Search Results (140)

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

We examine how institutional quality conditions the labor market returns to educational attainment, using individual-level microdata from the fourth round of the Life in Transition Survey across thirteen European Union member states. Linear probability models with country fixed effects show a tertiary employment premium of 12.4 percentage points. Contrary to our initial hypothesis, this premium is larger where institutional trust is lower: a one-point increase on the five-point trust index reduces the premium by 2.6 percentage points, consistent with formal credentials partly substituting for trust-based hiring mechanisms where perceived governance quality is weaker. Two-stage least squares estimates instrumenting tertiary attainment with parental education and childhood book ownership corroborate the baseline gradient, though we interpret them as supportive rather than definitive causal evidence. Tertiary-educated migrants face employment penalties of 4 to 12 percentage points relative to observably similar non-migrants, and 23.8 percent of employed respondents are over-qualified, with the highest incidence in the Baltic states. The findings suggest that educational expansion without parallel investment in credential recognition and institutional upgrading may generate systematic human capital misallocation. Full article

Mixed crop–livestock systems remain central to sustainable intensification because they reconnect feed, biomass, soil cover, livestock productivity, and household livelihoods. However, forage integration benefits are neither automatic nor uniformly transferable across regions. This structured critical integrative review asks when forage integration generates net system-level gains. It also asks when integration merely shifts costs among production, labor, water, nutrients, emissions, household risk, or territorial nutrient balances. The review synthesizes evidence on forage legumes, tropical and temperate grasses, dual-purpose crops, grazed cover crops, pasture rotations, silvopastoral arrangements, and beyond-farm feed–manure exchanges. Examples from the Brazilian Cerrado, western São Paulo, and Minas Gerais illustrate tropical pathways involving Urochloa/Brachiaria integration, crop–pasture rotations, crop–livestock–forest systems, and habitat-mediated pest regulation. These examples are interpreted alongside evidence from African, Asian, European, and temperate systems to maintain regional balance. The review contributes a diagnostic framework for assessing system design, evidence strength, and scaling feasibility. The framework links forage portfolios to integration niches, management quality, resource constraints, gendered labor implications, external-input dependency, and adaptive scaling pathways. Forage integration can improve feed quality, animal performance, soil cover, nutrient cycling, biodiversity functions, and emission intensity. These gains require careful management of establishment, grazing, manure distribution, phosphorus and potassium balances, water demand, labor allocation, markets, and governance. Future research should move beyond short-term demonstrations toward causal inference, longitudinal whole-system accounting, transparent evidence-quality grading, and documentation of failed, partial, or discontinued interventions. Full article

Digital technologies are reshaping economies, labor markets, social security systems, and societal well-being across the European Union, increasing the need for decision-support methods capable of systematically analyzing complex survey evidence. This study proposes B-MIDIA, a parametric extension of the belief-structure TOPSIS framework that integrates the MIDIA (Multi-Criteria Method Integrating Distances to Ideal and Anti-Ideal Points) aggregation mechanism with belief-structure-based multi-criteria decision analysis. The framework preserves the ordinal structure of survey responses and follows the standard belief-structure representation adopted in B-TOPSIS, in which uncertain (“Don’t know”) responses are incorporated into the evaluation through the normalization procedure. Using published country-level response distributions from Special Eurobarometer 554, the study evaluates public perceptions of recent digital technologies, including artificial intelligence (AI), across EU Member States in five domains: the economy, society, quality of life, current job, and social security benefits. Sensitivity analyses performed for alternative values of the aggregation parameter $\alpha$, together with comparisons with the benchmark B-TOPSIS rankings, demonstrate the robustness of the proposed aggregation mechanism within the adopted belief-structure framework. Across most dimensions, Lithuania and Malta achieved the highest evaluations, whereas France, Italy, and Romania generally ranked among the lowest. Economic and employment-related evaluations formed the central perception structure and were strongly associated with quality-of-life assessments. The proposed framework extends the analytical capabilities of belief-structure TOPSIS by enabling systematic sensitivity analysis of aggregation assumptions while preserving methodological compatibility with the original framework. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

