Category Archives: agenda setting

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

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.

The very-long version of this post takes us through those steps in the UK, and situates them in a wider political and policymaking context. This post is shorter, and only scratches the surface of analysis.

5 steps to policy analysis

  1. Define the problem.

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

  1. Identify technically and politically feasible solutions.

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

  1. Use values and goals to compare solutions.

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

  1. Predict the outcome of each feasible solution.

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

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

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

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

  1. Policymakers must ignore almost all evidence

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

  1. They do not control the policy process.

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

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

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

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

 

Further reading:

The longer report sets out these arguments in much more detail, with some links to further thoughts and developments.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

https://twitter.com/BorisJohnson/status/1246358936585986048

https://twitter.com/BorisJohnson/status/1243496858095411200

https://twitter.com/R_S_P_H/status/1242833029728477188

Step 1 Define the problem

Common advice in policy analysis texts:

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

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

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

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

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

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

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

https://twitter.com/AdamJKucharski/status/1238152492178976769

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

Uncertainty about the coronavirus plus uncertainty about policy impact

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

https://www.youtube.com/watch?time_continue=350&v=blkDulsgh3Q&feature=emb_logo

A summary of this video suggests:

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

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

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

https://twitter.com/ChrisGiles_/status/1247458186300456960

https://twitter.com/d_spiegel/status/1248157520943857665

https://twitter.com/d_spiegel/status/1247824140645683205

https://twitter.com/EmergMedDr/status/1250039068890726400

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

https://twitter.com/DrSamSims/status/1247445729439895555

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

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

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

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

https://twitter.com/STVNews/status/1238468179036459008

https://twitter.com/DHSCgovuk/status/1238540941717356548

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

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

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

https://twitter.com/luciadambruoso/status/1246361265909444608

https://twitter.com/anandMenon1/status/1246712962519310337

From uncertainty to ambiguity

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

  1. The health of the whole population or people who would be affected disproportionately by the illness.
  • For example, the difference in emphasis affects the health advice for the relatively vulnerable (and the balance between exhortation and reassurance)

https://twitter.com/colinrtalbot/status/1238227267471527937?s=09

https://twitter.com/hacscot/status/1240588827829436416?s=09

https://twitter.com/lisatrigg/status/1249670660802187266

 

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

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

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

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

https://twitter.com/closethepaygap/status/1244579870392422400

https://twitter.com/heyDejan/status/1238944695260233728?s=09

https://twitter.com/TimothyNoah1/status/1240375741809938433

https://twitter.com/politicshome/status/1249236632009691136?s=09

 

https://twitter.com/NPR/status/1246837779474120705?s=09

https://twitter.com/povertyscholar/status/1246487621230092294

https://twitter.com/Yamiche/status/1248028548998344708

https://twitter.com/MalindaSmith/status/1247281226274107392

https://twitter.com/Jas_Athwal/status/1248875273568878592?s=09

https://twitter.com/GKBhambra/status/1248874500764073989

cc

https://twitter.com/sunny_hundal/status/1247454112762990592

https://twitter.com/olivernmoody/status/1248260326140805125

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

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

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

https://twitter.com/kevinstoneUWE/status/1240000285046640645?s=09

https://twitter.com/colinimckay/status/1240721797731045378?s=09

https://twitter.com/heytherehurley/status/1242113416103432195

https://twitter.com/stellacreasy/status/1244022413865648128

https://twitter.com/NIOgov/status/1246482663738871811

https://twitter.com/refugeecouncil/status/1243842703680471040

https://twitter.com/libertyhq/status/1248173788598013953

https://twitter.com/TheLancet/status/1246039259880054784

https://twitter.com/profhrs/status/1247572112061222914

https://twitter.com/HumzaYousaf/status/1248262165657722885

  • For example, governments cannot ignore the impact of their actions on the economy, however much they emphasise mortality, health, and wellbeing. Most high-profile emphasis was initially on the fate of large and small businesses, and people with mortgages, but a long period of crisis will a tip the balance from low income to unsustainable poverty (even prompting Iain Duncan Smith to propose policy change), and why favour people who can afford a mortgage over people scraping the money together for rent?
  1. A need for more communication and exhortation, or for direct action to change behaviour.
  2. The short term (do everything possible now) or long term (manage behaviour over many months).
  1. How to maintain trust in the UK government when (a) people are more or less inclined to trust a the current part of government and general trust may be quite low, and (b) so many other governments are acting differently from the UK.

https://twitter.com/DrSophieHarman/status/1238893265782530059

https://twitter.com/Sander_vdLinden/status/1242168652180475906?s=09

https://twitter.com/policyatkings/status/1248318259029516289

  • 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):

https://twitter.com/AndyBurnhamGM/status/1239153510903619584

  • For example, note how often people are second guessing and criticising the UK Government position (and questioning the motives of Conservative ministers).

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

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

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

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

See also: ‘Othering the Virus‘ by Marius Meinhof

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

Step 2 Identify technically and politically feasible solutions

Common advice in policy analysis texts:

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

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

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

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

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

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

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

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

https://twitter.com/CairneyPaul/status/1244970044351791104

https://twitter.com/ChrisCEOHopson/status/1249617980859744256?s=09

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

Common advice in policy analysis texts:

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

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

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

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

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

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

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

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

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

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

Step 4 Predict the outcome of each feasible solution.

Common advice in policy analysis texts:

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

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

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

Step 5 Make a recommendation to your client

Common advice in policy analysis texts:

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

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

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

Would that policy analysis were so simple

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

cycle and cycle spirograph 18.2.20

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

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

Table for coronavirus 750

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

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

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

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

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

Evidence-based or expert-informed policymaking

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

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

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

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

box 7.1

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

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

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

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

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

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

See also:

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

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

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

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

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

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

See also:

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

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

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

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

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

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

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

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

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

See also: Rights and responsibilities in the Coronavirus pandemic

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

See also (Calderwood resigned as CMO that night):

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

The wider policymaking environment: 2. Limited control

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

image policy process round 2 25.10.18

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

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

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

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

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

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

Trial and error policymaking in complex policymaking systems (17.3.20)

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

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

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

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

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

[See also: Complex systems and systems thinking]

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

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

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

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

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

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

full fact coronavirus

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

See also:

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

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

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

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

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

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

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

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

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

See also: other rabbit holes are available

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

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

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

Compare with:

The take home messages

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

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

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

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

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

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

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

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

See also: Pandemic Science and Politics (Daniel Sarewitz)

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

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

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

Policy Analysis in 750 Words: entrepreneurial policy analysis

This post forms one part of the Policy Analysis in 750 words series overview and connects to ‘Three habits of successful policy entrepreneurs’.

The idea of a ‘policy entrepreneur’ is important to policy studies and policy analysis.

Let’s begin with its positive role in analysis, then use policy studies to help qualify its role within policymaking environments.

The take-home-messages are to

  1. recognise the value of entrepreneurship, and invest in relevant skills and strategies, but
  2. not overstate its spread or likely impact, and
  3. note the unequal access to political resources associated with entrepreneurs.

Box 11.3 UPP 2nd ed entrepreneurs

Entrepreneurship and policy analysis

Mintrom identifies the intersection between policy entrepreneurship and policy analysis, to highlighting the benefits of ‘positive thinking’, creativity, deliberation, and leadership.

He expands on these ideas further in So you want to be a policy entrepreneur?:

Policy entrepreneurs are energetic actors who engage in collaborative efforts in and around government to promote policy innovations. Given the enormous challenges now facing humanity, the need is great for such actors to step forward and catalyze change processes” (Mintrom, 2019: 307).

