Local and Regional Innovation

1. Local and Regional Innovation OECD Capacity Building Seminar Supporting SMEs in a Time of Crisis Jay Mitra 13 October, 2009

2. Definitional Issues • Entrepreneurship = new opportunity identification & realisation • (for the purpose of this paper ‘E’ = new business creation) • Innovation = successful exploitation of a new idea • Culture = a set of attitudes/beliefs common to a group • Culture = a set of activities concerned with moral, aesthetic, and intellectual aspects of life (activities include some element of creativity in production, communication of meaning & intellectual property) • Culture = a diverse way of life (from beef steak to Beethoven to • Eminem) • Entrepreneurial Culture = diverse set of attitudes, beliefs, systems, institutions and structures that are connected together with a view to supporting new venture creation, innovation and growth in a particular environment & in regional innovation systems.

3. Why Innovation Systems? • Innovation is non-linear but involves interaction between many actors • Interest resulting from research on the success of the Japanese model (Freeman, 1987) • Emergence of “innovation systems” models (Freeman, 1987)

4. Key Models of Innovation Systems • Nationalinnovation systems (Freeman, 1987; Lundvall, 2007) • Regional innovation systems (Cooke 1992; Braczyk et al., 1998)

5. Some Stylised Facts and Assumptions F/A 1: Innovation = source of economic growth • The endogenous model)– critical importance of technological change in economic growth ( total factor productivity accounted for 87.5% of economic growth – Solow, 1957) Romer, 1990, OECD 2003 • Strong emphasis on role of R&D, skilled labour & knowledge spillovers – greater productivity, product quality dependent on innovation

6. F/A 2: Innovation is not evenly spread but spatially concentrated Well-known concentrations = Oxford; Cambridge; SE, UK; Lombardy; Bangalore, Shanghai High urban focus – OCED countries Significant local differences within countries (Camagni & Capello, 1997; Keeble, 1996; Acs, 2002) Different measures – innovation output (patent applications) & input/output (employment in high technology manufacturing & knowledge-intensive industries) Some Stylised Facts and Assumptions

7. Subnational Variations in European Patent Applications, 2002 Source: adapted from Eurostat

8. Subnational Variations in Innovation-Related Employment- High Tech Manufacturing , 2003 Source: Eurostat

9. Subnational Variations in Innovation-Related Employment – Knowledge Intensive Industries Source: Eurostata * A = Employees In KI services as % of total employees

10. Some Stylised Facts and Assumptions F/A 3: SMEs participate in innovation process • Classic ‘structure-conduct-perfomance’ model = large firms have monopoly positions, commit substantial R&D • Alternative model = SMEs have more impact (more radical innovation, Baumol, 2002) • Importance of business churning (OECD, 2003) to national productivity • Empirical evidence suggests that both small and large firms play a part – dependent on active links to knowledge of market (users) & knowledge of materials & machinery (suppliers) & non-firm organisations • Small firms rely heavily on external environment • Spatial business clusters – association between spatial concentrations & rates of technological innovation (Baptista and Swann, 1998)

11. Frameworks for Analysing Innovation Process in Agglomerations Source: OECD, 2005ecd

12. Why is a Regional Innovation System important? • Innovation = 80% of productivity growth and comparable figure for GDP (Freeman, 1994) • Regional disparities in innovation & GDP (Acs, 2002; Cooke et al., 2002) • Innovation = higher in regions with more knowledge generation e.g. R&D by firms & institutions (Acs, 2002) • Region = new focus of economic policy (Cooke et al. 2003)

13. Why are Local/regional Innovation Systems Relevant? • Most processes driving innovation occur locally – knowledge embedded in people ; distance decay effects in rate of knowledge & information links; • SMEs have spatially restricted search patterns for collaborative partnerships or technological inputs; • Different localities have different sector specialisations & distinct sets of innovation processes; • Strong local differences in innovation performance

14. Market Failures and SME Innovation Source: OECD, 2005

15. System Failures & SME Innovationsource: OECD, 2005, Lundvall & Borras, 1997

16. What is Regional Innovation System? • “Regional innovation system consists of interacting • knowledge generation • and • exploitationsub-systems linked to global, national and other regional innovation systems for commercializing new knowledge” (Cooke, 2004 p.3) • Emphasis: Firms in interaction with other firms & knowledge infrastructure at regional level.

