Statistical analysis and modeling of regional economy and science relationship

Occupation: Principal Scientific Researcher
Affiliation: Central Economics and Mathematics Institute, Russian Academy of Sciences
Address: Moscow, Russian Federation

Alexander Kudrov

Occupation: Leading researcher
Affiliation: Central Economics and Mathematics Institute, Russian Academy of Sciences
Address: Nachimovky prospect 47

Iraida Tarakanova

Affiliation: Central Economics and Mathematics Institute, Russian Academy of Sciences
Address: Varshavskoe sh., 144-2-401

Journal name

Ekonomika i matematicheskie metody

Edition

Volume 58 Issue 4

Pages

56-70

Abstract

This paper proposes the analysis of regional economy dynamic model, reflecting the correlation between regional output, the number of employees and production funds. The attention is focused on the influence of different factors of science development on the economy. Those are: the number of PhD and researchers, the number of patents (of different kinds) and the new technologies. Besides, the sectoral orientation of the regional economy is taken into account, firstly, determined by the level of development of the mining and manufacturing. To build the models and make statistical calculations, the data of the official statistics is used. All the main parameters of the proposed model are estimated using modern computational optimization procedures given nonlinear dependencies. Statistical calculations showed that the obtained values of the main parameters of the models are significant, and the models themselves can be used for individual private estimations and forecasts. An important methodological feature of our approach: the necessary dependencies between model variables are identified with causal analysis methods and family hypothesis significance testing techniques. For this purpose, the general form of the production function is revealed by finding a graph of direct relationships of the entire system of the main variables. The resulting model is a methodological basis for constructing short-term forecasts of regional economic dynamics, which takes into account the contribution of science and the role of its individual components on economic growth. Besides, the dynamic model allows visualizing different variants of scenario analysis and gives an opportunity to optimize the trajectory. Selected computer calculations show how the qualitative nature of the trajectories of science and economy variables changes.

Keywords

indicators of regional science; causal analysis; immediate connections; structure of connections; science development index; index of sectoral orientation; dynamical model; trajectories of economic growth

Received

13.07.2022

Publication date

07.12.2022

Number of characters

34803

Cite

GOST	Gavrilets Y., Kudrov A., Tarakanova I. Statistical analysis and modeling of regional economy and science relationship // Ekonomika i matematicheskie metody – 2022. – V. 58. – Issue 4 C. 56-70 [Electronic resource]. URL: https://emm.jes.su/S042473880023019-9-1 (circulation date: 25.04.2024). DOI: 10.31857/S042473880023019-9
MLA	Gavrilets, Yuriy, Kudrov, Alexander, Tarakanova, Iraida "Statistical analysis and modeling of regional economy and science relationship." Ekonomika i matematicheskie metody 58.4 (2022):56-70. DOI: 10.31857/S042473880023019-9
APA	Gavrilets Y., Kudrov A., Tarakanova I. (2022). Statistical analysis and modeling of regional economy and science relationship. Ekonomika i matematicheskie metody 58(4), pp.56-70 DOI: 10.31857/S042473880023019-9

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1. Boeing P., Eberle J., Howell A. (2022). The impact of China's R&D subsidies on R&D in-vestment, technological upgrading and economic growth. Technological Forecasting and Social Change. Vol. 174. DOI:10.1016/j.techfore.2021.121212
2. Bonferroni C. (1936).Teoria statistica delle classi e calcolo delle probabilità.Publicazioni del R. Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 1–62.
3. David P., Hall B., Toole A. (2000). Is public R&D a complement or substitute for private R&D? A Review of the Econometric Evidence. Research Policy, 29 (4–5),497–529.
4. Gavrilets Y.N., Lebedev K.V., Tarakanova I.V. (2021). On statistical evaluation of science and education in the subjects of the Russian Federation in 2017–2019. In: Collection of articles of the international scientific-practical conference. Krasnodar: Prosveshenie-Yug (in Russian).
5. Gavrilets Yu.N. (1974). Social and economic planning. Systems and models. Moscow: Eco-nomics (in Russian).
6. Gavrilets Yu.N., Kudrov A.V., Tarakanova I.V. (2018). Analysis of the internal structure for the economic growth potential. Herald of CEMI, 1, 1.  Available at: https://cemi.jes.su/s111111110000009-2-1/  (in Russian).
7. Glazyev S.Y. (2019). The development of the Russian economy in the context of global technological shifts. The future of Russia. Executions and projects: Economics. Technique. Innovations. Moscow: URSS (in Russian).
8. Golichenko O.G. (2007). National innovation system of Russia: State and ways of develop-ment. Voprosy Ekonomiki, 7, 155–157 (in Russian).
9. Hayes D. (1981). Causal analysis in statistical research. Moscow: Finansy i statistika (in Rus-sian).
10. Holm S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 2, 65–70.
11. Lauritzen S. (1996). Graphical models. Oxford: Oxford University Press.
12. Liu F., Simon D., Sun Y., Cao C. (2011). China’s innovation policies: Evolution, institutional structure, and trajectory. Research Policy, 40, 917–931.
13. Makarov V.L. (2003). Contours of knowledge economy. The Economist, 3, 3–15 (in Russian).
14. Makarov V.L. (2013). Science cannot be effective. Direct Investments: Magazine about Real Economy, 5, 21–23 (in Russian).
15. Mazzucato M. (2015).The entrepreneurial state: Debunking public vs. private sector myths. London: Anthem Press.
16. OECD (2022a). Gross domestic spending on R&D (indicator). DOI: 10.1787/d8b068b4-en
17. OECD (2022b). Researchers (indicator). DOI: 10.1787/20ddfb0f-en
18. Ratkowsky D. (1993). Principles of nonlinear regression modeling. Journal of Industrial Mi-crobiology, 12, 195–199.
19. Seber G., Wild C. (2003).Nonlinear Regression. N.Y.: Wiley.
20. Stiglitz J., Lin Y., Monga C. (2013). The rejuvenation of industrial policy. World Bank Policy, Res. Work. Pap. 6628.
21. Varshavskiy A.E., Makarov V.L. (2004). Sustainable development strategy: The need for in-vesting in the future. In: Innovation management in Russia: Issues of strategic manage-ment and scientific and technological security. V.L. Makarov, A.E. Varshavskiy (head of author's team). Moscow: Nauka (in Russian).
22. Varshavsky A.E., Makarov V.L. (2015). Science, high-tech industries and innovation. In: Russian economy. Oxford compendium. Book 2. Moscow: Gaidar Institute Publishing House (in Russian).