Objectives The article analyzes the significance and value of the human being in the context of dynamic labor market transformations triggered by the development of artificial intelligence. Reflection is undertaken regarding the anthropological-pedagogical function of work and the role of the human being as a moral, creative subject responsible for shaping relationships with technology. Key competencies of the future, the development of which becomes indispensable in a professional environment dominated by algorithmic solutions, are discussed. The tasks of contemporary education and the pedagogy of work in preparing individuals for responsible coexistence with AI are also indicated Material and methods Subject material and analytical-synthetic method Results Working man model in AI Conclusions The conclusion emphasizes that, despite increasing automation, the human being remains the essential creator of meaning, value, and social order in the world of work.

This paper explores the divergent developmental trajectories of East-Central European countries by examining the cultural fault lines between Western (European) and Orthodox (Slavic/Russian) civilizations. The study assesses how these civilizational backgrounds have influenced economic performance, governance structures, social trust, labor market dynamics, and institutional development since the fall of communism. The analysis uses a comparative cross-country design based on secondary data from international sources. Countries are classified according to the cultural models of Huntington, De Blij & Muller, and Fellmann et al. A set of macroeconomic and societal indicators—including GDP per capita, Global Innovation Index rankings, labor force characteristics, and trust levels—is examined to identify patterns of divergence and convergence across cultural blocs. The results show that Western-aligned countries generally perform better economically and exhibit higher levels of social trust and institutional stability. At the same time, the relationship between culture and development is not deterministic. Several Orthodox countries demonstrate notable progress, indicating that integration dynamics, policy choices, and governance quality also play significant roles. The research offers an interdisciplinary perspective that connects cultural theory with observable economic and political outcomes. It contributes to a deeper understanding of how historical-cultural legacies shape developmental potential in East-Central Europe and provides insights for regional policy design, institutional reform, and future growth strategies.

The paper examines the influence of narrative economics on financial markets and employment outcomes, with a particular focus on job creation and labour sentiment. It examines how prevailing economic narratives shape investor behaviour, affect hiring decisions, and contribute to broader market fluctuations. This research has been conducted using secondary and tertiary sources, including economic journals, financial reports, and previously published studies. The analysis highlights that collective economic narratives, driven by optimism or pessimism, play a critical role in determining both financial performance and labour market dynamics.

校验后的结果： This study conducts a comprehensive analysis of the labor market dynamics within South- west China, covering the period from 2010 to 2025. Amidst China’s broader economic re- structuring, the southwestern region presents a unique case study characterized by rapid urbanization, industrial policy shifts, and significant rural-to-urban labor migration. This pa- per investigates the evolving trends in employment across primary, secondary, and tertiary sectors, alongside a critical examination of unemployment patterns. Utilizing statistical data from national and provincial sources, the analysis employs trend analysis, correlation matri- ces, and multiple regression models. The findings reveal a significant structural shift, with the tertiary (service) sector emerging asthe primary driver of job creation, partially mitigating un- employment pressures from industrial consolidation. The regression analysis indicates that GDP growth and foreign direct investment (FDI) remain significant factors in reducing un- employment, though the elasticity of employment to growth appears to be changing. These results underscore the importance of targeted labor policies that enhance skill development and facilitate labor mobility to align the workforce with the demands of a service-oriented economy.