Although many entrepreneurs seem to be exceptional people, Mintrom (2019: 308-20) identifies:

  1. Key attributes to compare
  • ‘ambition’, to invest resources for future reward
  • ‘social acuity’, to help anticipate how others are thinking
  • ‘credibility’, based on authority and a good track record
  • ‘sociability’, to empathise with others and form coalitions or networks
  • ‘tenacity’, to persevere during adversity
  1. The skills that can be learned
  • ‘strategic thinking’, to choose a goal and determine how to reach it
  • ‘team building’, to recognise that policy change is a collective effort, not the responsibility of heroic individuals (compare with Oxfam)
  • ‘collecting evidence’, and using it ‘strategically’ to frame a problem and support a solution
  • ‘making arguments’, using ‘tactical argumentation’ to ‘win others to their cause and build coalitions of supporters’ (2019: 313)
  • ‘engaging multiple audiences’, by tailoring arguments and evidence to their beliefs and interests
  • ‘negotiating’, such as by trading your support in this case for their support in another
  • ‘networking’, particularly when policymaking authority is spread across multiple venues.
  1. The strategies built on these attributes and skills.
  • ‘problem framing’, such as to tell a story of a crisis in need of urgent attention
  • ‘using and expanding networks’, to generate attention and support
  • ‘working with advocacy coalitions’, to mobilise a collection of actors who already share the same beliefs
  • ‘leading by example’, to signal commitment and allay fears about risk
  • ‘scaling up change processes’, using policy innovation in one area to inspire wider adoption.

p308 Mintrom for 750 words

Overall, entrepreneurship is ‘tough work’ requiring ‘courage’, but necessary for policy disruption, by: ‘those who desire to make a difference, who recognize the enormous challenges now facing humanity, and the need for individuals to step forward and catalyze change’ (2019: 320; compare with Luetjens).

Entrepreneurship and policy studies

  1. Most policy actors fail

It is common to relate entrepreneurship to stories of exceptional individuals and invite people to learn from their success. However, the logical conclusion is that success is exceptional and most policy actors will fail.

A focus on key skills takes us away from this reliance on exceptional actors, and ties in with other policy studies-informed advice on how to navigate policymaking environments (see ‘Three habits of successful policy entrepreneurs’, these ANZSOG talks, and box 6.3 below)

box 6.3

However, note the final sentence, which reminds us that it is possible to invest a huge amount of time and effort in entrepreneurial skills without any of that investment paying off.

  1. Even if entrepreneurs succeed, the explanation comes more from their environments than their individual skills

The other side of the entrepreneurship coin is the policymaking environment in which actors operate.

Policy studies of entrepreneurship (such as Kingdon on multiple streams) rely heavily on metaphors on evolution. Entrepreneurs are the actors most equipped to thrive within their environments (see Room).

However, Kingdon uses the additional metaphor of ‘surfers waiting for the big wave’, which suggests that their environments are far more important than them (at least when operating on a US federal scale – see Kingdon’s Multiple Streams Approach).

Entrepreneurs may be more influential at a more local scale, but the evidence of their success (independent of the conditions in which they operate) is not overwhelming. So, self-aware entrepreneurs know when to ‘surf the waves’ or try to move the sea.

  1. The social background of influential actors

Many studies of entrepreneurs highlight the stories of tenacious individuals with limited resources but the burning desire to make a difference.

The alternative story is that political resources are distributed profoundly unequally. Few people have the resources to:

  • run for elected office
  • attend elite Universities, or find other ways to develop the kinds of personal networks that often relate to social background
  • develop the credibility built on a track record in a position of authority (such as in government or science).
  • be in the position to invest resources now, to secure future gains, or
  • be in an influential position to exploit windows of opportunity.

Therefore, when focusing on entrepreneurial policy analysis, we should encourage the development of a suite of useful skills, but not expect equal access to that development or the same payoff from entrepreneurial action.

See also:

Compare these skills with the ones we might associate with ‘systems thinking

If you want to see me say these depressing things with a big grin:

https://youtu.be/L77Y7wynqXY

 

https://www.facebook.com/idsuk/videos/364796097654832/

 

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Filed under 750 word policy analysis, agenda setting, Evidence Based Policymaking (EBPM), public policy, Uncategorized

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

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

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

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

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

Does policymaking proceed through a series of stages?

The short answer is no.

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

cycle

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

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

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

 

  1. It arose from a misunderstanding in policy studies

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

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

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

  1. It is a functional requirement of policy analysis

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

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

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

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

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

should not be confused with

What you need versus what you can expect

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

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

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

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

See also:

The New Policy Sciences

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Policy Analysis in 750 Words: Defining policy problems and choosing solutions

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

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

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

The idea is that both elements are analytically separable but mutually informative: policy analysis is crucial to solving real policy problems, policy studies inform the feasibility of analysis, the study of policy analysts informs policy studies, and so on.

Both elements focus on similar questions – such as What is policy? – and explore their descriptive (what do policy actors do?) and prescriptive (what should they do?) implications.

  1. What is the policy problem?

Policy studies tend to describe problem definition in relation to framing, narrative, social construction, power, and agenda setting.

Actors exercise power to generate attention for their preferred interpretation, and minimise attention to alternative frames (to help foster or undermine policy change, or translate their beliefs into policy).

Policy studies incorporate insights from psychology to understand (a) how policymakers might combine cognition and emotion to understand problems, and therefore (b) how to communicate effectively when presenting policy analysis.

Policy studies focus on the power to reduce ambiguity rather than simply the provision of information to reduce uncertainty. In other words, the power to decide whose interpretation of policy problems counts, and therefore to decide what information is policy-relevant.

This (unequal) competition takes place within a policy process over which no actor has full knowledge or control.

The classic 5-8 step policy analysis texts focus on how to define policy problems well, but they vary somewhat in their definition of doing it well (see also C.Smith):

  • Bardach recommends using rhetoric and eye-catching data to generate attention
  • Weimer and Vining and Mintrom recommend beginning with your client’s ‘diagnosis’, placing it in a wider perspective to help analyse it critically, and asking yourself how else you might define it (see also Bacchi, Stone)
  • Meltzer and Schwartz and Dunn identify additional ways to contextualise your client’s definition, such as by generating a timeline to help ‘map’ causation or using ‘problem-structuring methods’ to compare definitions and avoid making too many assumptions on a problem’s cause.
  • Thissen and Walker compare ‘rational’ and ‘argumentative’ approaches, treating problem definition as something to be measured scientifically or established rhetorically (see also Riker).

These approaches compare with more critical accounts that emphasise the role of power and politics to determine whose knowledge is relevant (L.T.Smith) and whose problem definition counts (Bacchi, Stone). Indeed, Bacchi and Stone provide a crucial bridge between policy analysis and policy studies by reflecting on what policy analysts do and why.

  1. What is the policy solution?

In policy studies, it is common to identify counterintuitive or confusing aspects of policy processes, including:

  • Few studies suggest that policy responses actually solve problems (and many highlight their potential to exacerbate them). Rather, ‘policy solutions’ is shorthand for proposed or alleged solutions.
  • Problem definition often sets the agenda for the production of ‘solutions’, but note the phrase solutions chasing problems (when actors have their ‘pet’ solutions ready, and they seek opportunities to promote them).

Policy studies: problem definition informs the feasibility and success of solutions

Generally speaking, to define the problem is to influence assessments of the feasibility of solutions:

  • Technical feasibility. Will they work as intended, given the alleged severity and cause of the problem?
  • Political feasibility. Will they receive sufficient support, given the ways in which key policy actors weigh up the costs and benefits of action?

Policy studies highlight the inextricable connection between technical and political feasibility. Put simply, (a) a ‘technocratic’ choice about the ‘optimality’ of a solution is useless without considering who will support its adoption, and (b) some types of solution will always be a hard sell, no matter their alleged effectiveness (Box 2.3 below).

In that context, policy studies ask: what types of policy tools or instruments are actually used, and how does their use contribute to policy change? Measures include the size, substance, speed, and direction of policy change.

box 2.3 2nd ed UPP

In turn, problem definition informs: the ways in which actors will frame any evaluation of policy success, and the policy-relevance of the evidence to evaluate solutions. Simple examples include:

  • If you define tobacco in relation to: (a) its economic benefits, or (b) a global public health epidemic, evaluations relate to (a) export and taxation revenues, or (b) reductions in smoking in the population.
  • If you define ‘fracking’ in relation to: (a) seeking more benefits than costs, or (b) minimising environmental damage and climate change, evaluations relate to (a) factors such as revenue and effective regulation, or simply (b) how little it takes place.

