International Journal of Technology (Dec 2021)

The Interrelation between Digital and Tax Components of Sustainable Regional Development

  • Natalia Victorova,
  • Elena Vylkova,
  • Vladimir Naumov,
  • Natalia Pokrovskaia

DOI
https://doi.org/10.14716/ijtech.v12i7.5338
Journal volume & issue
Vol. 12, no. 7
pp. 1508 – 1517

Abstract

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The paper explores the relationship between digital characteristics and tax potential of the constituent entities of the Russian Federation as factors of sustainable development of territories and economic growth. The purpose of the study involves development and testing of a methodology for assessing the level of digitalization in Russian regions and its relationship with their tax potential, which has not been sufficiently developed in the available scientific research by Russian and foreign authors. For this purpose, cluster and factor analysis were applied with the use of Rstudio, the IBM SPSS statistics package, and the Anaconda Navigator graphical interface. The following data were studied: the number of active subscribers of fixed and mobile access, subscribers of fixed and mobile broadband access as well as mobile communication devices for all constituent entities of the Russian Federation. The authors identified the worst and best regions in terms of mobile and fixed communications. It was concluded that the regions’ readiness for digital transformation is determined by the general level of their economies. Significantly larger tax revenues per capita are generated in regions with a highly developed IT component, which is the basis for solving the problems of sustainable development of such territories. The economic situation in a region, its gross regional product, and its tax potential create the basis for digitalization of each constituent entity of the Russian Federation. Significant tax revenues per capita are the key to the success of the territories in the IT sector. Promising areas for further research are: (1) expansion of the indicators used and time horizons; (2) extrapolation of the results to other countries and groups of countries; (3) use of the methods and models that have proven themselves when working with short series, e.g., autoregressive integrated moving average models.

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