The persistent nature of socioeconomic inequalities across the European Union, despite the institutional and non-institutional efforts given so far for their reduction, motivates this paper investigating the structural characteristics and spatial dynamics of regional inequality across 209 NUTS 2 regions of the European Union, utilizing the most recent harmonized Eurostat data (2023, 2024). A comprehensive four-stage analytical framework is employed, integrating statistical inequality metrics (Gini coefficient and Theil’s index), correlation analysis, multivariate techniques (PCA and k-Means clustering), and Exploratory Spatial Data Analysis (Global Moran’s I and LISA). The results reveal that while social and demographic indicators exhibit strong convergence, economic output and R&D intensity remain highly polarized. The clustering analysis identifies a multi-speed Europe, distinguishing a “Knowledge-Driven Core” in Northern and Western Europe from a vast “Labor-Intensive” cluster in transition economies that relies on manufacturing efficiency rather than endogenous innovation. Furthermore, spatial analysis confirms the existence of significant Spatial Poverty Traps in the Southern and Eastern periphery, while highlighting the phenomenon of capital cities that fail to diffuse prosperity to their hinterlands. The findings suggest that bridging the remaining gap requires a shift in regional policy, prioritizing the diffusion of technological capacity over simple income redistribution.
The outbreak of the COVID-19 pandemic caused major disruptions in the education environment, leading to a shift from traditional classroom learning to online education, and accelerating the ongoing digital revolution. The expansion of digital infrastructure prompted by the pandemic has the potential to facilitate long-term systemic improvements within educational institutions. Building on this perspective, our research seeks to evaluate the transformative role of digital tools in Romanian education. Additionally, accounting for large regional disparities in economic development, as well as in education, the influence of digitalization was analysed through a spatial lens. The use of specific spatial econometric methods allows for capturing spatial interactions between regions, enhancing our understanding of these mechanisms. The findings indicate that while digital resources may not inherently improve academic performance under conventional circumstances, they remain indispensable for ensuring educational continuity during disruptive periods, such as global pandemics or extreme environmental events. The insights derived from the analysis offer valuable guidance for policymakers in designing frameworks that enhance the resilience and efficiency of digital integration in schools.
The book "Culture Based Development: Modelling Cultural Bias in Economic Choice" by Annie Tubadji is a significant interdisciplinary contribution to the subject of culture and its role in economic behavior and development. The book combines economics with wisdom from other social sciences and linguistics to demonstrate that cultural bias is a measurable and lasting factor in economic choice and decision-making at individual and group levels. Synthesizing the fruit of precise and original economic research spanning over 15 years and over 50 academic publications, the book is a major advancement and contribution to the mainstream economics view that development cannot be understood without integrating culture into economic analysis.
The study undertakes a comparative analysis of the 2021–2030 development strategies of Harghita, Covasna, and Mureș counties, focusing on the structure, objectives, implementation, and monitoring mechanisms of the strategic documents. The research is based on a qualitative document analysis, with particular attention to the integration of sustainable development principles into county-level planning. The findings highlight the distinct social and economic characteristics and development priorities of the three counties, which reflect their adaptation to both local specificities and the European Union’s policy frameworks. Harghita County’s decentralized and community-based approach, Covasna County’s exemplary monitoring system, and Mureș County’s innovation- and project-oriented strategy collectively illustrate the region’s diversity. Among the common development needs, the digital transition, infrastructure improvements, human capital retention, and the strengthening of territorial cohesion are particularly emphasized. The study underscores the importance of strategic flexibility, indicator-based monitoring, and institutional cooperation, while also providing recommendations for enhancing regional planning practices. The research contributes to a deeper understanding and advancement of sustainable and complementary regional development approaches.
Artificial intelligence and robotization are generating major occupational transformations in labor markets, affecting both the organization of work and individuals’ perceptions regarding displacement risks. While existing research has primarily focused on the objective effects of digital technologies on employment, productivity, and occupational restructuring, the perceptional dimension of job displacement remains relatively underexplored, particularly at regional level. By combining and objective and subjective approach, this study investigates the determinants of perceived occupational displacement associated with robotization and AI across 228 NUTS 2 regions in the European Union. The analysis integrates perception indicators collected from Eurobarometer surveys (101.3 and 101.4) with regional macroeconomic indicators derived from Eurostat database. The methodological framework combines exploratory spatial data analysis with classical and spatial local econometric techniques. Moran’s I statistic suggests significant spatial autocorrelation in the perceptions of job displacement, indicating the existence of regional clusters. clustering processes. The Multiscale Geographically Weighted Regression (MGWR) model outperforms the classical OLS regression and reveals the spatial variability of the relationships between explanatory variables and perceived job displacement. The results indicate that unemployment rates, tertiary education levels, positive perceptions of digital skills and perceptions regarding increased work rhythm generated by AI and robotization are positively associated with fears of job displacement. In contrast, employment in knowledge intensive and high technology services, together with positive perceptions regarding the use of AI and robots in the workplace reduce the perceived occupational displacement risk. However, these factors do not have a uniform effect at regional level, which is highlighted by the MGWR local regression models. By revealing geographically differentiated patterns, this study emphasizes the need for territorially driven policies aimed at supporting workforce adaptation to emerging digital technologies.
