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Research Collaboration and Regional Knowledge Production in Europe

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The Geography of Networks and R&D Collaborations

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

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Abstract

The focus of this study is on regional knowledge production in Europe, with special emphasis on the interplay between intra- and inter-regional research collaboration. The objective is to identify and measure effects of research collaboration on knowledge production at the level of European regions. We use a panel version of the spatial Durbin model (SDM) for empirical testing. The European coverage is achieved using 228 NUTS-2 regions covering all pre-2007 EU member states except Cyprus, Greece and Malta. The dependent variable, regional knowledge production, is measured in terms of fractional patent counts at the regional level in the time period 2000–2008, using patents applied at the European Patent Office (EPO). The independent variables include an agglomeration variable, reflecting intra-regional research collaboration, measured in terms of employment in knowledge intensive sectors, and a network variable, reflecting extra-regional research collaboration, measured in terms of a region’s collaboration activities in the EU Framework programmes (FPs), weighted by R&D expenditures in network partner regions. We implement a panel version of the standard SDM that controls for spatial autocorrelation as well as individual heterogeneity across regions, and allows for the estimation of spatial spillovers from neighbouring regions. The estimation results confirm the prevalence of agglomeration effects for regional knowledge production, and, by this, the importance of co-location of R&D actors. Furthermore, the study provides evidence that inter-regional R&D collaborations in the FPs significantly contribute to regional knowledge production.

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Notes

  1. 1.

    This policy focus has been mainly triggered by various considerations in theoretical and empirical literature of Economics of Innovation, Economic Geography, Regional Science and Management Science (see Fagerberg and Verspagen 2009 for an overview). In particular, two arguments are essential in this respect: First, innovation, knowledge creation and the diffusion of new knowledge are the key vehicles for sustained economic growth of firms, industries or regions, and, thus, are essential for achieving sustained competitive advantage in the economy (see, for example, Romer 1990). Second, as mentioned above, interactions, research collaborations and networks of actors are crucial for successful innovation (see, for instance, Fischer 2001).

  2. 2.

    Knowledge can be seen as a process, embedded in employees and firms’ routines (Fischer and Froehlich 2001). For the purpose of this study, it is useful to distinguish between two types of knowledge – tacit and codified (see, among others Polanyi 1967; Nonaka and Takeuchi 1995; Fischer 2001). Tacit knowledge is embodied in a person and can be obtained by experience. It requires rather interpersonal contact to diffuse, and, thus, is conditional on geographical proximity (Fischer 2001; Varga et al. 2010). On the contrary, codified (explicit) knowledge is stated in an explicit form, can be stored and transmitted easily over long distances almost frictionless (Bathelt et al. 2004).

  3. 3.

    Incentives to cooperate and advantages arising from R&D collaborations may also be identified using other theoretical arguments (Hagedoorn et al. 2000; Caloghirou et al. 2003). From the perspective of transaction costs, firms and organisations entering into collaborative arrangements can avoid high costs of internalising R&D activities. Industrial organisation theory argues that R&D collaborations are suitable strategies to capture external knowledge. In addition, the managerial perspective highlights an ability of a firm to learn from cooperation, thereby adopting new skills and abilities, and, thus, improving its own competitive position after all. Both, managerial and industrial organisation views, implicitly include further advantages arising from R&D collaborations, such as R&D costs sharing, economies of scale and scope, risk pooling or access to complementary resources. Close interactions build trust and reduce the uncertainty, and, thus, the complexity of production.

  4. 4.

    Two types of agglomeration economies may be specified. Localisation economies (called also intra-industry externalities) emerge from the spatial concentration of economic activity within one single industry, hence from the scale of the industrial specialisation (Marshall 1920; Arrow 1962; Romer 1986). Urbanisation economies (intra-industry externalities) arise from the industrial diversification, region-size (Jacobs 1969).

  5. 5.

    See, for instance, Breschi and Cusmano (2002) for a preliminary view on the emergent pan-European network of firms, public research organisations, universities, consultants and government institutions jointly collaborating on projects across different research areas.

  6. 6.

