All going well, it will be out in November 2019. We are now at the proofing stage.
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:
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:
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
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:
Where is the power analysis in all of this?
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:
However, they are no less important when they play out routinely:
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.
The EBPM talks begin with a discussion of the same three points: what counts as evidence, why we must ignore most of it (and how), and the policy process in which policymakers use some of it. However, the framing of these points, and the ways in which we discuss the implications, varies markedly by audience. So, in this post, I provide a short discussion of the three points, then show how the audience matters (referring to the city as a shorthand for each talk).
The overall take-home points are highly practical, in the same way that critical thinking has many practical applications (in other words, I’m not offering a map, toolbox, or blueprint):
3 ways to describe the use of evidence in policymaking
However, it only remains a valence issue when we refuse to define evidence and justify what counts as good evidence. After that, you soon see the political choices emerge. A reference to evidence is often a shorthand for scientific research evidence, and good often refers to specific research methods (such as randomised control trials). Or, you find people arguing very strongly in the almost-opposite direction, criticising this shorthand as exclusionary and questioning the ability of scientists to justify claims to superior knowledge. Somewhere in the middle, we find that a focus on evidence is a good way to think about the many forms of information or knowledge on which we might make decisions, including: a wider range of research methods and analyses, knowledge from experience, and data relating to the local context with which policy would interact.
So, what begins as a valence issue becomes a gateway to many discussions about how to understand profound political choices regarding: how we make knowledge claims, how to ‘co-produce’ knowledge via dialogue among many groups, and the relationship between choices about evidence and governance.
There is far more information about the world than we are able to process. A focus on evidence gaps often gives way to the recognition that we need to find effective ways to ignore most evidence.
There are many ways to describe how individuals combine cognition and emotion to limit their attention enough to make choices, and policy studies (to all intents and purposes) describe equivalent processes – described, for example, as ‘institutions’ or rules – in organisations and systems.
One shortcut between information and choice is to set aims and priorities; to focus evidence gathering on a small number of problems or one way to define a problem, and identify the most reliable or trustworthy sources of evidence (often via evidence ‘synthesis’). Another is to make decisions quickly by relying on emotion, gut instinct, habit, and existing knowledge or familiarity with evidence.
Either way, agenda setting and problem definition are political processes that address uncertainty and ambiguity. We gather evidence to reduce uncertainty, but first we must reduce ambiguity by exercising power to define the problem we seek to solve.
Policy textbooks (well, my textbook at least!) provide a contrast between:
Overall, policy theories have much to offer people with an interest in evidence-use in policy, but primarily as a way to (a) manage expectations, to (b) produce more realistic strategies and less dispiriting conclusions. It is useful to frame our aim as to analyse the role of evidence within a policy process that (a) we don’t quite understand, rather than (b) we would like to exist.
The events themselves
Below, you will find a short discussion of the variations of audience and topic. I’ll update and reflect on this discussion (in a revised version of this post) after taking part in the events.
Social science and policy studies: knowledge claims, bounded rationality, and policy theory
For Auckland and Wellington A, I’m aiming for an audience containing a high proportion of people with a background in social science and policy studies. I describe the discussion as ‘meta’ because I am talking about how I talk about EBPM to other audiences, then inviting discussion on key parts of that talk, such as how to conceptualise the policy process and present conceptual insights to people who have no intention of deep dives into policy theory.
I often use the phrase ‘I’ve read it, so you don’t have to’ partly as a joke, but also to stress the importance of disciplinary synthesis when we engage in interdisciplinary (and inter-professional) discussion. If so, it is important to discuss how to produce such ‘synthetic’ accounts.
I tend to describe key components of a policymaking environment quickly: many policy makers and influencers spread across many levels and types of government, institutions, networks, socioeconomic factors and events, and ideas. However, each of these terms represents a shorthand to describe a large and diverse literature. For example, I can describe an ‘institution’ in a few sentences, but the study of institutions contains a variety of approaches.
