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1DOI: 10.29141/2658-5081-2022-23-2-6 EDN: EQVMZL JEL classification: 010, O47, P51
Daria S. Bents Chelyabinsk State University, Chelyabinsk, Russia
Long-term trends in differentiation between regions: Sverdlovsk oblast vs Chelyabinsk oblast
Abstract. Threats of increased differentiation across regions, which have caused inefficient spatial development, are progressively coming into the scientists' focus. By and large, a peripheral region is unlikely to take the place of the center. In the Urals1, the Sverdlovsk oblast has long been the center and stayed ahead of its neighbours in terms of socioeconomic performance. Our previous research revealed a phenomenon called 'synchronisation of economies'. Accordingly, the Chelyabinsk oblast in many instances repeats the trends of the Sverdlovsk oblast, but remains at the periphery. In this regard, studying the differentiation between the two economies becomes a relevant issue. The research aims to construct long-term trends of differentiation between regions using the case of the Sverdlovsk and Chelyabinsk oblasts. The theories of spatial development, including the theory of cumulative growth, constitute the methodological basis of the research. Applying the methods of statistical comparison and times series analysis, the study interprets the data published by Russia's Federal State Statistics Service (Rosstat), the Unified Interdepartmental Statistical Information System (UISIS), and generated by FIRA PRO information analytics system (OOO "First Independent Rating Agency"). The author proposes a method for assessing differentiation across regions based on 12 indicators. The findings demonstrate that for 2001-2020, the variation between the regions in terms of GRP per capita (in 2001 prices) has increased, whereas in terms of wages in prices of the same year it decreased. In relation to the outsider region, the Sverdlovsk oblast has kept its position in terms of the real GRP per capita compared to the Chelyabinsk oblast, which is approaching the outsider. At the same time, for 2001-2020, both regions have become closer to the leader. With regard to the real wages, the positions of the regions have nearly equalized, the 'superiority' over the outsider has decreased.
Keywords: differentiation across regions; Sverdlovsk oblast, Chelyabinsk oblast; real GRP per capita; real wages; differentiation coefficients.
1 Here under the Urals we mean the Ural macroregion, not the Ural Federal District, where the Tyumen oblast taken together with Khanty-Mansi Autonomous Okrug - Yugra and Yamalo-Nenets Autonomous Okrug can outperform the Sverdlovsk oblast in a number of aspects.
For citation: Bents D. S. (2022). Long-term trends in differentiation between regions: Sverdlovsk oblast vs Chelyabinsk oblast. Journal of New Economy, vol. 23, no. 2, pp. 102124. DOI: 10.29141/2658-5081-2022-23-2-6. EDN: EQVMZL.
Article info: received January 25, 2022; received in revised form February 21, 2022; accepted March 5, 2022
Introduction
The issue of spatial development received particular relevance in the second half of the 20th century. A great number of strategic documents began to be drafted taking it into account. One of the key obstacles to efficient spatial development is the strengthening differentiation between regions. Moreover, the Strategy for spatial development of the Russian Federation expressed concerns about the strengthening territorial inequality in case of implementing an inertial scenario in this area.
The concept of territorial inequality was framed in the center - periphery theory by Friedman in 1966 [Friedman, 1966]. The center, which accumulates all resources, on the one hand, is fed by the reserves of the periphery, and on the other hand, it acts as a developer of innovations consumed by the periphery as well. Over time a separate direction of spatial development was bought out in the form of cumulative theories [Myrdal, 1957; Hirshman, 1958] describing the factors of successful specialisation and economies of scale as an advantage for further accelerated growth. Williamson showed a non-linear dependence of the differentiation across regions on the level of development of a territory [Williamson, 1965]. Kuznets also spoke about different rates of disproportions growth. According to his theory, these imbalances level out when the system reaches a higher level of development [Kuznets, 1955]. The concept of new economic geography has also contributed to the consideration of the spatial development issue. Its author Krugman divided the factors of regional growth into factors of the first and second nature. He included the geography and natural resources of a territory in the first group, and agglomeration effects in the second one [Krugman, 1991].
A great number of Russian scholars also paid attention to the issues of spatial development, interregional and intraregional differentiation, center and periphery. So-called geographical schools were set up, dealing with regional studies in general and issues of spatial development in particular. Their key figures are researchers from central Russia (Zubarevich [2019], Bukhvald [2020]), and its northwestern part (Kuznetsov, Mez-hevich, Lachininskiy [2015], Okrepilov, Kuznetsov, Lachininskiy [2020]), and representatives of the scientific communities of Siberia (Kolomak [2020]) and the Far East (Minakir [2021]).
Scientists in the northern part of the Urals are engaged in the studies of this issue as well. We can point to the research of Tatarkin [2016], Kuklin, Leontieva [2017], Petrov [2018], Kurushina, Lavrikova, Akberdina, Suvorova [2019, 2020], Animitsa, Vlasova [2021], and others. The colleagues of the author from Chelyabinsk oblast also explore the problem of differentiation (cf.: Barkhatov, Kapkaev, Pletnev [2019], Antonyuk, Ko-rnienko, Vansovich [2020], Danilova, Rezepin [2021]). A complete list of researchers is given in one of our publications [Bents, 2021].
The purpose of the study is to construct long-term trends in the differentiation between regions using the case of the Sverdlovsk and Chelyabinsk oblasts.
The objectives of the study are the following:
• to reveal the specifics of the socioeconomic development of these regions;
• to propose a method for assessing interregional differentiation that meets the objectives set forth below;
• to assess the depth of differentiation between the Sverdlovsk and Chelyabinsk oblasts;
• to analyse whether differentiation is strengthening or weakening in the long term;
• to identify the positions of the said regions in Russia;
• to examine the positions of the studied regions in relation to leaders and outsiders taking into account the long-term dynamics.
Peculiarities of socioeconomic development of the Sverdlovsk and Chelyabinsk oblasts
The territories located in the Urals, in particular the Sverdlovsk and Chelyabinsk oblasts, are traditionally referred to as old industrial regions. The industrial history of the Urals began three centuries ago, when its mining and metal-producing 'foundation' was set during the reforms of Peter the Great. Since then, the metal industries, including those relying on the mining of local minerals, have become the basic one [Gordeev, 2017, p. 76].
Key socioeconomic indicators reflecting the current state of the studied regions are presented in Table 1.
