Skip to main content
SpringerLink
Log in

Embeddedness of regions in European knowledge networks: a comparative analysis of inter-regional R&D collaborations, co-patents and co-publications

  • Special Issue Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

Abstract

This paper investigates the embeddedness of European regions in different types of inter-regional knowledge networks, namely project-based R&D collaborations within the European framework programmes (FPs), co-patent networks and co-publication networks. Embeddedness refers to the network positioning of regions captured in terms of social network analytic (SNA) centrality measures. The objective is to estimate how region-internal and region-external factors influence network embeddedness in the distinct network types, in order to identify differences in their driving factors at the regional level. In our modelling approach, we apply advanced spatial econometric techniques by means of a mixed effects panel version of the spatial Durbin model (SDM) and introduce a set of variables accounting for a capacity specific, a relational as well as a spatial dimension in regional knowledge production activities. The results reveal conspicuous differences between the knowledge networks. Internal capacity- and technology-related aspects but also spatial spillover impacts from surrounding regions prove to be particularly important for centrality in the co-patent network. We also find significant—region-internal and region-external—impacts of general economic conditions on a region’s centrality in the FP network. However, we cannot observe substantial spillover effects of region-external factors on centrality in the co-publication network. Thus, the distinctive knowledge creation foci in each network seem to find expression in the network structure as well as its regional determinants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. There is a growing body of literature dealing with the interlinkage of local “buzz” and global “pipelines” in regional systems (see, e.g. Morrison et al. 2013; Giuliani and Bell 2005; Bathelt et al. 2004). Following their argumentation, there need to be some key actors (e.g public research organisations, firms, etc.) that hold the necessary capacity to draw on outside sources but also to diffuse externally acquired knowledge within the local system, often referred to as ‘gatekeepers’ in the context of regional systems (see Giuliani and Bell 2005). However, regional systems with actors exclusively focusing on inter-regional linkages without cultivating region-internal cooperation networks would run the risk of developing into a fragmented regional system (‘hollow clusters’) that is not able to realise benefits from inter-regional knowledge networks (Bathelt et al. 2004). Case study evidence further suggests that a balanced mix between locally embedded interaction structures and external linkages is most important for sustained regional success (see, e.g. Broekel 2012; Graf 2011).

  2. While this study takes a regional science perspective focusing on inter-regional knowledge networks and the relations between regions, an increasing body of literature deals with network relations also in the context of inter-organisational structures focusing on network connectivity of different types of actors (see, e.g. Balland 2012; Broekel and Graf 2012; Ozman 2009).

  3. From a network perspective, this argument is also underpinned by the assumption that in social networks, often network triangles occur that corresponds to the concept of the transitivity of friendship (see, e.g. Wasserman and Faust 1994). In a regional context, this would imply that organisations with a specific set of partners influence other partner organisations, often located in neighbouring regions in their partner choice. Moreover, interdependencies between regions in their knowledge production and economic activities may not only arise from spatial neighbourhood but also from relational proximity in networks, or could also be due to organisational proximities, such as in the case of multinational enterprises and international value chains (see, for example, Borgatti 2005). Considering various forms of proximity and interactions (in terms of knowledge production as well as in a general economic sense) between regions may be also an important issue for future research on the drivers of knowledge network embeddedness, in particular with respect to spillover effects arising from other forms of regional interdependencies, for example technological or other types of network-based proximities.

  4. Note that we construct and analyse the individual networks under consideration separately but introduce our methodological approach in general terms for purposes of readability. The empirical measurement of collaborations in the FPs, co-patenting and co-publications is given in Sect. 6.

  5. We use full counting procedures for the construction of our collaboration matrices, assigning links for each participating organisation that is located in a different region.

  6. We are aware of the fact that organisations rather than regions are the essential actors in the knowledge network and thus would constitute a more appropriate unit of observation for such kind of analysis. Thus, an alternative approach would be to define network centrality by means of a bipartite graph directly at the organisational level in a first step and aggregate the observed organisation centralities to the regional level in a second step (see Wanzenböck and Heller-Schuh 2013). However, we do not have information on the network structures at the organisational level for all knowledge networks under consideration and thus directly stick to the regional level. However, statistical correlation test for the FP network shows a high correlation between a region’s network centrality calculated at the regional and network centrality calculated at the organisational level, and subsequently aggregated to regions. Results for Spearmans rank correlation coefficient show a statistically significant value of \(r_{s}=0.938\) (\(p<0.01\)) for eigenvector centrality in the FP network, and for betweenness centrality it is \(r_{s}=0.898\) (\(p<0.01\)), confirming that inter-regional collaboration matrices can serve as appropriate proxies for the network structure at the organisational level, at least when calculating the centrality of a region in a specific network.