    The geography of R&D collaborations within the FPs has attracted increasing attention in the recent past. The study of Constantelou et al. (2004) confirms significant collaborative activity among clusters of neighboring countries. Autant-Bernard et al. (2007) find that relational distance by means of the firms’ position within a network matters more than their geographical location. Maggioni et al. (2007) suggest that a region’s knowledge production is mainly influenced, besides by regions that are located close in geographical space, also by regions that are close in relational space. The study of Schnerngell and Barber (2009) provides evidence that geographical factors matters for interregional collaboration intensities, whereas the effect of technological proximity prevails. Schnerngell and Barber (2011) further show that geographical factors are less significant for public research networks in comparison with the greater impact of geography on patterns of industrial R&D collaboration networks.

  7. 7.

    Following previous empirical studies (Furman et al. 2002; Varga et al. 2010), we decide to impose a lag of 2 years on the network variable, as it takes some time between the inputs translate into measurable outputs. In case of the agglomeration variable, time lag is not necessary, as the variable varies only slightly over the analysed period.

  8. 8.

    The model with random effects is more appropriate in case of our sample data, because variables that do not change or change only slightly over time periods cannot be estimated using the model with fixed effects, since they are eliminated in estimation process (Elhorst 2010b). Such a variable in our model is the agglomeration variable and its spatial lag. Moreover, the model with fixed effects can be estimated consistently only when the time domain T is sufficiently large (Elhorst 2010b). As our sample comprises a relatively small number of time periods T = 9 as compared to the number of cross sectional observations N = 228, the model with random effects is more suitable.

  9. 9.

    There is also a possibility to derive a model where only independent variables exhibit spatial dependence and observations of the dependent variable are assumed to be spatially independent (ρ = 0). Finally, the restrictions ρ = 0 and δ 2 = γ 2 = 0 would result in a standard OLS model (Fischer and Wang 2011).

  10. 10.

    Taking only the parameter estimates δ 1 and γ 1 for the agglomeration and network variables into account would be an incorrect interpretation of the model, since they do not include the effect of so called feedback loops that arise as a result of impacts passing through neighboring regions and back to the regions themselves (LeSage and Pace 2009).

  11. 11.

    As inventions have to be novel, non-trivial and commercially applicable in order to be protected by a patent, patents can be recognised as quantitative indicators of inventions. Nevertheless, the use of patents has some limitations. Not all inventions that could be patented are actually patented, because patenting is a voluntary strategic decision. Further, not all inventions are allowed to be patented, for example a program code (OECD 1994).

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Acknowledgements

This work has been funded by the FWF Austrian Science Fund (Project Nr. P21450) and the Innovation Economic Talent Development Programme of the AIT Austrian Institute of Technology and the Vienna University of Economics and Business.

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Authors and Affiliations

  1. Institute for Economic Geography and GIScience, Vienna University of Economics and Business (WU Wien), Vienna, Austria

    Slavomir Hidas, Martyna Wolska & Manfred M. Fischer

  2. Innovation Systems Department, AIT Austrian Institute of Technology, Vienna, Austria

    Slavomir Hidas, Martyna Wolska & Thomas Scherngell

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  1. Slavomir Hidas
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  2. Martyna Wolska
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Correspondence to Thomas Scherngell .

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  1. Innovation Systems Department, AIT Austrian Institute of Technology, Wien, Austria

    Thomas Scherngell

Appendix: List of Regions

Appendix: List of Regions

In this study we use 228 NUTS-2 regions (revision 2003) covering all pre-2007 EU member states except Cyprus, Greece and Malta. In addition, the list does not include the Spanish North African territories of Ceuta y Melilla, the Portuguese non-continental territories Azores and Madeira, and the French Departments d’Outre-Mer Guadeloupe, Martinique, French Guayana and Reunion.

Austria: Burgenland, Kärnten, Niederösterreich, Oberösterreich, Salzburg, Steiermark, Tirol, Vorarlberg, Wien

Belgium: Prov. Antwerpen, Prov. Brabant-Wallon, Prov. Hainaut, Prov. Limburg (B), Prov. Liège, Prov. Luxembourg (B), Prov. Namur, Prov. Oost-Vlaanderen, Prov. Vlaams-Brabant, Prov. West-Vlaanderen, Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest

Czech Republic: Jihovýchod, Jihozápad, Moravskoslezsko, Praha, Severovýchod, Severozápad, Střední Morava, Střední Čechy

