Wouldn’t it be nice if policy scholars and professionals could have frequent and fruitful discussions about policy and policymaking? Both professions could make valuable contributions to our understanding of policy design in a wider political context.
However, it is notoriously difficult to explain what policy is and how it is made, and academics and practitioners may present very different perspectives on what policymakers or governments do. Without a common reference point, how can they cooperate to discuss how to (say) improve policy or policymaking?
One starting point is to visualize policymaking to identify overlaps in perspectives. To that end, if academics and policymakers were to describe ‘the policy process’, could they agree on what it looks like? To help answer this question, in this post I’m presenting some commonly-used images in policy research, then inviting you to share images that you would use to sum up policy work.
Why produce different images of policy processes?
One obstacle to a shared description is that we need different images for different aims, including:
To describe and explain what policymakers do. Academics describe one part of a complex policy process, accompanied by a technical language to understand each image.
To describe what policymakers need to do. Practitioners visualise a manageable number of aims or requirements (essential steps, stages, or functions), accompanied by a professional in-house language (such as in the Green Book).
To describe what they would like to do. Governments produce images of policymaking to tell stakeholders or citizens what they do, accompanied by an aspirational language related to what is expected of elected governments.
Why seek a common image? Would it help or hinder discussion?
If we have such different aims, is it (a) possible, and (b) desirable to produce an image that satisfies each aim? For example, it is possible but undesirable to use the policy cycle image to that end.
This image may be shared by academics and practitioners, but it means something different each time:
1.Most policy scholars use the cycle to describe what does not happen. It is a teaching tool, to (a) describe the ideal-type, (b) explain its descriptive inaccuracy, and (c) introduce the search for better models, which (d) might help to visualise a messier reality (for example, by using Spirograph).
2. Practitioners often find it more useful to sum up the steps they need to take – to get from defining to addressing a policy problem. For example, the ROAMEF cycle looks fairly similar to the one in my textbook. However, most policymakers would describe their actual steps in different ways or – more importantly – accept that no-one really makes policy this way.
3. Policymakers find it useful to project to the public that their process is orderly. You will find many versions of this image in UK government and European Commission documents, using images to summarise how they would like to be seen.
In each case, the policy cycle image represents a confusing mix of (1) valuable to prompt further discussion, and (2) not valuable because it is so misleading. Indeed, even (one small part of) the European Commission presents a very different image, to superimpose an unwieldy mess onto the traditional cyclical image.
What images do academics use to explain complexity?
While an image of messy policymaking makes a simple point well (policymaking is far messier than the cycle suggests), it does not do much else. What other images convey this complexity while also providing specific insights to guide research or action?
Policy theories help to visualise complexity in a range of useful ways. What follows are some examples…
The multiple streams framework: much like a space launch, major policy change will not happen unless many requirements come together simultaneously. In policymaking, the requirements are: attention rises to a problem, a feasible solution already exists, and policymakers have the motive and opportunity to select it. Policy entrepreneurs may help, but as surfers riding a wave, not controllers of the sea (apologies for the mixed metaphors).
Take home message from image 1: ‘stages’ of a policy cycle matter, but the process (1) is not linear, and (2) does not lead inevitably to policy change.
Visualising data
Source: True, J.L., Jones, B.D. and Baumgartner, F.R. (2007) Punctuated Equilibrium Theory’ in P. Sabatier (ed.) Theories of the Policy Process, 2nd edn (Cambridge, MA: Westview Press)
Punctuated equilibrium theory: this image sums up the distribution of policy change in liberal democracies: there is a huge number of very small changes, and a very small number of huge changes. This distribution is akin to the frequency and magnitude of earthquakes! What is the cause? (1) Policymaker attention to problems does not relate strongly to (a) the size of the problem, or (b) the available information. (2) A lack of attention results – in most cases – in limited change (since high attention may be required to help overcome existing rules and practices).
Take home message from image 2: Policymaking is largely about governments managing existing policies which can change very little for long periods. Major changes can happen, but they are rare. They can be explained, but are not easy to predict.
Visualising important factors
Source: Weible, C., Heikkila, T., Ingold, K. and Fischer, M. (2016) ‘Introduction’ in (eds) Weible, C., Heikkila, T., Ingold, K. and Fischer, M. (eds) Policy Debates on Hydraulic Fracturing(London: Palgrave)
The advocacy coalition framework flow diagram: people join ‘advocacy coalitions’ to turn their beliefs into policy and they compete with other coalitions to influence policy in subsystems (specialist networks of policymakers and influencers). Policy change relates to how coalitions manage internal dynamics (such as learning from policy failure) or deal with external events (such as a crisis or change of government).
Take home message from image 3: Most policy is processed in a large number of specialist policy networks, which are more or less insulated from the wider political system.
Visualising concepts in a non-threatening way
The blue turtle: – my aim is to introduce concepts in a visually pleasing way (to compete with the policy cycle). The image provides an introductory story about how policymakers deliberate and make choices (drawing on psychology to show how they frame problems and identify trusted sources of information) while surrounded by their policymaking environment (consisting of many policy actors spread across many venues, each with their own rules, networks, and reference points).
Take home message from image 4: Policy is processed by many different ‘centres’ – each with their own ways of working – rather than one single central government. The overall effect cannot be summed up by one single cycle of activity, and the overall ‘policy mix’ does not emerge from one source.
What images do you find more useful?
My main aim has been to present these images to prompt discussion: what does each image say about how we describe policymaking, our role in policy processes, and how we would like others to understand what we do? Do you prefer other images, such as to describe the ‘strategic triangle’?
I would welcome your thoughts in the comments below. Or, if you have some valuable images to share, please send them to p.a.cairney@stir.ac.uk
The next post
My plan is to write a follow-up post to collate many more images, with early suggestions including:
‘Political science remains indebted to approaches, debates, and categories that emerged to make sense of the challenges that imperial centers faced in ruling over the colonial margins that they had created’ (Shilliam, 2021: 3)
Shilliam (2018: 18) aims to ‘decolonize the academic study of politics’, partly by identifying the historic impact of Western imperialism (including the violence to centre one world or perspective) and colonialism (including ways to govern marginalised populations) on how we still think about politics. These legacies have helped to set limits on whose perspectives matter in political research and whose written knowledge we have treated as canonical (the sacred sources that we treat as foundational to our approach).
I would summarise part of Shilliam’s approach as follows:
First, ask: which sources are treated as canon in my field, and why?
Second, identify the political context in which that work was produced, re-engaging with conventional accounts of key texts.
Third, identify the legacy of past choices. For example, what limits do conventional accounts of key sources place on our understanding of political research? Whose knowledge and voices matter in these accounts? Whose knowledge is diminished and whose voices went unheard? What has been said, and what remains unsaid?
Shilliam’s examples include:
Political theory. The usual story of Aristotle helps to downplay – for example – the limits on who would be treated as citizens entitled to deliberate and pursue ‘the good life’. The Enlightenment also took place at a time of imperialism and an assumption that only some humans were ‘properly human’ (2021: 21),
The study of political behaviour emerged during concern about the forms of social mixing (such as between races) that could undermine ‘democracy’.
Comparative politics developed during and after the Cold War, focusing on the acknowledgement of difference (as a basis for comparison) but also a belief that some differences should be discouraged (such as in the battle to ensure that decolonised states became liberal democracies, not communist).
Strands of international relations have focused on how to deal with international anarchy via globalised orders overseen by elites.
What is the relevance to the study of policy analysis?
We can tell a similar story about the development of post-war (US and UK) policy analysis, although ‘mainstream’ and critical/ interpretive accounts may tell it in different ways.
On the one hand, both reject old stories of ‘rationalist’ policymaking which romanticised the idea of a centralised and exclusive policy process, where elite professional analysts translated the highest quality science to produce the correct diagnosis of a problem and an optimal solution (see Radin and Thissen/Walker).
On the other, note the potential for different take-home messages relating to their treatment of wider context:
Rejecting the description or prescription? Mainstream approaches seek more accurate accounts of the policy processes in which analysts engage (e.g. 1000 series). Critical approaches also reject the ideal, or the assertion that policy analysis could or should be a depoliticised process driven primarily by experts and scientific evidence. Defining problems and establishing the feasibility of solutions is inevitably a political process and it should be based on citizen and stakeholder participation and deliberation, including steps to include marginalised groups.
Rejecting rationalism in political science? ‘Mainstream’ tends to describe the largely-US ‘positivist’ approaches that also tend to dominate political science. Critical or interpretive approaches are not ‘canon’ in mainstream policy theory journals (such as Policy Studies Journal) or the influential Theories of the Policy Process series.
‘Each framework must do a reasonably good job of meeting the criteria of a scientific theory; that is, its concepts and propositions must be relatively clear and internally consistent, it must identify clear causal drivers, it must give rise to falsifiable hypotheses, and it must be fairly broad in scope (i.e., apply to most of the policy process in a variety of political systems) … Each framework must be a positive theory seeking to explain much of the policy process. The theoretical framework may also contain some explicitly normative elements, but these are not required’ (Sabatier, 2007: 8).
This description – of what methods to gather knowledge should be included – should seem familiar if you read Linda Tuhiwai Smith (2012), who describes:
The exercise of power to determine whole rules – about knowledge and how to gather and use it – matter in research, and
How scientific research (in the ‘European Enlightenment’ mould) went hand in hand with colonialism, to the extent that “the term ‘research’ is inextricably linked to European imperialism and colonialism” (2012: 1; 21-6).
Consequently, while it is relatively straightforward to consider (1) how we might share insights from knowledge based on mainstream or interpretive approaches, it is harder to (2) reconcile what each approach may represent in a wider political context.
For example, mainstream accounts focus primarily on explanation, with normative issues an optional extra.
In contrast, critical accounts:
Come with an explicit commitment to emancipation or social justice in relation to research (challenging the idea that scientific knowledge trumps all others) and politics (fostering more inclusive, participatory, deliberative approaches to knowledge gathering and use), and
If so, could scholars from each approach really share insights at a superficial level while ignoring the wider political context that underpins anything they discuss?
Other relevant posts:
Many posts in this (and other) series could be usefully read together, including:
The recent PAR editorial ‘Epistemic decolonization of public policy pedagogy and scholarship’ engages with a call to ‘reflect on the intrinsic whiteness, colonial legacies, and power imbalances implicit in knowledge production practices in the field of philosophy of science’.
My role was to set the scenes and raise problems. Think of most of these problems as political policymaking dilemmas that produce the need to engage with uncertainty, ambiguity, and trade-offs, not technical problems amenable to simple fixes.
What does complexity mean in relation to policymaking and administration?
It takes time to understand what people mean whey they describe complexity, but this process is essential. Otherwise, we will be talking at cross-purposes, or in vague platitudes, without demonstrating why the language of complexity matters to policymaking.
For example, some people use the common term ‘complex’ when describing something very complicated.
Some refer to ‘complexity theory’ to describe the properties of ‘complex systems’, including:
Interdependence.
