The application of multivariate classification in evaluating the regional differentiation by population income in Russia

TL;DR

This study applies multivariate classification to analyze regional income disparities in Russia, identifying distinct groups of high-income and low-income regions to inform policy decisions during crises.

Contribution

It introduces an innovative approach to selecting characteristics for clustering regions, enhancing evaluation of spatial income differentiation in Russia.

Findings

High-income regions are relatively stable in their status.

Low-income regions face ongoing economic struggles.

The analysis highlights regions needing targeted support.

Abstract

The article presents the results of multivariate classification of Russian regions by the indicators characterizing the population income and their concentration. The clusterization was performed upon an author approach to selecting the characteristics which determines the academic novelty in the evaluation of regional differentiation by population income and the interconnected characteristics. The performed analysis was aimed at the evaluation of the real scale of disproportions in spatial development of the country territories by the considered characteristics. The clusterization results allowed to formulate the condition of a relatively "strong" position of a group of high-income regions (the changes in the array of regions constituting it is highly unlikely in the foreseeable future). Additionally there has been revealed a group of Russian regions that the population is struggling to live on quite low income. These so-called "poor" regions, within the crisis conditions caused by Covid-19 are in need of additional public support, without which their population will impoverish.