Almost. I have sent a full draft following external feedback and review (next stage: copy-editing). All going well, it will be out in November 2019.
Since 2016, my most common academic presentation to interdisciplinary scientist/ researcher audiences is a variant of the question, ‘why don’t policymakers listen to your evidence?’
I tend to provide three main answers.
Few policymakers know or care about the criteria developed by some scientists to describe a hierarchy of scientific evidence. For some scientists, at the top of this hierarchy is the randomised control trial (RCT) and the systematic review of RCTs, with expertise much further down the list, followed by practitioner experience and service user feedback near the bottom.
Yet, most policymakers – and many academics – prefer a wider range of sources of information, combining their own experience with information ranging from peer reviewed scientific evidence and the ‘grey’ literature, to public opinion and feedback from consultation.
While it may be possible to persuade some central government departments or agencies to privilege scientific evidence, they also pursue other key principles, such as to foster consensus driven policymaking or a shift from centralist to localist practices.
Consequently, they often only recommend interventions rather than impose one uniform evidence-based position. If local actors favour a different policy solution, we may find that the same type of evidence may have more or less effect in different parts of government.
Many scientists articulate the idea that policymakers and scientists should cooperate to use the best evidence to determine ‘what works’ in policy (in forums such as INGSA, European Commission, OECD). Their language is often reminiscent of 1950s discussions of the pursuit of ‘comprehensive rationality’ in policymaking.
The key difference is that EBPM is often described as an ideal by scientists, to be compared with the more disappointing processes they find when they engage in politics. In contrast, ‘comprehensive rationality’ is an ideal-type, used to describe what cannot happen, and the practical implications of that impossibility.
The ideal-type involves a core group of elected policymakers at the ‘top’, identifying their values or the problems they seek to solve, and translating their policies into action to maximise benefits to society, aided by neutral organisations gathering all the facts necessary to produce policy solutions. Yet, in practice, they are unable to: separate values from facts in any meaningful way; rank policy aims in a logical and consistent manner; gather information comprehensively, or possess the cognitive ability to process it.
Instead, Simon famously described policymakers addressing ‘bounded rationality’ by using ‘rules of thumb’ to limit their analysis and produce ‘good enough’ decisions. More recently, punctuated equilibrium theory uses bounded rationality to show that policymakers can only pay attention to a tiny proportion of their responsibilities, which limits their control of the many decisions made in their name.
More recent discussions focus on the ‘rational’ short cuts that policymakers use to identify good enough sources of information, combined with the ‘irrational’ ways in which they use their beliefs, emotions, habits, and familiarity with issues to identify policy problems and solutions (see this post on the meaning of ‘irrational’). Or, they explore how individuals communicate their narrow expertise within a system of which they have almost no knowledge. In each case, ‘most members of the system are not paying attention to most issues most of the time’.
This scarcity of attention helps explain, for example, why policymakers ignore most issues in the absence of a focusing event, policymaking organisations make searches for information which miss key elements routinely, and organisations fail to respond to events or changing circumstances proportionately.
In that context, attempts to describe a policy agenda focusing merely on ‘what works’ are based on misleading expectations. Rather, we can describe key parts of the policymaking environment – such as institutions, policy communities/ networks, or paradigms – as a reflection of the ways in which policymakers deal with their bounded rationality and lack of control of the policy process.
Scientists often appear to be drawn to the idea of a linear and orderly policy cycle with discrete stages – such as agenda setting, policy formulation, legitimation, implementation, evaluation, policy maintenance/ succession/ termination – because it offers a simple and appealing model which gives clear advice on how to engage.
Indeed, the stages approach began partly as a proposal to make the policy process more scientific and based on systematic policy analysis. It offers an idea of how policy should be made: elected policymakers in central government, aided by expert policy analysts, make and legitimise choices; skilful public servants carry them out; and, policy analysts assess the results with the aid of scientific evidence.
