This post summarises new research on the health equity strategy Health in All Policies. As our previous post suggests, it is common to hope that a major event will create a ‘window of opportunity’ for such strategies to flourish, but the current COVID-19 experience suggests otherwise. If so, what do HIAP studies tell us about how to respond, and do they offer any hope for future strategies? The full report is on Open Research Europe, accompanied by a brief interview on its contribution to the Horizon 2020 project – IMAJINE – on spatial justice.
COVID-19 should have prompted governments to treat health improvement as fundamental to public policy
Many had made strong rhetorical commitments to public health strategies focused on preventing a pandemic of non-communicable diseases (NCDs). To do so, they would address the ‘social determinants’ of health and health inequalities, defined by the WHO as ‘the unfair and avoidable differences in health status’ that are ‘shaped by the distribution of money, power and resources’ and ‘the conditions in which people are born, grow, live, work and age’.
COVID-19 reinforces the impact of the social determinants of health. Health inequalities result from factors such as income and social and environmental conditions, which influence people’s ability to protect and improve their health. COVID-19 had a visibly disproportionate impact on people with (a) underlying health conditions associated with NCDs, and (b) less ability to live and work safely.
Yet, the opposite happened. The COVID-19 response side-lined health improvement
This experience shows that the evidence does not speak for itself
The evidence on social determinants is clear to public health specialists, but the idea of social determinants is less well known or convincing to policymakers.
It also challenges the idea that the logic of health improvement is irresistible
Health in All Policies (HIAP) is the main vehicle for health improvement policymaking, underpinned by: a commitment to health equity by addressing the social determinants of health; the recognition that the most useful health policies are not controlled by health departments; the need for collaboration across (and outside) government; and, the search for high level political commitment to health improvement.
Its logic is undeniable to HIAP advocates, but not policymakers. A government’s public commitment to HIAP does not lead inevitably to the roll-out of a fully-formed HIAP model. There is a major gap between the idea of HIAP and its implementation. It is difficult to generate HIAP momentum, and it can be lost at any time.
Instead, we need to generate more realistic lessons from health improvement and promotion policy
However, most HIAP research does not provide these lessons. Most HIAP research combines:
functional logic (here is what we need)
programme logic (here is what we think we need to do to achieve it), and
Policy theory-informed empirical studies of policymaking could help produce a more realistic agenda, but very few HIAP studies seem to exploit their insights.
To that end, this review identifies lessons from studies of HIAP and policymaking
It summarises a systematic qualitative review of HIAP research. It includes 113 articles (2011-2020) that refer to policymaking theories or concepts while discussing HIAP.
We produced these conclusions from pre-COVID-19 studies of HIAP and policymaking, but our new policymaking context – and its ironic impact on HIAP – is impossible to ignore.
It suggests that HIAP advocates produced a 7-point playbook for the wrong game
The seven most common pieces of advice add up to a plausible but incomplete strategy:
adopt a HIAP model and toolkit
raise HIAP awareness and support in government
seek win-win solutions with partners
avoid the perception of ‘health imperialism’ when fostering intersectoral action
find HIAP policy champions and entrepreneurs
use HIAP to support the use of health impact assessments (HIAs)
challenge the traditional cost-benefit analysis approach to valuing HIAP.
Yet, two emerging pieces of advice highlight the limits to the current playbook and the search for its replacement:
treat HIAP as a continuous commitment to collaboration and health equity, not a uniform model; and,
address the contradictions between HIAP aims.
As a result, most country studies report a major, unexpected, and disappointing gap between HIAP commitment and actual outcomes
These general findings are apparent in almost all relevant studies. They stand out in the ‘best case’ examples where: (a) there is high political commitment and strategic action (such as South Australia), or (b) political and economic conditions are conducive to HIAP (such as Nordic countries).
These studies show that the HIAP playbook has unanticipated results, such as when the win-win strategy leads to HIAP advocates giving ground but receiving little in return.
HIAP strategies to challenge the status quo are also overshadowed by more important factors, including (a) a far higher commitment to existing healthcare policies and the core business of government, and (b) state retrenchment. Additional studies of decentralised HIAP models find major gaps between (a) national strategic commitment (backed by national legislation) and (b) municipal government progress.
Some studies acknowledge the need to use policymaking research to produce new ways to encourage and evaluate HIAP success
Studies of South Australia situate HIAP in a complex policymaking system in which the link between policy activity and outcomes is not linear.
Studies of Nordic HIAP show that a commitment to municipal responsibility and stakeholder collaboration rules out the adoption of a national uniform HIAP model.
However, most studies do not use policymaking research effectively or appropriately
Almost all HIAP studies only scratch the surface of policymaking research (while some try to synthesise its insights, but at the cost of clarity).
Most HIAP studies use policy theories to:
produce practical advice (such as to learn from ‘policy entrepreneurs’), or
supplement their programme logic (to describe what they think causes policy change and better health outcomes).
Most policy theories were not designed for this purpose.
Policymaking research helps primarily to explain the HIAP ‘implementation gap’
Its main lesson is that policy outcomes are beyond the control of policymakers and HIAP advocates. This explanation does not show how to close implementation gaps.
Its practical lessons come from critical reflection on dilemmas and politics, not the reinvention of a playbook
It prompts advocates to:
Treat HIAP as a political project, not a technical exercise or puzzle to be solved.
Re-examine the likely impact of a focus on intersectoral action and collaboration, to recognise the impact of imbalances of power and the logic of policy specialisation.
Revisit the meaning-in-practice of the vague aims that they take for granted without explaining, such as co-production, policy learning, and organisational learning.
Engage with key trade-offs, such as between a desire for uniform outcomes (to produce health equity) but acceptance of major variations in HIAP policy and policymaking.
Avoid reinventing phrases or strategies when facing obstacles to health improvement.
This post first appeared on LSE British Politics and Policy (27.11.20) and is based on this article in British Politics.
Paul Cairneyassesses government policy in the first half of 2020. He identifies the intense criticism of its response so far, encouraging more systematic assessments grounded in policy research.
In March 2020, COVID-19 prompted policy change in the UK at a speed and scale only seen during wartime. According to the UK government, policy was informed heavily by science advice. Prime Minister Boris Johnson argued that, ‘At all stages, we have been guided by the science, and we will do the right thing at the right time’. Further, key scientific advisers such as Sir Patrick Vallance emphasised the need to gather evidence continuously to model the epidemic and identify key points at which to intervene, to reduce the size of the peak of population illness initially, then manage the spread of the virus over the longer term.
Both ministers and advisors emphasised the need for individual behavioural change, supplemented by government action, in a liberal democracy in which direct imposition is unusual and unsustainable. However, for its critics, the government experience has quickly become an exemplar of policy failure.
Initial criticisms include that ministers did not take COVID-19 seriously enough in relation to existing evidence, when its devastating effect was apparent in China in January and Italy from February; act as quickly as other countries to test for infection to limit its spread; or introduce swift-enough measures to close schools, businesses, and major social events. Subsequent criticisms highlight problems in securing personal protective equipment (PPE), testing capacity, and an effective test-trace-and-isolate system. Some suggest that the UK government was responding to the ‘wrong pandemic’, assuming that COVID-19 could be treated like influenza. Others blame ministers for not pursuing an elimination strategy to minimise its spread until a vaccine could be developed. Some criticise their over-reliance on models which underestimated the R (rate of transmission) and ‘doubling time’ of cases and contributed to a 2-week delay of lockdown. Many describe these problems and delays as the contributors to the UK’s internationally high number of excess deaths.
How can we hold ministers to account in a meaningful way?
I argue that these debates are often fruitless and too narrow because they do not involve systematic policy analysis, take into account what policymakers can actually do, or widen debate to consider whose lives matter to policymakers. Drawing on three policy analysis perspectives, I explore the questions that we should ask to hold ministers to account in a way that encourages meaningful learning from early experience.
These questions include:
Was the government’s definition of the problem appropriate? Much analysis of UK government competence relates to specific deficiencies in preparation (such as shortages in PPE), immediate action (such as to discharge people from hospitals to care homes without testing them for COVID-19), and implementation (such as an imperfect test-trace-and-isolate system). The broader issue relates to its focus on intervening in late March to protect healthcare capacity during a peak of infection, rather than taking a quicker and more precautionary approach. This judgment relates largely to its definition of the policy problem which underpins every subsequent policy intervention.
Did the government select the right policy mix at the right time? Who benefits most from its choices?
Most debates focus on the ‘lock down or not?’ question without exploring fully the unequal impact of any action. The government initially relied on exhortation, based on voluntarism and an appeal to social responsibility. Initial policy inaction had unequal consequences on social groups, including people with underlying health conditions, black and ethnic minority populations more susceptible to mortality at work or discrimination by public services, care home residents, disabled people unable to receive services, non-UK citizens obliged to pay more to live and work while less able to access public funds, and populations (such as prisoners and drug users) that receive minimal public sympathy. Then, in March, its ‘stay at home’ requirement initiated a major new policy and different unequal impacts in relation to the income, employment, and wellbeing of different groups. These inequalities are list in more general discussions of impacts on the whole population.
Did the UK government make the right choices on the trade-offs between values, and what impacts could the government have reasonably predicted?
Initially, the most high-profile value judgment related to freedom from state coercion to reduce infection versus freedom from the harm of infection caused by others. Then, values underpinned choices on the equitable distribution of measures to mitigate the economic and wellbeing consequences of lockdown. A tendency for the UK government to project centralised and ‘guided by the science’ policymaking has undermined public deliberation on these trade-offs between policies. The latter will be crucial to ongoing debates on the trade-offs associated with national and regional lockdowns.
Did the UK government combine good policy with good policymaking?
A problem like COVID-19 requires trial-and-error policymaking on a scale that seems incomparable to previous experiences. It requires further reflection on how to foster transparent and adaptive policymaking and widespread public ownership for unprecedented policy measures, in a political system characterised by (a) accountability focused incorrectly on strong central government control and (b) adversarial politics that is not conducive to consensus seeking and cooperation.
These additional perspectives and questions show that too-narrow questions – such as was the UK government ‘following the science’ – do not help us understand the longer term development and wider consequences of UK COVID-19 policy. Indeed, such a narrow focus on science marginalises wider discussions of values and the populations that are most disadvantaged by government policy.
Many public health bodies are responding to crisis by shifting their attention and resources from (1) a long-term strategic focus on reducing non-communicable diseases (such as heart diseases, cancers, diabetes), to (2) the coronavirus pandemic.
