Theorizing on the metagovernance of collaborative policy innovations

Theorizing on the metagovernance of collaborative policy innovations Vidar Stevens ([email protected]) and prof. dr. Koen Verhoest ([email protected])

Keywords: collaborative policy innovation, metagovernance, horizontal coordination, backward mapping

Conference Paper prepared for the 2015 ICPP conference in Milan, T02P07

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1. Introduction Coordination, and challenges posed to coordination, have been recurrent themes in the study of public policy1. In contemporary times, the issue of horizontal coordination has become more pressing due to the cross-cutting nature of many of today’s policy problems2. To efficiently respond to these cross-cutting policy problems, public officials have over the years come up with various governmental strategies, like e.g.: holistic governance3, whole-of-government4, joined-up government5 and network governance6. Simultaneously, different analytical concepts such as overlapping sub-systems7, boundary spanning policy regimes8, policy networks9 and functional regulatory spaces10, have been used by scholars for the analysis of the governance of these ‘crosscutting’ policy issues. The latest contribution to this pile of conceptual and analytical tools is the literature on collaborative policy innovation, which is particularly interested in how multi-actor governance arrangements (referred to as collaborative policy innovation networks 11) can foster innovation in public policies as a means to address unmet societal and public policy challenges 12. The literature on collaborative policy innovation is, nevertheless, still in its infancy. The concept was only first-mentioned by Christian Bason and Helle Vibeke Carstensen in 2012. The authors applied it to the case of the Danish MindLab13. Following this seminal work, a few other scholars have looked at collaborative policy innovations and foremost considered the problems and potential of processes of collaborative policy innovation14. Only very limited scholarly attention has so far been devoted to the meta-governance of these innovation processes; that is to say, a focus on the manner in which collaborative policy innovation networks are coordinated and governed with the ambition to bring the innovation process to a good end (but see the theoretically focussed 2014conference paper of Sørensen15). This lacunae in the literature is striking as it creates the misleading image that achieving unison among involved actors for concerted innovative policy action is an easy exercise – neglecting the possibility that coordination of the innovation processes is essential as actors may hold different problem perceptions, are reluctant to collaborate or paralyze the innovation process for strategic reasons. To this end, Sørensen16 has declared that the research field is in need for more theoretical reflections and, most of all, empirical studies on the meta-governance dynamics in processes of collaborative policy innovation. Therefore, the prime purpose of this conference paper is to explore how processes of collaborative policy innovation, which involve the participation of a multitude of organizations, are metagoverned. Our focus is both on cases in which involved actors, with (some) help of the metagovernor (which is, the actor that meta-governs the collaborative policy innovation processes), managed to reach unison on a collaborative policy innovation as well as on cases wherein this was not the result. In this way, we will get a full understanding of the ‘challenges’ a metagovernor faces in these kinds of innovation processes and the ‘action strategies’ it uses to (try and) turn ‘deadlock’ situations around.

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Alexander, 1995; Bouckaert et. al., 2010. Halligan, 2008; Laegreid et. al., 2014, Knill and Tosun, 2012. 3 Perri 6, 1997. 4 Christensen and Laegreid, 2007; Christensen and Laegreid, 2006. 5 Ling, 2002; Perri 6, 2004; Mulgan, 2005. 6 Sørensen and Torfing, 2007; Coen and Thatcher, 2008; Koppenjan and Klijn, 2004; Rhodes, 1997. 7 Davis and Davis, 1988; Thomas, 1996. 8 Jochim and May, 2010; Bump, 2011. 9 Marsh and Rhodes, 1992; Rhodes, 1997; Marsh, 1998; Waarden, 1992; Klijn and Koppenjan, 2000. 10 Varone, Nahrath, Aubin and Gerber, 2013. 11 See for definition section 3. 12 Bason and Carstensen, 2012; Sørensen and Waldorff, 2014; Sørensen, 2014. 13 A permanent governance network between the Ministries of Business and Growth, Taxation and Employment designed to foster crossgovernmental innovation in public policies. 14 Sørensen and Waldorff, 2014; Van Buuren and Loorbach, 2009. 15 Sørensen, 2014. 16 Sørensen, 2014:10. 2

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The paper advances as follows. First attention is devoted to the concepts of innovation, policy innovation and collaborative policy innovation (networks). Then the paper elaborates on what metagovernance entails and what so far has been written on strategies and activities of meta-governance in the academic literature. We end the paper by proposing a methodology that allows us to perform the envisioned cross-case comparison. The data collection and empirical analysis is planned for next months. As such, this conference paper is a provisional account of our ongoing research project. So, let us proceed.

