UDC 377:331.5:005.52:332.1; JEL J24, I28, R58, C53
Hrynkevych, O. S., Pykus, I. O., & Baranyak, I. Ye. (2026). A multilevel model for regional planning and forecasting of skilled workforce training needs in Ukraine: integrating strategic and statistical analysis. Rehional'na ekonomika - Regional Economy, 120 (2), 17-31. DOI: https://doi.org/10.36818/1562-0905-2026-2-2.
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Authors



Hrynkevych (Polevska) Olha Stepanivna

Doctor of Economics, Professor

Head of the Department of statistics of the Faculty of Economics of the Ivan Franko National University of Lviv

Contacts: ogrynkevych@gmail.com

Webpages:

Pykus Ivan Oleksiyovych

Contacts: pykusivan@ukr.net

Webpages:

Baranyak Ihor Yevhenovych

Ph.D. of Economics

Senior Researcher, Scientific Secretary of the unit of the Department of problems of social and humanitarian development of the regions of the Dolishniy Institute of Regional Research of NAS of Ukraine

Contacts: ihorbaranyak@gmail.com, (067)961-2002, (032)270-6445

Webpages:

Resume

Purpose. This study develops and substantiates a multilevel organisational and analytical model for regional planning and forecasting of skilled workforce training in Ukraine, taking into account the interests of key labour market stakeholders and the influence of political, legal, economic, social, and technological factors shaping demand for skilled workers and vocational education.
Methodology. The study employs a combination of strategic and statistical methods. Strategic analysis tools, including PEST analysis and stakeholder-oriented approaches, are used to assess the external environment and institutional factors influencing workforce training. Quantitative methods, including correlation analysis and regression modelling with lagged variables, are applied to evaluate and forecast the impact of demographic trends and educational enrolment patterns on demand for vocational education and training.
Findings. The results demonstrate that demand for skilled workforce training is shaped by a complex interaction of economic, demographic, social, technological, and political and legal factors. In particular, the study highlights the significant impact of Russia’s full-scale military aggression, which has intensified labour market imbalances through demographic losses, migration processes, and structural changes in regional economies. At the same time, recent updates to the legislative framework regulating vocational education and regional workforce planning have strengthened the institutional basis for forecasting and aligning training provision with labour market needs. Empirical analysis based on data from the Lviv region confirms a strong relationship between enrolment in vocational education institutions, demographic indicators, and higher education enrolment. The forecasting results indicate a potential decline in vocational education enrolment in the medium term, driven by demographic changes and persistent educational preferences. Based on the integration of strategic and statistical analysis, a three-level model for regional planning and forecasting of skilled workforce training -comprising basic (inertial), forecasting (analytical), and strategic (corrective) levels - is proposed.
Originality. The originality of the study lies in the development of an integrated conceptual framework for regional workforce training planning and forecasting that combines strategic analysis tools with quantitative forecasting methods. The proposed approach contributes to the formation of a regional workforce forecasting system aligned with European practices of skills anticipation and labour market analysis.
Practical Value. The proposed model can be used to improve the analytical basis for regional decision-making in workforce development, enhance coordination between vocational education systems and labour market needs, and increase the effectiveness of planning and forecasting of workforce training in the context of economic transformation and post-war recovery.

Keywords:

regional workforce training plan, workforce forecasting, skills demand, vocational education and training (VET), higher education, regional labour market, multilevel model, human capital, skilled workers, strategic analysis, statistical analysis, regression analysis