Yet, few policy theories describe this cycle as useful, while most – including the advocacy coalition framework , and the multiple streams approach – are based on a rejection of the explanatory value of orderly stages.
Policy theories also suggest that the cycle provides misleading practical advice: you will generally not find an orderly process with a clearly defined debate on problem definition, a single moment of authoritative choice, and a clear chance to use scientific evidence to evaluate policy before deciding whether or not to continue. Instead, the cycle exists as a story for policymakers to tell about their work, partly because it is consistent with the idea of elected policymakers being in charge and accountable.
Some scholars also question the appropriateness of a stages ideal, since it suggests that there should be a core group of policymakers making policy from the ‘top down’ and obliging others to carry out their aims, which does not leave room for, for example, the diffusion of power in multi-level systems, or the use of ‘localism’ to tailor policy to local needs and desires.
Now go to:
In policy studies, there is a profound difference between uncertainty and ambiguity:
Both concepts relate to ‘bounded rationality’: policymakers do not have the ability to process all information relevant to policy problems. Instead, they employ two kinds of shortcut:
I make an artificially binary distinction, uncertain versus ambiguous, and relate it to another binary, rational versus irrational, to point out the pitfalls of focusing too much on one aspect of the policy process:
Actors can try to solve uncertainty by: (a) improving the quality of evidence, and (b) making sure that there are no major gaps between the supply of and demand for evidence. Relevant debates include: what counts as good evidence?, focusing on the criteria to define scientific evidence and their relationship with other forms of knowledge (such as practitioner experience and service user feedback), and what are the barriers between supply and demand?, focusing on the need for better ways to communicate.
Actors try to solve ambiguity by exercising power to increase attention to, and support for, their favoured interpretation of a policy problem. You will find many examples of such activity spread across the 500 and 1000 words series:
A focus on reducing uncertainty gives the impression that policymaking is a technical process in which people need to produce the best evidence and deliver it to the right people at the right time.
In contrast, a focus on reducing ambiguity gives the impression of a more complicated and political process in which actors are exercising power to compete for attention and dominance of the policy agenda. Uncertainty matters, but primarily to describe the role of a complex policymaking system in which no actor truly understands where they are or how they should exercise power to maximise their success.
For a longer discussion, see Fostering Evidence-informed Policy Making: Uncertainty Versus Ambiguity (PDF)
Or, if you fancy it in French: Favoriser l’élaboration de politiques publiques fondées sur des données probantes : incertitude versus ambiguïté (PDF)
Here is the relevant opening section in UPP:
Today, I’m helping to deliver a new course – Engaging Policymakers Training Programme – piloted by the Alliance for Useful Evidence and the UCL. Right now, it’s for UCL staff (and mostly early career researchers). My bit is about how we can better understand the policy process so that we can engage in it more effectively. I have reproduced the brief guide below (for my two 2-hour sessions as part of a wider block). If anyone else is delivering something similar, please let me know. We could compare notes.
This module will be delivered in two parts to combine theory and practice
Policy theories provide a wealth of knowledge about the role of evidence in policymaking systems. They prompt us to understand and respond to two key dynamics:
In this seminar, we discuss how to respond effectively to these dynamics. We focus on unresolved issues:
The format will be relatively informal. Cairney will begin by making some introductory points (not a powerpoint driven lecture) and encourage participants to relate the three questions to their research and engagement experience.
Gateway to further reading:
In this seminar, we move from abstract theory and general advice to concrete examples and specific strategies. Each participant should come prepared to speak about their research and present a theoretically informed policy analysis in 3 minutes (without the aid of powerpoint). Their analysis should address:
After each presentation, each participant should be prepared to ask questions about the problem raised and the strategy to engage. Finally, to encourage learning, we will reflect on the memorability and impact of presentations.
