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25COMPETITIVENESS OF RUSSIAN REGIONS IN FOREIGN ECONOMIC ACTIVITY: METHODS OF ANALYSIS AND FORECASTING
Anna Yu. Kosobutskaya
Voronezh State University, Russia E-mail: anna.rodnina@mail.ru
Yuri I. Treshchevsky
Voronezh State University, Russia E-mail: utreshevski@yandex.ru
Elena A. Opoikova
Voronezh State University, Russia E-mail: oea.voronezh@yandex.ru
Abstract. The paper looks to identify and forecast the trends in foreign economic activity of Russian regions that reflect the state of their competitiveness. The study used: cluster analysis - to identify homogeneous groups of regions with similar parameters of different types of foreign economic activity; comparative analysis - to establish model regions reflecting the specific aspects of development of the respective clusters; correlation and regression analysis - to identify and predict the dynamics of foreign economic activity parameters reflecting the competitiveness of Russian regions. The study reveals the specific aspects of changes in the competitiveness of the typical «average» regions of Russia — Voronezh and Yaroslavl oblasts. We have determined the actual dynamics and forecast of indicators of various aspects of regional competitiveness, such as imports from non-CIS countries, exports to non-CIS countries, imports and exports of technologies and technical services, foreign direct investment. The forecast range is
Keywords: Russian regions, foreign economic activity, competitiveness, export, import, foreign investments. JEL codes: C1; R11; R 13
For citation: Kosobutskaya, A. Y., Treshchevsky, Y. I., & Opoikova, E. A. (2021). COMPETITIVENESS OF RUSSIAN REGIONS IN FOREIGN ECONOMIC ACTIVITY: METHODS OF ANALYSIS AND FORECASTING. JOURNAL OF REGIONAL AND INTERNATIONAL COMPETITIVENESS, 2(1), 44-54. Retrieved from http://jraic.com/index.php/tor/article/view/18
Introduction
Numerous works of Russian and foreign researchers are devoted to the assessment and forecast of processes in foreign economic activity of the regions. (Kuzmina, Timchenko, Naumik, 2020; Timokhin, 2019; Treshchevsky et al., 2020)
Various research papers note the significant impact of globalization in the economic and political areas of countries and regions on their competitiveness and the development of foreign economic relations (Freire, 2019; Head, Mayer, 2010; Dorin et al. 2016; Endovitsky, Treshchevsky, Terzi, 2020).
Researchers around the world note the diversity of regional systems competitiveness factors (Bitarova et al., 2019). One of the most important conditions for regional competitiveness is the overall state of financial systems at different levels — from the global to the micro-level (Radyukova et al., 2018; Endovitskaya, Risin, Treshchevsky, 2018; Lanskaya et al., 2018).
Authors of these works have different views on the purposes of development of foreign economic relations and the factors influencing them, but they are unanimous in the idea that their condition characterizes competitiveness of the countries, regions, and economic subjects.
2020-2024.
Study methods
The analysis and forecast of the regions' competitiveness in foreign economic activity was done in several stages.
The first stage is the construction of virtual clusters of foreign economic activity of regions based on a wide range of indicators (Treshchevsky et al., 2020). We use a generally accepted method to analyze them (Hartigan, Wong, 1979; Mandel, 1988). The total number of clusters adopted for calculations is five (this number of clusters in most cases reflects the nature and level of differentiation of Russian regions by various combinations of social and economic indicators). Moscow is excluded from the calculations because its level of development of foreign economic activity is significantly higher than that of other regions. As such, the differences between the clusters are smoothed out against the advancement level of Moscow. The «second order» regions included in the larger ones are excluded from the calculations in order to avoid double counting. The regions for which no data are available for the entire analyzed period (2000-2019) are also excluded from the calculations.
The second stage is the selection of the time periods for which the virtual clusters are formed. Here is the list of years we have taken the data of to form the clusters: 2000 (the beginning of a new economy); 2005 (high conjuncture, but no overheating); 2009 (crisis worldwide — macroeconomic shock); 2012 (quiet year); 2015 (sanctions and counter-sanctions involving Russia - macroeconomic shock); 2018 (the last year for which all necessary statistics for clustering are available).
