This study presents a novel decomposition of the traditional tourism rate, defined as tourist arrivals per resident, into distinct components that reflect demographic intensity, pressure on tourism enterprises, demand for employment, the influence of crime, and the resilience of local business structures. The aim is to establish a multidimensional framework for analyzing tourism pressure in Italy.
The analysis applies an Index Decomposition Analysis framework to provincial-level data for Italy in 2023. Tourist arrivals, population, and police reports were obtained from ISTAT, while enterprise data were sourced from Movimprese and employment data from the Excelsior Information System. A two-level multiplicative decomposition separates the tourism rate into demand- and supply-side components, further refined by labour market, organisational, and social outcomes. A graphical analysis and a K-means clustering procedure identify homogeneous groups of provinces with similar tourism profiles.
Results show heterogeneity in tourism pressure across Italian provinces. Northern and metropolitan areas face high labour intensity and, in some cases, elevated crime rates, while alpine and North-Eastern provinces demonstrate strong enterprise resilience despite high tourist inflows. Three distinct clusters emerge: high-intensity metropolitan economies, balanced intermediate provinces, and resilient North-Eastern tourism systems.
This study is subject to several limitations. First, tourism pressure is measured using official tourist arrivals, which exclude non-commercial stays and informal accommodation, potentially underestimating actual flows. Second, labour market indicators rely on formally registered employment and do not capture undeclared or highly seasonal work. Third, the crime variable reflects reported offences rather than actual criminal activity and may be influenced by local reporting behavior. Finally, the analysis is conducted at the provincial level, which may mask intra-provincial heterogeneity. Despite these limits, the framework provides a transferable and policy-relevant tool for comparative and longitudinal analyses of tourism pressure.
The proposed decomposition provides policymakers and tourism managers with an operational tool to diagnose tourism pressure beyond simple arrival counts. By distinguishing demand pressure, labour intensity, enterprise density, crime exposure, and business resilience, the indicators support targeted, place-based interventions. Urban and high-intensity destinations can use the framework to manage carrying capacity. It also supports workforce planning and safety policies. Intermediate areas may apply it to reduce tourism dependency. They can do this through diversification and seasonality management. Resilient destinations can monitor sustainability risks over time. The indicators can also be adopted as monitoring KPIs. These KPIs guide strategic planning and evaluate tourism policies.
The study highlights how tourism pressure affects local communities through labour market strain, public safety, and the stability of local businesses. High-intensity destinations may experience increased social stress, seasonal employment insecurity, and perceived declines in safety, while other areas face risks of economic dependence on tourism. By integrating crime and employment indicators, the framework helps identify territories where tourism growth may undermine social cohesion or exacerbate inequalities. This evidence supports more socially balanced tourism policies, aimed at protecting residents' well-being, improving job quality, and ensuring that tourism development remains compatible with community needs and local quality of life.
This study contributes to tourism research by extending the conventional tourism rate through a two-level decomposition that integrates business, labour, and social dimensions. Unlike standard measures, the proposed framework uncovers the mechanisms through which tourism pressure is given and sustained. The approach offers a replicable tool for time and cross-territorial analysis.
