CC BY
5DOI: 10.29141/2658-5081-2023-24-3-5 EDN: WKGNWP JEL classification: R10, R15, L61
Daria S. Bents Chelyabinsk State University, Chelyabinsk, Russia
Aleksandr V. Rezepin South Ural State University, Chelyabinsk, Russia
Medium-term trends in economic and technological development of metals industry regions
Abstract. Since metals industry regions are dependent on the location of production resources and infrastructure, as well as have highly capital-intensive production, they usually feature a slowly changing industrial and technological structure of production. In line with the hypothesis of the study, the technological structure of the manufacturing industry determines the differences in their economic development. The study aims to explore the medium-term trends and patterns in economic and technological development of the metals industry regions. The methodological basis of the research is the structural dynamic approach to studying regional economic development. The methods include multivariate data analysis and clustering algorithms, time series estimation and correlation analysis. The study uses the data on large and medium-sized enterprises by types of economic activities for 2006-2021 taken from an information and analytics system FIRA PRO. According to the findings, the Chelyabinsk, Sverdlovsk, Lipetsk, Vologda and Tula oblasts are metals industry regions. The identified medium-term trends in their economic development show different dynamics of input and output indicators of production both by all types of economic activities and in metals industry in particular. The study proves that the dynamics of metals industry regions' development largely depends on technological characteristics of the manufacturing industry. The results of the research contribute to the understanding of the adaptation mechanisms of regions' industry to the changing conditions of external environment.
Keywords: mono-industrial region; Russia's regions; technological structure; metals industry; manufacturing industry.
Acknowledgements: The research is funded by the grant of the Russian Science Foundation and the Chelyabinsk oblast (project no. 23-28-10167, https://rscf.ru/pro-ject/23-28-10167/).
For citation: Bents D. S., Rezepin A. V. (2023). Medium-term trends in economic and technological development of metals industry regions. Journal of New Economy, vol. 24, no. 3, pp. 91-118. DOI: 10.29141/2658-5081-2023-24-3-5. EDN: WKGNWP. Article info: received February 27, 2023; received in revised form May 22, 2023; accepted June 23, 2023
Introduction
Regions that traditionally specialise in metals industry and manufacturing of the fabricated metal products, such as the Chelyabinsk oblast, are usually notable for their slowly changing industrial and technological structure of production due to the high dependence on the location of the raw materials, production and transport infrastructure, high capital intensity and rigidity of production technologies.
Despite the objective trend of accelerating technological changes, the analysis of medium-term (from 6 to 16 years) trends in the development of metals industry regions and the study of the dependence of these regions on the internal development programme, the influence of inherited characteristics and the path dependence are of scientific interest. We can logically assume that changes in the general external environment may cause differences in the dynamics of economic and technological development of these territories. In line with the hypothesis of the study, the technological structure of basic industries' production determines these differences.
The research subject is the medium-term trends in the economic and technological development of the metals industry regions, which are similar to the Chelyabinsk oblast. The similarity is determined by two criteria: the regional specialisation in metals industry and the technological intensity in the manufacturing industry.
The purpose of the study is determining the medium-term trends in the economic and technological development of the metals industry regions.
In order to achieve this purpose, the following objectives were set out:
- to determine metals industry regions similar to the Chelyabinsk oblast using two criteria: technological intensity in the manufacturing industries and the level of monospecialisation;
- to identify medium-term trends in the technological intensity of metals industry regions and hold their comparative assessment;
- to locate these regions in the economic space of the Russian Federation;
- to assess the economic development synchronisation of these regions with one another and with the national economy as a whole;
- to reveal the dependence between the input and output indicators (resources and performance) of the economic activity in the regions under consideration.
The study does not intend to comprehensively take into account the factors influencing the medium-term trends in the economic and technological development of the metals industry regions, and is based on several prerequisites.
A criterion for the technological intensity of manufacturing industry was the share of the average number of employees involved in the production belonging to various technological levels (in pure OKVED (Russian Classification of Economic Activities)).
Since information about the employees' actual type of activity is generalised only for legal entities and does not include small businesses, the study used statistical data on medium and large businesses.
According to the applied method, "Manufacture of basic metals" and "Manufacture of fabricated metal products" belong to the same technological intensity- medium-low-tech production, the trends in metals industry are considered in general, without regarding the production of fabricated metal products, ferrous and non-ferrous metals industry.
Literature review
What factors determine the universality in economic development of the regions belonging to one cluster is a complex and debatable question. There is not any uniformity even among mono-industrial regions. There is not also any universal model according to which the metals industry regions develop [Danilova, Salimonenko, 2020].
Romanova and Sirotin discuss the need and possibility of repositioning the metals complex of the Urals as a whole and the Sverdlovsk oblast in particular. In their opinion, the technological image of the metals industry is currently associated with a large-scale type of environmentally unfriendly production and the predominance of low value-added products [Romanova, Sirotin, 2017]. However, there is a reasonable potential for the development of the metals industry as a science-intensive and hightech complex [Romanova, Sirotin, 2019].
The metals industry is regarded as a once good basis for the transition to the second and third technological paradigms and, at the same time, as the industry that is 'stuck' at the stage of transition to later technological paradigms [Sorokina, Latov, 2018]. Researchers call the institutional inertia, preventing the necessary changes in the region, the path dependence [Gordeev, Zyryanov, Podoprigora, 2019]. In foreign literature, this effect is associated with dependence on previous outcomes [Martin, Sunley, 2006]. The path dependence manifests itself in the long run. At certain moments, it helps to resist negative trends. However, it hinders the dynamic growth of a region. An analysis of medium-term trends in regional development allows experiencing this effect.
