MODELING OF INCOME OF SOCIO-ECONOMIC SYSTEMS ON THE BASIS OF THE PRODUCTION FUNCTION
https://doi.org/10.26794/2587-5671-2018-22-1-118-127
Abstract
Topic. The article examines the problems associated with forecasting of the development prospects of the economy.
Purpose. The creation of a model used to predict the replenishment of budgets of all levels. Analysis of the state of the Russian economy in general and its 85 re gions.
Methodology. The research was conducted on the basis of economic and statistical techniques, system analysis, and scientific methods of comparison and mapping. The work applied the proposed the authors the term “macroeconomic production function” — an analog of the production function, which expresses the dependence of production results of the enterprise and factors of production. We used data on tax revenues for all kinds of taxes, employment and gross regional product contained in consolidated informationanalytical system of regional tax revenues “Taxes of Russia”. Data analysis and estimation of parameters was performed using the programs of statistical data processing — IBM SPSS Statistics 20. In the multiple regression procedure of SPSS it was used methods of inclusion, allowing step-by-step selection in a regression equation only the significant independent variables.
Results. Based on the proposed model, we conducted the comparison of actual and estimated values of tax revenues for all the subjects of the Russian Federation in 2011 and 2014. We obtained the values of point estimates of the model parameters of macroeconomic production functions. Further, we provided simulation of the values of tax revenues for all the subjects of the Russian Federation for 2014 year. Next, we compared the actual and calculated (from the model) values of tax revenues. Finally, we present the results of comparing the actual and estimated (from the model) values of tax revenues for all the subjects of the Russian Federation for 2014 year.
Conclusions. The dependence of tax revenues from the factor of labor productivity is constant for every year. With the growth of capital, the amount of tax incomes of subjects of the Russian Federation was threefold higher than increase in productivity of labor. It can be used for planning of economic development of regions.
About the Authors
A. Sh. KamaletdinovRussian Federation
Anvar Sh. Kamaletdinov — Cand. Sci. (Phys.-Math.), Associate Professor, Department of management
Moscow
A. A. Ksenofontov
Russian Federation
Andrei A. Ksenofontov — Cand. Sci. (Phys.-Math.), Associate Professor, Department of management
Moscow
References
1. Эскиндаров М. А., Баранов Э. Ф., Лобзова А. Ф. и др. Российская экономика в 2011–2013 годах: тенденции, анализ, прогноз. Аналитический доклад. М.: Финансовый университет при Правительстве РФ, 2013. 118 с. Eskindarov M. A., Baranov E. F., Lobzova A. F. et al. The Russian economy in 2011–2013: trends, analysis, forecast. Analytical report. Moscow: Financial University under the Government of the Russian Federation, 2013. 118 p. (In Russ.).
2. Касаев Б. С., Ртищев А. В. Трехсекторная модель экономики и проблемы снижения пространственной поляризации регионов России // Инновации и инвес тиции. 2013. № 5. С. 113–116. Kasaev B. S., Rtishchev A. V. Three-sector model of the economy and the problem of reducing the spatial polarization of regions of Russia. Innovatsii i investitsii = Innovations and investments, 2013, no. 5, pp. 113–116. (In Russ.).
3. Усманова Т. Х. Менеджмент устойчивого социально-экономического развития регионов в рамках бюджетно-налоговой и денежно-кредитной политики России // МИР (Модернизация. Инновации. Развитие). 2016. Т. 7. № 1 (25). С. 123–131. Usmanova T. H. The management of sustainable socio-economic development of the regions in the framework of fiscal and monetary policy of Russia. MIR (Modernizatsiya. Innovatsii. Razvitie) = MIR (Modernization. Innovations. Development), 2016, vol. 7, no. 1 (25), pp. 123–131. (In Russ.).
4. Мхитарян В. С., Архипова. М.Ю., Сиротин В. П. Эконометрика: учебно-методический комплекс. М.: Изд. центр ЕАОИ. 2008. 144 с. Mkhitaryan V. S., Arkhipova M. Yu., Sirotin V. P. Econometrics: educational-methodical complex. Moscow: Publishing centre EAOI, 2008. 144 p. (In Russ.).
5. Zhang F., Tan Q., Zhang C., Guo S., Guo P. A Regional Water Optimal Allocation Model Based on the Cobb-Douglas Production Function under Multiple Uncertainties. Journal Water, 2017, vol. 9, no. 12, p. 923.
6. Klump R., McAdam P., Willman A. The normalized CES production function: theory and empirics. Journal of Economic Surveys, 2012, vol. 25, no. 5, pp. 769–799.
7. Vilcu G. On a generalization of a class of production functions. Journal Applied Economics Letters, 2018, vol. 25, no. 2, pp. 106–110.
8. Grassetti F., Hunanyan G. On the economic growth theory with Kadiyala production function. Communications in Nonlinear Scienc e and Numerical Simulation Journal, 2016, vol. 58, pp. 220–232.
9. Arrow K., Chenery H., Minhas B., Solow R. Capital-labor substitution and economic efficiency. The Review of Economics and Statistics, 1961, vol. 43, no. 3, pp. 225–250.
