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Skilled labour mobility, networks and knowledge creation in regions: a panel data approach

Abstract

The aim of this paper is to investigate the relative contribution of different features of the local labour market for inventors on regional patenting. By means of a knowledge production function and a sample of 276 European regions, we assess whether local labour mobility of inventors, as well as the scale and extent of their collaborative research networks, correlates with innovation outcomes. In the second part of the paper, we extend the analysis to the role of spatial mobility of knowledge workers and cross-regional research networks as predictors of regional patenting.

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Notes

  1. NUTS stands for the French acronym “nomenclature d’unités territoriales statistiques” and refers to administrative divisions within Europe devised for statistical purposes.

  2. We are aware about an important debate within the literature on whether the strength of social ties matters for innovation (Granovetter 1973) and the detrimental role of too much social proximity (socially embedded relations between actors) for knowledge diffusion and innovation due to the problem of lock-in (Boschma 2005; Uzzi 1997). The present inquiry does not, however, enter this debate and, as such, we prefer to leave this discussion and potential empirical analysis for further research.

  3. SNA has been widely applied to collaboration in research and innovation studies, although a review of detailed methodological contributions falls outside the scope of this paper. In fact, in recent years, many contributions have been made to economics and economic geography using SNA tools, most notably Balconi et al. (2004), Breschi and Catalini (2009), and Ter Wal and Boschma (2009). For a more complete theoretical discussion of the methods and applications of SNA, see Wasserman and Faust (1994).

  4. We have omitted the regions of Las Canarias, Ceuta, Melilla, Madeira, Açores, Guadeloupe, Martinique, Guyane and Reunion due to their distance from continental Europe. We do not expect this omission to alter significantly our results.

  5. EPO stands for European Patent Office.

  6. Although this variable captures inventors’ overall propensity to be connected, it overlooks that invention networks tend to be highly fragmented in different components of relatively small size. This variable, therefore, could overestimate the connectivity level, especially in the absence of a direct control for the size of the largest component or, perhaps better, the heterogeneity of the component size distribution (as discussed in Schilling and Phelps 2007, and Lobo and Strumsky 2008). This important limitation must be acknowledged, and we thank a referee for raising this point.

  7. We added a small value, 0.01, to all the explanatory variables presenting zero values in at least one observation to allow for a logarithmic transformation.

  8. International patent classification.

  9. For the calculation of this index, we have used the technological sector classification jointly produced by Fraunhofer Gesellschaft-ISI (Karlsruhe), Institut National de la Propriété In-dus-trielle (INPI, Paris) and Observatoire des Sciences and des Techniques (OST, Paris).

  10. The spatial lag of R&D expenditures is the average value of R&D expenditures in the neighbouring regions, where ‘neighbouring’ is defined through a predefined standardized weights matrix, as is customary in the literature. In this paper, the weights matrix is built as the inverse of the Euclidean distance between regions’ centroids. Following Bottazzi and Peri (2003), a cut-off of 300 km is introduced. Other definitions of the weights matrix barely alter the results.

    Table 5 External links and innovation: mobility, networks and spatial spillovers

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Acknowledgments

We acknowledge financial support from the Ministerio de Ciencia e Innovación, ECO2011-30260-C03-03 and the project ‘Knowledge, Innovation, Territory (KIT)’—Programme ESPON 2013 (2010–2013). The views expressed in this article are those of the authors and do not necessarily reflect those of the World Intellectual Property Organization. We are very grateful to CRENoS (University of Cagliari and University of Sassari) for providing us with data on patents, R&D and human capital, and also to two anonymous referees for their useful comments. However, any mistakes or omissions are ours.

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Miguélez, E., Moreno, R. Skilled labour mobility, networks and knowledge creation in regions: a panel data approach. Ann Reg Sci 51, 191–212 (2013). https://doi.org/10.1007/s00168-012-0526-0

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