Academic-practitioner discussions: improving the use of research evidence in policy
For Wellington B and Melbourne, the audience is an academic-practitioner mix. We discuss ways in which we can encourage the greater use of research evidence in policy, perhaps via closer collaboration between suppliers and users.
Discussions with scientists: why do policymakers ignore my evidence?
Sydney UNSW focuses more on researchers in scientific fields (often not in social science). I frame the question in a way that often seems central to scientific researcher interest: why do policymakers seem to ignore my evidence, and what can I do about it?
Then, I tend to push back on the idea that the fault lies with politics and policymakers, to encourage researchers to think more about the policy process and how to engage effectively in it. If I’m trying to be annoying, I’ll suggest to a scientific audience that they see themselves as ‘rational’ and politicians as ‘irrational’. However, the more substantive discussion involves comparing (a) ‘how to make an impact’ advice drawn from the personal accounts of experienced individuals, giving advice to individuals, and (b) the sort of advice you might draw from policy theories which focus more on systems.
Background post: What can you do when policymakers ignore your evidence?
Early career researchers: the need to build ‘impact’ into career development
Canberra UNSW is more focused on early career researchers. I think this is the most difficult talk because I don’t rely on the same joke about my role: to turn up at the end of research projects to explain why they failed to have a non-academic impact. Instead, my aim is to encourage intelligent discussion about situating the ‘how to’ advice for individual researchers into a wider discussion of policymaking systems.
Similarly, Brisbane A and B are about how to engage with practitioners, and communicate well to non-academic audiences, when most of your work and training is about something else entirely (such as learning about research methods and how to engage with the technical language of research).
2. European Health Forum Gastein 2018 ‘Policy in Evidence’ (from 6 minutes)
|Evidence-based policymaking and the new policy sciences|
This post describes a new article published in British Politics (Open Access). Please find:
(1) A super-exciting video/audio powerpoint I use for a talk based on the article
(2) The audio alone (link)
(4) A written/ tweeted discussion of the main points
In retrospect, I think the title was too subtle and clever-clever. I wanted to convey two meanings: imaginative as a euphemism for ridiculous/ often cynical and to argue that a government has to be imaginative with evidence. The latter has two meanings: imaginative (1) in the presentation and framing of evidence-informed agenda, and (2) when facing pressure to go beyond the evidence and envisage policy outcomes.
So I describe two cases in which its evidence-use seems cynical, when:
Then I describe more difficult cases in which supportive evidence is not clear:
As scientists, we can shrug our shoulders about the uncertainty, but elected policymakers in government have to do something. So what do they do?
At this point of the article it will look like I have become an apologist for David Cameron’s government. Instead, I’m trying to demonstrate the value of comparing sympathetic/ unsympathetic interpretations and highlight the policy problem from a policymaker’s perspective:
I suggest that they use evidence in a mix of ways to: describe an urgent problem, present an image of success and governing competence, and provide cover for more evidence-informed long term action.
The result is the appearance of top-down ‘muscular’ government and ‘a tendency for policy to change as is implemented, such as when mediated by local authority choices and social workers maintaining a commitment to their professional values when delivering policy’
I conclude by arguing that ‘evidence-based policy’ and ‘policy-based evidence’ are political slogans with minimal academic value. The binary divide between EBP/ PBE distracts us from more useful categories which show us the trade-offs policymakers have to make when faced with the need to act despite uncertainty.
As such, it forms part of a far wider body of work …
In both cases, the common theme is that, although (1) the world of top-down central government gets most attention, (2) central governments don’t even know what problem they are trying to solve, far less (3) how to control policymaking and outcomes.
In that wider context, it is worth comparing this talk with the one I gave at the IDS (which, I reckon is a good primer for – or prequel to – the UK talk):
(found by searching for early intervention)
(found by searching for prevention)
Powerpoint for guest lecture: Paul Cairney UK Government Evidence Policy
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.