Artificial intelligence (AI) is revolutionizing talent acquisition by streamlining recruitment processes, improving efficiency, and reshaping workforce management. AI-powered tools automate repetitive tasks such as resume screening, candidate assessments, and interview scheduling, allowing HR professionals to focus on strategic workforce planning and decision-making. Moreover, AI enhances the candidate experience by utilizing chatbots and virtual assistants, offering personalized interactions, skill assessments, and real-time feedback, leading to a more engaging recruitment process. Despite its advantages, AI-driven recruitment presents ethical challenges, particularly concerning bias and fairness in decision-making. While AI has the potential to minimize human biases, flawed algorithms and biased training data can lead to unintended discrimination. Therefore, continuous monitoring, refinement, and transparency in AI implementation are crucial to ensuring ethical and inclusive hiring practices. The rapid evolution of AI in the job market also necessitates workforce adaptation. According to the World Economic Forum, AI could displace approximately 75 million jobs, while simultaneously creating 113 million new opportunities. This shift underscores the need for organizations to invest in continuous learning, employee reskilling, and career development initiatives to prepare the workforce for emerging roles. Furthermore, AI’s role in recruitment extends beyond hiring; it influences talent management, employee retention, and long-term workforce planning. Organizations leveraging AI can enhance decision-making through predictive analytics, enabling HR teams to identify skill gaps, anticipate workforce trends, and optimize hiring strategies. Future research should focus on ethical AI deployment, workforce transformation, and AI’s broader impact on labor market dynamics. In conclusion, AI-driven talent acquisition is poised to become a fundamental component of modern recruitment. Companies must strike a balance between technological advancements and ethical considerations, ensuring AI is used responsibly to foster a fair, efficient, and inclusive hiring landscape.

This paper has analysed the dynamic associations between GDP growth and the rates of urban unemployment and urban labour participation rate (LFPR) in the urban labour markets of India. The analysis utilised the secondary data of the Indian labour force survey which included Maximum Likelihood estimation under a structural equation modelling approach to demonstrate that the economic growth was associated with low urban unemployment, and unemployment was negatively related to participation in the labour force. These results underscored the role of macroeconomic growth in absorbing labour but high unemployment will deter people to involve themselves in active work, particularly in the more complicated urban environment. The paper combined stringent econometric methods to measure and confirm such interactions putting the findings in the wider context of the labour market issues in India which include skill gaps, informal sector dominance, and the gendered nature of India labour markets. Through critical examination of these essential variables, the study adds value to the study of labour market friction associated with economic development of cities and workforce participation.

Ex ante heterogeneity, separations, and labor market dynamics

The middle class can play a pivotal role as a growth driver in achieving Indonesia’s Golden Vision of becoming a high-income country by 2045. However, it remains narrow, at under 20 percent of the total population. It is also highly vulnerable, given a waning purchasing power, and unfavorable labor market dynamics. In contrast with the steady progress of the bottom half of the income distribution, the middle-class share has declined since 2019, driven, inter alia, by labor market shifts toward informality, falling real incomes, pandemic scarring. Reversing this trajectory requires broad-based structural reforms focused on revitalizing private-sector led growth, including investment to create formal sector jobs, aligning education with labor market needs and develop skills to raise economic sophistication, and enhancing productivity and resilience. Reforms that enhance the ease of doing business, such as reducing regulatory barriers and uncertainty and improving governance, can help facilitate convergence to high-income status and benefit the middle class.

This study examines recent shifts in U.S. immigration policy, with particular attention to regulations governing the F-1 visa, and considers their implications for global STEM education and labor market dynamics. Growing uncertainty surrounding post-graduation employment and residency opportunities in the United States has led many international students to redirect their educational trajectories toward countries offering more predictable post-study pathways, including Canada, the United Kingdom, Germany, and Australia. Such patterns signal not only changes in student mobility but also broader transformations in the distribution of scientific talent, the configuration of research networks, and the capacity for innovation across national contexts.

