Tax Clustering of Russian Regions as an Instrument to Classify Territories

Abstract

The paper is devoted to issues of diagnosing territories in terms of the effect that taxes are made on their development. The message of the research is competition that occurs between territories to possess a tax base. The purpose of the research is related to grouping regions into clusters by a set of indicators, which reflect tax status, tax administration and tax policy. The authors selected the official statistics of 2018, and using these data they calculated 14 indicators. Initially, the research covered 84 Russia's federal constituent entities. However, five Russia's federal constituent entities were excluded from the survey due to their abnormal data: Moscow, Sevastopol, Ingushetia, Khanty-Mansi and Yamalo-Nenets autonomous areas. The cluster analysis is conducted using the software SPSS, Rstudio, Anaconda Navigator. The analysis resulted into highlighting three regional clusters: 1) least functionally proportional (7 regions), 2) medium functionally proportional (50 regions), 3) diversely most successful (22 regions). It is found out that the leaders of the third cluster by digital indicators are Tyumen region, Murmansk region, Republic of Tatarstan, Leningrad region. These regions are highly rated by accessibility of digital technologies, internet usage and dissemination of digital competences. The authors of this paper suggest considering tax clustering of regions as an instrument to establish the tax policy at the sub-federal level. The given research has the following promising areas to develop.1) Adding non-typical indicators for specification of tax environment to the cluster analysis, which fully reflect the influence of miscellaneous external factors on regions' tax status. 2) Extrapolating results to assessment of the tax status for territories of other states. 3) Necessity to improve the method of tax clustering using the technology of artificial intelligence.