Typology of Russian regions according to human potential indicators

Abstract

The article presents the results of creatiing a typology of the country’s regions according to the characteristics of various components of human potential. The purpose of the study is to group regions by the level of human potential development and identify exemplary and lagging regions among them in this aspect. The object of the study is 85 Russian regions and indicators of their human potential. First, 22 indicators of human potential were considered, characterizing various aspects of human potential: demographic, labor, educational, cultural, environmental and related to social health, including its negative components — alcohol consumption and crime. Further, the selected indicators were analyzed and checked for multicollinearity, resulting in 9 indicators reflecting life expectancy, fertility, morbidity, the proportion of highly skilled workers, the proportion of managers and specialists, alcohol consumption, the number of visits to museums and theaters, the number of divorces, environmental behavior. Creation of a typology was carried out using the methods of cluster analysis: hierarchical and k-means. The official statistics data on the socio-economic development of the regions for 2021 were used. As a result of clustering, 9 groups of regions were obtained, from 1 to 22 regions in the group. Each group is given a meaningful characteristic. The grouping of regions differs greatly from the usual typologies of economic development. For example, the Moscow and Leningrad oblasts are defined as «below average» in terms of human potential characteristics, while the highly economically developed Republic of Tatarstan and the subsidized Pskov oblast are in the same group. The resulting typology expands our understanding of the regional differentiation of human potential and allows us to take a different look at the level of its development in separate regions and their groups. The main benefit of the presented typology is that groups with high values of human potential indicators should become the objects of analysis of the effective measures implemented by regions, thanks to which the regions have achieved good results. This experience should be extended to other regions that are less successful in terms of human development.

About the authors

Elena V. Ryumina

ISESP FCTAS RAS

Email: ryum50@mail.ru
ORCID iD: 0000-0002-7386-1077
SPIN-code: 6902-7304
Doctor of Economics, Professor, Chief Researcher Moscow, Russia

Artem A. Fedotov

ISESP FCTAS RAS

Email: fedotov.arr@gmail.com
ORCID iD: 0000-0003-4185-4013
SPIN-code: 5172-1491
Candidate of Economics, Senior Researcher Moscow, Russia

References

  1. Локосов, В.В. Человеческий потенциал: концептуальные подходы и методики измерения / В.В. Локосов // Народонаселение. — 2023. — Т. 26. — № 4. — С. 4–14. doi: 10.19181/population.2023.26.4.1; EDN: FFZUND
  2. Токсанбаева, М.С. Социально-экономические факторы, влияющие на качество трудового потенциала населения регионов России / М.С. Токсанбаева, О.А. Коленникова, Р.И. Попова // Народонаселение. — 2024. — Т. 27. — № 3. — С. 98–110. doi: 10.24412/1561-7785-2024-3-98-110; EDN: WEHHTT
  3. Everitt B.S. Cluster analysis. Fifth Edition / Brian S. Everitt. Sabine Landau, Morven Leese, Daniel Stahl. — U.K : John Wiley & Sons, — 2011. — 330 p.
  4. Lior R. A Survey of Clustering Algorithms / R. Lior // Data Mining and Knowledge Discovery Handbook. Second Edition / Ed. Oded Maimon and Lior Rokach. — New York : Springer, 2010 — P. 269–298. doi: 10.1007/978-0-387-09823-4
  5. Simovici, D.A. Mathematical Tools for Data Mining. Second edition / D.A. Simovici, C. Djeraba. — London : Springer-Verlag, 2014. — 831 p. doi: 10.1007/978-1-4471-6407-4
  6. Рюмина, Е.В. Анализ факторов региональной дифференциации показателей потребления электроэнергии населением России / Е.В. Рюмина // Народонаселение. — 2023. — Т. 26. — № 3. — С. 107–116. doi: 10.19181/population.2023.26.3.9; EDN: YRYQVK

Supplementary files

Supplementary Files
Action
1. JATS XML


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

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).