  7. We refrain using the weighted version of betweenness centrality, such as for instance defined by Newman (2001), since interpretation of shortest paths in terms of the weighted graphs that we use in this study that is collaboration intensities between regions is problematic.

  8. A common notation used in this context is the eigenvector equation as given by \(\lambda \,\mathbf{x} = \mathbf{A} \mathbf{x}\), where x is a vector of centralities \(\mathbf{x} = (x_{1}, x_{2},\ldots \)) denoting the eigenvector of the adjacency matrix A with eigenvalue \(\lambda \) (see Bonacich 1987).

  9. Since their launch in 1984, the overall objectives of the FPs have been to strengthen the scientific and technological bases of the European scientific community and the European economy to foster international competitiveness, and the promotion of research activities in support of other EU policies (see, for instance, Scherngell and Barber 2009). Funding is open to all legal entities established in the Member States of the European Union—e.g. individuals, industrial and commercial firms, universities, research organisations, etc.—and can be applied by at least two independent legal entities established in different EU Member States or in an EU Member State and an Associated State. Proposals to be funded are selected on the basis of criteria including scientific excellence, added value for the European Community, the potential contribution to furthering the economic and social objectives of the Community, the innovative nature, the prospects for disseminating and exploiting the results and effective transnational cooperation.

  10. EUPRO has been constructed and maintained by AIT Austrian Institute of Technology. It contains systematic information on project objectives and achievements, project costs, project funding and contract type as well as on the participating organisations including the full name, type of the organisation and geographical location for FP1 to FP7 (see, for instance, Scherngell and Barber 2011). Moreover, the database includes the address of the specific department participating in a project, i.e. bias towards headquarters is minimised.

  11. Note that network fragmentation is much higher for the co-patenting network at the organisational level, leading to the fact that centrality measures calculated at the organisational level, especially measures of global network centrality as used in this study, would be subject to considerable bias.

  12. Concerning model specification, it seems to be reasonable to assume that network-specific processes influence structural network positioning, especially when measures of global network embeddedness (such as betweenness and eigenvector centrality) are regarded. Controlling for potential network-specific effects—resulting from formation processes or network dynamics—would be essential when analytical focus is on the probability of tie formation or the process of network formation. However, we focus on network positioning of regions in a structural sense by calculating our centrality measures on the basis of region-by-region network matrices and aim to identify region-internal and spatial factors that influence such a specific positioning. Thus, including a network weight matrix and controlling for structural network configuration (i.e. network autocorrelation) would cause severe endogeneity problems in the specification of our empirical model.

  13. We use a row-standardised version of \(W\) allowing interpretation of the spatial lags of the independent variables being the weighted average impact on region \(i\) by their neighbouring regions. Note further that the meaning of neighbourhood is used in the sense of spatial relatedness throughout this study.

  14. Note further that the spatial fixed effects model could not be estimated consistently due to relatively time-invariant independent variables. Further, the number of observations for \(n=241\) regions, in contrast to \(t=8\) time periods, is relatively large (Elhorst 2003; Baltagi 2008).

  15. At the regional level, the level of financial and human resources is to be considered as a general indicator for absorptive capacity. However, absorptive capacity has an additional dimension in the context of networks, referring to the capacity to establish and maintain a set of knowledge network relations simultaneously. We are aware of the fact that relational capacity may be particularly determined by structural characteristics of organisations, such as the size or the institutional background, but also the research field or the specific knowledge base of the organisations. Thus, the share of universities, research organisations or the relative dominance of large or small firms in a region might exert additional influence on the embeddedness of regions in knowledge networks. Due to a lack of information on organisation-specific characteristics and their distribution across our regional units of observation, we are not able to consider such factors in this study, but want to raise an issue for future research in this regard.

  16. The index is defined by \(c_{it}^{(4)}={\frac{1}{2}}\sum _{P}|s_{ip}-{\bar{s}_{p}}|\) where \(s_{ip}\) is the region’s \(i\) share of patents in a specific IPC class \(p\) and \({\bar{s}}_p\) is the mean of IPC class \(p\). Patents were taken into account at a three-digit level corresponding to the International Patent Classification (IPC).

