Mario Intini

1. In recent decades, regional inequality has re-emerged as an urgent consideration in economic research, propelled by the disparate consequences of globalization, fiscal austerity, and demographic...

Fuel price fluctuations affect not only the energy sector but also the entire macroeconomic system. Over the past decade, the Italian fuel sector has faced unprecedented events, most notably the Covid-19 pandemic, the Russia–Ukraine war, and the introduction of the regional average price policy, which have altered both demand conditions and competitive mechanisms. These shocks have generated heterogeneous effects across Italian provinces, reflecting underlying differences in mobility patterns and market structure. Using high-frequency data covering 2016–2024, this work provides new evidence on how these shocks have affected the Italian fuel market. Combining structural break tests with a Local Projection framework and a VAR-X counterfactual approach, we show that the three events generated distinct and time-varying adjustments in gasoline and diesel prices. The findings highlight that the evolution of fuel prices in Italy depends less on general market trends than on the way shocks interact with local market conditions and institutional frameworks. Fully assessing the impact of future regulatory or geopolitical events requires accounting for these structural discontinuities and for the spatial heterogeneity that characterizes the Italian fuel market.

This study investigates the relationship between EU Cohesion Policy-funded tourism investments and the attractiveness of Italian municipalities. Utilizing data from OpenCoesione, the research classifies tourism projects to differentiate between various investment types and estimates their association with alternative tourism outcomes through spatial panel models. The findings reveal that Cohesion Policy funds are generally linked to higher tourism attractiveness, although the relationship varies depending on the type of investment. Investments for businesses demonstrate synergies with investments for transportation, while all categories show non-linear dynamics, with diminishing marginal returns at higher expenditure levels. These results underscore the intricate nature of tourism funding, providing valuable insights for policymakers aiming to optimize investment strategies.

Statistical modeling often faces the problem of missing information. This could be due either to data privacy reasons or to the lack of data detection in a specific territorial domain. In this paper, we show a methodology to downscale estimators from larger to smaller territorial domains. Our method is embedded in a small area estimation context, in particular at area-level scale. The proposed method allows us to exploit information from the direct estimators measured in the larger territorial domains that are planned by the sampling design combined with some auxiliary variables at a finer unplanned territorial level. The resulting estimators are a convex linear combination on a different scale of the direct estimators and the synthetic parts based on a statistical model. As an illustration, we use this methodology to highlight differences in the school performances of Italian students. We downscale validated estimators from the planned regional to the unplanned provincial territorial level with the scope to provide more reliable estimators in such a spatial domain than the existing unreliable data. We use data from the Italian National Institute for the Evaluation of the School System (INVALSI), which releases validated sample data at the regional spatial scale, and not validated census data at the provincial level. We use the latter as auxiliary information to predict the provincial scores.

The digital economy can function either as a catalyst to stimulate economic growth or else as a driver of socioeconomic inequality when its benefits are unevenly distributed. This study investigates the effect of rural digital connectivity on income inequality in Italy. Utilizing NUTS 3 panel data spanning 2014–2022, we conduct a counterfactual Difference-in-Differences approach with continuous treatment intensity to estimate the impact of introducing rural broadband coverage at speeds of 30 and 100 Mbps on multiple measures of income distribution, including the Gini, Theil, and Atkinson indices. The empirical framework incorporates a comprehensive set of socioeconomic controls, as well as provincial and time fixed effects, to account for unobserved heterogeneity and regional path dependencies. Our findings indicate that broadband expansion is significantly associated with increasing inequality, suggesting that access alone does not guarantee inclusive outcomes, particularly in localities characterized by structural fragility and limited human capital. Additional heterogeneity and spatial analyses demonstrate that these inequality effects are more evident in southern provinces and localities with a higher concentration of inner areas, where the digital divide remains more pronounced. These findings accentuate the dual role of digitalization and highlight the necessity of coordinated policy interventions that combine infrastructure investment with digital skills development, institutional capacity-building, and spatially integrated governance strategies.