The third stage is the selection of «model» regions representing clusters with different levels of foreign economic activity development. For competitiveness analysis and forecast, we selected the medium development level cluster. In 2018, it included 21 regions. For further analysis, we used data on the development of the main parameters of foreign economic activity in the regions which were the closest to the center of the virtual cluster in 2018 and, accordingly, which characterized the cluster as a whole to the greatest extent. These regions were: Voronezh region, distance from the center 0.040449; Yaroslavl region, distance 0.040822; Vladimir region, distance 0.046200; Novosibirsk region, distance 0.047657. Given the limited length of the paper, we used only the data from the Voronezh and Yaroslavl regions.
The fourth stage is the selection of the main indicators reflecting its manufacturing, technical, technological, and financial aspects to analyze the region's foreign economic relations: imports from non-CIS countries; exports to non-CIS countries; imports of technologies and technical services; exports of technologies and technical services; foreign direct investments (inflow).
The fifth stage is choosing the time period to analyze the actual dynamics of the main indicators of foreign economic activity. Statistical data for the analysis were obtained from official statistical handbooks for 2002-2020, which helped with analyzing the data for 2000-2019 (Regions of Russia: 2002-2020).
The sixth stage is determining the forecast threshold. 2024 is chosen as the forecast threshold, which is a year in the middle of the time range of the Strategies of Russian Regions, which are planned until 2035.
The seventh stage is the selection of functions that reflect the dynamics of social and economic processes in Russian regions. The following functions are used to analyze the processes taking place in foreign economic activity and to forecast them: linear, power, logarithmic (by natural logarithm); polynomial; exponential. During the logical analysis, only those functions that have a sufficiently high determination factor (R2 > 0.5) are used for forecasting.
The methodological basis for the analysis and forecast of each process is, any process develops along several trends at the same time, each of which can develop further in the future with a sufficient level of function reliability. Thus, there is a basis to predict not one but several scenarios of development of the analyzed processes.
Results
The data on the regions' foreign economic relations with non-CIS countries that shows the competitiveness the best are used for analysis and forecast.
Estimation of the import volume as a competitiveness indicator stems from the fact that their growth
demonstrates positive changes in competitiveness. On the one hand, there is a demand for quality products in the manufacturing and consumer sectors; on the other hand, there are resources that are offered in payment for imported products and services. Actual and projected import trends from non-CIS countries in the Voronezh region are shown in Figure 1 and in Formulas 1-4.
1200
i Voronezh region --Linear (Voronezh region)
— . . Polynomial (Voronezh region) - Power (Voronezh region)
— — — Logarithmic (Voronezh region)
Figure 1. Imports to Voronezh region from non-CIS countries (million US dollars)
Source: composed by the authors
y = 38.452x + 32.969 (1); R2 = 0.772 y = -0.5947x2 + 50.942x - 12.826 (2); R2 = 0.7769 y = 48.916x0.9241 (3); R2 = 0.8996 y = 266.42ln(x) - 127.23 (4); R2 = 0.6994
As it is seen from the data presented in Figure 1 and Formulas 1-4, there was a development along several trends with high determination factors throughout 2000-2019. This makes it possible to make at least three forecasts regarding imports to the Voronezh region from non-CIS countries. An optimistic (and hence conservative) forecast of the import volume dynamics is based on a linear dependence or a power dependence which is close to it; basic - by polynomial function, pessimistic - by logarithmic function. Actual and projected volumes of imports to the Voronezh and Yaroslavl regions from non-CIS countries are shown in Table 1.
Data on actual and projected imports from non-CIS countries in Yaroslavl region are shown in Figure 2 and in Formulas 5-8.
y = 35.25x + 96.519 (5); R2 = 0.717 y = -1.1367x2 + 59.696x + 8.9923 (6); R2 = 0.736 y = 105.96x0.6314 (7); R2 = 0.7192 y = 244.78ln(x) - 45.456 (8); R2 = 0.6317
As it is seen from the data in Figure 2 and in Formulas 5-8, the nature of actual and projected import dynamics to the Yaroslavl region is close to the dynamics of the Voronezh region. The optimistic forecast is represented by a linear function; the basic one is either power or polynomial; pessimistic — logarithmic. The data on actual and projected imports to the Yaroslavl region are presented in Table 1.