Positive and negative feedback.
Sensitivity to initial conditions.
Strange attractors.
Emergence.
This language comes with the exhortation to see the world differently: as less amenable to understanding (with traditional research techniques) or to solutions (with traditional policy processes).
For some, it describes implications for policy. For others, the policy process itself.
Problem 1 – The tension between a focus on requirement versus reality when we seek concrete meaning
A focus on policy analysis/ design usually begins with the complexity 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 (Dunn, 2017: 73; compare with ‘wicked’ problems)
Then, analysts identify a list of policymaking requirements to deal with the problem:
If policy problems are complex,
they spill over traditional government boundaries,
so we require concerted efforts towards integration, joining up, whole of government approaches.
A focus on policymaking complexity usually suggests that such requirements will not be met. Holistic government is a pipedream or ideal-type to compare with what happens.
Problem 2 – The lack of central control and a lack of coordinative capacity; the system is not so amenable
This focus on complex policymaking systems provides a list of cautionary tales, such as:
A policy that was successful in one context may not have the same effect in another.
Expect policy interventions to not have the desired effect.
It also comes with a call to do things differently:
Policymaking is too driven by the idea of order, maintaining rigid hierarchies and producing top-down, centrally driven policy strategies.
Or, people have too much faith in the coordinative capacity of organisations working together.
Policymaking systems change quickly, so adapt quickly and do not rely on a single policy strategy.
Problem 3 – Let go or hold on?
One solution to this problem is to give up on the idea of central coordination: let go, in favour of decentralised responses.
However, this general response leaves unresolved questions about how to meet expectations for elected government and holding specific organisations to account for their actions. If everyone is responsible, is no-one responsible?
Below is the introduction to an article that I wrote for a Special Issue paper on Teaching Policy Analysis for Gestión y Análisis de Políticas Públicas (GAPP).
When we teach policy analysis, we focus on how to be a policy analyst or how to situate the act of policy analysis within a wider policymaking context. Ideally, students would learn about both. This aim is central to Lasswell’s vision for the policy sciences, in which the analysis of policy and policymaking informs analysis for policy, and both are essential to the pursuit of human equality and dignity (Lasswell, 1951; 1956; 1971; see Cairney and Weible, 2017).
There is the potential to achieve this vision for the policy sciences. Policy analysis texts focus on the individual and professional skills required to act efficiently and effectively in a time-pressured political environment. Further, they are supported by the study of policy analysts to reflect on how analysis takes place, and policy is made, in the real world (Radin, 2019; Brans et al, 2017; Thissen and Walker, 2013; Geva-May, 2005). The next steps would be to harness the wealth of policy concept- and theory-informed studies to help understand how real-world contexts inform policy analysis insights.
First, almost all mainstream policy theories assume or demonstrate that there is no such thing as a policy cycle. It would be misleading to suggest that the policy process consists of clearly defined and well-ordered stages of policymaking, from defining problems and generating solutions to implementing solutions and evaluating their effects. If so, there is no clear route to influence via analysis unless we understand a far messier reality. In that context, how can policy analysts understand their complex policymaking environment, and what skills and strategies do they need to develop to engage effectively? These discussions may be essential to preventing the demoralisation of analysts: if they do not learn in advance about the processes and factors that can minimise their influence, how can they generate realistic expectations?
Second, if the wider aim is human equality and dignity, insights from critical policy analysis are essential. They help analysts think about what those values mean, how to identify and support marginalised populations, and how policy analysis skills and techniques relate to those aims. In particular, they warn against treating policy analysis as a technocratic profession devoid of politics. This rationalist story may contribute to exclusive research gathering practices, producing too-narrow definitions of problems, insufficient consideration of feasible solutions, and recommendations made about target populations without engaging with the people they claim to serve (Bacchi, 2009; Stone, 2012).
However, this aim is much easier described than achieved. Policy analysis texts, focusing on how to do it, often use insights from policy studies but without fully explaining key concepts and theories or exploring their implications. There is not enough time and space to do justice to every element, from the technical tools of policy analysis (including cost-benefit analysis) to the empirical findings from policy theories and normative insights from critical policy analysis approaches (e.g. Weimer and Vining, 2017 is already 500 pages long). Policy process research, focusing on what happens, may have practical implications for analysts. However, they are often hidden behind layers of concepts and jargon, and most of their authors seem uninterested in describing the normative importance of, or practical lessons from, theory-informed empirical studies. The cumulative size of this research is overwhelming and beyond the full understanding of experienced specialist scholars. Further, it is difficult to recommend a small number of texts to sum up each approach, which makes it difficult to predict how much time and energy it would take to understand this field, or to demonstrate the payoff from that investment. In addition, critical policy analysis is essential, but often ignored in policy analysis texts, and the potential for meaningful conversations between critical or interpretive versus mainstream policy scholars remains largely untapped (e.g. Durnova and Weible, 2020) or resisted (e.g. Jones and Radaelli, 2016).
In that context, policy analysis students embody the problem of ‘bounded rationality’ described famously by Simon (1976). Simon’s phrase ‘to satisfice’ sums up a goal-oriented response to bounded rationality: faced with the inability to identify, process, or understand all relevant information, they seek ways to gather enough information to inform ‘good enough’ choices. More recently, policy studies have sought to incorporate insights from individual human, social, and organisational psychology to understand (1) the cognitive shortcuts that humans use, including gut-level instinct, habit, familiarity with an issue, deeply-held beliefs, and emotions, and (2) their organisation’s equivalents (organisations use rules and standard operating procedures to close off information searches and limit analysis – Koski and Workman, 2018). Human cognitive shortcuts can be described negatively as cognitive biases or more positively as ‘thinking fast and slow’ (Kahneman, 2012) or ‘fast and frugal heuristics’ (Gigerenzer, 2001). However, the basic point remains: if people seek shortcuts to information, we need to find ways to adapt to their ways of thinking, rather than holding onto an idealised version of humans that do not exist in the real world (Cairney and Kwiatkowski, 2017).
While these insights focus on policymakers, they are also essential to engaging with students. Gone – I hope – are the days of lecturers giving students an overwhelmingly huge reading list and expecting them to devour every source before each class. This approach may help some students but demoralise many others, especially since it seems inevitable that students’ first engagement with specialist texts and technical jargon will already induce fears about their own ignorance. Rather, we should base teaching on a thoughtful exploration of how much students can learn about the wider policy analysis context, focusing on (1) the knowledge and skills they already possess, (2) the time they have to learn, and (3) how new knowledge or skills would relate to their ambitions. For example, if students are seeking fast and frugal heuristics to learn about policy analysis, how can we help?
To help answer this question, I focus on what students should learn, can learn, and how blog posts and coursework can contribute to that learning. First, I describe the valuable intersection between policy analysis, policy process research, and critical policy analysis to demonstrate the potential payoffs to wider insights. In other words, what should policy analysis students learn from mainstream policy process research and critical policy analysis? Second, I describe the rationale for the blog that I developed in tandem with teaching public policy. I taught initially at an undergraduate level as part of a wider politics programme, before developing a Master of Public Policy and contributing to shorter executive courses and one-off workshops. This range of audiences matters, since the answer to the question ‘what can people learn?’ will vary according to their existing knowledge and time. Third, I summarise the rationale for the coursework that I use to encourage the application of public policy theories and knowledge to policy analysis (as part of a wider degree programme), including skills in critical thinking about policymaking dilemmas, to accompany more specialist research and analytical skills.
Could policy theories help to understand and facilitate the pursuit of equity (or reduction of unfair inequalities)?
We are producing a series of literature reviews to help answer that question, beginning with the study of equity policy and policymaking in health, education, and gender research.
Each field has a broadly similar focus. Most equity researchers challenge the ‘neoliberal’ approaches to policy that favour low state action in favour of individual responsibility and market forces. They seek ‘social justice’ approaches, favouring far greater state intervention to address the social and economic causes of unfair inequalities, via redistributive or regulatory measures. They seek policymaking reforms to reflect the fact that most determinants of inequalities are not contained to one policy sector and cannot be solved in policy ‘silos’. Rather, equity policy initiatives should be mainstreamed via collaboration across (and outside of) government. Each field also projects a profound sense of disenchantment with limited progress, including a tendency to describe a too-large gap between their aspirations and actual policy outcomes. They describe high certainty about what needs to happen, but low confidence that equity advocates have the means to achieve it (or to persuade powerful politicians to change course).
Policy theories could offer some practical insights for equity research, but not always offer the lessons that some advocates seek. In particular, health equity researchers seek to translate insights on policy processes into a playbook for action, such as to frame policy problems to generate more attention to inequalities, secure high-level commitment to radical change, and improve the coherence of cross-cutting policy measures. Yet, policy theories are more likely to identify the dominance of unhelpful policy frames, the rarity of radical change, and the strong rationale for uncoordinated policymaking across a large number of venues. Rather than fostering technical fixes with a playbook, they encourage more engagement with the inescapable dilemmas and trade-offs inherent to policy choice. This focus on contestation (such as when defining and addressing policy problems) is more of a feature of education and gender equity research.
While we ask what policy theories have to offer other disciplines, in fact the most useful lessons emerge from cross-disciplinary insights. They highlight two very different approaches to transformational political change. One offers the attractive but misleading option of radical change through non-radical action, by mainstreaming equity initiatives into current arrangements and using a toolbox to make continuous progress. Yet, each review highlights a tendency for radical aims to be co-opted and often used to bolster the rules and practices that protect the status quo. The other offers radical change through overtly political action, fostering continuous contestation to keep the issue high on the policy agenda and challenge co-option. There is no clear step-by-step playbook for this option, since political action in complex policymaking systems is necessarily uncertain and often unrewarding. Still, insights from policy theories and equity research shows that grappling with these challenges is inescapable.
Ultimately, we conclude that advocates of profound social transformation are wasting each other’s time if they seek short-cuts and technical fixes to enduring political problems. Supporters of policy equity should be cautious about any attempt to turn a transformational political project into a technical process containing a ‘toolbox’ or ‘playbook’.
You can read the original research in Policy & Politics:
Paul Cairney, Emily St.Denny, Sean Kippin, and Heather Mitchell (2022) ‘Lessons from policy theories for the pursuit of equity in health, education, and gender policy’, Policy and Politicshttps://doi.org/10.1332/030557321X16487239616498
By James Nicholls and Paul Cairney, for the University of Stirling MPH and MPP programmes.
There are strong links between the study of public health and public policy. For example, public health scholars often draw on policy theories to help explain (often low amounts of) policy change to foster population health or reduce health inequalities. Studies include a general focus on public health strategies (such as HiAP) or specific policy instruments (such as a ban on smoking in public places). While public health scholars may seek to evaluate or influence policy, policy theories tend to focus on explaining processes and outcomes.