There are equally important lessons – such as on health equity – from the experiences of longer-term and lower-profile ‘preventive’ public health agendas such as ‘Health in All Policies’ (HIAP).*
What is ‘Health in All Policies’?
HIAP is a broad (and often imprecise) term to describe:
The policy problem. Address the ‘social determinants’ of health, defined by the WHO as ‘the unfair and avoidable differences in health status … shaped by the distribution of money, power and resources [and] the conditions in which people are born, grow, live, work and age’.
The policy solutions. Identify a range of policy instruments, including redistributive measures to reduce economic inequalities, distributive measures to improve public services and the physical environment (including housing), regulations on commercial and individual behaviour, and health promotion via education and learning.
The policy style. An approach to policymaking that encourages meaningful collaboration across multiple levels and types of government, and between governmental and non-governmental actors (partly because most policy solutions to improve health are not in the gift of health departments).
Political commitment and will. High level political support is crucial to the production of a holistic strategy document, and to dedicate resources to its delivery, partly via specialist organisations and the means to monitor and evaluate progress.
As two distinctive ‘Marmot reviews’ demonstrate, this problem (and potential solutions) can be described differently in relation to:
Either way, each of the 4 HIAP elements highlights issues that intersect with the impact of the coronavirus: COVID-19 has a profoundly unequal impact on populations; there will be a complex mix of policy instruments to address it, and many responses will not be by health departments; an effective response requires intersectoral government action and high stakeholder and citizen ownership; and, we should not expect current high levels of public, media, and policymaker attention and commitment to continue indefinitely or help foster health equity (indeed, even well-meaning policy responses may exacerbate health inequalities).
A commitment to health equity, or the reduction of health inequalities
At the heart of HIAP is a commitment to health equity and to reduce health inequalities. In that context, the coronavirus provides a stark example of the impact of health inequalities, since (a) people with underlying health conditions are the most vulnerable to major illness and death, and (b) the spread of underlying health conditions is unequal in relation to factors such as income and race or ethnicity. Further, there are major inequalities in relation to exposure to physical and economic risks.
A focus on the social determinants of health inequalities
A ‘social determinants’ focus helps us to place individual behaviour in a wider systemic context. It is tempting to relate health inequalities primarily to ‘lifestyles’ and individual choices, in relation to healthy eating, exercise, and the avoidance of smoking and alcohol. However, the most profound impacts on population health can come from (a) environments largely outside of an individual’s control (e.g. in relation to threats from others, such as pollution or violence), (b) levels of education and employment, and (c) economic inequality, influencing access to warm and safe housing, high quality water and nutrition, choices on transport, and access to safe and healthy environments.
In that context, the coronavirus provides stark examples of major inequalities in relation to self-isolation and social distancing: some people have access to food, private spaces to self-isolate, and open places to exercise away from others; many people have insufficient access to food, no private space, and few places to go outside (also note the disparity in resources between countries).
The pursuit of intersectoral action
A key aspect of HIAP is to identify the ways in which non-health sectors contribute to health. Classic examples include a focus on the sectors that influence early access to high quality education, improving housing and local environments, reducing vulnerability to crime, and reforming the built environment to foster sustainable public transport and access to healthy air, water, and food.
The response to the coronavirus also appears to be a good advert for the potential for intersectoral governmental action, demonstrating that measures with profound impacts on health and wellbeing are made in non-health sectors, including: treasury departments subsidising business and wages, and funding additional healthcare; transport departments regulating international and domestic travel; social care departments responsible for looking after vulnerable people outside of healthcare settings; and, police forces regulating social behaviour.
However, most (relevant) HIAP studies identify a general lack of effective intersectoral government action, related largely to a tendency towards ‘siloed’ policymaking within each department, exacerbated by ‘turf wars’ between departments (even if they notionally share the same aims) and a tendency for health departments to be low status, particularly in relation to economic departments (also note the frequently used term ‘health imperialism’ to describe scepticism about public health in other sectors). Some studies highlight the potential benefits of ‘win-win’ strategies to persuade non-health sectors that collaboration on health equity also helps deliver their core business (e.g. Molnar et al 2015), but the wider public administration literature is more likely to identify a history of unsuccessful initiatives with a cumulative demoralising effect (e.g. Carey and Crammond, 2015; Molenveld et al, 2020).
The pursuit of wider collaboration
HIAP ambitions extend to ‘collaborative’ or ‘co-produced’ forms of governance, in which citizens and stakeholders work with policymakers in health and non-health sectors to define the problem of health inequalities and inform potential solutions. These methods can help policymakers make sense of broad HIAP aims through the eyes of citizens, produce priorities that were not anticipated in a desktop exercise, help non-health sector workers understand their role in reducing health inequalities, and help reinforce the importance of collaborative and respectful ways of working.
An excellent example comes from Corburn et al’s (2014) study of Richmond, California’s statutory measures to encourage HIAP. They describe ‘coproducing health equity in all policies’ with initial reference to WHO definitions, but then to social justice in relation to income and wealth, which differs markedly according to race and immigration status. It then reports on a series of community discussions to identify key obstacles to health:
“For example, Richmond residents regularly described how, in the same day, they might experience or fear violence, environmental pollution, being evicted from housing, not being able to pay health care bills, discrimination at work or in school, challenges accessing public services, and immigration and customs enforcement (ICE) intimidation … Also emerging from the workshops and health equity discussions was that one of the underlying causes of the multiple stressors experienced in Richmond was structural racism. By structural racism we meant that seemingly neutral policies and practices can function in racist ways by disempowering communities of color and perpetuating unequal historic conditions” (2014: 627-8).
Yet, a tiny proportion of HIAP studies identify this level of collaboration and new knowledge feeding into policy agendas to address health equity.
The cautionary tale: HIAP does not cause health equity
Rather, most of the peer-reviewed academic HIAP literature identifies a major gap between high expectations and low implementation. Most studies identify an urgent and strong impetus for policy action to be proportionate to the size of the policy problem, and ideas about the potential implementation of a HIAP agenda when agreed, but no studies identify implementation success in relation to health equity. In fact, the two most-discussed examples – in Finland and South Australia – seem to describe a successful reform of processes that have a negligible impact on equity.
A window of opportunity for what?
It is common in the public health field to try to identify ‘windows of opportunity’ to adopt (a) HIAP in principle, and (b) specific HIAP-friendly policy instruments. It is also common to try to identify the factors that would aid HIAP implementation, and to assume that this success would have a major impact on the social determinants of health inequalities. Yet, the cumulative experience from HIAP studies is that governments can pursue health promotion and intersectoral action without reducing health inequalities.
For me, this is the context for current studies of the unequal impact of the coronavirus across the globe and within each country. In some cases, there are occasionally promising discussions of major policymaking reforms, or to use the current crisis as an impetus for social justice as well as crisis response. Yet, the history of the pursuit of HIAP-style reforms should help us reject the simple notion that some people saying the right things will make that happen. Instead, right now, it seems more likely that – in the absence of significantly new action** – the same people and systems that cause inequalities will undermine attempts to reduce them. In other words, health equity will not happen simply because it seems like the right thing to do. Rather, it is a highly contested concept, and many people will use their power to make sure that it does not happen, even if they claim otherwise.
*These are my early thoughts based on work towards a (qualitative) systematic review of the HIAP literature, in partnership with Emily St Denny, Sean Kippin, and Heather Mitchell.
**No, I do not know what that action would be. There is no magic formula to which I can refer.
Paul Cairney (2020) ‘The UK Government’s COVID-19 policy: assessing evidence-informed policy analysis in real time’, British Politicshttps://rdcu.be/b9zAk (PDF)
The coronavirus feels like a new policy problem that requires new policy analysis. The analysis should be informed by (a) good evidence, translated into (b) good policy. However, don’t be fooled into thinking that either of those things are straightforward. There are simple-looking steps to go from defining a problem to making a recommendation, but this simplicity masks the profoundly political process that must take place. Each step in analysis involves political choices to prioritise some problems and solutions over others, and therefore prioritise some people’s lives at the expense of others.
My article in British Politics takes us through those steps in the UK, and situates them in a wider political and policymaking context. This post is shorter, and only scratches the surface of analysis.
5 steps to policy analysis
Define the problem.
Perhaps we can sum up the initial UK government approach as: (a) the impact of this virus and illness will be a level of death and illness that could overwhelm the population and exceed the capacity of public services, so (b) we need to contain the virus enough to make sure it spreads in the right way at the right time, so (c) we need to encourage and make people change their behaviour (primarily via hygiene and social distancing). However, there are many ways to frame this problem to emphasise the importance of some populations over others, and some impacts over others.
Identify technically and politically feasible solutions.
Solutions are not really solutions: they are policy instruments that address one aspect of the problem, including taxation and spending, delivering public services, funding research, giving advice to the population, and regulating or encouraging changes to social behaviour. Each new instrument contributes an existing mix, with unpredictable and unintended consequences. Some instruments seem technically feasible (they will work as intended if implemented), but will not be adopted unless politically feasible (enough people support their introduction). Or vice versa. From the UK government’s perspective, this dual requirement rules out a lot of responses.
Use values and goals to compare solutions.
Typical judgements combine: (a) broad descriptions of values such as efficiency, fairness, freedom, security, and human dignity, (b) instrumental goals, such as sustainable policymaking (can we do it, and for how long?), and political feasibility (will people agree to it, and will it make me more or less popular or trusted?), and (c) the process to make choices, such as the extent to which a policy process involves citizens or stakeholders (alongside experts) in deliberation. They combine to help policymakers come to high profile choices (such as the balance between individual freedom and state coercion), and low profile but profound choices (to influence the level of public service capacity, and level of state intervention, and therefore who and how many people will die).
Predict the outcome of each feasible solution.
It is difficult to envisage a way for the UK Government to publicise all of the thinking behind its choices (Step 3) and predictions (Step 4) in a way that would encourage effective public deliberation. People often call for the UK Government to publicise its expert advice and operational logic, but I am not sure how they would separate it from their normative logic about who should live or die, or provide a frank account without unintended consequences for public trust or anxiety. If so, one aspect of government policy is to keep some choices implicit and avoid a lot of debate on trade-offs. Another is to make choices continuously without knowing what their impact will be (the most likely scenario right now).
Make a choice, or recommendation to your client.
