TYPOLOGIZATION OF REGIONS ACCORDING TO THE PACE OF LIFE BASED ON CLUSTER ANALYSIS

UDC 332.1

  • Rasseko Yuliya Yur’yevna − Master of Economics, Senior Lecturer, the Department of International Management. Belarusian State University (4, Nezavisimosti Ave., 220010, Minsk, Republic of Belarus). E-mail: 10886alica@mail.ru

  • Karpenko Elena Mikhaylovna − DSc (Economics), Professor, Head of the Department of International Management. Belarusian State University (4, Nezavisimosti Ave., 220010, Minsk, Republic of Belarus). E-mail: emkarpenko@mail.ru

DOI: https://doi.org/10.52065/2520-6877-2022-262-2-49-57

Key words: cluster, cluster analysis, type, pace of life, physical component, information component, regional development.

For citation: Rassekо Yu. Yu., Karpenko E. M. Typologization of regions according to the pace of life based on cluster analysus. Proceedings of BSTU, issue 5, Economics and Management, 2022, no. 2 (262), pp. 49–57 (In Russian). DOI: https://doi.org/10.52065/2520-6877-2022-262-2-49-57.

Abstract

The use of the pace of life indicator in the system of regional development dictates the need to form a set of effective tools for its regulation. However, the heterogeneity of the regions does not allow for a universal approach and requires the identification of groups with similar development priorities. This kind of problem can be solved by cluster analysis implemented on the data of the economic assessment of the pace of life of thirty countries (Albania, Austria, Belarus, Belgium, Bulgaria, Bosnia and Herzegovina, Croatia, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Iceland, Ireland, Latvia, Lithuania, Luxembourg, Moldova, Montenegro, the Netherlands, North Macedonia, Norway, Portugal, Romania, Serbia, Slovakia, Slovenia, Sweden, Switzerland) in the period 1999–2020. The clustering of regions is based on a combination of the values of the informational and physical components of the pace of life. The result of the study was the identification of three types of regions (type A, type C, type B), which reflect the direction of the processes of regional development, its growth points, priorities. The procedure secondary clustering allowed us to identify three possible combinations of values for the component in each type of region (А24, А34, А35,С14,С25,С36,В15,В16,В26), which allow to determine the effectiveness of the chosen course of regional development.

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References

  1. Rasseko Yu. Yu., Karpenko Ye. M. Assessment of the influence of the pace of life in the region on its socio-economic development. Zhurnal Belorusskogo gosudarstvennogo universiteta. Ekonomika [Journal of the Belarusian State University. Economy], 2022, no. 1, pp. 59–65 (In Russian).
  2. Rasseko Yu. Yu., Karpenko E. M. Methods of choosing tools for managing the regional pace of life. Vestnik Vitebskogo gosudarstvennogo tekhnologicheskogo universiteta [Bulletin of the Vitebsk State Technological University], 2022, no. 1 (42), pp. 205–213 (In Russian).
  3. Rasseko Yu. Yu., Karpenko E. M. Methodological approach to assessing the pace of life of the population of the region: theory and practice. Teoriya i praktika khozyaystvennogo razvitiya: razrabotka novykh i adaptatsiya sushchestvuyushchikh metodov, mekhanizmov i instrumentov: materialy natsional’noy nauchno-prakticheskoy conferentsii s mezhdunarodnym uchastiem [Theory and practice of economic development: development of new and adaptation of existing methods, mechanisms and tools: materials of the national scientific and practical conference with international participation]. Elista, 2020, pp. 10–14 (In Russian).
  4. Karpenko E. M., Rasseko Yu. Yu. Construction of an integral indicator for assessing the regional pace of life using multidimensional statistical methods. Vestnik Polotskogo gosudarstvennogo universiteta [Bulletin of the Polotsk State University], series D, Economic and legal sciences, 2022, no. 5, pp. 44–50 (In Russian).
  5. Suslov S. A. Cluster analysis: essence, advantages and disadvantages. Vestnik NGIEI [Bulletin of the NGIEI], 2010, no. 1. Available at: https://cyberleninka.ru/article/n/klasternyy-analiz-suschnost-preimuschestva-i-nedostatki (accessed 22.06.2022) (In Russian).
  6. Lutsenko E. V., Korzhakov V. E. Some problems of classical cluster analysis. Vestnik Adygeyskogo gosudarstvennogo universiteta [Bulletin of the Adygei State University], series 4, Natural-mathematical and technical sciences, 2011, no. 2. Available at: https://cyberleninka.ru/article/n/nekotorye-problemyklassicheskogo-klasternogo-analiza (accessed 13.07.2022) (In Russian).
  7. Karpenko E. M., Karpenko V. M., Kovalevich V. S. Cluster analysis of growth factors in the sphere of information and communication technologies. Trudy BGTU [Proceedings of BSTU], series 5, Economics and Management, 2018, no. 2 (214). Available at: https://cyberleninka.ru/article/n/klasternyy-analizfaktorov-rosta-sfery-informatsionno-kommunikatsionnyh-tehnologiy (accessed 09.08.2022) (In Russian).
  8. UN statistics. Available at: http://data.un.org (accessed 30.07.2021) (In Russian).
  9. Data from the Statistical Office of the European Union. Available at: https://ec.europa.eu/eurostat (accessed 29.07.2021) (In Russian).
  10. Economic statistics. Available at: https://knoema.com/atlas/topics/Economy (accessed 26.07.2021) (In Russian).
  11. Annual data of the National Statistical Commitete of the Republic of Belarus. Available at: https://www.belstat.gov.by (accessed 10.12.2020) (In Russian).
  12. Karpenko E. M., Rasseko Yu. Yu. Management of the pace of life of the Republic of Belarus. Izvestiya Gomel’skogo gosudarstvennogo universiteta imeni F. Skoriny [Proceedings of the Gomel State University named after F. Skorina], 2022, no. 5 (38), pp. 52–61 (In Russian). 
14.09.2022