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The Location of Industry R&D and the Location of University R&D: How Are They Related?

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New Directions in Regional Economic Development

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

At the same time as we can observe strong tendencies of globalization of R&D (Florida 1997; Cantwell 1998), we also observe strong spatial clustering of R&D and related innovation activities (Audretsch and Feldman 1996). The standard explanation in the literature of the clustering phenomenon is that clustering brings about external knowledge economies, typically in the form of knowledge flows,1 which tend to be spatially bounded (Jaffe et al. 1993). R&D is a typical innovation activity; irrespective of whether it is focused on new products or new processes. Industry – i.e., private firms – and universities are the major performers of R&D. It is well established in the literature that both university and industry R&D have a positive effect on innovation output (often measured by patent applications), but that the effect diminishes with distance because knowledge flows are spatially bound and that clustering is consequently an effective spatial configuration.

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Notes

  1. 1.

    For reasons given in Sect. 15.2 we use the general term “knowledge flows” instead of the term “knowledge spillovers” commonly used in the literature.

  2. 2.

    Obviously, not all types of university R&D attract industrial R&D. There are reasons to believe that, in particular, university R&D in natural, technical and medical sciences attract industrial R&D but that there are also strong reasons to believe that there are variations between different sectors of industry in terms of how dependent their R&D is on proximity to university R&D. However, although a distinction between different science areas would be interesting, the present paper focuses on the aggregate pattern.

  3. 3.

    Many scholars use very crude measures of physical accessibility. By focusing on physical accessibility and relying on actual travel time distances in the accessibility calculations, we believe that proximity is measured in a coherent fashion, (see also Karlsson and Manduchi 2001; Andersson and Karlsson 2004).

  4. 4.

    Von Hippel (1994) persuasively demonstrates that highly contextual and uncertain knowledge, i.e. what he refers to as “sticky knowledge”, is best transmitted via (preferably frequent) face-to-face interactions. This is in line with the claim by Teece (1998) that knowledge assets are often inherently difficult to copy. Von Hippel’s sticky knowledge is also referred to as tacit knowledge in many studies from the last decade (Kogut and Zander 1992). Tacit knowledge cannot be codified easily in the form of a blueprint or a contract (Mowery and Ziedonis 2001), or a published article (Audretsch and Feldman 1996).

  5. 5.

    Knowledge exchange is defined here as any face-to-face interaction that can contribute to the process of the disclosure, dissemination, transmission, and/or communication of knowledge.

  6. 6.

    In this way face-to-face contacts become a necessary or facilitating condition, though not a sufficient condition, for knowledge transfer.

  7. 7.

    Historically, the transfer/communication of rich information has required proximity and specialized channels to customers, suppliers, and distributors. However, we must acknowledge the possibility that the new developments are undermining the traditional chains and business models, and that new structures – generally less dependent on physical communication channels – might become more and more often an economically viable option (cf. Teece 1998).

  8. 8.

    Interestingly, some authors assume that geography plays no role for the costs of accessing knowledge (Spence 1984; Cohen and Levinthal 1990).

  9. 9.

    Functional regions are delimited based upon the spatial interaction patterns of the economic agents in a country. A functional region is fundamentally characterized by its size, by its density of economic activities, social opportunities and interaction options, and by the frequency of spatial interaction between the actors within the region (Johansson 1997).

  10. 10.

    There is no generally accepted definition of a university. We use the term university here as a collective term for institutions of higher education, whether they are major R&D performers or not. Major R&D performing universities are termed as research universities in this paper.

  11. 11.

    However, there are authors that claim that R&D-intensive and high-tech industries do not necessarily agglomerate (Devereux et al. 1999; Shaver and Flyer (2000); Barrios et al. 2003; Alecke et al., 2003). In her study of Japanese investments in Europe Mariani (2002) found that R&D tends to locate close to production activities.

  12. 12.

    Since p and c are monetary values, \( \alpha \) and \( \sigma \) translate these values to a common preference base (cf. Johansson et al. 2002).

  13. 13.

    This condition is derived in several texts, see inter alia Train (1993).

  14. 14.

    These municipalities are Stockholm, Uppsala, Göteborg, Lund, Umeå, Solna Linköping, Huddinge, Malmö and Luleå.

  15. 15.

    These municipalities are Stockholm, Göteborg, Mölndal, Linköping, Lund, Södertälje, Trollhättan, Malmö, Västerås, Uppsala, Järfälla, Karlstad, Karlskoga, Luleå, Sandviken, Jönköping and Solna.

  16. 16.

    Lagged values are used as instruments.

  17. 17.

    We are grateful to an anonymous referee for emphasizing this point.

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  1. Jönköping International Business School, Jönköping University, Högskoleområdet, Gjuterigatan 5, 553 18, Jönköping, Sweden

    Charlie Karlsson & Martin Andersson

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  1. Charlie Karlsson
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  1. Jönköping Int. Business School, Jönköping University, Jönköping, 551 11, Sweden

    Charlie Karlsson

  2. Jönköping Int. Business School, Jönköping University, Jönköping, 551 11, Sweden

    Ake E. Andersson

  3. Dept. Geography & Environment, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom

    Paul C. Cheshire

  4. School of Public Policy, George Mason University, University Drive 4400, Fairfax, 22030, U.S.A.

    Roger R. Stough

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Karlsson, C., Andersson, M. (2009). The Location of Industry R&D and the Location of University R&D: How Are They Related?. In: Karlsson, C., Andersson, A., Cheshire, P., Stough, R. (eds) New Directions in Regional Economic Development. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01017-0_15

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