17. Regional Innovation Systems (RIS) ESSENTIAL NOTIONS: • Tacit knowledge = Innovation involves face-face interaction between actors due to tacit knowledge e.g. experience (Maskell and Malmberg, 1999) • Costs of interaction = Regional level has lower distance, transportation & communication costs (Audretsch, 1998; Krugman, 1991) • Local networks = Innovation is higher in regions with local networks of SMEs and R&D(Maskell & Malmberg, 1999; Asheim & Gertler, 2004)

18. Sub-Systems of RIS • Knowledge Generation: • Public & private research laboratories • Universities & Colleges for scientific & technical training • Firms thattransfer knowledge • Knowledge Exploitation: • Firms with regional & global value chain relationships • Venture capitalists • Consultants Adapted from: Cooke et. al., (2003)

19. Basic Arguments of RIS • Innovation process is social • Innovation = involves face-face interaction between actors internal & external to the firm(Maskell and Malmberg, 1999)

20. Basic Arguments of RIS 2. Region facilitates interaction • Region = lower distance, transportation & communication costs for interaction (Krugman, 1993) • Face-to-face interaction and cooperation are easier at the regional level

21. Basic Arguments of RIS 3. Regional concentration of R&D firms & institutions boosts innovation • Combination of knowledge generation (e.g. by universities) & exploitation (by SMEs with local networks) boosts innovation • Local concentration increases capacity to use external knowledge for innovation Adapted from: Cooke et al., 2003 ; Asheim & Gertler, 2004

22. Basic Arguments of RIS 4. External Links boost innovation • Entering global markets • Sourcing Knowledge from global sources (e.g. R&D)

23. Links between RIS and Entrepreneurship • Entrepreneurship – requires knowledge and resource seeking (e.g. technical knowledge, finance, consultancy etc.) • Innovative activity of firms and entrepreneurs are largely based on localised resources (Asheim et. al., 2003; Cooke et. al., 2000) • RIS provides access to critical resources for entrepreneurship within proximity

24. RIS Public Governance System • Grass roots – SME dominated or industrial district (less public governance) • Networked – Associated between regional governance & industry pronounced • Centralist – Governance is strongly centralised Cooke et. al (2003)

25. Problem of RIS: Few Regions in the world are high-tech clusters

26. Typology of Regional Innovation Systems Business innovation system Public Governance System Source: Braczyk et. al. 1998; Cooke et. al. (2003 p.368)

27. Developing Innovation Systems • Identify Strong Sectors/Candidate Clusters • Investigate Regional Clusters • Identify Competitive Advantage • Identify Innovation Practices • Cooperative or Individualistic? • Innovation Support System

28. Conditions for Assessing RIS • Infrastructure issues • Superstructure

29. Infrastructure level Regional private equity Policy influence on infrastructure Regional university-industry strategy Superstructural level Institutional dimension Co-operative culture Interactive learning Associative consensus Organisational Dimension (firms) Worker mentoring Externalisation Interactive innovation Organisational dimension (policy) Monitoring Consultative Networking Decentralised spending National financing organisation Limited influence on infrastructure Competitive culture Individualistic Institutional dissension Self acquired skills Internationalisation Stand alone R&D Reacting Authoritative Hierarchical Conditions for Higher & Lower RIS Potential Higher RIS potential Lower RIS potential Adapted from: Cooke et. al. (2001)

30. Regional Enterprise Support System for Innovation National Policy Information Legitimation Ministrics Assembly Programme approval Information National Research Institutes Reporting National technology agency Requirement Advice Proposals Information Strategy SME Agency Business associates FDI Agency Training agency Trade Board Universities Regional steering Committee Measures Coordination Social partners Research Community Venture Capitalists Local Cooperative Forum Local Government Technology Consultants Chambers of commerce Source: Braczyk, Cooke and Heinreich, eds. (1998)

31. Policy Levers to Strengthen Local Innovation Systems Public investment in technology development Creation of S&T parks Attracting inward investment Supporting access to finance Creation & strengthening of local networks Encouraging local innovation collaborations Creation of bridging institutions Ensuring openness of local innovation system to sources of knowledge outside system Education & Training of individuals Advice, training & consultancy to SMEs Influencing motivation & abilities of universities & Research organisations in collaborative research with SMEs