Background and Aim: China's demographic transition from a "demographic dividend" phase, characterized by a youthful population and rapid economic growth, to an aging society presents multifaceted socioeconomic challenges. This shift has been accelerated by the legacy of the one-child policy, rising life expectancy, and pronounced regional disparities. Provincial variations in aging trajectories are critical. Heilongjiang faces severe aging due to outmigration and shrinking labor pools, Hebei exhibits deep urban-rural divides in aging impacts, and Hunan demonstrates moderate aging mitigated by intergenerational family support systems. This study investigates the heterogeneous economic consequences of population aging across these provinces, with a focus on labor market dynamics, fiscal sustainability, and long-term growth trajectories. By contextualizing aging within China's unique "getting old before getting rich" paradigm, we aim to inform region-specific policy responses. Materials and Methods: An augmented Solow growth model is employed to disentangle aging effects, incorporating province-level panel data (2004–2024) from China's National Bureau of Statistics and official provincial yearbooks. The model explicitly integrates aging-specific variables—including elderly dependency ratio, working-age population share, human capital stock, savings rate, and labor force participation rate—as both independent and interactive predictors. Econometric techniques include unit root testing for stationarity, cointegration analysis to validate long-term relationships, and fixed-effects regression to control for province-invariant characteristics. Model transparency is enhanced by specifying the functional form of aging variable incorporation and data standardization processes. Results: The elderly dependency ratio exhibits negligible direct effects on real per capita GDP. However, working-age population proportion (β=2.29, p<0.1), human capital (β=5.08, p<0.01), savings rate (β=3.86, p<0.05), and labor participation (β=1.96, p<0.01) significantly drive growth. Regional heterogeneity emerges. Heilongjiang’s labor shortages contrast with Hunan’s resilience from human capital investments, while Hebei’s urban-rural disparities underscore uneven aging impacts. Conclusion: Policy measures must prioritize region-specific strategies: enhancing vocational education in human-capital-deficient areas, incentivizing labor participation through flexible retirement, and expanding multi-pillar pension systems. Lessons from Japan’s automation adoption and Nordic flexible work models highlight feasible pathways. Aligning reforms with China’s “getting old before getting rich” context ensures sustainable adaptation to demographic shifts.

The article explores the competence-based approach in specialized (profile) education, emphasizing its role in differentiating and individualizing the learning process. It examines the historical development of profile-based education, including Soviet-era experiments and their limitations, and highlights the evolution of pedagogical thought towards competency-oriented training. The study defines key concepts such as competence, competency, and professional qualification, and distinguishes between professionally significant and professionally important qualities. It argues that specialized education should focus on forming both general and profile-specific competencies, integrating theoretical knowledge with practical skills to prepare students for professional and personal development. The competence-based approach ensures that students acquire the necessary abilities, knowledge, and attitudes to succeed in a dynamic labor market and adapt to changing professional requirements.

ABSTRACT In this paper, I study which is the trade‐off that workers face when accepting a wage cut after a job‐to‐job (JTJ) transition. Using data from the Chilean Unemployment Insurance (UI) registry, I show that JTJ transitions are positively associated with ex‐post wage growth. Besides, conditional on a JTJ transition, workers who accept wage cuts show higher wage growth rates in their destination firms. I rationalize these findings in a parsimonious job search model that features exogenous wage‐wage growth offers. Workers maximize the expected present value of moving JTJ or staying in their current firm when a job offer arrives. The model is calibrated in order to replicate most stylized facts documented in the empirical section and, through comparative statics exercises, the model highlights the relevance of the layoff probability, the correlation of wage levels and growth rates within a job offer, the value of unemployment and the offered ‐ratio in explaining some of the observed labor market and wage dynamics. The evidence that I provide suggests that, even though a JTJ transition implies a wage cut, workers may also enjoy higher continuation values in their new job.

BackgroundChild labour remains a critical issue in SAARC countries, driven by various socio-economic factors. While previous studies have explored individual determinants, limited research has been conducted on their collective long-term impact. Understanding how structural and economic conditions shape child labour trends is essential for designing effective policy interventions.MethodsThis study engages panel cointegration techniques to examine the long-term relationship between child labour and key socio-economic drivers in SAARC countries. It assesses the impact of education, access to healthcare, economic conditions, labour market dynamics, foreign investment, and urbanisation on the prevalence of child labour.ResultsThe findings confirm a stable, long-term relationship between child labour and these determinants in each SAARC country. Improvements in education and health significantly reduce child labour. However, economic growth and urbanisation have complex, country-specific effects. Higher unemployment and increased FDI may also influence child labour, emphasising the need for targeted policy responses.ConclusionsThe study highlights the significance of ongoing investments in education and healthcare. Labour market reforms are crucial to mitigate the impact of unemployment, while inclusive economic policies ensure that growth benefits vulnerable populations. Targeted strategies for FDI and urbanisation are necessary to prevent unintended consequences on child labour. Combating child labour in SAARC countries requires a multi-sectoral approach. Regional collaboration is crucial for sharing best practices, developing unified strategies, and enhancing cross-border initiatives. Holistic policies integrating education, health, and economic planning are key to reducing child labour.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-25399-w.