Powerpoint: Paul Cairney A4UE UCL 2017
The JRC’s annual conference has become a key forum in which to discuss the use of evidence in policy. At this scale, in which many hundreds of people attend plenary discussions, it feels like an annual mass rally for science; a ‘call to arms’ to protect the role of science in the production of evidence, and the protection of evidence in policy deliberation. There is not much discussion of storytelling, but we tell each other a fairly similar story about our fears for the future unless we act now.
Last year, the main story was of fear for the future of heroic scientists: the rise of Trump and the Brexit vote prompted many discussions of post-truth politics and reduced trust in experts. An immediate response was to describe attempts to come together, and stick together, to support each other’s scientific endeavours during a period of crisis. There was little call for self-analysis and reflection on the contribution of scientists and experts to barriers between evidence and policy.
This year was a bit different. There was the same concern for reduced trust in science, evidence, and/ or expertise, and some references to post-truth politics and populism, but with some new voices describing the positive value of politics, often when discussing the need for citizen engagement, and of the need to understand the relationship between facts, values, and politics.
For example, a panel on psychology opened up the possibility that we might consider our own politics and cognitive biases while we identify them in others, and one panellist spoke eloquently about the importance of narrative and storytelling in communicating to audiences such as citizens and policymakers.
A focus on narrative is not new, but it provides a challenging agenda when interacting with a sticky story of scientific objectivity. For the unusually self-reflective, it also reminds us that our annual discussions are not particularly scientific; the usual rules to assess our statements do not apply.
As in studies of policymaking, we can say that there is high support for such stories when they remain vague and driven more by emotion than the pursuit of precision. When individual speakers try to make sense of the same story, they do it in different – and possibly contradictory – ways. As in policymaking, the need to deliver something concrete helps focus the mind, and prompts us to make choices between competing priorities and solutions.
I describe these discussions in two ways: tables, in which I try to boil down each speaker’s speech into a sentence or two (you can get their full details in the programme and the speaker bios); and a synthetic discussion of the top 3 concerns, paraphrasing and combining arguments from many speakers:
The key distinction began as between politics-values-facts which is impossible to maintain in practice.
Yet, subsequent discussion revealed a more straightforward distinction between facts and opinion, ‘fake news’, and lies. The latter sums up an ever-present fear of the diminishing role of science in an alleged ‘post truth’ era.
The tables below provide a range of concerns about the problem, from threats to democracy to the need to communicate science more effectively. A theme of growing importance is the need to deal with the cognitive biases and informational shortcuts of people receiving evidence: communicate with reference to values, beliefs, and emotions; build up trust in your evidence via transparency and reliability; and, be prepared to discuss science with citizens and to be accountable for your advice. There was less discussion of the cognitive biases of the suppliers of evidence.
Not all speakers described scientists as the heroes of this story:
Often in such discussions it is difficult to know if some scientists are naïve actors or sophisticated political strategists, because their public statements could be identical. For the former, an appeal to objective facts and the need to privilege science in EBPM may be sincere. Scientists are, and should be, separate from/ above politics. For the latter, the same appeal – made again and again – may be designed to energise scientists and maximise the role of science in politics.
Yet, energy is only the starting point, and it remains unclear how exactly scientists should communicate and how to ‘know your audience’: would many scientists know who to speak to, in governments or the Commission, if they had something profoundly important to say?
