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Scientometrics

, Volume 87, Issue 1, pp 63–74 | Cite as

Factors affecting inter-regional academic scientific collaboration within Europe: the role of economic distance

  • Manuel Acosta
  • Daniel Coronado
  • Esther Ferrándiz
  • M. Dolores León
Article

Abstract

This paper offers some insights into scientific collaboration (SC) at the regional level by drawing upon two lines of inquiry. The first involves examining the spatial patterns of university SC across the EU-15 (all countries belonging to the European Union between 1995 and 2004). The second consists of extending the current empirical analysis on regional SC collaboration by including the economic distance between regions in the model along with other variables suggested by the extant literature. The methodology relies on co-publications as a proxy for academic collaboration, and in order to test the relevance of economic distance for the intensity of collaboration between regions, we put forward a gravity equation. The descriptive results show that there are significant differences in the production of academic scientific papers between less-favoured regions and core regions. However, the intensity of collaboration is similar in both types of regions. Our econometric findings suggest that differences in scientific resources (as measured by R&D expenditure) between regions are relevant in explaining academic scientific collaborations, while distance in the level of development (as measured by per capita GDP) does not appear to play any significant role. Nevertheless, other variables in the analysis, including geographical distance, specialization and cultural factors, do yield significant estimated coefficients, and this is consistent with the previous literature on regional SC.

Keywords

Economic distance Academic scientific collaboration Gravity equation Co-authorship 

Mathematics Subject Classification (2000)

62P20 62J02 97K40 

JEL Classification

R11 O33 I23 

Notes

Acknowledgements

The authors would like to thank Martin Feldkircher (Austrian National Bank) for providing the spatial weight contiguity matrix. They are also grateful to Raffaele Paci and Barbara Dettori from the Centro Ricerche Economiche Nord Sud (CRENOS) at the University of Cagliari for their assistance in the construction of the distance matrix and for providing the coordinates of the centre regions, and Robert Tijssen of the Centre for Science and Technology Studies (CWTS) at Leiden University for providing the updated classification. An early version of this paper was presented at the 12th European Network on Industrial Policy (EUNIP) International Conference held in Reus, Spain, from 9–11 June 2010. We thank the conference participants, particularly James Wilson, for their helpful comments and suggestions. The authors are also very grateful to the reviewers for constructive and insightful comments. This work was supported by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía-Spain) [Grants P06-SEJ-02087 and P08-SEJ-3981].

References

  1. Boshoff, N. (2010). South–South research collaboration of countries in the Southern African Development Community (SADC). Scientometrics, 84, 481–503.CrossRefGoogle Scholar
  2. Cameron, C., & Trivedi, P. K. (1998). Regression analysis of count data. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  3. Cameron, C., & Trivedi, P. K. (2009). Microeconometrics using Stata. College Station Texas: Stata Press.zbMATHGoogle Scholar
  4. Hoekman, J., Frenken, K., & Oort, F. (2009). The geography of collaborative knowledge production in Europe. Annals of Regional Science, 43, 721–738.CrossRefGoogle Scholar
  5. Hoekman, J., Frenken K., Tijssen, R. J. W. (2010). Research collaboration at a distance: changing spatial patterns of scientific collaboration within Europe. Research Policy, 39, 662–673.Google Scholar
  6. Hwang, K. (2008). International collaboration in multilayered center-periphery in the globalization of science and technology. Science Technology Human Values, 33, 101–133.CrossRefGoogle Scholar
  7. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26, 1–18.CrossRefGoogle Scholar
  8. Kim, M.-J. (2005). Korean science and international collaboration, 1995–2000. Scientometrics, 63, 321–339.CrossRefGoogle Scholar
  9. Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11, 3–15.CrossRefGoogle Scholar
  10. Liang, L., & Zhu, L. (2002). Major factors affecting china’s inter-regional research collaboration: regional scientific productivity and geographical proximity. Scientometrics, 55, 287–316.CrossRefGoogle Scholar
  11. Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.zbMATHGoogle Scholar
  12. Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36, 363–377.CrossRefGoogle Scholar
  13. Okubo, Y., & Zitt, M. (2004). Searching for research integration across Europe: A closer look at international and inter-regional collaboration in France. Science and Public Policy, 31, 213–226.CrossRefGoogle Scholar
  14. Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87, 308–322.CrossRefGoogle Scholar
  15. Ponds, R., van Oort, F., & Frenken, K. (2007). The geographical and institutional proximity of research collaboration. Papers in Regional Science, 86, 423–443.CrossRefGoogle Scholar
  16. Scherngell, T., & Barber, M. J. (2009). Spatial interaction modelling of cross-region R&D collaborations: Empirical evidence from the 5th EU framework programme. Papers in Regional Science, 88, 531–546.CrossRefGoogle Scholar
  17. Schott, T. (1998). Ties between center and periphery in the scientific world-system: Accumulation of rewards, dominance and self-reliance in the center. Journal of World-Systems Research, 4, 112–144.Google Scholar
  18. Schubert, T., & Sooryamoorthy, R. (2010). Can the centre-periphery model explain patterns of international scientific collaboration among threshold and industrialised countries? The case of South Africa and Germany. Scientometrics, 83, 181–203.CrossRefGoogle Scholar
  19. Sonnenwald, D. H. (2007). Scientific collaboration: A synthesis of challenges and strategies. In B. Cronin (Ed.), Annual review of information science and technology (Vol. 41). Medford NJ: Information Today, Inc.Google Scholar
  20. Tijssen, R.J.W, van Leeuwen, T.N., (2003). Bibliometric analyses of world science, extended technical annex to chapter 5 of the third European report on science & technology indicators.Google Scholar
  21. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, 307–333.zbMATHCrossRefMathSciNetGoogle Scholar
  22. Wagner, C. S., & Leydesdorff, L. (2005). Mapping the network of global science: Comparing international co-authorship from 1990 to 2000. International Journal of Technology and Globalization, 1, 185–208.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2010

Authors and Affiliations

  • Manuel Acosta
    • 1
  • Daniel Coronado
    • 1
  • Esther Ferrándiz
    • 1
  • M. Dolores León
    • 1
  1. 1.Facultad de Ciencias Económicas y EmpresarialesUniversidad de CádizCadizSpain

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