Osimo codagnone

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Osimo codagnone

  1. 1. Evidence-based and open: myths and reality of policy-making David Osimo Open University of Catalunya, Cristiano Codagnone, London School of Economics ICPP 2015, Milan
  2. 2. Two “recent” trends Evidence based policy-making • Long tradition, but emerging in 1990s under New Labour as a answer to “end of ideology” • “New Labour is a party of ideas and ideals but not of outdated ideology. What counts is what works” • Further developer in automated, data-intensive decision making Open policy-making • Roots in direct democracy but emerging as part of “open government” and “government 2.0 trend around 2008 with strong techno-driven approach • Open data, crowdsourcing and policy co-design to increase effectiveness of public policy
  3. 3. Failed expectations? • No evidence of increased quality of policy-making • Evaluation carried out keep mentioning the same systemic weaknesses (Hallsworth, 2011) • Permanent low participation in online initiatives and loudest voice effect (National Audit Office, 2012) (Prieto-martín, Marcos, & Martínez, 2011). • Financial crisis showed limitations of tools: “in the face of the crisis, we felt abandoned by conventional tools” (Trichet 2010)
  4. 4. Objectives • As practitioners and advocates of open and evidence-based policy, we aim to set the correct expectations about what “science” and “openness” can bring. • We aim to understand what are the factors behind the failed expectations of EB and Open Policy-making, and identify possible common patterns and lessons learnt.
  5. 5. Method • Umbrella review (Grant & Booth, 2009) • Scientific literature, grey literature such as reports of the UK National Audit Office, and online blog entries • Integrated by personal experience of the authors
  6. 6. Bottlenecks of EBPM • Adoption of a rigid linear model from empirical data to scientific evidence to policy decisions (Pielke, 2007). Policy is considered as the continuation of science by other means. • The positivist assumption of a predictable world, governed by laws that are fully understandable and discoverable, without recognizing the complexity of science and the rise of post-normal science. • The obsessive removal of values from the equation and the trend towards technocracy and scientization (Jasanoff, 1990). “Normative” today has become a “killer criticism” in any policy discussion within government. • The notion that full scientific knowledge will lead to optimal policy decision, failing to recognize the importance of the “hiding hand” (Hirschman, 1967) where ignorance can be a prerequisite of ambitious decisions. • The sterilization of the relation between scientific advisors and policy-makers, failing to understand the complexity of the factors that lead to policy decisions (Strassheim & Kettunen, 2014) • The importance of “framing the debate” to manipulate and manage policy decisions (Lakoff, Dean, & Hazen, 2005). • The difficulty in moving from decision to implementation in a highly distributed system. Too often “right” and evidence-based decisions encounter resistance in their implementation at the level of civil servants or intermediate institutions (Hallsworth, 2011)
  7. 7. Bottlenecks of open policy-making • Top down policy mandates on openness struggle to be implemented at lower level of government • Inflated expectations of willingness of citizens to participate: unequal participation is normal on online initiatives (Shirky, 2003). The key is looking for insight, not representativeness. One open data reuser could be enough as in Reinhart Rogoff case (Konczal, 2013) • Excessive focus on the “decision”, rather than what happens before and after. Expectation that crowdsourcing substitutes government decision, rather than supporting it. • Need to account for different level of openness between and within initiatives • Metrics are excessively focussed on quantity of participation rather than quality
  8. 8. Design Implement Evaluate Set agenda Brainstorming solutions Drafting proposals Revising proposals Induce behavioural change Collaborativ e action Ensure Buy-in Monitor executionCollect feedback Identify problems Collect evidence Set priorities Analyze data Uservoice, ideascale Etherpad Co-ment.com Social networks Persuasive technologies Challenge. gov Open data Participatr y sensing Open Data visualization Evidencechall enge.com Collaborati ve visualizatio n Open discussion Policy cycle Simulate impact of options Model and simulation Source: CROSSOVER roadmap
  9. 9. It’s doesn’t have to be totally open to the crowd Open Declaration on European Public Services Open to all Digital Agenda Mid Term review Open to all, 2000 comments received, 1500 participants Pledge Tracker Only to those organisations committing to the Grand Coalition for Digital Jobs OpenIdeo Members of the OpenIdeo community Daeimplementation Collaborative platform for EU MS representative Young Advisors to VP Neelie Kroes Appointed Young Advisors Need for restricted online spaces
  10. 10. Not all the time open Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux- people-autumn-2010-talks/ Open brainstorming Small groups drafting Open commenting Small group re-drafting Open endorsement EU Open Declaration:
  11. 11. Common issues • open and evidence-based are not contradictory but complementary. • the expectation that evidence, data or the “crowd” could substitute the role of government as decision-makers, rather than supporting them. • decision-making in the public sphere is never done in a vacuum, but under competing pressures of different forces: the “Bermuda Triangle” composed by scientific evidence, politics and values (ECGT, 2014) • Stereotyped perspective on the different stakeholders and lack of a systemic view of their interaction • Need to consider the full policy cycle, not only decisions • Lack of a robust evaluation framework
  12. 12. Conclusions • Integrate open and evidence-based approach. The challenges are similar and the benefits mutual. • Adopt a systemic perspective of the interactions in the decision-making process, covering the different stakeholders and different phases of the policy cycle • Undertand obth approaches as a support, not a susbsitute to the role of policy-makers • Recognize the limitations of scientific knowledge and human behaviour
  13. 13. A proposed evaluation framework Source: Open Evidence / UNDP
  14. 14. There’s elite and elite: who benefits? 14 Usual suspects No problem Not interested/interesting Missed opportunity Low quality of ideas High quality of ideas Don’t participate in policy debate Participate in policy debate Source: adapted from Kublai evaluation

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