  17. We include five different main economic sectors, namely agriculture, manufacturing, construction, private services and non-market service sector. The index of specialisation to account for industrial diversity is defined as \(z_{it}^{(1)}={\frac{1}{2}}\sum _{P}|o_{ip}-{\bar{o}}_{p}|\) where \(o_{ip}\) is the region’s \(i\) share of gross value added in a specific sector \(p\) (indexed \(p=1, \ldots , 5\)) and \({\bar{o}}_{p}\) is the mean of sector \(p\) for \(n=241\) regions.

  18. Differences between SDM coefficient estimates and impact estimates give indication on the magnitude of the feedback effects.

References

  • Asheim B, Lawton Smith H (2011) Regional innovation systems: theory, empirics and policy. Reg Stud 45(7):875–891

    Google Scholar 

  • Autant-Bernard C (2012) Spatial econometrics of innovation: recent contributions and research perspectives. Spat Econ Anal 7(4):403–419

    Article  Google Scholar 

  • Autant-Bernard C, Billand P, Frachisse D, Massard N (2007) Social distance versus spatial distance in R&D cooperation: empirical evidence from European collaboration choices in micro and nanotechnologies. Pap Reg Sci 86:495–519

    Article  Google Scholar 

  • Balland PA (2012) Proximity and the evolution of collaborative networks: evidence from R&D projects within the GNSS industry. Reg Stud 46:741–756

    Article  Google Scholar 

  • Baltagi B (2008) Econometric analysis of panel data, 4th edn. Wiley, NY

    Google Scholar 

  • Bathelt H, Malmberg A, Maskell P (2004) Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Prog. Hum Geogr 28:31–56

    Article  Google Scholar 

  • Bergman E, Maier G (2009) Network central: regional positioning for innovative advantage. Ann Reg Sci 43:615–644

    Article  Google Scholar 

  • Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92:1170–1182

    Article  Google Scholar 

  • Borgatti SP (2005) Centrality and network flow. Social Netw 27:55–71

    Article  Google Scholar 

  • Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Social Netw 28:466–484

    Article  Google Scholar 

  • Boschma R (2005) Proximity and innovation: a critical assessment. Reg. Stud. 39:61–74

    Article  Google Scholar 

  • Breschi S, Lissoni F (2009) Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows. J Econ Geogr 9:439–468

    Article  Google Scholar 

  • Breschi S, Malerba F (2009) ERA and the role of networks. In: Delanghe H, Muldur U, Soete L (eds) European science and technology policy. Towards integration or fragmentation?. Edward Elgar, Cheltenham, pp 160–174

    Google Scholar 

  • Broekel T (2012) Collaboration intensity and regional innovation efficiency in Germany—a conditional efficiency approach. Ind Innov 19:155–179

    Article  Google Scholar 

  • Broekel T, Brenner T (2011) Regional factors and innovativeness: an empirical analysis of four German industries. Ann Reg Sci 47:169–194

    Article  Google Scholar 

  • Broekel T, Graf H (2012) Public research intensity and the structure of German R&D networks: a comparison of 10 technologies. Econ Innov New Technol 21:345–372

    Article  Google Scholar 

  • Cohen WM, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35:128–152

    Article  Google Scholar 

  • Commission of the European Communities (CEC) (2007) Green paper ‘The European Research Area: New Perspectives’. SEC(2007) 412, COM(2007) 161 final, Brussels, 4 April 2007

  • Elhorst JP (2003) Specification and estimation of spatial panel data models. Int Reg Sci Rev 26(3):244–268

    Article  Google Scholar 

  • Elhorst JP (2012) Matlab software for spatial panels. Int Reg Sci Rev. doi:10.1177/0160017612452429

  • Feldman MP, Kogler D (2010) Stylized facts in the geography of innovation. In: Hall H, Rosenberg N (eds) Handbook of the economics of innovation, vol 1. North Holland, Elsevier, pp 382–410

    Google Scholar 

  • Fischer MM, Varga A (2003) Spatial knowledge spillovers and university research. Ann Reg Sci 37:302–322

    Article  Google Scholar 

  • Fischer MM, Wang J (2011) Spatial data analysis: models, methods and techniques. Springer briefs in regional science. Springer, Berlin

    Book  Google Scholar 

  • Fischer MM, Scherngell T, Jansenberger E (2006) The geography of knowledge spillovers between high-technology firms in Europe. Evidence from a spatial interaction modelling perspective. Geogr Anal 38:288–309

    Article  Google Scholar 

  • Fischer MM, Scherngell T, Reismann M (2009) Knowledge spillovers and total factor productivity. Evidence using a spatial panel data model. Geogr Anal 41:204–220

    Article  Google Scholar 

  • Freeman LC (1979) Centrality in social networks: conceptual clarification. Social Netw 1(3):215–239