Despite the fact that the area of the Sverdlovsk oblast exceeds the area of the Chelyabinsk oblast more than twice, the population there is only 20 % higher. The average annual number of the employed population has an even smaller discrepancy. While investments in fixed assets in the Sverdlovsk oblast are only 20 % higher, and fixed assets in terms of value - by 40 %, the oblast produces 60 % more regional product. In terms of GRP per capita, in the Sverdlovsk oblast it is 30 % higher than in Chelyabinsk oblast.
For all types of industry (in accordance with the Russian Classification of Economic Activities 2), Sverdlovsk oblast produces 44 % more than the Chelyabinsk oblast, with the exception of the mining industry. Here the volumes in the Chelyabinsk oblast are
Table 1. The most important socioeconomic indicators of the Sverdlovsk and Chelyabinsk oblasts
No. Indicators Ural Federal District Sverdlovsk oblast Chelyabinsk oblast Ratio between the indicators of the Sverdlovsk and Chelyabinsk oblasts
1 Territory area, thousand km2 1,818.5 194.3 88.5 2.2
2 Population (January 1, 2021), thousand people 12, 329.5 4,290.0 3,442.8 1.2
3 Average annual number of employed people, thousand people 6,177.7 1,954.9 1,717.0 1.1
4 Average per capita income, rubles per month 37,204.0 37,374.0 26,628.0 1.4
5 Average consumer spending per capita, rubles per month 27,217.0 29,868.0 20,457.0 1.5
6 Average monthly nominal wages of employees, rubles 54,603.0 43,256.0 39,349.0 1.1
7 Investments in fixed assets, billion rubles 3,146.9 381.1 322.2 1.2
8 Fixed assets in the economy (gross book value), billion rubles 49,847.6 8,183.2 5,810.1 1.4
9 Number of large industrial enterprises (January 1, 2021) mining 96 10 7 1.4
10 manufacturing 211 94 72 1.3
11 supply of electricity, gas and steam; air conditioning 49 12 9 1,3
12 water supply; water disposal, organisation of waste collection and disposal, activities to eliminate pollution 28 14 8 1.8
13 Volume of shipped goods of own production, performed works and services by types of economic activities, billion rubles mining 5,435.8 92.5 139.6 0.7
14 manufacturing 5,134.9 2,072.2 1,412.2 1.5
15 supply of electricity, gas and steam; air conditioning 722.1 245.2 129.5 1.9
16 water supply; water disposal, organisation of waste collection and disposal, activities to eliminate pollution 174.0 77.6 37.2 2.1
17 Gross regional product, billion rubles 13,227.7 2,529.5 1,545.6 1.6
18 Gross regional product per capita, thousand rubles 1,070.6 586.5 445.3 1.3
Source: Russia's Federal State Statistics Service (Rosstat) data (items 1-8 and 13-18) and FIRA PRO information analytics system (OOO "First Independent Rating Agency") (items 9-12).
Note: unless otherwise noted, the data is as of January 1, 2020.
51 % higher than in Sverdlovsk oblast, despite the fact that there are fewer large mining companies in the region.
Studying long-term trends, some researchers emphasise the manifestation of industrial despecialisation trends in the Sverdlovsk oblast, which are not typical for the neighboring oblast [Grebenkin, 2020, p. 76]. If we turn to the figures that fix the sectoral structure of value added, in the long term the situation will be as follows: in 2001, the share of industry in the gross regional product was 43.7 % in the Chelyabinsk oblast, 42.9 % in Sverdlovsk oblast; by 2019 the values decreased and amounted to 39.1 and 39.0 % respectively1.
There are a lot of typologies of regions, but the terms "old industrial region" or "traditional industrial region" have found their place in scientific discourse. The subjects of the Russian Federation under consideration are typical representatives of traditional industrial regions, since they have such characteristic properties as evolutionary nature, stability of internal content, and industrialism [Dvoryadkina, Dzhalilov, 2021, p. 54].
Another peculiarity of the long-term development of the studied regions is the synchronisation of development dynamics. The relationship was especially close in terms of growth rates of average per capita incomes of the population, industrial production, and nominal GRP per capita. All other key indicators of socioeconomic development also demonstrate the presence of synchronisation effects, but to a lesser extent [Bents, 2020b]. If we calculate the correlation coefficients between their growth rates, almost all values will exceed 0.7. In Table 2 there are the results of the correlation analysis, sorted by the degree of decreasing relationship.
Table 2. Correlation analysis results
Indicator Value of the correlation coefficient
Growth rates of average per capita monetary income of the population* 0.95
Industrial production indices* 0.91
Growth rates of nominal GRP per capita** 0.90
Physical volume indices of GRP*** 0.87
Nominal GRP growth rates*** 0.86
Growth rates of investment in fixed assets* 0.78
Growth rate of consumer spending per capita* 0.76
Growth rate of the average annual number of employed people* 0.70
Growth rate of emissions into the environment* 0.62
Growth rates in the value of fixed assets* 0.47
Source: own representation based on Rosstat data. The sample included annual values for the following periods: * - 1996-2020; ** - 1998-2019; *** - 1996-2019.
1 Rosstat data. https://rosstat.gov.ru.
There are other publications, which show that the studied oblasts demonstrate similar results. For example, Zemtsov and Barinova classified both Ural regions as "specialised creative regions", which they define as "regions of average potential with high scientific and production potential" [Zemtsov, Barinova, 2016, pp. 74-75]. In terms of the prospects for innovative development of regional economies, the Sverdlovsk and Chelyabinsk oblasts are included in the group of regions with an above-average rating [Valentey et al., 2014].
Previously built regression models made it possible to find out that the source of long-term socioeconomic growth of these territories is industrial production [Bents, 2020a, pp. 119-120]. As for the Chelyabinsk oblast, this factor is more significant - its elasticity is higher in relation to the growth rate of GRP. The labour factor also demonstrated a highly elastic impact on the economic growth of this region.
Materials and methods
There is a vast economic literature that provides an overview of methods for quantifying differences between regions [Glushchenko, 2015] or uses these methods [Krivoshlykov, Zhakhov, 2017; Malkina, 2017; Zharomskiy, Migranova, Toksanbaeva, 2018; Voroshilov, Gubanova, 2018; Malkina, 2019]. The researchers often apply the following indicators to assess the level of territorial inequality: coefficient of funds; coefficients of income differentiation considering decile groups; Atkinson, Gini, Hatchman indices, Theil, variation, and oscillation coefficients, etc.
