems, and others about social systems (or systems of policy problems), without making a clear connection between both endeavours.
For example, if you use approach 9 to rethink societal cause-and-effect, are you then going to pretend that you can use approach 7 to do something about it? Or, will our group have a difficult discussion about the greater likelihood of 6 (metaphorical policymaking) in the context of 1 (the inability of governments to control the policymaking systems we need to solve the problems raised by 9).
In that context, the reason that I am sitting in the corner, looking so morose, is that too much collective effort goes into (a) restating, over and over and over again, the potential benefits of systems thinking, leaving almost no time for (b) clarifying systems thinking well enough to move on to these profound differences in thinking. Systems thinking has not even helped us solve these problems with systems thinking.
Throughout this series you may notice three different conceptions about the scope of policy analysis:
‘Ex ante’ (before the event) policy analysis. Focused primarily on defining a problem, and predicting the effect of solutions, to inform current choice (as described by Meltzer and Schwartz and Thissen and Walker).
‘Ex post’ (after the event) policy analysis. Focused primarily on monitoring and evaluating that choice, perhaps to inform future choice (as described famously by Weiss).
Some combination of both, to treat policy analysis as a continuous (never-ending) process (as described by Dunn).
As usual, these are not hard-and-fast distinctions, but they help us clarify expectations in relation to different scenarios.
The impact of old-school ex ante policy analysis
Radin provides a valuable historical discussion of policymaking with the following elements:
a small number of analysts, generally inside government (such as senior bureaucrats, scientific experts, and – in particular- economists),
giving technical or factual advice,
about policy formulation,
to policymakers at the heart of government,
on the assumption that policy problems would be solved via analysis and action.
This kind of image signals an expectation for high impact: policy analysts face low competition, enjoy a clearly defined and powerful audience, and their analysis is expected to feed directly into choice.
Radin goes on to describe a much different, modern policy environment: more competition, more analysts spread across and outside government, with a less obvious audience, and – even if there is a client – high uncertainty about where the analysis fits into the bigger picture.
Yet, the impetus to seek high and direct impact remains.
This combination of shifting conditions but unshifting hopes/ expectations helps explain a lot of the pragmatic forms of policy analysis you will see in this series, including:
Keep it catchy, gather data efficiently, tailor your solutions to your audience, and tell a good story (Bardach)
Speak with an audience in mind, highlight a well-defined problem and purpose, project authority, use the right form of communication, and focus on clarity, precision, conciseness, and credibility ( Smith)
Address your client’s question, by their chosen deadline, in a clear and concise way that they can understand (and communicate to others) quickly (Weimer and Vining)
Client-oriented advisors identify the beliefs of policymakers and anticipate the options worth researching (Mintrom)
Identify your client’s resources and motivation, such as how they seek to use your analysis, the format of analysis they favour (make it ‘concise’ and ‘digestible’), their deadline, and their ability to make or influence the policies you might suggest (Meltzer and Schwartz).
‘Advise strategically’, to help a policymaker choose an effective solution within their political context (Thissen and Walker).
Focus on producing ‘policy-relevant knowledge’ by adapting to the evidence-demands of policymakers and rejecting a naïve attachment to ‘facts speaking for themselves’ or ‘knowledge for its own sake’ (Dunn).
The impact of research and policy evaluation
Many of these recommendations are familiar to scientists and researchers, but generally in the context of far lower expectations about their likely impact, particularly if those expectations are informed by policy studies (compare Oliver & Cairney with Cairney & Oliver).
In that context, Weiss’ work is a key reference point. It gives us a menu of ways in which policymakers might use policy evaluation (and research evidence more widely):
to inform solutions to a problem identified by policymakers
as one of many sources of information used by policymakers, alongside ‘stakeholder’ advice and professional and service user experience
as a resource used selectively by politicians, with entrenched positions, to bolster their case
as a tool of government, to show it is acting (by setting up a scientific study), or to measure how well policy is working
as a source of ‘enlightenment’, shaping how people think over the long term (compare with this discussion of ‘evidence based policy’ versus ‘policy based evidence’).
In other words, researchers may have a role, but they struggle (a) to navigate the politics of policy analysis, (b) find the right time to act, and (c) to secure attention, in competition with many other policy actors.