    Article  Google Scholar 

  • Gilsing V, Nooteboom B, Vanhaverbekec W, Duystersd G, van den Oorda A (2008) Network embeddedness and the exploration of novel technologies: technological distance, betweenness centrality and density. Res Policy 37:1717–1731

    Article  Google Scholar 

  • Giuliani E, Bell M (2005) The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Res Policy 34:47–68

    Article  Google Scholar 

  • Goetze C (2010) An empirical enquiry into co-patent networks and their stars: the case of cardiac pacemaker technology. Technovation 30(7/8):436–446

    Article  Google Scholar 

  • Graf H (2011) Gatekeepers in regional networks of innovators. Camb J Econ 35:173–198

    Article  Google Scholar 

  • Hagedoorn J, Link AN, Vonortas NS (2000) Research partnerships. Res Policy 29(4/5):567–586

    Article  Google Scholar 

  • Hicks DM, Katz JS (1996) Where is science going? Sci Technol Hum Values 21:379–406

    Article  Google Scholar 

  • Hoekman J, Frenken K, van Oort F (2009) The geography of collaborative knowledge production in Europe. Ann Reg Sci 43:721–738

    Article  Google Scholar 

  • Hoekman J, Frenken K, Tijssen RJW (2010) Research collaboration at a distance: changing spatial patterns of scientific collaboration within Europe. Res Policy 39:662–673

    Article  Google Scholar 

  • Karlsson C, Manduchi A (2001) Knowledge spillovers in a spatial context—a critical review and assessment. In: Fischer MM, Fröhlich J (eds) Knowledge, complexity and innovation systems. Springer, Berlin, pp 101–124

  • Katz JS, Martin BR (1997) What is research collaboration? Res Policy 26:1–18

    Article  Google Scholar 

  • Knoke D, Young S (2008) Social network analysis. Sage, Los Angeles

    Google Scholar 

  • Lagendijk A (2001) Scaling knowledge production: how significant is the region? In: Fischer MM, Fröhlich J (eds) Knowledge, complexity and innovation systems. Springer, Berlin, pp 79–100

    Chapter  Google Scholar 

  • Le Sage J, Pace RK (2009) Introduction to spatial econometrics. CRC Press, Boca Raton

    Google Scholar 

  • Maggioni MA, Uberti TE (2009) Knowledge networks across Europe: which distance matters? Ann Reg Sci 43:691–720

    Article  Google Scholar 

  • Mairesse J, Turner L (2005) Measurement and explanation of the intensity of co-publication in scientific research: an analysis at the laboratory level. NBER Working Paper, No. 11172, Cambridge/MA

  • Morrison A, Rabellotti R, Zirulia L (2013) When do global pipelines enhance the diffusion of knowledge in clusters? Econ Geogr 89:77–96

    Article  Google Scholar 

  • Mur J, Angula A (2009) Model selection strategies in a spatial setting: some additional results. Reg Sci Urban Econ 39:200–213

    Article  Google Scholar 

  • Newman MEJ (2001) Scientific collaboration networks. I. Network construction and fundamental results. Phys Rev E Stat Nonlinear Soft Matter Phys 64(I II):016131/1–016131/8

    Google Scholar 

  • Ozman M (2009) Inter-firm networks and innovation: a survey of literature. Econ Innov New Technol 18:39–67

    Article  Google Scholar 

  • Paier M, Scherngell T (2011) Determinants of collaboration in European R&D networks: empirical evidence from a discrete choice model. Ind Innov 18:89–104

    Article  Google Scholar 

  • Park HM (2011) Practical guides to panel data modeling: a step-by-step analysis using stata. Graduate School of International Relations, International University of Japan, Tutorial Working Paper

  • Ponds R, van Oort F, Frenken K (2007) The geographical and institutional proximity of research collaboration. Pap Reg Sci 86(3):423–443

    Article  Google Scholar 

  • Porter K, Whittington BK, Powell WW (2005) The institutional embeddedness of high-tech regions: relational foundations of the Boston biotechnology community. In: Breschi S, Malerba F (eds) Clusters, networks and innovation. Oxford University Press, Oxford, pp 261–296

    Google Scholar 

  • Powell WW, Giannella E (2010) Collective invention and inventor networks. In: Hall H, Rosenberg N (eds) Handbook of the economics of innovation, vol 1. North Holland, Elsevier, pp 576–605

    Google Scholar 

  • Powell WW, Grodal S (2005) Networks of innovators. In: Fagerberg J, Mowery DC, Nelson RR (eds) The oxford handbook of innovation. Oxford University Press, Oxford, pp 56–85

