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Innovation and Regional Competitiveness

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Innovation and Regional Competitiveness The Spatial Dimensions of Knowledge Flows: Implications for Innovation Policy Philip McCann University of Groningen Special Adviser to the EU Commissioner for Regional Policy Johannes Hahn Features of Knowledge • Knowledge is: - sticky with respect to institutions, organisations and location - sticky with respect to geography and location – clustering • Issue of boundaries or barriers to knowledge or innovation • Issue of systems of knowledge or innovation Features of Innovation • Features of Innovation: (i) newness (ii) improvement (iii) reduction of uncertainty by creation of a monopoly position • Types of Innovation (OECD Oslo Manual): (i) product innovation (ii) process innovation (iii) radical innovation – new products to market Systems of Innovation • Sectoral innovation systems (SIS) Technological systems of innovation (TIC) National systems of innovation (NIS) Regional systems of innovation (RIS) • Which specific innovation system is dominant depends on the context: the prevailing industrial, organizational and geographical structure Systems of Innovation • Innovation via large firms with large R&D budgets • Innovation via groups of SMEs • Innovations via networks of firms and organisations • Innovation via publicly funded universities and research institutes • Systems mixtures of each Agglomeration and Clustering • Sources of externalities - Knowledge spillovers (learning) - Non-traded specialist inputs (sharing) - Specialist skilled labour pool (matching) Outcomes of externalities - Localisation economies - Urnanisation economies - Internal returns to scale Agglomeration and Clustering • Marshall: specialisation • Jacobs: variety • Porter: mutual transparency and competition • Chinitz: nursery • Vernon: life cycle • Putnam: social capital and shared goals or values • Florida: creativity Table 1. Industrial clusters: a transactions costs perspective Characteristics Pure agglomeration Industrial complex Social network Firm size atomistic some firms are large variable Characteristics of non-identifiable identifiable trust relations fragmented stable and frequent trading loyalty unstable frequent trading joint lobbying joint ventures non-opportunistic Membership open closed partially open Access to cluster rental payments internal investment history location necessary location necessary experience location necessary but not sufficient Space outcomes rent appreciation no effect on rents partial rental capitalisation Example of cluster competitive urban economy steel or chemicals new industrial areas production complex Analytical models of pure agglomeration location-production theory social network theory approaches (Granovetter) input-output analysis Notion of space urban local or regional but not local or regional but not urban urban Table 2. Industrial clusters: knowledge, technology and cluster dynamics Characteristics Pure agglomeration Industrial complex Social network New SN Old SN Nature of technical codified, explicit and mixed, systemic, routinised, tacit, new, generic, mixed, mature, knowledge mobile R&D-intensive non-systemic, sticky incremental and leaky transmitted by way of specific, based on non- transmitted within information transferable experience transmitted within localised networks cognitive networks Technological oriented to processes, oriented to complex oriented to radically oriented to trajectory problem-solving products, cost-cutting new products processes, customer-driven Dynamics stochastic strategic mixed mixed Sources of external to the firm internal to the firm mixed external to the firm innovation Appropriability of low, perfect or high, private creation of mixed, public- low, collaboration innovation returns monopolistic competition new knowledge, private creation of and competition oligopolistic competition new knowledge Technological medium low very high, uncertain low opportunities Degree of low high low high cumulativeness Knowledge-base diversified specialised research-based specialised along the filière Modes of market hierarchies relational and social and governance cognitive networks historical networks Examples finance, banking, steel, chemicals, SME high-tech Customised Industrial insurance, business automotive, clusters in general traditional goods specialisation services, retailing pharmaceuticals, machine purpose textiles, footwear, tools, medical instruments, technologies furniture, tourism ICT hardware Example of cluster ‘Silicon Valley’ ‘Silicon Glen’ (Scottish ‘Silicon Fen’ Italian industrial (California) Electronics Industry) (Cambridge UK) districts (Emilia- Romagna) Pavitt Classification Information Intensive, Production Intensive Firms Science-Based Supplier Supplier Dominated (Scale Intensive & Firms Dominated Firms Firms Specialised Suppliers) Knowledge Spillovers • Benefits of inward knowledge spillovers • Problems or costs of unintended outward knowledge spillovers • Depends on degrees of trust in actors and institutions • Social capital – common interests and goals • Stakeholders – networked organisations and agencies Innovation and Regional Policy • Smart Specialisation and EU Cohesion Policy: - embeddedness - specialised technological diversification - connectivity - Aim to promote both inter- and intra- regional knowledge flows without the Krugman shadow effect Innovation and Regional Policy • Identification of regional strengths and specialisations • Identification of regional linkages • Identification of crucial missing elements • Identification of sources of technology, skills and knowledge (local or otherwise) • Facilitation of networking – public and private partnerships Innovation and Regional Policy • Identification of the provision of possible local public goods to respond to: - externalities - network effects - agglomeration/congestion effects • May involve cross-border discussions • Use of outcome indicators is essential .
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