Silvia Mattiozzi (p), Barbara Ermini, Raffaella Santolini

Valentina Diana Rusu

The NextGenerationEU plan emphasizes digital transformation as a key objective, recognizing the essential role of digital technologies in modernizing public administration. Through targeted initiatives and investments, the plan aims to enhance the efficiency, transparency, and accessibility of public services. Core actions include developing digital infrastructures, creating interoperable platforms, and promoting data-driven governance. These measures aim to streamline bureaucratic processes, improve service delivery, and create a responsive, citizen-focused administration, ultimately strengthening institutional effectiveness and economic resilience.

Within the framework of the Italian local government, this study examines the presence of spatial interactions in the participation of municipalities in projects designed to promote digitalization, financed through the National Recovery and Resilience Plan (NRRP). To analyze spatial interaction patterns among approximately 6,500 municipalities, we utilized the data on projects from the National Recovery and Resilience Plan (NRRP) falling under Mission 1, for which municipalities serve as implementing entities.

As it is well known, spatial patterns can be analyzed using various spatial econometric models. In particular, we compare several widely used spatial econometric models, including the Spatial Lag of X (SLX), Spatial Durbin Model (SDM), and Spatial Durbin Error Model (SDEM). These models have been selected for their effectiveness in capturing a broad range of spatial dependencies (LeSage, 2015). To determine the optimal spatial model for analyzing relationships among municipalities, we utilize multiple spatial weight matrices, including first- and second-order binary contiguity matrices and geographical distance matrices, as well as matrices incorporating up to 16 nearest neighbours. Model comparison employs a Bayesian approach (LeSage,2015; Yesilyurt and Elhorst, 2017).

Further investigation focuses on the determinants of spatial interactions, particularly the influence of municipal administrative quality and the technological and human capital endowments of territories. We assess administrative capacity using the Municipal Administration Quality Index (Cerqua et al., 2025), which includes bureaucratic efficiency, local politicians' quality, and economic performance. Proxies for technological background include specialization in high-tech sectors and broadband access, while human capital is measured by the proportion of the population with a high level of education.

Preliminary results from the selected Spatial Durbin Model (SDM) show a statistically significant effect of neighbouring municipalities on NRRP municipal performance. In investigating the factors driving digital transformation across Italian municipalities due to NRRP initiatives, our main finding suggests that municipalities with stronger administrative capacities do not align their performance with that of their neighbours, likely due to their more efficient political and bureaucratic structures.

Valentina Diana Rusu (p), Angela Roman

Wen-Chung Guo

Due to its substantial contribution to the creation of new jobs, added value, and innovations, the small and medium-sized business (SME) sector is essential to a nation's economic and social development. However, small and medium-sized businesses encounter many barriers to financing when compared to larger companies. The global business landscape has changed dramatically in recent years due to advancements made possible by digital technologies. They also provided SMEs with new chances to enter new markets, strengthen their resilience, and remove barriers to funding. Therefore, we investigate the degree to which SMEs' access to financing would be enhanced by the use of digital technology. Furthermore, we seek to determine whether the relationship between a firm's access to financing and the use of digital technology varies depending on the size of the firm. Our investigation, which is based on statistical data for 2009–2023, applies panel data estimation techniques to a selection of European nations. Two indicators that gauge SMEs' financial accessibility serve as the dependent variable in the econometric model, while several indicators pertaining to the use of digital technologies serve as the independent variables. Overall, our findings demonstrate that by reducing financial barriers, digital technology use can have a major impact on SMEs' access to funding. Policymakers who are interested in implementing certain policies to promote SMEs' use of digital technologies to reduce barriers to obtaining outside funding sources could find our results useful.

Wen-Chung Guo (p)

Juan R. Cuadrado-Roura

This study investigates the transformative impact of Artificial Intelligence (AI) on market competition and social welfare through a spatial oligopoly framework. AI technologies, including algorithmic pricing, machine learning, and data analytics, have revolutionized market structures, fostering efficiency but also raising concerns over inequality and monopolization. The research highlights the digital divide between large firms with advanced AI capabilities and smaller firms constrained by limited resources, analyzing how this disparity affects market competition.

Using a modified spatial oligopoly model, the study examines AI's role in cost reduction, quality enhancement, and strategic pricing, particularly for dominant firms. The analysis identifies significant shifts in equilibrium market structures, revealing how AI alters firm entry behavior, consumer surplus, and overall social welfare. Key findings demonstrate that while AI enhances efficiency, it may also exacerbate market concentration and competitive inequalities. Large firms gain competitive advantages, leveraging AI to improve product quality and reduce costs, while smaller firms face higher barriers to entry, intensifying digital disparities.

The study also explores regulatory and policy implications, advocating for measures such as the EU’s Digital Markets Act to address challenges posed by AI-driven market dynamics. It integrates theoretical insights with empirical observations to propose strategies promoting fair competition and inclusive growth, ensuring that AI's benefits are equitably distributed. By addressing issues like algorithmic pricing, tacit collusion, and data privacy, the research contributes to the broader discourse on AI's role in economic transitions.

Through equilibrium analysis, the study evaluates the trade-offs between competitive intensity and market efficiency, identifying scenarios of excessive and insufficient market entry under varying levels of AI adoption. Policy recommendations emphasize balancing innovation with consumer protection, fostering an environment that mitigates digital disparities while maximizing social welfare. The findings provide actionable insights for regulators and stakeholders navigating the evolving competitive landscape shaped by AI technologies.

Juan R. Cuadrado-Roura (p)

Silvia Mattiozzi

As highlighted in the introduction, telework, as a complex phenomenon, can be studied from different perspectives. In this paper we will focus exclusively on its economic and territorial dimensions, exploring the differences that exist when comparing the numbers of teleworkers by country and their recent evolution. The sectoral structure of each economy is a major determinant of telework potential, both now and in the future. But the level of ICT development and the education level play also an important role, as well as the type of jobs. The factors that seem to determine the reduction of their growth expectations in the medium-long term are particularly considered.

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