The method of our research allows reaching the above objectives: firstly, to assess the level of differentiation between the studied regions in relation to each other; secondly, to show their position in relation to the leading regions, outsider regions, and average regions of Russia; thirdly, to evaluate both of these parameters in dynamics in order to build long-term trends. We suggest relying on indicators measured in monetary units (absolute) and indicators having dimensionless units of measurement (relative). The proposed method includes two indicators: the range of variation and the differentiation coefficient. The author has proposed six versions of each indicator (Table 3).
Actually, to determine the depth of differentiation between regions any socioeconomic indicators characterising the state and development of regions can be used. In this paper, we take two indicators: 1) real gross domestic product per capita; 2) the amount of real wages for a full range of organisations (hereinafter referred to as real wages). The statistical data come from Rosstat. To form relatively long-term trends, the time sample included the period from 2001 to the year for which data are currently available. The sample in terms of GRP per capita included 2001-2019, for wages it was 2001-2020.
Table 3. The method for assessing differentiation between regions
Version Absolute indicators Relative indicators
1 RV1 = Xso - Xcho CDx= Xso / Xcho
2 RV2 = Xso — Xmax reg-out> RV2 = Xcho — Xmax reg-out CD2 = Xso /Xmax reg-out ; CD2 = Xcho /Xmax reg-out
3 RV3 = Xmin reg-lead — Xso; RV3 = Xmin reg-lead — Xcho CD3 = Xmin reg-lead /Xso; CD3 = Xmin reg-lead /Xcho
4 RV4 = Xso — Xav reg-out> RV4 = Xcho — Xav reg-out CD4 = Xso /Xav reg-out; CD4 = Xcho /Xav reg-out
5 RV5 = Xav reg-lead — Xso; RV5 = Xav reg-lead — Xcho CD5 = Xav reg-lead / Xso; CD5 = Xav reg-lead /Xcho
6 RV6 = Xso - Xav 80 %; RV6 = Xcho - Xav 80 % CD6 = Xso /Xav 80 %; CD6 = Xcho /Xav 80 %
Notes: RV is range of variation; CD is differentiation coefficient; Xso is an indicator typical for the Sverdlovsk oblast; Xcho is an indicator typical for the Chelyabinsk oblast; Xmax reg_out is an indicator characteristic of the 'best outsider' (in the lower decile group); Xmin reg-iead is an indicator characteristic of the 'worst leader' (in the upper decile group); Xav reg-out is the average value of the indicator among 10 % of outsider regions (in the lower decile group); Xav reg-lead is the average value of the indicator among 10 % of the leading regions (in the upper decile group); Xav 80 % is the average value of the indicator among 80 % of the regions of the central sample (minus 10 % of the leading regions and 10 % of the outsider regions, but taking into account the 'worst leader' and 'best outsider').
In order to level the influence of prices, we decided to move from nominal values to real ones. This is especially important when assessing the differentiation between regions with absolute values. For this purpose, using the Unified Interdepartmental Statistical Information System (UISIS) we selected price indices specific to each region. Due to the lack of some data (consumer price indices and /or GRP per capita, and/or the amount of nominal wages) for a number of territories, 79 regions were included in the sample. Therefore, it did not include the Republic of Crimea, the city of Sevastopol and the Chechen Republic. The Arkhangelsk oblast includes data for the Nenets Autonomous Okrug, the Perm krai - for the Komi-Permyatsky Okrug, the Tyumen oblast - for the Khanty-Mansi Autonomous Okrug - Yugra and Yamalo-Nenets Autonomous Okrug.
Thus, the algorithm of our work was as follows:
1) compiling a data set for the indicators of nominal GRP per capita and nominal wages for 79 regions since 2001;
2) compiling a data set for regional consumer price indices for the same period;
3) calculating consumer price indices in relation to 2001 as the base year (let us call these indices regional deflators);
4) calculating GRP per capita and wages for each of the 79 regions taking into account regional deflators (let us call these values real);
5) calculating indicators for two studied indicators - "real GRP per capita" and "real wages" (Table 3);
6) registering the obtained results (Figures 1-12 and Appendices 1, 2).
Version (1) of both the range of variation and the differentiation coefficient demonstrates the level of differentiation between the studied regions. If we assume that in the "center - periphery" relationship the role of the center is assigned to the Sverdlovsk oblast, and the role of the periphery belongs to the Chelyabinsk oblast, this explains the arrangement of indicators in the proposed formulas. With a greater degree of probability, the indicators in the Sverdlovsk oblast will be higher than in Chelyabinsk oblast.
Versions of the range of variation and differentiation coefficient (2) and (4) show the positions of the Ural regions in relation to outsiders; versions (3) and (5) - to the leaders; version (6) - to the average regions of Russia.
In the economic literature on the topic, a decile approach [Bakhtizin, Bukhvald, Kol-chugina, 2016] is widely present, according to which the regions with extreme points in the spatial sample should be removed from the calculation. In other words, to obtain a statistically significant data, it is necessary to remove 10 % of the leading regions and 10 % of the outsider regions. Therefore, it is considered correct to compare the values obtained for the region under study not with the 'best leader' or 'worst outsider', but with the 'worst leader' and 'best outsider'. Based on the size of our spatial sample (79 regions), 10 % is eight regions. Therefore, indicators for versions (2) and (3) assume the following: the 'worst leader' is ranked 8th (if the results are ordered from highest to lowest), and the 'best outsider' is 8th from the bottom, i.e., 72th.
We decided to add indicators that allow comparing the indicators for the studied regions with 'average leaders' and 'average outsiders'. Comparison of the calculation results of indicators for versions (2) and (4), as well as for versions (3) and (5) will provide complete data. If the results obtained in versions (2) and (3) are relatively far from the results obtained in versions (4) and (5), this will indicate a high differentiation within the extreme decile groups.
Version (6) of the proposed indicators will make it possible to determine the positions of the Ural regions in relation to the average region, the value for which is calculated according to the 'middle' sample of the rating obtained, namely, 80 % of the regions, with the exception of the extreme decile groups.