    Google Scholar 

  • Romer PM (1990) Endogenous technological change. J Polit Econ 98:71–102

    Article  Google Scholar 

  • Scherngell T (2013) The geography of networks and R&D collaborations. Springer advances in spatial science series. Springer, Berlin

    Google Scholar 

  • Scherngell T, Barber M (2011) Distinct spatial characteristics of industrial and public research collaborations: evidence from the 5th EU framework programme. Ann Reg Sci 46:247–266

    Article  Google Scholar 

  • Scherngell T, Barber M (2009) Spatial interaction modelling of cross-region R&D collaborations. Empirical evidence from the 5th EU Framework Programme. Pap Reg Sci 88:531–546

    Article  Google Scholar 

  • Scherngell T, Lata R (2013) Towards an integrated European research area? Findings from eigenvector spatially filtered spatial interaction models using European framework programme data. Pap Reg Sci 92:555–577

    Google Scholar 

  • Sebestyén T, Varga A (2013) Research productivity and the quality of interregional knowledge networks. Ann Reg Sci 51:155–189

    Article  Google Scholar 

  • Singh J (2005) Collaborative networks as determinants of knowledge diffusion patterns. Manag Sci 51(5):756–770

    Article  Google Scholar 

  • Sorenson O, Rivkin JW, Fleming L (2006) Complexity, networks and knowledge flow. Res Policy 35(7): 994–1017

    Google Scholar 

  • Stephan PE (2010) The economics of science. In: Hall H, Rosenberg N (eds) Handbook of the economics of innovation, vol 1. North Holland, Elsevier, pp 217–273

    Chapter  Google Scholar 

  • Ter Wal ALJ, Boschma R (2009) Applying social network analysis in economic geography: framing some key analytical issues. Ann Reg Sci 43:739–756

    Article  Google Scholar 

  • Varga A, Pontikakis D, Chorafakis G (2012) Metropolitan Edison and cosmopolitan Pasteur? Agglomeration and interregional research network effects on European R&D productivity. J Econ Geogr. doi:10.1093/jeg/lbs041

  • Wanzenböck I, Heller-Schuh B (2013) The embeddedness of regions in R&D collaboration networks of the EU framework programmes. In: Scherngell T (ed) The geography of networks and R&D collaborations. Springer advances in spatial science series. Springer, Berlin

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in the production of knowledge. Science 316:1036–1039

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to Manfred M. Fischer (Vienna University of Economics), Michael Barber (AIT) and Yuanjia Hu (University of Macau) for valuable comments made on an earlier version of the manuscript. They also thank two anonymous referees for valuable comments. This work was funded by the FWF Austrian Science Fund [Project Number I 886 G11] and the Multi-Year Research Grant (MYRG) - Level iii [RC Reference Number MYRG119(Y1-L3)-ICMS12-HYJ] by the University of Macau.

Author information

Authors and Affiliations

  1. Innovations Systems Department, Austrian Institute of Technology (AIT), Vienna, Austria

    Iris Wanzenböck & Thomas Scherngell

  2. Economic Geography and Location Research, Philipps University Marburg, Marburg, Germany

    Thomas Brenner

Authors
  1. Iris Wanzenböck
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Scherngell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Brenner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iris Wanzenböck.

Appendix

Appendix

1.1 List of regions

NUTS is an acronym of the French for the ”nomenclature of territorial units for statistics”, which is a hierarchical system of regions used by the statistical office of the European Community for the production of regional statistics. At the top of the hierarchy are NUTS-0 regions (countries) below which are NUTS-1 regions and then NUTS-2 regions. This study disaggregates Europe’s territory into 241 NUTS-2 regions located in the EU-25 member states (except Cyprus and Malta). We exclude 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. Thus, we include the following NUTS 2 regions:

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

Greece::

Anatoliki Makedonia, Thraki; Attiki; Ipeiros; Voreio Aigaio; Dytiki Ellada; Dytiki Makedonia; Thessalia; Ionia Nisia; Kentriki Makedonia; Kriti; Notio Aigaio; Peloponnisos; Sterea Ellada

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

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wanzenböck, I., Scherngell, T. & Brenner, T. Embeddedness of regions in European knowledge networks: a comparative analysis of inter-regional R&D collaborations, co-patents and co-publications. Ann Reg Sci 53, 337–368 (2014). https://doi.org/10.1007/s00168-013-0588-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-013-0588-7

JEL Classification

Search

Navigation