As it can be seen, the initial positions of the Voronezh and Yaroslavl regions (2000) are completely different. Import volumes into the Yaroslavl region are three times higher. Further volume dynamics vary by year, but both areas have reached the same level of imports in 2019. The forecast of import volumes in 2024 according to the optimistic and pessimistic variants is almost the same for both regions; the basic variants
are very different. However, the same range of possible values suggests that the regions have reached the peak of competitiveness in terms of imports from non-CIS countries and the projected growth in its volumes is associated with positive quantitative changes in the economy as a whole. This is evidenced, in particular, by the linear nature of the optimistic trend. At the same time, some growth in imports from non-CIS countries is projected, even if the economic situation worsens.
1200
1000
» Yaroslavl region --Linear (Yaroslavl region)
- Power (Yaroslavl region) —■■ Polynomial (Yaroslavl region)
— — — Logarithmic (Yaroslavl region)
Figure 2. Imports to the Yaroslavl region from non-CIS countries (million US dollars)
Source: composed by the authors
Table 1 - Actual and projected dynamics of imports from non-CIS countries to the Voronezh and Yaroslavl regions
External Trade (Imports) with the Non-CIS countries (at actual prices; million US dollars)
Region 2000 2005 2009 2015 2019 2024 (linear) 2024 (polynomial) 2024 (logarithmic)
Voronezh region 48.7 258.7 196.9 462.4 653.3 955.8 867.2 719.5
Yaroslavl region 150.9 151.3 385.2 536.1 665.4 956.3 786.9 732.5
Source: composed by the authors
The actual and projected dynamics of exports from the Voronezh region to non-CIS countries is shown in Figure 3 and in Formulas 9-12.
y = 40.052x + 172.58 (9); R2 = 0.7456 y = -2.7042x2 + 96.84x - 35.648 (10); R2 = 0.8353 y = 111.16x0,719 (11); R2 = 0.8556
y = 298.62ln(x) - 38.983 (12); R2 = 0.7821
As it is seen, four functions have a high degree of reliability, as in the case of imports. The optimistic (conservative) forecast variant is represented by a linear function (just below the value of the power function); base - logarithmic; pessimistic - polynomial.
Actual and projected export volumes to non-CIS countries from the Yaroslavl region are shown in Figure 4 and in Formulas 13-17.
1400
1200
Voronezh region — — Linear (Voronezh region)
Polynomial (Voronezh region) - Power (Voronezh region)
Logarithmic (Voronezhregion)
Figure 3. Exports from Voronezh region to non-CIS countries (million US dollars)
Source: composed by the authors
1600 1400 1200 1000 SOO 600 400 200 fl \ * / A / / \ / / \ 1 \ y I V / _ -r-r..*___
# # "P -P # "i? v & v
m Yaroslavl region
--Linear (Yaroslavl region) -Power (Yaroslavl region)
— ■ Polynomial (Yaroslavl region) — - - Logarithmic (Yaroslavl region)
Figure 4. Exports from Yaroslavl region to non-CIS countries (million US dollars)
Source: composed by the authors
y = 12.003x + 412.94 (13); R2 = 0.0396 y = 4.7214x2 - 87.146x + 776.49 (14); R2 = 0.2012 y = 402.35x0.0533 (15); R2 = 0.0052 y = -3.829ln(x) + 547.08 (16); R2 = 8E-05 y = 318.23e0.0331x (17); R2 = 0.1059
As it is seen, the exports from the Yaroslavl region as a whole is not lower than that of the Voronezh region. However, low determination factors and strong, but short-lived «bursts» of activity in this area allow
us to conclude that random factors have a significant impact on the Yaroslavl region competitiveness in the export of products and services. In this case it is not possible to make a sufficiently reliable forecast of export volumes.
An important area of foreign economic activity characterizing the competitiveness of regions is technology exchange, which reflected in the indicators of technology import and export. The actual and projected volumes of technology imports from the Voronezh region are shown in Figure 5 and in Formulas 18-21. It should be noted that the methodology for calculating this indicator has changed over the period under analysis. Because of this, it is impossible to analyze the actual state of the process over the entire time period. Therefore, the analysis is based on actual data for the period from 2007 to 2019.