The potential for a form of continuous impact
Dunn suggests that the idea of ‘ex ante’ policy analysis is misleading, since policymaking is continuous, and evaluations of past choices inform current choices. Think of each policy analysis steps as ‘interdependent’, in which new knowledge to inform one step also informs the other four. For example, routine monitoring helps identify compliance with regulations, if resources and services reach ‘target groups’, if money is spent correctly, and if we can make a causal link between the policy solutions and outcomes. Its impact is often better seen as background information with intermittent impact.
Key conclusions to bear in mind
The demand for information from policy analysts may be disproportionately high when policymakers pay attention to a problem, and disproportionately low when they feel that they have addressed it.
Common advice for policy analysts and researchers often looks very similar: keep it concise, tailor it to your audience, make evidence ‘policy relevant’, and give advice (don’t sit on the fence). However, unless researchers are prepared to act quickly, to gather data efficiently (not comprehensively), to meet a tight brief for a client, they are not really in the impact business described by most policy analysis texts.
A lot of routine, continuous, impact tends to occur out of the public spotlight, based on rules and expectations that most policy actors take for granted.
When describing ‘the policy sciences’, Lasswell distinguishes between:
‘knowledge of the policy process’, to foster policy studies (the analysis of policy)
‘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.
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
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.
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:
define the problem
identify potential solutions
choose the criteria to compare them
evaluate them in relation to their predicted outcomes
Overall, this discussion of policy studies and policy analysis reminds us of a major difference between:
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).
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.
When describing ‘the policy sciences’, Lasswell distinguishes between:
‘knowledge of the policy process’, to foster policy studies (the analysis of policy)
‘knowledge in the process’, to foster policy analysis (analysis for policy)
The idea is that both elements are analytically separable but mutually informative: policy analysis is crucial to solving real policy problems, policy studies inform the feasibility of analysis, the study of policy analysts informs policy studies, and so on.
Both elements focus on similar questions – such as What is policy? – and explore their descriptive (what do policy actors do?) and prescriptive (what should they do?) implications.
Policy studies focus on the power to reduce ambiguity rather than simply the provision of information to reduce uncertainty. In other words, the power to decide whose interpretation of policy problems counts, and therefore to decide what information is policy-relevant.
This (unequal) competition takes place within a policy process over which no actor has full knowledge or control.
The classic 5-8 step policy analysis texts focus on how to define policy problems well, but they vary somewhat in their definition of doing it well (see also C.Smith):
Bardach recommends using rhetoric and eye-catching data to generate attention
Weimer and Vining and Mintrom recommend beginning with your client’s ‘diagnosis’, placing it in a wider perspective to help analyse it critically, and asking yourself how else you might define it (see also Bacchi, Stone)
Meltzer and Schwartz and Dunn identify additional ways to contextualise your client’s definition, such as by generating a timeline to help ‘map’ causation or using ‘problem-structuring methods’ to compare definitions and avoid making too many assumptions on a problem’s cause.
Thissen and Walker compare ‘rational’ and ‘argumentative’ approaches, treating problem definition as something to be measured scientifically or established rhetorically (see also Riker).
These approaches compare with more critical accounts that emphasise the role of power and politics to determine whose knowledge is relevant (L.T.Smith) and whose problem definition counts (Bacchi, Stone). Indeed, Bacchi andStone provide a crucial bridge between policy analysis and policy studies by reflecting on what policy analysts do and why.
What is the policy solution?
In policy studies, it is common to identify counterintuitive or confusing aspects of policy processes, including:
Few studies suggest that policy responses actually solve problems (and many highlight their potential to exacerbate them). Rather, ‘policy solutions’ is shorthand for proposed or alleged solutions.
Problem definition often sets the agenda for the production of ‘solutions’, but note the phrase solutions chasing problems (when actors have their ‘pet’ solutions ready, and they seek opportunities to promote them).
Policy studies: problem definition informs the feasibility and success of solutions
Generally speaking, to define the problem is to influence assessments of the feasibility of solutions:
Technical feasibility. Will they work as intended, given the alleged severity and cause of the problem?
Political feasibility. Will they receive sufficient support, given the ways in which key policy actors weigh up the costs and benefits of action?
Policy studies highlight the inextricable connection between technical and political feasibility. Put simply, (a) a ‘technocratic’ choice about the ‘optimality’ of a solution is useless without considering who will support its adoption, and (b) some types of solution will always be a hard sell, no matter their alleged effectiveness (Box 2.3 below).
In turn, problem definition informs: the ways in which actors will frame any evaluation of policy success, and the policy-relevance of the evidence to evaluate solutions. Simple examples include:
If you define tobacco in relation to: (a) its economic benefits, or (b) a global public health epidemic, evaluations relate to (a) export and taxation revenues, or (b) reductions in smoking in the population.