2. The concept of innovation Innovation is one of the magic concepts that over the years has been embraced by many OECD governments as a modernization strategy for the public sector17. Confronted with major budgetary pressures and grand societal challenges, these governments have felt the need to step beyond their conventional wisdoms and their sedimented practices of organizing public services, and instead come up with innovative solutions to address unmet public needs. ‘Innovation’ is, however, quite an ‘elusive concept’ that often lacks a precise definition 18, and with the growing attention that it receives in the public sector, there is the risk that the notion of innovation looses its distinct meaning and becomes synonymous with other terms, like e.g.: ‘reform’, ‘change’ or ‘new ideas’19. To avoid this, Sørensen and Torfing 20 have done a lot of effort to come up with a definition that captures the exact gist of the concept. They defined innovation as, “the intentional and proactive process of actors that involves the generation and practical adoption and spread of new and creative ideas, which aim to produce a qualitative change in a specific context.” This definition ascribes five key aspects to innovations that make them distinctively different from the other, aforementioned, analytical terms. The first aspect being the intentional and proactive action of involved actors. As Sørensen and Torfing argue, “although the process of innovation is an open and unpredictable process, involved actors will deliberately try to change, or even improve, the current state of affairs.” Secondly, the definition makes clear that innovation is not merely about generating a new idea. A ‘creative’ or ‘new’ idea only becomes an innovation when it is implemented and therewith able to produce some significant effects. Thirdly, Sørensen and Torfing foresee with their definition that innovations are not about delivering more or less the same kind of goods, services or solutions21, but rather about changing the form, content, repertoire of goods, services and organizational routines 22 or even transforming the underlying problem understanding, objectives and program theory23. Such a radical transition is by Sørensen and Torfing seen as a ‘qualitative’ rather than a ‘quantitative’ change. Fourthly, innovation is always relative to a specific context. The new is not necessarily novel to the world but merely perceived to be new in a particular context or domain24. Fifthly, although innovation carries a positive connotation, the concept itself is not about whether the consequences of an innovation are good or bad 25. There are different ways in which governments can ‘innovate’. The academic literature makes mention of administrative process26 innovations27, technological process28 innovations29, product or service30 innovations31, governance innovations32, conceptual innovations33 and policy innovations 34. 17

Borins, 2008; Osborne and Brown, 2011; OECD, 2014. Lloyd-Reason, Wall and Muller, 2002. 19 Sørensen and Torfing, 2011: 849. 20 Sørensen and Torfing 2011: 849-851. 21 This is by Hall (1993) understood as a first order change. 22 This is by Hall (1993) understood as a second order change. 23 This is by Hall (1993) understood as a third order change. 24 Zaltman, Duncan and Holbek, 1973; Sørensen and Torfing, 2011:850. 25 Hartley, 2005. 26 The creation of a ‘one-stop shop’. 27 Daft, 1978; Meeus and Enquist, 2006. 28 The digital assessment of taxes. 29 Damanpour and Gopalakrishnan, 2001; Edquist et. al., 2001. 30 The creation of youth work disability benefits. 31 Damanpour et. al., 2009. 32 Moore and Hartley, 2008; Bekkers et. al., 2011. 33 Bekkers et. al., 2011. 34 Sørensen and Waldorff, 2014. 18

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This article is interested in the latter category. Policy innovation is by Sørensen and Waldorff 35 defined as the radical transformation of problem understandings, policy visions, objectives, strategies and/or policy instruments for solving a specific policy problem.

