Analysis of Socioeconomic Indices in Russia’s Regions

The subject of this study is the socioeconomic indicators of Russian regions. The relevance of this work is determined by issues of spatial heterogeneity, where donor regions, concentrating financial flows and high-tech industries, coexist with territories that remain focused on raw materials or are experiencing a structural crisis. The goal is to identify and quantify statistically significant spatial autocorrelation relationships, requiring consideration, analysis, and evaluation of the spatial context, as well as the specific development patterns of the identified clusters. The research methodology is based on the calculation of global and local Moran’s I indices using a binary matrix of spatial weights and randomized testing, as well as an analysis of the dynamics of eleven representative indicators. The study revealed the presence of stable spatial autocorrelation for six key indicators out of eleven representative ones: retail turnover dynamics, consumer price indices, changes in the cost of a fixed set of goods and services, prices on the primary housing market, nominal wages, and the unemployment rate. LISA clustering was used to identify persistent spatial patterns, including the formation of contrasting areas of synchronous growth and decline, as well as zones of spatial outliers where local development barriers impede the spread of positive economic impulses. It was concluded that the identified polarization between central regions demonstrating accelerated growth in income and consumer activity and peripheral territories experiencing structural constraints indicates the persistence of fundamental imbalances in spatial development, reproduced through mechanisms of spatial spillover and competition. The authors recommended more active use of the obtained results, which have significant practical implications for the development of differentiated regional policies that require consideration, analysis, and assessment of the spatial context and specific features of the identified cluster development.

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