Abstract
This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group’s ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions.
Highlights
The current debate on post-2020 European Cohesion Policy confirms the need for public policies targeting the reduction of regional disparities and the enhancement of complementarities and synergies within macro-regions
We aim at interpreting the overall framework of interconnected structural socioeconomic and demographic features and policy programmes on smart specialisation strategy in the EU
By identifying clusters of EU regions, we provide policy makers with a more systematic and informed tool they can use to learn from other regions, when they focus on the projects implemented within the various priorities
Summary
The current debate on post-2020 European Cohesion Policy confirms the need for public policies targeting the reduction of regional disparities and the enhancement of complementarities and synergies within macro-regions Such interventions, supported by the European Structural & Investment Funds, are key instruments for the implementation of EU policies and programmes, aimed at fostering the cohesion and competitiveness across larger EU spaces, encompassing neighbouring member and non-member States (European Commission, 2016). In the programming period 2014-2020, the European Commission has adopted the Research and Innovation Smart Specialisation Strategy (RIS3) as an ex-ante conditionality for access of regions to European Regional Development Funds (ERDFs) Such policies are built on specific guidelines and on a very detailed process of implementation (European Commission 2012, 2017; Foray et al 2012; McCann and Ortega, 2015). It embraces a broad view of innovation that goes beyond research-oriented and technology-based activities, and requires a sound intervention strategy supported by effective monitoring mechanisms” (European Commission, 2017, p. 11)
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