Policy analysis: recognising and pushing boundaries

Policy analysis texts tend to accommodate these insights when giving advice:

  • Bardach recommends identifying solutions that your audience might consider, perhaps providing a range of options on a notional spectrum of acceptability.
  • Smith highlights the value of ‘precedent’, or relating potential solutions to previous strategies.
  • Weimer and Vining identify the importance of ‘a professional mind-set’ that may be more important than perfecting ‘technical skills’
  • Mintrom notes that some solutions are easier to sell than others
  • Meltzer and Schwartz describe the benefits of making a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups
  • Dunn warns against too-narrow forms of ‘evidence based’ analysis which undermine a researcher’s ability to adapt well to the evidence-demands of policymakers
  • Thissen and Walker relate solution feasibility to a wide range of policy analysis ‘styles’

Still, note the difference in emphasis.

Policy analysis education/ training may be about developing the technical skills to widen definitions and apply many criteria to compare solutions.

Policy studies suggest that problem definition and a search for solutions takes place in an environment where many actors apply a much narrower lens and are not interested in debates on many possibilities (particularly if they begin with a solution).

I have exaggerated this distinction between each element, but it is worth considering the repeated interaction between them in practice: politics and policymaking provide boundaries for policy analysis, analysis could change those boundaries, and policy studies help us reflect on the impact of analysts.

I’ll take a quick break, then discuss how this conclusion relates to the idea of ‘entrepreneurial’ policy analysis.

Further reading

Understanding Public Policy (2020: 28) describes the difference between governments paying for and actually using the ‘tools of policy formulation’. To explore this point, see ‘The use and non-use of policy appraisal tools in public policy making‘ and The Tools of Policy Formulation.

p28 upp 2nd ed policy tools

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Policy Analysis in 750 Words: What can you realistically expect policymakers to do?

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

One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.

In this case, modern theories of the policy process help you identify your audience and their capacity to follow your advice. This simple insight may have a profound impact on the advice you give.

Policy analysis for an ideal-type world

For our purposes, an ideal-type is an abstract idea, which highlights hypothetical features of the world, to compare with ‘real world’ descriptions. It need not be an ideal to which we aspire. For example, comprehensive rationality describes the ideal type, and bounded rationality describes the ‘real world’ limitations to the ways in which humans and organisations process information.

 

Imagine writing policy analysis in the ideal-type world of a single powerful ‘comprehensively rational’ policymaker at the heart of government, making policy via an orderly policy cycle.

Your audience would be easy to identify, your analysis would be relatively simple, and you would not need to worry about what happens after you make a recommendation for policy change.

You could adopt a simple 5-8 step policy analysis method, use widely-used tools such as cost-benefit analysis to compare solutions, and know where the results would feed into the policy process.

I have perhaps over-egged this ideal-type pudding, but I think a lot of traditional policy analyses tapped into this basic idea and focused more on the science of analysis than the political and policymaking context in which it takes place (see Radin and Brans, Geva-May, and Howlett).

Policy analysis for the real world

Then imagine a far messier and less predictable world in which the nature of the policy issue is highly contestedresponsibility for policy is unclear, and no single ‘centre’ has the power to turn a recommendation into an outcome.

This image is a key feature of policy process theories, which describe:

  • Many policymakers and influencers spread across many levels and types of government (as the venues in which authoritative choice takes place). Consequently, it is not a straightforward task to identify and know your audience, particularly if the problem you seek to solve requires a combination of policy instruments controlled by different actors.
  • Each venue resembles an institution driven by formal and informal rules. Formal rules are written-down or widely-known. Informal rules are unwritten, difficult to understand, and may not even be understood in the same way by participants. Consequently, it is difficult to know if your solution will be a good fit with the standard operating procedures of organisations (and therefore if it is politically feasible or too challenging).
  • Policymakers and influencers operate in ‘subsystems’, forming networks built on resources such as trust or coalitions based on shared beliefs. Effective policy analysis may require you to engage with – or become part of – such networks, to allow you to understand the unwritten rules of the game and encourage your audience to trust the messenger. In some cases, the rules relate to your willingness to accept current losses for future gains, to accept the limited impact of your analysis now in the hope of acceptance at the next opportunity.
  • Actors relate their analysis to shared understandings of the world – how it is, and how it should be – which are often so well-established as to be taken for granted. Common terms include paradigms, hegemons, core beliefs, and monopolies of understandings. These dominant frames of reference give meaning to your policy solution. They prompt you to couch your solutions in terms of, for example, a strong attachment to evidence-based cases in public health, value for money in treasury departments, or with regard to core principles such as liberalism or socialism in different political systems.
  • Your solutions relate to socioeconomic context and the events that seem (a) impossible to ignore and (b) out of the control of policymakers. Such factors range from a political system’s geography, demography, social attitudes, and economy, while events can be routine elections or unexpected crises.

What would you recommend under these conditions? Rethinking 5-step analysis

There is a large gap between policymakers’ (a) formal responsibilities versus (b) actual control of policy processes and outcomes. Even the most sophisticated ‘evidence based’ analysis of a policy problem will fall flat if uninformed by such analyses of the policy process. Further, the terms of your cost-benefit analysis will be highly contested (at least until there is agreement on what the problem is, and how you would measure the success of a solution).

Modern policy analysis texts try to incorporate such insights from policy theories while maintaining a focus on 5-8 steps. For example:

  • Meltzer and Schwartz contrast their ‘flexible’ and ‘iterative’ approach with a too- rigid ‘rationalistic approach’.
  • Bardachand Dunn emphasise the value of political pragmatism and the ‘art and craft’ of policy analysis.
  • Weimer and Vininginvest 200 pages in economic analyses of markets and government, often highlighting a gap between (a) our ability to model and predict economic and social behaviour, and (b) what actually happens when governments intervene.
  • Mintrom invites you to see yourself as a policy entrepreneur, to highlight the value of of ‘positive thinking’, creativity, deliberation, and leadership, and perhaps seek ‘windows of opportunity’ to encourage new solutions. Alternatively, a general awareness of the unpredictability of events can prompt you to be modest in your claims, since the policymaking environment may be more important (than your solution) to outcomes.
  • Thissen and Walker focus more on a range of possible roles than a rigid 5-step process.

Beyond 5-step policy analysis

  1. Compare these pragmatic, client-orientated, and communicative models with the questioning, storytelling, and decolonizing approaches by Bacchi, Stone, and L.T. Smith.
  • The latter encourage us to examine more closely the politics of policy processes, including the importance of framing, narrative, and the social construction of target populations to problem definition and policy design.
  • Without this wider perspective, we are focusing on policy analysis as a process rather than considering the political context in which analysts use it.
  1. Additional posts on entrepreneurs and ‘systems thinking’ [to be added] encourage us to reflect on the limits to policy analysis in multi-centric policymaking systems.

 

 

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Policy Analysis in 750 Words: Reflecting on your role as a policy analyst

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

One aim of this series is to combine insights from policy research (1000, 500) and policy analysis texts.

If we take key insights from policy theories seriously, we can use them to identify (a) the constraints to policy analytical capacity, and (b) the ways in which analysts might address them. I use the idea of policy analyst archetypes to compare a variety of possible responses.

Key constraints to policy analytical capacity

Terms like ‘bounded rationality’ highlight major limits on the ability of humans and organisations to process information.

Terms like policymaking ‘context’, ‘environments’, and multi-centric policymaking suggest that the policy process is beyond the limits of policymaker understanding and control.

  • Policy actors need to find ways to act, with incomplete information about the problem they seek to solve and the likely impact of their ‘solution’.
  • They gather information to help reduce uncertainty, but problem definition is really about exercising power to reduce ambiguity: select one way to interpret a problem (at the expense of most others), and limit therefore limit the relevance and feasibility of solutions.
  • This context informs how actors might use the tools of policy analysis. Key texts in this series highlight the use of tools to establish technical feasibility (will it work as intended?), but policymakers also select tools for their political feasibility (who will support or oppose this measure?).

box 2.3 2nd ed UPP

How might policy analysts address these constraints ethically?

Most policy analysis texts (in this series) consider the role of professional ethics and values during the production of policy analysis. However, they also point out that there is not a clearly defined profession and associated code of conduct (e.g. see Adachi). In that context, let us begin with some questions about the purpose of policy analysis and your potential role:

  1. Is your primary role to serve individual clients or some notion of the ‘public good’?
  2. Should you maximise your role as an individual or play your part in a wider profession?
  3. What is the balance between the potential benefits of individual ‘entrepreneurship’ and collective ‘co-productive’ processes?
  4. Which policy analysis techniques should you prioritise?
  5. What forms of knowledge and evidence count in policy analysis?
  6. What does it mean to communicate policy analysis responsibly?
  7. Should you provide a clear recommendation or encourage reflection?