To demonstrate these links, we present:
A long-read blog post to (a) use an initial description of a key alcohol policy instrument (minimum unit pricing, adopted by the Scottish Government but not the UK Government) to (b) describe the application of policy concepts and theories and reflect on the empirical and practical implications. We then added some examples of further reading.
A 45 minute podcast to describe and explain these developments (click below or scroll to the end)
Minimum Unit Pricing in Scotland: background and development
Minimum Unit Pricing for alcohol was introduced in Scotland in 2018. In 2012, the UK Government had also announced plans to introduce MUP, but within a year dopped the policy following intense industry pressure. What do these two journeys tell us about policy processes?
When MUP was first proposed by Scottish Health Action on Alcohol Problems in 2007, it was a novel policy idea. Public health advocates had long argued that raising the price of alcohol could help tackle harmful consumption. However, conventional tax increases were not always passed onto consumers, so would not necessarily raise prices in the shops (and the Scottish Government did not have such taxation powers). MUP appeared to present a neat solution to this problem. It quickly became a prominent policy goal of public health advocates in Scotland and across the UK, while gaining increasing attention, and support, from the global alcohol policy community.
In 2008, the UK Minister for Health, Dawn Primarolo, had commissioned researchers at the University of Sheffield to look into links between alcohol pricing and harm. The Sheffield team developed economic models to analysis the predicted impact of different systems. MUP was included, and the ‘Sheffield Model’ would go on to play a decisive role in developing the case for the policy.
What problem would MUP help to solve?
Descriptions of the policy problem often differed in relation to each government. In the mid-2000s, alcohol harm had become a political problem for the UK government. Increasing consumption, alongside changes to the night-time economy, had started to gain widespread media attention. In 2004, just as a major liberalisation of the licensing system was underway in England, news stories began documenting the apparent horrors of ‘Binge Britain’: focusing on public drunkenness and disorder, but also growing rates of liver disease and alcohol-related hospital admissions.
In 2004, influential papers such as the Daily Mail began to target New Labour alcohol policy
Politicians began to respond, and the issue became especially useful for the Conservatives who were developing a narrative that Britain was ‘broken’ under New Labour. Labour’s liberalising reforms of alcohol licensing could conveniently be linked to this political framing. The newly formed Alcohol Health Alliance, a coalition set up under the leadership of Professor Sir Ian Gilmore, was also putting pressure on the UK Government to introduce stricter controls. In Scotland, while much of the debate on alcohol focused on crime and disorder, Scottish advocates were focused on framing the problem as one of public health. Emerging evidence showed that Scotland had dramatically higher rates of alcohol-related illness and death than the rest of Europe – a situation strikingly captured in a chart published in the Lancet.
Source: Leon, D. and McCambridge, J. (2006). Liver cirrhosis mortality rates in Britain from 1950 to 2002: an analysis of routine data. Lancet 367
The notion that Scotland faced an especially acute public health problem with alcohol was supported by key figures in the increasingly powerful Scottish National Party (in government since 2007), which, around this time, had developed working relationships with Alcohol Focus Scotland and other advocacy groups.
What happened next?
The SNP first announced that it would support MUP in 2008, but it did not implement this change until 2018. There are two key reasons for the delay:
Its minority government did not achieve enough parliamentary support to pass legislation. It then formed a majority government in 2011, and its legislation to bring MUP into law was passed in 2012.
Court action took years to resolve. The alcohol industry, which is historically powerful in Scotland, was vehemently opposed. A coalition of industry bodies, led by the Scotch Whisky Association, took the Scottish Government to court in an attempt to prove the policy was illegal. Ultimately, this process would take years, and conclude in rulings by the European Court of Justice (2016), Scottish Court of Session Inner House (2016), and UK Supreme Court (2017) which found in favour of the Scottish Government.
Once again, the alcohol industry swung into action, launching a campaign led by the Wine and Spirits Trade Association, asking ‘Why should moderate drinkers pay more?’
This public campaign was accompanied by intense behind-the-scenes lobbying, aided by the fact that the leadership of industry groups had close ties to Government and that the All-Party Parliamentary Group on Beer had the largest membership of any APPG in Westminster. The industry campaign made much of the fact there was little evidence to suggest MUP would reduce crime, but also argued strongly that the modelling produced by Sheffield University was not valid evidence in the first place. A year after the adopting the policy, the UK Government announced a U-turn and MUP was dropped.
How can we use policy theories and concepts to interpret these dynamics?
Here are some examples of using policy theories and concepts as a lens to interpret these developments.
1. What was the impact of evidence in the case for policy change?
First, many political actors (including policymakers) have many different ideas about what counts as good evidence.
The assessment, promotion, and use of evidence is highly contested, and never speaks for itself.
Second, policymakers have to ignore almost all evidence to make choices.
They address ‘bounded rationality’ by using two cognitive shortcuts: ‘rational’ measures set goals and identify trusted sources, while ‘irrational’ measures use gut instinct, emotions, and firmly held beliefs.
Third, policymakers do not control the policy process.
There is no centralised and orderly policy cycle. Rather, policymaking involves policymakers and influencers spread across many authoritative ‘venues’, with each venue having its own rules, networks, and ways of thinking.
In that context, policy theories identify the importance of contestation between policy actors, and describe the development of policy problems, and how evidence fits in. Approaches include:
The acceptability of a policy solution will often depend on how the problem is described. Policymakers use evidence to reduce uncertainty, or a lack of information around problems and how to solve them. However, politics is about exercising power to reduce ambiguity, or the ability to interpret the same problem in different ways.
By suggesting MUP would solve problems around crime, the UK Government made it easier for opponents to claim the policy wasn’t evidence-based. In Scotland, policymakers and advocates focused on health, where the evidence was stronger. In addition, the SNP’s approach fitted within a wider political independence frame, in which more autonomy meant more innovation.
Policy actors tell stories to appeal to the beliefs (or exploit the cognitive shortcuts) of their audiences. A narrative contains a setting (the policy problem), characters (such as the villain who caused it, or the victim of its effects), plot (e.g. a heroic journey to solve the problem), and moral (e.g. the solution to the problem).
Supporters of MUP tended to tell the story that there was an urgent public health crisis, caused largely by the alcohol industry, and with many victims, but that higher alcohol prices pointed to one way out of this hole. Meanwhile opponents told the story of an overbearing ‘nanny state’, whose victims – ordinary, moderate drinkers – should be left alone by government.
Policymakers make strategic and emotional choices, to identify ‘good’ populations deserving of government help, and ‘bad’ populations deserving punishment or little help. These judgements inform policy design (government policies and practices) and provide positive or dispiriting signals to citizens.
For example, opponents of MUP rejected the idea that alcohol harms existed throughout the population. They focused instead on dividing the majority of moderate drinkers from irresponsible minority of binge drinkers, suggesting that MUP would harm the former more than help the latter.
This competition to frame policy problems takes place in political systems that contain many ‘centres’, or venues for authoritative choice. Some diffusion of power is by choice, such as to share responsibilities with devolved and local governments. Some is by necessity, since policymakers can only pay attention to a small proportion of their responsibilities, and delegate the rest to unelected actors such as civil servants and public bodies (who often rely on interest groups to process policy).
For example, ‘alcohol policy’ is really a collection of instruments made or influenced by many bodies, including (until Brexit) European organisations deciding on the legality of MUP, UK and Scottish governments, as well as local governments responsible for alcohol licensing. In Scotland, this delegation of powers worked in favour of MUP, since Alcohol Focus Scotland were funded by the Scottish Government to help deliver some of their alcohol policy goals, and giving them more privileged access than would otherwise have been the case.
The role of evidence in MUP
In the case of MUP, similar evidence was available and communicated to policymakers, but used and interpreted differently, in different centres, by the politicians who favoured or opposed MUP.
In Scotland, the promotion, use of, and receptivity to research evidence – on the size of the problem and potential benefit of a new solution – played a key role in increasing political momentum. The forms of evidence were complimentary. The ‘hard’ science on a potentially effective solution seemed authoritative (although few understood the details), and was preceded by easily communicated and digested evidence on a concrete problem:
There was compelling evidence of a public health problem put forward by a well-organised ‘advocacy coalition’ (see below) which focused clearly on health harms. In government, there was strong attention to this evidence, such as the Lancet chart which one civil servant described as ‘look[ing] like the north face of the Eiger’. There were also influential ‘champions’ in Government willing to frame action as supporting the national wellbeing.
Reports from Sheffield University appeared to provide robust evidence that MUP could reduce harm, and advocacy was supported by research from Canada which suggested that similar policies there had been successful elsewhere.
Advocacy in England was also well-organised and influential, but was dealing with a larger – and less supportive – Government machine, and the dominant political frame for alcohol harms remained crime and disorder rather than health.
Debates on MUP modelling exemplify these differences in evidence communication and use. Those in favour appealed to econometric models, but sometimes simplifying their complexity and blurring the distinction between projected outcomes and proof of efficacy. Opponents went the other way and dismissed the modelling as mere speculation. What is striking is the extent to which an incredibly complex, and often poorly understand, set of econometric models – and the ’Sheffield Model’ in particular – came to occupy centre stage in a national policy debate. Katikireddi and colleagues talked about this as an example of evidence as rhetoric:
Support became less about engagement with the econometric modelling, and more an indicator of general concern about alcohol harm and the power of the industry.
Scepticism was often viewed as the ‘industry position’, and an indicator of scepticism towards public health policy more broadly.
2. Who influences policy change?
Advocacy plays a key role in alcohol policy, with industry and other actors competing with public health groups to define and solve alcohol policy problems. It prompts our attention to policy networks, or the actors who make and influence policy.
People engage in politics to turn their beliefs into policy. They form advocacy coalitions with people who share their beliefs, and compete with other coalitions. The action takes place within a subsystem devoted to a policy issue, and a wider policymaking process that provides constraints and opportunities to coalitions. Beliefs about how to interpret policy problems act as a glue to bind actors together within coalitions. If the policy issue is technical and humdrum, there may be room for routine cooperation. If the issue is highly charged, then people romanticise their own cause and demonise their opponents.
MUP became a highly charged focus of contestation between a coalition of public health advocates, who saw themselves as fighting for the wellbeing of the wider community (and who believed fundamentally that government had a duty to promote population health), and a coalition of industry actors who were defending their commercial interests, while depicting public health policies as illiberal and unfair.
3. Was there a ‘window of opportunity’ for MUP?
Policy theories – including Punctuated Equilibrium Theory – describe a tendency for policy change to be minor in most cases and major in few. Paradigmatic policy change is rare and may take place over decades, as in the case of UK tobacco control where many different policy instruments changed from the 1980s. Therefore, a major change in one instrument could represent a sea-change overall or a modest adjustment to the overall approach.
Multiple Streams Analysis is a popular way to describe the adoption of a new policy solution such as MUP. It describes disorderly policymaking, in which attention to a policy problem does not produce the inevitable development, implementation, and evaluation of solutions. Rather, these ‘stages’ should be seen as separate ‘streams’. A ‘window of opportunity’ for policy change occurs when the three ‘streams’ come together:
Problem stream. There is high attention to one way to define a policy problem.