3. The concepts of (collaborative) policy innovation (network) Whereas the definition given by Sørensen and Waldorff is relatively ‘new’, the phenomenon of ‘policy innovation’ itself definitely has some history in the public sector. For example, public officials have under the slogan of ‘reinventing government’ in the 1980s and 1990s tried to render public policies more efficient 36. What makes contemporary policy innovations, nonetheless, distinct from their predecessors is the collaborative manner in which they tend to emerge. That is to say, it is not uncommon that the generation of a policy innovation is the outcome of an innovation process that involves a multitude of (public) actors 37. The collaborative character of recent policy innovations has for most part been a consequence 38 of the ‘wickedness’ of many of today’s policy issues and the inability of ‘traditional’ policy responses to get a hold on these problems. 'Wicked issues39' is a term that was coined by Rittel and Webber in 1973. Herewith they referred to policy issues that possess 10 specific properties, to mention: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Wicked problems have no definitive formulation; Wicked problems have no stopping rule; Solutions to wicked problems are not true or false, but good or bad; There is no immediate test of a solution to a wicked problem; Wicked problems do not have a well-described set of potential solutions; Each wicked problem is essentially unique; Every implemented solution to a wicked problem has consequences; Each wicked problem can be considered a symptom of another problem; The causes of a wicked problem can be explained in numerous ways; The policy planner has not right to be wrong.

In more laymen’s terms, wicked problems can be defined as policy problems that are persistent and generally dealt with in a context of great uncertainty with regard to the nature of the matter and possible solutions40. Causal relations underlying these policy problems are often numerous and difficult to identify. Developments in one seemingly unrelated policy field can impinge in unpredictable and intricate ways on realities of another policy sector41. This means that wicked policy problems typically transcend the portfolios of individual public sector organizations. ‘Traditional’ public management responses to complexity and uncertainty, like technical (expertdriven) solutions and routine administrative solutions 42, for most part take place in the hierarchical silo structures of individual public sector organizations and do often not consider the involvement of other actors43. Hence, public sector organizations have had great difficulty in taming the crosscutting nature of wicked policy problems. In consequence, governments have set up collaborative governance arrangements as a means to tackle these daunting and wicked policy problems. Here the rationale was that through collaboration across the conventional borders in the public sector, innovative policy solutions would emerge that better fit the wicked policy context, as more stakeholders and thus more knowledge, 35

Sørensen and Waldorff, 2014. Osborne and Gaebler, 1992; Sørensen, 2014. 37 Van Buuren and Loorbach, 2009. 38 Apart from wicked policy issues, many governments are confronted with rising expectations of citizens to the quality, availability and effectiveness of public policies (Sørensen and Torfing, 2010) and are additionally stimulated to rethink their institutional designs due to the decreasing amount of available resources in the public sector as a consequence of the financial and economic crises (Bekkers et. al., 2013; Keast and Mandell, 2014). 39 There are many examples of such kinds of wicked issues, like e.g.: youth unemployment, population ageing, obesity, intermodal transport, poverty, energy, sustainability, immigration, security, etc. 40 Rittel and Webber, 1973. 41 Ney, 2009. 42 Like e.g.: markets, outsourcing or regulatory prescription. 43 Hartley, 2005. 36

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information, resources and experiences are included in the policymaking process44. Carstensen and Bason45 have called these processes in which multiple public sector organizations interact and participate to come up with policy innovations, ‘collaborative policy innovations’ (CPIs). In similar fashion, we use the term ‘collaborative policy innovation networks’ (CPINs) to explicitly refer to the multi-actor collaborative governance arrangements that are central in these innovation processes.

