Socio-economic transformations are complex, multidimensional processes shaped by globalization challenges, technological progress, institutional change, and crisis events, notably the COVID-19 pandemic and the full-scale invasion of Ukraine. Based on bibliometric and cluster analyses, six conceptual domains of socio-economic transformation are identified: production–financial, international trade, innovation–technological, welfare-related, demographic, and governance–political. Google Trends data for 2019–2025 indicate rising public attention to lifelong learning, increasing interest in digital adaptation in education and employment, and growing demand for stable and legal employment opportunities abroad. A theoretical model of competence formation and transfer within the “education–labour market” system identifies key barriers to effective knowledge integration into employment, including the complexity of knowledge transfer, mismatches between acquired competencies and labour market requirements, and the risk of underutilization of human capital («skills spillover»).
To uncover macroeconomic regularities, a comparative analysis of 28 countries (EU Member States and candidate countries) was conducted for 2011, 2016, and 2021. The results reveal both upward and downward trends across key socio-economic dimensions. A hierarchical cluster analysis (Ward’s method) distinguishes five country groups: Cluster 1 (advanced economies with high levels of social protection); Cluster 2 (countries with highly developed social systems and strong innovation capacity); Cluster 3 (countries facing structural development challenges); Cluster 4 (countries undergoing active economic reforms); and Cluster 5 (transition economies characterized by relatively high levels of trust in government). Ukraine is classified within Cluster 5, reflecting its ongoing reform efforts and potential for strengthening socio-economic development. Empirical results from confirmatory factor analysis confirm the dominant role of the «Education» component within the «education–migration–labour market» nexus. Specifically, a strong direct relationship between higher education and migration is observed, alongside a moderate effect of education on labour market outcomes and an indirect effect whereby improvements in labour market conditions contribute to reduced migration. An adapted gravity model identifies factors that positively influence migration (financial and production stability, scientific and technological advancement, and improved social conditions), as well as those exerting a negative influence (adverse demographic trends and geographical distance). These findings provide a robust basis for modelling migration processes and informing evidence-based socio-economic policy.
The application of an adapted Mendelow matrix, combined with stakeholder engagement standards, enables a comprehensive understanding of stakeholder interests and interdependencies, thereby facilitating the development of strategically aligned cooperation within the «education–labour market» system. This approach supports improved coordination among education and labour market actors in the context of ongoing socio-economic transformations.
An empirical assessment of stakeholder interactions in higher education and the labour market, which aimed at mitigating labour emigration, was conducted using the Stakeholder Value Network (SVN) framework, in conjunction with the Hub-and-Spoke model and the Design Structure Matrix (DSM). This approach enables the systematic identification and prioritization of key value flows among stakeholders. The most influential flows include educational content, financial resources, human capital, and reputational linkages.
A benchmark analysis of countries that have experienced armed conflict or war and faced challenges in post-crisis recovery identifies effective policy approaches to educational re-emigration and labour market reintegration. These include diaspora engagement in economic development, financial incentives for return, the expansion of reskilling and retraining programmes, and infrastructure reconstruction. The consistency of these approaches across different national contexts underscores their broader applicability and relevance for shaping Ukraine’s post-war recovery strategy.