Policy stream. A technically and politically feasible solution already exists (and is often pushed by a ‘policy entrepreneur’ with the resources and networks to exploit opportunities).
Politics stream. Policymakers have the motive and opportunity to choose that solution.
However, these windows open and close, often quickly, and often without producing policy change.
This approach can help to interpret different developments in relation to Scottish and UK governments:
Problem stream
The Scottish Government paid high attention to public health crises, including the role of high alcohol consumption.
The UK government paid often-high attention to alcohol’s role in crime and anti-social behaviour (‘Binge Britain’ and ‘Broken Britain’)
Policy stream
In Scotland, MUP connected strongly to the dominant framing, offering a technically feasible solution that became politically feasible in 2011.
The UK Prime Minister David Cameron’s made a surprising bid to adopt MUP in 2012, but ministers were divided on its technical feasibility (to address the problem they described) and its political feasibility seemed to be more about distracting from other crises than public health.
Politics stream
The Scottish Government was highly motivated to adopt MUP. MUP was a flagship policy for the SNP; an opportunity to prove its independent credentials, and to be seen to address a national public health problem. It had the opportunity from 2011, then faced interest group opposition that delayed implementation.
The Coalition Government was ideologically more committed to defending commercial interests, and to framing alcohol harms as one of individual (rather than corporate) responsibility. It took less than a year for the alcohol industry to successfully push for a UK government U-turn.
As a result, MUP became policy (eventually) in Scotland, but the window closed (without resolution) in England.
Paul Cairney and Donley Studlar (2014) ‘Public Health Policy in the United Kingdom: After the War on Tobacco, Is a War on Alcohol Brewing?’ World Medical and Health Policy, 6, 3, 308-323PDF
Niamh Fitzgerald and Paul Cairney (2022) ‘National objectives, local policymaking: public health efforts to translate national legislation into local policy in Scottish alcohol licensing’, Evidence and Policy, https://doi.org/10.1332/174426421X16397418342227, PDF
Podcast
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By James Nicholls and Paul Cairney, for the University of Stirling MPH and MPP programmes.
There are strong links between the study of public health and public policy. For example, public health scholars often draw on policy theories to help explain (often low amounts of) policy change to foster population health or reduce health inequalities. Studies include a general focus on public health strategies (such as HiAP) or specific policy instruments (such as a ban on smoking in public places). While public health scholars may seek to evaluate or influence policy, policy theories tend to focus on explaining processes and outcomes,.
To demonstrate these links, we present this podcast and blog post to (1) use an initial description of a key alcohol policy instrument (minimum unit pricing in Scotland) to (2) describe the application of policy concepts and theories and reflect on the empirical and practical implications.
Using policy theories to interpret public health case studies: the example of a minimum unit price for alcohol | Paul Cairney: Politics & Public Policy (wordpress.com)
I apologise for every word in this post, and the capitalised 5-letter words in particular.
WORDLE is a SIMPLE word game (in US English). The aim is to identify a 5-letter word correctly in 6 guesses or fewer. Each guess has to be a real word, and you receive informative feedback each time: GREEN means you have the letter RIGHT and in the right position; yellow means the right letter in the wrong position; grey MEANS the letter does not appear in the word.
One strategy involves trial-and-error learning via 3 or 4 simple steps:
1. Use your initial knowledge of the English language to inform initial guesses, such as guessing a word with common vowels (I go for E and A) and consonants (e.g. S, T).
2. Learn from feedback on your correct and incorrect estimates.
3. Use your new information and deduction (e.g. about which combinations work when you exclude many options) to make informed guesses.
4. Do so while avoiding unhelpful heuristics, such as assuming that each letter will only appear once (or that the spelling is in UK English).
At least, that is how I play it. I get it in 3 just over half the time, and 4 or 5 in the rest. I make 2-4 ‘errors’ then succeed. In the context of the game’s rules, that is consistent success, RIGHT?
[insert crowbar GIF to try to get away with the segue]
That is the spirit of the idea of trial-and-error learning.
It is informed by previous knowledge, but also a recognition of the benefits of trying things out to generate new information, update your knowledge and skills (the definition of learning), and try again.
A positive normative account of this approach can be found in classic discussions of incrementalism and modern discussions of policymaking informed by complex systems insights:
‘To deal with uncertainty and change, encourage trial-and-error projects, or pilots, that can provide lessons, or be adopted or rejected, relatively quickly’.
Advocates of such approaches also suggest that we change how we describe them, replacing the language of policy failure with ERROR, at least when part of a process of continuous policy learning in the face of uncertainty.
At the heart of such advice are two guiding principles:
1. Recognise the limits to centralism when giving policy advice. There is no powerful centre of government, able to carry out all of its aims successfully, so do not build policy advice on that assumption.
2. Recognise the limits to our knowledge. Policymakers must make and learn from choices in the face of uncertainty, so do not kid yourself that one piece of analysis and action will do.
Much like the first two WORDLE guesses, your existing knowledge alone does not tell you how to proceed (regardless of the number of times that people repeat the slogan of ‘evidence-based policymaking’).
Political problems with trial and error
The main political problem with this approach is that many political systems – including adversarial and/or Westminster systems – are not conducive to learning from error. You may think that adapting continuously to uncertainty is crucial, but also be wary of recommending it to:
1. Politicians who will be held to account for failure. A government’s apparent failure to deliver on promises represents a resource for its opposition.
2. Organisations subject to government targets. Failure to meet strict statutory requirements is not seen as a learning experience.
More generally, your audience may face criticism whenever errors are associated with negative policy consequences (with COVID-19 policy representing a vivid, extreme example).
These limitations produce a major dilemma in policy analysis, in which you believe that you will not learn how to make good policy without trial-and-error but recognise that this approach will not be politically feasible. In many political systems, policymakers need to pretend to their audience that they know what the problem is and that they have the knowledge and power to solve it. You may not be too popular if you encourage open-minded experimentation. This limitation should not warn you against trial-and-error recommendations completely, but rather remind you to relate good-looking ideas to your policymaking context.
Please note that I missed my train stop while writing this post, despite many opportunities to learn from the other times it happened.
Policy studies and policy analysis guidebooks identify the importance of feasible policy solutions:
Technical feasibility: will this solution work as intended if implemented?
Political feasibility: will it be acceptable to enough powerful people?
For example, Kingdon treats feasibility as one of three conditions for major policy change during a ‘window of opportunity’: (1) there is high attention to the policy problem, (2) a feasible solution already exists, and (3) key policymakers have the motive and opportunity to select it.
Guidebooks relate this requirement initially to your policymaker client: what solutions will they rule out, to the extent that they are not even worth researching as options (at least for the short term)?
Further, this assessment relates to types of policy ‘tool’ or ‘instrument’: one simple calculation is that ‘redistributive’ measures are harder to sell than ‘distributive’, while both may be less attractive than regulation (although complex problems likely require a mix of instruments).
Incremental analysis. It is better to research in-depth a small number of feasible options than spread your resources too thinly to consider all possibilities.
Strategic analysis. The feasibility of a solution relates strongly to current policy. The more radical a departure from the current negotiated position, the harder it will be to sell.
As many posts in the Policy Analysis in 750 words series describe, this advice is not entirely useful for actors who seek rapid and radical departures from the status quo. Lindblom’s response to such critics was to seek radical change via a series of non-radical steps (at least in political systems like the US), which (broadly speaking) represents one of two possible approaches.
While incrementalism is not as popular as it once was (as a description of, or prescription for, policymaking), it tapped into the enduring insight that policymaking systems produce huge amounts of minor change. Rapid and radical policy change is rare, and it is even rarer to be able to connect it to influential analysis and action (at least in the absence of a major event). This knowledge should not put people off trying, but rather help them understand the obstacles that they seek to overcome.
Relating feasible solutions and strategies to ‘policy success’
One way to incorporate this kind of advice is to consider how (especially elected) policymakers would describe their own policy success. The determination of success and failure is a highly contested and political process (not simply a technical exercise called ‘evaluation’), and policymakers may refer – often implicitly – to the following questions when seeking success:
Political. Will this policy boost my government’s credibility and chances of re-election?
Process. Will it be straightforward to legitimise and maintain support for this policy?
Programmatic. Will it achieve its stated objectives and produce beneficial outcomes if implemented?
The benefit to analysts, in asking themselves these questions, is that they help to identify the potential solutions that are technically but not politically feasible (or vice versa).
The absence of clear technical feasibility does not necessarily rule out solutions with wider political benefits (for example, it can be beneficial to look like you are trying to do something good). Hence the popular phrase ‘good politics, bad policy’.
Nor does a politically unattractive option rule out a technically feasible solution (not all politicians flee the prospect of ‘good policy, bad politics’). However, it should prompt attention to hard choices about whose support to seek, how long to wait, or how hard to push, to seek policy change. You can see this kind of thinking as ‘entrepreneurial‘ or ‘systems thinking’ depending on how much faith you have in agency in highly-unequal political contexts.
Further reading
It is tempting to conclude that these obstacles to ‘good policy’ reflect the pathological nature of politics. However, if we want to make this argument, we should at least do it well:
1. You can find this kind of argument in fields such as public health and climate change studies, where researchers bemoan the gap between (a) their high-quality evidence on an urgent problem and (b) a disproportionately weak governmental response. To do it well, we need to separate analytically (or at least think about): (a) the motivation and energy of politicians (usually the source of most criticism of low ‘political will’), and (b) the policymaking systems that constrain even the most sincere and energetic policymakers. See the EBPM page for more.
2. Studies of Social Construction and Policy Design are useful to connect policymaking research with a normative agenda to address ‘degenerative’ policy design.
How should policy actors seek radical changes to policy and policymaking?
This question prompts two types of answer:
1. Be pragmatic, and change things from the inside
Pragmatism is at the heart of most of the policy analysis texts in this series. They focus on the needs and beliefs of clients (usually policymakers). Policymakers are time-pressed, so keep your analysis short and relevant. See the world through their eyes. Focus on solutions that are politically as well as technically feasible. Propose non-radical steps, which may add up to radical change over the long-term.
This approach will seem familiar to students of research ‘impact’ strategies which emphasise relationship-building, being available to policymakers, and responding to the agendas of governments to maximise the size of your interested audience.
It will also ring bells for advocates of radical reforms in policy sectors such as (public) health and intersectoral initiatives such as gender mainstreaming:
Health in All Policies is a strategy to encourage radical changes to policy and policymaking to improve population health. Common advice includes to: identify to policymakers how HiAP fits into current policy agendas, seek win-win strategies with partners in other sectors, and go to great lengths to avoid the sense that you are interfering in their work (‘health imperialism’).
Gender mainstreaming is a strategy to consider gender in all aspect of policy and policymaking. An equivalent playbook involves steps to: clarify what gender equality is, and what steps may help achieve it; make sure that these ideas translate across all levels and types of policymaking; adopt tools to ensure that gender is a part of routine government business (such as budget processes); and, modify existing policies or procedures while increasing the representation of women in powerful positions.