Your recommendation or choice would build on these four steps. Define the problem with one framing at the expense of the others. Romanticise some people and not others. Decide how to support some people, and coerce or punish others. Prioritise the lives of some people in the knowledge that others will suffer or die. Do it despite your lack of expertise and profoundly limited knowledge and information. Learn from experts, but don’t assume that only scientific experts have relevant knowledge (decolonise; coproduce). Recommend choices that, if damaging, could take decades to fix after you’ve gone. Consider if a policymaker is willing and able to act on your advice, and if your proposed action will work as intended. Consider if a government is willing and able to bear the economic and political costs. Protect your client’s popularity, and trust in your client, at the same time as protecting lives. Consider if your advice would change if the problem seemed to change. If you are writing your analysis, maybe keep it down to one sheet of paper (in other words, fewer words than in this post up to this point).
Policy analysis is not as simple as these steps suggest, and further analysis of the wider policymaking environment helps describe two profound limitations to simple analytical thought and action.
Policymakers must ignore almost all evidence
The amount of policy relevant information is infinite, and capacity is finite. So, individuals and governments need ways to filter out almost all of it. Individuals combine cognition and emotion to help them make choices efficiently, and governments have equivalent rules to prioritise only some information. They include: define a problem and a feasible response, seek information that is available, understandable, and actionable, and identify credible sources of information and advice. In that context, the vague idea of trusting or not trusting experts is nonsense, and the larger post highlights the many flawed ways in which all people decide whose expertise counts.
They do not control the policy process.
Policymakers engage in a messy and unpredictable world in which no single ‘centre’ has the power to turn a policy recommendation into an outcome.
There are many policymakers and influencers spread across a political system. For example, consider the extent to which each government department, devolved governments, and public and private organisations are making their own choices that help or hinder the UK government approach.
Most choices in government are made in ‘subsystems’, with their own rules and networks, over which ministers have limited knowledge and influence.
The social and economic context, and events, are largely out of their control.
The take home messages (if you accept this line of thinking)
The coronavirus is an extreme example of a general situation: policymakers will always have very limited knowledge of policy problems and control over their policymaking environment. They make choices to frame problems narrowly enough to seem solvable, rule out most solutions as not feasible, make value judgements to try help some more than others, try to predict the results, and respond when the results do not match their hopes or expectations.
This is not a message of doom and despair. Rather, it encourages us to think about how to influence government, and hold policymakers to account, in a thoughtful and systematic way that does not mislead the public or exacerbate the problem we are seeing. No one is helping their government solve the problem by saying stupid shit on the internet (OK, that last bit was a message of despair).
The article (PDF) sets out these arguments in much more detail, with some links to further thoughts and developments.
This series of ‘750 words’ posts summarises key texts in policy analysis and tries to situate policy analysis in a wider political and policymaking context. Note the focus on whose knowledge counts, which is not yet a big feature of this crisis.
These series of 500 words and 1000 words posts (with podcasts) summarise concepts and theories in policy studies.
This is the long version. It is long. Too long to call a blog post. Let’s call it a ‘living document’ that I update and amend as new developments arise (then start turning into a more organised paper). In most cases, I am adding tweets, so the date of the update is embedded. If I add a new section, I will add a date. If you seek specific topics (like ‘herd immunity’), it might be worth doing a search. The short version is shorter.
The coronavirus feels like a new policy problem. Governments already have policies for public health crises, but the level of uncertainty about the spread and impact of this virus seems to be taking it to a new level of policy, media, and public attention. The UK Government’s Prime Minister calls it ‘the worst public health crisis for a generation’.
As such, there is no shortage of opinions on what to do, but there is a shortage of well-considered opinions, producing little consensus. Many people are rushing to judgement and expressing remarkably firm opinions about the best solutions, but their contributions add up to contradictory evaluations, in which:
the government is doing precisely the right thing or the completely wrong thing,
we should listen to this expert saying one thing or another expert saying the opposite.
Lots of otherwise-sensible people are doing what they bemoan in politicians: rushing to judgement, largely accepting or sharing evidence only if it reinforces that judgement, and/or using their interpretation of any new development to settle scores with their opponents.
Yet, anyone who feels, without uncertainty, that they have the best definition of, and solution to, this problem is a fool. If people are also sharing bad information and advice, they are dangerous fools. Further, as Professor Madley puts it (in the video below), ‘anyone who tells you they know what’s going to happen over the next six months is lying’.
In that context, how can we make sense of public policy to address the coronavirus in a more systematic way?
Studies of policy analysis and policymaking do not solve a policy problem, but they at least give us a language to think it through.
In each step, note how quickly it is possible to be overwhelmed by uncertainty and ambiguity, even when the issue seems so simple at first.
Note how difficult it is to move from Step 1, and to separate Step 1 from the others. It is difficult to define the problem without relating it to the solution (or to the ways in which we will evaluate each solution).
Let’s relate that analysis to research on policymaking, to understand the wider context in which people pay attention to, and try to address, important problems that are largely out of their control.
Throughout, note that I am describing a thought process as simply as I can, not a full examination of relevant evidence. I am highlighting the problems that people face when ‘diagnosing’ policy problems, not trying to diagnose it myself. To do so, I draw initially on common advice from the key policy analysis texts (summaries of the texts that policy analysis students are most likely to read) that simplify the process a little too much. Still, the thought process that it encourages took me hours alone (spread over three days) to produce no real conclusion. Policymakers and advisers, in the thick of this problem, do not have that luxury of time or uncertainty.
In our latest guest blog, Jonny Pearson-Stuttard, RSPH Trustee and Public Health Doctor @imperialcollege sets out what we know about the spread of coronavirus to date, and why the Government has taken the measures it hashttps://t.co/XM7zKKjwtE
Provide a diagnosis of a policy problem, using rhetoric and eye-catching data to generate attention.
Identify its severity, urgency, cause, and our ability to solve it. Don’t define the wrong problem, such as by oversimplifying.
Problem definition is a political act of framing, as part of a narrative to evaluate the nature, cause, size, and urgency of an issue.
Define the nature of a policy problem, and the role of government in solving it, while engaging with many stakeholders.
‘Diagnose the undesirable condition’ and frame it as ‘a market or government failure (or maybe both)’.
Coronavirus as a physical problem is not the same as a coronavirus policy problem. To define the physical problem is to identify the nature, spread, and impact of a virus and illness on individuals and populations. To define a policy problem, we identify the physical problem and relate it (implicitly or explicitly) to what we think a government can, and should, do about it. Put more provocatively, it is only a policy problem if policymakers are willing and able to offer some kind of solution.
This point may seem semantic, but it raises a profound question about the capacity of any government to solve a problem like an epidemic, or for governments to cooperate to solve a pandemic. It is easy for an outsider to exhort a government to ‘do something!’ (or ‘ACT NOW!’) and express certainty about what would happen. However, policymakers inside government:
Do not enjoy the same confidence that they know what is happening, or that their actions will have their intended consequences, and
Will think twice about trying to regulate social behaviour under those circumstances, especially when they
Know that any action or inaction will benefit some and punish others.
For example, can a government make people wash their hands? Or, if it restricts gatherings at large events, can it stop people gathering somewhere else, with worse impact? If it closes a school, can it stop children from going to their grandparents to be looked after until it reopens? There are 101 similar questions and, in each case, I reckon the answer is no. Maybe government action has some of the desired impact; maybe not. If you agree, then the question might be: what would it really take to force people to change their behaviour?
The ideal spread involves all well people sharing the virus first, while all vulnerable people (e.g. older, and/or with existing health problems that affect their immune systems) protected in one isolated space, but it won’t happen like that; so, we are trying to minimise damage in the real world.
We mainly track the spread via deaths, with data showing a major spike appearing one month later, so the problem may only seem real to most people when it is too late to change behaviour
The choice in theory is between a rapid epidemic with a high peak, or a slowed-down epidemic over a longer period, but ‘anyone who tells you they know what’s going to happen over the next six months is lying’.
Maybe this epidemic will be so memorable as to shift social behaviour, but so much depends on trying to predict (badly) if individuals will actually change (see also Spiegelhalter on communicating risk).
None of this account tells policymakers what to do, but at least it helps them clarify three key aspects of their policy problem:
The impact of this virus and illness could overwhelm the population, to the extent that it causes mass deaths, causes a level of illness that exceeds the capacity of health services to treat, and contributes to an unpredictable amount of social and economic damage.
We need to contain the virus enough to make sure it (a) spreads at the right speed and/or (b) peaks at the right time. The right speed seems to be: a level that allows most people to recover alone, while the most vulnerable are treated well in healthcare settings that have enough capacity. The right time seems to be the part of the year with the lowest demand on health services (e.g. summer is better than winter). In other words, (a) reduce the size of the peak by ‘flattening the curve’, and/or (b) find the right time of year to address the peak, while (c) anticipating more than one peak.
My impression is that the most frequently-expressed aim is (a) …
… while the UK Government’s Deputy Chief Medical Officer also seems to be describing (b):
We need to encourage or coerce people to change their behaviour, to look after themselves (e.g. by handwashing) and forsake their individual preferences for the sake of public health (e.g. by self-isolating or avoiding vulnerable people). Perhaps we can foster social trust and empathy to encourage responsible individual action. Perhaps people will only protect others if obliged to do so (compare Stone; Ostrom; game theory).
See also: From across the Ditch: How Australia has to decide on the least worst option for COVID-19 (Prof Tony Blakely on three bad options: (1) the likelihood of ‘elimination’ of the virus before vaccination is low; (2) an 18-month lock-down will help ‘flatten the curve’; (3) ‘to prepare meticulously for allowing the pandemic to wash through society over a period of six or so months. To tool up the production of masks and medical supplies. To learn as quickly as possible which treatments of people sick with COVID-19 saves lives. To work out our strategies for protection of the elderly and those with a chronic condition (for whom the mortality from COVID-19 is much higher’).
If you are still with me, I reckon you would have worded those aims slightly differently, right? There is some ambiguity about these broad intentions, partly because there is some uncertainty, and partly because policymakers need to set rather vague intentions to generate the highest possible support for them. However, vagueness is not our friend during a crisis involving such high anxiety. Further, they are only delaying the inevitable choices that people need to make to turn a complex multi-faceted problem into something simple enough to describe and manage. The problem may be complex, but our attention focuses only on a small number of aspects, at the expense of the rest. Examples that have arisen, so far, include to accentuate:
The health of the whole population or people who would be affected disproportionately by the illness.
For example, the difference in emphasis affects the health advice for the relatively vulnerable (and the balance between exhortation and reassurance)
Inequalities in relation to health, socio-economic status (e.g. income, gender, race, ethnicity), or the wider economy.
