T he geography of university-industry research collaborations

1. DIME Conference: Regional innovation and growth: Theory, empirics and policy analysis Pécs, Hungary, March 31-April 1, 2011 The geography of university-industry research collaborations Simona Iammarino (London School of Economics, London, UK)

2. Trends in U-I knowledge linkages 1980s onwards – Changes driven by increased globalisation, competition and emphasis on innovation: firms need to get closer to knowledge sources increased speedand complexity of knowledge creation and exploitation budgetary constraints faced by governments and universities: search for new funding sources government policies – at both national and local level – encouraging technology transfer, collaborative research in key areas, U-I links, commercialisation of research examples/‘heroic myths’ highly localised: e.g. MIT & Route 128, Stanford & Silicon Valley, ‘the Cambridge Phenomenon’ declining profits and/or increasing costs of research encouraged many firms to outsource more basic research (not only to U) industry increasingly interested in university research as well as highly specialised personnel offering specific opportunities for cooperation in some fields knowledge may flow directly from U to I resource flow to U from I no longer limited to endowments

3. The rising importance of (localised)U-I knowledge links • Universities as key actors in the generation of new knowledge and externalities (spillovers), and at the centre of academic and policy attention • 3 broad strands of literature interested in U-I linkages for the creation and diffusion of new knowledge: 1) studies on localised knowledge spillovers (LKS); 2) studies on the systemic nature of knowledge and innovation, i.e. ‘Systems of Innovation’, ‘Triple Helix’; 3) studies on industrial clustering, local and regional systems and development

4. 1. LKS 1. Large empirical literature finding support to localised knowledge spillovers (LKS) from university research to industrial innovation (e.g. Jaffe, 1989; Acs et al., 1994; Feldman, 1994; Mansfield & Lee, 1996; Audretsch & Feldman, 1996, 1999; Anselin et al., 1997, 2000; Henderson et al., 1998; Varga, 1998; Audretsch et al. 2005; Abramovsky et al., 2007; Fritsch & Slavtchev, 2007). Literature on LKS generally vague about the actual transport mechanisms channelling knowledge between U and I, often failing to disentangle knowledge flows mediated through market-related exchanges from pure unintended knowledge spillovers(e.g. Breschi & Lissoni, 2001a,b, 2003, 2004; Breschi et al., 2005; Autant-Bernard et al., 2009; Massard & Mehier, 2010)

5. 2. SI & TH 2. Knowledge and innovation as interactive phenomena at the core of U-I linkages in both: Systems of Innovation (SI): interactions and networks among a variety of actors and institutions within national, sectoral or technological systems privileging the firm as core agent (e.g. Lundvall, 1992; Nelson, 1993; Nelson & Rosenberg, 1993; Breschi & Malerba 1997; Edquist, 1997; Arundel and Geuna, 2004). Triple Helix (TH): Universityat the centre of a triadic relationship with Industry and Government (e.g. Etzkowitz & Leydesdorff, 1997, 2000; Leydesdorff & Etzkowitz, 1996, 1998) In original formulations little attention to spatial dimensions other than the national one. Later, ‘national bias’ overcome introducing more fine-grained geography

6. 3. RSI & clustering 3. Regional and local innovation systems and industrial clusters: variety of U-I knowledge linkages, sectoral and technological structural differences, knowledge flows and spillovers outcome of intense interactions, favouring local and regional levels over others (e.g. Howells, 1999; Morgan, 1997; Braczyk et al. 1998; Fritsch & Schwirten, 1999; Keane & Allison, 1999; Cooke, 2000, 2001, 2004; Charles, 2003, 2006; Gunasekara, 2006; Lawton Smith, 2007; Laranja et al., 2008; Huggins et al., 2008, 2010) Empirical studies mostly based either on cluster case studies, subject to confirmation bias (Silicon Valley-type, e.g. Saxenian, 1990, 1994) or on loosely specified interactions (e.g. CIS-based)

7. Common grounds While LKS places more weight on externalities (geographical proximity) from academic research, and the SI/TH/industrial clustering literatures emphasise U-I interactions and networks (regional location), ALL 1-2-3 share similar underlying assumptions: • Spatial proximity favours U-I linkages because of the tacit and sticky nature of knowledge • Thus, knowledge that spills over “is a public good, but a local one” (Breschi and Lissoni, 2001b, 980) Problem with tacit vs. codified dichotomy: what is "tacit" (the transit) depends on the shared codification capabilities of the actors (e.g. Steinmueller, 2000; Cowan, David & Foray, 2000; Antonelli, 2003; Foray, 1998, 2004)

