CLUSTERING OF RUSSIAN REGIONS BY SOCIALLY IMPORTANT INDICATORS OF TRADE DEVELOPMENT

The article aims at forming clusters of Russian regions based on socially significant indicators of trade development. Clustering was conducted using the k-means method based on statistical data from 85 regions. As a result, five clusters were identified. The first cluster is characterized by a high share of chain retail turnover in the total retail trade volume. The second cluster shows no extreme values and is defined as average. The third cluster stands out for its well-developed market and fair trade. The distinctive feature of the fourth cluster is high consumer prices. The fifth cluster is marked by many retail facilities. In the context of fulfilling the social functions of trade, the most challenging is the fourth cluster, which includes four regions in the Far Eastern Federal District. To increase the social impact of trade in this cluster, it is advisable to develop foreign trade with neighboring Asian countries, including China, and to promote the expansion of Russian and cross-border e-commerce. The value of the results lies in the fact that clustering based on indicators reflecting the social significance of trade enables the adaptation of trade development policies to the specific characteristics of each region. This will contribute to a more comprehensive realization of trade’s social functions and help mitigate regional socioeconomic disparities.