T. Yu. Kudryavtseva, A. E. Skhvediani, M. A. Rodionova, V. V. Iakovleva. Identification of Russian Clusters Based on the Synthesis of Functional and Spatial Approaches

UDК 332.12(470+571)

doi: 10.15507/2413-1407.122.031.202301.046-069


Introduction. The study continues the approbation of the methodology of cluster identification, developed earlier by the authors and the study of regional industry specialization, within the framework of which the database “Clusters of Russian Regions” was developed. The relevance of the topic is the necessity of the methodology for complex clustering of regions in order to provide further recommendations for the development of industrial sectors. The purpose of the article is to develop and test the methodology for identifying clusters on the territory of Russia based on the synthesis of functional and spatial approaches.

Materials and Methods. The analysis of intersectoral relations within the framework of the functional approach consisted in the application of the maximum method, which allows to trace the chain of consumption relative to the main suppliers and main consumers between industries based on the Russian “Input – Output” table of 2016. The spatial approach was implemented by calculating location quotients, determining z-scores, correlation coefficients analysis between clusters’ location quotients to establish regional and interregional links.

Results. The results of the article have tested the methods proposed by the authors for the clustering process of regions. The results obtained after applying the methods revealed the localization of the cluster “Chemical Products” in the territories of certain regions of the Russian Federation and its existing significant functional and spatial relationship with the clusters: “Construction”, “Production Equipment” and others. Moreover, it has been determined that the chemical industry has different types of connections: both the functional connection (with the “Metallurgy” cluster) and the presence of spatial communication: interregional (“Construction”), regional (“Production equipment” and others). Therefore, it has been proved that an integrated approach is necessary to identify industrial clusters.

Discussion and Conclusion. Considerations of previous studies on regional clustering and our obtained results on the cluster “Chemical products” have confirmed the need to use the complex methodology of regional clustering, which includes the synthesis of functional and spatial approaches, since both approaches separately have their limitations: functional connection does not mean the existence of spatial (analysis of clusters “Chemical products” and “Metallurgy” interconnection) and vice versa. This result will help to comprehensively solve the problem of the chemical industry development in Russia, due to the understanding of the competent placement of enterprises and taking into account the relationship with enterprises of various industries. The materials of the article can be useful both for scientists dealing with the problems of regional economic development, and for governmental bodies whose goals include making managerial decisions in the field of industrial development.

Keywords: cluster identification, “Input Output” table, location quotient, intersectoral links, cluster algorithm, cluster structure of the territory

Conflict of interest. The authors declare that there is no conflict of interest.

Acknowledgements. This research was funded by the Russian Science Foundation. Project No. 20-78-10123.

For citation: Kudryavtseva T.Yu., Skhvediani A.E., Rodionova M.A., Iakovleva V.V. Identification of Russian Clusters Based on the Synthesis of Functional and Spatial Approaches. Russian Journal of Regional Studies. 2023;31(1):46–69. doi: https://doi.org/10.15507/2413-1407.122.031.202301.046-069


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Submitted 01.08.2022; revised 10.10.2022; accepted 19.10.2022.

Аbout the authors:

Tatiana Yu. Kudryavtseva, Dr. Sci. (Economics), Associate Professor, Professor, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University (29 Polytechnicheskaya St., St. Petersburg 195251, Russian Federation), ORCID: https://orcid.org/0000-0003-1403-3447, Scopus ID: 56023272600, Researcher ID: S-8637-2017, kudryavtseva_tyu@spbstu.ru

Angi E. Skhvediani, Cand. Sci. (Economics), Associate Professor, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University (29 Polytechnicheskaya St., St. Petersburg 195251, Russian Federation), ORCID: https://orcid.org/0000-0001-7171-7357, Scopus ID: 57194696524, Researcher ID: S-8668-2017, shvediani_ae@spbstu.ru

Maria A. Rodionova, Specialist, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University (29 Polytechnicheskaya St., St. Petersburg 195251, Russian Federation), ORCID: https://orcid.org/0000-0002-6972-2082, rodionova.mariia@yandex.ru

Valeriia V. Iakovleva, Postgraduate Student, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University (29 Polytechnicheskaya St., St. Petersburg 195251, Russian Federation), ORCID: https://orcid.org/0000-0003-0361-5003, yakovleva2.vv@edu.spbstu.ru

Contribution of the authors:

T. Yu. Kudryavtseva – critical analysis and revision of the text; data curation; scientific supervision; resource provision; project administration; funding.

A. E. Skhvediani – computer work; methodology development; data and evidence collection; formalized data analysis.

M. A. Rodionova – visualization/presentation of data in the text; critical analysis and revision of the text; formalized data analysis; study of the concept.

V. V. Iakovleva – visualization/presentation of data in the text; computer work; preparation of the initial version of the text; collection of data and evidence.

The authors have read and approved the final version of the manuscript.


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