—Voronezh region --Linear (Voronezh region)
— - ■ Polynomial (Voronezh region) - Power (Voronezh region)
---Logarithmic (Voronezh region)
Figure 5. Imports of technologies and technical services to Voronezh region (thousand US dollars)
Source: composed by the authors
y = 828.82x - 827.94 (18); R2 = 0.226 y = -96.601x2 + 2181.2x - 4209 (19); R2 = 0.2598 y = 12.372x2.7455 (20); R2 = 0.7093 y = 4213.8ln(x) - 2336.3 (21); R2 = 0.2257
As it is seen, all equations describing the actual dynamics of imports of technologies and services of technical nature have low determination factors. The only function where this factor has a sufficiently high value is a 2.7 power function, which cannot be considered it reliable. The function recorded a high trend from 2007-2013 and a «spike» in activity in 2015. The dynamics of2016-2019 show that such optimistic developments in this area of economic relations are impossible. It is not possible to predict reliably the dynamics of import of technologies, services of technical nature and, accordingly, the competitiveness of the Voronezh region within the technical and technological relations with foreign countries.
The situation is similar in the Yaroslavl region (Figure 6, Formulas 22-25).
y = 956.04x + 1875.9 (22); R2 = 0.3866 y = -61.434x2 + 1816.1x - 274.26 (23); R2 = 0.4042 y = 366.73x1.5186 (24); R2 = 0.6236 y = 5112ln(x) - 300.04 (25); R2 = 0.427
As we can see, the Yaroslavl region surpasses the Voronezh region by the overall level of competitiveness in the field of technology import (Table 2), however, its further development cannot be predicted.
Figure 6. Imports of technologies and technical services to Yaroslavl region (thousand US dollars)
Source: composed by the authors
Table 2 - Imports of technologies and technical services to Voronezh and Yaroslavl regions (thousand US dollars)
Region
Import of Technologies and Technical Services (thousand US dollars)
2007 2009 2012 2013 2015 2019
Voronezh region 25.9 623.8 1574.0 3541.5 25569.0 4799.4
Yaroslavl region 440.8 3732.4 9491.1 9801.9 4910.6 8455.7
Source: composed by the authors
Table 2 demonstrates: higher level of competitiveness of the Yaroslavl region in the field of import of technologies and technical services; sharp changes in the indicator values in both areas and their high dependence on external factors. None of the functions used for the analysis showed a sufficient value of the determination factor.
Dynamics of exports of technologies and technical services are uneven, demonstrating the same trends as imports (Table 3).
Table 3 - Exports of technologies and technical services from Voronezh and Yaroslavl regions (thousand US dollars)
Region Export of Technologies and Technical Services (thousand US dollars)
2007 2009 2012 2013 2015 2019
Voronezh region 183.1 8883.3 3197.2 3660.5 5303.5 139.4
Yaroslavl region 8.6 6830.7 10058.7 14095 1538.7 478.8
Source: composed by the authors
One important area of competitiveness of a region is its ability to attract foreign direct investment (FDI). The dynamics of FDI (inflow) in the Voronezh region is shown in Figure 7, Formulas 26-27. The specifics of the analysis of this indicator is the change in the methodology of calculations during the analyzed period. Unchanged methodology is specific to the period 2011-2019 only. This somewhat impairs forecasting
capabilities, but a period of eight years is considered acceptable if the determination factor is sufficient.
600,0 500,0 ■400,0 300,0 200,0 100,0 0,0
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
—' Voronezh region - Power (Voronezh region)
......... Exponential (Voronezh region)
Figure 7. Foreign direct investments (inflow) in Voronezh region (million US dollars)
Source: composed by the authors
y = 616.67x-0.621 (26); R2 = 0.8515 y = 526.37e-0.145x (27); R2 = 0.6729
As in the previous cases, there is a contradiction between the sufficient level of determination factor and the logic of the analyzed economic process. In this case, such a contradiction is inherent to the polynomial function which has the maximum determination factor (0.9036), but demonstrates an overly optimistic forecast for the period up to 2024. The opposite dynamics was demonstrated by the linear and logarithmic functions, which took negative values in the forecast period. In this regard, two functions are used for forecasting, on the basis of which the forecasts until 2024 are built. The optimistic forecast is a power function; the baseline forecast is an exponential function.
However, it should be noted that the «optimism» of the power function is rather relative — it shows a decline in FDI in the period up to 2024. To an even greater extent, the falling trend is characteristic of the exponential function. Quantitative indicators reflecting actual and projected FDI dynamics are presented in Table 4.