If you define ‘fracking’ in relation to: (a) seeking more benefits than costs, or (b) minimising environmental damage and climate change, evaluations relate to (a) factors such as revenue and effective regulation, or simply (b) how little it takes place.
Policyanalysis: recognising and pushing boundaries
Policy analysis texts tend to accommodate these insights when giving advice:
Bardach recommends identifying solutions that your audience might consider, perhaps providing a range of options on a notional spectrum of acceptability.
Smith highlights the value of ‘precedent’, or relating potential solutions to previous strategies.
Weimer and Vining identify the importance of ‘a professional mind-set’ that may be more important than perfecting ‘technical skills’
Mintrom notes that some solutions are easier to sell than others
Meltzer and Schwartz describe the benefits of making a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups
Dunn warns against too-narrow forms of ‘evidence based’ analysis which undermine a researcher’s ability to adapt well to the evidence-demands of policymakers
Thissen and Walker relate solution feasibility to a wide range of policy analysis ‘styles’
Still, note the difference in emphasis.
Policy analysis education/ training may be about developing the technical skills to widen definitions and apply many criteria to compare solutions.
Policy studies suggest that problem definition and a search for solutions takes place in an environment where many actors apply a much narrower lens and are not interested in debates on many possibilities (particularly if they begin with a solution).
I have exaggerated this distinction between each element, but it is worth considering the repeated interaction between them in practice: politics and policymaking provide boundaries for policy analysis, analysis could change those boundaries, and policy studies help us reflect on the impact of analysts.
I’ll take a quick break, then discuss how this conclusion relates to the idea of ‘entrepreneurial’ policy analysis.
One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.
In this case, modern theories of the policy process help you identify your audience and their capacity to follow your advice. This simple insight may have a profound impact on the advice you give.
Policy analysis for an ideal-type world
For our purposes, an ideal-type is an abstract idea, which highlights hypothetical features of the world, to compare with ‘real world’ descriptions. It need not be an ideal to which we aspire. For example, comprehensive rationality describes the ideal type, and bounded rationality describes the ‘real world’ limitations to the ways in which humans and organisations process information.
Imagine writing policy analysis in the ideal-type world of a single powerful ‘comprehensively rational’ policymaker at the heart of government, making policy via an orderly policy cycle.
Your audience would be easy to identify, your analysis would be relatively simple, and you would not need to worry about what happens after you make a recommendation for policy change.
You could adopt a simple 5-8 step policy analysis method, use widely-used tools such as cost-benefit analysis to compare solutions, and know where the results would feed into the policy process.
I have perhaps over-egged this ideal-type pudding, but I think a lot of traditional policy analyses tapped into this basic idea and focused more on the science of analysis than the political and policymaking context in which it takes place (see Radin and Brans, Geva-May, and Howlett).
This image is a key feature of policy process theories, which describe:
Many policymakers and influencers spread across many levels and types of government (as the venues in which authoritative choice takes place). Consequently, it is not a straightforward task to identify and know your audience, particularly if the problem you seek to solve requires a combination of policy instruments controlled by different actors.
Each venue resembles an institution driven by formal and informal rules. Formal rules are written-down or widely-known. Informal rules are unwritten, difficult to understand, and may not even be understood in the same way by participants. Consequently, it is difficult to know if your solution will be a good fit with the standard operating procedures of organisations (and therefore if it is politically feasible or too challenging).
Policymakers and influencers operate in ‘subsystems’, forming networks built on resources such as trust or coalitions based on shared beliefs. Effective policy analysis may require you to engage with – or become part of – such networks, to allow you to understand the unwritten rules of the game and encourage your audience to trust the messenger. In some cases, the rules relate to your willingness to accept current losses for future gains, to accept the limited impact of your analysis now in the hope of acceptance at the next opportunity.
Actors relate their analysis to shared understandings of the world – how it is, and how it should be – which are often so well-established as to be taken for granted. Common terms include paradigms, hegemons, core beliefs, and monopolies of understandings. These dominant frames of reference give meaning to your policy solution. They prompt you to couch your solutions in terms of, for example, a strong attachment to evidence-based cases in public health, value for money in treasury departments, or with regard to core principles such as liberalism or socialism in different political systems.
Your solutions relate to socioeconomic context and the events that seem (a) impossible to ignore and (b) out of the control of policymakers. Such factors range from a political system’s geography, demography, social attitudes, and economy, while events can be routine elections or unexpected crises.
