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27.02.202402.2024с 01.01.2024
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Journal section "Life quality and human potential of territories"

Typologization and Socio-Economic Aspects of the Formation of Demographic Aging of Russian Regions

Dobrokhleb V.G., Kondakova N.A.

Volume 26, Issue 4, 2022

Dobrokhleb V.G., Kondakova N.A. (2022). Typologization and socio-economic aspects of the formation of demographic aging of Russian regions. Problems of Territory's Development, 26 (4), 98–110. DOI: 10.15838/ptd.2022.4.120.7

DOI: 10.15838/ptd.2022.4.120.7

Abstract   |   Authors   |   References
The article is devoted to one of the main demographic challenges of Russia – the population aging. The purpose of the work is to identify homogeneous territorial formations according to the selected parameters of demographic aging and factors affecting the population aging using mathematical analysis. We use clustering, correlation and regression analysis methods as the main methods of processing and analyzing empirical data. The first part of the paper presents an analysis of the demographic aging level of Russia’s regions. With the help of cluster analysis, we have carried out the typologization of Russia’s territories according to the following parameters: the share of the population older than working age in the total population, life expectancy, the aging depth (the share of people older than 75 years in the total elderly population). We have concluded that the trend of the population aging can be traced throughout Russia, but its relevance for the regions is different which indicates their significant differentiation. We have revealed that geographical proximity does not always indicate the similarity of regions among themselves. The second part of the article presents a regression model of the population aging which allows identifying the most significant regional factors and assessing the direction of their impact. The model includes the following factors: employment level, gross regional product per capita, urbanization level, and production volume in the agriculture industry

Keywords

region, regression model, cluster analysis, population aging, elderly population, population aging factors

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