Generative artificial intelligence is gradually infiltrating contemporary socio-technical systems, affecting work practices, labor market participation, and the dynamics of labor-related costs. In this paper, we analyze cross-sectional relationships between generative AI use among active individuals and two macro-level labor-market indicators: the labor force participation rate and the evolution of nominal unit labor costs. The research is based on comparative European data. It analyses the use of AI in personal, professional, educational, and recent contexts as an expression of the interaction of human agency, technological infrastructure, and the institutional environment. The methodology combines multivariate modelling and regression with exploratory factor analysis and clustering to examine the statistical associations between AI use and labour-market indicators, as well as the emergent typologies of countries with diverse profiles. Results indicate stable links between the extent of generative AI use and employment, and unit labour cost disparities, suggesting that generative AI use is associated with broader socio-economic development patterns, without implying a causal relationship. The results highlight the need for an integrative view of AI adoption, linking human behaviors to organizational procedures and governance structures to ensure a responsible and fair transition. Full article

After EU accession, Croatia has leveraged the advantages of EU membership, such as access to a large market and EU funds, to accelerate economic growth and reduce the development gap in comparison to advanced EU economies. Although EU membership has stimulated economic growth, it has also brought negative effects, such as labor emigration to more developed EU economies with higher wages and increased inflation due to price convergence and the adoption of the Euro. The weak growth of labor productivity in Croatia is a consequence of the slow transformation towards technology-intensive industries, the dominance of traditional labor-intensive sectors such as construction and hospitality, and the rapid growth of employment in the public sector. The novelty of the research lies in applying an input–output model to estimate direct and indirect labor requirements in Croatia, an example of a small, service-oriented economy that, after joining the EU, witnessed a significant increase in final demand. Research is based on the Eurostat FIGARO database. The increase in gross value added across industries during 2015–2024 is separated into price and real growth effects. Analysis indicates that the current Croatian growth model is unsustainable because of high labor requirements and slow productivity growth. Results imply that European Union membership brings many advantages, but if not coupled with an adequate industrial development strategy, economic growth based exclusively on increasing final demand could reach its limits. Labor constraints and continued demand growth without substantial structural changes could result in rising wages and prices rather than real GDP growth. Full article

This article uses Jacob Gorender’s theory of colonial slavery to challenge a core premise of modern social theory—that labor inherently generates freedom. In Hegel and Marx, productive activity enables recognition, consciousness, and ultimately emancipation. The article argues that this mechanism depended on a historically specific condition: the worker’s juridical possession of labor-power. Plantation slavery abolished that condition. The enslaved laborer did not sell labor but was owned as labor, making alienation, recognition, and class formation structurally impossible. Building on Gorender’s claim that colonial slavery constituted a distinct mode of production integrated into global markets, this article shows that plantation economies produced accumulation without proletarianization, commercialization without citizenship, and economic modernization without social emancipation. Drawing on institutional theory, it further argues that the legal and political arrangements created to manage coerced labor persisted after abolition and continue to structure inequality and coercive governance across the Americas. American modernity therefore followed a trajectory different from the European one: rather than emerging from the emancipation of labor, it developed from its permanent subordination, indicating the existence of multiple modernities. Full article

Digital infrastructure expansion in Europe raises a spatial planning problem: early-stage screening needs to compare regional conditions while also checking whether rankings remain stable when decision priorities change. This study evaluates 24 European Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) regions for geo-distributed data center development. The 2022 decision matrix uses five Eurostat criteria: information and communications technology (ICT) specialists’ share in employment, average hourly labor cost, renewable electricity share, non-household electricity price and population density. Four criteria are national intensive proxies assigned to the selected NUTS-2 regions, while population density is directly observed at the NUTS-2 level. After a log₁₀ transformation of population density and min–max normalization, we compare the weighted sum model (WSM), TOPSIS and VIKOR across four weighting scenarios. We then apply a random-weighting audit based on Stochastic Multicriteria Acceptability Analysis (SMAA) principles, using 10,000 Dirichlet weight draws, followed by a local Dirichlet sensitivity analysis around the Balanced profile. Results show that the most stable high-performing profiles are not limited to the established FLAP-D market reference. Latvija (LV00), Stockholm (SE11), Helsinki-Uusimaa (FI1B), Eesti (EE00) and Área Metropolitana de Lisboa (PT17) form the main high-performing set across stochastic rank metrics, while several mature Western metropolitan regions remain more sensitive to cost and territorial-pressure criteria. The study provides a reproducible spatial decision support framework for macro-regional screening rather than micro-siting. Full article