What would you recommend under these conditions? Rethinking 5-step analysis
There is a large gap between policymakers’ (a) formal responsibilities versus (b) actual control of policy processes and outcomes. Even the most sophisticated ‘evidence based’ analysis of a policy problem will fall flat if uninformed by such analyses of the policy process. Further, the terms of your cost-benefit analysis will be highly contested (at least until there is agreement on what the problem is, and how you would measure the success of a solution).
Modern policy analysis texts try to incorporate such insights from policy theories while maintaining a focus on 5-8 steps. For example:
Meltzer and Schwartz contrast their ‘flexible’ and ‘iterative’ approach with a too- rigid ‘rationalistic approach’.
Weimer and Vininginvest 200 pages in economic analyses of markets and government, often highlighting a gap between (a) our ability to model and predict economic and social behaviour, and (b) what actually happens when governments intervene.
One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.
If we take key insights from policy theories seriously, we can use them to identify (a) the constraints to policy analytical capacity, and (b) the ways in which analysts might address them. I use the idea of policy analyst archetypes to compare a variety of possible responses.
Key constraints to policy analytical capacity
Terms like ‘bounded rationality’ highlight major limits on the ability of humans and organisations to process information.
Policy actors need to find ways to act, with incomplete information about the problem they seek to solve and the likely impact of their ‘solution’.
They gather information to help reduce uncertainty, but problem definition is really about exercising power to reduce ambiguity: select one way to interpret a problem (at the expense of most others), and limit therefore limit the relevance and feasibility of solutions.
This context informs how actors might use the tools of policy analysis. Key texts in this series highlight the use of tools to establish technical feasibility (will it work as intended?), but policymakers also select tools for their political feasibility (who will support or oppose this measure?).
How might policy analysts address these constraints ethically?
Most policy analysis texts (in this series) consider the role of professional ethics and values during the production of policy analysis. However, they also point out that there is not a clearly defined profession and associated code of conduct (e.g. see Adachi). In that context, let us begin with some questions about the purpose of policy analysis and your potential role:
Is your primary role to serve individual clients or some notion of the ‘public good’?
Should you maximise your role as an individual or play your part in a wider profession?
What is the balance between the potential benefits of individual ‘entrepreneurship’ and collective ‘co-productive’ processes?
Which policy analysis techniques should you prioritise?
What forms of knowledge and evidence count in policy analysis?
What does it mean to communicate policy analysis responsibly?
Should you provide a clear recommendation or encourage reflection?
Policy analysis archetypes: pragmatists, entrepreneurs, manipulators, storytellers, and decolonisers
In that context, I have created a story of policy analysis archetypes to identify the elements that each text emphasises.
The pragmatic policy analyst
Bardach provides the classic simple, workable, 8-step system to present policy analysis to policymakers while subject to time and resource-pressed political conditions.
Dunn also uses Wildavsky’s famous phrase ‘art and craft’ to suggest that scientific and ‘rational’ methods can only take us so far.
The professional, client–oriented policy analyst
Weimer and Vining provide a similar 7-step client-focused system, but incorporating a greater focus on professional development and economic techniques (such as cost-benefit-analysis) to emphasise a particular form of professional analyst.
Meltzer and Schwartz also focus on advice to clients, but with a greater emphasis on a wide variety of methods or techniques (including service design) to encourage the co-design of policy analysis with clients.
The communicative policy analyst
C. Smith focuses on how to write and communicate policy analysis to clients in a political context.
Riker helps us understand the relationship between two aspects of agenda setting: the rules/ procedures to make choice, and the framing of policy problems and solutions.
The entrepreneurial policy analyst
Mintrom shows how to combine insights from studies of policy entrepreneurship and policy analysis, to emphasise the benefits of collaboration and creativity.
The questioning policy analyst
Bacchi analyses the wider context in which people give and use such advice, to identify the emancipatory role of analysis and encourage policy analysts to challenge dominant social constructions of problems and populations.
The storytelling policy analyst
Stone identifies the ways in which people use storytelling and argumentation techniques to define problems and justify solutions. This process is about politics and power, not objectivity and optimal solutions.
The decolonizing policy analyst.
L.T. Smith does not describe policy analysis directly, but shows how the ‘decolonization of research methods’ can inform the generation and use of knowledge.
Compare with Hindess on the ways in which knowledge-based hierarchies rely on an untenable, circular logic.