For example, restrictive measures may reduce the risk of harm to some, but increase the burden on people with no savings or reliable sources of income.
For example, some people are hoarding large quantities of home and medical supplies that (a) other people cannot afford, and (b) some people cannot access, despite having higher need.
For example, social distancing will limit the spread of the virus (see the nascent evidence), but also produce highly unequal forms of social isolation that increase the risk of domestic abuse (possibly exacerbated by school closures) and undermine wellbeing. Or, there will be major policy changes, such as to the rules to detain people under mental health legislation, regarding abortion, or in relation to asylum (note: some of these tweets are from the US, partly because I’m seeing more attention to race – and the consequence of systematic racism on the socioeconomic inequalities so important to COVID-19 mortality – than in the UK).
COVID-19 has brought new focus to women’s continued inequality. Without a gendered response to both the health and economic crises, gender inequality will be further cemented. Read more on the blog: https://t.co/zYxSFpUTNE
Available evidence (though injuriously limited) shows that Black people are being infected & dying of #coronavirus at higher rates. Disproportionate Black suffering is what many of us have suspected and feared because it is consistent with the entirety of American history. https://t.co/qzmXvGCGvV
“I believe that the actions and omissions of world leaders in charge of fighting the #COVID19 pandemic will reveal historical and current impacts of colonial violence and continued health inequities” https://t.co/nUuBIKfrVL
BBC news reports on the disproportionate deaths of African Americans & minorities in the US from #COVID19, but silence on similar issues in the UK. Why? Where is the reporting? Where is the accountability? https://t.co/DkGPjfnWG1
What the coronavirus bill will do: https://t.co/qoBdKKr64H Mental Health Act – detention implemented using just one doctor’s opinion (not 2) & AMHP, & temporarily allow extension or removal of time limits to allow for greater flexibility where services are less able to respond
Abortion services for women from Northern Ireland remain available free of charge in England. This provision will continue until services are available to meet these needs in Northern Ireland. For more information, visit: https://t.co/YYjop5lSgUpic.twitter.com/M8k95aIisM
BREAKING NEWS!!!! The Home Office have confirmed that ALL evictions and terminations of asylum support have been paused for 3 months. Find out more and read the letter from Home Office Minister Chris Philp confirming this on our website at: https://t.co/KDlVr4PHyP
In relation to Prison Rule Changes – these would only ever be used as an absolute last resort, in order to protect staff & those in our care. I can confirm that emergency changes to showering have not been implemented in any establishment.
For example, governments cannot ignore the impact of their actions on the economy, however much they emphasise mortality, health, and wellbeing. Most high-profile emphasis was initially on the fate of large and small businesses, and people with mortgages, but a long period of crisis will a tip the balance from low income to unsustainable poverty (even prompting Iain Duncan Smith to propose policy change), and why favour people who can afford a mortgage over people scraping the money together for rent?
A need for more communication and exhortation, or for direct action to change behaviour.
The short term (do everything possible now) or long term (manage behaviour over many months).
How to maintain trust in the UK government when (a) people are more or less inclined to trust a the current part of government and general trust may be quite low, and (b) so many other governments are acting differently from the UK.
For example, note the visible presence of the Prime Minister, but also his unusually high deference to unelected experts such as (a) UK Government senior scientists providing direct advice to ministers and the public, and (b) scientists drawing on limited information to model behaviour and produce realistic scenarios (we can return to the idea of ‘evidence-based policymaking’ later). This approach is not uncommon with epidemics/ pandemics (LD was then the UK Government’s Chief Medical Officer):
For example, note how often people are second guessing and criticising the UK Government position (and questioning the motives of Conservative ministers).
For example, people often try to lay blame for viruses on certain populations, based on their nationality, race, ethnicity, sexuality, or behaviour (e.g. with HIV).
For example, the (a) association between the coronavirus and China and Chinese people (e.g. restrict travel to/ from China; e.g. exacerbate racism), initially overshadowed (b) the general role of international travellers (e.g. place more general restrictions on behaviour), and (c) other ways to describe who might be responsible for exacerbating a crisis.
Under ‘normal’ policymaking circumstances, we would expect policymakers to resolve this ambiguity by exercising power to set the agenda and make choices that close off debate. Attention rises at first, a choice is made, and attention tends to move on to something else. With the coronavirus, attention to many different aspects of the problem has been lurching remarkably quickly. The definition of the policy problem often seems to be changing daily or hourly, and more quickly than the physical problem. It will also change many more times, particularly when attention to each personal story of illness or death prompts people to question government policy every hour. If the policy problem keeps changing in these ways, how could a government solve it?
Step 2 Identify technically and politically feasible solutions
As a result, what we call ‘policy’ is really a complex mix of instruments adopted by one or more governments. A truism in policy studies is that it is difficult to define or identify exactly what policy is because (a) each new instrument adds to a pile of existing measures (with often-unpredictable consequences), and (b) many instruments designed for individual sectors tend, in practice, to intersect in ways that we cannot always anticipate. When you think through any government response to the coronavirus, note how every measure is connected to many others.
Further, it is a truism in public policy that there is a gap between technical and political feasibility: the things that we think will be most likely to work as intended if implemented are often the things that would receive the least support or most opposition. For example:
Redistributing income and wealth to reduce socio-economic inequalities (e.g. to allay fears about the impact of current events on low-income and poverty) seems to be less politically feasible than distributing public services to deal with the consequences of health inequalities.
Providing information and exhortation seems more politically feasible than the direct regulation of behaviour. Indeed, compared to many other countries, the UK Government seems reluctant to introduce ‘quarantine’ style measures to restrict behaviour.
Under ‘normal’ circumstances, governments may be using these distinctions as simple heuristics to help them make modest policy changes while remaining sufficiently popular (or at least looking competent). If so, they are adding or modifying policy instruments during individual ‘windows of opportunity’ for specific action, or perhaps contributing to the sense of incremental change towards an ambitious goal.
Right now, we may be pushing the boundaries of what seems possible, since crises – and the need to address public anxiety – tend to change what seems politically feasible. However, many options that seem politically feasible may not be possible (e.g. to buy a lot of extra medical/ technology capacity quickly), or may not work as intended (e.g. to restrict the movement of people). Think of technical and political feasibility as necessary but insufficient on their own, which is a requirement that rules out a lot of responses.
Typical value judgements relate to efficiency, equity and fairness, the trade-off between individual freedom and collective action, and the extent to which a policy process involves citizens in deliberation.
Normative assessments are based on values such as ‘equality, efficiency, security, democracy, enlightenment’ and beliefs about the preferable balance between state, communal, and market/ individual solutions
‘Specify the objectives to be attained in addressing the problem and the criteria to evaluate the attainment of these objectives as well as the satisfaction of other key considerations (e.g., equity, cost, equity, feasibility)’.
‘Effectiveness, efficiency, fairness, and administrative efficiency’ are common.
Identify (a) the values to prioritise, such as ‘efficiency’, ‘equity’, and ‘human dignity’, and (b) ‘instrumental goals’, such as ‘sustainable public finance or political feasibility’, to generate support for solutions.
Instrumental questions may include: Will this intervention produce the intended outcomes? Is it easy to get agreement and maintain support? Will it make me popular, or diminish trust in me even further?
Step 3 is the most simple-looking but difficult task. Remember that it is a political, not technical, process. It is also a political process that most people would like to avoid doing (at least publicly) because it involves making explicit the ways in which we prioritise some people over others. Public policy is the choice to help some people and punish or refuse to help others (and includes the choice to do nothing).
Policy analysis texts describe a relatively simple procedure of identifying criteria and producing a table (with a solution in each row, and criteria in each column) to compare the trade-offs between each solution. However, these criteria are notoriously difficult to define, and people resolve that problem by exercising power to decide what each term means, and whose interests should be served when they resolve trade-offs. For example, see Stone on whose needs come first, who benefits from each definition of fairness, and how technical-looking processes such as ‘cost benefit analysis’ mask political choices.
Right now, the most obvious and visible trade-off, accentuated in the UK, is between individual freedom and collective action, or the balance between state, communal, and market/ individual solutions. In comparison with many countries (and China and Italy in particular), the UK Government seems to be favouring individual action over state quarantine measures. However, most trade-offs are difficult to categorise
What should be the balance between efforts to minimise the deaths of some (generally in older populations) and maximise the wellbeing of others? This is partly about human dignity during crisis, how we treat different people fairly, and the balance of freedom and coercion.
How much should a government spend to keep people alive using intensive case or expensive medicines, when the money could be spent improving the lives of far more people? This is partly about human dignity, the relative efficiency of policy measures, and fairness.
If you are like me, you don’t really want to answer such questions (indeed, even writing them looks callous). If so, one way to resolve them is to elect policymakers to make such choices on our behalf (perhaps aided by experts in moral philosophy, or with access to deliberative forums). To endure, this unusually high level of deference to elected ministers requires some kind of reciprocal act:
Still, I doubt that governments are making reportable daily choices with reference to a clear and explicit view of what the trade-offs and priorities should be, because their choices are about who will die, and their ability to predict outcomes is limited.
Focus on the outcomes that key actors care about (such as value for money), and quantify and visualise your predictions if possible. Compare the pros and cons of each solution, such as how much of a bad service policymakers will accept to cut costs.
‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.
Estimate the cost of a new policy, in comparison with current policy, and in relation to factors such as savings to society or benefits to certain populations. Use your criteria and projections to compare each alternative in relation to their likely costs and benefits.
Explain potential solutions in sufficient detail to predict the costs and benefits of each ‘alternative’ (including current policy).
Short deadlines dictate that you use ‘logic and theory, rather than systematic empirical evidence’ to make predictions efficiently.
Monitoring is crucial because it is difficult to predict policy success, and unintended consequences are inevitable. Try to measure the outcomes of your solution, while noting that evaluations are contested.
It is difficult to envisage a way for the UK Government to publicise the thinking behind its choices (Step 3) and predictions (Step 4) in a way that would encourage effective public deliberation, rather than a highly technical debate between a small number of academics:
Further, people often call for the UK Government to publicise its expert advice and operational logic, but I am not sure how they would separate it from their normative logic, or provide a frank account without unintended consequences for public trust or anxiety. If so, government policy involves (a) to keep some choices implicit to avoid a lot of debate on trade-offs, and (b) to make general statements about choices when they do not know what their impact will be.
Examine your case through the eyes of a policymaker. Keep it simple and concise.
