Abstract
Starting in 2007–2008, an economic crisis with no comparable precedent after WWII has affected most of the World, and Europe in particular. Yet, despite the pervasiveness of the crisis, its impact was highly differentiated across countries. The macroeconomic country-level effects are very important, but also within countries the impact on the various regions has been far from uniform, with some regions, often the most urban, able to resist the crisis better than others. Among the many factors which can have influenced the differential impact of the crisis in Europe, this paper looks at the regional endowment of structural territorial assets, those which have been labelled as “territorial capital”. Territorial capital comprehends all those assets, being material or immaterial, public or private, which represent the development potential of places. Territorial capital enhances regional growth in ordinary times, and, being structural, can be expected to also act as a factor of resilience in times of crisis. To investigate this hypothesis, a database of territorial capital indicators for all regions of the European Union at NUTS3 level is exploited, and a classification of regions based on the endowment of territorial capital is built. It appears that regions belonging to different groups, i.e. being differently endowed with territorial capital, have had different degrees of resilience, with some being able to maintain their income levels better than their country and others losing ground. The structure of regions is hence an important determinant of how they can afford periods of distress, and in particular, more resilient have been those regions endowed with less mobile territorial capital assets and with those territorial capital assets of mixed levels of materiality and rivalry.
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Notes
For a review of the papers analysing the association between territorial capital and regional development, see Tóth (2015).
This is valid for all countries except Germany. In the latter case most of the data are not available at this level of territorial disaggregation. For this reason, in the case of Germany we consider data at the NUTS2 level, because the average size of German NUTS3 regions is smaller than the area of NUTS3 regions in another EU countries. Other scholars have made the same choice in previous works on EU NUTS3 regions (e.g. Paci 1997).
Seven components of territorial capital out of nine have been empirically measured. Relational capital and cooperation networks are missing, since a satisfactory empirical measure is not available for all regions of Europe. Summary statistics of all empirical measurements are presented in Appendix A of Electronic Supplementary Material.
The clustering analysis was carried through model-based clustering and, more specifically, the (R Development Core Team 2008) library mclust (Fraley and Raftery 1999), in order to test several parameterizations with a variety of distributions (spherical, diagonal and ellipsoidal) and variable shape and volume. The drawback of the most popular clustering techniques is that they are not able to identify clusters with varying shape and volume, as in the case of k-means clustering, which leads to the identification of spherical clusters of identical volume and shape. Model-based clustering selects the “best” model by fitting models with differing parameterizations and/or numbers of components to the data by maximum likelihood and then applying the Bayesian Information Criterion for model selection. This approach selected diagonal clusters with varying volume and shape.
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