Leonidova G.V., Basova E.A., Rassadina M.N. (2022). Cluster analysis of income inequality of the Russian population. Problems of Territory's Development, 26 (6), 94–114. DOI: 10.15838/ptd.2022.6.122.6
The problem of uneven distribution of income is particularly relevant for Russian society due to the excessive scale of stratification of the population. Significant interregional differences and the lack of a scientifically based methodology for dividing Russia’s entities into clusters according to the degree of citizens’ income differentiation complicate the implementation of state social policy in the same type of regions. The presented article examines the features of population inequality in contemporary Russia and trends of its changes in the case study of Russia’s regions for 2010–2018, and also suggests a methodology for combining territories into homogeneous groups according to the income inequality rate based on cluster analysis tools. The sources of information are official Rosstat data. We have shown that the income inequality of the Russian population remains at an excessively high level. There are four groups of regional clusters according to the level of monetary inequality and poverty. We have determined that a high level of income polarization is especially characteristic of entities belonging to a regional cluster with the maximum amount of per capita monetary income and GRP per capita. We have proposed management measures to reduce inequality and poverty in the context of homogeneous groups. The results obtained have scientific significance for the study of income inequality of the population in terms of the analysis of regional imbalances. The practical value of the research materials lies in the possibility of using them in the development of measures to reduce poverty in the regions. In the future, the study of monetary (income) inequality may include the problems of economic behavior taking into account the motivational and adaptive strategies of citizens
Keywords
regions, income inequality, hierarchical cluster analysis, Ward’s method, multidimensional data classification