Cluster-Econometric Analysis of Russian Regions: Implications for Differentiated Economic Policys
DOI:
https://doi.org/10.17059/ekon.reg.2025-2-3Keywords:
cluster econometric analysis, region, factor potentials, spatial development, economic structure, diversification, economic growthAbstract
In a rapidly changing economic landscape, a crucial challenge for state economic policy is the need for a differentiated approach to regional development. Despite its importance, this aspect has been insufficiently explored within the framework of macroeconomic regulation. This study investigates regional identity through the unique configurations of economic, innovative, technological, and transport potentials, and their influence on economic growth. At the first stage, a cluster analysis was performed to classify Russian regions into eight groups, based on 27 economic and structural indicators. These indicators encompass various dimensions such as economic capacity, innovation, technological readiness, and transport infrastructure. In the second stage, multiple regression analysis was used to evaluate the effect of these factors on per capita gross regional product. The econometric modelling revealed key drivers of regional growth and identified the factors most significantly influencing development. Analysis of each cluster highlighted the varying degrees of factor potential and future development prospects. The findings suggest that many regions are not fully exploiting their available resources, while some are facing significant constraints to growth, underscoring the need for technological transformation. The study proposes an interregional cooperation policy aimed at redistributing technological capabilities and fostering technology transfer. This approach can help policymakers design more effective economic policies that promote sustainable growth, improve technological development, and balance regional potential.
References
Akberdina, V. V., & Romanova, O. A. (2021). Regional Industrial Development: Review of Approaches to Regulation and Determining of Priorities. Economy of region, 17 (3), 714–736. https://doi.org/10.17059/ekon.reg.2021-3-1
Balland, P. A., & Boschma, R. (2021). Complementary interregional linkages and Smart Specialisation: an empirical study on European regions. Regional Studies, 55 (6), 1059–1070. https://doi.org/10.1080/00343404.2020.1861240
Blanco, E., Elosegui, P., Izaguirre, A., & Montes-Rojas, G. (2019). Regional and state heterogeneity of monetary shocks in Argentina. The Journal of Economic Asymmetries, 20, e00129. https://doi.org/10.1016/j.jeca.2019.e00129
Demichev, V. V., Maslakova, V. V., & Nestratova, A. A. (2020). Clustering Russian regions by the level of agricultural efficiency. Bukhuchet v sel'skom khozyaystve [Accounting in Agriculture], (12), 58–66. https://doi.org/10.33920/sel-11-2012-06 (In Russ.)
Dzemydaitė, G. (2021). The Impact of Economic Specialization on Regional Economic Development in the European Union: Insights for Formation of Smart Specialization Strategy. Economies, 9 (2), 76. https://doi.org/10.3390/economies9020076
Eferin, Ya. Yu., & Kutsenko, E. S. (2021). Adjusting smart specialization concept for Russian regions. Voprosy gosudarstvennogo i munitsipal'nogo upravleniya [Public Administration Issues], (3), 75–110. (In Russ.)
Ehrlich, M., & Overman, H. G. (2020). Place-Based Policies and Spatial Disparities across European Cities. Journal of Economic Perspectives, 34 (3), 128–149. http://dx.doi.org/10.1257/jep.34.3.128
Gamidullaeva, L. A., & Roslyakova, N. A. (2023). An integrated methodological approach to the structural transformation of the regional economy. Trudy III Granbergovskoi konferentsii: Sbornik dokladov Vserossiiskoi konferentsii s mezhdunarodnym uchastiem, posvyashchennoi pamyati akademika A. G. Granberga, Novosibirsk, 11–13 oktyabrya 2023 goda [Proceedings of III Granberg Conference: Collected papers of National Conference dedicated to the memory of Academician A. G. Granberg] (pp. 106-112). Novosibirsk: Institut ekonomiki i organizatsii promyshlennogo proizvodstva SO RAN. (In Russ.)
Gamidullaeva, L., & Roslyakova, N. (2024). A methodological approach to complex territorial development based on agglomeration effects: “Smart” specialization perspective. Journal of Infrastructure, Policy and Development, 8 (7), 4986. https://doi.org/10.24294/jipd.v8i7.4986
Golova, I. M., & Sukhovey, A. F. (2019). Differentiation of innovative development strategies considering specific characteristics of the Russian regions. Economy of Region, 15 (4), 1294–1308. https://doi.org/10.17059/2019-4-25
Hewings, G. J. D. (2014). Spatially blind trade and fiscal impact policies and their impact on regional economies. The Quarterly Review of Economics and Finance, 54 (4), 590–602. http://dx.doi.org/10.1016/j.qref.2014.04.007
Ketova, K. V., Kasatkina, E. V., & Vavilova, D. D. (2021). Clustering Russian Federation regions according to the level of socio-economic development with the use of machine learning methods. Ekonomicheskiye i sotsial'nyye peremeny: fakty, tendentsii, prognoz [Economic and Social Changes: Facts, Trends, Forecast], 14 (6), 70–85. https://doi.org/10.15838/esc.2021.6.78.4 (In Russ.)
