Research aimed at studying the influence of the place of residence of the population on income differentiation in society is essential for Russia due to the vast territory of the country. Along with the diversity of natural and climatic conditions, the uneven spatial development and resource capabilities of regions and municipalities, there are high gaps in the density and settlement of the territory and the development of infrastructure that provides basic human needs. All this determines the isolation of local populations and their dependence on the general characteristics of the locality of which they are residents.

The purpose of this article is to develop approaches to the extended measurement and analysis of income inequality of the Russian population taking into account their place of residence and available information on income at the municipal level. The main indicators for measuring income inequality at the national level were the Gini coefficient and the Palm ratio, which complement each other quite harmoniously, characterizing the overall level of inequality and the income gap between the rich and poor. These coefficients are very popular among researchers and their interpretation requires no effort.

Measuring inequality at the local level requires special tools that could make it possible to characterize this phenomenon based on sample data. The publication uses the intra-cluster correlation coefficient (ICC) as such a tool, thanks to which it is possible to assess the degree of uniformity of income of the population within clusters and, accordingly, income differentiation between clusters.

The ICC made it possible to link the level of per capita income of the population with the municipality as the place of permanent residence of the household. It was used to assess the ongoing changes in income inequality, taking into account the differences between and within territorial entities, both from the perspective of income inequality within population groups and through the difference in average incomes among local populations.