RCI 2010 : Some in-depth analysis
In: 2011; (2011)
Online
Elektronische Ressource
Zugriff:
This document is the final delivery of the two-year joint project DG Joint Research Centre and DG Regional Policy on the measurement of the level of regional competitiveness, launched in November 2008. Within this project, the European Commission has recently published the first edition of the Regional Competitiveness Index (RCI). The index provides a tool to improve the understanding of competitiveness at the regional level by showing the strengths and weaknesses of each of the European regions at the NUTS2 level in a number of dimensions related to competitiveness. The analysis offered by the first edition of the RCI is a snapshot of regional competitiveness as it is in 2010 and is based upon data mostly spanning between 2007 and 2009. The present document takes a step further and offers a two-fold analysis based on the RCI indices: an exploratory spatial data analysis and an analysis of possible relationships between exogenous indicators and the RCI index and sub-indices. The exploratory spatial data analysis shows the existence of spatial dependence among EU regions, with different patterns for different areas within the EU. This can be taken as an indication for the existence of spatial externalities among regions and, when observed for high performing regions, as evidence, or better, as necessary condition for spillover effects. LISA analysis allowed us to distinguish between two sub-areas in the EU: group A which comprises regions with high RCI performance surrounded by regions with similar strong competitive performance and group B, comprising low-performing regions surrounded by low RCI performers.The analysis has been extended to better explore the structure of spatial autocorrelation within the two main sub-areas – A and B - of low-low and high-high clusters as detected by LISA. The analysis of sub-area B is meant to further investigate the possible presence of ‘negative’ spillover effects where low performing regions negatively affect their neighbours.Spatial autocorrelation structure is investigated by using variogram analysis, a tool typical of Kriging for describing spatial dependences. Variogram analysis provides as additional information the ‘range of action’ of spatial dependence, which is the maximum distance beyond which the correlation can be considered null. Variogram analysis is carried out using three different distances between region centroids: Euclidean distance, distance along the road (ferry) network and the travel time distance. Results indicate the existence of a clearstructure of correlation for the sub-area A of high-high clusters. On the contrary, sub-area B seems to be characterized mostly by low performing regions with some rare and sparse picks of relatively higher performers(some capital regions). With regards to the analysis of possible relationships between exogenous indicators and RCI index and subindices, we have looked at bivariate correlations with five exogenous indicators (population change in the period 2001-2007; natural population change in the period 2001-2007; net migration in the period 2001-2007; share of population which live in Large Urban Zones, LUZ; GDP growth average 2000-2007) for all EU NUTS 2 regionsas well as for two sub-areas as identified by the ESDA analysis. We find that the number of significant results from the correlation analysis increases when we distinguish between the sub-areas. Results show that population dynamics and demographic trends are highly relevant for territorial competitiveness while therelationship with GDP growth remains ambiguous
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RCI 2010 : Some in-depth analysis
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Autor/in / Beteiligte Person: | Commission, European ; Joint Research Centre ; Annoni, Paola ; Kozovska, Kornelia |
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Quelle: | 2011; (2011) |
Veröffentlichung: | 2011 |
Medientyp: | Elektronische Ressource |
ISBN: | 978-92-79-19078-0 (print) |
ISSN: | 1018-5593 (print) |
DOI: | 10.2788/28476 |
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