Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces

Area of Science:

• Regional economics
• Spatial analysis
• Data science

Background:

• The EU Regional Competitiveness Index (RCI) framework lacks detailed NUTS3-level attractiveness indicators.
• Italian provinces (NUTS3) require granular data for effective policy design.

Purpose of the Study:

• To propose a dashboard of territorial attractiveness indicators at the NUTS3 level.
• To apply a Fuzzy C-Medoids Clustering model for partitioning Italian provinces.
• To analyze the impact of contiguity constraints on clustering results.

Main Methods:

• Development of a dashboard of territorial attractiveness indicators at NUTS3 level.
• Application of the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints.
• Comparative analysis of clustering results with and without contiguity constraints.

Main Results:

• Identification of elementary indicators for eleven composite competitiveness pillars.
• Deep analysis of the positioning of Italian provinces within identified clusters.
• Demonstration of the influence of contiguity constraints on territorial partitioning.

Conclusions:

• The proposed NUTS3-level attractiveness indicators and clustering provide a valuable information base for policy design.
• The findings support the Italian government's consideration of NUTS3-level policies.
• The analysis is crucial for the effective implementation of the National Recovery and Resilience Plan (NRRP).