Results and discussion
The dynamics of the ranks of the Sverdlovsk and Chelyabinsk oblasts for the two studied indicators is shown in Figure 1 and 2. For both regions, it is typical that there is practically no connection between a region's position in terms of real GRP per capita and its position in terms of real wages. On the one hand, we can point to some long-term stability. For example, the Sverdlovsk oblast in 2001 ranked 22nd in terms of real GRP per capita, and 19 years later, it remained at approximately the same position (23rd place). As for the
real wages, there was a decrease by five positions - from 23rd to 28th place. The opposite situation is typical of the Chelyabinsk oblast: by GRP per capita, the region moved from 29th place to 34th, and in terms of real wages it almost retained its positions (27th place in 2001, 29th place in 2020). As for the correlation of the forces of the regions against each other, in terms of the level of GRP per capita, the Chelyabinsk oblast, with the exception of 2004, has always been inferior to the neighboring oblast. The situation is different when it comes to real wages: since 2015, the Chelyabinsk oblast has been ranked higher. Nominal wages in this region have been lower since 2015, but here we are considering prices in 2001. Indeed, from 2001 to 2021, prices in the Chelyabinsk oblast increased 4.81 times, while in the Sverdlovsk oblast - 5.37 times.
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Fig. 2. Dynamics of the Sverdlovsk and Chelyabinsk oblasts' ranks by the indicator of real wages
Figures 3 and 4 show the values of the studied indicators for the Sverdlovsk and Chelyabinsk oblasts.
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Fig. 4. Dynamics of real wages in the Sverdlovsk and Chelyabinsk oblasts
The geometry of the graphs allows us to draw the following conclusions. First of all, there are the synchronisation effects, which have already been mentioned. The graphs practically copy each other if we compare the oblasts by each indicator. A higher level of synchronisation is characteristic of the dynamics of real wages. The situation repeats itself from year to year: if the value grows in one region, the same happens in another one, and vice versa. Only in relation to real GRP per capita in the period from 2012 to 2015, there was a certain imbalance: the indicator in the Chelyabinsk oblast was growing, while it was decreasing in the neighboring oblast.
Secondly, a different level of differentiation between regions by the two studied indicators is observed. In terms of GRP, the Chelyabinsk oblast only once, in 2004, was ahead of the Sverdlovsk oblast, whereas with respect to wages, there is a slightly different trend. Since 2015, the Chelyabinsk oblast has been ahead of the Sverdlovsk oblast in this value.
However, in 2020 this advance was steadily falling, it can be seen from the range of variation (1) (Appendix 2).
Thirdly, we can argue that there are divergent trends in relation to the studied indicators: in the long term, the range of variation grows in terms of GRP, but falls in terms of real wages. At the same time, for real GRP, the situation is not so clear - on the one hand, in 2019 it was higher than at the early 2000, but on the other hand, the maximum amount of differentiation was in 2012, not at present (Appendix 1), which manifests in the distance between the graphs in Figure 3.
Given the large amount of statistical data that was examined, it is impossible to display trends for all indicators proposed in Table 3. Therefore, we will demonstrate only some graphs. All other results are presented in Appendices 1 and 2. Let us assume that the range of variation reflects the trends of differentiation between regions less clearly than the coefficients. Therefore, further we identify the trends obtained from the calculation of relative indicators, i.e., differentiation coefficients.
Figure 5 shows the dynamics of the differentiation coefficient (1), which is the ratio of GRP in 2001 prices per capita of the Sverdlovsk oblast to same indicator of the Chelyabinsk oblast. A certain cyclicity is visible. Every 4-7 years, the value of the coefficient increases, reaching a certain maximum, and then it decreases. In 2001-2004, we can see the descending part of this conditional parabola, and in 2015-2019, we see the ascending part. Two complete cycles are presented in 2004-2008 and 2008-2015. The long-term dynamics is generally upward: if in 2001 the Sverdlovsk oblast generated 13 % more GRP than the Chelyabinsk oblast, in 2019 the excess was 19 %. If we talk about the medium term, then from 2012, when the level of differentiation reached its maximum, the value of the coefficient went down from 1.37 to 1.19 by 2019.
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Fig. 5. Dynamics of differentiation coefficient (1) for the indicator of real GRP per capita
Analysis of real wages (Figure 6) does not reveal any cycles. The overall long-term trend is descending. In 2015-2019, the differentiation coefficient CD1 was completely
below one, which indicates that the periphery has overtaken the center. By 2020, the coefficient reached one, which actually indicates the absence of interregional differentiation in terms of real wages. Points
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The differentiation coefficients CD2 and CD3 show the positions of the regions of the Urals in relation to the 'best outsider' and 'worst leader' according to the size of real GRP per capita. The dynamics of these coefficients is presented in Figures 7 and 8. Again, we note the phenomenon of synchronisation of neighboring regions: the geometry of the presented graphs is almost the same. This means that both regions either simultaneously strengthen their positions in relation to other regions, or simultaneously lose them. The exception was 2009-2015. While the Sverdlovsk oblast increased the gap from the 'best outsider', overtook it, the Chelyabinsk oblast, on the contrary, reduced this gap, i.e., became closer to the outsider. This suggests that in the post-crisis period, the Sverdlovsk oblast demonstrated a stronger ability to grow. Points
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Fig. 7. Dynamics of differentiation coefficient (2) for the indicator of real GRP per capita
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This situation is different from those presented in some publications [Bakhtizin, Bukhvald, Kolchugina, 2016]. There is a hypothesis in the economic literature, according to which, during a crisis, strong regions demonstrate a poorer ability to grow than weaker ones. Therefore, during periods of economic growth, differentiation increases, and during periods of stagnation and crisis, it decreases. This is usually explained by the implementation of an equalisation policy. However, in the case under consideration, we see a different trend, but it is a relatively short-term one: three years later, the vectors of development changed, and in 2012-2015 the Sverdlovsk oblast reduced the advance over the outsider, and the Chelyabinsk oblast managed to increase this advance from 1.71 times in 2013 to 2.02 times in 2015 (Figure 7).
In the same period, 2009-2015, there is an asynchronisation of the regions in relation to their positions towards the 'worst leader'. The Chelyabinsk oblast was losing these positions: the differentiation coefficient CD3 was growing, which means that the leading region increased the indicator (GRP per capita) to a greater extent. The Sverdlovsk oblast, on the contrary, was narrowing the gap: in 2012 the leader was only 30 % ahead of the GRP of the Sverdlovsk oblast (Figure 8).
