Priority directions of tax policy change in the information sphere

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Authors:

O. Lagovska, Dr. Sc. (Econ.), Prof., Professor of the Department of Accounting and Auditing, orcid.org/0000-0001-9517-0499, Zhytomyr Polytechnic State University, Zhytomyr, Ukraine

V. Ilin, Dr. Sc. (Econ.), Prof., Professor of the Department of Accounting and Auditing, orcid.org/0000-0002-4223-1865, University of the State Fiscal Service of Ukraine, Irpin, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M. Kotsupatriy, Cand. Sc. (Econ.), Prof., Professor of the Accounting and Taxation Department, orcid.org/0000-0001-5528-3210, Kyiv National Economic University named after Vadym Hetman, Kyiv, Ukraine

M. Ishchenko, Dr. Sc. (Econ.), Prof., Professor of the Department of Accounting, Taxation, Public Administration and Administration, orcid.org/0000-0002-6820-9455, Kryvyi Rih National University, Kryvyi Rih, Ukraine

L. Verbivska, Cand. Sc. (Econ.), Assoc. Prof., Associate Professor of the Department of Business, Trade and Stock Exchange Operations, orcid.org/0000-0002-2768-9157, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine

 

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020, (3): 183-190

https://doi.org/10.33271/nvngu/2020-3/183

 

повний текст / full article

 

Abstract:

Purpose. To substantiate the proposals about priority directions of tax policy change in the information sphere for various sectors of economy, in particular, mining industry. For development of proposals to analyze the current state of the information sphere, to identify and evaluate the possible risks from changes in taxation, to forecast tax rates using modern mathematical methods and algorithms, to set the limit for increasing the tax burden on experts in the information sector.

Methodology. The improved method of modeling interval time series was used for prognostication of volume of tax receipts from experts in the information sector and the volume of export of information services. The parameter of volume of export matches the requirements for applying the specified method. This parameter applies to flow variables. The use of the interval series method gives an opportunity to get sums of their levels, which is convenient for solving the task of forecasting. As the model of the so-called mathematical apparatus of forecasting, “Generalized AutoRegressive Conditional Heteroscedasticity” – UARUG (GARCH) was chosen, the Adaptive Rejection Metropolis Sampling (ARMS) was used. The use of ARMS makes provisions for the use of Hastings-Metropolis and adaptive rejection sampling (ARS) methods. The obtained results were approximated to construct analytical equations of predictive parameters over time.

Findings. A detailed analysis of the situation in the field of information services for various sectors of the economy, in particular, mining industry, was conducted. The risks of increasing the tax burden beyond certain boundary values were identified. The calculations of the forecast values of the volume of export of services and the amount of tax burden on the information sector up to 2025 were carried out. This made it possible to scientifically explain possible changes in the tax policy in the information sector. Suggestions for priority directions of tax policy change in the information sector for various sectors of economy, in particular, mining industry, were given.

Originality. The specific proposals for priority directions of tax policy change in the information sphere for various sectors of economy are developed and substantiated. The limit of possible increase in taxation on experts in the information sector was grounded and the value of this limit was calculated for the first time. The improved interval time series modeling method was used to forecast the volume of the single social security tax of the future periods and the volume of export of IT services for the first time. In contrast to traditional methods, which rely on existing forecasts, the polynomial approach reducing the relative error is proposed.

Practical value. The detailed recommendations on tax policy change in the field of information services for various sectors of the economy, in particular, the mining industry are given. The need for a change in tax policy, for individual entrepreneurs in particular, is pointed out. It is noted that the proposed changes should be implemented gradually, as transparently as possible and on the condition of careful monitoring in the industry. The analytically presented forecasting makes their practical application convenient.

References.

1. Legislation of Ukraine (2010). Tax Code of Ukraine of 02.12.2010 No. 2755-VI. Vidomosti Verkhovnoi Rady Ukrainy, 13-14, 15-16, 17. Retrieved from https://zakon.rada.gov.ua/laws/show/2755-17.

2. Liashenko, V. I., & Vyshnevskyi, O. S. (2018). Digital Modernization of the Ukrainian Economy as a Breakthrough Opportunity: monograph. Kyiv: NAS of Ukraine, Institute of Industrial Economics. ISBN 978-966-02-8440-1.

3. IT Ukraine Association (2017). Ukrainian IT Industry: General Data and Expertise Level. Retrieved from http://itukraine.org.ua/news/infografika_ukrayinska_it_industriya_zagalni_dani_ta_profesiynyy_profil_specialistiv.