 

Policy analysis archetypes: pragmatists, entrepreneurs, manipulators, storytellers, and decolonisers

In that context, I have created a story of policy analysis archetypes to identify the elements that each text emphasises.

The pragmatic policy analyst

  • Bardach provides the classic simple, workable, 8-step system to present policy analysis to policymakers while subject to time and resource-pressed political conditions.
  • Dunn also uses Wildavsky’s famous phrase ‘art and craft’ to suggest that scientific and ‘rational’ methods can only take us so far.

The professional, clientoriented policy analyst

  • Weimer and Vining provide a similar 7-step client-focused system, but incorporating a greater focus on professional development and economic techniques (such as cost-benefit-analysis) to emphasise a particular form of professional analyst.
  • Meltzer and Schwartz also focus on advice to clients, but with a greater emphasis on a wide variety of methods or techniques (including service design) to encourage the co-design of policy analysis with clients.

The communicative policy analyst

  •  C. Smith focuses on how to write and communicate policy analysis to clients in a political context.
  • Compare with Spiegelhalter and Gigerenzer on how to communicate responsibly when describing uncertainty, probability, and risk.

The manipulative policy analyst.

  • Riker helps us understand the relationship between two aspects of agenda setting: the rules/ procedures to make choice, and the framing of policy problems and solutions.

The entrepreneurial policy analyst

  • Mintrom shows how to combine insights from studies of policy entrepreneurship and policy analysis, to emphasise the benefits of collaboration and creativity.

The questioning policy analyst

  • Bacchi  analyses the wider context in which people give and use such advice, to identify the emancipatory role of analysis and encourage policy analysts to challenge dominant social constructions of problems and populations.

The storytelling policy analyst

  • Stone identifies the ways in which people use storytelling and argumentation techniques to define problems and justify solutions. This process is about politics and power, not objectivity and optimal solutions.

The decolonizing policy analyst.

  • L.T. Smith does not describe policy analysis directly, but shows how the ‘decolonization of research methods’ can inform the generation and use of knowledge.
  • Compare with Hindess on the ways in which knowledge-based hierarchies rely on an untenable, circular logic.
  • Compare with Michener’s thread, discussing Doucet’s new essay on (a) the role of power and knowledge in limiting (b) the ways in which we gather evidence to analyse policy problems.

https://twitter.com/povertyscholar/status/1207054211759910912

Using archetypes to define the problem of policy analysis

Studies of the field (e.g. Radin plus Brans, Geva-May, and Howlett) suggest that there are many ways to do policy analysis. Further, as Thissen and Walker describe, such roles are not mutually exclusive, your views on their relative value could change throughout the process of analysis, and you could perform many of these roles.

Further, each text describes multiple roles, and some seem clustered together:

  • pragmatic, client-orientated, and communicative could sum-up the traditional 5-8 step approaches, while
  • questioning, storytelling, and decolonizing could sum up an important (‘critical’) challenge to narrow ways of thinking about policy analysis and the use of information.

Still, the emphasis matters.

Each text is setting an agenda or defining the problem of policy analysis more-or-less in relation to these roles. Put simply, the more you are reading about economic theory and method, the less you are reading about dominance and manipulation.

How can you read further?

Michener’s ‘Policy Feedback in a Racialized Polity’ connects to studies of historical institutionalism, and reminds us to use insights from policy theories to identify the context for policy analysis.

I have co-authored a lot about uncertainty/ ambiguity in relation to ‘evidence based policymaking’, including:

See also The new policy sciences for a discussion of how these issues inform Lasswell’s original vision for the policy sciences (combining the analysis of and for policy).

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Policy Analysis in 750 words: Rachel Meltzer and Alex Schwartz (2019) Policy Analysis as Problem Solving

Please see the Policy Analysis in 750 words series overview before reading the summary. This post might well represent the largest breach of the ‘750 words’ limit, so please get comfortable. I have inserted a picture of a cat hanging in there baby after the main (*coughs*) 1400-word summary. The rest is bonus material, reflecting on the links between this book and the others in the series.

Meltzer Schwartz 2019 cover

Rachel Meltzer and Alex Schwartz (2019) Policy Analysis as Problem Solving (Routledge)

We define policy analysis as evidence-based advice giving, as the process by which one arrives at a policy recommendation to address a problem of public concern. Policy analysis almost always involves advice for a client’ (Meltzer and Schwartz, 2019: 15).

Meltzer and Schwartz (2019: 231-2) describe policy analysis as applied research, drawing on many sources of evidence, quickly, with limited time, access to scientific research, or funding to conduct a lot of new research (2019: 231-2). It requires:

  • careful analysis of a wide range of policy-relevant documents (including the ‘grey’ literature often produced by governments, NGOs, and think tanks) and available datasets
  • perhaps combined with expert interviews, focus groups, site visits, or an online survey (see 2019: 232-64 on methods).

Meltzer and Schwartz (2019: 21) outline a ‘five-step framework’ for client-oriented policy analysis. During each step, they contrast their ‘flexible’ and ‘iterative’ approach with a too- rigid ‘rationalistic approach’ (to reflect bounded, not comprehensive, rationality):

  1. ‘Define the problem’.

Problem definition is a political act of framing, not an exercise in objectivity (2019: 52-3). It is part of a narrative to evaluate the nature, cause, size, and urgency of an issue (see Stone), or perhaps to attach to an existing solution (2019: 38-40; compare with Mintrom).

In that context, ask yourself ‘Who is defining the problem? And for whom?’ and do enough research to be able to define it clearly and avoid misunderstanding among you and your client (2019: 37-8; 279-82):

  • Identify your client’s resources and motivation, such as how they seek to use your analysis, the format of analysis they favour, their deadline, and their ability to make or influence the policies you might suggest (2019: 49; compare with Weimer and Vining).
  • Tailor your narrative to your audience, albeit while recognising the need to learn from ‘multiple perspectives’ (2019: 40-5).
  • Make it ‘concise’ and ‘digestible’, not too narrowly defined, and not in a way that already closes off discussion by implying a clear cause and solution (2019: 51-2).

In doing so:

  • Ask yourself if you can generate a timeline, identify key stakeholders, and place a ‘boundary’ on the problem.
  • Establish if the problem is urgent, who cares about it, and who else might care (or not) (2019 : 46).
  • Focus on the ‘central’ problem that your solution will address, rather than the ‘related’ and ‘underlying’ problems that are ‘too large and endemic to be solved by the current analysis’ (2019: 47).
  • Avoid misdiagnosing a problem with reference to one cause. Instead, ‘map’ causation with reference to (say) individual and structural causes, intended and unintended consequences, simple and complex causation, market or government failure, and/ or the ability to blame an individual or organisation (2019: 48-9).
  • Combine quantitative and qualitative data to frame problems in relation to: severity, trends in severity, novelty, proximity to your audience, and urgency or crisis (2019: 53-4).

During this process, interrogate your own biases or assumptions and how they might affect your analysis (2019: 50).

2. ‘Identify potential policy options (alternatives) to address the problem’.

Common sources of ideas include incremental changes from current policy, ‘client suggestions’, comparable solutions (from another time, place, or policy area), reference to common policy instruments, and ‘brainstorming’ or ‘design thinking’ (2019: 67-9; see box 2.3 and 7.1, below, from Understanding Public Policy).

box 2.3 2nd ed UPP

Identify a ‘wide range’ of possible solutions, then select the (usually 3-5) ‘most promising’ for further analysis (2019: 65). In doing so:

  • be careful not to frame alternatives negatively (e.g. ‘death tax’ – 2019: 66)
  • compare alternatives in ‘good faith’ rather than keeping some ‘off the table’ to ensure that your preferred solution looks good (2019: 66)
  • beware ‘ best practice’ ideas that are limited in terms of (a) applicability (if made at a smaller scale, or in a very different jurisdiction), and (b) evidence of success (2019: 70; see studies of policy learning and transfer)
  • think about how to modify existing policies according to scale or geographical coverage, who to include (and based on what criteria), for how long, using voluntary versus mandatory provisions, and ensuring oversight (2019: 71-3)
  • consider combinations of common policy instruments, such as regulations and economic penalties/ subsidies (2019: 73-7)
  • consider established ways to ‘brainstorm’ ideas (2019: 77-8)
  • note the rise of instruments derived from the study of psychology and behavioural public policy (2019: 79-90)
  • learn from design principles, including ‘empathy’, ‘co-creating’ policy with service users or people affected, ‘prototyping’ (2019: 90-1)

box 7.1

3. ‘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)’.