Make a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups
Client-oriented advisors identify the beliefs of policymakers and tailor accordingly.
‘Unless your client asks you not to do so, you should explicitly recommend one policy’
I now invite you to make a recommendation (step 5) based on our discussion so far (steps 1-4). Define the problem with one framing at the expense of the others. Romanticise some people and not others. Decide how to support some people, and coerce or punish others. Prioritise the lives of some people in the knowledge that others will suffer or die. Do it despite your lack of expertise and profoundly limited knowledge and information. Learn from experts, but don’t assume that only scientific experts have relevant knowledge (decolonise; coproduce). Recommend choices that, if damaging, could take decades to fix after you’ve gone. Consider if a policymaker is willing and able to act on your advice, and if your proposed action will work as intended. Consider if a government is willing and able to bear the economic and political costs. Protect your client’s popularity, and trust in your client, at the same time as protecting lives. Consider if your advice would change if the problem would seem to change. If you are writing your analysis, maybe keep it down to one sheet of paper (and certainly far fewer words than in this post). Better you than me.
Please now watch this video before I suggest that things are not so simple.
Would that policy analysis were so simple
Imagine writing policy analysis in an imaginary world, in which there is a single powerful ‘rational’ policymaker at the heart of government, making policy via an orderly series of stages.
Your audience would be easy to identify at each stage, your analysis would be relatively simple, and you would not need to worry about what happens after you make a recommendation for policy change (since the selection of a solution would lead to implementation). You could adopt a simple 5 step policy analysis method, use widely-used tools such as cost-benefit analysis to compare solutions, and know where the results would feed into the policy process.
Studies of policy analysts describe how unrealistic this expectation tends to be (Radin, Brans, Thissen).
For example, there are many policymakers, analysts, influencers, and experts spread across political systems, and engaging with 101 policy problems simultaneously, which suggests that it is not even clear how everyone fits together and interacts in what we call (for the sake of simplicity) ‘the policy process’.
Instead, we can describe real world policymaking with reference to two factors.
The wider policymaking environment: 1. Limiting the use of evidence
Limited attention, and lurches of attention. Policymakers can only pay attention to a tiny proportion of their responsibilities, and policymaking organizations struggle to process all policy-relevant information. They prioritize some issues and information and ignore the rest.
Power and ideas. Some ways of understanding and describing the world dominate policy debate, helping some actors and marginalizing others.
Beliefs and coalitions. Policymakers see the world through the lens of their beliefs. They engage in politics to turn their beliefs into policy, form coalitions with people who share them, and compete with coalitions who don’t.
Dealing with complexity. They engage in ‘trial-and-error strategies’ to deal with uncertain and dynamic environments (see the new section on trial-and-error- at the end).
Framing and narratives. Policy audiences are vulnerable to manipulation when they rely on other actors to help them understand the world. People tell simple stories to persuade their audience to see a policy problem and its solution in a particular way.
They need to find ways to ignore most evidence so that they can focus disproportionately on some. Otherwise, they will be unable to focus well enough to make choices. The cognitive and organisational shortcuts, described above, help them do it almost instantly.
They also use their experience to help them decide – often very quickly – what evidence is policy-relevant under the circumstances. Relevance can include:
How it relates to the policy problem as they define it (Step 1).
If it relates to a feasible solution (Step 2).
If it is timely, available, understandable, and actionable.
If it seems credible, such as from groups representing wider populations, or from people they trust.
They use a specific shortcut: relying on expertise.
However, the vague idea of trusting or not trusting experts is a nonsense, largely because it is virtually impossible to set a clear boundary between relevant/irrelevant experts and find a huge consensus on (exactly) what is happening and what to do. Instead, in political systems, we define the policy problem or find other ways to identify the most relevant expertise and exclude other sources of knowledge.
In the UK Government’s case, it appears to be relying primarily on expertise from its own general scientific advisers, medical and public health advisers, and – perhaps more controversially – advisers on behavioural public policy.
Right now, it is difficult to tell exactly how and why it relies on each expert (at least when the expert is not in a clearly defined role, in which case it would be irresponsible not to consider their advice). Further, there are regular calls on Twitter for ministers to be more open about their decisions.
However, don’t underestimate the problems of identifying why we make choices, then justifying one expert or another (while avoiding pointless arguments), or prioritising one form of advice over another. Look, for example, at the kind of short-cuts that intelligent people use, which seem sensible enough, but would receive much more intense scrutiny if presented in this way by governments:
Sophisticated speculation by experts in a particular field, shared widely (look at the RTs), but questioned by other experts in another field:
Experts in one field trusting certain experts in another field based on personal or professional interaction:
Experts in one field not trusting a government’s approach based on its use of one (of many) sources of advice:
Experts representing a community of experts, criticising another expert (Prof John Ashton), for misrepresenting the amount of expert scepticism of government experts (yes, I am trying to confuse you):
Expert debate on how well policymakers are making policy based on expert advice
Finding quite-sensible ways to trust certain experts over others, such as because they can be held to account in some way (and may be relatively worried about saying any old shit on the internet):
There are many more examples in which the shortcut to expertise is fine, but not particularly better than another shortcut (and likely to include a disproportionately high number of white men with STEM backgrounds).
Update: of course, they are better than the volume trumps expertise approach:
(f) use their expertise on governance to highlight problems with thoughtless criticism
However, note that most of these experts are from a very narrow social background, and from very narrow scientific fields (first in modelling, then likely in testing), despite the policy problem being largely about (a) who, and how many people, a government should try to save, and (b) how far a government should go to change behaviour to do it (Update 2.4.20: I wrote that paragraph before adding so many people to the list). It is understandable to defer in this way during a crisis, but it also contributes to a form of ‘depoliticisation’ that masks profound choices that benefit some people and leave others vulnerable to harm.
The wider policymaking environment: 2. Limited control
Second, policymakers engage in a messy and unpredictable world in which no single ‘centre’ has the power to turn a policy recommendation into an outcome. I normally use the following figure to think through the nature of a complex and unwieldy policymaking environment of which no ‘centre’ of government has full knowledge or control.
It helps us identify (further) the ways in which we can reject the idea that the UK Prime Minister and colleagues can fully understand and solve policy problems:
Actors. The environment contains many policymakers and influencers spread across many levels and types of government (‘venues’).
For example, consider how many key decisions that (a) have been made by organisations not in the UK central government, and (b) are more or less consistent with its advice, including:
Devolved governments announcing their own healthcare and public health responses (although the level of UK coordination seems more significant than the level of autonomy).
Public sector employers initiating or encouraging at-home working (and many Universities moving quickly from in-person to online teaching)
Private organisations cancelling cultural and sporting events.
Context and events. Policy solutions relate to socioeconomic context and events which can be impossible to ignore and out of the control of policymakers. The coronavirus, and its impact on so many aspects on population health and wellbeing, is an extreme example of this problem.
Networks, Institutions, and Ideas. Policymakers and influencers operate in subsystems (specialist parts of political systems). They form networks or coalitions built on the exchange of resources or facilitated by trust underpinned by shared beliefs or previous cooperation. Many different parts of government have practices driven by their own formal and informal rules. Formal rules are often written down or known widely. Informal rules are the unwritten rules, norms and practices that are difficult to understand, and may not even be understood in the same way by participants. Political actors relate their analysis to shared understandings of the world – how it is, and how it should be – which are often so established as to be taken for granted. These dominant frames of reference establish the boundaries of the political feasibility of policy solutions. These kinds of insights suggest that most policy decisions are considered, made, and delivered in the name of – but not in the full knowledge of – government ministers.
Trial and error policymaking in complex policymaking systems (17.3.20)
There are many ways to conceptualise this policymaking environment, but few theories provide specific advice on what to do, or how to engage effectively in it. One notable exception is the general advice that comes from complexity theory, including:
Law-like behaviour is difﬁcult to identify – so a policy that was successful in one context may not have the same effect in another.
Policymaking systems are difﬁcult to control; policy makers should not be surprised when their policy interventions do not have the desired effect.
Policy makers in the UK have been too driven by the idea of order, maintaining rigid hierarchies and producing top-down, centrally driven policy strategies. An attachment to performance indicators, to monitor and control local actors, may simply result in policy failure and demoralised policymakers.
Policymaking systems or their environments change quickly. Therefore, organisations must adapt quickly and not rely on a single policy strategy.
On this basis, there is a tendency in the literature to encourage the delegation of decision-making to local actors:
Rely less on central government driven targets, in favour of giving local organisations more freedom to learn from their experience and adapt to their rapidly-changing environment.
To deal with uncertainty and change, encourage trial-and-error projects, or pilots, that can provide lessons, or be adopted or rejected, relatively quickly.
Encourage better ways to deal with alleged failure by treating ‘errors’ as sources of learning (rather than a means to punish organisations) or setting more realistic parameters for success/ failure (although see this example and this comment).
Encourage a greater understanding, within the public sector, of the implications of complex systems and terms such as ‘emergence’ or ‘feedback loops’.
In other words, this literature, when applied to policymaking, tends to encourage a movement from centrally driven targets and performance indicators towards a more flexible understanding of rules and targets by local actors who are more able to understand and adapt to rapidly-changing local circumstances.
Now, just imagine the UK Government taking that advice right now. I think it is fair to say that it would be condemned continuously (even more so than right now). Maybe that is because it is the wrong way to make policy in times of crisis. Maybe it is because too few people are willing and able to accept that the role of a small group of people at the centre of government is necessarily limited, and that effective policymaking requires trial-and-error rather than a single, fixed, grand strategy to be communicated to the public. The former highlights policy that changes with new information and perspective. The latter highlights errors of judgement, incompetence, and U-turns. In either case, the advice is changing as estimates of the coronavirus’ impact change:
I think this tension, in the way that we understand UK government, helps explain some of the criticism that it faces when changing its advice to reflect changes in its data or advice. This criticism becomes intense when people also question the competence or motives of ministers (and even people reporting the news) more generally, leading to criticism that ranges from mild to outrageous:
For me, this casual reference to a government policy to ‘cull the heard of the weak’ is outrageous, but you can find much worse on Twitter. It reflects wider debate on whether ‘herd immunity’ is or is not government policy. Much of it relates to interpretation of government statements, based on levels of trust/distrust in the UK Government, its Prime Minister and Secretaries of State, and the Prime Minister’s special adviser
However, I think that some of it is also about:
1. Wilful misinterpretation (particularly on Twitter). For example, in the early development and communication of policy, Boris Johnson was accused (in an irresponsibly misleading way) of advocating for herd immunity rather than restrictive measures.
