Detecting multidimensional clustering across EU regions

This paper applies multidimensional clustering of EU-regions to identify similar specialization strategies. Different techniques are applied to an original dataset, created by the research team, where EU-28 regions are classified according to their socioeconomic features and to the strategic features of their research and innovation smart specializations strategy (RIS3). In the first classification, each region is associated to one categorical variable (with 19 modalities). In the classification of RIS3, two clustering of “descriptions” and “codes” of RIS3 priorities were considered (respectively made of 23 and 21 Boolean categories). The configuration of data is discussed in the paper. Two techniques of clustering have been applied: Correspondence Analysis and Infomap multilayer algorithm. The most effective clustering, in terms of both the characteristics of the data and the emerging results, is the one obtained with a Correspondence Analysis. On the contrary, given the very dense network does not produce significant results when Infomap is applied. A classification of regions encompassing the three dimensions under analysis is of particular interest in the current debate on post 2020 European Cohesion Policy, aiming at orienting public policies on the reduction of regional disparities.