4. The metagovernance of collaborative policy innovations Looking at the dynamics within these CPINs, theorists tend to agree that it is not something apparent that actors are impartial about the joint formation of a policy innovation. Commonly, there is a certain degree of ‘cognitive diversity and distance’ between the organizations46 which has to be overcome in order to reach an agreement on an innovative policy action. ‘Cognitive diversity and distance’ refers to the multiple alternative perspectives and objectives that the actors hold which participate in a collaborative policy innovation process47. Ideally, actors get a shared understanding of each other’s issues, concerns and interests through processes of mutual learning48, in which the fundamental problems and tensions are discussed in an atmosphere of mutual trust, and wherein the involved actors eventually arrive, in a transparent manner, on the most-suited solution for the targeted policy problem 49. Though, meaningful interaction and fruitful collaboration across cognitive distance, of course, is only possible as long as the participants are willing to make sense of each other’s perspectives 50. If this is not the case, different managerial strategies may contribute to improving the quality 51 of the interactions among participants within collaborative policy innovations. Such a view on the use of managerial strategies in self-steering collaborative policy innovation networks is by certain scholars, with Sørensen52 and Torfing as frontrunners, understood as metagovernance53. To elucidate, Sørensen and Torfing54 define metagovernance as, “the endeavor to regulate self-steering [policy networks 55] by shaping the conditions under which they operate.” There are also other conceptualizations of metagovernance in the academic literature 56. In this conference-paper we, however, use the conceptualization from the strand of Sørensen and Torfing as, in conceptual terms, this strand offers a promising operationalization of different strategies metagovernors can enact to facilitate collaborative policy innovation processes 57. A metagovernor is here understood as an actor, or a group of actors, who ‘guide(s)’the process of collaborative policy innovation. Other scholars see metagovernors as ‘process facilitators58’ or ‘mediators59’. These scholars do, nonetheless, share the common perception that the activities of a metagovernor are aimed at establishing (more) coherence between the involved actors60. Within this strand of the metagovernance literature, there are four distinct metagovernance ‘strategies’ that metagovernors can adopt vis-à-vis the other actors in the collaborative policy innovation network to stimulate the establishment of concerted (innovative) policy action, to mention: framing, designing, facilitation and participation.

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Bekkers et. al., 2013:13. Carstensen and Bason, 2012. 46 , Van Buuren and Loorbach, 2009. 47 Van Buuren and Loorbach, 2009:381. 48 Vinke-de Kruijf, Bressers and Augustijn, 2014. 49 Sørensen and Torfing, 2011:852. 50 Grabher, 2004:108. 51 Torfing et. al., 2012:135. 52 Sørensen, 2014; Sørensen, 2006, Sørensen and Torfing, 2009; Torfing, et. al., 2012. 53 Meuleman, 2008:70; Voets et. al., 2015:3. 54 Sørensen and Torfing, 2005:202. 55 Policy networks are seen as self-steering collaborative arrangements in which a multitude of actors participate. 56 Jessop, 2002; Kooiman, 2003; Koppejan and Klijn, 2004. 57 Voets et. al., 2015:3; Sørensen, 2014. 58 Provan and Kenis, 2007:8. 59 Driessen and Vermeulen, 1995:168. 60 Sørensen and Torfing, 2009:246-250. 45

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> 4.1.

The metagovernance strategy of framing

The first metagovernance strategy, that of framing, focusses on ‘the formulation of political goals and objectives to be pursued by the actors in the collaborative policy innovation network’61. There are three different activities that fall within this first category. 1. First of all, metagovernors can introduce intermediate policy objectives. Most of the time, the policy objectives of a collaborative arrangement are broadly formulated and offer considerable leeway to achieve these. The meta-governor can intervene by introducing measurements for intermediate steps which have to be met in order to reach the overall goals. This allows the metagovernor to supervise the policy-making process, while the network constellation does not lose its self-steering capacity. Sørensen and Torfing 62 specifically refer to the use of performance indicators and floor targets. 2. Second, metagovernors can shape the discussion on a certain issue. Sørensen and Torfing63 have called these shaping-activities ‘storytelling’ or ‘discursive framing’. Especially, when deliberations on a policy innovation are bogged down to nothing more than ordinary ‘trench-fighting64’, the framing activities of a metagovernor can unfold or present stories in a plain and holistic manner and thereby reshape the interests of participants, create a common vision or point out some common strategies. This can, for example, be done by developing reports on best practices. 3. Third, metagovernors can use incentives to artificially strengthen the relationships between involved actors. A much used action is the introduction of ‘carrots’ – in terms of financial compensations, political importance or increased autonomy – that reward network members after they bring the innovation processes to a good end. The idea is that these ‘carrots’ will during the innovation processes act as motivators to improve the collaborations65. > 4.2.

