The aim of our paper is to analyze the structural diversity of the European regions assuming the complexity of production spaces approach (Hidalgo C.A., B. Klinger, A.L. Barabási, R. Hausmann 2007). This stream of economic literature is the natural companion of the evolutionary theory of economics, where development is seen as the endogenous learning process led by the initial knowledge basis, which tends to expand in its proximity (Boschma 2005). The first step of our analysis is to map the EU regions according to their economic structure. We exploit information conveyed by Eurostat data, which are available for a balanced panel of 241 regions and 86 economic branches in 2010 and 2015. In this way we are able to construct a space characterized by technological proximity of regions. The underlying assumption is that territories with similar production structures display similar production knowledge. The second step is the construction of the network space based on the correlation matrix. In order to obtain the clusters of regions based on the similarity of their economic structure, we apply a modularity algorithm to the network. Such measures define groups based on the degree of connectedness of the observations between them and allows to measure how such groups explain the network connections using as benchmark a case in which edges where assigned randomly. Our findings suggest that regions, which are more dynamic in terms of structural change, are those with manufacturing capabilities located in Eastern European countries. Such regions were able to upgrade their competences towards more complex productions and this resulted also in a fast catch-up of their GDP per capita level with respect to other mid income regions in Western Europe. Most prosperous regions are found to be urban areas with developed creative service activities and in regions with advanced manufactures (machinery, automotive, electronics, etc.); whereas backwardness is detected in regions with a cumbersome weight of tourism related activities.

Structural change and convergence across European regions

Corò Giancarlo
2018-01-01

Abstract

The aim of our paper is to analyze the structural diversity of the European regions assuming the complexity of production spaces approach (Hidalgo C.A., B. Klinger, A.L. Barabási, R. Hausmann 2007). This stream of economic literature is the natural companion of the evolutionary theory of economics, where development is seen as the endogenous learning process led by the initial knowledge basis, which tends to expand in its proximity (Boschma 2005). The first step of our analysis is to map the EU regions according to their economic structure. We exploit information conveyed by Eurostat data, which are available for a balanced panel of 241 regions and 86 economic branches in 2010 and 2015. In this way we are able to construct a space characterized by technological proximity of regions. The underlying assumption is that territories with similar production structures display similar production knowledge. The second step is the construction of the network space based on the correlation matrix. In order to obtain the clusters of regions based on the similarity of their economic structure, we apply a modularity algorithm to the network. Such measures define groups based on the degree of connectedness of the observations between them and allows to measure how such groups explain the network connections using as benchmark a case in which edges where assigned randomly. Our findings suggest that regions, which are more dynamic in terms of structural change, are those with manufacturing capabilities located in Eastern European countries. Such regions were able to upgrade their competences towards more complex productions and this resulted also in a fast catch-up of their GDP per capita level with respect to other mid income regions in Western Europe. Most prosperous regions are found to be urban areas with developed creative service activities and in regions with advanced manufactures (machinery, automotive, electronics, etc.); whereas backwardness is detected in regions with a cumbersome weight of tourism related activities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3704493
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