In other words, the first approach is to pursue your radical agenda via non-radical means, using a playbook that is explicitly non-confrontational. Use your insider status to exploit opportunities for policy change.
2. Be radical, and challenge things from the outside
Challenging the status quo, for the benefit of marginalised groups, is at the heart of critical policy analysis:
Reject the idea that policy analysis is a rationalist, technical, or evidence-based process. Rather, it involves the exercise of power to (a) depoliticise problems to reduce attention to current solutions, and (b) decide whose knowledge counts.
Identify and question the dominant social constructions of problems and populations, asking who decides how to portray these stories and who benefits from their outcomes.
This approach resonates with frequent criticisms of ‘impact’ advice, emphasising the importance of producing research independent of government interference, to challenge policies that further harm already-marginalised populations.
It will also rings bells among advocates of more confrontational strategies to seek radical changes to policy and policymaking. They include steps to: find more inclusive ways to generate and share knowledge, produce multiple perspectives on policy problems and potential solutions, focus explicitly on the impact of the status quo on marginalised populations, politicise issues continuously to ensure that they receive sufficient attention, and engage in outsider strategies to protest current policies and practices.
Does this dichotomy make sense?
It is tempting to say that this dichotomy is artificial and that we can pursue the best of both worlds, such as working from within when it works and resorting to outsider action and protest when it doesn’t.
However, the blandest versions of this conclusion tend to ignore or downplay the politics of policy analysis in favour of more technical fixes. Sometimes collaboration and consensus politics is a wonderful feat of human endeavour. Sometimes it is a cynical way to depoliticise issues, stifle debate, and marginalise unpopular positions.
This conclusion also suggests that it is possible to establish what strategies work, and when, without really saying how (or providing evidence for success that would appeal to audiences associated with both approaches). Indeed, a recurrent feature of research in these fields is that most attempts to produce radical change prove to be dispiriting struggles. Non-radical strategies tend to be co-opted by more powerful actors, to mainstream new ways of thinking without changing the old. Radical strategies are often too easy to dismiss or counter.
The latter point reminds us to avoid excessively optimistic overemphasis on the strategies of analysts and advocates at the expense of context and audience. The 500 and 1000 words series perhaps tip us too far in the other direction, but provide a useful way to separate (analytically) the reasons for often-minimal policy change. To challenge dominant forms of policy and policymaking requires us to separate the intentional sources of inertia from the systemic issues that would constrain even the most sincere and energetic reformer.
This post forms one part of the Policy Analysis in 750 words series. It draws on work for an in-progress book on learning to reduce inequalities. Some of the text will seem familiar if you have read other posts. Think of it as an adventure game in which the beginning is the same but you don’t know the end.
Policy learning is the use of new information to update policy-relevant knowledge. Policy transfer involves the use of knowledge about policy and policymaking in one government to inform policy and policymaking in another.
These processes may seem to relate primarily to research and expertise, but they require many kinds of political choices (explored in this series). They take place in complex policymaking systems over which no single government has full knowledge or control.
Therefore, while the agency of policy analysts and policymakers still matters, they engage with a policymaking context that constrains or facilitates their action.
Two approaches to policy learning: agency and context-driven stories
Policy analysis textbooks focus on learning and transfer as an agent-driven process with well-established guidance (often with five main steps). They form part of a functionalist analysis where analysts identify the steps required to turn comparative analysis into policy solutions, or part of a toolkit to manage stages of the policy process.
Analysts compete to define problems and determine the manner and sources of learning, in a multi-centric environment where different contexts will constrain and facilitate action in different ways. For example, varying structural factors – such as socioeconomic conditions – influence the feasibility of proposed policy change, and each centre’s institutions provide different rules for gathering, interpreting, and using evidence.
Think of two different ways to respond to this description of the policy process with this lovely blue summary of concepts. One is your agency-centred strategic response. The other is me telling you why it won’t be straightforward.
There are many policy makers and influencers spread across many policymaking ‘centres’
Find out where the action is and tailor your analysis to different audiences.
There is no straightforward way to influence policymaking if multiple venues contribute to policy change and you don’t know who does what.
Each centre has its own ‘institutions’
Learn the rules of evidence gathering in each centre: who takes the lead, how do they understand the problem, and how do they use evidence?
There is no straightforward way to foster policy learning between political systems if each is unaware of each other’s unwritten rules. Researchers could try to learn their rules to facilitate mutual learning, but with no guarantee of success.
Each centre has its own networks
Form alliances with policymakers and influencers in each relevant venue.
The pervasiveness of policy communities complicates policy learning because the boundary between formal power and informal influence is not clear.
Well-established ‘ideas’ tend to dominate discussion
Learn which ideas are in good currency. Tailor your advice to your audience’s beliefs.
The dominance of different ideas precludes many forms of policy learning or transfer. A popular solution in one context may be unthinkable in another.
Many policy conditions (historic-geographic, technological, social and economic factors) command the attention of policymakers and are out of their control. Routine events and non-routine crises prompt policymaker attention to lurch unpredictably.
Learn from studies of leadership in complex systems or the policy entrepreneurs who find the right time to exploit events and windows of opportunity to propose solutions.
The policy conditions may be so different in each system that policy learning is limited and transfer would be inappropriate. Events can prompt policymakers to pay disproportionately low or high attention to lessons from elsewhere, and this attention relates weakly to evidence from analysts.
Feel free to choose one or both forms of advice. One is useful for people who see analysts and researchers as essential to major policy change. The other is useful if it serves as a source of cautionary tales rather than fatalistic responses.
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.
Entrepreneurial policy analysis warns against a too-strong focus on the agency – rather than the unequal status and resources – of successful political actors.
A key argument in policy studies is that it is impossible to separate facts and values when making policy. We often treat our beliefs as facts, or describe certain facts as objective, but perhaps only to simplify our lives or support a political strategy (a ‘self-evident’ fact is very handy for an argument). People make empirical claims infused with their values and often fail to realise just how their values or assumptions underpin their claims.
This is not an easy argument to explain. One strategy is to use extreme examples to make the point. For example, Herbert Simon points to Hitler’s Mein Kampf as the ultimate example of value-based claims masquerading as facts. We can also identify historic academic research which asserts that men are more intelligent than women and some races are superior to others. In such cases, we would point out, for example, that the design of the research helped produce such conclusions: our values underpin our (a) assumptions about how to measure intelligence or other measures of superiority, and (b) interpretations of the results.
‘Wait a minute, though’ (you might say). “What about simple examples in which you can state facts with relative certainty – such as the statement ‘there are X number of words in this post’”. ‘Fair enough’, I’d say (you will have to speak with a philosopher to get a better debate about the meaning of your X words claim; I would simply say that it is trivially true). But this statement doesn’t take you far in policy terms. Instead, you’d want to say that there are too many or too few words, before you decided what to do about it.
In that sense, we have the most practical explanation of the unclear fact/ value distinction: the use of facts in policy is to underpin evaluations (assessments based on values). For example, we might point to the routine uses of data to argue that a public service is in ‘crisis’ or that there is a public health related epidemic (note: I wrote the post before COVID-19; it referred to crises of ‘non-communicable diseases’). We might argue that people only talk about ‘policy problems’ when they think we have a duty to solve them.
Or, facts and values often seem the hardest to separate when we evaluate the success and failure of policy solutions, since the measures used for evaluation are as political as any other part of the policy process. The gathering and presentation of facts is inherently a political exercise, and our use of facts to encourage a policy response is inseparable from our beliefs about how the world should work.
‘Modern science remains value-laden … even when so many people employ so many systematic methods to increase the replicability of research and reduce the reliance of evidence on individual scientists. The role of values is fundamental. Anyone engaging in research uses professional and personal values and beliefs to decide which research methods are the best; generate research questions, concepts and measures; evaluate the impact and policy relevance of the results; decide which issues are important problems; and assess the relative weight of ‘the evidence’ on policy effectiveness. We cannot simply focus on ‘what works’ to solve a problem without considering how we used our values to identify a problem in the first place. It is also impossible in practice to separate two choices: (1) how to gather the best evidence and (2) whether to centralize or localize policymaking. Most importantly, the assertion that ‘my knowledge claim is superior to yours’ symbolizes one of the most worrying exercises of power. We may decide to favour some forms of evidence over others, but the choice is value-laden and political rather than objective and innocuous’.
Implications for policy analysis
Many highly-intelligent and otherwise-sensible people seem to get very bothered with this kind of argument. For example, it gets in the way of (a) simplistic stories of heroic-objective-fact-based-scientists speaking truth to villainous-stupid-corrupt-emotional-politicians, (b) the ill-considered political slogan that you can’t argue with facts (or ‘science’), (c) the notion that some people draw on facts while others only follow their feelings, and (d) the idea that you can divide populations into super-facty versus post-truthy people.
A more sensible approach is to (1) recognise that all people combine cognition and emotion when assessing information, (2) treat politics and political systems as valuable and essential processes (rather than obstacles to technocratic policymaking), and (3) find ways to communicate evidence-informed analyses in that context. This article and 750 post explore how to reflect on this kind of communication.
This post forms one part of the Policy Analysis in 750 words series overview. The title comes from this article by Cairney and Kwiatkowski on ‘psychology based policy studies’.
One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts. How might we combine insights to think about effective communication?
1. Insights from policy analysis texts
Most texts in this series relate communication to understanding your audience (or client) and the political context. Your audience has limited attention or time to consider problems. They may have a good antennae for the political feasibility of any solution, but less knowledge of (or interest in) the technical details. In that context, your aim is to help them treat the problem as worthy of their energy (e.g. as urgent and important) and the solution as doable. Examples include:
Bardach: communicating with a client requires coherence, clarity, brevity, and minimal jargon.
Dunn: argumentation involves defining the size and urgency of a problem, assessing the claims made for each solution, synthesising information from many sources into a concise and coherent summary, and tailoring reports to your audience.
Smith: your audience makes a quick judgement on whether or not to read your analysis. Ask yourself questions including: how do I frame the problem to make it relevant, what should my audience learn, and how does each solution relate to what has been done before? Maximise interest by keeping communication concise, polite, and tailored to a policymaker’s values and interests.
2. Insights from studies of policymaker psychology
‘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.
Policy process texts focus on policymaking reality: showing that ideal-types such as the policy cycle do not guide real-world action, and describing more accurate ways to guide policy analysts.
For example, they help us rethink the ‘know your audience’ mantra by:
Showing that many policymaking ‘centres’ create the instruments that produce policy change
Gone are the mythical days of a small number of analysts communicating to a single core executive (and of the heroic researcher changing the world by speaking truth to power). Instead, we have many analysts engaging with many centres, creating a need to not only (a) tailor arguments to different audiences, but also (b) develop wider analytical skills (such as to foster collaboration and the use of ‘design principles’).
