Abstract
Innovation is essential for the competitiveness and prosperity of regions. The appropriate measurement of innovation is of crucial importance in order to design suitable policies to foster innovation and regional growth. However, current approaches to measuring regional innovation neglect the multidimensionality of the innovation concept. In this context, the present analysis extends the multidimensional approach to evaluate regional innovation by Siller et al. (The multiple facets of regional innovation, University of Innsbruck: Working Papers in Economics and Statistics 2014-19, 2014) to a set of 196 European regions. This is accomplished by combining original innovation indicators from the Community Innovation Survey and Eurostat with data from the Regional Innovation Scoreboard. The applied methodology allows a comprehensive analysis of regional innovation performance at the European level based on three independent dimensions, i.e. Technological Innovation, Commercial Viability of Innovation and Service Innovation. The classification of regions in innovation regimes based on their performance with regard to the different innovation aspects identifies territories pursuing specialized innovation strategies, regions with strong performance on more than a single aspect, as well as regions with low overall innovation activities. The differentiated evaluation of regional strengths and weaknesses may contribute to designing better targeted innovation policies and may thus be profitable for regional policy making.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For a detailed description of the estimation methodology, see Capello and Lenzi (2013b).
- 2.
In the original article the second dimension is labeled Commercial Innovation. However, in order to better illustrate the underlying concept, the present article denotes this dimension as Commercial Viability of Innovation.
- 3.
Original innovation data for only 45 regions can be employed in the analysis. This is because CIS data and RIS data have to be available at the same NUTS level, which is not the case for some regions used in Siller et al. (2014).
- 4.
CIS data for Germany, the Czech Republic and the United Kingdom are based on the NACE rev. 2 divisions for CIS core and additional coverage as set out in the CIS2008 methodological recommendations, whereas the data for Austria include only the CIS core-coverage industries. For information regarding the Nomenclature statistique des activités économiques dans la Communauté européenne (NACE), see: http://ec.europa.eu/eurostat/web/nace-rev2. For information regarding the CIS methodological recommendations, see: http://ec.europa.eu/eurostat/cache/metadata/en/inn_cis2_esms.htm.
- 5.
Regional values were calculated for both reference years. However, only results for the year 2010 are presented and discussed in this article. Results for the reference year 2008 can be made available by the authors on request.
- 6.
Indicators from the CIS consider only small and medium enterprises (SME) within the regional CIS samples. Consequently, all firms with more than 249 enterprises are excluded.
- 7.
The EU Member States Cyprus, Estonia, Latvia, Lithuania, Luxembourg and Malta are not included in the RIS dataset.
- 8.
More specifically, the following indicators (regions) are missing: Non-R&D innovation expenditures (all regions of the UK); Non-technological innovators (all regions of Switzerland); public and business R&D expenditures (all regions of Greece); EPO patent applications (the two Spanish exclaves Melilla and Ceuta and the two Finnish regions East and North Finland) and employment in knowledge-intensive services (the French Overseas Departments and Territories). Since Annex 5 of the RIS2012 report presents data for these indicators without missing values, these data are used to produce estimates of the missing values using OLS regressions.
- 9.
The RIS includes data for Austria at the NUTS1 level. However, this NUTS level is very artificial and the historically evolved level corresponds to NUTS2 (Bundesländer). In order to account for this, Austrian RIS data are converted from the NUTS1 to the NUTS2 level by using original CIS and Eurostat data and applying the following steps: Firstly, the CIS/Eurostat variable is power-transformed using the same parameter as used for the relative variable in the RIS2014 (Hollanders et al. 2014). Secondly, the RIS values for Austrian NUTS2 regions are calculated by transferring the percentage deviation of original values for NUTS2 regions from the corresponding value for the NUTS1 region.
- 10.
Localisation and urbanisation economies refer to different effects resulting from the agglomeration of firms. The former arise from the spatial concentration of firms from the same industries, whereas the latter refers to benefits due to the agglomeration of firms from different industries. Localisation economies are predominantly found in cluster-type agglomerations, whereas urbanisation economies are associated with densely populated areas, such as cities.
- 11.