The metagovernance strategy of design

The second metagovernance strategy, that of design, targets the structure, scope, composition and procedures of the collaborative multi-actor arrangement66. Quite similarly, Kickert, Klijn and Koppejan67 speak of network constitution or network structuring. There are three activities that fall within the boundaries of this second metagovernance strategy, that is to say: 1. First of all, metagovernors can change the rules68, structure, statute or decision-making procedures of a collaborative policy innovation network. The weak performance of network interactions can be a reason to do this. Another reason to alter the structural dynamics of a collaborative policy innovation network can be that the metagovernor finds it necessary for the success of the collaboration to adapt the constellation to the demands of specific actors that were identified as either ‘important’ or as ‘impeding’ to the network activities. 2. Second, the metagovernor can increase the political scope of the network constellation. By doing so, the metagovernor can enlarge the ‘playing-field’ within the collaborative policy innovation network in times of severe deadlocks and thereby create more ‘political space’ for the actors to come up with ‘give-and-take’ compromises. 3. Third, the metagovernor can define milestones and concrete deadlines for the completion of specific tasks needed to realize output “[…] in terms of reports, conferences, plans, policy proposals and direct interventions 69.” This safeguards timely and sufficient interaction between the involved actors and, in the worst case, might avoid that the process is threatened with total collapse. 61

Sørensen, 2014. Sørensen and Torfing, 2005:204. 63 Sørensen and Torfing, 2009. 64 See for an example Stevens and Verhoest, 2014. 65 Sørensen and Torfing, 2009:249. 66 Sørensen and Torfing, 2005. 67 Kickert, Klijn and Koppenjan, 1997. 68 These can be rules about the: (1) objectives (2) participation, access, exit and role distribution, (3) structuring of activities, (4) sharing of information, (5) process steps and time schedule, (6) conflict regulation, and (7) communication with the political environment, see Koppejan and Klijn, 2004:192-203. 69 Sørensen and Torfing, 2009:249. 62

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> 4.3.

The meta-governance strategy of facilitation

The third metagovernance strategy, that of facilitation, is designed at endorsing cooperation through interaction; yet, not by way of the direct participation of the metagovernor itself. Moreover, this metagovernance strategy is unlike ‘design’ as it focusses less on changing the structure of the collaborative policy innovation network70. There are four activities that belong to this third metagovernance strategy. 1. First of all, metagovernors can through agenda control 71, joint fact-finding72 and crossframe learning73 enhance the interaction among the participants in the processes of collaborative policy innovation74. The specific aim of such activities is to reduce the destructive tensions in an actor constellation. However, the proposed actions do not imply the creation of a complete consensus about a problem situation or objectives. This would ignore the fact that the different perceptions, objectives and preferences that involved actors hold are institutionally anchored and hard to change. The reality is that, despite such differences, the parties must arrive at cooperation. The enlisted actions, as such, are concerned with the creation of ‘a common ground’ which enables the mutual adjustment of organizational strategies and joint action75. 2. Second, metagovernors can make use of arbitration and conflict management to mediate conflicts between network participants. 3. Third, metagovernors can activate actors by providing them with veto power. The activity of activating actors is based on the strategy of ‘selective activation’ which was originally formulated by Scharpf76. According to Scharpf, an empirical network has many potential relations of which not all are constantly activated. Selective activation, thus, involves identifying and activating the parties that are necessary for establishing a particular policy innovation. By providing certain actors with ‘veto power’, implying that they are given the power to block proposals, these actors most likely feel the responsibility to actively participate in the collaborative arrangement and do everything in their power to bring the collaboration to a satisfying end. 4. Fourth, the metagovernor can contribute to increasing transparency within the collaborative policy innovation networks by ensuring that (all) relevant information is circulated to involved actors in a clear and accessible way. > 4.4.

The meta-governance strategy of participation

The fourth metagovernance strategy, that of participation, creates the complicated situation wherein the metagovernor becomes one among the many participants, but retains a reflexive gaze on the collaborative policy innovation network in order to influence its operations and promote effective interaction77. According to Sørensen78, this strategy is useful to support the other strategies of metagovernance from ‘within’ the CPIN, and thereby, align the network activities with the more general governance ambitions pursued by the metagovernor.

