Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering

Angelo Leogrande, Carlo Drago, Giulio Mallardi, Alberto Costantiello and Nicola Magaletti

No nftv3_v1, SocArXiv from Center for Open Science

Abstract: This article focuses on the propensity to patent across Italian regions, considering data from ISTAT-BES between 2004 and 2019 to contribute to analyzing regional gaps and determinants of innovative performances. Results show how the North-South gap in innovative performance has persisted over time, confirming the relevance of research intensity, digital infrastructure, and cultural employment on patenting activity. These relations have been analyzed using the panel data econometric model. It allows singling out crucial positive drivers like R&D investment or strongly negative factors, such as limited mobility of graduates. More precisely, given the novelty of approaches applied in the used model, the following contributions are represented: first, the fine grain of regional differentiation, from which the sub-national innovation system will be observed. It also puts forward a set of actionable policy recommendations that would contribute to more substantial inclusive innovation, particularly emphasizing less-performing regions. By focusing on such dynamics, this study will indirectly address how regional characteristics and policies shape innovation and technological competitiveness in Italy. Therefore, it contributes to the debate on regional systems of innovation and their possible role in economic development in Europe since the economic, institutional, and technological conditions are differentiated between various areas in Italy.

Date: 2024-12-27
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Related works:
Working Paper: Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering (2024)
Working Paper: Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering (2024)
Working Paper: Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:nftv3_v1

DOI: 10.31219/osf.io/nftv3_v1

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