Denmark: Danmark

Estonia: Eesti

Finland: Åland, Etelä-Suomi, Itä-Suomi, Länsi-Suomi, Pohjois-Suomi

France: Alsace, Aquitaine, Auvergne, Basse-Normandie, Bourgogne, Bretagne, Centre, Champagne-Ardenne, Corse, Franche-Comté, Haute-Normandie, Île de France, Languedoc-Roussillon, Limousin, Lorraine, Midi-Pyrénées, Nord – Pas-de-Calais, Pays de la Loire, Picardie, Poitou-Charentes, Provence-Alpes-Côte d’Azur, Rhône-Alpes

Germany: Arnsberg, Berlin, Brandenburg, Braunschweig, Bremen, Chemnitz, Darmstadt, Dessau, Detmold, Dresden, Düsseldorf, Freiburg, Gießen, Halle, Hamburg, Hannover, Karlsruhe, Kassel, Koblenz, Köln, Leipzig, Lüneburg, Magdeburg, Mecklenburg-Vorpommern, Mittelfranken, Münster, Niederbayern, Oberbayern, Oberfranken, Oberpfalz, Rheinhessen-Pfalz, Saarland, Schleswig-Holstein, Schwaben, Stuttgart, Thüringen, Trier, Tübingen, Unterfranken, Weser-Ems

Hungary: Dél-Alföld, Dél-Dunántúl, Észak-Alföld, Észak-Magyarország, Közép-Dunántúl, Közép-Magyarország, Nyugat-Dunántúl

Ireland: Border, Midland and Western; Southern and Eastern

Italy: Abruzzo, Basilicata, Calabria, Campania, Emilia-Romagna, Friuli-Venezia Giulia, Lazio, Liguria, Lombardia, Marche, Molise, Piemonte, Puglia, Sardegna, Sicilia, Toscana, Trentino-Alto Adige, Umbria, Valle d’Aosta/Vallée d’Aoste, Veneto

Latvia: Latvija

Lithuania: Lietuva

Luxembourg: Luxembourg (Grand-Duché)

Netherlands: Drenthe, Flevoland, Friesland, Gelderland, Groningen, Limburg (NL), Noord-Brabant, Noord-Holland, Overijssel, Utrecht, Zeeland, Zuid-Holland

Poland: Dolnośląskie, Kujawsko-Pomorskie, Lubelskie, Lubuskie, Łódzkie, Mazowieckie, Małopolskie, Opolskie, Podkarpackie, Podlaskie, Pomorskie, Śląskie, Świętokrzyskie, Warmińsko-Mazurskie, Wielkopolskie, Zachodniopomorskie

Portugal: Alentejo, Algarve, Centro (P), Lisboa, Norte

Slovakia: Bratislavský kraj, Stredné Slovensko, Východné Slovensko, Západné Slovensko

Slovenia: Slovenija

Spain: Andalucía, Aragón, Cantabria, Castilla y León, Castilla-La Mancha, Cataluña, Comunidad Foral de Navarra, Comunidad Valenciana, Comunidad de Madrid, Extremadura, Galicia, Illes Balears, La Rioja, País Vasco, Principado de Asturias, Región de Murcia

Sweden: Mellersta Norrland, Norra Mellansverige, Småland med öarna, Stockholm, Sydsverige, Västsverige, Östra Mellansverige, Övre Norrland

United Kingdom: Bedfordshire & Hertfordshire; Berkshire, Buckinghamshire & Oxfordshire; Cheshire; Cornwall & Isles of Scilly; Cumbria; Derbyshire & Nottinghamshire; Devon; Dorset & Somerset; East Anglia; East Riding & North Lincolnshire; East Wales; Eastern Scotland; Essex; Gloucestershire, Wiltshire & North Somerset; Greater Manchester; Hampshire & Isle of Wight; Herefordshire, Worcestershire & Warkwickshire; Highlands and Islands; Inner London; Kent; Lancashire; Leicestershire, Rutland and Northamptonshire; Lincolnshire; Merseyside; North Eastern Scotland; North Yorkshire; Northern Ireland; Northumberland and Tyne and Wear; Outer London; Shropshire & Staffordshire; South Western Scotland; South Yorkshire; Surrey, East & West Sussex; Tees Valley & Durham; West Midlands; West Wales & The Valleys; West Yorkshire

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Hidas, S., Wolska, M., Fischer, M.M., Scherngell, T. (2013). Research Collaboration and Regional Knowledge Production in Europe. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_17

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