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Sørensen and Torfing, 2009:250. Agenda control refers to the act of an metagovernor to put a tensioned issue on the agenda for discussion. 72 We perceive joint fact-finding as a strategy for resolving factual disputes. We envision that a metagovernor sets-up a project team, comprised of officials from both sides of a conflict, that has the task to come to an agreement regarding relevant trends, developments and facts that are central in a political dispute. In this sense, ‘joint fact-finding’ is an attempt to resolve a ‘sub-conflict’ over different interpretations of facts as a part of an effort to deal with the overall conflict. 73 When parties insufficiently consider the fact that they have different problem frames, knowledge conflicts and asymmetrical debates are the result. Only when parties are aware that different frames of reference are involved, it will be possible to discover the real substantive questions that have to be addressed in the processes of collaborative policy innovation. ‘Cross-frame learning’ is in this regard considered as processes initiated by the metagovernor where through information gathering, the use of experts and conducting research involved actors get a shared understanding of each other’s perspectives, see Koppejan and Klijn, 2004:37-38. 74 Sørensen and Torfing, 2009:250, Koppenjan and Klijn, 2004: 160-183. 75 Koppejan and Klijn, 2004:162. 76 Scharpf, 1997. 77 Sørensen and Torfing, 2009:250-251. 78 Sørensen, 2014:7. 71

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Following the latter statement, Sørensen79 further argues that the four metagovernance strategies should most of all be viewed as complementary to each other rather than being separate alternatives. She states, “that the metagovernance of [collaborative policy innovation networks 80] is a complex and difficult matter, which can easily go wrong. It consists of a careful balancing of two opposites: being able to control the network interactions and granting it the autonomy to function well.” Too much control or coordination undermines the self-governing capacity of the collaborative arrangements, whilst too little intervention might result in fragmentation and a lack of direction in the governance initiatives. In this vein, Sørensen foresees a dynamic in which the different metagovernance strategies and corresponding activities are used in a ‘supplementary’ manner as a means to overcome the earlier discussed ‘cognitive distance’ between involved actors and bring the collaborative policy innovation processes to a good end (Ibidem).

5. Research design Open to question is whether this foreseen dynamic is a good reflection of how metagovernors commonly act. To elucidate, the work of Sørensen, as earlier denoted, is mere a theoretical exploration of the metagovernance dynamics in processes of collaborative policy innovation. So far, there has not been any empirical account on the metagovernance practices. Hence, scholars still need to explore which metagovernance strategies and activities are used when in the processes of collaborative policy innovation by the metagovernor and in what way do the metagovernance strategies and activities mutually reinforce or hinder each other’s (potential) impact on the activities and interactions within the CPINs when being deployed in a confluent manner by the metagovernor? This article attempts to find an answers to these questions, as well as, identify what the differences and similarities in the behavioural manifestations of the metagovernor are in cases where the involved actors, with (some) help of the metagovernor, eventually managed to create unison on a policy innovation and cases where no unison was reached despite the efforts of the metagovernor. As such, a research design should be developed that both allows to analyze the interplay between the behaviour of involved actors, their interactions and the metagovernance strategies (and corresponding activities) in CPINs and to compare the dynamics of ‘successful attempts’ of metagovernance to that of ‘unsuccessful’ meta-governance attempts. > 5.1.

Backward mapping: a comparison between ‘successful’ and ‘unsuccessful’ CPI

Within the research niche of collaborative (policy) innovation there is no commonly accepted framework for analyzing empirical cases. Though, a few scholars have made use of a rather similar research tactic called backward mapping. Backward mapping is a research tactic that, based on a certain outcome, intends to trace back the origins of the development of a specific process 81. Backward mapping is, as such, in its core quite comparable with the general aspects of processtracing82 (hereafter referred to as PT). Just like PT, backward mapping has the ambition to trace causal mechanisms83. Backward mapping is thus very useful to get a view on how collaborative policy innovations are metagoverned successfully and unsuccessfully, as it allows us to connect the result of a process of collaborative policy innovation (‘outcome’) with the deployed metagovernance strategies, the behaviour of involved actors and their reciprocal interactions within collaborative policy innovation networks (‘process development’). Following the definition (and therewith the selection criteria) of Sørensen and Waldorff 84 and Bason and Carstensen85, we decided to apply the research tactic of backward mapping to two specific 79