Below is one of the most misleading videos of its type. Look at how it cuts each segment into a narrative not provided by ministers or their advisors (see also this stinker):
2. The accentuation of a message not being emphasised by government spokespeople.
See for example this interview, described by Sky News (13.3.20) as: The government’s chief scientific adviser Sir Patrick Vallance has told Sky News that about 60% of people will need to become infected with coronavirus in order for the UK to enjoy “herd immunity”. You might be forgiven for thinking that he was on Sky extolling the virtues of a strategy to that end (and expressing sincere concerns on that basis). This was certainly the write-up in respected papers like the FT (UK’s chief scientific adviser defends ‘herd immunity’ strategy for coronavirus). Yet, he was saying nothing of the sort. Rather, when prompted, he discussed herd immunity in relation to the belief that COVID-19 will endure long enough to become as common as seasonal flu.
The same goes for Vallance’s interview on the same day (13.3.20) during Radio 4’s Today programme (transcribed by the Spectator, which calls Vallance the author, and gives ittheheadline “How ‘herd immunity’ can help fight coronavirus” as if it is his main message). The Today Programme also tweeted only 30 seconds to single out that brief exchange:
Yet, clearly his overall message – in this and other interviews – was that some interventions (e.g. staying at home; self-isolating with symptoms) would have bigger effects than others (e.g. school closures; prohibiting mass gatherings) during the ‘flattening of the peak’ strategy (‘What we don’t want is everybody to end up getting it in a short period of time so that we swamp and overwhelm NHS services’). Rather than describing ‘herd immunity’ as a strategy, he is really describing how to deal with its inevitability (‘Well, I think that we will end up with a number of people getting it’).
[OK, that proved to be a big departure from the trial-and-error discussion. Here we are, back again]
In some cases, maybe people are making the argument that trial-and-error is the best way to respond quickly, and adapt quickly, in a crisis but that the UK Government version is not what, say, the WHO thinks of as good kind of adaptive response. It is not possible to tell, at least from the general ways in which they justify acting quickly.
See also the BBC’s provocative question (which I expect to be replaced soon):
The take home messages
The coronavirus is an extreme example of a general situation: policymakers will always have very limited knowledge of policy problems and control over their policymaking environment. They make choices to frame problems narrowly enough to seem solvable, rule out most solutions as not feasible, make value judgements to try help some more than others, try to predict the results, and respond when the results to not match their hopes or expectations.
This is not a message of doom and despair. Rather, it encourages us to think about how to influence government, and hold policymakers to account, in a thoughtful and systematic way that does not mislead the public or exacerbate the problem we are seeing.
Further reading, until I can think of a better conclusion:
This series of ‘750 words’ posts summarises key texts in policy analysis and tries to situate policy analysis in a wider political and policymaking context. Note the focus on whose knowledge counts, which is not yet a big feature of this crisis.
These series of 500 words and 1000 words posts (with podcasts) summarise concepts and theories in policy studies.
On the 17th May, Professor Paul Cairney (University of Stirling) and Dr John Boswell (University of Southampton) led a discussion on ‘institutionalising’ preventive health with key people working with the Scottish Government and COSLA to reform public health in Scotland, including members of the Programme Board, the Oversight Board, Commission leads and members of the senior teams in NHS Health Scotland and Public Health and Intelligence. They drew on their published work, co-authored with Dr Emily St Denny (University of Stirling), to examine the role of evidence in policy and the lessons from comparable experiences in other public health agencies (in England, New Zealand and Australia).
This post summarises their presentation, reflections from the panel, group-work in the afternoon, and post-event feedback.
The Academic Argument
Governments face two major issues when they try to improve population health and reduce health inequalities:
Should they ‘mainstream’ policies – to help prevent ill health and reduce health inequalities – across government and/ or set up a dedicated government agency?
Should an agency ‘speak truth to power ‘and seek a high profile to set the policy agenda?
Our research provides three messages to inform policy and practice:
When governments have tried to mainstream ‘preventive’ policies, they have always struggled to explain what prevention means and reform services to make them more preventive than reactive.
Public health agencies could set a clearer and more ambitious policy agenda. However, successful agencies keep a low profile and make realistic demands for policy change. In the short term, they measure success according to their own survival and their ability to maintain the positive attention of policymakers.
Advocates of policy change often describe ‘evidence based policy’ as the answer. However, a comparison between (a) specific tobacco policy change and (b) very general prevention policy shows that the latter’s ambiguity hinders the use of evidence for policy. Governments use three different models of evidence-informed policy. These models are internally consistent but they draw on assumptions and practices that are difficult to mix and match. Effective evidence use requires clear aims driven by political choice.
Overall, they warn against treating any response – (a) the idiom ‘prevention is better than cure’, (b) setting up a public health agency, or (c) seeking ‘evidence based policy’ – as a magic bullet. Major public health changes require policymakers to define their aims, and agencies to endure long enough to influence policy and encourage the consistent use of models of evidence-informed policy.
The Panel Discussion
The panel discussion produced a series of positive and sensible suggestions about the way forward, including the need to:
Make a strong political case for the idea of a ‘social return on investment’, in which every £1 spent on preventive work produces far more valuable long term returns.
Establish respect for the work of a public health agency in a political context.
Build on the fact that the broad argument for prevention has been won within Scottish central and local government.
Ensure a shift in culture, to maximise partnership working and foster leadership skills among a larger number of people (than associated with a hierarchical model of leadership).
Take forward work by the Christie Commission on reforming public services (such as to ‘empower individuals and communities’, ‘integrate service provision’, ‘prevent negative outcomes from arising’, and ‘become more efficient’).
However, we noted that Christie – and the Scottish Government’s ‘decisive shift to prevention’ – took place eight years ago. We also describe (in Why Isn’t Government Policy More Preventive?) a historic tendency for the ‘same cycle to be repeated without resolution’: an ‘initial period of enthusiasm and activity’ is replaced in a few years by ‘disenchantment and inactivity’.
In that context, our challenge is: what will make the difference this time?
The group discussion
The group discussion took on a ‘world café’ format in which people moved around each space, providing ideas according to theme. The main questions – and three key answers per question – include:
How can we engage well with members of the public?
Establish a brand, digital presence, public role, and approach to ‘social marketing’.
Choose a consistent model of ‘co-production’ based on what you want from your relationship with service users.
Choose how to balance the need to give consistent population-wide advice, and advice tailored to specific communities.
How can we encourage and maintain a public health community?
Address perceptions of power and status in the NHS and local government.
Clarify what evidence counts, and how to gather and use it.
Balance the need for modest ‘quick wins’ (for PHS endurance) with the need to maintain an ambitious advocacy-focused agenda (for community morale).
How can the NHS and local government work well in partnership?
Address immediate important issues: contracts of employment, union recognition and support, location.
Identify cross-system partnership issues: the boundaries between NHS/ Local authority work, working with local governments directly or via COSLA, how to balance your time between core work and partnership work, and how to work with each other’s stakeholders.
Address the possible tensions between national NHS work and local variation and accountability.
How can PHS keep public health high on the ministerial agenda?
Use advocacy to generate public attention to evidence-informed policy solutions.
Frame solutions in different ways to different audiences, to appeal to national ministers and local politicians.
Generate an understanding of how to work closely with stakeholders and policymakers without undermining an image of PHS independence.
How can PHS focus on the bigger picture?
Develop a strategy to stay informed about, and seek to influence, policies reserved to the UK.
Develop a more detailed ‘health in all policies’ strategy: clarify aims, identify key policymakers, develop a strategy to influence policymakers beyond ‘health’.
Develop a strategy to deal with a complex media landscape: from personal relationships with key journalists to less personal messaging for social media.
Post Event Feedback
Feedback from the event was generally positive. Attendees appreciated the time and space to come together with PHS team leaders to discuss next steps. The feedback suggests that the academic presentation helped challenge or shape group assumptions, by:
Questioning if attendees agreed on key issues. What is prevention? What counts as good evidence? What models of evidence-informed policy should we recommend? From whom should we learn?
Shifting attitudes about what counts as agency success (survival!) and what strategies help achieve it (such as by stealth rather than always speaking truth to power).
From this discussion, it is clear that Public Health Scotland will happen, and its general remit and ambition is clear. However, to ensure that PHS becomes successful requires grappling with the inevitable dilemmas that confront policymakers – and advisers to policymakers – in such complex terrain. Perhaps the key theme of the reflective discussion was the role of clear choice to address important trade-offs:
balancing the imperative to speak ‘uncomfortable truths’ with the need to retain the trust and attention of government
pursuing evidence-informed policymaking but with sufficient flexibility to enable cooperation across different approaches
choosing with whom to collaborate to maximise impact but maintain credibility
working out how to retain long-term support from the public health community in the face of short-term disagreements and disappointments
to work for the public (in the background) or with the public (in the foreground) in pursuit of preventive aims.
Some of these strategic choices are more pressing than others. Some can be resolved decisively while others will require an ongoing balancing act. However, each choice requires a commitment to realistic and continuous dialogue and reflection on what (a) PHS can seek to achieve, and (b) what it can realistically expect central and local governments to do.
This post contains preliminary notes for my keynote speech ‘The politics of evidence-based policymaking’ for the COPOLAD annual conference, ‘From evidence to practice: challenges in the field of drugs policies’ (14th June). I may amend them in the run up to the speech (and during their translation into Spanish).
COPOLAD (Cooperation Programme on Drugs Policies) is a ‘partnership cooperation programme between the European Union, Latin America and the Caribbean countries aiming at improving the coherence, balance and impact of drugs policies, through the exchange of mutual experiences, bi-regional coordination and the promotion of multisectoral, comprehensive and coordinated responses’. It is financed by the EU.
My aim is to draw on policy studies, and the case study of tobacco/ public health policy, to identify four lessons:
‘Evidence-based policymaking’ is difficult to describe and understand, but we know it’s a highly political process which differs markedly from ‘evidence based medicine’.
Actors focus as much on persuasion to reduce ambiguity as scientific evidence to reduce uncertainty. They also develop strategies to navigate complex policymaking ‘systems’ or ‘environments’.
Tobacco policy demonstrates three conditions for the proportionate uptake of evidence: it helps ‘reframe’ a policy problem; it is used in an environment conducive to policy change; and, policymakers exploit ‘windows of opportunity’ for change.