|Keynotes and introductory statements from panel chairs|
|Vladimír Šucha: We need to understand the relationship between politics, values, and facts. Facts are not enough. To make policy effectively, we need to combine facts and values.|
|Tibor Navracsics: Politics is swayed more by emotions than carefully considered arguments. When making policy, we need to be open and inclusive of all stakeholders (including citizens), communicate facts clearly and at the right time, and be aware of our own biases (such as groupthink).|
|Sir Peter Gluckman: ‘Post-truth’ politics is not new, but it is pervasive and easier to achieve via new forms of communication. People rely on like-minded peers, religion, and anecdote as forms of evidence underpinning their own truth. When describing the value of science, to inform policy and political debate, note that it is more than facts; it is a mode of thinking about the world, and a system of verification to reduce the effect of personal and group biases on evidence production. Scientific methods help us define problems (e.g. in discussion of cause/ effect) and interpret data. Science advice involves expert interpretation, knowledge brokerage, a discussion of scientific consensus and uncertainty, and standing up for the scientific perspective.|
|Carlos Moedas: Safeguard trust in science by (1) explaining the process you use to come to your conclusions; (2) provide safe and reliable places for people to seek information (e.g. when they Google); (3) make sure that science is robust and scientific bodies have integrity (such as when dealing with a small number of rogue scientists).|
|Pascal Lamy: 1. ‘Deep change or slow death’ We need to involve more citizens in the design of publicly financed projects such as major investments in science. Many scientists complain that there is already too much political interference, drowning scientists in extra work. However, we will face a major backlash – akin to the backlash against ‘globalisation’ – if we do not subject key debates on the future of science and technology-driven change (e.g. on AI, vaccines, drone weaponry) to democratic processes involving citizens. 2. The world changes rapidly, and evidence gathering is context-dependent, so we need to monitor regularly the fitness of our scientific measures (of e.g. trade).|
|Jyrki Katainen: ‘Wicked problems’ have no perfect solution, so we need the courage to choose the best imperfect solution. Technocratic policymaking is not the solution; it does not meet the democratic test. We need the language of science to be understandable to citizens: ‘a new age of reason reconciling the head and heart’.|
|Panel: Why should we trust science?|
|Jonathan Kimmelman: Some experts make outrageous and catastrophic claims. We need a toolbox to decide which experts are most reliable, by comparing their predictions with actual outcomes. Prompt them to make precise probability statements and test them. Only those who are willing to be held accountable should be involved in science advice.|
|Johannes Vogel: We should devote 15% of science funding to public dialogue. Scientific discourse, and a science-literature population, is crucial for democracy. EU Open Society Policy is a good model for stakeholder inclusiveness.|
|Tracey Brown: Create a more direct link between society and evidence production, to ensure discussions involve more than the ‘usual suspects’. An ‘evidence transparency framework’ helps create a space in which people can discuss facts and values. ‘Be open, speak human’ describes showing people how you make decisions. How can you expect the public to trust you if you don’t trust them enough to tell them the truth?|
|Francesco Campolongo: Claude Juncker’s starting point is that Commission proposals and activities should be ‘based on sound scientific evidence’. Evidence comes in many forms. For example, economic models provide simplified versions of reality to make decisions. Economic calculations inform profoundly important policy choices, so we need to make the methodology transparent, communicate probability, and be self-critical and open to change.|
|Panel: the politician’s perspective|
|Janez Potočnik: The shift of the JRC’s remit allowed it to focus on advocating science for policy rather than policy for science. Still, such arguments need to be backed by an economic argument (this policy will create growth and jobs). A narrow focus on facts and data ignores the context in which we gather facts, such as a system which undervalues human capital and the environment.|
|Máire Geoghegan-Quinn: Policy should be ‘solidly based on evidence’ and we need well-communicated science to change the hearts and minds of people who would otherwise rely on their beliefs. Part of the solution is to get, for example, kids to explain what science means to them.|
|Panel: Redesigning policymaking using behavioural and decision science|
|Steven Sloman: The world is complex. People overestimate their understanding of it, and this illusion is burst when they try to explain its mechanisms. People who know the least feel the strongest about issues, but if you ask them to explain the mechanisms their strength of feeling falls. Why? People confuse their knowledge with that of their community. The knowledge is not in their heads, but communicated across groups. If people around you feel they understand something, you feel like you understand, and people feel protective of the knowledge of their community. Implications? 1. Don’t rely on ‘bubbles’; generate more diverse and better coordinated communities of knowledge. 2. Don’t focus on giving people full information; focus on the information they need at the point of decision.|
