UDC: 331.5:332.1
BBC: У9(2) + Т1(2Рос)6
Arkadeva O.G.
Structural imbalances in employment and unemployment in regional labor markets
Keywords: regional differentiation, demographic factors, migration processes, industry specialization, state employment policy
Modern Russian economy is characterized by a combination of demographic shifts, diverse industry specialization and steady migration flows, which creates structural imbalances in territorial labor markets. This study aims to identify structural imbalances in the development of Russia's regional labor markets based on the analysis of participation of population aged 15–72 years in the labor force and the unemployment rate. The purpose of the study is to identify, based on generalized modern scientific experience, spatially heterogeneous forms of labor market response to demographic challenges and institutional measures. Materials and methods. The empirical base is based on Rosstat data on employment and unemployment for 2024. The study used indicators of the level of population participation as part of the workforce, the unemployment rate and their ratio, which made it possible to reveal inter-territorial differences. The choice of the age range of 15–72 years is dictated by the need to minimize distortions associated with the aging of the population, as well as taking into account the institutional and economic characteristics of regional labor markets. Methods of comparative analysis, cartographic modeling and interpretation of statistical indicators were used. Implementation is made in Python using Pandas, NumPy, Matplotlib, Scikit-learn (KMeans, StandardScaler), Kneed (KneeLocator) libraries and modules for working with GeoJSON. Fixed parameters were used: IQR coefficient 1.5 for outlier detection; range k for elbow method 1–7; random_state = 42, n_init = 10 for K-means. Results. The constructed cluster model made it possible to identify three stable groups of regions: cluster A – territories with high population involvement in the labor force and low unemployment; cluster B – regions with average employment and high unemployment; cluster C – subjects with relatively low labor force participation and moderate unemployment. Outlier regions (cluster D) with extreme values of indicators were singled out separately. The comparative analysis showed that the differences between the clusters are due to demographic structures, industry specialization, the scale of migration and the institutional features of regional employment. Conclusions. The results confirmed the systemic nature of inter-regional differences and showed the need for a differentiated employment policy. For sustainable regions (cluster A), the focus should be made on maintaining human capital and stimulating innovative industries. Clusters B and C require active employment measures and entrepreneurship incentives, while cluster D requires adaptive federal–level programs, including mobility support and employment infrastructure development. The developed clustering makes it possible to identify stable types of regions in terms of employment and unemployment parameters, which can serve as the basis for a targeted labor market regulation policy. The methodological approach, including statistical and cartographic analysis, showed effectiveness in identifying spatial heterogeneity and factors of structural imbalances reproduction. Further development of the research involves integration of cluster analysis with factor modeling using machine learning models and forecasting tension in regional labor markets.
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About authors
- Arkadeva Olga G.
- Candidate of Economics Sciences, Associate Professor, Department of Finance, Credit and Economic Security, Chuvash State University, Russia, Cheboksary (knedlix@yandex.ru; ORCID: https://orcid.org/0000-0003-4868-2365)
Article link
Arkadeva O.G. Structural imbalances in employment and unemployment in regional labor markets [Electronic resource] // Oeconomia et Jus. – 2025. – №4. P. 1-16. – URL: https://oecomia-et-jus.ru/en/single/2025/4/1/. DOI: 10.47026/2499-9636-2025-4-1-16.