Actual and projected volumes of FDI in the Yaroslavl region are shown in Figure 8 and in Formulas
28-29.
1 200,0 1000,0 800,0 600,0 400,0 200,0 0,0
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
—Yaroslavl region ......... Exponential (Yaroslavl region)
- Power (Yaro s lavl re gion)
Figure 8. Foreign direct investments (inflow) in Yaroslavl region (million US dollars)
Source: composed by the authors
y = 1129.4x-0.854 (28); R2 = 0.5995 y = 844.96e-0.185x (29); R2 = 0.4076
As can be seen from the data presented in Figures 7, 8, in Formulas 26-29, the trends in both regions are quite similar. As in the prediction of FDI dynamics in the Voronezh region, in the Yaroslavl region the polynomial and linear functions were excluded from consideration, despite rather high values of determination factors. Among the remaining functions, the optimistic forecast is demonstrated by the power function, while the basic one - by the exponential function (with an insufficiently high determination factor). The actual and projected values of FDI in the Voronezh and Yaroslavl regions are shown in Table 4.
Table 4 - Foreign direct investments (inflow) in Voronezh and Yaroslavl region (million US dollars)
Region
Foreign Direct Investment in Voronezh and Yaroslavl Regions (mln US dollars)
2011 2012 2013 2015 2017 2019 2024 (optimistic forecast) 2024 (base forecast)
Voronezh region 655.0 491.0 239.0 166.0 172.0 174.0 85.7 16.2
Yaroslavl region 1,316.0 813.0 481.0 138.0 452.0 308.0 74.8 9.9
Source: composed by the authors
As it is seen from the data presented in Figures 7, 8 and Table 4, the competitiveness of regions in terms of attracting foreign direct investment is declining. A further decline in the competitiveness of both regions in this area of foreign economic activity is projected for the period up to 2024.
Conclusion
The analysis of competitiveness dynamics in the Voronezh and Yaroslavl regions, which represent a large group of averagely-developed Russian regions, showed different trends in trade, technical, technological, and financial relations.
Stable positive dynamics is forecast in the sphere of commodity relations. The actual dynamics and forecast of competitiveness of the Voronezh and Yaroslavl regions in terms of imports from non-CIS countries demonstrates three possible scenarios: optimistic, basic, and pessimistic. Either way, a fairly steady growth in imports from non-CIS countries is projected, indicating an increase in competitiveness in this area.
The actual and projected dynamics of exports to non-CIS countries in the analyzed regions are different. The Voronezh region can rather reliably forecast a steady growth of exports to non-CIS countries under any scenario. The optimistic (conservative) forecast variant is represented by a linear function (just below the value of the power function); base - logarithmic; pessimistic - polynomial.
In the Yaroslavl region the actual export dynamics is extremely uneven. It is impossible to sufficiently reliably characterize the actual trends of the indicator and predict the dynamics of exports in this region, which indicates a high dependence of the competitiveness of products and services on external factors.
By the general level of competitiveness in the export and import of technologies the Yaroslavl region exceeds the Voronezh region, however, due to the volatility of the dynamics of indicators, their forecasting for any long period is impossible. Regional competitiveness in this area depends mainly on external factors.
One of the most important indicators characterizing regions' competitiveness in foreign economic activity is the volume of foreign direct investment. In both regions, the trend of declining competitiveness in attracting foreign direct investment is steadily negative.
References
1. Kuzmina, V. M., Timchenko, M. V., & Naumik, V. A. (2020). Dynamics of Russia's export-import transactions with CIS countries in 2017-2019. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Ekonomika. Sotsiologiya. Menedgment, 10(3), 36-49. (in Russian).
2. Timokhin, A. D. (2019). Assessment of the dynamics of export-import transactions of the most important for the domestic economy types of goods in trade relations between Russia and China. Ekonomika i biznes: teoriia i praktika, (3-4), 150-154. (in Russian).
3. Treshchevsky, Yu. I., Kosobutskaya, A. Yu., Opoikova, E. A., & Papina, O. N. (2020). The Effects of Macroeconomic Shocks on Trends of Region's Foreign Economic Activity. Proceedings of the 36th International Business Information Management Association (IBIMA). (pp. 8015-8022). Granada, Spain.