|Stephan Lewandowsky: 97% of scientists agree that human-caused climate change is a problem, but the public thinks it’s roughly 50-50. We have a false-balance problem. One solution is to ‘inoculate’ people against its cause (science denial). We tell people the real figures and facts, warn them of the rhetorical techniques employed by science denialists (e.g. use of false experts on smoking), and mock the false balance argument. This allows you to reframe the problem as an investment in the future, not cost now (and find other ways to present facts in a non-threatening way). In our lab, it usually ‘neutralises’ misinformation, although with the risk that a ‘corrective message’ to challenge beliefs can entrench them.|
|Françoise Waintrop: It is difficult to experiment when public policy is handed down from on high. Or, experimentation is alien to established ways of thinking. However, our 12 new public innovation labs across France allow us to immerse ourselves in the problem (to define it well) and nudge people to action, working with their cognitive biases.|
|Simon Kuper: Stories combine facts and values. To change minds: persuade the people who are listening, not the sceptics; find go-betweens to link suppliers and recipients of evidence; speak in stories, not jargon; don’t overpromise the role of scientific evidence; and, never suggest science will side-line human beings (e.g. when technology costs jobs).|
|Panel: The way forward|
|Jean-Eric Paquet: We describe ‘fact based evidence’ rather than ‘science based’. A key aim is to generate ‘ownership’ of policy by citizens. Politicians are more aware of their cognitive biases than we technocrats are.|
|Anne Bucher: In the European Commission we used evidence initially to make the EU more accountable to the public, via systematic impact assessment and quality control. It was a key motivation for better regulation. We now focus more on generating inclusive and interactive ways to consult stakeholders.|
|Ann Mettler: Evidence-based policymaking is at the heart of democracy. How else can you legitimise your actions? How else can you prepare for the future? How else can you make things work better? Yet, a lot of our evidence presentation is so technical; even difficult for specialists to follow. The onus is on us to bring it to life, to make it clearer to the citizen and, in the process, defend scientists (and journalists) during a period in which Western democracies seem to be at risk from anti-democratic forces.|
|Mariana Kotzeva: Our facts are now considered from an emotional and perception point of view. The process does not just involve our comfortable circle of experts; we are now challenged to explain our numbers. Attention to our numbers can be unpredictable (e.g. on migration). We need to build up trust in our facts, partly to anticipate or respond to the quick spread of poor facts.|
|Rush Holt: In society we can find the erosion of the feeling that science is relevant to ‘my life’, and few US policymakers ask ‘what does science say about this?’ partly because scientists set themselves above politics. Politicians have had too many bad experiences with scientists who might say ‘let me explain this to you in a way you can understand’. Policy is not about science based evidence; more about asking a question first, then asking what evidence you need. Then you collect evidence in an open way to be verified.|
That was 10 hours of discussion condensed into one post. If you can handle more discussion from me, see:
Psychology and policymaking: Three ways to communicate more effectively with policymakers
The role of evidence in policy: EBPM and How to be heard
This is a guest post by William L. Swann (left) and Seo Young Kim (right), discussing how to use insights from the Institutional Collective Action Framework to think about how to improve collaborative governance. The full paper has been submitted to the series for Policy and Politics called Practical Lessons from Policy Theories.
Many public policy problems cannot be addressed effectively by a single, solitary government. Consider the problems facing the Greater Los Angeles Area, a heavily fragmented landscape of 88 cities and numerous unincorporated areas and special districts. Whether it is combatting rising homelessness, abating the country’s worst air pollution, cleaning the toxic L.A. River, or quelling gang violence, any policy alternative pursued unilaterally is limited by overlapping authority and externalities that alter the actions of other governments.
Problems of fragmented authority are not confined to metropolitan areas. They are also found in multi-level governance scenarios such as the restoration of Chesapeake Bay, as well as in international relations as demonstrated by recent global events such as “Brexit” and the U.S.’s withdrawal from the Paris Climate Agreement. In short, fragmentation problems manifest at every scale of governance, horizontally, vertically, and even functionally within governments.