Sørensen, 2014:7. We added the part between the brackets. Sørensen, 2009:7 uses the term ‘governance networks’. 81 Elmore, 1979:602. 82 See for the general aspects of PT Beach and Pedersen, 2011 or Beach and Pedersen, 2013. Our research focus comes perhaps most close to the aspects of theory building PT, see Beach and Pedersen, 2011:6. 83 Causal mechanisms can be defined as, ‘… a complex system, which produces an outcome by the intervention of a number of parts’ (Glennan, 1996:52). 84 Sørensen and Waldorff, 2014. For more information see the different definitions in section three of this conference paper. 85 Bason and Carstensen, 2012. For more information see the different definitions in section three of this conference paper. 80

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cases, to mention: the generation process for the establishment of the National Plan on Climate Change and the National Plan against Child Poverty in the Belgian federal state. These cases can be regarded as ‘collaborative policy innovations’, as the multitude of involved actors, in both cases, had the intention to agree on a governmental strategy that would radically alter the way in which the policy problems of climate change and child poverty were addressed86. In the case of the National Plan on Climate Change, the actors failed to reach unison87, whereas in the case of the National Plan against Child Poverty 88 the involved actors did come to an agreement. Therefore, the generation process of the National Plan on Climate Change is perceived as a ‘failed case’ of collaborative policy innovation and the generation process of the National Plan against Child Poverty is marked as a ‘successful case’ of CPI in this research. To this end, the ‘meta-governance dynamics’ of these cases will be compared to each other in the empirical analysis. > 5.2.

Backward mapping: an actor analysis, network analysis and game analysis

Yet, in order to be able to analyze the interplay between the behaviour of the involved actors, their interactions in the innovation process and the deployed metagovernance strategies (and corresponding activities) for both cases, we believe that the backward mapping process should consist of three types of analysis, to mention: an actor analysis, a network analysis and a game analysis. The actor analysis will be used to demarcate the population of actors which will be analyzed89, to indicate what the organizations’ initial perceptions and interests were with regard to the problem situation and to show what ‘power positions90’ these parties occupied in the multiactor constellation (in terms of the resources they possessed and the substitutability of these). In this way, we get an indication of the main concerns of the involved organizations and we are able to make an assessment of which actors were most relevant for the fulfillment of the collaborative policy innovation and which of the organizations were less ‘critical’ actors. The latter can be an important determinant for explaining why some meta-governance strategies and activities (like e.g.: assigning veto power to one of the involved organizations) were targeted at certain actors and not at others. Subsequently, the network analysis provides us with data on the morphology of the collaborative policy innovation network. With this we mean, the overall shape of the CPIN and the general contact patterns between the nexus of actors. Which actors occupied a central place in the network 86

