In Italy, the National Recovery and Resilience Plan (PNRR), launched in 2021, was designed to address the socio-economic consequences of the COVID-19 crisis and to foster sustainable growth through targeted investments. To evaluate the territorial effects of these interventions, it is essential to establish a detailed baseline of municipal conditions at the beginning of the plan’s implementation. This study develops a data-driven classification of Italian municipalities, excluding regional and provincial capitals, based on Principal Component Analysis (PCA) and hierarchical clustering. Using 25 socio-economic, demographic, infrastructural, and environmental indicators drawn from ISTAT and other official sources, we identify eight municipal profiles grouped into three macro-categories: Metropolitan Areas, Productive Areas, and Fragile Peripheral Areas. The classification is conceived as a baseline territorial taxonomy based on conditions observed in 2022, after the main pandemic shock and before the territorial effects of PNRR implementation became fully visible. Comparative validation against the National Strategy for Inner Areas (SNAI), Local Labour Systems (SLL), and Italy’s geographical macro-divisions confirms the robustness and interpretive consistency of the proposed typology. The results highlight not only persistent territorial divides, but also substantial intra-regional heterogeneity that is not captured by broader institutional classifications. Beyond its analytical contribution, the study is supported by an interactive online tool that enables users to explore the classification and municipal profiles in an accessible way. The proposed framework offers a replicable and multidimensional basis for monitoring territorial disparities and for supporting future ex post evaluations of the long-term effects of the PNRR and other cohesion policies.

Mapping Socio-Economic Disparities in Italy: A Statistical Classification of Municipalities for Regional Development

Pavone, Pasquale
2026-01-01

Abstract

In Italy, the National Recovery and Resilience Plan (PNRR), launched in 2021, was designed to address the socio-economic consequences of the COVID-19 crisis and to foster sustainable growth through targeted investments. To evaluate the territorial effects of these interventions, it is essential to establish a detailed baseline of municipal conditions at the beginning of the plan’s implementation. This study develops a data-driven classification of Italian municipalities, excluding regional and provincial capitals, based on Principal Component Analysis (PCA) and hierarchical clustering. Using 25 socio-economic, demographic, infrastructural, and environmental indicators drawn from ISTAT and other official sources, we identify eight municipal profiles grouped into three macro-categories: Metropolitan Areas, Productive Areas, and Fragile Peripheral Areas. The classification is conceived as a baseline territorial taxonomy based on conditions observed in 2022, after the main pandemic shock and before the territorial effects of PNRR implementation became fully visible. Comparative validation against the National Strategy for Inner Areas (SNAI), Local Labour Systems (SLL), and Italy’s geographical macro-divisions confirms the robustness and interpretive consistency of the proposed typology. The results highlight not only persistent territorial divides, but also substantial intra-regional heterogeneity that is not captured by broader institutional classifications. Beyond its analytical contribution, the study is supported by an interactive online tool that enables users to explore the classification and municipal profiles in an accessible way. The proposed framework offers a replicable and multidimensional basis for monitoring territorial disparities and for supporting future ex post evaluations of the long-term effects of the PNRR and other cohesion policies.
2026
Data-driven classification, Socio-economic disparities, Municipal typology, Territorial inequalities, Regional and local development, Place-based policy
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12607/77865
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