In many cases governments would be better off coordinating and working together, but they face barriers that prevent them from doing so. These barriers are what the policy literature refers to as ‘institutional collective action’ (ICA) dilemmas, or collective action problems in which a government’s incentives do not align with collectively desirable outcomes. For example, all governments in a region benefit from less air pollution, but each government has an incentive to free ride and enjoy cleaner air without contributing to the cost of obtaining it.
The ICA Framework, developed by Professor Richard Feiock, has emerged as a practical analytical instrument for understanding and improving fragmented governance. This framework assumes that governments must match the scale and coerciveness of the policy intervention (or mechanism) to the scale and nature of the policy problem to achieve efficient and desired outcomes.
For example, informal networks (a mechanism) can be highly effective at overcoming simple collective action problems. But as problems become increasingly complex, more obtrusive mechanisms, such as governmental consolidation or imposed collaboration, are needed to achieve collective goals and more efficient outcomes. The more obtrusive the mechanism, however, the more actors’ autonomy diminishes and the higher the transaction costs (monitoring, enforcement, information, and agency) of governing.
We explored what actionable steps policymakers can take to improve their results with collaboration in fragmented systems. Our study offers three general practical recommendations based on the empirical literature that can enhance institutional collaborative governance.
First, institutional collaboration is more likely to emerge and work effectively when policymakers employ networking strategies that incorporate frequent, face-to-face interactions.
Government actors networking with popular, well-endowed actors (“bridging strategies”) as well as developing closer-knit, reciprocal ties with a smaller set of actors (“bonding strategies”) will result in more collaborative participation, especially when policymakers interact often and in-person.
Policy network characteristics are also important to consider. Research on estuary governance indicates that in newly formed, emerging networks, bridging strategies may be more advantageous, at least initially, because they can provide organizational legitimacy and access to resources. However, once collaboratives mature, developing stronger and more reciprocal bonds with fewer actors reduces the likelihood of opportunistic behavior that can hinder collaborative effectiveness.
Second, policymakers should design collaborative arrangements that reduce transaction costs which hinder collaboration.
Well-designed collaborative institutions can lower the barriers to participation and information sharing, make it easier to monitor the behaviors of partners, grant greater flexibility in collaborative work, and allow for more credible commitments from partners.
Research suggests policymakers can achieve this by
Considering the context, however, is crucial. Collaboratives that thrive on informal, close-knit, reciprocal relations, for example, may be severely damaged by the introduction of monitoring mechanisms that signal distrust.
Third, institutional collaboration is enhanced by the development and harnessing of collaborative capacity.
Research suggests signaling organizational competencies and capacities, such as budget, political support, and human resources, may be more effective at lowering barriers to collaboration than ‘homophily’ (a tendency to associate with similar others in networks). Policymakers can begin building collaborative capacity by seeking political leadership involvement, granting greater managerial autonomy, and looking to higher-level governments (e.g., national, state, or provincial governments) for financial and technical support for collaboration.
Finally, we recognize that not all policymakers operate in similar institutional contexts, and collaboration can often be mandated by higher-level authorities in more centralized nations. Nonetheless, visible joint gains, economic incentives, transparent rules, and equitable distribution of joint benefits and costs are critical components of voluntary or mandated collaboration.
The recommendations offered here are, at best, only the tip of the iceberg on valuable practical insight that can be gleaned from collaborative governance research. While these suggestions are consistent with empirical findings from broader public management and policy networks literatures, much could be learned from a closer inspection of the overlap between ICA studies and other streams of collaborative governance work.
Collaboration is a valuable tool of governance, and, like any tool, it should be utilized appropriately. Collaboration is not easily managed and can encounter many obstacles. We suggest that governments generally avoid collaborating unless there are joint gains that cannot be achieved alone. But the key to solving many of society’s intractable problems, or just simply improving everyday public service delivery, lies in a clearer understanding of how collaboration can be used effectively within different fragmented systems.