Incisive essay from @bailabomba on studying the use of research evidence through critical perspectives that center the marginalized. There is so too much good stuff in here to summarize via twitter (you should just read it). But let me point out a few things that resonated (1/n) https://t.co/nIahyIjwBo
Using archetypes to define the problem of policy analysis
Studies of the field (e.g. Radin plus Brans, Geva-May, and Howlett) suggest that there are many ways to do policy analysis. Further, as Thissen and Walker describe, such roles are notmutually exclusive, your views on their relative value could change throughout the process of analysis, and you could perform many of these roles.
Further, each text describes multiple roles, and some seem clustered together:
pragmatic, client-orientated, and communicative could sum-up the traditional 5-8 step approaches, while
questioning, storytelling, and decolonizing could sum up an important (‘critical’) challenge to narrow ways of thinking about policy analysis and the use of information.
Still, the emphasis matters.
Each text is setting an agenda or defining the problem of policy analysis more-or-less in relation to these roles. Put simply, the more you are reading about economic theory and method, the less you are reading about dominance and manipulation.
These choices are not mutually exclusive, but there are key tensions between them that should not be ignored, such as when we ask:
how many people should be involved in policy analysis?
whose knowledge counts?
who should control policy design?
Perhaps we can only produce a sensible combination of the two if we clarify their often very different implications for policy analysis. Let’s begin with one story for each and see where they take us.
A story of ‘evidence-based policymaking’
One story of ‘evidence based’ policy analysis is that it should be based on the best available evidence of ‘what works’.
Often, the description of the ‘best’ evidence relates to the idea that there is a notional hierarchy of evidence according to the research methods used.
At the top would be the systematic review of randomised control trials, and nearer the bottom would be expertise, practitioner knowledge, and stakeholder feedback.
This kind of hierarchy has major implications for policy learning and transfer, such as when importing policy interventions from abroad or ‘scaling up’ domestic projects.
Put simply, the experimental method is designed to identify the causal effect of a very narrowly defined policy intervention. Its importation or scaling up would be akin to the description of medicine, in which the evidence suggests the causal effect of a specific active ingredient to be administered with the correct dosage. A very strong commitment to a uniform model precludes the processes we might associate with co-production, in which many voices contribute to a policy design to suit a specific context (see also: the intersection between evidence and policy transfer).
A story of co-production in policymaking
One story of ‘co-produced’ policy analysis is that it should be ‘reflexive’ and based on respectful conversations between a wide range of policymakers and citizens.
Often, the description is of the diversity of valuable policy relevant information, with scientific evidence considered alongside community voices and normative values.
This rejection of a hierarchy of evidence also has major implications for policy learning and transfer. Put simply, a co-production method is designed to identify the positive effect – widespread ‘ownership’ of the problem and commitment to a commonly-agreed solution – of a well-discussed intervention, often in the absence of central government control.
Its use would be akin to a collaborative governance mechanism, in which the causal mechanism is perhaps the process used to foster agreement (including to produce the rules of collective action and the evaluation of success) rather than the intervention itself. A very strong commitment to this process precludes the adoption of a uniform model that we might associate with narrowly-defined stories of evidence based policymaking.
Where can you find these stories in the 750-words series?
My interest has been to understand how governments juggle competing demands, such as to (a) centralise and localise policymaking, (b) encourage uniform and tailored solutions, and (c) embrace and reject a hierarchy of evidence. What could possibly go wrong when they entertain contradictory objectives? For example:
Paul Cairney (2019) “The myth of ‘evidence based policymaking’ in a decentred state”, forthcoming in Public Policy and Administration(Special Issue, The Decentred State) (accepted version)
Paul Cairney (2019) ‘The UK government’s imaginative use of evidence to make policy’, British Politics, 14, 1, 1-22 Open AccessPDF
Paul Cairney and Kathryn Oliver (2017) ‘Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy?’ Health Research Policy and Systems (HARPS), DOI: 10.1186/s12961-017-0192-xPDF
Paul Cairney (2017) “Evidence-based best practice is more political than it looks: a case study of the ‘Scottish Approach’”, Evidence and Policy, 13, 3, 499-515 PDF
These posts introduce you to key concepts in the study of public policy. They are all designed to turn a complex policymaking world into something simple enough to understand. Some of them focus on small parts of the system. Others present ambitious ways to explain the system as a whole. The wide range of concepts should give you a sense of a variety of studies out there, but my aim is to show you that these studies have common themes.