Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Clustering of Russian Regions : The Relationship between Digital and Tax Components. / Victorova, Natalia; Vylkova, Elena; Naumov, Vladimir; Pokrovskaia, Natalia; Yevstigneev, Yevgeniy.
Proceedings - International Scientific Conferenc: Digital Transformation on Manufacturing, Infrastructure and Service, DTMIS 2020. Association for Computing Machinery, 2020. (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Clustering of Russian Regions
T2 - 2020 International Scientific Conference on Digital Transformation on Manufacturing, Infrastructure and Service, DTMIS 2020
AU - Victorova, Natalia
AU - Vylkova, Elena
AU - Naumov, Vladimir
AU - Pokrovskaia, Natalia
AU - Yevstigneev, Yevgeniy
N1 - Publisher Copyright: © 2020 ACM.
PY - 2020/11/18
Y1 - 2020/11/18
N2 - The article discusses the impact of digital development of Russian regions on their tax status. Inside the research goal, authors put forward two hypotheses: regions with a developed digital infrastructure are characterized by a stable tax situation and tax status of the least successful regions is only partly related to the level of digitalization in the region. The study included three stages: clustering of Russian regions according to the level of their digital development; clustering of regions according to their tax status and analyzing of relationship between digital and tax clustering's results. Fifteen indicators that characterize the development of the information society at the regional level were used to identify digital clusters. Fourteen indicators describing the tax status, tax administration and tax policy of the regions were used for tax clusters. It was found that the distribution of regions into these two types of clusters is uneven, without unambiguous relationship between cluster types. Research contribution is related to authors' hypotheses. First hypothesis was confirmed: Russian regions with a developed digital infrastructure also have stable tax status. As for the second hypothesis, it can be assumed, that the least successful regions have a high level of tax evasion. The results of the study showed the need to search for new indicators that characterize the relationship between digitalization and taxation in the regions. Cluster analysis of regions by digital and tax parameters can serve as a tool for the formation of regional tax policy aimed at digital economy development.
AB - The article discusses the impact of digital development of Russian regions on their tax status. Inside the research goal, authors put forward two hypotheses: regions with a developed digital infrastructure are characterized by a stable tax situation and tax status of the least successful regions is only partly related to the level of digitalization in the region. The study included three stages: clustering of Russian regions according to the level of their digital development; clustering of regions according to their tax status and analyzing of relationship between digital and tax clustering's results. Fifteen indicators that characterize the development of the information society at the regional level were used to identify digital clusters. Fourteen indicators describing the tax status, tax administration and tax policy of the regions were used for tax clusters. It was found that the distribution of regions into these two types of clusters is uneven, without unambiguous relationship between cluster types. Research contribution is related to authors' hypotheses. First hypothesis was confirmed: Russian regions with a developed digital infrastructure also have stable tax status. As for the second hypothesis, it can be assumed, that the least successful regions have a high level of tax evasion. The results of the study showed the need to search for new indicators that characterize the relationship between digitalization and taxation in the regions. Cluster analysis of regions by digital and tax parameters can serve as a tool for the formation of regional tax policy aimed at digital economy development.
KW - cluster analysis
KW - digital development of the region
KW - Digital economy
KW - tax state of the region
UR - http://www.scopus.com/inward/record.url?scp=85123043275&partnerID=8YFLogxK
U2 - 10.1145/3446434.3446484
DO - 10.1145/3446434.3446484
M3 - Conference contribution
AN - SCOPUS:85123043275
T3 - ACM International Conference Proceeding Series
BT - Proceedings - International Scientific Conferenc
PB - Association for Computing Machinery
Y2 - 18 November 2020 through 19 November 2020
ER -
ID: 92308422