Preview

Bulletin of the State University of Education. Series: Economics

Advanced search

CLUSTERIZATION OF THE REGIONS OF THE RUSSIAN FEDERATION BY THEIR LEVEL OF SOCIO-ECONOMIC DEVELOPMENT

https://doi.org/10.18384/2310-6646-2022-2-95-103

Abstract

Aim. Clusterization and comparative analysis of the regions of the Russian Federation according to the level of their socio-economic development.

Methodology. Clusterization was carried out according to the main characteristics reflecting the level of socio-economic development of the regions of the Russian Federation by the k-means method using the «Deductor» analytical platform.

Results.Based on the availability of statistical data, three clusters have been identified into which the regions of the Russian Federation can be divided according to their social and economic status. An analysis of the identified clusters was carried out to identify significant cases in their socioeconomic situation and in search of sustainable development.

Research implications. The results obtained can be used to obtain high-quality and reasonable decisions in the social and economic sphere, taking into account the level of development of different clusters.

About the Authors

Yu. M. Protasov
Moscow Region State University
Russian Federation

Yury M. Protasov – Cand. Sci. (Technical), Assoc. Prof., Department of Primary Education

Very Voloshinoi ul. 24, Mytischi 141014, Moscow region



V. M. Yurov
LEONOV Moscow Region University of Technology
Russian Federation

Vladimir M. Yurov – Cand. Sci. (Technical), Assoc. Prof., Department of Quality Management and Standardization

Gagarina ul. 42, Korolev 141070, Moscow region



References

1. Aksenov I . A . [Clusterization of foreign economic activity of regions] . In: Ekonomika i menedzhment sistem upravleniya [Economics and management of control systems], 2016, no . 1-3, pp . 309–315.

2. Gichiev N . S . [Cluster analysis in economics: theoretical aspect] . In: Regionalnyye problemy preobrazovaniya ekonomiki [Regional problems of economic transformation], 2020, no . 8 (118), pp . 176–186.

3. Gordopolov Yu . V ., Lukashevich N . S . [Clusterization of regions to ensure socio-economic development based on self-organizing Kohonen maps] . In: Nauchno-tekhnicheskiye vedomosti Sankt-Peterburgskogo gosudarstvennogo politekhnicheskogo universiteta. Ekonomicheskiye nauki [Scientific and technical statements of St . Petersburg State Polytechnic University . Economic sciences], 2010, iss . 3, pp . 27–33.

4. Demichev V . V ., Maslakova V . V ., Nestratova A . A . [Clusterization of Russian regions according to the level of agricultural efficiency] . In: Bukhuchet v selskom khozyaystve [Accounting in agriculture], 2020, no . 12, pp . 58–66.

5. Isaev V . G ., Protasov Yu . M ., Yurov V . M . [Clusterization of organizations of higher education in Moscow region based on the results of monitoring their activities] . In: Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya: Ekonomika [Bulletin of Moscow Region State University . Series: Economy], 2021, no . 1, pp . 93–101.

6. Kostina S . N ., Trynov A . V . [Cluster analysis of the dynamics of the birth rate of fourth and subsequent children in the regions of the Russian Federation] . In: Ekonomicheskiye i sotsialnyye peremeny: fakty, tendentsii, prognoz [Economic and social changes: facts, trends, forecast], 2021, vol . 14, no . 3, pp . 232–245.

7. Kuznetsov V . N . [Application of cluster analysis to assess the transport system of regions of the Russian Federation] . In: Universitetskaya nauka [University Science], 2020, no . 1 (9), pp . 71–73.

8. Lavrinenko P . A ., Rybakova D . A . [Comparative analysis of regional differences in the spheres of public health, ecology and healthcare] . In: Ekonomicheskiye i sotsialnyye peremeny: fakty, tendentsii, prognoz [Economic and social changes: facts, trends, forecast], 2015, no . 5 (41), pp . 198–210.

9. Lokosov V . V ., Ryumina E . V ., Ulyanov V . V . [Clusterization of Russian regions in terms of quality of life and population quality] . In: Narodonaseleniye [Population], 2019, vol . 22, no . 4, pp . 4–17.

10. Lee C . H ., Steigerwald D . G . Inference for clustered data . In: Stata Journal, 2018, vol . 18, no . 2, pp . 447–460 . DOI:10.1177/1536867X1801800210


Review

Views: 323


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


ISSN 2949-5040 (Print)
ISSN 2949-5024 (Online)