• Autocorrelation model
• Cluster dynamics
• Geographical clusters
• Patent-based indicators
• Regional competitiveness
• Sectoral clusters
• Smart Specialisation

The thesis work done is part of the MIT-UNIPI project, which aims to seek the scientific and technological paths describing Regions’ development in order to provide policy makers useful indications for a better allocation of resources. The thesis explored European clusters and their evolutionary dynamics, assessing the policy implications for the Smart Specialisation Strategies (SSS), which aimed to consolidate the regional strengths and make effective and efficient use of public investment in R&D. Three topics are investigated: 1) the possible relevance of the geographical proximity to the innovative activity; 2) major current clusters of sector (e.g. Mechanical Engineering, Chemistry); 3) space-time evolution of the clusters. The work required a review of the literature on the topic of regional clusters, as well as econometric analysis and spatial autocorrelation analysis, performed with patent data, socio-economic (GDP per capita) and spatial (Longitude and Latitude) data, relative to 203 EU Regions. The main results show the importance of geographical proximity to innovation activity, highlighting the technological diversification through complementary sectors as a fundamental driver to promote economic growth within the EU Regions.