Your objectives relate to your problem definition and aims: what is the problem, what do you want to happen when you address it, and why?

  • For example, questions to your client may include: what is your organization’s ‘mission’, what is feasible (in terms of resources and politics), which stakeholders to you want to include, and how will you define success (2019: 105; 108-12)?

In that values-based context, your criteria relate to ways to evaluate each policy’s likely impact (2019: 106-7). They should ensure:

  • Comprehensiveness. E.g. how many people, and how much of their behaviour, can you influence while minimizing the ‘burden’ on people, businesses, or government? (2019: 113-4)
  • Mutual Exclusiveness. In other words, don’t have two objectives doing the same thing (2019: 114).

Common criteria include (2019: 116):

  1. Effectiveness. The size of its intended impact on the problem (2019: 117).
  2. Equity (fairness). The impact in terms of ‘vertical equity’ (e.g. the better off should pay more), ‘horizontal equity’ (e.g. you should not pay more if unmarried), fair process, fair outcomes, and ‘intergenerational’ equity (e.g. don’t impose higher costs on future populations) (2019: 118-19).
  3. Feasibility (administrative, political, and technical). The likelihood of this policy being adopted and implemented well (2019: 119-21)
  4. Cost (or financial feasibility). Who would bear the cost, and their willingness and ability to pay (2019: 122).
  5. Efficiency. To maximise the benefit while minimizing costs (2019: 122-3).

 

4. ‘Assess the outcomes of the policy options in light of the criteria and weigh trade-offs between the advantages and disadvantages of the options’.

When explaining objectives and criteria,

  • ‘label’ your criteria in relation to your policy objectives (e.g. to ‘maximize debt reduction’) rather than using generic terms (2019: 123-7)
  • produce a table – with alternatives in rows, and criteria in columns – to compare each option
  • quantify your policies’ likely outcomes, such as in relation to numbers of people affected and levels of income transfer, or a percentage drop in the size of the problem, but also
  • communicate the degree of uncertainty related to your estimates (2019: 128-32; see Spiegelhalter)

Consider using cost-benefit analysis to identify (a) the financial and opportunity cost of your plans (what would you achieve if you spent the money elsewhere?), compared to (b) the positive impact of your funded policy (2019: 141-55).

  • The principle of CBA may be intuitive, but a thorough CBA process is resource-intensive, vulnerable to bias and error, and no substitute for choice. It requires you to make a collection of assumptions about human behaviour and likely costs and benefits, decide whose costs and benefits should count, turn all costs and benefits into a single measure, and imagine how to maximise winners and compensate losers (2019: 155-81; compare Weimer and Vining with Stone).
  • One alternative is cost-effectiveness analysis, which quantifies costs and relates them to outputs (e.g. number of people affected, and how) without trying to translate them into a single measure of benefit (2019: 181-3).
  • These measures can be combined with other thought processes, such as with reference to ‘moral imperatives’, a ‘precautionary approach’, and ethical questions on power/ powerlessness (2019: 183-4).

 

5. ‘Arrive at a recommendation’.

Predict the most likely outcomes of each alternative, while recognising high uncertainty (2019: 189-92). If possible,

  • draw on existing, comparable, programmes to predict the effectiveness of yours (2019: 192-4)
  • combine such analysis with relevant theories to predict human behaviour (e.g. consider price ‘elasticity’ if you seek to raise the price of a good to discourage its use) (2019: 193-4)
  • apply statistical methods to calculate the probability of each outcome (2019: 195-6), and modify your assumptions to produce a range of possibilities, but
  • note Spiegelhalter’s cautionary tales and anticipate the inevitable ‘unintended consequences’ (when people do not respond to policy in the way you would like) (2019: 201-2)
  • use these estimates to inform a discussion on your criteria (equity, efficiency, feasibility) (2019: 196-200)
  • present the results visually – such as in a ‘matrix’ – to encourage debate on the trade-offs between options
  • simplify choices by omitting irrelevant criteria and options that do not compete well with others (2019: 203-10)
  • make sure that your recommendation (a) flows from the analysis, and (b) is in the form expected by your client (2019: 211-12)
  • consider making a preliminary recommendation to inform an iterative process, drawing feedback from clients and stakeholder groups (2019: 212).

 

hang-in-there-baby

 

Policy analysis in a wider context

Meltzer and Schwartz’s approach makes extra sense if you have already read some of the other texts in the series, including:

  1. Weimer and Vining, which represents an exemplar of an X-step approach informed heavily by the study of economics and application of economic models such as cost-benefit-analysis (compare with Radin’s checklist).
  2. Geva-May on the existence of a policy analysis profession with common skills, heuristics, and (perhaps) ethics (compare with Meltzer and Schwartz, 2019: 282-93)
  3. Radin, on:
  • the proliferation of analysts across multiple levels of government, NGOs, and the private sector (compare with Meltzer and Schwartz, 2019: 269-77)
  • the historic shift of analysis from formulation to all notional stages (contrast with Meltzer and Schwartz, 2019: 16-7 on policy analysis not including implementation or evaluation)
  • the difficulty in distinguishing between policy analysis and advocacy in practice (compare with Meltzer and Schwartz, 2019: 276-8, who suggest that actors can choose to perform these different roles)
  • the emerging sense that it is difficult to identify a single client in a multi-centric policymaking system. Put another way, we might be working for a specific client but accept that their individual influence is low.
  1. Stone’s challenge to
  • a historic tendency for economics to dominate policy analysis,
  • the applicability of economic assumptions (focusing primarily on individualist behaviour and markets), and
  • the pervasiveness of ‘rationalist’ policy analysis built on X-steps.

Meltzer and Schwartz (2019: 1-3) agree that economic models are too dominant (identifying the value of insights from ‘other disciplines – including design, psychology, political science, and sociology’).

However, they argue that critiques of rational models exaggerate their limitations (2019: 23-6). For example:

  • these models need not rely solely on economic techniques or quantification, a narrow discussion or definition of the problem, or the sense that policy analysis should be comprehensive, and
  • it is not problematic for analyses to reflect their client’s values or for analysts to present ambiguous solutions to maintain wide support, partly because
  • we would expect the policy analysis to form only one part of a client’s information or strategy.

Further, they suggest that these critiques provide no useful alternative, to help guide new policy analysts. Yet, these guides are essential:

to be persuasive, and credible, analysts must situate the problem, defend their evaluative criteria, and be able to demonstrate that their policy recommendation is superior, on balance, to other alternative options in addressing the problem, as defined by the analyst. At a minimum, the analyst needs to present a clear and defensible ranking of options to guide the decisions of the policy makers’ (Meltzer and Schwartz, 2019: 4).

Meltzer and Schwartz (2019: 27-8) then explore ways to improve a 5-step model with insights from approaches such as ‘design thinking’, in which actors use a similar process – ‘empathize, define the problem, ideate, prototype, test and get feedback from others’ – to experiment with policy solutions without providing a narrow view on problem definition or how to evaluate responses.