There is another point, clearly shown in Figures 7 and 8. On the one hand, there is a certain pattern: if the advance of outsiders increases, the gap from the leaders decreases. However, this relationship is not so linear. For example, in terms of economic growth, the Sverdlovsk oblast outstripped the outsiders to the maximum in 2006, when the CD2 value was 2.83. At the same time, the gap from the leaders was minimal in 2012, when CD3 was 1.30. The Chelyabinsk oblast has a similar situation: the maximum advance over the outsider was 2.46 in 2004-2005 (Figure 7), and the minimum gap from the leader was 1.30 in 2008 (Figure 8).
Sverdlovsk oblast
Chelyabinsk oblast
If we analyse only the extreme values of CD2 and CD3 (in 2001 and 2019), the long-term trend of the considered Ural regions is the same: a decrease in the advance over the outsider and a decrease in the advance of the leader over the regions of the Urals. However, this dynamics does not show significant changes. Such a trend can be regarded as a signal to reduce differentiation between regions on the scale of the entire national economy.
The dynamics of the coefficients CD4 and CD5 is given in Table 4. These coefficients also show the advance or lag of the Ural regions from the regions-outsiders and regions-leaders, but here we are talking about the average value of GRP among 10 % of the regions-leaders and 10 % of the regions-outsiders. In addition, if the long-term trend in relation to CD4 is comparable to the trend shown in Figure 7 (CD2), the long-term dynamics of CD5 is not similar to the dynamics of CD3 (Figure 8). The value of CD3 from 2001 to 2019 is declining, while the value of CD5, on the contrary, is growing. In general, all CD5 values are higher than CD3 values. This suggests that among the 10 % of the leading regions there is a high level of differentiation, and over the period under the study, the 'average' leader has become even more ahead of the regions of the Urals.
Figures 9 and 10 also show the dynamics of the CD2 and CD3 coefficients, but now in terms of real wages. Comparing the dynamics of the same coefficients calculated for real GRP per capita, one could identify some similarities, although there are also a few differences. There is a synchronisation of trends in the two Ural regions - the graphs in Figures 9 and 10 are almost the same. Moreover, long-term trends (both CD2 and CD3) are descending. In other words, there is an obvious reduction in the advance in terms of wages from outsider regions (Figure 9). As for the long-term dynamics of CD3, in the Chelyabinsk oblast it is again downward. The advance of the 'worst leader' over the Chelyabinsk oblast is falling. At the same time, in 2020, the leading region is ahead of the Sverdlovsk oblast to a greater extent than in 2001 (1.60 vs 1.53). This is due to the rather high rate of price growth in the Sverdlovsk oblast compared to both the Chelyabinsk oblast and a number of other regions, including the leaders.
As for the coefficients of CD4 and CD5, the situation is almost similar to that observed in terms of real GRP per capita. The data in the Table 5 shows that the long-term dynamics of CD4 is downward, i.e., the gap from the 'average outsider' region is narrowing. Nevertheless, at the same time, the values of the CD5 indicator have grown significantly over a twenty-year period. If in 2001 the 'average leader' was ahead of the Sverdlovsk oblast by 1.99 times, and the Chelyabinsk oblast by 2.18 times, then by 2020 the gap for the regions of the Urals was already by 2.78 times.
Graphs of regions in Figures 9 and 10 are placed closer to each other than in Figures 7 and 8, and in dynamics this gap is only reduced. After 2014, the graphs almost overlap
each other. This indicates the almost identical position of the regions under consideration both in relation to outsiders and in relation to leaders.
Points
1.90 185 , „ , *n 1-84
1.83 1.84
1.00
OOOOOOOOO'-H'—I t—I T-H1,-HT-H1,—1 i—( T—I T—I <N OOOOOOOOOOOOOOOOOOOO <N<N<N<N<N<N<N<N<N<N<N<N<N<N<N<N<N<N<N<N
Sverdlovsk oblast
Chelyabinsk oblast
Fig. 9. Dynamics of differentiation coefficient (2) for the indicator of real wages
<N<N<N<NCN<N<N<N
00 ON O O O T-H
o o o o
<N CO LO
<N
<N<N<N<N<N<NCS<N<N<N<N
Sverdlovsk oblast
Chelyabinsk oblast
Fig. 10. Dynamics of differentiation coefficient (3) for the indicator of real wages
The dynamics of the differentiation coefficients CD6 for the two studied indicators is shown in Figures 11 and 12. CD6 shows the positions of the regions of the Urals in relation to the average Russian region, i.e., for a sample of 80 % of the regions, whose values do not belong to leaders or outsiders. For GRP per capita, the geometry of the graphs is not completely synchronous (Figure 11). In both regions, the coefficient is higher than one, which indicates the excess of real GRP per capita over the average Russian region.
However, in the Chelyabinsk oblast, this resource is not very significant: in 2019, the value of real GRP per capita was only 3 % higher than the value of the 'average region'. The positions of the Sverdlovsk oblast are more stable and persist over time: in 2001, it produced 22 % of the regional GRP more than the average region of Russia; by 2019, this excess was 23 %. In the long term, the Chelyabinsk oblast has lost its positions: the gap from the 'average region' has decreased from 8 to 3 %.
As for wages, both regions have lost their 'superiority' (Figure 12): the Sverdlovsk oblast has reduced the gap from 20 % in 2001 to 5 % in 2020, the Chelyabinsk oblast -from 9 to 5 %, respectively.
^CNrOTfinvorvOOONO^ OOOOOOOOO'-H'—I OOOOOOOOOOOO
<N<N<N<N<N<N<N<N<N<N<N<N
-♦- Sverdlovsk oblast -■- Chelyabinsk oblast Fig. 11. Dynamics of differentiation coefficient (6) for the indicator of real GRP per capita
Points
1.29
1.261.25 1.26-&-127
1.25 1201.21 1.20
L16117ll71.15
1.15,
1.05 1 09 1.09 1.06
1.00
1.05
1.08 10910-U ' 109L081.09L08io7
1.05
1.05
OOOOOOOOO*—II-HI*—ii-H1,-HI—1,—if—I,-H<N OOOOOOOOOOOOOOOOOOOO <N<NC^<N<NCN<N<N<N<N<N<N<N<N<N<N<N<N<N<N
Sverdlovsk oblast
Chelyabinsk oblast
Fig. 12. Dynamics of differentiation coefficient (6) for the indicator of real wages
Let us again point to that all indicators that appear in our method, but are not presented in figures, are given in Appendices 1 and 2.
