| Annotation |
The problem of assessing the level of social well-being is very relevant and is actively studied in both the Russian and world scientific literature. The purpose of the study is to test the possibility of using mathematical methods in specific socio – economic problems related to the assessment of the well-being of the population of Russian regions.
The paper presents an attempt to use simulation methods to assess the impact of quantitative factors that affect well-being at the regional level. In particular, the sensitivity of the extended Sen’s welfare to changes in the values of factors up to a certain “reference” level was assessed. Five Eastern regions of the Russian Federation were selected as model territories, and the Republic of Tatarstan was chosen as the “reference” region. The main attention is paid to the main effects of factors that determine the value of the multiplicative A. Sen function, in the framework of a factor plan of type 2k (k = 6, according to the number of factors studied).
The analysis showed that for this sample, the most significant factors are not only the per capita gross regional product, but also the ratio of citizens’ income to gross value added, as well as the ratio of net income in the region to the national level. The results of our analysis show that the per capita GRP cannot serve as the main indicator. It is important that investments also provide a sufficiently high share of income in gross regional product.
It is concluded that the desire for economic growth “at any cost” is not always justified. If such growth is provided by resource industries and accompanied by the displacement of other sectors from the economy, their impact on welfare can be negative. This factor should be taken into account within programs of spatial development in Russia |
| References |
1. Belousova A. V., Gritsko M. A., Naiden S. N. Vlast i upravlenie na Vostoke Rossii (Power and Administration in the East of Russia), 2019, no. 4, pp. 50–65.
2. Bobkov V. N., Stepanov V. S. Uroven zhizni naseleniya regionov Rossii (Living standarts of the population in the regions of Russia), 2014, no. 1, pp. 104–110.
3. Vasilieva E. V. Regionalnaya ekonomika: teoriya i praktika (Regional economics: theory and practice), 2014, no. 44, pp. 14–24.
4. Glazyrina I. P., Faleychik L. M., Faleychik A. A. Izvestiya RAN. Seriya geograficheskaya (Proceedings of the RAS. Geographical Series), 2020, vol. 84, no. 3, pp. 341–358.
5. Domnina S. V. Region: ekonomika i sociologiya (Region: economics and sociology), 2011, no. 3, pp. 70–77.
6. Kelton W. D., Lou A. M. Imitatsionnoe modelirovanie. Klassika CS (Simulation modeling. CS Classics). St. Petersburg: Piter; Kiev: BHV, 2004. 847 p.
7. Malkina M. Yu. Terra Economicus (Terra Economicus), 2016, vol. 14, no. 3, pp. 29–49.
8. Malkina M. Yu. Ekonomika regiona (Economy of region), 2017, vol. 13, no. 1, pp. 49–62.
9. Naiden S. N., Belousova A. V. Ekonomika regiona (Economy of region), 2018, vol. 14, no. 1, pp. 53–68.
10. Pyzhev A. I., Pyzheva Yu. I. Regionalnaya ekonomika: teoriya i praktika (Regional economics: theory and practice), 2015, no. 34, pp. 30–40.
11. Ryumina E. V. Upravlenie ekonomicheskimi sistemami: elektronny nauchny zhurnal (Management of Economic Systems: electronic scientific journal), 2018, no. 9, pp. 24.
12. Brown C., Lazarus E. Ecological Indicators (Ecological Indicators), 2018, vol. 93, pp. 1143–1151.
13. Castaneda B. Ecological Economics (Ecological Economics), 1999, vol. 28, no. 2, pp. 231–244.
14. Clarke M., Islam S. M. N. Ecological Economics (Ecological Economics), 2005, vol. 54, no. 1, pp. 81–93.
15. Sen A. Review of Economic Studies (Review of Economic Studies), 1976, vol. 43, pp. 19–39.
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