Policy analysis and policy theory

One benefit to Meltzer and Schwartz’s approach is that it seeks to incorporate insights from policy theories and respond with pragmatism and hope. However, I think you also need to read the source material to get a better sense of those theories, key debates, and their implications. For example:

  1. Meltzer and Schwartz (2019: 32) note correctly that ‘incremental’ does not sum up policy change well. Indeed, Punctuated Equilibrium Theory shows that policy change is characterised by a huge number of small and a small number of huge changes.
  • However, the direct implications of PET are not as clear as they suggest. Baumgartner and Jones have both noted that they can measure these outcomes and identify the same basic distribution across a political system, but not explain or predict why particular policies change dramatically.
  • It is useful to recommend to policy analysts that they invest some hope in major policy change, but also sensible to note that – in the vast majority of cases – it does not happen.
  • On his point, see Mintrom on policy analysis for the long term, Weiss on the ‘enlightenment’ function of research and analysis, and Box 6.3 (from Understanding Public Policy), on the sense that (a) we can give advice to ‘budding policy entrepreneurs’ on how to be effective analysts, but (b) should note that all their efforts could be for nothing.

box 6.3

  1. Meltzer and Schwartz (2019: 32-3) tap briefly into the old debate on whether it is preferable to seek radical or incremental change. For more on that debate, see chapter 5 in the 1st ed of Understanding Public Policy in which Lindblom notes that proposals for radical/ incremental changes are not mutually exclusive.
  2. Perhaps explore the possible tension between Meltzer and Schwartz’s (2019: 33-4) recommendation that (a) policy analysis should be ‘evidence-based advice giving’, and (b) ‘flexible and open-ended’.
  • I think that Stone’s response would be that phrases such as ‘evidence based’ are not ‘flexible and open-ended’. Rather, they tend to symbolise a narrow view of what counts as evidence (see also Smith, and Hindess).
  • Further, note that the phrase ‘evidence based policymaking’ is a remarkably vague term (see the EBPM page), perhaps better seen as a political slogan than a useful description or prescription of policymaking.

 

Finally, if you read enough of these policy analysis texts, you get a sense that many are bunched together even if they describe their approach as new or distinctive.

  • Indeed, Meltzer and Schwarz (2019: 22-3) provide a table (containing Bardach and Patashnik, Patton et al, Stokey and Zeckhauser, Hammond et al, and Weimer & Vining) of ‘quite similar’ X-step approaches.
  • Weimer and Vining also discuss the implications of policy theories and present the sense that X-step policy analysis should be flexible and adaptive.
  • Many texts – including Radin, and Smith (2016) – focus on the value of case studies to think through policy analysis in particular contexts, rather than suggesting that we can produce a universal blueprint.

However, as Geva-May might suggest, this is not a bad thing if our aim is to generate the sense that policy analysis is a profession with its own practices and heuristics.

 

 

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Policy in 500 Words: Punctuated Equilibrium Theory

See also the original – and now 6 years old – 1000 Words post.

This 500 Words version is a modified version of the introduction to chapter 9 in the 2nd edition of Understanding Public Policy.  

UPP p147 PET box

 Punctuated equilibrium theory (PET) tells a story of complex systems that are stable and dynamic:

  • Most policymaking exhibits long periods of stability, but with the ever-present potential for sudden instability.
  • Most policies stay the same for long periods. Some change very quickly and dramatically.

We can explain this dynamic with reference to bounded rationality: since policymakers cannot consider all issues at all times, they ignore most and promote relatively few to the top of their agenda.

This lack of attention to most issues helps explain why most policies may not change, while intense periods of attention to some issues prompts new ways to frame and solve policy problems.

Some explanation comes from the power of participants, to (a) minimize attention and maintain an established framing, or (b) expand attention in the hope of attracting new audiences more sympathetic to new ways of thinking.

Further explanation comes from policymaking complexity, in which the scale of conflict is too large to understand, let alone control.

The original PET story

The original PET story – described in more detail in the 1000 Words version – applies two approaches – policy communities and agenda setting – to demonstrate stable relationships between interest groups and policymakers:

  • They endure when participants have built up trust and agreement – about the nature of a policy problem and how to address it – and ensure that few other actors have a legitimate role or interest in the issue.
  • They come under pressure when issues attract high policymaker attention, such as following a ‘focusing event’ or a successful attempt by some groups to ‘venue shop’ (seek influential audiences in another policymaking venue). When an issue reaches the ‘top’ of this wider political agenda it is processed in a different way: more participants become involved, and they generate more ways to look at (and seek to solve) the policy.

The key focus is the competition to frame or define a policy problem (to exercise power to reduce ambiguity). The successful definition of a policy problem as technical or humdrum ensures that issues are monopolized and considered quietly in one venue. The reframing of that issue as crucial to other institutions, or the big political issues of the day, ensures that it will be considered by many audiences and processed in more than one venue (see also Schattschneider).

The modern PET story

The modern PET story is about complex systems and attention.

Its analysis of bounded rationality and policymaker psychology remains crucial, since PET measures the consequences of the limited attention of individuals and organisations.

However, note the much greater quantification of policy change across entire political systems (see the Comparative Agendas Project).

PET shows how policy actors and organisations contribute to ‘disproportionate information processing’, in which attention to information fluctuates out of proportion to (a) the size of policy problems and (b) the information on problems available to policymakers.

It also shows that the same basic distribution of policy change – ‘hyperincremental’ in most cases, but huge in some – is present in every political system studied by the CAP (summed up by the image below)

True et al figure 6.2

See also:

5 images of the policy process

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Policy Analysis in 750 words: Michael Mintrom (2012) Contemporary Policy Analysis

Please see the Policy Analysis in 750 words series overview before reading the summary. This summary is not 750 words. I can only apologise.

Michael Mintrom (2012) Contemporary Policy Analysis (Oxford University Press)

https://twitter.com/CairneyPaul/status/1195241605831315456

Mintrom (2012: xxii; 17) describes policy analysis as ‘an enterprise primarily motivated by the desire to generate high quality information to support high-quality decisions’ and stop policymakers ‘from making ill-considered choices’ (2012: 17). It is about giving issues more ‘serious attention and deep thought’ than busy policymakers, rather than simply ‘an exercise in the application of techniques’ to serve clients (2012: 20; xxii).

It begins with six ‘Key Steps in Policy Analysis’ (2012: 3-5):

  1. ‘Engage in problem definition’

Problem definition influences the types of solutions that will be discussed (although, in some cases, solutions chase problems).

Define the nature and size of a policy problem, and the role of government in solving it (from maximal to minimal), while engaging with many stakeholders with different views (2012: 3; 58-60).

This task involves a juggling act. First, analysts should engage with their audience to work out what they need and when (2012 : 81). However, second, they should (a) develop ‘critical abilities’, (b) ask themselves ‘why they have been presented in specific ways, what their sources might be, and why they have arisen at this time’, and (c) present ‘alternative scenarios’ (2012: 22; 20; 27).

  1. ‘Propose alternative responses to the problem’

Governments use policy instruments – such as to influence markets, tax or subsidize activity, regulate behaviour, provide services (directly, or via commissioning or partnership), or provide information – as part of a coherent strategy or collection of uncoordinated measures (2012: 30-41). In that context, try to:

  • Generate knowledge about how governments have addressed comparable problems (including, the choice to not intervene if an industry self-regulates).
  • Identify the cause of a previous policy’s impact and if it would have the same effect now (2012: 21).
  • If minimal comparable information is available, consider wider issues from which to learn (2012: 76-7; e.g. alcohol policy based on tobacco).

Consider the wider:

 

  1. ‘Choose criteria for evaluating each alternative policy response’

There are no natural criteria, but ‘effectiveness, efficiency, fairness, and administrative efficiency’ are common (2012: 21). ‘Effective institutions’ have a marked impact on social and economic life and provide political stability (2012: 49). Governments can promote ‘efficient’ policies by (a) producing the largest number of winners and (b) compensating losers (2012: 51-2; see Weimer and Vining on Kaldor-Hicks). They can prioritise environmental ‘sustainability’ to mitigate climate change, the protection of human rights and ‘human flourishing’, and/or a fair allocation of resources (2012: 52-7).

  1. ‘Project the outcomes of pursuing each policy alternative’

Estimate the costs of a new policy, in comparison with current policy, and in relation to factors such as (a) overall savings to society, and/or (b) benefits to certain populations (any policy will benefit some social groups more than others). Mintrom (2012: 21) emphasises ‘prior knowledge and experience’ and ‘synthesizing’ work by others alongside techniques such as cost-benefit analyses.

  1. ‘Identify and analyse trade-offs among alternatives’

Use your criteria and projections to compare each alternative in relation to their likely costs and benefits.