Conclusion
Having constructed the long-term trends of differentiation between the regions we draw the following conclusions. In terms of real GRP per capita, in 2019, the Sverdlovsk oblast ranked 23rd out of 79 regions, the Chelyabinsk oblast took the 34th place. Since 2001, the Sverdlovsk oblast has lost only one place; the Chelyabinsk oblast, which was 29th in 2001, has been outstripped by five regions. Changes in real wages turned out to be different: the Chelyabinsk oblast almost retained its place in the ranking, while the Sverdlovsk oblast lost five positions.
All coefficients obtained from the study demonstrate different and sometimes even opposite results in relation to two indicators: real GRP per capita and real wages. If according to the first indicator, in the long-term dynamics, the regions move away from each other, then according to the second one, they, on the contrary, converge. However, the long-term and medium-term dynamics are again different. The largest range of variation in the level of real GRP per capita was in 2012. Since then, the range has only been decreasing. As for differentiation in terms of wages, here, on the contrary, around the same period, in 2014, the range of variation was minimal, after which the Chelyabinsk oblast began to outpace the Sverdlovsk oblast in terms of real wages, although it was inferior to it in terms of nominal wages.
The differentiation coefficient, expressed as the ratio of the values of the indicators of the Sverdlovsk oblast to the same values of the indicators of the Chelyabinsk oblast, shows the same trends. In terms of the level of real GRP per capita, the ratio in the long term grows, though insignificantly, whereas in terms of the level of real wages, it decreases and becomes equal to one. In other words, if we compare the well-being of the population of the regions, focusing on wages in 2001 prices, today it is the same. In the sphere of wages, we observe an almost strict downward trend, while in terms of GRP, the trend is cyclical.
In relation to the leader, which occupies the last place within the decile group, the studied regions of the Urals become closer in terms of real GRP per capita. Again, this is a long-term trend, and it is strictly ascending. If in 2003 the region - 'worst leader' was ahead of the Sverdlovsk oblast by 38 %, then in 2019 this advance was already 57 %. The same trends are typical for the Chelyabinsk oblast: the corresponding values were 41 % in 2003 and 87 % in 2019. The outrunning of outsiders by the Ural regions has approximately, although not absolutely, a mirror image. In the long-term dynamics, the Ural regions almost retain their positions in relation to the region -'best outsider', narrowing the gap slightly. If we compare the current situation with the maximum gap, there is a weakening of positions. Both oblasts reached their maximum 'advance' over the outsider in 2006, when the ratio of indicators was 2.83 times and 2.46 times, respectively. In 2019, in the first case, the ratio was 2.12, in the second - 1.78 times.
The trends are more stable for wages than for the level of real GRP, which is characterised by cycles. It is more about positioning regions in relation to outsiders and narrowing the gap from them. In relation to the region - 'worst leader', the positions remain practically unchanged, but the long-term dynamics is characterised by a parabolic trend. From 2001 to 2007, it was descending. For example, in 2007 the leader was ahead of the Sverdlovsk oblast by only 22 %. After 2007, the trend changed to an upward one, and the leaders again increased their advance, returning approximately to the level of the base year of 2001.
The foregoing leads to the following conclusion. On the one hand, the Ural regions have some stability in their positions both in relation to the leaders and in relation to the outsiders. On the other hand, the differentiation between regions both deepened in terms of real GRP per capita and decreased in terms of real wages.
Appendix 1. Assessment results of differentiation between regions by real GRP per capita
Indicator Region 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
rvi, rubles 4,903 3,262 865 -231 5,173 12,795 10,734 6,733 9,909 17,345 24,778 30,436 29,441 22,513 11,276 16,323 17,911 17,793 19,934
rv2, rubles SO 23,616 24,685 28,453 34,837 43,244 56,842 62,447 59,864 41,756 52,178 63,059 67,343 63,641 58,879 57,595 61,171 64,921 67,389 65,116
CHO 18,714 21,423 27,588 35,068 38,071 44,047 51,713 53,131 31,847 34,834 38,281 36,907 34,200 36,366 46,319 44,847 47,010 49,595 45,183
rv3, rubles SO 31,416 20,601 19,383 31,386 29,072 22,894 18,180 20,344 37,700 44,421 44,164 34,598 37,046 39,677 51,199 50,203 55,526 64,412 70,031
CHO 36,318 23,863 20,248 31,155 34,245 35,689 28,914 27,076 47,609 61,765 68,942 65,034 66,487 62,190 62,474 66,526 73,437 82,205 89,964
rv4, rubles SO 28,314 28,777 32,317 38,664 47,359 61,986 68,282 64,710 47,424 59,271 70,253 74,567 70,376 66,545 64,854 69,639 74,187 77,004 76,033
CHO 23,412 25,515 31,452 38,895 42,187 49,191 57,548 57,977 37,515 41,926 45,474 44,131 40,934 44,032 53,578 53,316 56,276 59,211 56,099
rvs, rubles SO 71,614 73,084 80,298 87,541 99,697 99,260 113,127 130,541 137,452 138,355 155,040 150,818 150,062 163,857 163,725 162,355 167,635 217,685 223,705
CHO 76,517 76,346 81,163 87,310 104,870 112,055 123,861 137,274 147,361 155,700 179,818 181,254 179,504 186,370 175,000 178,678 185,546 235,478 243,639
rvs, rubles SO 7,844 7,372 8,506 10,580 16,434 27,160 31,124 24,982 14,293 21,617 26,931 31,211 28,823 25,055 21,201 24,733 26,434 24,631 22,763
CHO 2,942 4,109 7,642 10,811 11,262 14,365 20,390 18,250 4,384 4,272 2,152 775 -618 2,542 9,925 8,409 8,523 6,838 2,829
cd4 SO 2.79 2.68 2.78 2.91 3.16 3.40 3.28 3.04 2.49 2.84 2.96 2.92 2.69 2.65 2.71 2.62 2.69 2.71 2.62
CHO 2.48 2.49 2.73 2.92 2.92 2.90 2.92 2.83 2.18 2.30 2.27 2.13 1.98 2.09 2.41 2.24 2.29 2.31 2.19
cd5 so 2.62 2.59 2.59 2.49 2.44 2.13 2.15 2.35 2.73 2.51 2.46 2.33 2.34 2.53 2.59 2.44 2.42 2.78 2.82
CHO 2.95 2.79 2.64 2.48 2.64 2.49 2.42 2.53 3.13 3.10 3.21 3.18 3.18 3.21 2.91 2.86 2.85 3.26 3.36
Note: Appendices 1, 2 are own calculations based on statistical data from Rosstat. https://rosstat.gov.ru.