Abstract: The global landscape of management education is undergoing a paradigm shift. Traditional MBA programs - once considered gold standards for preparing future business leaders—are now facing significant scrutiny for their ability to stay relevant amid rapid technological change, evolving industry demands, and the need for more dynamic leadership capabilities (Zeidan &Bishnoi, 2020). With the rise of Industry 4.0, digital transformation, and sustainable innovation, there is a growing need for educational ecosystems that are agile, practice-oriented, and deeply integrated with industry realities (Esangbedo et al., 2024). India’s current approach to skill development is comprehensive, encompassing both the skilling of new entrants into the labor market and the up-skilling and reskilling of the existing workforce to meet evolving industry benchmarks. The National Skill Development Mission has set an ambitious target of skilling 403 million individuals by 2022. As the fastest-growing service economy, with services contributing approximately 61% to national GDP, India is witnessing rapid expansion in sectors such as Information Technology–Business Process Management (IT-BPM), healthcare, tourism, and emerging technologies. However, despite a robust demand for labor both domestically and internationally, a significant gap persists in the employability of Indian youth. Notably, this gap is prevalent not only among the uneducated and untrained, but also among educated individuals, whose skills often fall short of industry requirements. This study explores the evolving landscape of India’s higher education system in the context of labor market dynamics, skill demands, and the employability index across high-growth sectors. The study highlights the strategic value of embedding structured, long-term partnerships between industry and academia to foster innovation, skill alignment, and graduate employability. These insights provide actionable recommendations for business schools, policy-makers, and corporate stakeholders aiming to co-create impactful management education ecosystems. By analysing the education profiles of jobseekers and existing skill gaps, the paper proposes strategic interventions to enhance workforce readiness. The findings aim to inform policy and institutional frameworks that can better integrate education and skills development, ensuring alignment with national and global economic needs.

This paper reviews contemporary research on the integration of Artificial Intelligence (AI) and Business Analytics (BA) in transforming decision-making within technology-driven firms. AI, through machine learning, natural language processing, and predictive modelling, enhances BA’s capacity for data analysis, enabling faster, more accurate, and adaptive decision-making across strategic, operational, and financial domains. This integration is particularly vital for technology-intensive firms that operate in dynamic digital environments where data-driven insights determine competitiveness and innovation. The reviewed literature highlights substantial benefits, including improved cost efficiency, productivity, and real-time responsiveness, while also identifying challenges such as data privacy risks, algorithmic bias, high implementation costs, and workforce skill gaps. Economically, the adoption of AI-enhanced analytics is linked to increased firm competitiveness, innovation-driven growth, and evolving labour market dynamics. The paper underscores that while AI and BA collectively empower firms to achieve superior agility and performance, they simultaneously raise ethical and governance concerns that demand attention. The purpose of this review is to synthesise existing studies to understand how AI and business analytics transform organisational decision-making and what implications this holds for firms and economies, offering a foundation for future inquiry into sustainable, equitable, and autonomous decision systems.