  1. ‘Report findings and make an argument for the most appropriate response’

Mintrom (2012: 5) describes a range of advisory roles.

(a) Client-oriented advisors identify the beliefs of policymakers and anticipate the options worth researching (although they should not simply tell clients what they want to hear – 2012: 22). They may only have the time to answer a client’s question quickly and on their own. Or, they need to create and manage a team project (2012: 63-76).

(b) Other actors, ‘who want to change the world’, research options that are often not politically feasible in the short term but are too important to ignore (such as gender mainstreaming or action to address climate change).

In either case, the format of a written report – executive summary, contents, background, analytical strategy, analysis and findings (perhaps including a table comparing goals and trade-offs between alternatives), discussion, recommendation, conclusion, annex – may be similar (2012: 82-6).

Wider context: the changing role of policy analysts

Mintrom (2012: 5-7) describes a narrative – often attributed to Radin – of the changing nature of policy analysis, comparing:

  1. (a) a small group of policy advisors, (b) with a privileged place in government, (c) giving allegedly technical advice, using economic techniques such as cost-benefit analysis.
  2. (a) a much larger profession, (b) spread across – and outside of – government (including external consultants), and (c) engaging more explicitly in the politics of policy analysis and advice.

It reflects wider changes in government, (a) from the ‘clubby’ days to a much more competitive environment debating a larger number and wider range of policy issues, subject to (b) factors such as globalisation that change the task/ context of policy analysis.

If so, any advice on how to do policy analysis has to be flexible, to incorporate the greater diversity of actors and the sense that complex policymaking systems require flexible skills and practices rather than standardised techniques and outputs.

The ethics of policy analysis

In that context, Mintrom (2012: 95-108) emphasises the enduring role for ethical policy analysis, which can relate to:

  1. ‘Universal’ principles such as fairness, compassion, and respect
  2. Specific principles to project the analyst’s integrity, competence, responsibility, respectfulness, and concern for others
  3. Professional practices, such as to
  • engage with many stakeholders in problem definition (to reflect a diversity of knowledge and views)
  • present a range of feasible solutions, making clear their distributional effects on target populations, opportunity costs (what policies/ outcomes would not be funded if this were), and impact on those who implement policy
  • be honest about (a) the method of calculation, and (b) uncertainty, when projecting outcomes
  • clarify the trade-offs between alternatives (don’t stack-up the evidence for one)
  • maximise effective information sharing, rather than exploiting the limited attention of your audience (compare with Riker).
  1. New analytical strategies (2012: 114-15; 246-84)
  1. the extent to which social groups are already ‘systematically disadvantaged’,
  2. the causes (such as racism and sexism) of – and potential solutions to – these outcomes, to make sure
  3. that new policies reduce or do not perpetuate disadvantages, even when
  4. politicians may gain electorally from scapegoating target populations and/ or
  5. there are major obstacles to transformative policy change.

Therefore, while Mintrom’s (2012: 3-5; 116) ‘Key Steps in Policy Analysis’ are comparable to Bardach and Weimer and Vining, his emphasis is often closer to Bacchi’s.

The entrepreneurial policy analyst

Mintrom (2012: 307-13) ends with a discussion of the intersection between policy entrepreneurship and analysis, highlighting the benefits of ‘positive thinking’, creativity, deliberation, and leadership. He expands on these ideas further in So you want to be a policy entrepreneur?

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Policy Analysis in 750 words: William Riker (1986) The Art of Political Manipulation

Please see the Policy Analysis in 750 words series overview before reading the summary.

William H. Riker (1986) The Art of Political Manipulation (New Haven: Yale University Press)

Most texts in this series describe the politics of policy analysis, in which your aim is to communicate with a client to help them get what they want, subject to professional standards and ethics (Smith, Bardach, and Weimer and Vining).

Such texts suggest that the evidence will not speak for itself, and that your framing of information could make a big difference between success and failure. However, they tend to dance around the question of how to exercise power to maximise your success.

The consequence may be some bland Aristotle-style advice, in which you should seek to be a persuasive narrator by combining:

  • Pathos. The appeal to an audience’s emotions to maximise interest in a problem.
  • Logos. The concise presentation of information and logic to make a persuasive case.
  • Ethos. The credibility of the presenter, based on their experience and expertise.

Studies of narrative suggest that these techniques have some impact. Narrators tap into their audience’s emotions and beliefs, make a problem seem ‘concrete’ and urgent, and romanticise a heroic figure or cause. However, their success depends heavily on the context, and stories tend to be most influential of the audiences predisposed to accept them.

If so, a key option is to exploit a tendency for people to possess many contradictory beliefs, which suggests that (a) they could support many different goals or policy solutions, and (b) their support may relate strongly to the context and rules that determine the order and manner in which they make choices.

In other words, you may not be able to ‘change their minds’, but you can encourage them to pay more attention to, and place more value on, one belief (or one way to understand a policy problem) at the expense of another. This strategy could make the difference between belief and action.

Riker (1986: ix) uses the term ‘heresthetic’ to describe ‘structuring the world so you can win’. People ‘win politically because they have set up the situation in such a way that other people will want to join them’. Examples include:

  1. Designing the order in which people make choices, because many policy preferences are ‘intransitive’: if A is preferred to B and B to C, A is not necessarily preferred to C.
  2. Exploiting the ways in which people deal with ‘bounded rationality’ (the limits to their ability to process information to make choices).

For example, what if people are ‘cognitive misers’, seeking to process information efficiently rather than comprehensively? What if they combine cognition and emotion to make choices efficiently? Riker highlights the potential value of some combination of the following strategies:

  1. Make your preferred problem framing or solution as easy to understand as possible.
  2. Make other problems/ solutions difficult to process, such as by presenting them in the abstract and providing excessive detail.
  3. Emphasize the high cognitive cost to the examination of all other options.
  4. Experiment with choice-rule options that consolidate the vote for your preferred option while splitting the vote of others.
  5. Design the comparison of a small number of options to make sure that yours is the most competitive.
  6. Design the framing of choice (for example, is a vote primarily about the substantial issue or confidence in its proponents?).
  7. Design the selection of criteria to evaluate options.
  8. Design a series of votes, in sequence, to allow you to trade votes with others.
  9. Conspire to make sure that the proponent of your preferred choice is seen as heroic (and the proponent of another choice as of flawed character and intellect).
  10. Ensure that people make or vote for choices quickly, to ward off the possibility of further analysis and risk of losing control of the design of choice.
  11. Make sure that you engage in these strategies without being detected or punished.

The point of this discussion is not to recommend that policy analysts become Machiavellian manipulators, fixing their eye on the prize, and doing anything to win.

Rather, it is to highlight the wider agenda setting context that you face when presenting evidence, values, and options.

It is a truism in policy studies that the evidence does not speak for itself. Instead, people engage in effective communication and persuasion to assign meaning to the evidence.

Similarly, it would be a mistake to expect success primarily from a well written and argued policy analysis document. Rather, much of its fate depends on who is exploiting the procedures and rules that influence how people make choices.

See also:

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

Three habits of successful policy entrepreneurs

Evidence-informed policymaking: context is everything

Please note: some of this text comes from Box 4.3 in Understanding Public Policy 2nd ed

box 4.3 Riker topbox 4.3 Riker bottom

 

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A general theory of public policy

This is a placeholder for future work and discussion. It tails off at the end.

People sometimes talk about a ‘general theory’ of public policy to put in our minds a comparison with the physical sciences. Usually, the punchline is that there are ‘no general theories of public policy that are not bounded by space or time’ (p21). There may be some reference to the accumulation of knowledge or wisdom in policy studies, but based rarely on the understanding that policy studies contain the equivalent of general laws (I can only think of one possible exception).

This outcome is not too surprising in the social sciences, in which context really matters and we would expect a lot of variation in policy, policymaking, and outcomes.

On the other hand, we still need a way to communicate our findings, relate them to other studies, compare them, and wonder what it all adds up to. Few people go as far as expressing the sense that every study is unique (to the point of non-comparability) and that every description of policymaking does not compare to another.

In other words, we may be looking for a happy medium, to reject the idea of general laws but encourage – when appropriate or necessary – enough of a sense of common outlook and experience to help us communicate with each other (without descending too quickly into heated debate on our cross-purposes). Or, we can at least tell a story of policy studies and invite others to learn from, or challenge, its insights.