10. Mallick D. The role of the elasticity of substitution in economic growth: A cross-country investigation. Labour Economics, 2012, vol. 19, no. 5, pp. 682–694.
11. Brianzoni S., Mammana C., Michetti E. Local and global dynamics in a neoclassical growth model with nonconcave production function and nonconstant population growth rate. Siam Journal on Applied Mathematics. 2015, vol. 75, no. 1, pp. 61–74.
12. Dreger C. Long-term growth perspectives in Japan and the Euro area. Asia Europe Journal, 2017, vol. 15, no. 4, pp. 363–375.
13. Daniels G., Kakar V. Economic Growth and the CES Production Function with Human Capital. Economics Bulletin, 2017, vol. 37, no. 2, pp. 930–941.
14. Willetts R., Burdon J., Glass J. Fostering sustainability in infrastructure development schemes. Proceedings of the institution of civil engineers-engineering susta inability, 2010, vol. 163, pp. 159–166.
15. Gunhan S., Arditi D. Factors affecting international construction. Journal of Construction Engineering and Management, 2005, vol. 131, no. 3, pp. 273–283.
16. Xu Qun, Yalin L., Jianping G. Did investment become green in China? Evidence from a sectoral panel analysis from 2003 to 2012. Journal of Cleaner P roduction, 2017, vol. 156, pp. 500–506.
17. Lee Kang-Wook, Wooyong Jung, Heon Seung. Country Selection Model for Sustainable Construction Businesses Using Hybrid of Objective and Subjective Information. Sustainability, 2017, vol. 9, no. 5, p. 800.
18. Bu-Qammaz A.S., Dikmen I., Talat M. Risk assessment of international construction projects using the analytic network process. Canadian Journal of Civil Engineering, 2011, vol. 36, no. 7, pp. 1170–1181.
19. Усманова Т. Х. Системный подход в решении социально-экономических задач в рамках бюджетно-налоговой и денежно-кредитной политики России // Сборник трудов IV Международной научно-практической конференции «Системный анализ в экономике — 2016–биеннале». М., 2016. С. 297–300. Usmanova T. H. A systematic approach in solving socio-economic tasks in the framework of fiscal and monetary policy of Russia. In Proceedings of IV International scientific-practical conference “System analysis in the ec onomy of the 2016 Biennale ”. Moscow, 2016, pp. 297–300. (In Russ.).
20. Ксенофонтов А. А. Камалетдинов А. Ш. Управление финансовой деятельностью социально-экономических систем // Вестник Университета (Государственный университет управления). 2017. № 3. С. 120–127. Ksenofontov A. A., Kamaletdinov A. Sh. Financial management of socio-economic systems. Vestnik Universiteta (Gosudarstvennyi universitet upravleniya) = Bulletin of University (State University of Management), 2017, no. 3, pp. 120–127. (In Russ.).
21. Косарев И. М., Камалетдинов А. Ш., Ксенофонтов А. А., Москаленко Л. А. Применение информационных технологий при обработке и анализе данных о налоговых поступлениях // Международный научно-исследовательский журнал «Успехи современной науки и образования». 2016. № 11. С. 104–107. Kosarev I. M., Kamaletdinov A. Sh., Ksenofontov A. A., Moskalenko L. A. Application of information technology in the processing and analysis of data on tax revenue. Mezhdunarodnyi nauchnoissledovatel’skii zhurnal “Uspekhi sovremennoi nauki i obrazovaniya” = International research journal “Advances in modern scienc e and education”, 2016, no. 11, pp. 104–107. (In Russ.).
22. Наследов А. Д. IBMSPSS Statistics20 и AMOS: профессиональный статистический анализ данных. СПб.: Питер, 2013. 416 с. Nasledov A. D. IBMSPSS Statistics20 and AMOS: Professional statistical analysis of data. St. Petersburg: Peter, 2013. 416 p. (In Russ.).
23. Айвазян С. А. Прикладная статистика. Основы эконометрики. Т. 2. М.: ЮНИТИ-ДАНА. 2001. 432 с. Ayvazyan S. A. Applied statistics. The basics of econometrics. Vol. 2. Moscow: YUNITI-DANA. 2001. 432 p. (In Russ.).
24. Астафьева О. В., Астафьев Е. В. Формирование индустриальной траектории развития национальной экономики для обеспечения перехода к новому технологическому укладу // Региональная экономика: теория и практика. 2016. № 5. С. 109–120. Astafieva O. V., Astafiev E. V. Formation of an industrial development trajectory of the national economy to ensure the transition to a new technological order. Regional’naya ekonomika: teoriya i praktika = Regional economy: theory and practice, 2016, no. 5, pp. 109–120. (In Russ.).
Review
For citations:
Kamaletdinov A.Sh., Ksenofontov A.A. MODELING OF INCOME OF SOCIO-ECONOMIC SYSTEMS ON THE BASIS OF THE PRODUCTION FUNCTION. Finance: Theory and Practice. 2018;22(1):118-127. (In Russ.) https://doi.org/10.26794/2587-5671-2018-22-1-118-127