32. Comparison RIS & other Regional Models

33. Problems with Public Support for RIS • RISs are rare and newly discovered • Hard to detect systemic regional innovation • In Europe = high dependence on public expenditure Source: Cooke (2001)

34. Problems with Public Support for RIS Isaksen (2001)

35. Differences: National vs. Regional Systems Source: Acs (2002)

36. Case Study: Silicon Valley • A Region of 1500 Square Miles in California, US • One of the “most” innovative high-tech regions in the world • 1.35 million jobs • Headquarters for over 400 public companies • Average salary of $65,000 • Venture Capital Investments of over $8 billion Source: Stanford University

37. “knowledge generation” in Silicon Valley (1) Past: • Linkages to Federal funding agencies and flood of Government Sponsored Research at universities (Cold war effect in1950s) Present: • Cutting-edge education to company employees • Small Business Innovation Research (SBIR) grants: Over $2B awarded in U.S. in 2006 Source: Stanford University

38. “knowledge generation” in Silicon Valley (2) Source: Stanford University

39. “knowledge Exploitation” in Silicon Valley • Stanford graduates, faculty & staff have launched approximately 1200 companies in the last 50 years • More than 50% of Silicon Valley product is due to companies started by Stanford alumni Source: Stanford University

40. Silicon Valley Innovations: Past & Present Source: Stanford University

41. Some Silicon Valley companies

42. Conclusions • RIS consists of knowledge generation and exploitation subsystems • New focus of economic policy • Think local, act global - External links are important for RIS • RISs are rare and rely heavily on public expenditure

43. Some Preliminary Questions • Can/does higher education make a difference? • Does it make a difference by itself or in collaboration with other institutions? • Do small businesses interact with this collaborative venture? • Does such interaction generate new businesses?

44. University Culture and Entrepreneurship • What unites academics more? Car Parking or intellectual discourse? • What price entrepreneurship? • “Loosely coupled systems” (Weick 1976) • Collegial academy of chaos • Four cultures of “collegium”, “bureaucracy”, “corporation” and “enterprise” (McNay, 1995)

45. University Offerings versus Entrepreneurs’ Learning Needs

46. Some Stylised Observations 1/2 • Patterns of use of university (especially research) output: • Economic stability = pure research; instability = commercialisation • But note a few caveats: • a) Origins of university activity– industry focused • Technische Mittelschulen, Technische Hochschulen, Fachhoschulen in Germany; USA – University of Akron (polymers & elastomers), Cornell’s electrical engineering dept. • b) advent of science in engineering with government superseding industry & emergence of divide between corporate and university R&D

47. Some stylised observations 2/2 • Economic sectors with most rapid growth are closest to science – microelectronics, software, biotech and new materials. • Above industries also have high ‘social qualities’ – high wages, good environmental characteristics, low barriers to entry for small firms, relative independence from geographic constraints • Universities benefit from government policy to encourage entrepreneurship (licensed inventions from govt. grants (Mowrey, Nelson & Sampat, 1999) • Real spur to entrepreneurship in universities = business opportunity from basic science • Most revenues from patents of a very basic nature for process or tools & licensed non-exclusively

48. The Forces At Work - Regionalisation • New & diverse client bases for teaching & research • From traditional relationships with large corporations to regional clusters of firms (not just money but changes in nature & scope of technologies) • Regionalisation of regulating institutions leads to regional networking & institutional capacity building • Universities as regional intermediaries & commentators • Regional networking as institutional survival • Ambivalent relationship with territory

49. The Forces at Work – Forms of Learning • New mode of learning production from inter-disciplinary research centres & reliance on external funding (Gibbon, 1994) • Interactive forms of learning inherently bound in time & space – regional context for learning & knowledge • International research transferred to specific localities through universities

50. Forces at Work – The New Culture • The new student – decentred world & multiple lives • Diverse forms of preparation • Episodic & fragmented engagement not holistic, intense, linear forms of learning • Research generated in heterogeneous environments of producers, brokers and users • Knowledge is more contextualised & intensely reflexive • Communicative culture – from cerebral, objective, codified & symbolic (logos) to visual, intuitive, volatile, subjective • Wider social distribution of knowledge generation • (source: Scott, 2004)