Formerly, the various public sector organizations intended to improve the situation with regard to climate change and child poverty by launching initiatives that fit their organizational portfolio. There was little cross-boundary collaboration. 87 http://deredactie.be/cm/vrtnieuws/politiek/1.2343699 88 http://www.mi-is.be/sites/default/files/doc/nationaal_kinderamoedebestrijdingsplan_nl.pdf 89 An immanent problem emanates when scholars are confronted ‘compound actors’ (Scharpf, 1997). ‘Compound actors’ are organizations which are represented by more than one unit in a multi-actor constellation. A Ministry of Transport and Mobility can, for example, be represented by both the Department of Fluvial Transport as well as the Department of Rail Transport in multi-actor policy games on issues intermodality and sustainable mobility. To assure the retrieval of the right amount of actors and avoid possible confusion on compound actors, we will use the rule of thumb that is proposed by Koppejan and Klijn to identify the right amount of actors (2004:139), which proceeds in the following manner: select an ‘organizational’ or ‘aggregation’ level as high as possible, without losing information or including irrelevant objectives into the analysis. This means, for example, that an aggregation level as high as ‘the government’ is not appropriate as it tangibly reduces the information quality for the analysis (Ibidem). 90 Agranoff (2006) and Scharpf (1997) have argued that multi-actor collaborative arrangements are not without power asymmetries. In most cases, certain actors are more ‘powerful’ than others, and therewith some organizations will be more in the position to influence and control interaction processes and network activities than others. According to Scharpf (1997), the ‘power position’ of an actor is determined by the resources an organization possesses and the substitutability of these. He begins his argument by stating that complex policy problems require a combination of essential resources in order to be tamed. Unison on a policy action is thus reached when the essential combination of resources for taming a policy issue is acquired with the support of the actors that possess the resources. Here a resource is understood as a supply that is necessary to produce a certain benefit (Aldrich, 1979; Benson, 1982). Within the literature a variety of resources are distinguished, to mention: financial resources, production resources, competencies, legitimacy and knowledge (Aldrich, 1979; Benson, 1982; Koppejan and Klijn, 2004:144). For most of these categories it is obvious what they entail. Perhaps the ‘vaguer’ resources are production resources and legitimacy. Production resources are necessary for enabling policy initiatives. One can think of, for instance, owning land in an urban restructuring issue. In other cases, production resources entail technology, personnel, equipment, etc. (Ibidem). Legitimacy is synonymous for authority or support. It should be separated from the concept of ‘legality’, as there is the possibility that a government action can be legal whilst not being legitimate. In this sense, the concept of legality more aligns with the competencies that an organizations possesses and legitimacy denotes whether an organization is in the position to perform a certain task or action (Locke, 1689).The essential resources are within network constellations, however, generally not owned by a single actor but rather spread over a multitude of organizations (Koppejan and Klijn, 2004; Scharpf, 1997). Hence, the resource(s) an actor possesses, determines the amount of ‘leverage’ an organization has in steering the course of action of the innovation process. Yet, it might be possible that some resources of an organization can also be acquired by another organization in the network constellation. Scharpf (1997) has described this as ‘the substitutability’ of a resource. Therefore, Scharpf further argued that if a resource an organization possesses is substitutable, the position of the actor in the network constellation will be weaker than if this is not the case (Ibidem).

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and which actors a more peripheral? Which of the actors were the gatekeepers (or the transmission belts) of various subsets of actors in the network? Which actors had frequent contact with each other and which met rather sporadic? How frequent did the different actors meet? Was the contact between the actors more formal or informal? With whom did the metagovernor most of all interact? As such, the relational data that is gathered with the help of a Social Network Analysis (SNA) provides us with a clear image of the interactions in the collaborative policy innovation process. Some authors argue, however, that the relational data that follows from a SNA is quite basic 91. To this end, a third step in the backward mapping process is the so-called game92 analysis. The game analysis zooms in on the stagnations and breakthroughs of the collaborative policy innovation process, and intends to enlighten where the relevant decisions for the advancement of the innovation process were taken, how each of the individual actors behaved in these specific decision-making arenas, what strategies and activities the metagovernor utilized to facilitate the network activities and how in the end deadlocks in the collaborative policy innovation process were overcome. Altogether, we believe that these three types of analysis will give us detailed information on the meta-governance dynamics and thereby allows us to compare the cases under study in a systematic manner. Information for the whole empirical analysis will be collected through an extensive document analysis of primary and secondary data-sources and a considerable number of interviews with relevant government officials.

6. Closing remarks To sum up, this conference paper is a provisional account of our ongoing research project on the ‘successful’ metagovernance of processes of collaborative policy innovation. In this way, we adhere to the call of Sørensen93 to devote more scholarly attention to the empirical analysis of the metagovernance practices and dynamics in policy innovation processes that involve various public actors. As could be read, we have intended to delineate the conceptual meanings of the terms ‘innovation’, ‘policy innovation’, ‘collaborative policy innovation’ and ‘collaborative policy innovation networks’ and we have summarized the various metagovernance strategies and corresponding activities that have been mentioned in the academic literature. The next step is to inductively discover under which conditions certain metagovernance strategies and activities are deployed by a metagovernor and how these different tools hinder or reinforce each other’s impact on the network activities. For this we will follow the proposed methodology of section 5.

91

See Koppejan and Klijn, 2004:154 for a reference to these authors. The game analysis does not make any reference to the rational choice school and does as such not argue that the actors only act out of self-interest and that the deliberations on a collaborative policy innovation are mere strategic interactions. 93 Sørensen, 2014. 92

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