How to communicate effectively with policymakers
In that context, we argue that effective communication requires analysts to:
1. Understand your audience and tailor your response (using insights from psychology)
2. Identify ‘windows of opportunity’ for influence (while noting that these windows are outside of anyone’s control)
3. Engage with real world policymaking rather than waiting for a ‘rational’ and orderly process to appear (using insights from policy studies).
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.
Some notes for my guest appearance on @urbaneprofessor ‘s module
Peter’s description
Paul comes from a Political Science background and started off his project trying to understand why politicians don’t make good policy. He uses a lot of Political Science theory to understand the policy process (what MPP students have been learning) and theory from Public Policy about how to make the policy process better.
I come from a Social Policy background. I presume policy will be bad, and approach policy analysis from a normative position, analysing and criticising it from theoretical and critical perspectives.
Paul’s description
I specialize in the study of public policy and policymaking. I ‘synthesise’ and use policy concepts and theories to ask: how do policy processes work, and why?
Most theories and concepts – summarized in 1000 and 500 words – engage with that question in some way.
As such, I primarily seek to describe and explain policymaking, without spending much time thinking about making it better (unless asked to do so, or unless I feel very energetic).
In particular, I can give you a decent account of how all of these policy theories relate to each other, which is more important that it first seems.
Individual policymakers can only pay attention to and understand a tiny proportion of (a) available information (b) the policy problems of which they are ostensibly responsible
So, they find cognitive shortcuts to pay attention to some issues/ information and ignore the rest (goal setting, relying on trusted advisors, belief translation, gut instinct, etc.)
Governmental organisations have more capacity, but also develop ‘standard operating procedures’ to limit their attention, and rely on many other actors for information and advice
Complex Policymaking Environments consisting of:
Many actors in many venues
Institutions (formal and informal rules)
Networks (relationships between policymakers and influencers)
Ideas (dominant beliefs, influencing the interpretation of problems and solutions)
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.
Trust is essential during a crisis. It is necessary for cooperation. Cooperation helps people coordinate action, to reduce the need for imposition. It helps reduce uncertainty in a complex world. It facilitates social order and cohesiveness. In a crisis, almost-instant choices about who to trust or distrust make a difference between life and death.
Put simply, we need to trust: experts to help us understand and address the problem, governments to coordinate policy and make choices about levels of coercion, and each other to cooperate to minimise infection.
Yet, there are three unresolved problems with understanding trust in relation to coronavirus policy.
What does trust really mean?
Trust is one of those words that could mean everything and nothing. We feel like we understand it intuitively, but would also struggle to define it well enough to explain how exactly it works. For example, in social science, there is some agreement on the need to describe individual motivation, social relationships, and some notion of the ‘public good’:
the production of trust helps boost the possibility of cooperation, partly by
reducing uncertainty (low information about a problem) and ambiguity (low agreement on how to understand it) when making choices, partly by
helping you manage the risk of making yourself vulnerable when relying on others, particularly when
people demonstrate trustworthiness by developing a reputation for competence, honesty, and/ or reliability, and
you combine cognition and emotion to produce a disposition to trust, and
social and political rules facilitate this process, from the formal and well-understood rules governing behaviour to the informal rules and norms shaping behaviour.
As such, trust describes your non-trivial belief in the reliability of other people, organisations, or processes. It facilitates the kinds of behaviour that are essential to an effective response to the coronavirus, in which we need to:
Make judgements about the accuracy of information underpinning our choices to change behaviour (such as from scientific agencies).
Assess the credibility of the people with whom we choose to cooperate or take advice (such as more or less trust in each country’s leadership).
Measure the effectiveness of the governments or political systems to which we pledge our loyalty.
Crucially, in most cases, people need to put their trust in actions or outcomes caused by people they do not know, and the explanation for this kind of trust is very different to trusting people you know.
What does trust look like in policymaking?
Think of trust as a mechanism to boost cooperation and coalition formation, help reduce uncertainty, and minimise the ‘transactions costs’ of cooperation (for example, monitoring behaviour, or producing or enforcing contracts). However, uncertainty is remarkably high because the policy process is not easy to understand. We can try to understand the ‘mechanisms’ of trust, to boost cooperation, with reference to these statements about trustees and the trusted:
Individuals need to find ways to make choices about who to trust and distrust.
However, they must act within a complex policymaking environment in which they have minimal knowledge of what will happen and who will make it happen.
To respond effectively, people seek ways to cooperate with others systematically, such as by establishing formal and informal rules.
People seeking to make and influence policy must act despite uncertainty about the probability of success or risk of failure. In a crisis, it happens almost instantly. People generate beliefs about what they want to happen and how their reliance on others can help it happen. This calculation depends on:
Another person or organisation’s reputation for being trustworthy, allowing people the ability to increase certainty when they calculate the risk of engagement.
The psychology of trust and perceptions of another actor’s motives. To some extent, people gather information and use logic to determine someone’s competence. However, they also use gut feeling or emotion to help them decide to depend on someone else. They may also trust a particular source if the cognitive load is low, such as because (a) the source is familiar (e.g. a well-known politician or a celebrity, or oft-used source), or (b) the information is not challenging to remember or accept.
If so, facilitators of trust include:
People share the same characteristics, such as beliefs, norms, or expectations.
Some people have reputations for being reliable, predictable, honest, competent, and/ or relatively selfless.
Good experiences of previous behaviour, including repeated interactions that foster rewards and help predict future risk (with face to face contact often described as particularly helpful).
People may trust people in a position of authority (or the organisation or office), such as an expert or policymaker (although perhaps the threat of rule enforcement is better understood as a substitute for trust, and in practice it is difficult to spot the difference).
High levels of trust are apparent when effective practices – built on reciprocity, emotional bonds, and/ or positive expectations – become the norms or formalised and written down for all to see and agree. High levels of distrust indicate a need to deter the breach of agreements, by introducing expectations combined with sanctions for not behaving as expected.
Who should you trust?
These concepts do not explain fully why people trust particular people more than others, or help us determine who you should trust during a crisis.
Rather, first, they help us reflect on the ways in which people have been describing their own thought processes (click here, and scroll to ‘Limiting the use of evidence’), such as trusting an expert source because they: (a) have a particular scientific background, (b) have proven to be honest and reliable in the past, (c) represent a wider scientific profession/ community, (d) are part of a systematic policymaking machinery, (e) can be held to account for their actions, (f) are open about the limits to their knowledge, and/or (g) engage critically with information to challenge simplistic rushes to judgement. Overall, note how much trust relates to our minimal knowledge about their research skills, prompting us to rely on an assessment of their character or status to judge their behaviour. In most cases, this is an informal process in which people may not state (or really know) why they trust or distrust someone so readily.
Then, we can reflect on who we trust, and why, and if we should change how we make such calculations during a crisis like the coronavirus. Examples include:
A strong identity with a left or right wing cause might prompt us only to trust people from one political party. This thought process may be efficient during elections and debates, but does it work so well during a crisis necessitating so high levels of cross-party cooperation?
People may be inclined to ignore advice because they do not trust their government, but maybe (a) high empathy for their vulnerable neighbours, and (b) low certainty about the impact of their actions, should prompt them to trust in government advice unless they have a tangible reason not to (while low empathy helps explain actions such as hoarding).
Government policy is based strongly on the extent to which policymakers trust people to do the right thing. Most debates in liberal democracies relate to the idea that (a) people can be trusted, so give advice and keep action voluntary, or cannot be trusted, so make them do the right thing, and that (b) citizens can trust their government. In other words, it must be a reciprocal relationship (see the Tweets in Step 3).
Finally, governments make policy based on limited knowledge and minimal control of the outcomes, and they often respond with trial-and-error strategies. The latter is fine if attention to policy is low and trust in government sufficiently high. However, in countries like the UK and US, each new choice prompts many people to question not only the competence of leaders but also their motivation. This is a worrying development for which everyone should take some responsibility.
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).
Further reading:
The article (PDF) sets out these arguments in much more detail, with some links to further thoughts and developments.
This series of ‘750 words’ posts summarises key texts in policy analysis and tries to situate policy analysis in a wider political and policymaking context. Note the focus on whose knowledge counts, which is not yet a big feature of this crisis.
These series of 500 words and 1000 words posts (with podcasts) summarise concepts and theories in policy studies.
This 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 answer is: often too much for a government to consider (in a liberal democracy), particularly if policymakers are informed that it will not have the desired impact.
A couple of key takeaways from our analysis of early COVID-19 dynamics in Wuhan:
1. We estimated that the control measures introduced – unprecedented interventions that will have had a huge social and psychological toll – reduced transmission by around 55% in space of 2 weeks 1/
If so, the UK government’s definition of the policy problem will incorporate this implicit question: what can we do if we can influence, but not determine (or even predict well) how people behave?
Uncertainty about the coronavirus plus uncertainty about policy impact
Now, add that general uncertainty about the impact of government to this specific uncertainty about the likely nature and spread of the coronavirus:
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
A lot of the spread will happen inside homes, where the role of government is minimal (compared to public places). So, for example, the impact of school closures could be good (isolation) or make things worse (children spreading the virus to vulnerable relatives) (see also ‘we don’t know [if the UKG decision not to close schools] was brilliant or catastrophic’). [Update 18.3.20: as it turned out, the First Minister’s argument for closing Scottish schools was that there were too few teachers available).
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) …
Yesterday we entered the Delay phase of our #COVID_19uk Action Plan. @UKScienceChief explained why this is important.
It allows us to #FlattenTheCurve, which means reducing the impact in the short-term to ensure our health & care system can effectively protect vulnerable people pic.twitter.com/1I45C3v38V
— Department of Health and Social Care (@DHSCgovuk) March 13, 2020
… while the UK Government’s Deputy Chief Medical Officer also seems to be describing (b):
Dr Jenny Harries, Deputy Chief Medical Officer, came into Downing Street to answer some of the most commonly asked questions on coronavirus. pic.twitter.com/KCdeHsaz6a
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’).
Why politicians fear being accused of over reaction. Which in turn might prevent them from reacting appropriately when a real crisis hits 👇🏽👇🏽 https://t.co/UrxHTAs2z5
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
“The epidemic has had a huge impact on domestic violence,” said Wan. “According to our statistics, 90% of the causes of violence are related to the COVID-19 epidemic.” https://t.co/xswemtf548
I just asked a DC cop what he’s noticed since the coronavirus sent people home. “More domestic violence,” he said, without missing a beat. https://t.co/kv9zH5VNj1
While black people make up about 12% of Michigan’s population, they make up about 40% of all COVID-19 deaths reported.