This study investigates the dynamic relationships between net migration, the unemployment rate, and GDP per capita in Morocco using a Vector Error Correction Model (VECM) with annual data from 1991 to 2024. The research aims to fill a notable gap in the existing literature by examining the long-run equilibrium relationships and short-run dynamics among these key macroeconomic variables. Our findings reveal that all three variables are integrated of order one, I(1), and share a single, stable long-run equilibrium relationship. The long-run analysis demonstrates that net migration has a significant and positive effect on GDP per capita, while the unemployment rate has a negative effect. These results are consistent with established economic theories, such as the “migration-led growth” hypothesis and Okun's Law. The short-run dynamics indicate that GDP per capita actively adjusts to restore equilibrium following a shock, while both net migration and unemployment appear to be weakly exogenous, responding very little to short-run deviations from the long-run path. This suggests that migration flows and labor market dynamics in Morocco are primarily driven by their own inertia and factors external to the immediate economic system. In conclusion, this paper provides robust empirical evidence that net migration plays a vital and positive role in Morocco's long-term economic growth, while high unemployment acts as a persistent drag on per-capita output. The findings offer important policy implications for fostering sustainable economic development through well-managed migration and targeted employment strategies.

Abstract For many years, Italy has reported the highest NEET rates among EU countries—that is, the share of young people Not in Employment, Education, or Training. Between 2012 and 2021, several events influenced Italy’s NEET rates, including the recovery from the 2007–2010 financial and economic crisis, the COVID-19 pandemic, and recent labour market reforms, such as the Youth Guarantee and the Citizenship Income. Furthermore, the digital and green transitions have significantly reshaped labour market dynamics, affecting both how individuals work and how they interact with one another. Although NEET rates have declined across Europe, the reduction in Italy has been less marked compared to the EU-27 average. Understanding the determinants of these dynamics and producing accurate forecasts is essential, as it can help identify best practices to accelerate NEET reduction. Traditional statistical analyses can only partially meet these needs, due to the complexity of the phenomenon and the underlying assumptions, such as the linearity relationships. In this paper, in addition to employing a modern time series approach, we adopted three advanced statistical techniques from the field of machine learning: regression trees, random forests, and XGBoost algorithms. The findings provide a clear identification of the main NEET related factors, while the analysis of the predictive accuracy of the models in an out-of-sample framework reveals heterogeneous results, with the random forest performing slightly better overall.

The politically charged domains of immigration policy, labor market dynamics, and human rights discourse intersect in scholarly analysis of migrant labor. This exploratory study employs a randomized survey methodology to investigate self-perceptions, employment conditions, and social integration among migrant laborers in Singapore. Over a one-month period, 45 semi-structured interviews were conducted with randomly selected participants working in the house help, construction, and food and beverage industries. Findings indicate that respondents overwhelmingly reported positive experiences, with the majority expressing feelings of safety and stable/gainful employment. However, respondents also identified communication regarding support networks as a key area for improvement.

ABSTRACT This study explores the planning trade-offs generated by the conflicting infill development and green space preservation objectives in the Frankfurt Rhine-Main (FRM) region through a social-ecological systems (SES) perspective. The main research question is: How can the conflict between infill development and green space preservation objectives in the growing mid-sized towns be resolved by planning instruments? To answer this question, the analysis combines spatial assessment of settlement development and green space parameters in selected towns with an evaluation of institutional conditions. Findings show that limited capacities at administrations, bureaucratic hurdles and policy-practice divides are the main systemic impediments weakening institutional conditions to effectively handle trade-offs. A city-wide decision-making instrument is proposed to be coordinated regionally, which systematically evaluates spatial reserves either for infill development (e.g., residential development at the site of an underused parking lot with close proximity to everyday utilities) or for green space preservation (e.g., green space protection to ensure connectivity for fresh air corridors). Since housing and labour market dynamics, commuting, and regionally-significant green spaces function across municipal borders, the studied trade-offs are inherently regional. Regional-level institutions should contribute through initiating and coordinating this at the stage of preparatory land-use planning. Coordinating decisions at the regional-scale with the proposed SES-based tool sustains long-term regional attractiveness and competitiveness by avoiding and managing externalities such as congestion, urban overheating and ecological fragmentation. This instrument would require the SES approach to be operationalised at the regional-level administration, but can in turn ensure a systemic, unbiased and transparent practice for land-use decisions at the local level.

Local labor market dynamics and agglomeration effects