Appendix 2. Assessment results of differentiation between regions by real wages
Indicator Region 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
RVi, rubles 287 497 525 641 705 776 923 921 664 568 532 554 143 1 -359 -350 -115 -131 -44 -9
rv2, rubles SO 1,494 1,760 1,976 2,250 2,426 2,748 3,287 3,597 2,769 2,964 3,077 3,061 2,946 2,760 2,358 2,266 2,636 2,597 2,665 2,687
CHO 1,207 1,263 1,451 1,609 1,721 1,973 2,364 2,676 2,105 2,396 2,545 2,507 2,803 2,759 2,717 2,616 2,751 2,728 2,709 2,696
RV3, rubles SO 1,733 1,777 1,770 1,645 1,769 1,849 1,627 2,040 2,880 2,767 2,779 2,975 3,242 3,271 3,245 3,500 3,827 4,359 4,754 5,192
CHO 2,020 2,273 2,295 2,286 2,474 2,625 2,550 2,960 3,544 3,335 3,311 3,529 3,385 3,272 2,886 3,150 3,712 4,228 4,710 5,183
rv4, rubles SO 1,633 1,914 2,128 2,428 2,636 2,992 3,592 3,948 3,016 3,275 3,479 3,409 3,236 3,023 2,736 2,712 2,930 2,948 3,070 2,991
CHO 1,345 1,418 1,603 1,788 1,931 2,216 2,669 3,027 2,352 2,707 2,947 2,855 3,092 3,023 3,095 3,062 3,045 3,079 3,114 3,000
RVs, rubles SO 3,199 3,501 3,764 3,750 4,069 4,155 4,339 4,894 5,492 5,604 5,993 6,449 7,008 7,454 7,293 7,616 7,761 8,194 8,902 15,528
CHO 3,486 3,998 4,289 4,391 4,774 4,930 5,262 5,815 6,156 6,172 6,525 7,003 7,151 7,455 6,934 7,265 7,646 8,063 8,858 15,519
RVs, rubles SO 541 695 832 1,023 1,118 1,292 1,629 1,675 971 1,073 1,153 1,098 946 862 649 542 627 595 550 379
CHO 254 198 307 382 413 516 706 754 307 505 621 543 803 862 1,008 892 742 726 594 388
cd4 SO 2.01 1.95 1.92 1.97 1.91 1.91 1.98 2.00 1.72 1.79 1.80 1.70 1.61 1.58 1.57 1.57 1.60 1.55 1.55 1.52
CHO 1.83 1.70 1.70 1.71 1.67 1.67 1.73 1.77 1.57 1.65 1.68 1.59 1.59 1.58 1.65 1.64 1.62 1.58 1.56 1.52
cd5 so 1.99 1.89 1.85 1.76 1.74 1.66 1.60 1.62 1.77 1.75 1.76 1.78 1.82 1.91 1.97 2.02 1.99 1.99 2.03 2.78
CHO 2.18 2.16 2.10 2.02 1.99 1.89 1.83 1.83 1.95 1.90 1.89 1.91 1.85 1.91 1.88 1.93 1.96 1.96 2.02 2.78
References
Animitsa E. G., Vlasova N. Yu. (2021). Large and largest cities in the spatial development of the Urals. Proc. Int. Sci. Conf. "The Present and Future of Russia in a Changing World: Socio-Geographical Analysis and Forecast" (pp. 111-116). Izhevsk. (In Russ.)
Antonyuk V. S., Kornienko E. L., Vansovich E. R. (2020). The spatial development patterns of the Russian Federation productive forces in the current context. Zhurnal ekonomicheskoy teorii = Russian Journal of Economic Theory, vol. 17, no. 2, pp. 314-327. https://doi.org/10.31063/2073-6517/2020.17-2.6. (In Russ.)
Barkhatov V. I., Kapkaev Yu. Sh., Pletnev D. A. (2019). The Ural and Volga regions' centers and periphery in the context of "new normality". Sotsium i vlast = Society and Power, no. 5 (79), pp. 65-82. DOI: 10.22394/1996-0522-2019-5-65-83. (In Russ.)
Bakhtizin A. R., Bukhvald E. M., Kolchugina A. V. (2016). Alignment of regions in Russia: Illusions of the program and reality of the economy. Vestnik Instituta ekonomiki Rossiyskoy akademii nauk = Vestnik of the Institute of Economics of the Russian Academy of Sciences, no. 1, pp. 76-91. (In Russ.)
Bents D. S. (2020a). Modelling of the economic growth factors: The case of the Ural regions and the Russian Federation. Journal of New Economy, vol. 21, no. 3, pp. 112-131. DOI: 10.29141/2658-5081- 2020-21-3-6. (In Russ.)
Bents D. S. (2020b). On synchronization of the Sverdlovsk and Chelyabinsk region economies. Vestnik Chelyabinskogo gosudarstvennogo universiteta = Bulletin of the Chelyabinsk State University, no. 11 (445), pp. 244-253. DOI: 10.47475/1994-2796-2020-11129. (In Russ.)
Bents D. S. (2021). The effectiveness of a territory's spatial development as an indicator of regional authorities performance: The case of Chelyabinsk oblast. Upravlenets = The Manager, vol. 12, no. 6, pp. 49-66. DOI:10.29141/2218-5003-2021-12-6-4. (In Russ.)
Bukhvald E. M. (2020). Managing the spatial development of the Russian economy: Goals and tools. Upravlenets = The Manager, vol. 11, no. 6, pp. 2-14. DOI: 10.29141/2218-5003-202011-6-1. (In Russ.)
Valentey S. D., Bakhtizin A. R., Bukhvald E. M., Kolchugina A. V. (2014). Development trends of the Russian regions. Ekonomika regiona = Economy of Region, no. 3 (39), pp. 9-22. (In Russ.)
Glushchenko K. P. (2015). On estimation of inter-regional inequality. Prostranstvennaya ekonomika = Spatial Economics, no. 4, pp. 39-58. DOI: 10.14530/se.2015.4.039-058. (In Russ.)