Even the ‘best cases’ of tobacco control highlight a gap of 20-30 years between the production of scientific evidence and a proportionate policy response. In many countries it could be 50. I’ll use this final insight to identify some scenarios on how evidence might be used in areas, such as drugs policy, in which many of the ‘best case’ conditions are not met.
‘Evidence-based policymaking’ is highly political and difficult to understand
Evidence-based policymaking (EBPM) is so difficult to understand that we don’t know how to define it or each word in it! People use phrases like ‘policy-based evidence’, to express cynicism about the sincere use of evidence to guide policy, or ‘evidence informed policy’, to highlight its often limited impact. It is more important to try to define each element of EBPM – to identify what counts as evidence, what is policy, who are the policymakers, and what an ‘evidence-based’ policy would look like – but this is easier said than done.
In fact, it is far easier to say what EBPM is not:
Policymakers translate their values into policy in a straightforward manner – they know what they want and about the problem they seek to solve.
Policymakers and governments can gather and understand all information required to measure the problem and determine the effectiveness of solutions.
Instead, we talk of ‘bounded rationality’ and how policymakers deal with it. They employ two kinds of shortcut: ‘rational’, by pursuing clear goals and prioritizing certain kinds and sources of information, and ‘irrational’, by drawing on emotions, gut feelings, deeply held beliefs, habits, and what is familiar to them, to make decisions quickly.
It does not take place in a policy cycle with well-ordered stages
‘Policy cycle’ describes the ides that there is a core group of policymakers at the ‘centre’, making policy from the ‘top down’, and pursuing their goals in a series of clearly defined and well-ordered stages, such as: agenda setting, policy formulation, legitimation, implementation, and evaluation.
It does not describe or explain policymaking well. Instead, we tend to identify the role of environments or systems.
a wide range of actors (individuals and organisations) influencing policy at many levels of government
a proliferation of rules and norms followed by different levels or types of government
important relationships (‘networks’) between policymakers and powerful actors (with material resources, or the ability to represent a profession or social group)
a tendency for certain ‘core beliefs’ or ‘paradigms’ to dominate discussion
shifting policy conditions and events that can prompt policymaker attention to lurch at short notice.
When describing complex policymaking systems we show that, for example, (a) the same inputs of evidence or policy activity can have no, or a huge, effect, and (b) policy outcomes often ‘emerge’ in the absence of central government control (which makes it difficult to know how, and to whom, to present evidence or try to influence).
It does not resemble ‘evidence based medicine’ or the public health culture
In health policy we can identify an aim, associated with ‘evidence-based medicine’ (EBM), to:
(a) gather the best evidence on the effectiveness of policy interventions, based on a hierarchy of research methods which favours, for example, the systematic review of randomised control trials (RCTs)
(b) ensure that this evidence has a direct impact on healthcare and public health, to exhort practitioners to replace bad interventions with good, as quickly as possible.
Instead, (a) policymakers can ignore the problems raised by scientific evidence for long periods of time, only for (b) their attention to lurch, prompting them to beg, borrow, or steal information quickly from readily available sources. This can involve many sources of evidence (such as the ‘grey literature’) that some scientists would not describe as reliable.
Actors focus as much on persuasion to reduce ambiguity as scientific evidence to reduce uncertainty.
In that context, ‘evidence-based policymaking’ is about framing problems and adapting to complexity.
Framing refers to the ways in which policymakers understand, portray, and categorise issues. Problems are multi-faceted, but bounded rationality limits the attention of policymakers, and actors compete to highlight one ‘image’ at the expense of others. The outcome of this process determines who is involved (for example, portraying an issue as technical limits involvement to experts), who is responsible for policy, how much attention they pay, their demand for evidence on policy solutions, and what kind of solution they favour.
Scientific evidence plays a part in this process, but we should not exaggerate the ability of scientists to win the day with reference to evidence. Rather, policy theories signal the strategies that actors adopt to increase demand for their evidence:
to combine facts with emotional appeals, to prompt lurches of policymaker attention from one policy image to another (punctuated equilibrium theory)
to tell simple stories which are easy to understand, help manipulate people’s biases, apportion praise and blame, and highlight the moral and political value of solutions (narrative policy framework)
to interpret new evidence through the lens of the pre-existing beliefs of actors within coalitions, some of which dominate policy networks (advocacy coalition framework)
to produce a policy solution that is feasible and exploit a time when policymakers have the opportunity to adopt it (multiple streams analysis).
This takes place in complex ‘systems’ or ‘environments’
A focus on this bigger picture shifts our attention from the use of evidence by an elite group of elected policymakers at the ‘top’ to its use by a wide range of influential actors in a multi-level policy process. It shows actors that:
They are competing with many others to present evidence in a particular way to secure a policymaker audience.
Support for particular solutions varies according to which organisation takes the lead and how it understands the problem.
Some networks are close-knit and difficult to access because bureaucracies have operating procedures that favour particular sources of evidence and some participants over others
There is a language – indicating which ideas, beliefs, or ways of thinking are most accepted by policymakers and their stakeholders – that takes time to learn.
Well-established beliefs provide the context for policymaking: new evidence on the effectiveness of a policy solution has to be accompanied by a shift of attention and successful persuasion.
In some cases, social or economic ‘crises’ can prompt lurches of attention from one issue to another, and some forms of evidence can be used to encourage that shift. However, major policy shifts are rare.
In other words, successful actors develop pragmatic strategies based on the policy process that exists, not the process they’d like to see
We argue that successful actors: identify where the ‘action is’ (in networks and organisations in several levels of government); learn and follow the ‘rules of the game’ within networks to improve strategies and help build up trust; form coalitions with actors with similar aims and beliefs; and, frame the evidence to appeal to the biases, beliefs, and priorities of policymakers.
Tobacco policy demonstrates three conditions for the proportionate uptake of evidence
Case studies allow us to turn this general argument into insights generated from areas such as public health.
There are some obvious and important differences between tobacco and (illegal) drugs policies, but an initial focus on tobacco allows us to consider the conditions that might have to be met to use the best evidence on a problem to promote (what we consider to be) a proportionate and effective solution.
We can then use the experience of a ‘best case scenario’ to identify the issues that we face in less ideal circumstances (first in tobacco, and second in drugs).
Our studies help us identify the conditions under which scientific evidence, on the size of the tobacco problem and the effectiveness of solutions, translates into a public policy response that its advocates would consider to be proportionate.
Actors are able to use scientific evidence to persuade policymakers to pay attention to, and shift their understanding of, policy problems.
Although scientific evidence helps reduce uncertainty, it does not reduce ambiguity. Rather, there is high competition to define problems, and the result of this competition helps determine the demand for subsequent evidence.
In tobacco, the evidence on smoking and then passive smoking helped raise attention to public health, but it took decades to translate into a proportionate response, even in ‘leading’ countries such as the UK.
The comparison with ‘laggard’ countries is crucial to show that the same evidence can produce a far more limited response, as policymakers compare the public health imperative with other ‘frames’, relating to their beliefs on personal responsibility, civil liberties, and the economic consequences of tobacco controls.
The policy environment becomes conducive to policy change.
Public health debates take place in environments more or less conducive to policy change. In the UK, actors used scientific evidence to help reframe the problem. Then, this new understanding helped give the Department of Health a greater role, the health department fostered networks with public health and medical groups at the expense of the industry and, while pursuing policy change, policymakers emphasised the reduced opposition to tobacco control, smoking prevalence, and economic benefits to tobacco,.
In many other countries, these conditions are far less apparent: there are multiple tobacco frames (including economic and civil liberties); economic and trade departments are still central to policy; the industry remains a key player; and, policymakers pay more attention to opposition to tobacco controls (such as bans on smoking in public places) and their potential economic consequences.
Further, differences between countries have largely endured despite the fact that most countries are parties to the FCTC. In other words, a commitment to evidence based ‘policy transfer’ does not necessarily produce actual policy change.
Even in favourable policy environments, it is not inevitable that major policy changes will occur. Rather, the UK’s experience of key policy instruments – such as legislation to ban smoking in public places (a major commitment of the FCTC) – shows the high level of serendipity involved in the confluence of three necessary but insufficient conditions:
high policymaker attention to tobacco as a policy problem
the production of solutions, introducing partial or comprehensive bans on smoking in public places, that are technically and politically feasible
the willingness and ability of policymakers to choose the more restrictive solution.
In many other countries, there has been no such window of opportunity, or only an opportunity for a far weaker regulation.
So, this condition – the confluence of three ‘streams’ during a ‘window of opportunity’ – shows the major limits to the effect of scientific evidence. The evidence on the health effects of passive smoking have been available since the 1980s, but they only contributed to comprehensive smoking bans in the UK in the mid-2000s, and they remain unlikely in many other countries.
Comparing ‘best case’ and ‘worst case’ scenarios for policy change
These discussions help us clarify the kinds of conditions that need to be met to produce major ‘evidence based’ policy change, even when policymakers have made a commitment to it, or are pursuing an international agreement.
I provide a notional spectrum of ‘best’ and ‘worst’ case scenarios in relation to these conditions:
Actors agree on how to gather and interpret scientific evidence.
Best case: governments fund effective ways to gather and interpret the most relevant evidence on the size of policy problems and the effectiveness of solutions. Policymakers can translate large amounts of evidence on complex situations into simple and effective stories (that everyone can understand) to guide action. This includes evidence of activity in one’s own country, and of transferable success from others.
Worst case: governments do not know the size of the problem or what solutions have the highest impacts. They rely on old stories that reinforce ineffective action, and do not know how to learn from the experience of other regions (note the ‘not invented here’ issue).
Actors ‘frame’ the problem simply and/or unambiguously.
Best case: governments maintain a consensus on how best to understand the cause of a policy problem and therefore which evidence to gather and solutions to seek.
Worst case: governments juggle many ‘frames’, there is unresolved competition to define the problem, and the best sources of evidence and solutions remain unclear.
A new policy frame is not undermined by the old way of thinking about, and doing, things
Best case: the new frame sets the agenda for actors in existing organisations and networks; there is no inertia linked to the old way of thinking about and doing things.
Worst case: there is a new policy, but it is undermined by old beliefs, rules, pre-existing commitments (for example, we talk of ‘path dependence’ and ‘inheritance before choice’), or actors opposed to the new policy.
There is a clear ‘delivery chain’ from policy choice to implementation
Best case: policymakers agree on a solution, they communicate their aims well, and they secure the cooperation of the actors crucial to policy delivery in many levels and types of government.