4. Dyba, M., & Gernego, I. (2018). Digitalization of Economy: International Expirience and Possibilities of Development in Ukraine. Finansy Ukrainy, 7, 50-63.

5. Mishchenko, L. (2020). Improvement of the state budget of Ukraine projecting income. Efektyvna ekonomika, 4, 1-9. https://doi.org/10.32702/2307-2105-2020.4.51.

6. Synyutka, N. H., & Lutsyk, A. I. (2018). Transformation of Fiscal Policy and Fiscal Space under Conditions of Digital Technologies Expansion. Accounting and Finance, 4(82), 108-113.

7. Matveichuk, L. O. (2016). Information technologies and systems in public administration of taxation. Public Administration and Customs Administration, 1, 77-84.

8. Delaney, T. K. (2018). Taxing the Gig Economy. University of Pennsylvania Law Review, 166, 1-60, https://doi.org/10.2139/ssrn.2894394.

9. Adams, A., Freedman, J., & Prassl, J. (2018). Rethinking legal taxonomies for the gig economy. Oxford Review of Economic Policy, 34(3), 475-494. https://doi.org/10.1093/oxrep/gry006.

10. Oei, S.-Y., & Ring, D. (2017). Tax Issues in the Gig Economy: Implications for Workers. Cambridge Handbook of the Sharing Economy, 343-356. https://doi.org/10.1017/9781108255882.026.

11. Lynch, T. D., Sun, J., & Smith, R. W. (2017). Public budgeting in America. Irvine, California: Melving & Leigh Publishers. Retrieved from https://www.amazon.com/Public-Budgeting-America-Sixth-Thomas/dp/0997308443.

12. Rekova, N., Dolozina, I., Nitsenko, V., Zaitsev, Y., & Za­mlynskyi, V. (2018). Budgetary revenue structure at central level of public administration in the federal countries. Administratie si Management Public, 30, 37-50. https://doi.org/10.24818/amp/2018.30-03.

13. Nitsenko, V., Mardani, A., Kuksa, I., & Sudarkina, L. (2018). Additional opportunities of systematization the marketing research for resource conservation practice. Management Theory and Studies for Rural Business and Infrastructure Development, 40(3), 361-368. https://doi.org/10.15544/mts.2018.34.

14. Aleksynska, M., Bastrakova, A., & Kharchenko, N. (2018). Work on Digital Labour Platforms in Ukraine: Issues and Policy Perspectives. International Labor Organization. ISBN: 978-92-2-030734-2.

15. Neuvoo (2020). Income Tax Calculator, California, USA. Find how much you salary is after tax. Retrieved from https://neuvoo.com/taxcalculator/?iam=&uet_calculate=calculate&salary=18000&from=year&region=California.

16. Higgins, P., Zha, T., & Zhong, K. (2016). Forecasting China’s economic growth and inflation NBER working paper series. National bureau of economic research. Retrieved from https://www.nber.org/papers/w22402.pdf.

17. Ministry for Development of Economy, Trade and Agriculture of Ukraine (2019). Forecast of economic and social development of Ukraine for 2020-2022. Retrieved from https://www.me.gov.ua/Documents/Detail?lang=ukUA&id=9cdcccd2-10de-4073-bb63-09a94f88a741&title=PrognozEkonomichnogoISotsialnogoRozvitkuUkrainiNa2020-2022-Roki.

18. Ministry of Finance of Ukraine (2019). Information on fiscal risks and their potential impact on the state budget in 2019. Retrieved from https://mof.gov.ua/storage/files/39ae660ef0cad97119c95ff41bfbf9e1.pdf.

19. Financial and Economic Analysis Office in the VRU (2020). Analysis of the forecasting methodology of main budget-forming taxes. Retrieved from https://feao.org.ua/products/budgetoutvoruyutchi-podatky/.

20. Nitsenko, V., Mardani, A., Streimikis, J., Ishchenko, M., Chaikovsky, M., Stoyanova-Koval, S., & Arutiunian, R. (2019). Automatic Information System of Risk Assessment for Agricultural Enterprises of Ukraine. Montenegrin Journal of Economics, 15(2), 139-152. https://doi.org/10.14254/1800-5845/2019.15-2.11.

21. Kuznietsova, N. V. (2017). Information Technologies for Clients’ Database Analysis and Behaviour Forecasting. CEUR Workshop Proceeding, 2067, 56-62.

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