8. Our specific focus • Contention that spatial proximity favours U-I linkages as a consequence of the tacit and sticky nature of knowledge particularly applicable to interactions involving advanced scientific knowledge. While technological and academic knowledge tends to circulate in global networks, F-2-F remain critical for the generation and exchange of non-standardised and complex knowledge (e.g. van Oort et al., 2008) • U-I research collaborations favour both intended and unintended exchanges of knowledge, entail bi-directional knowledge transfer (both tacitand codified!) , require ex ante high threshold of both absorptive capacity and cognitive closeness, and facilitate learning processes and the establishment of enduring social relationships between the partners involved (e.g. Katz and Martin, 1997; Ponds et al., 2007)

9. 3(among others) issues The recent empirical research on the geography of research collaborations (e.g. Autant-Bernard, 2007; Maggioni et al., 2007; Ponds et al., 2007; Ferru, 2010; Hoekman et al., 2010, Laursen et al. 2010) try to address the following limitations: • Tendency to concentrate on spatial co-location, more than on actual interactions • Attitude to overlook partnerscharacteristics (e.g. both firm- and university-level) and spatial features within specific interactions • Focuson geographical proximity: other dimensions of proximity, and their interaction with space, still under-investigated

10. E.g. U-I research collaboration: a non-linear relationship across space D‘Este & Immarino (2010)

11. Geography and proximity Effects of geographical space depend on other forms of proximity (i.e. cognitive, organisational, social, relational, institutional) (e.g. Nooteboom, 1999; Torre & Gilly, 2000; Boschma, 2005; Moodysson & Jonsson, 2007; Ponds et al., 2007; Massard & Mehier, 2010) Indirect role of space in fostering knowledge creation, interactive learning and innovative networks by bridging and reinforcing other forms of proximity among different actors (U-I) involved in knowledge creation and diffusion “Geographical proximity can be considered a necessary, but not sufficient precondition for the existence of a territorially based system of innovation [...] “ (Fisher, 2001, 210) “[...] geographical proximity per se is neither a necessary nor a sufficient condition for learning to take place” (Boschma, 2005, 62)

12. Some evidence (D’Este, Guy & Iammarino, 2011) • Collaborative research grants awarded by the UK Engineering and Physical Sciences Research Council (EPSRC), 1999–2003 • Each partnership includes • One university • One or more firms • 2210 research grants involving 4525 partnerships • 2031 business units and 1566 PIs in 318 university departments (87 UK universities) • NEW!geocoding; great circle distance for all u-f and f-f

13. What affects the likelihood of U-I collaboration formation? • Ad hoc built indicators of different proximity dimensions • Case-control approach: pairing each instance of actual collaboration with a critical number of U-I pairs that could have happened but did not • Probability of partnership between business unit iand university m (2003) • = Logit/REL [β1* Geographical Proximity (GeoProx) +*** • + β2*Prior Partnerships +*** • + β3 * Prior Partnerships * GeoProx- not sig. • + β4 * Clustering of Firms (wout/with TechRel) + not sig./+*** • + β5 * Clustering of Firms (wout/with TechRel) * GeoProx-**/-*** • + β6 * University clustering - (sig. varies) • + β7 * University clustering * GeoProx+ not sig. • + β8 * Services dummy - (sig. varies) • + β9 * Services dummy * GeogProx+ not sig. • + Constant] +***

15. Some e.g. of implications • If technologically dynamic clusters have social value, exhibit increasing returns, and depend on nearby universities, scarce public research resources should be concentrated in universities proximate to existing clusters, and/or in a very small number of places where the prospect for cluster development appears especially good. Such is, indeed, the de facto policy in the UK, (the ‘golden triangle’ of the Southeast: greater London, Cambridge, Oxford) • Our results support an entirely different policy direction: when firms located in dense clusters of technologically related firms engage in collaborative research with universities, they do so essentially independently of the university’s location. Firms in dense clusters appear to have capabilities in the area of collaboration which enable them to ignore distances, at least on the scale of a country the size of the UK