This study examines the relationship between financial inclusion and economic development in the European Union by analyzing its macroeconomic determinants across 26 countries over the period of 2004–2023. Using a balanced panel dataset, the empirical analysis employs econometric techniques that account for heterogeneity, autocorrelation, and cross-sectional dependence, leading to the estimation of a Panel-Corrected Standard Errors (PCSE) model. Financial inclusion is proxied by the number of automated teller machines per 100,000 adults, while the explanatory variables include GDP per capita, personal remittances, inflation, years of schooling, unemployment, and foreign direct investment. The results show that GDP per capita, remittances, inflation, and unemployment have a positive and statistically significant effect on financial inclusion, whereas education and foreign direct investment exhibit a negative and significant relationship. These findings suggest that financial inclusion in the European Union is shaped by a complex interplay of economic development, labor market conditions, and external financial flows, rather than by structural factors alone. Notably, the results reveal counterintuitive relationships that challenge conventional assumptions about the roles of education and foreign investment in promoting financial access. This study contributes to the literature by providing updated panel evidence for advanced economies and by emphasizing the multidimensional nature of financial inclusion in a context of increasing digitalization and economic integration. The findings also offer relevant policy implications, suggesting that strategies to enhance financial inclusion should go beyond expanding financial infrastructure and instead focus on improving the effective use of financial services, strengthening financial capabilities, and reducing structural disparities across countries. Full article

Active labor market policies (ALMPs) targeting self-employment have become a well-established and relevant instrument within employment promotion strategies across many European countries. However, despite their strategic and economic importance, there is limited evidence on their potential performance prior to implementation. This paper aims to address this gap by conducting an ex-ante cost–benefit simulation of different types of ALMPs designed to promote self-employment in Spain. The methodology is based on estimating public costs per beneficiary and quantifiable potential benefits, including avoided welfare payments, additional tax revenues, and the generation of economic activity. These benefits are adjusted using two key parameters: additionality (the proportion of the effect genuinely attributable to the policy) and persistence (the duration of the impact over time). In addition, three sensitivity scenarios (conservative, baseline, and favorable) are developed. The results suggest that financing and access to credit policies exhibit the most robust returns, while direct subsidies, general tax incentives, and emergency policies are more sensitive to intervention design features. Consequently, the effectiveness of ALMPs targeting self-employment depends fundamentally on their ability to align with the specific frictions faced by potential entrepreneurs and on the persistence of their effects. Full article

Central European, especially the Hungarian and Polish experiences, reveal a profound paradox, anti-immigration policy narratives, yet immigration laws and policies support reliance on migrant workforce (non-EU migrants). The question arises: why is that? The aim of this research is to examine the ways in which Poland and Hungary have managed the challenges of labor migration in the region, arising from the demographic crisis and labor shortages in the region. The research will use a socio-legal approach in the analysis of the changes in the laws of the two countries, government strategies, statistics, and political discourse in the period from 2023 to 2025. The assessment of the two countries will reveal a contrast in the political narrative and the implementation of the laws. Hungary maintains a narrative of strict migration and quotas, while at the same time liberalizing economic migration. Poland, on the contrary, has adopted a liberal yet selective migration strategy in the new laws that incorporate digital administrative tools, integration, and a points system for economic migrants. The research will reveal that both countries have moved from being net emigration countries to being net immigration countries, despite the political narrative. The research will conclude that the migration policies of the two countries have been influenced by the need to address the structural labor shortages in the region and not political ideologies. Experiences in Central Europe, specifically those of Hungary and Poland, show a unique contradiction of having anti-immigration politics and legislation providing for easier access to the countries’ borders to non-EU workers to solve problems of labor shortages. This paper will discuss the approaches of these two countries to dealing with labor migration in light of declining populations and increased need for migrant workers. Comparative socio-legal research is conducted in the course of this project, where recent legislative amendments, policies, statistics, and political discourse in relation to labor migration are reviewed within the period from 2023 to 2025. The research shows that while maintaining its conservative and securitized narrative, Hungary makes some concessions for economic migration through specific legal channels. Meanwhile, Poland has managed to build up an open and selective approach by combining labor market demands with digitization and points-based policy making. The results suggest that both nations operate in an environment of net immigration despite their official rhetoric implying otherwise. In conclusion, policies towards labor migration in Central Europe remain economic in nature, which produces contradiction between politics and reality. Full article