This is a draft of my review of Justin Parkhurst (2017) The Politics of Evidence (Routledge, Open Access)
Justin Parkhurst’s aim is to identify key principles to take forward the ‘good governance of evidence’. The good governance of scientific evidence in policy and policymaking requires us to address two fundamentally important ‘biases’:
To address both problems, Parkhurst seeks pragmatic ways to identify principles to decide what counts as ‘good evidence to inform policy’ and ‘what constitutes the good use of evidence within a policy process’:
‘it is necessary to consider how to establish evidence advisory systems that promote the good governance of evidence – working to ensure that rigorous, systematic and technically valid pieces of evidence are used within decision-making processes that are inclusive of, representative of and accountable to the multiple social interests of the population served’ (p8).
Parkhurst identifies some ways in which to bring evidence and policy closer together. First, to produce evidence more appropriate for, or relevant to, policymaking (‘good evidence for policy’):
Second, to produce the ‘good use of evidence’, combine three forms of ‘legitimacy’:
In the final chapter, Parkhurst suggests that these aims can be pursued in many ways depending on how governments want to design evidence advisory systems, but that it’s worth drawing on the examples of good practice he identifies. Parkhurst also explores the role for Academies of science, or initiatives such as the Cochrane Collaboration, to provide independent advice. He then outlines the good governance of evidence built on key principles: appropriate evidence, accountability in evidence use, transparency, and contestability (to ensure sufficient debate).
The overall result is a book full of interesting discussion and very sensible, general advice for people new to the topic of evidence and policy. This is no mean feat: most readers will seek a clearly explained and articulate account of the subject, and they get it here.
For me, the most interesting thing about Parkhurst’s book is the untold story, or often-implicit reasoning behind the way in which it is framed. We can infer that it is not a study aimed primarily at a political science or social science audience, because most of that audience would take its starting point for granted: the use of evidence is political, and politics involves values. Yet, Parkhurst feels the need to remind the reader of this point, in specific (“it is worth noting that the US presidency is a decidedly political role”, p43) and general circumstances (‘the nature of policymaking is inherently political’, p65). Throughout, the audience appears to be academics who begin with a desire for ‘evidence based policy’ without fully thinking through the implications, either about the lack of a magic bullet of evidence to solve a policy problem, how we might maintain a political system conducive to democratic principles and good evidence use, how we might design a system to reduce key ‘barriers’ between the supply of evidence by scientists and its demand by policymakers, and why few such designs have taken off.
In other words, the book appeals primarily to scientists trained outside social science, some of whom think about politics in their spare time, or encounter it in dispiriting encounters with policymakers. It appeals to that audience with a statement on the crucial role of high quality evidence in policymaking, highlights barriers to its use, tells scientists that they might be part of the problem, but then provides them with the comforting assurance that we can design better systems to overcome at least some of those barriers. For people trained in policy studies, this concluding discussion seems like a tall order, and I think most would read it with great scepticism.
Policy scientists might also be sceptical about the extent to which scientists from other fields think this way about hierarchies of scientific evidence and the desire to depoliticise politics with a primary focus on ‘what works’. Yet, I too hear this language regularly in interdisciplinary workshops (often while standing next to Justin!), and it is usually accompanied by descriptions of the pathology of policymaking, the rise of post-truth politics and rejection of experts, and the need to focus on the role of objective facts in deciding what policy solutions work best. Indeed, I was impressed recently by the skilled way in which another colleague prepared this audience for some provocative remarks when he suggested that the production and use of evidence is about power, not objectivity. OMG: who knew that policymaking was political and about power?!
So, the insights from this book are useful to a large audience of scientists while, for a smaller audience of policy scientists, they remind us that there is an audience out there for many of the statements that many of us would take for granted. Some evidence advocates use the language of ‘evidence based policymaking’ strategically, to get what they want. Others appear to use it because they believe it can exist. Keep this in mind when you read the book.