4. Freire, M. R. (2019). The quest for status: how the interplay of power, ideas, and regime security shapes Russia's policy in the post-Soviet space. International Politics, 56(1), 795-809.
5. Head, K., & Mayer, T. Gravity, market potential and economic development. Journal of Economic Geography, 2010, 11(2), 281-294.
6. Dorin, M., Olaru, M., Andreea, M., & Eidenmüller, T. (2016). Importance of Innovation in the Context of Changes Arising from Economic Globalization. Proceedings of the 27th International Business Information Management Association Conference "Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth". (pp. 3102-3108). Milan: IBIMA.
7. Endovitsky, D. A., Treshchevsky, Y. I., & Terzi, I. V. (2020). Global Competitiveness as a Background of the Digital Economy Development. Lecture Notes in Networks and Systems, 87, 499-509.
8. Bitarova, M. A., Getmantsev, K. V., Ilyasova, E. V., Krylova, E. M., & Treshchevsky, Y. I. (2019). Factors of socio-economic development of rural regions in the area of influence of city agglomerations. Lecture Notes in Networks and Systems, 57, 183-194.
9. Sutyagin, V., Radyukova, Y., Treschevsky, Y. I., & Artemenko, D. (2018). The Problems of Financial Security of Modern Russia. International Business Information Management Conference (32nd IBIMA) -Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth. Khalid S. Soliman (Ed.). (pp. 2877-2888). Seville, Spain.
10. Endovitskaya, E. V., Risin, I. E., & Treshchevsky, Yu. I. (2018). Strategic Goals of Socio-Economic Development of Regions in the Conditions of Economic and Financial Limitations. The Future of the Global Financial System: Downfall or Harmony Lecture Notes in Networks and Systems. Vol. 57. Elena G. Popkova (Ed.). (pp. 229-235). Springer Nature Switzerland AG. Cham, Switzerland.
11. Lanskaya, D. V., Treschevsky, Y. I., Getmantsev, K. V., & Myasnikova, T. A. (2018). Strategizing as a Tool of Information and Analytical Provision and a Technology of Regional Development Management. The Future of the Global Financial System: Downfall or Harmony Lecture Notes in Networks and Systems. Vol. 57. Elena G. Popkova (Ed.). (pp. 348-356). Springer Nature Switzerland AG. Cham, Switzerland.
12. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics), 28(1), 100-108.
13. Mandel, I. D. (1988). Cluster analysis. Moscow: Finansi i statistika. (in Russian).
14. Russian Regions. Social and economic indicators. 2020: Statistics comp. (2002). (p. 586). Moscow: Goskomstat Rossii. (in Russian).
15. Russian Regions. Social and economic indicators. 2003: Statistics comp. (2003). (pp. 890, 984). Moscow: Goskomstat Rossii. (in Russian).
16. Russian Regions. Social and economic indicators. 2005: Statistics comp. (2005). (pp. 928, 932). Moscow: Goskomstat Rossii. (in Russian).
17. Russian Regions. Social and economic indicators. 2007: Statistics comp. (2007). (pp. 986, 990). Moscow: Goskomstat Rossii. (in Russian).
18. Russian Regions. Social and economic indicators. 2008: Statistics comp. (2008). (pp. 994, 998). Moscow: Goskomstat Rossii. (in Russian).
19. Russian Regions. Social and economic indicators. 2009: Statistics comp. (2009). (pp. 984, 988). Moscow: Goskomstat Rossii. (in Russian).
20. Russian Regions. Social and economic indicators. 2010: Statistics comp. (2010). (pp. 359, 361, 988, 994). Moscow: Goskomstat Rossii. (in Russian).
21. Russian Regions. Social and economic indicators. 2011: Statistics comp. (2011). (pp. 351, 353).
Moscow: Goskomstat Rossii. (in Russian).
22. Russian Regions. Social and economic indicators. 2019: Statistics comp. (2019). (pp. 552-553). Moscow: Goskomstat Rossii. (in Russian).
23. Russian Regions. Social and economic indicators. 2020: Statistics comp. (2020). (pp. 490, 492, 576577, 1214-1215, 1240-1241). Moscow: Goskomstat Rossii. (in Russian).
© Anna Yu. Kosobutskaya, Yuri I. Treshchevsky, Elena A. Opoikova, 2021
Received 15.01.2021 Accepted 20.02.2021
