Regional Innovation Cultures Rune Dahl Fitj ar, IRIS Andrés Rodríguez-Pose, London S chool of Economics
Leveranser så langt • 4 artikler publisert (eller akseptert) i internasj onale tidsskrifter på nivå 2 • Ytterligere 4 p.t. inne til vurdering • Og 4 til for tiden under utarbeidelse • S tor oppmerksomhet fra medier i inn- og utland, samt fra politikere og næringsorganisasj oner • 3 ekstern finansierte prosj ekter (2 fra Norges Forskningsråd og 1 fra Regionalt forskningsfond Vestlandet) med totalramme > 13 mill kr
Andrés Rodríguez-Pose and Rune Dahl Fitj ar (2012): Buzz, Archipelago Economies and the Future of Intermediate and Peripheral Areas in a S piky World. European Planning S t udies (forthcoming)
Regional development trends • Large urban agglomerations increasingly regarded as engines of economic development – Attract capital, human resources and knowledge – Economies of scale and scope – Local buzz – diffusion of tacit knowledge – Global pipelines between largest metropoli (archipelago economies)
What are the options for peripheral areas? • Decay and vanish (do nothing) – S pread effects from dynamic agglomerations – More likely: Backwash effects t 0 t (McCann 2008)
What are the options for peripheral areas? • Fight for survival – Build economic dynamism through knowledge and innovation – Buzz option • Popular policy tool: Industrial districts, learning regions, clusters, regional systems of innovation, innovative milieus • Encouragement of agglomeration of firms in the same or related sectors • Often leads to «Cathedrals in the desert» (Lipietz 1980) and problems of lock-in
What are the options for peripheral areas? • Fight for survival – Pipeline option • Alternative: Encourage interaction at a distance • Building bridges from the local economy to the outside world • Difficult, costly and uncertain, but may deliver greater results
”I trust other business managers in this region" 1 2 2 2 3 1 1 3 2 2 9 18 18 20 20 38 36 35 38 36 45 43 41 36 34 Oslo Bergen S t avanger Trondheim Krist iansand Fully agree Partly agree Neut ral Partly disagree Fully disagree
”I find it easier to cooperate with local and regional actors than with people from outside the region” 15 16 17 19 30 10 15 15 21 10 15 16 21 20 11 23 20 16 21 16 45 32 31 29 18 Oslo Bergen S t avanger Trondheim Krist iansand Fully agree Partly agree Neut ral Partly disagree Fully disagree
Regional cooperation and product innovation 1 Likelihood of product innovation 0.8 0.6 0.4 0.2 0 0 2 4 6 No. of regional partners
International cooperation and product innovation 1 Likelihood of product innovation 0.8 0.6 0.4 0.2 0 0 2 4 6 No. of international partners
Rune Dahl Fitj ar and Andrés Rodríguez-Pose (2012): Firm collaboration and modes of innovation in Norway. Research Policy (forthcoming)
Research questions • How does industrial (DUI) and scientific (S TI) collaboration affect the innovative capacity of firms? • Does it matter whether industrial and scientific partners are located nearby or at a distance?