Gordeev S. S. (2017). Growth points and new trend of industrial development of Ural. Sotsium i vlast = Society and Power, no. 5 (67), pp. 74-82. (In Russ.)
Grebenkin I. V. (2020). Trends in industrial specialization and development dynamics in the Russian regions. Ekonomika regiona = Economy of Region, vol. 16, issue 1, pp. 69-83. https://doi. org/10.17059/2020-1-6. (In Russ.)
Danilova I. V., Rezepin A. V. (2021). Spatial economic systems: Methodology and theoretical approaches to research. Vestnik Altayskoy akademii ekonomiki i prava = Bulletin of the Altai Academy of Economics and Law, no. 7-1, pp. 24-32. DOI: 10.17513/vaael.1776. (In Russ.)
Dvoryadkina E. B., Dzhalilov E. V. (2021). Properties of traditional industrial regions' economic space: Theoretical foundations. Journal of New Economy, vol. 22, no. 4, pp. 45-61. DOI: 10.29141/2658-5081-2021-22-4-3. (In Russ.)
Zharomskiy V. S., Migranova L. A., Toksanbaeva M. S. (2018). Methods of estimating the level and differentiation of population income. Narodonaselenie = Population, vol. 21, no. 4, pp. 79-95. https://doi.org/10.19181/population.2020.23.43. (In Russ.)
Zemtsov S. P., Barinova V. A. (2016). The paradigm changing of regional innovation policy in Russia: From equalization to smart specialization. Voprosy ekonomiki = The Issues of Economics, no. 10, pp. 65-81. https://doi.org/10.32609/0042-8736-2016-10-65-81. (In Russ.)
Zubarevich N. V. (2019). Spatial development strategy: Priorities and instruments. Voprosy ekonomiki = The Issues of Economics, no. 1, pp. 135-145. https://doi.org/10.32609/0042-8736-2019-1-135-145. (In Russ.)
Kolomak E. A. (2020). Urbanization and income inequality: Cause or solution? Prikladnaya ekonometrika=AppliedEconometrics,no.3(59), pp.55-70. DOI: 10.22394/1993-7601-2020-59-5570. (In Russ.)
Krivoshlykov V. S., Zhakhov N. V. (2017). Economics and management of interregional differentiation. Vestnik NGIEI = Bulletin of NGII, no. 1 (68), pp. 119-129. (In Russ.)
Kuznetsov S. V., Mezhevich N. M., Lachininskiy S. S. (2015). The spatial recourses and limitations of the Russian economy modernization: The example of the North-West Macro Region. Ekonomika regiona = Economy of Region, no. 3 (43), pp. 25-38. DOI: 10.17059/2015-3-3. (In Russ.)
Kuklin A. A., Leontyeva A. G. (2011). Differentiation of population incomes and the problem of poverty (the case of the Tyumen oblast). Ekonomika regiona = Economy of Region, no. 1 (25), pp. 93-98. (In Russ.)
Kurushina E. V., Petrov M. B. (2018). Performance criteria of spatial development projects based on interregional integration. Ekonomika regiona = Economy of Region, vol. 14, no. 1, pp. 176-189. DOI: 10.17059/2018-1-14. (In Russ.)
Lavrikova Yu. G., Akberdina V. V., Suvorova A. V. (2019). Coordinating the priorities of scientific, technological and spatial development of industrial regions. Ekonomika regiona = Economy of Region, vol. 15, no. 4, pp. 1022-1035. https://doi.org/10.17059/2019-4-5. (In Russ.)
Lavrikova Yu. G., Suvorova A. V. (2020). Optimal spatial organisation of the regional economy: Search for parameters and dependencies. Ekonomika regiona = Economy of Region, vol. 16, issue 4, pp. 1017-1030. https://doi.org/10.17059/ekon.reg.2020-4-1. (In Russ.)
Malkina M. Yu. (2017). Social well-being of the Russian Federation regions. Ekonomika regiona = Economy of Region, vol. 13, no. 1, pp. 49-62. DOI: 10.17059/2017-1-5. (In Russ.)
Minakir P. A. (2021). Strategies for Russia and Russia. Prostranstvennaya ekonomika = Spatial Economics, vol. 17, no. 1, pp. 7-17. https://dx.doi.org/10.14530/se.2021.L007-017. (In Russ.)
Okrepilov V. V., Kuznetsov S. V., Lachininskiy S. S. (2020). Priorities of economic development of North-West regions in the context of the strategy of spatial development of Russia. Problemy prognozirovaniya = Problems of Forecasting, no. 2 (179), pp. 72-81. (In Russ.)
Tatarkin A. I. (2016). Regional targeting of the economic policy of the Russian federation as an institution of regional spatial development. Ekonomika regiona = Economy of Region, vol. 12, no. 1, pp. 9-27. (In Russ.)
Friedmann J. (1966). Regional development policy: A case study of Venezuela. Cambridge: MIT Press. 279 p.
Hirshman A. (1958). The strategy in economic development. New Haven: Yale University Press. 217 p.
Krugman P. R. (1991). Geography and trade. Cambridge: The MIT Press. 156 p.
Kuznets S. (1955). Economic growth and income inequality. The American Economic Review, vol. XLV, no. 1, pp. 1-28.
Malkina M. (2019). Spatial wage inequality and its sectoral determinants: The case of modern Russia. Oeconomia Copernicana, vol. 10, no. 1, pp. 69-87. DOI:10.24136/oc.2019.004.
Myrdal G. (1957). Economic theory and underdeveloped regions. London: G. Duckworth. 168 p.
Voroshilov N. V., Gubanova E. S. (2018). Territorial differentiation and mechanism for its reduction. Economic and Social Changes: Facts, Trends, Forecast, vol. 11, no. 6, pp. 57-72. DOI: 10.15838/esc.2018.6.60.4.
Williamson J. G. (1965). Regional development and the process of national development: A description of the patterns. Economic Development and Cultural Change, vol. 13, no. 4, pp. 1-84. https://doi.org/10.1086/450136.
Information about the author
Darya S. Bents, Cand. Sc. (Econ.), Associate Prof., Prof. of Industries and Markets Dept., Chelyabinsk State University, 129 Bratyev Kashirinykh St., Chelyabinsk, 454001, Russia Phone: +7 (351) 799-71-46, e-mail: benz@csu.ru
© Bents D. S., 2022