The computation is performed firstly, by multiplying the proportion of explained variance for each dimension shown in Table 4 by the R2 values from the regressions, where values for the respective dimension are employed as dependent variables (Table A.2), and secondly, by summing up the resulting percentage values for all three dimensions.
References
Ajmone Marsan G, Maguire K (2011) Categorisation of OECD regions using innovation-related variables, OECD Regional Development Working Papers, 2011/03. OECD Publishing
Arundel A, Kabla I (1998) What percentage of innovations are patented? Empirical estimates for European firms. Res Pol 27(2):127–141
Capello R, Lenzi C (2013a) Territorial patterns of innovation: a taxonomy of innovative regions in Europe. Ann Reg Sci 51(1):119–154
Capello R, Lenzi C (2013b) The knowledge-innovation nexus in European regions. In: Capello R, Lenzi C (eds) Territorial patterns of innovation. An inquiry on the knowledge economy in European regions. Routledge, London
Cooke P (2002) Regional innovation systems: general findings and some new evidence from biotechnology clusters. J Technol Transf 27(1):133–145
European Commission (2005) Working together for growth and jobs: a new start for the Lisbon Strategy, Communication to the Spring European Council COM(2005) 24
European Commission (2010) Europe 2020: a strategy for smart, sustainable and inclusive growth, Communication from the Commission COM(2010) 2020
Evangelista R, Iammarino S, Mastrostefano V, Silvani A (2001) Measuring the regional dimension of innovation. Lessons from the Italian innovation survey. Technovation 21(11):733–745
Griliches Z (1990) Patent statistics as economic indicators–A survey. J Econ Lit 28(4):1661–1707
Hasan I, Tucci CL (2010) The innovation–economic growth nexus: global evidence. Res Pol 39(10):1264–1276
Hollanders H, Rivera-Leon L, Roman L, Regional Innovation Scoreboard (RIS) (2012) INNO Metrics 2011–2012 report, Brussels: European Commission, DG Enterprise
Hollanders H, Es-Sadki N, Rivera-Leon L, Griniece E, Roman L, Regional Innovation Scoreboard (RIS) (2014) European Innovation Scoreboard 2013–2014 report, Brussels: European Commission, DG Enterprise
Kleinknecht A, van Montfort K, Brouwer E (2002) The non-trivial choice between innovation indicators. Econ Innov New Tech 11(2):109–121
Lundvall B-Å (1992) National systems of innovation. Towards a theory of innovation and interactive learning. Pinter, London
Navarro M, Gibaja JJ, Bilbao-Osorio B, Aguado R (2009) Patterns of innovation in EU-25 regions: a typology and policy recommendations. Environ Plann C-Govern Pol 27(5):815–840
OECD (1996) The knowledge based economy. OECD Publishing, Paris
OECD (2005) Oslo manual: guidelines for collecting and interpreting innovation data. The measurement of scientific and technological activities. OECD Publishing, Paris
OECD (2010) The OECD innovation strategy: getting a head start on tomorrow. OECD Publishing, Paris
Porter ME (1990) The competitive advantage of nations. Macmillan, London
Siller M, Hauser C, Walde J, Tappeiner G, (2014) The multiple facets of regional innovation, University of Innsbruck: Working Papers in Economics and Statistics 2014-19
Smith K (2005) Measuring innovation. In: Fagerberg J, Mowery DC, Nelson RR (eds) The Oxford handbook of innovation. Oxford University Press, Oxford, pp 148–177
Acknowledgements
This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.
We thank the ONS (Office for National Statistics, London), ZEW (Centre for European Economic Research, Mannheim), Statistics Austria (Vienna) and the Český Statistický Úřad (Czech Statistical Office, Prague) for their kind assistance in supplying regional CIS data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Siller, M., Schatzer, T., Tappeiner, G. (2016). Regional Innovation in Europe: A Multidimensional Approach. In: Hilpold, P., Steinmair, W., Perathoner, C. (eds) Europa der Regionen. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48205-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-48205-6_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48204-9
Online ISBN: 978-3-662-48205-6
eBook Packages: Social Science and Law (German Language)