Dr. Rudy Fernandez-Escobedo (p)

Digital transformation is increasingly reshaping regional economic performance, yet its effects remain spatially uneven and strongly conditioned by regional industrial structures. While firm-level studies often document productivity gains associated with technology adoption, less is known about how these processes translate into regional returns to labour. This paper examines how industrial clusters, digital transformation, and regional competitiveness interact to shape labour returns from a spatial perspective.
The analysis adopts a quantitative, descriptive, and exploratory approach based on a cross-sectional design. First, a cluster-mapping exercise is conducted using establishment-level data to identify the spatial distribution and intensity of industrial clusters across regions. This mapping reveals pronounced territorial heterogeneity in cluster presence and specialisation, indicating that agglomeration advantages are highly uneven across space.
Second, the paper combines cluster indicators with regional measures of digital transformation, competitiveness, and innovation to explore their relationship with returns to labour. The empirical analysis uses regional and NUTS-3 level data for Spain, combining establishment-based information on industrial activity with regional indicators of digital transformation, competitiveness, and labour outcomes. A multivariate modelling approach is employed to assess how digital adoption and regional competitiveness operate as complementary mechanisms linking industrial clustering to labour outcomes. The modelling strategy is intended to structure observed spatial relationships rather than to establish causal identification.
The findings suggest that industrial clusters are positively associated with regional returns to labour, but only where digital transformation and competitive conditions jointly enable the translation of agglomeration advantages into labour-market outcomes. Clustered regions lacking these complementary capacities exhibit substantially weaker returns to labour, despite comparable levels of industrial concentration.
The paper contributes to regional science by providing spatially explicit evidence on how industrial structure, digital transformation, and competitiveness jointly shape regional labour outcomes. From a policy perspective, the findings underscore the importance of place-based digital and industrial strategies that move beyond cluster promotion alone and address the complementary regional capacities required to generate inclusive economic returns.

Prof. Wen-Chung Guo (p)

This study investigates how personalized pricing and privacy protection jointly influence spatial competition, welfare distribution, and market structure in data-driven oligopolistic markets. As digital technologies have reduced the cost of collecting and tracking consumer data, firms are increasingly able to tailor prices based on consumer characteristics or purchasing histories. At the same time, privacy regulations such as the EU’s GDPR have begun to restrict the scope of data use, making the interaction between personalized pricing and privacy protection a central issue in both policy and industry practice.

We develop a spatial competition model featuring one firm with a data or brand advantage and multiple disadvantaged rivals. Only a subset of consumers are subject to personalized pricing, while others face uniform prices. This framework allows us to examine how the relaxation of privacy protection affects spatial competition equilibria, incentives for personalized pricing, social welfare, and the possibility of excess entry. The analysis is expected to reveal nonlinear competitive effects of privacy relaxation. The model will further be extended to allow disadvantaged firms to adopt personalized pricing, to endogenize the degree of personalization, to incorporate consumer privacy choices, network externalities, and elastic demand. The anticipated findings will contribute to the literatures on personalized pricing and spatial competition, highlight the implications of personalized pricing for consumer surplus, industry structure, and social welfare, and provide theoretical and policy insights for data-economy governance.

Dr. Patricia Ikouta Mazza (p), Prof Maria Mavri, Mr Dimitris Papandreou

Technological innovation constitutes a central driver of economic growth, productivity, and structural transformation in contemporary economies. In the context of the global digital transition, regions are confronted with both new development opportunities and intensified risks of spatial inequality. In Greece, digital transformation has accelerated in recent years, supported by national recovery strategies and European Union initiatives. However, its uneven regional diffusion has contributed to widening economic disparities, reflected in productivity levels, employment structures, and income distribution.
Despite notable policy efforts, Greece continues to lag behind the EU average in key digital performance indicators. Only 52.4% of the population possesses at least basic digital skills (EU average: 55.6%), while ICT specialists represent just 2.4% of total employment, compared to 4.8% at the EU level. In 2023, only 43.3% of Greek SMEs achieved basic digital intensity (EU average: 57.7%), and the adoption of advanced technologies such as artificial intelligence and cloud computing remains limited (Digital Decade 2024 Report). These shortcomings are geographically uneven and disproportionately affect rural and less densely populated regions, where infrastructure deficits and lower levels of human capital constrain participation in the digital economy.
Urban centers, particularly, the metropolitan Attica and the region of Thessaloniki, attract the majority of digital investment, innovation activity, skilled labour, reinforcing agglomeration economies and higher productivity growth (Digital Transformation in Greece 2024-2025). Conversely, peripheral regions characterized by weaker digital infrastructure, limited SME digitalisation, and structural economic vulnerabilities struggle to integrate into emerging digital value chains. As a result, technological transition risks reinforcing a dual spatial structure: dynamic metropolitan cores and lagging rural peripheries.
The study adopts a mixed analytical approach combining EU Digital Decade indicators, national digital performance reports, and regional economic data (GDP per capita, employment composition, sectoral structure) to examine the spatial dimensions of digital inequality in Greece. Comparative regional analysis highlights how variations in digital readiness interact with pre-existing economic structures to shape divergent development trajectories.
By situating the Greek case within the broader global challenge of digital transformation, this paper contributes to debates on territorial resilience and place-based policy in a transition era. It argues that technological innovation, while growth-enhancing, may exacerbate spatial inequalities unless accompanied by targeted regional strategies that strengthen digital infrastructure, expand digital skills, support SME digital upgrading, and foster inclusive innovation ecosystems.

Prof. Olga Demidova (p), Svetlana Kazakova

The identification of factors influencing inclusive economic growth is gaining prominence. This concept implies that the benefits of economic development are distributed more evenly among all segments of the population, reducing inequality and poverty. Information and communication technologies (ICT) are among such factors and are currently among the most rapidly developing ones. There are few studies devoted to identifying factors influencing the inclusive development of Russian regions (Barinova, Zemtsov, 2019; Mikheeva, 2022), and these studies do not focus on ICT.
The aim of this study is to determine the impact of ICT on the inclusiveness of Russian regions. We used data from 2014 to 2023 for 80 Russian regions. The inclusiveness indicator for Russian regions was calculated based on the following metrics: median household income, the Gini index, and the poverty rate.
The model incorporated variables representing the development of ICT in the region:
Computer — the share of households with a personal computer;
Internet — the share of households with internet access;
BroadInternet — the share of households with broadband internet access;
PopulInternet — the share of the population using the internet;
EverydayInternet — the share of the population using the internet daily or almost daily.
The models also included control variables characterizing the socio-economic status of the regions.
According to the results of global panel data models with fixed effects, the more developed a region's ICT is, the higher its level of inclusiveness; however, the proportion of households with a personal computer does not affect the level of inclusiveness. Because the influence of factors affecting inclusiveness may vary across regions, we also estimated a geographically weighted panel data regression.
The coefficients for all variables characterizing ICT, except for the Computer variable, were significant and positive. However, the estimates of local coefficients varied; thus, ICT increases the degree of inclusiveness of Russian regions, but not equally.
At the same time, the proportion of households with a personal computer can have both a positive and negative impact on the degree of inclusiveness. This issue requires further research. Perhaps personal computers are concentrated in the hands of the wealthy and educated, while socially vulnerable groups (rural populations, people with disabilities, the elderly) remain digital outsiders. In this case, despite a formally "high share," actual inclusivity does not increase and may even decline due to the widening gap between groups.