A social epidemiologist says the numbers don’t say everything, but there's something that can’t be ignored: inequality. @MichiganRadiohttps://t.co/bWsqFaCrUJ
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
#Coronavirus is not the 'great equalizer'—race matters:
“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
— Dr. Malinda S. Smith (@MalindaSmith) April 6, 2020
BAME lives matter, so far they account for:
– 100% of Dr deaths – 50% nurse deaths – 35% of Patients in ICU
Yet account for only 14% of population and account for 44% of NHS staff. Who is asking the questions, why the disparity?https://t.co/VOL8FAmy45
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
English obviously, but fascinating that have issued an explicitly ethical framework for COVID decisions re mental health and incapacity. Can Scotland do same? https://t.co/WccPntZOwf
WOW – government has relaxed restrictions on WHERE abortions can take place, temporary inclusion of 'the home' as a legal site for abortion: https://t.co/Vw714fWXEM
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
NEW Editorial: While responding to #COVID19, policy makers should consider the risk of deepening health inequalities. If vulnerable groups are not properly identified, the consequences of this pandemic will be even more devastating https://t.co/BrypuXH6vSpic.twitter.com/hka3nLzxdv
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?
So…. Govt income protection package includes….. 1. 80% of wage costs up to £2500 2. Deferred VAT. 3. £7 billion uplift to Universal Credit and Woring Tax crdit. 4. £1 billion to cover 30% of house rental costs. 5. Self employed to get same as sickness benefit payments.
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).
The Imperial College COVID report is being discussed. But a major takeaway from it will likely survive discussion: the human cost of a pure mitigation strategy is inacceptable, whilst a pure suppression strategy is unsustainable over time (thread)
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.
For social scientists wondering “what can I do now?” here’s a challenge:@cp_roth@LukasHenselEcon & others ran a survey with 2500 Italians yday & found that:
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?
@alexwickham doing fine work as a journalist again. Gets right into Government somehow and tells people what is going on.
10 Days That Changed Britain: "Heated" Debate Between Scientists Forced Boris Johnson To Act On Coronavirus https://t.co/hDLEAPT3Z0
Public expenditure (e.g. to boost spending for emergency care, crisis services, medical equipment)
Economic incentives and disincentives (e.g. to reduce the cost of business or borrowing, or tax unhealthy products)
Linking spending to entitlement or behaviour (e.g. social security benefits conditional on working or seeking work, perhaps with the rules modified during crises)
Formal regulations versus voluntary agreements (e.g. making organisations close, or encouraging them to close)
Public services: universal or targeted, free or with charges, delivered directly or via non-governmental organisations
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.
Add in the UK legislation and we see that it is a major feat simply to account for all of the major moving parts (while noting that much policy change is not legislative)https://t.co/gKsIx7aHr2pic.twitter.com/Ms6fjaDbhF
A few 'somewhat overwritten' newspaper stories today using some of our quotes on PPE. Here is exactly what we are saying, in the tone in which we are saying it: https://t.co/j6PO420WSF
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?
How to weigh the many future health problems and deaths caused by the lockdown against those saved? How to account for the worse effects of the lockdown on the young and the poor? Near impossible ethical choices that government will have to make. https://t.co/DJgwE4b3rd
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:
"I hope the UK government will be transparent about its decision-making; willing to listen to NHS staff concerns; humble in learning from other countries’ experiences; and pro-active in building relationships with them."https://t.co/CYUyvij2bK
I agree. There is a need to show that divergent opinons in the public health/virology expert sector have been heard, debates have been had and conclusions explained. This is what I need as a citizen. Also casting the public not a bog roll stowing mob is not helpful or kind. https://t.co/g61Nypcqlc
The Guardian calls this document a “secret” briefing from Public Health England. At a time of national crisis there is no place for secrecy from health experts. If you want public support, share your data, scenarios, and forecasts. Now. https://t.co/O8BpDlCJ7H
I am glad Johnson has listened, but we shouldn't have to drag him kicking and screaming to these decisions. A daily update is a basic step. Transparency, honesty, compassion are vital in this time of a global crisis! no more secret briefings PM.https://t.co/eMxZnMehUp
The CSA and CMO say they will publish the models underlying their strategy on Covid-19. Sharing the data and models is important for accountability, testing and learning. https://t.co/rOuJWwy93i
Dear Boris – Number 10 needs a professional communications operation, immediately. (Open letter to the Prime Minister. Britain has some great comms specialists. He needs to hire one of them urgently) https://t.co/8w6MBYHHbm
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:
Ferguson et al (link) simulate outbreak response. Positive: They show suppression (lockdown R0<1) is essential as mitigation (R0>1, “flattening the curve”) results in massive hospital overload and many dead. BUT 1/3 (review attached)https://t.co/srbBS7F1s5pic.twitter.com/qbEymBdOqm
I’m conscious that lots of people would like to see and run the pandemic simulation code we are using to model control measures against COVID-19. To explain the background – I wrote the code (thousands of lines of undocumented C) 13+ years ago to model flu pandemics…
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
First, policymakers face ‘bounded rationality’, in which they only have the ability to pay attention to a tiny proportion of available facts, are unable to separate those facts from their values (since we use our beliefs to evaluate the meaning of facts), struggle to make clear and consistent choices, and do not know what impact they will have. The consequences can include:
Limited attention, and lurches of attention. Policymakers can only pay attention to a tiny proportion of their responsibilities, and policymaking organizations struggle to process all policy-relevant information. They prioritize some issues and information and ignore the rest.
Power and ideas. Some ways of understanding and describing the world dominate policy debate, helping some actors and marginalizing others.
Beliefs and coalitions. Policymakers see the world through the lens of their beliefs. They engage in politics to turn their beliefs into policy, form coalitions with people who share them, and compete with coalitions who don’t.
Dealing with complexity. They engage in ‘trial-and-error strategies’ to deal with uncertain and dynamic environments (see the new section on trial-and-error- at the end).
Framing and narratives. Policy audiences are vulnerable to manipulation when they rely on other actors to help them understand the world. People tell simple stories to persuade their audience to see a policy problem and its solution in a particular way.
The social construction of populations. Policymakers draw on quick emotional judgements, and social stereotypes, to propose benefits to some target populations and punishments for others.
Rules and norms. Institutions are the formal rules and informal understandings that represent a way to narrow information searches efficiently to make choices quickly.
Learning. Policy learning is a political process in which actors engage selectively with information, not a rational search for truth.
Evidence-based or expert-informed policymaking
Don’t think science can or should make decisions Donna. In conditions of uncertainty, it must inform decision makers who must be transparent about the choices they make and be held to account for them https://t.co/Wj4s9IS6fO
Put simply, policymakers cannot oversee a simple process of ‘evidence-based policymaking’. Rather, to all intents and purposes:
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.
Not a thread but an interesting exchange on #coronavirus & Behavioural Sciences including readings from @LiamDelaneyEcon https://t.co/7Yn89XwOk6
Here’s my article on why I wish my fellow psychologists and “behavioural scientists” would just stop talking about the coronavirus: https://t.co/ofjJWdIY9v
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.
Key point from @jameswilsdon 'It is problematic if political choices are being made and then the science advice system has to front them up. There needs to be a clearer sense of where science advice ends and political judgement begins.'https://t.co/TjLCJDZijO via @timeshighered
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:
2. This all assumes I'm correct in what I think the govt are doing and why. I could be wrong – and wouldn't be surprised. But it looks to me like. . .
— Professor Ian Donald 3.5% (@iandonald_psych) March 13, 2020
As many have said, it would be good to get an official version of this, with acknowledged uncertainties and sources of evidence https://t.co/jxgoysYb3L
But what happened is that they have as a group fallen into a logical error in their attempts to model what will bring this epidemic under control. They have not appreciated that the answer to this question is adaptive behavior change. 3/17
It would be really helpful to project risk of covid death with and without mitigation strategies? Possible to map with inside / outside projections (ie what we gain/ don’t gain with current measures ?)
Experts in one field trusting certain experts in another field based on personal or professional interaction:
Lots of concern about UK's approach to #COVID19. I'm not an epidemiologist or a virologist (=> can't judge the detail) but I knew Patrick Vallance before he was famous and I believe he is a man of integrity. Same for Chris Whitty. Science, not politics, is driving their thinking.
— Trisha Greenhalgh 😷 #BlackLivesMatter (@trishgreenhalgh) March 14, 2020
Experts in one field not trusting a government’s approach based on its use of one (of many) sources of advice:
Why is UK government listening to the ‘nudge unit’ on the pandemic, and not expert epidemiologists and the WHO? You would think the ‘anti-experts’ approach would have at least on this occasion, with so many lives at risk, given way to a scientific approach https://t.co/QZIicXYpsj
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):
The Chief Medical Officer @CMO_England and his team have the 100% support and backing of the Public Health community. Every DPH I know thinks he is doing an amazing job in difficult circumstances Sorry but JRA is just demonstrating he is out of touch on this https://t.co/ExmOjEgum0
Expert debate on how well policymakers are making policy based on expert advice
Disagree.
Not much audible consensus amongst scientists anywhere for UK approach. Science can only illuminate value judgements yet now used a shield for determining them. UK science advice has always been characterised by old boys, political operators. Blurring is concerning. https://t.co/iBt07QfvqH
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):
My current approach to making sense of conflicting expert opinion on #coronavirus: no expert is infallible, but some are accountable and others are not, and I will value the opinions of those who are accountable above the opinions of those who are not.
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:
This meme is spreading (you could say, in a not very funny joke, that it has gone viral). The WHO Director-General did not say this (brief thread). https://t.co/3eMfy70tKZ
For what it’s worth, I tend to favour experts who:
(a) establish the boundaries of their knowledge, (b) admit to high uncertainty about the overall problem:
After having spent considerable time thinking how to mitigate and manage this pandemic, and analysing the available data. I failed to identify the best course of action. Even worse, I'm not sure there is such a thing as an acceptable solution to the problem we are facing. (2/12)
— Prof Francois Balloux (@BallouxFrancois) March 14, 2020
I would challenge anyone to provide an accurate estimate of prevalence. The difference between models & real life is that with models we can set the parameters as if they are known. In real life these parameters are as clear as mud. Extract 04/13/2020 https://t.co/Qg2OrCo8tR
(c) (in this case) make it clear that they are working on scenarios, not simple prediction
I am deeply uncomfortable with the message that UK is actively pursuing ‘herd immunity’ as the main COVID-19 strategy. Our group’s scenario modelling has focused on reducing two main things: peak healthcare demand and deaths… 1/
"Prediction models are just estimates of what might happen and a model is only as good as the data that goes into it." https://t.co/KXDILsbZgr via @ConversationUK
(d) examine critically the too-simple ideas that float around, such as the idea that the UK Government should emulate ‘what works’ somewhere else
It's easy to say 'let's just do what Wuhan did', but the measures there have involved a change to daily life that really has been unimaginable in scale and impact. And as we've seen, China cannot sustain them indefinitely. 3/
A lot of my colleagues in the @LSHTM modelling centre (@cmmid_lshtm) have been working extremely hard to help expand the COVID-19 evidence base over the past two months. I'd like to take a moment to highlight some of their work… 1/
8. There's no gotcha-ism. Updating your models and predictions in light of new evidence and new inferential methods and insightful counterpoints from colleagues isn't a sign of weakness, it's *doing science*.