Lavrovskiy, B. L. (2015). The state policy of regional development: questions of the theory. Federalizm [Federalism], (4), 121–130. https://doi.org/10.21686/2073–1051-2015-4-121-130 (In Russ.)
Lokosov, V. V., Ryumina, E. V., & Ulyanov, V. V. (2019). Clustering of regions by indicators of quality of life and quality of population. Narodonaseleniye [Population], 22 (4), 4–17. https://doi.org/10.19181/1561–7785-2019–00035 (In Russ.)
Marchenko, E. M., & Belova, T. D. (2016). Clustering of regions taking into account the energy efficiency. Regional'naya ekonomika: teoriya i praktika [Regional Economics: Theory and Practice], (1(424)), 51–60. (In Russ.)
Orlova, I. V., & Filonova, E. S. (2015). Cluster analysis of the regions of the central federal district socio-economic and demographic indicators. Statistika i ekonomika [Statistics and Economics], (5), 111–115. (In Russ.)
Pankova, Yu. V. (2022). The problems of the differentiated impact of macro-regulatory measures on the socio-economic space. In A. O. Baranov, A. A. Shirov (Eds.), Ekonomicheskaya politika Rossii v mezhotraslevom i prostranstvennom izmerenii: Materialy IV Vserossiyskoy nauchno-prakticheskoy konferentsii IEOPP SO RAN i INP RAN po mezhotraslevomu i regional’nomu analizu i prognozirovaniyu, Belokurikha, 24–25 marta 2022 goda. Tom 4. [Russia’s economic policy in the intersectoral and spatial dimension: Proceedings of the IV All-Russian Scientific and Practical Conference of INP SB RAS on intersectoral and regional analysis and forecasting, Belokurikha, March 24-25, 2022. Vol. 4] (pp. 163–166). Novosibirsk: Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences. https://doi.org/10.36264/978-5-89665-367-7-2022-005/33-180 (In Russ.)
Petrykina, I. N. (2013). Cluster analysis of regions of the Central Federal District in terms of human capital development. Vestnik Voronezhskogo gosudarstvennogo universiteta. Ekonomika i upravleniye [Eurasian Journal of Economics and Management], (1), 72–80. (In Russ.)
Piskun, E. I., & Khokhlov, V. V. (2019). Economic development of the Russian Federation’s regions: factor-cluster analysis. Ekonomika regiona [Economy of region], 15 (2), 363–376. https://doi.org/10.17059/2019-2-5 (In Russ.)
Regal, A., Gonzalez-Feliu, J., & Rodriguez, M. (2023). A spatio-functional logistics profile clustering analysis method for metropolitan areas. Transportation Research Part E: Logistics and Transportation Review, 179, 103312. https://doi.org/10.1016/j.tre.2023.103312
Rhoden, I., Weller, D., Voit, A-K. (2022). Spatio-Temporal Dynamics of European Innovation — An Exploratory Approach via Multivariate Functional Data Cluster Analysis. Journal of Open Innovation: Technology, Market, and Complexity, 8 (1), 6, https://doi.org/10.3390/joitmc8010006
Serkov, L. A., Petrov, M. B., & Kozhov, K. B. (2021). Сluster-based econometric analysis to study the heterogeneity of Russian regions. Journal of New Economy, 22 (4), 78–96. https://doi.org/10.29141/2658–5081-2021-22-4-5 (In Russ.)
Soboleva, T. S. (2009). The analysis of disproportions of is innovative-investment development of regions of Russia. Upravleniye obshchestvennymi i ekonomicheskimi sistemami [Management in social and economic systems], (1), 56–66. (In Russ.)
Uitermark, J., Hochstenbach, C., & Groot, J. (2023). Neoliberalization and urban redevelopment: the impact of public policy on multiple dimensions of spatial inequality. Urban Geography, 45 (4), 541–564. https://doi.org/10.1080/02723638.2023.2203583
World Bank. (2009). World Development Report. Reshaping economic geography. 383. https://hdl.handle.net/10986/5991 (Date of access: 17.09.2024).
World Bank. (2018). Re-mapping Opportunity. Making best use of the economic potential of Russia’s regions. World Bank Group.
Yang, J., Yang, C., & Hu, X. (2021). Economic policy uncertainty dispersion and excess returns: Evidence from China. Finance Research Letters, 40, 101714. https://doi.org/10.1016/j.frl.2020.101714
Zhu, S., & Yu, G. (2022). The impact of economic policy uncertainty on industrial output: The regulatory role of technological progress. Sustainability, 14 (16), 10428. https://doi.org/10.3390/su141610428
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Гамидуллаева Лейла Айваровна , Рослякова Наталья Андреевна

This work is licensed under a Creative Commons Attribution 4.0 International License.

