Suchergebnisse

In the article, we study the relationship between the information technology component and the tax potential of Russian regions as factors of economic growth and sustainable development. The aim of the research is to develop and test a methodology for assessing the level of information technology development of the Russian regions and its relationship with their tax status. The research methodology involves the use of factor analysis and the method of principal components using the Rstudio integrated development environment and the statistical package IBM SPSS statistics. The indicators traditionally used in international rankings of the information society development are supplemented with data on the assessment of efforts aimed at creating and implementing innovations. The study is based on the following data for the whole Russia, for federal districts, and for regions of the Russian Federation: the number of active fixed and mobile subscribers, the number of fixed and mobile broadband access subscribers, and the number of mobile communication devices. Our calculations made it possible to identify the worst and best regions in terms of the information and technological development of the territory. We have found that the worst regions are Sevastopol, Crimea, Ingushetia, Dagestan, Adygea. The regions with a highly developed information technology component include St. Petersburg, Moscow, Novosibirsk, Karelia, and Murmansk Oblast. The successful experience of the best regions in terms of the level of digitalization should be studied in detail and competently used in other Russian regions. The study showed that the readiness of the territories for digital transformations is determined by the level of their economic development. The tax potential of the regions is the main factor determining the growth of the gross regional product of the subject of the Russian Federation. Territories with highly developed information technology component form significantly higher tax revenues of the budget system per capita. This creates the basis not only for further increasing of the level of informatization, but also for its successful socio-economic development for regions. Thus, the level of the economic development of the region, its gross regional product and tax potential are the basis for effective digitalization. The accumulation of significant tax revenues creates opportunities for financing activities for the digitalization of the region. Further research could be in the following main areas: a competent increase in the number of indicators used and the expansion of time horizons; application of successfully tested research methods for other states, their groups and international associations; supplementing of the applied methods with models that have proven themselves well when working with short series, for example, ARIMA models

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.