This paper investigates the relationship between green vocational education and training (VET), structural economic features, and green employment in Central and Eastern European (CEE) economies. For the purpose of the research, an initial database covering the post-2010 period was assembled from Eurostat and related statistical sources. Due to data availability and cross-country comparability constraints, the final empirical analysis employs a balanced panel of six EU Member States covering the period 2018–2022. The empirical analysis employs pooled OLS and fixed-effects estimators over the period 2018–2022, following a stepwise modeling strategy to assess baseline relationships and robustness. The results show that VET enrollment alone is not a reliable predictor of green employment growth, while VET graduation rates exhibit a more consistent—yet not robust—association once country-specific heterogeneity is controlled for. By contrast, structural reliance on industrial sectors is consistently linked to lower green employment shares, while environmental tax revenues demonstrate modest positive effects. Overall, the findings suggest that green employment dynamics are driven primarily by structural and macroeconomic conditions rather than by skill formation alone. The study contributes to the literature on the green transition by providing an integrated perspective on the interaction between skills, structural transformation, and policy incentives in shaping sustainable labor market outcomes. Full article

The employment and labor market landscape has undergone significant transformations globally, including the three Central European countries examined in this study. Over the past decades, organizations in this region have transitioned from a state of full employment to labor shortages, raising the question: What factors have driven these changes? Our study aims to present a theoretical framework highlighting key macro-level factors, such as demographic trends, economic development, labor market dynamics, the impact of the COVID-19 pandemic, and the role of robotization and artificial intelligence. Based on two empirical studies conducted in 2019 and 2022 among Czech, Hungarian, and Slovak organizations, we analyzed the extent and causes of labor shortages, as well as the labor market effects of robotization. Using descriptive and non-parametric statistical methods, including frequency analysis and Mann–Whitney U tests, the study examined key trends and compared the two periods to identify significant shifts. The analytical approach of this study primarily aims to compare perceptions across occupational groups and between the two survey waves (2019 and 2022). Because most variables were measured on ordinal Likert-type scales and the datasets represent independent cross-sectional samples rather than a panel dataset, non-parametric methods were considered the most appropriate. More advanced causal modeling techniques, such as regression or factor analysis, were not applied because the objective of the research was exploratory and comparative rather than to establish causal relationships between variables. The findings reveal significant shifts in the perceived causes of labor shortages across occupational groups in the surveyed Central European organizations. In particular, increasing labor shortages were observed in specific job categories, alongside changes in the relative importance of the underlying drivers of labor shortages. While adopting robotization and artificial intelligence has been positively received, demographic decline and emigration remain critical challenges. The study provides practical insights for policymakers and corporate leaders regarding labor market challenges, workforce planning, and the potential role of robotization and artificial intelligence in addressing labor shortages. Although the research is based on a non-representative sample, it offers valuable insights into the Central European region’s employment and labor market trends. Future research could examine whether, in hard-to-fill positions, robotization and AI primarily provide indirect support by augmenting and reallocating human work, or whether they may serve as direct substitutes. Full article

Amid Europe’s demographic decline and the intensifying global “war for talent,” migration is increasingly viewed as a critical source of human capital capable of sustaining economic growth and welfare systems. Nevertheless, the literature on Macro-Talent Management (MTM) has primarily focused on the attraction of highly skilled expatriates, paying limited attention to how national integration systems shape the broader capacity of countries to attract and retain migrant talent. Addressing this gap, the present study conceptualizes migrant integration as a strategic component of macro-level talent management and evaluates the “talent attractiveness” of different European welfare and migration regimes. Methodologically, the study develops a multi-criteria evaluation framework based on the PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations) outranking method, enabling the simultaneous assessment of institutional, socio-economic, and administrative dimensions of migration governance. The model integrates nine indicators combining policy inclusiveness (e.g., Migrant Integration Policy Index—MIPEX (Migrant Integration Policy Index), citizenship accessibility), labor market outcomes (employment and gender gaps), and systemic pressures on migration management (asylum applications). By integrating policy indicators with real-world labor market performance and administrative capacity, the proposed framework offers a novel analytical tool for comparative migration policy evaluation and decision support. The empirical application covers six European countries representing distinct migration regimes: Portugal, Sweden, France, Poland, Greece, and Germany. The results challenge the conventional assumption that economic strength alone determines migrant attractiveness. Portugal emerges as the most attractive destination, demonstrating that inclusive rights-based integration policies can offset lower GDP levels. In contrast, Germany ranks last in the sample, revealing signs of systemic overextension due to extreme administrative pressure, while Greece occupies the fifth position characterized by structural integration deficits. The study contributes to the literature by linking migration governance, integration policy effectiveness, and macro-level talent management and by introducing a multi-criteria decision-analytic approach for evaluating national migration systems in Europe. The study offers a reassessment of the ‘talent attractiveness’ of European welfare models in a post-pandemic context (2023). Full article