КЛАСТЕРНЫЙ АНАЛИЗ РЕГИОНОВ РФ ДЛЯ ВЫЯВЛЕНИЯ ТЕРРИТОРИЙ – ДРАЙВЕРОВ УСТОЙЧИВОГО РАЗВИТИЯ: НАЛОГОВАЯ КОМПОНЕНТА

Abstract

The relevance of tax clustering is due to the need for a competent scientifically grounded definition of territories that are drivers of economic growth. The aim of the study was to identify, on the basis of econometric methods, clusters of the regions of the Russian Federation by a set of indicators reflecting their tax status, tax administration, informatization of the tax environment. The Russian regions were grouped into clusters by a set of tax indicators based on official statistical data for 2018 using SPSS, Rstudio, Anaconda Navigator software. As a result of the anomalous values, five federal subjects were excluded from the analysis: Moscow, Sevastopol, Ingushetia, Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs. Econometric analysis made it possible to conclude that there are three clusters of regions according to the analyzed parameters: 1) the least functionally proportional (7 regions), which have the lowest tax intensity of the gross regional product, the highest debt intensity of the gross regional product and the highest level of tax debt of the employed population, companies, and individual entrepreneurs; 2) medium functionally proportional (50 regions) with the lowest efficiency of tax administration, the highest coefficient of tax collection, the lowest level of taxation of the employed population and individual entrepreneurs (but not companies), the lowest level of tax debt of all analyzed subjects, and the lowest additional tax charges and sanctions for violation of tax legislation from tax audits, 3) the most comprehensively successful (22 regions), which are characterized by the highest tax intensity of the gross regional product and the highest level of tax revenues generated by the employed population, companies, and individual entrepreneurs. The regions of this cluster have the most effective taxation of value added and financial results of organizations. Among the regions of the third group, the leaders in terms of digital indicators are: Tyumen Oblast, Murmansk Oblast, Republic of Tatarstan, Leningrad Oblast. The study can develop in the following promising directions: 1) inclusion in the cluster analysis of indicators, not typical for the characteristics of the tax environment, that most fully reflect the influence of external diverse factors on the tax state of the regions; 2) extrapolation of the results to assess the tax status of the territories of other states; 3) the need to improve the tax clustering method based on artificial intelligence technology.