Worst case: policymakers communicate an ambiguous message and/ or the actors involved in policy delivery pursue different – and often contradictory – ways to try to solve the same problem.
In international cooperation, it is natural to anticipate and try to minimise at least some of these worst case scenarios. Problems are more difficult to solve when they are transnational. Our general sense of uncertainty and complexity is more apparent when there are many governments involved and we cannot rely on a single authoritative actor to solve problems. Each country (and regions within it) has its own beliefs and ways of doing things, and it is not easy to simply emulate another country (even if we think it is successful and know why). Some countries do not have access to the basic information (for example, on health and mortality, alongside statistics on criminal justice) that others take for granted when they monitor the effectiveness of policies.
Further, these obstacles exist in now-relatively-uncontroversial issues, such as tobacco, in which there is an international consensus on the cause of the problem and the appropriateness and effectiveness of public solutions. It is natural to anticipate further problems when we also apply public health (and, in this case, ‘harm reduction’) measures to more controversial areas such as illegal drugs.
Three things to remember when you are trying to close the ‘evidence-policy gap’
Last week, a new major report on The Science of Using Science: Researching the Use of Research Evidence in Decision-Making suggested that there is very limited evidence of ‘what works’ to turn scientific evidence into policy. There are many publications out there on how to influence policy, but few are proven to work.
This is because scientists think about how to produce the best possible evidence rather than how different policymakers use evidence differently in complex policymaking systems (what the report describes as the ‘capability, motivation, and opportunity’ to use evidence). For example, scientists identify, from their perspective, a cultural gap between them and policymakers. This story tells us that we need to overcome differences in the languages used to communicate findings, the timescales to produce recommendations, and the incentives to engage.
This scientist perspective tends to assume that there is one arena in which policymakers and scientists might engage. Yet, the action takes place in many venues at many levels involving many types of policymaker. So, if we view the process from many different perspectives we see new ways in which to understand the use of evidence.
First, we must choose what counts as ‘the evidence’. In some academic disciplines there is a strong belief that some kinds of evidence are better than others: the best evidence is gathered using randomised control trials and accumulated in systematic reviews. In others, these ideas have limited appeal or are rejected outright, in favour of (say) practitioner experience and service user-based feedback as the knowledge on which to base policies. Most importantly, policymakers may not care about these debates; they tend to beg, borrow, or steal information from readily available sources.
Second, we must choose the lengths to which we are prepared to go ensure that scientific evidence is the primary influence on policy delivery. When we open up the ‘black box’ of policymaking we find a tendency of central governments to juggle many models of government – sometimes directing policy from the centre but often delegating delivery to public, third, and private sector bodies. Those bodies can retain some degree of autonomy during service delivery, often based on governance principles such as ‘localism’ and the need to include service users in the design of public services.
This presents a major dilemma for scientists because policy solutions based on RCTs are likely to come with conditions that limit local discretion. For example, a condition of the UK government’s license of the ‘family nurse partnership’ is that there is ‘fidelity’ to the model, to ensure the correct ‘dosage’ and that an RCT can establish its effect. It contrasts with approaches that focus on governance principles, such as ‘my home life’, in which evidence – as practitioner stories – may or may not be used by new audiences. Policymakers may not care about the profound differences underpinning these approaches, preferring to use a variety of models in different settings rather than use scientific principles to choose between them.
Third, scientists must recognise that these choices are not ours to make. We have our own ideas about the balance between maintaining evidential hierarchies and governance principles, but have no ability to impose these choices on policymakers.
This point has profound consequences for the ways in which we engage in strategies to create impact. A research design to combine scientific evidence and governance seems like a good idea that few pragmatic scientists would oppose. However, this decision does not come close to settling the matter becausethese compromises look very different when designed by scientists or policymakers.
Take for example the case of ‘improvement science’ in which local practitioners are trained to use evidence to experiment with local pilots and learn and adapt to their experiences. Improvement science-inspired approaches have become very common in health sciences, but in many examples the research agenda is set by research leads and it focuses on how to optimise delivery of evidence-based practice.
Consequently, improvement science appears to offer pragmatic solutions to the gap between divergent approaches, but only because they mean different things to different people. Its adoption is only one step towards negotiating the trade-offs between RCT-driven and story-telling approaches.
These examples help explain why we know so little about how to influence policy. They take us beyond the bland statement – there is a gap between evidence and policy – trotted out whenever scientists try and maximise their own impact. The alternative is to try to understand the policy process, and the likely demand for and uptake of evidence, before working out how to produce evidence that would fit into the process. This different mind-set requires a far more sophisticated knowledge of the policy process than we see in most studies of the evidence-policy gap. Before trying to influence policymaking, we should try to understand it.
The initial further reading uses this table to explore three ways in which policymakers, scientists, and other groups have tried to resolve the problems we discuss:
This academic journal article (in Evidence and Policy) highlights the dilemmas faced by policymakers when they have to make two choices at once, to decide: (1) what is the best evidence, and (2) how strongly they should insist that local policymakers use it. It uses the case study of the ‘Scottish Approach’ to show that it often seems to favour one approach (‘approach 3’) but actually maintains three approaches. What interests me is the extent to which each approach contradicts the other. We might then consider the cause: is it an explicit decision to ‘let a thousand flowers bloom’ or an unintended outcome of complex government?
Well, it’s really a set of messages, geared towards slightly different audiences, and summed up by this table:
This academic journal article (in Evidence and Policy) highlights the dilemmas faced by policymakers when they have to make two choices at once, to decide: (1) what is the best evidence, and (2) how strongly they should insist that local policymakers use it. It uses the case study of the ‘Scottish Approach’ to show that it often seems to favour one approach (‘approach 3’) but actually maintains three approaches. What interests me is the extent to which each approach contradicts the other. We might then consider the cause: is it an explicit decision to ‘let a thousand flowers bloom’ or an unintended outcome of complex government?
This is the second of three posts which use case studies of cross-cutting and specific policy areas to add more depth to our discussion of Scottish politics and policymaking.
Most aspects of health policy have been devolved since 1999, and many were devolved before 1999, so we can generate a relatively long term picture of policy change/ divergence in three key areas: healthcare, mental health, and public health. We can then revisit the idea of prevention and inequalities raised in the first lecture.
The NHS has always been a little bit different in Scotland, which enjoyed administrative devolution – through the Scottish Office (a UK Government Department) – before 1999 and maintained its own links with professional groups.
Scotland has traditionally trained a disproportionate number of UK doctors and maintained an unusually high presence of Royal Colleges. This greater medical presence boosted the Scottish Office’s policymaking image as ‘professionalised’, or more likely to pursue policies favoured by the medical profession than the UK’s Department of Health. For example, it appeared to be less supportive of reforms based on the ‘marketisation’ of the NHS.
For example, while the UK Labour Government furthered the ‘internal market’ established by its Conservative predecessors, the Labour-led Scottish Government seemed to dismantle it (for example, there are no Foundation hospitals). It also bought (and effectively renationalised) a private hospital, which had a symbolic importance way above its practical effect.
Since 2007, the SNP-led Scottish Government – often supported publicly by UK-wide groups such as the British Medical Association (and nursing and allied health professions) – has gone big on this difference between Scottish and UK Government policies, criticising the marketization of the NHS in England and expressing, at every opportunity, the desire to maintain the sort of NHS portrayed by Danny Boyle at the Olympics opening ceremony.
This broad approach is generally supported, at least implicitly, by the important political parties in Scotland (the SNP is competing with a centre-left Labour Party and the Conservatives are less important). It is also supported by a medical profession and a public that, in practice, tends to be more committed to the NHS (in other words, opinion polls may not always show a stark difference in attitudes, but there is not the same fear in Scotland, as in the South-East of England, that doctors and patients might defect to the private sector if the NHS is not up to scratch).
To some extent, early Scottish Governments developed an international reputation for innovation in some areas relating to wellbeing. It also reformed mental health and capacity legislation in a relatively quick and smooth way – at least compared to the UK Labour Government, which had a major stand-off with virtually all mental health advocacy groups on psychiatric-based reforms. Part of the difference relates to the size of Scotland and its government’s responsibilities which can produce a distinctive policy style; it often has the ability to coordinate cross-cutting policy, in consultation with stakeholders, in a more personal way. However, this is a field in which there tend to be often-similar policies beyond the Sun-style headlines.
The bigger picture of continuity: a tax funded service
These Scottish-UK differences should be seen in the context of a shared history and some major similarities. Both NHS systems are primarily tax-funded and free at the point of use, with the exception of some charges in England (which should not be exaggerated – for example, 89% of prescriptions in England are tax-funded). Both governments have sought to assure the public in similar ways by, for example, maintaining high profile targets on waiting times. Both systems face similar organisational pressures, such as the balance between a public demand for local hospitals and medical demand for centralised services. Both governments face similar demographic changes which put pressure on services. Both have similarly healthy (or unhealthy) populations.
The bigger picture of prevention and health inequality
Although the Scottish Government pursues an agenda on prevention to reduce service demand and health inequalities, many other policies based on the idea of universal provision have the potential to exacerbate inequalities.
For example, a real rise in spending (cash spending adjusted with the GDP deflator) on health policy of 68% from 2000-11 did not have a major effect on health inequalities (Cairney and McGarvey, 2013: 229). Instead, Scottish Governments tended to use the money in areas such as acute care to, for example, maintain high profile waiting list (non-emergency operations) and waiting times (A&E) targets which did not have a health inequalities component (Cairney, 2011: 177-9). It has also phased out several charges, such on prescriptions and eye tests, which increase spending without decreasing inequalities (particularly since the lowest paid already qualified for exemptions for charges).
It has pursued strongly a public health strategy geared, in part, towards reducing health inequalities, but with the same tendency as in the UK for healthcare to come first. This process includes interesting overlaps in aims and outcomes, such as in tobacco control where smoking is addressed strongly partly because it represents the single biggest element of health inequalities, but most initiatives do not necessarily reduce inequalities in smoking.
The United Kingdom now has one of the most comprehensive tobacco control policies in the world, a far cry from its status two decades ago. Some influential public health voices have called for a similar campaign against alcohol consumption. But is the comparison appropriate? We identify the factors which were important in the relatively successful campaign for tobacco control, then analyse the obstacles and opportunities facing the movement for more stringent alcohol control. Alcohol policy today bears a striking resemblance to tobacco policy pre-1990s, when the UK started on its path to becoming a major regulatory state in the world. Can alcohol policy be changed in a similar way?
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.