Sources of innovation • S cientific/ technical • Learning by doing knowledge – Regional innovation systems (Lundvall 1992, Cooke and – Linear model of innovation Morgan 1998), industrial (Bush 1945, Maclaurin 1953) districts (Becattini 1987), – Knowledge spillovers learning regions (Morgan (Audretsch and Feldman 1997), innovative milieux 1996, S onn and S torper (Aydalot 1986) 2008) – Key variables: Informal – Key variables: R&D interaction, social capital, investment, human capital, organisations, institutions, links to scientific partners markets – Key skills: Know-why, – Key skills: Know-how, know-what (Jensen et al. know-who (Jensen et al. 2007) 2007) • S TI mode of innovation • DUI mode of innovation
Two types of interaction in DUI mode • Within supply-chain • Outside supply-chain – With suppliers and – With other firms, such as customers competitors – Close complementary bonds – Transfer of knowledge not within supply chain the main purpose – Clear economic purpose, – Unintended knowledge j oint aim of improving spillovers may happen products – Externalities from – Contractual links diversification (Jacobs), potential for excessive – Externalities from cognitive distance specialisation (Marshall) or (Boschma 2005) related variety (Frenken et al. 2007, Boschma and Iammarino 2009)
The geography of STI and DUI • S TI mode • DUI mode – Costly search for knowledge – Based on shared problems requires purpose-built and experiences connections – global – Tacit knowledge pipelines (Bathelt et al. – More frequent in industries 2004) with synthetic or symbolic – Analytical and codified knowledge base (Moodysson knowledge travels well et al. 2008) (Asheim and Gertler 2005) – Local buzz (S torper and – Geographical distance not Venables 2004), informal necessarily a problem interaction – Top research centres often – ’ Being there’ (Gertler 1995) located far away – S trong value-added of local cooperation
The case of Norway – S mall and relatively remote – Population of around 4.5 million – Performs poorly on traditional (S TI-based) indicators of innovation (R&D investments, patenting) – From a DUI perspective, insufficient agglomeration and the long distance between maj or cities is a drawback – Yet high levels of productivity and growth – Firms invest little in intramural R&D and frequently pursue collaborative innovation strategies (Fagerberg et al. 2009) – Innovation policy increasingly focused on regions – Main assets: Good institutions, high level of trust, solid endowments of human capital, open economy, rich
Norwegian city regions (Tromsø) Population Businesses S ample (2009) > 10 empl Oslo 1.400.000 4921 403 Bergen 375.000 1210 401 S tavanger 310.000 1282 400 Trondheim 240.000 901 300 Trondheim Kristiansand 150.000 469 100 Total 2.475.000 8783 1604 Bergen Oslo Stavanger (Fredrikstad) Map from the Norwegian Government’s white paper no. 31, 2002-03: The Metropolitan Region Report: On the development of policies for Kristiansand metropolitan regions.
Data • Tailor-made survey of firms with more than 10 employees in Norway • Targeting the managers of those firms • Conducted by telephone • In the five largest urban agglomerations in Norway • In the spring of 2010 • Examining – Innovation during the last three years – The use of external partners in innovation processes – The location of external partners used
Innovation in Norwegian city regions Product Process (% yes) Tot al Radical Tot al Radical N Oslo 59.6 % 34.0 % 50.4 % 20.4 % 403 Bergen 46.4 % 25.1 % 42.4 % 16.5 % 401 S tavanger 54.0 % 33.8 % 46.8 % 18.8 % 400 Trondheim 52.3 % 29.0 % 48.7 % 19.7 % 300 Kristiansand 58.0 % 30.0 % 47.0 % 20.0 % 100 Total 53.4 % 30.5 % 46.9 % 18.8 % 1604
Percent of companies using partner type 60 50 40 30 20 10 0 Internal S uppliers Customers Competitors Consultants Universities Research inst. Regional National International
Innovation and collaboration with partner types Product New to Process New to market industry Wit hin congl 0.39** 0.20 -0.02 0.10 (0.12) (0.13) (0.12) (0.15) * p < 0.05, ** p < 0.01, *** p < 0.001 0.39** 0.33* 0.76*** 0.38* S uppliers (0.14) (0.16) (0.14) (0.19) Cust omers 0.36** 0.54*** 0.03 -0.03 (0.13) (0.15) (0.13) (0.17) Compet it ors -0.39*** -0.55*** -0.14 -0.09 (0.12) (0.13) (0.12) (0.15) Consult ancies 0.15 0.18 0.16 0.03 (0.12) (0.13) (0.12) (0.15) Universit ies 0.30* 0.53*** 0.21 0.13 (0.16) (0.15) (0.15) (0.18) Research inst 0.26 0.20 0.26 0.79*** (0.16) (0.16) (0.16) (0.18) Logistic regression models, N = 1604. Controls: S ector, region, education, age, board memberships, ownership, size
Fitted probabilities of innovation Product New to Process New to market industry 0.34 0.15 0.30 0.10 No part ners 0.43 0.17 0.30 0.11 Wit hin congl 0.43 0.19 0.48 0.14 S uppliers 0.43 0.23 0.31 0.10 Cust omers 0.26 0.09 0.27 0.10 Compet it ors Consult ancies 0.38 0.17 0.34 0.11 0.41 0.23 0.35 0.12 Universit ies 0.40 0.17 0.36 0.20 Research inst
Geographical dimension of DUI and S TI Regional Non-regional 28.4 19.0 DUI non-supply-chain (1.1) (1.0) 67.0 61.5 DUI supply-chain (1.2) (1.2) 48.0 29.1 S TI (1.2) (1.1)
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