In my case, there are two examples in which it is necessary to project some sense of a common and initially-not-too-complicated story:

  1. When describing policy theory insights to students, on the assumption that it may be their gateway to more reading.

It is possible to choose how many words to devote to each topic, including 500 Words, 1000 Words, a 9000 word Understanding Public Policy chapter, more in the source material, and even more if students start to ‘snowball’.

It is also possible, if you have a clearly defined audience, to introduce some level of uncertainty about these descriptions and their limitations.

For example, I try to describe ‘the policy process’ in 500 words and 1000 words, but in the context of a wider discussion of images of the policy process.

Circle image policy process 24.10.18

It is also possible to provide more context, such as in this kind of introductory box, coupled with 12 things to know about studying public policy

Introduction box

(from Chapter 1)

You can also get into the idea that my story is one of many, particularly after students have invested in many versions of that story by the end of an introductory book

conclusion box

(from Chapter 13)

  1. When describing these insights to people – from other disciplines or professions – who do not have the time, inclination, or frame of reference to put in that kind of work.

In this case, one presentation or article may be the limit. People may want to know the answer to a question – e.g. Why don’t policymakers listen to your evidence?rather than hear all about the explanation for the answer.

You do your best, and then – if there is time – you talk about what you missed out.

For example, in this talk, the first question was: why didn’t you mention the role of power?

 

A general theory or a general understanding? Two key issues

That was a long-winded introduction to a more philosophical point about what we might want from general theories. My impression is that you might be seeking one of these two possibilities:

  1. To use theories and concepts to describe material reality. In producing a general theory, we seek a general understanding of the ways in which the real world works. If so, we may focus primarily on how well these concepts describe the world, and the extent to which we can produce methods to produce systematic and consistent findings. The lack of a general theory denotes too much complexity and context.
  2. To use theories and concepts to represent a useful story. In producing a general understanding, we focus on the ways in which people generate and communicate their understanding. If so, we may focus more on how people come together to produce and share meaning through concepts. The lack of a general theory could reflect the lack of agreement on how to study policymaking. Or, the presence of a general understanding could represent the exercise of power, to set the agenda and limit scholarly attention to a small number of theories.

I describe this distinction in the following audio clip, produced halfway through a run with the dogs, while jetlagged. The large gap in the middle happens when I am trying to see if the voice to text is working well enough for me to copy/paste it here (no).

Key examples of the exercise of power include:

  1. The act of dismissing an individual, social group, or population by undermining the value of their knowledge or claim to knowledge (discussed in power and knowledge and Chapter 3).
  2. Ongoing discussions about how we deal with (a) a relatively new focus (among the most-established policy theories) on policy studies in countries in the Global South, given that (b) the dominant interpretations of policymaking come from experiences in the Global North.

box 13.4 part 1box 13.4 part 2

So, if you read these posts or Chapter 13 you will find a story of a general understanding of policy followed, almost immediately, by a list of reasons for why you should engage with it critically and perhaps not accept it. I’m setting your agenda but also reminding you that I’m doing it.

That’s it really. To be continued.

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Understanding Public Policy 2nd edition

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

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

2nd ed cover

titlechapter 1chapter 2chapter 3chapter 4.JPG

chapter 5

chapter 6chapter 7.JPG

chapter 8

chapter 9

chapter 10

chapter 11

chapter 12

chapter 13

 

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Policy in 500 Words: Power and Knowledge

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

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

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

In practice, these more-or-less-observable forms of power co-exist and often reinforce each other:

Example 1. The control of elected office is highly skewed towards men. Male incumbency, combined with social norms about who should engage in politics and public life, signal to women that their efforts may be relatively unrewarded and routinely punished – for example, in electoral campaigns in which women face verbal and physical misogyny – and the oversupply of men in powerful positions tends to limit debates on feminist issues.

Example 2. ‘Epistemic violencedescribes the act of dismissing an individual, social group, or population by undermining the value of their knowledge or claim to knowledge. Specific discussions include: (a) the colonial West’s subjugation of colonized populations, diminishing the voice of the subaltern; (b) privileging scientific knowledge and dismissing knowledge claims via personal or shared experience; and (c) erasing the voices of women of colour from the history of women’s activism and intellectual history.

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

See also:

Policy Concepts in 1000 Words: Power and Ideas

Evidence-informed policymaking: context is everything

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Evidence-informed policymaking: context is everything

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

https://www.facebook.com/idsuk/videos/364796097654832/

 

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

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

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

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

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

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

Limiting participation as a strategic aim

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

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

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

NASA launch

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

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

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

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

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

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

Where is the power analysis in all of this?

image policy process round 2 25.10.18

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

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

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

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

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

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

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

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

See also:

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

listening-new-york-1-11-16

See also:

Policy in 500 Words: Power and Knowledge

The politics of evidence-based policymaking

Palgrave Communications: The politics of evidence-based policymaking

Using evidence to influence policy: Oxfam’s experience

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

https://youtu.be/A7qYj8nRkYg

 

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Filed under agenda setting, Evidence Based Policymaking (EBPM), Policy learning and transfer, Psychology Based Policy Studies, public policy, Storytelling

Evidence-based policymaking and the ‘new policy sciences’

image policy process round 2 25.10.18

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

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

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

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

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

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

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

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

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

The example of EBPM

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

SLIDE simple advice from hexagon image policy process 24.10.18

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

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

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

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

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

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

From one story to many?

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

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

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

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

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

My not-brilliant conclusion is that:

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

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

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

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

 

Paul Cairney Brisbane EBPM New Policy Sciences 25.10.18

 

 

 

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

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Filed under agenda setting, Evidence Based Policymaking (EBPM), Policy learning and transfer, Psychology Based Policy Studies, public policy

Evidence-based policymaking and the ‘new policy sciences’

Circle image policy process 24.10.18

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

SLIDE simple advice from hexagon image policy process 24.10.18

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

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

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

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

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

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

The new policy sciences

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

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

This focus on psychology is not new …

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

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

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

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

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

Theory-driven policy analysis

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

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

Epilogue

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

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

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

 

Paul Cairney Brisbane EBPM New Policy Sciences 25.10.18

 

 

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

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Filed under agenda setting, Evidence Based Policymaking (EBPM), Policy learning and transfer, Psychology Based Policy Studies, public policy, Storytelling

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

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

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

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

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

slide 3 24.10.18

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

Under those circumstances, key responses include:

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

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

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

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

In that context, I suggest that CS should:

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

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

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

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

Figure 1

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

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

Table 1 3 ideal types of EBBP

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

  1. Storytelling

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

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

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

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

See: Storytelling for Policy Change: promise and problems

  1. Evidence for advocacy

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

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

oxfam table 2

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

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

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

 

 

 

 

 

 

 

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Taking lessons from policy theory into practice: 3 examples

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

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

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

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

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

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

Example 1: the policy cycle endures despite its descriptive inaccuracy

cycle

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

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

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

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

However, the cycle metaphor endures because:

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

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

  1. One option is via complexity theory, in which scholars generally encourage policymakers to accept and describe their limits:
  • Accept routine error, reduce short-term performance management, engage more in trial and error, and ‘let go’ to allow local actors the flexibility to adapt and respond to their context.
  • However, would a government in the Westminster tradition really embrace this advice? No. They need to balance (a) pragmatic policymaking, and (b) an image of governing competence.
  1. Another option is to try to help improve an existing approach.

Further reading (blog posts):

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

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

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

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

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

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

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

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

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

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

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

Cairney 2017 image of the policy process

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

Examples include:

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

Further reading:

Is Evidence-Based Policymaking the same as good policymaking?

A 5-step strategy to make evidence count

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

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

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

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

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

For me, the context is potentially overwhelming:

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

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

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

 

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

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

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Filed under agenda setting, Evidence Based Policymaking (EBPM), IMAJINE, Policy learning and transfer

Managing expectations about the use of evidence in policy

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

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

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

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

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

Put less gloomily:

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

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

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

They include:

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

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

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

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

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

hang-in-there-baby

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Filed under agenda setting, Evidence Based Policymaking (EBPM), public policy, UK politics and policy