I do not agree with this interpretation. Multiple papers that tested people at high risk found that asymptomatic infection is relatively uncommon, in the range of 6-32%. https://t.co/gv5e2upEwz
(e) situate their own position (in Prof Sridhar’s case, for mass testing) within a broader debate
Scientific community is well-intentioned but split in two camps: one argues why sacrifice short-term social/economic well-being if everyone will get virus regardless, & other which says we have to buy time in short-term & save lives now while figuring out exit plan.
How much effort does your govt want to put into suppressing this outbreak? There is no quick fix or easy solution. S.Korea & Germany show what huge govt effort, planning, strong leadership, & doing utmost to protect population look like. Do everything v. do minimum.
Been saying 3 objectives for weeks. Not to attack anyone, but to highlight what we have learned so far: 1. Testing, tracing, isolating 2. Protect health workers with PPE & testing 3. Buy time for NHS
Two weeks ago Boris Johnson said Britain was aiming to eventually test 250,000 people a day. The reality is still far off the aspiration https://t.co/2SHX40B9Ul
My new blog on whether Covid raises everyone’s relative risk of dying by a similar amount. https://t.co/76NSNuDJ3i Latest ONS data shows that, of recent death registrations, the proportion linked to Covid does not depend on age.
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.
— Louis M M Coiffait (@LouisMMCoiffait) April 6, 2020
See also: ‘What’s important is social distancing’ coronavirus testing ‘is a side issue’, says Deputy Chief Medical Officer [Professor Jonathan Van-Tam talks about the important distinction between a currently available test to see if someone has contracted the virus (an antigen test) and a forthcoming test to see if someone has had and recovered from COVID-19 (an antibody test)]. The full interview is here (please feel free to ignore the editorialising of the uploader):
We might need to change our criteria to decide on capacity and resources. COVID-19 showed that the standard CEO approach of doing more with less is no good. German planners have apparently safely ignored this holy managerial mantra. @Breconomicshttps://t.co/MKi3f1Pueq
Cross country comparisons of the efficacy of anti covid19 policies are going to be hard. There are so many likely inputs; and data on them is scarce and noisy.
The UK Govts chief medical officer has conceded that Germany “got ahead” in testing people for Covid-19 and said the UK needed to learn from that. Ministers have been challenged repeatedly during the pandemic over their failure to increase testing. https://t.co/V0bgcMR7l0
He says there is not as much scrutiny as we might normally wish and says concerns raised about human rights, the length of powers and need for safeguards should be heeded in Westminster. He also commits to legislate for reporting requirements for use of powers by SG 4/5
Glad Scottish Government recognise need for ethical guidance on Covid 19, and hope they can say more on human rights in next version https://t.co/GiyTd2Xksu
This is an excellent initiative from @policescotland – commissioning @johndscott to provide independent scrutiny of new Coronavirus Emergency Powers. Policing is by consent of the people, this step hopefully gives further public reassurance on the application of powers https://t.co/6MtrqdTqIm
Unprecedented restrictions are in force in order to limit social contact and slow the spread of the coronavirus. But the govt and police must make clear what is enforceable and what is guidance if they are to retain the trust and confidence of the public https://t.co/ieLcg2qVE5pic.twitter.com/mBOK2fppH2
— Institute for Gov (@instituteforgov) April 5, 2020
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.
There’s some coverage today suggesting Scotland proposing different policy to rest of UK on over 70s. This isn’t so. The policy of social distancing, not isolation, set out here by @jasonleitch is the policy all 4 nations have been discussing at COBR – and will do so again today. https://t.co/D89nwUDZTb
This is interesting, particularly the contrast with the approach to Brexit. The key difference is that Brexit blurred the boundaries between reserved and devolved competences in a way that health does not. https://t.co/4kSIcQFmJf
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)
One way of viewing the UK's COVID-19 policy is that it changed to reflect changing evidence. That is fair; it's both how science-guided policy *should* work, and how I think the govt's advisors are seeing it, as per the Imperial paper. But… 1/
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:
Incredible detail in this FT story: up until last week, the UK was basing its coronavirus control policy on a model based on hospitalisation rates for 😲a different disease😲 with lower rates of intensive care need than coronavirus pic.twitter.com/7rJYh9sqg2
Laura Kuenssberg says (BBC) that, “The science has changed.” This is not true. The science has been the same since January. What has changed is that govt advisors have at last understood what really took place in China and what is now taking place in Italy. It was there to see.
We can’t keep changing our #COVID19 control policies whenever the results of the “mathematical modelling” change. We need to implement standard WHO-approved epidemic control policies hard and fast, as well as providing more support to frontline NHS staff. https://t.co/HAM9OqbmqW
There may be perfectly valid or at least debatable reasons for each but obfuscation does not help public to understand uncertainty around decisions. In other words, not communicating rationale = incompetence (as in incompetent in terms of state craft, not nec individual decision)
One wonders if Brit leaders have decided that the ultimate way to cut national budgets is to cull the herd of the weak, those who require costly NHS care, and pray for "herd immunity" among the rest. Cruel, cost effective #COVID19 strategy?@richardhorton1
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
I have enormous respect for the SAGE team and scientific advisors trying to understand the situation & inform the UK's response. If this article is accurate & partisan hacks were deliberately sacrificing lives based on their information, its scandalous. A week ago I was saying… https://t.co/WYsHbj6o0a
If you read the whole article you will see that Dominic Cummings has been, for the last 10 days, the most zealous advocate of a tough lockdown. Which is what his critics seem to want. The world is not black and white
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):
The herd immunity strategy would’ve likely caused hundreds of thousands of deaths. They even told us so.
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:
Sir Patrick Vallance, the govt chief scientific adviser, says the thinking behind current approach to #coronavirus is to try and "reduce the peak" and to build up a "degree of herd immunity so that more people are immune to the disease". #R4Today
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’).
For anyone who thinks it was all obvious in January and February reading these minutes is a sobering experience. What comes over is the real uncertainty about what could be foretold from the Chinese experience and the ease with which the disease could be transmitted.4/n
Toby Young 'expert'. Nobody, including the Oxford team, believes this is true. Shame on The Sun for publishing this irresponsible rubbish. Shame on Toby Young for cynical misrepresentation of the science. pic.twitter.com/17hrOPW9b8
[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.
Dr Michael J Ryan, Executive Director at WHO. An off the cuff answer to a question at today's virtual press conference. Inspiring stuff! pic.twitter.com/Q4EUs8V1dG
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.
The scientific response to COVID-19 demands speed. But changing incentives and norms in academic science may be pushing the enterprise toward fast science at the expense of good science. Read Dan Sarewitz's editor's journal in the Spring 2020 ISSUES: https://t.co/JSSS45eTze
— Issues in Science and Technology (@ISSUESinST) April 7, 2020
#politvirus Public Health has always been #political because it’s actions impact on politics, economics, commercial interests, personal freedoms – this becomes most obvious in crisis – it will be key to analyse the political responses to #Covid_19 if we want to be better prepared https://t.co/JkUZrVeAxv
An assessment of the Government's response to date – written by Chair of Global Health at Edinburgh University..Prof Devi Sridhar https://t.co/N31QtFmQ2p
This is a really important paper. Partisanship is a huge influence on timing of state public health measures- Republican governors and Trump majorities slow adoption of measures. This might have big mortality effects in a few weeks. https://t.co/BEOAM69aSw
One reason Germany has so many ventilators (and intensive care beds) given in The Times: Not just more money in the system but design of hospital payment rates through the insurance system has driven up ICU investment be hospital managers pic.twitter.com/7R062IJI2k
This is worrying. Singapore was held up as one of the models for how to control #COVID19 through a sophisticated programme of testing and tracing without having to resort to the kinds of lockdowns many other countries are going through. https://t.co/6R0LY4IhuO
Today’s reflection- A number of Swedes are pretty shit at social distancing and probably need at least a modicum of discipline- the notion that we should be so very different here is ludicrous
WATCH: "Some countries initially talked about herd immunity as a strategy. In New Zealand we never, ever considered that. It would have meant tens of thousands of New Zealanders dying" — New Zealand Prime Minister @jacindaardernpic.twitter.com/W1ei6OUUyr
An online form to report lockdown breaches undermines the trust we have in each other – unhelpful in even the most benign of situations, and downright dangerous right now, writes Michael Macaulay. https://t.co/XCrnpfEVJt
Speechless every time someone says that this was totally unexpected & nobody saw this coming. See chapter 3: 'Preparing for the Worst: A Rapidly Spreading, Lethal Respiratory Pathogen' published by the @WHO Sept 2019. https://t.co/23qTrz7dN9
People are facing uncertainty for days, weeks & months. We need a manageable way forward to keep the health, social & economic costs at a minimum. My analysis on where COVID-19 response is heading & how it could end: https://t.co/qLDm8tv8a9
I wish the late great Mick Moran were still around – it feels like the next chapter of his analysis of the modern British state urgently needs to be written. https://t.co/ffxegGKVCu
I’m writing a book about @ExtinctionR. Here are some thoughts about today’s controversy. 1. This may or may not be a legit XR group. 2. That may matter because it may be done in order to smear XR & climate activism generally 1/n https://t.co/NyQhbv53a3
Cautionary words for anyone tempted to say "this must be good for the climate" or, worse, "this shows we can tackle climate change".
COVID19 is a re-framing of the climate issues – a dramatically changed context for the response – but those climate issues haven't gone away. https://t.co/gixVwnk6gq
We are concerned about regulation rollbacks which impact the food system slipping under the radar at the moment – we are going to be keeping an eye on things and use hashtag #Covid19Watchdoghttps://t.co/niinfSWv6f#TuesdayThoughts
A study in politics – when leadership fails. Would those that were ready to bash the @WHO take the time to read this? The critical issue for all countries is: what did they do after the PHEIC was declared? Why did USA and China not work together to fight #COVID19https://t.co/zK7hcEbU80
Not a single voice from the Global South – that’s not good enough if you are reporting on a global organisation – @who has 194 member states – it’s not the donors who should be running it #COVID19#geopoliticshttps://t.co/xqTaFEYLap
— Professor Paul Cairney (@CairneyPaul) April 9, 2020
The Australian #COVID19 modelling was published today. My thanks to James McCaw (@j_mccaw) for checking this thread. I’ll do two threads – one explaining the results and how we might interpret them; and another to try to explain how these models work. https://t.co/O6sGwggY9W
This was so predictable. Ireland was already closing pubs and restaurants. #COVIDー19 . Cheltenham Festival ‘spread coronavirus across country’ | News | The Times https://t.co/QVQnJblJiH
— Andrea Catherwood (@acatherwoodnews) April 3, 2020
expert comments about comparison between the COVID-19 situation in Ireland and the UKhttps://t.co/y4OBOhdbtT
‘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).
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.
Paul Cairney: Politics & Public Policy 