ЭКОНОМИКА РЕГИОНА

Public Employment Services provide support for firms and individuals in finding new employment opportunities. They are important actors at the labour market, since well-functioning services reduce costs of search friction and increase matching efficiency. In this paper we adopt the regional classification scheme to identify similarities of regions and their PES on the basis of regional labour market-oriented characteristics. The purpose of the scientific search is the theoretical justification and empirical confirmation of Russian regions’ similarity in terms of employment level and the formulation of areas for increasing the efficiency of public employment services. The tasks were solved using expert analytical methods, analysis of statistical rows, clustering and cartography. The clustering is based on Ward’s hierarchical method, clusters are plotted on weighted standardised data. The official information from the Federal State Statistics Service of the Russian Federation (Rosstat) are analysed. We identified 7 clusters, in which PES have rather similar conditions. The heterogeneity of conditions is higher between clusters. PES within a cluster are valid for comparison and the adoption of new services and best practice examples. We show that the classification of the Russian Economic Zones does not necessarily cover similarities at local labour markets. The practical significance of the results is due to the possibility of using them to develop decisions for long - and short-term support for employment and the formation of an optimal labour market structure both at the state level and at the level of constituent entities of the Federation.

Digitalisation is often perceived as a driver of operational performance in manufacturing, but the mechanisms by which advanced digital skills influence productivity remain poorly understood. Digitalisation processes are heterogeneous in nature and are shaped by regional factors. The study aims to explore how workers’ digital human capital affects the performance of production systems in the metallurgy sector considering differences in regional digitalisation contexts. The research methods are based on multigroup analysis of partial least squares structural equation models (MGA PLS-SEM), in which the dependent variable is the performance of production systems. The research measured accumulated human capital as a stock of relevant digital basic and specific skills using a survey of 2 570 employees conducted in 2022 in Sverdlovsk, Chelyabinsk, Rostov, and Volgograd oblasts, which differ in their levels of digitalisation, innovation, industrial specialisation, and gross income. The findings indicate that advanced digital skills not only complement basic ones but also significantly enhance production performance, as the standardised path coefficients are ranging between 0.4 and 0.7. Specifically, the industrially advanced Chelyabinsk oblast shows a more significant impact of basic digital competencies on Industry 4.0 skills, though path coefficients are still less than 0.2, suggesting a moderate overall effect of Industry 4.0 skills on performance across all regions. This study contributes to the contextual economics perspective by demonstrating the heterogeneous nature of digital human capital accumulation within a single industry.