There have been numerous studies to investigate such trends. For instance, Bents built long-term trends of interregional differentiation for the Chelyabinsk and Sverdlovsk oblasts [Bents, 2022] and showed that the long-term development of the former brings it closer to outsiders in terms of real GRP per capita, while the latter keeps a stable position. In addition, the author determined the convergence the regional economies under consideration - in many respects, long-term trends demonstrate a synchronising effect.
Silin, Animitsa and Novikova [2019] studied long-term trends of industrialisation processes in the economic space of the Ural macroregion. They consistently showed the long-term changes in the development of the industry, which began in the period of the pre-industrial era (1710-1855), and completed their work by demonstrating the trends typical for 2018. The upper time limit of the study was the period 2035-2040, after which the Ural regions will be able to fully use the results of the fourth industrial revolution. It is noteworthy that, studying the pre-industrial (proto-industrial) period, the authors revealed the formation dynamics of the Urals as a metals industry region. In 1720-1860, the share of the Urals in the all-Russian space in terms of iron smelting grew from 21.6 to 79.7 %. It should be clear that when it comes to preserving the Urals' industrial specialisation, its technological development is predicted not at the expense of the metals industry, but at the expense of the production of vehicles, machinery, equipment, fabricated products, electrical equipment, etc. [Silin, Animitsa , Novikova, 2019].
Such a factor as technology is increasingly becoming the focus of attention of scientists studying the specifics of regional development [Gambeeva, Smey, 2021; Antonyuk, Kornienko, 2022], and the technological component is also researched in the context of creative industries. Gambeeva and Smey find there is a great potential for the creative economy and point to the insufficiency of its implementation.
Traditionally, many researchers refer the Chelyabinsk oblast to old industrial regions. Antonyuk and Kornienko devoted their work to the peculiarities of these regions' development [Antonyuk, Kornienko, 2022]. In total, they identified 41 old industrial regions, which they divided into 24 middle and 17 border regions. The scientists found that the border old industrial regions lag behind the territories that are not old industrial in terms of GRP per capita. In addition, the authors proposed a composite indicator characterising the development level of economic factors (labour/capital/technology), on the basis of which they ranked old industrial border regions. According to this ranking, the Chelyabinsk oblast is the leader among all border regions. However, it is accompanied by a negative state in terms of the economic factor "capital".
Doroshenko, Starikova and Ryapukhina defined the Chelyabinsk oblast as an industrially developed region with relatively high innovation performance. The same group included the Lipetsk and Tula oblasts (which are also in our sample), but the authors did not group the regions by the criterion "share of metal production". The indicators were the share of manufacturing industry in the total GRP; share of organisations implementing technological innovations; the share of innovative goods, works, services in the total volume of shipped goods, performed works and services [Doroshenko, Starikova, Ryapukhina, 2022].
Dzhurka [2018] looked at the specifics of industrial production location. The spatial distribution of manufacturing industries is made dependent on domestic demand. In metals industry, the effect of the domestic market is not as tangible as, for example, in the oil refining industry.
The Chelyabinsk oblast is a region featuring remarkable monospecialisation. It is distinguished not only by a high share of the manufacturing industry, but by a large share of metal production. Mono-industry puts the region's development at risk - it is impossible to call its economy highly diversified. The loss of industry diversity is a risk inherent in the industry [Treyvish, 2019]. Savelyeva, Danilova, and Pravdina [2022], who examined the specialisation of the Chelyabinsk oblast's economy and stated an increase in the share of medium-low-tech industries in the region over the period 2014-2018, also emphasise such a risk.
At the same time, it may be worth not considering monospecialisation as an aggravating factor in the development of a territory, since the metals cluster of the Urals demonstrates competitiveness even globally [Samarina, Martirosyan, Ilyicheva, 2019; Emelyanov, Kelchevskaya, Pelymskaya, 2020]. This competitiveness is also enhanced due to objective advantages, in particular, the presence of own ore deposits and coal mines [Orekhova, Dubrovskiy, 2018]. In addition, if we take into account the shift of economic activity from mining regions to the manufacturing ones [Kolomak, 2019], the manufacturing industry of the region itself is likely to become a driver of its economic development.
If a territory has a low growth rate of capital costs (positions of the metals industry regions under consideration in the all-Russian space in terms of the share of fixedas-sets are weakening), then in order to get started with Industry 4.0, it is necessary to follow the model of labour-intensive growth, otherwise the technological level of a region will remain unchanged or even decline [Sukharev, 2022]. And here again there are some risks - the risks of the increasing technological backwardness of the Chelyabinsk oblast.
High technology and innovation formed the basis of the so-called smart specialisation, the project launched in the European Union. Those regions that work on "innovative strategies for the development of smart specialisation" may qualify for subsidies from a relevant fund [Asheim, 2019]. We can conclude that European countries have taken the path of supranational achievement of results in the innovative development of regions [Bailey, Christos, Tomlinson, 2018].
Foreign scientists understand the necessity to diversify regional production in order to avoid the risks of monospecialisation as well. Related industries will have the key role to play in technological diversification. The decisive role is assigned to firms and industries operating in a region [Neffke, Henning, Boschma, 2011]. New activities are formed on the basis of existing ones [Boschma, 2016]. This predetermines
the vector of a region's development. The concept of smart specialisation is based on the knowledge economy, which has long been receiving much attention. Foreign researchers have long argued that the importance of low-tech industries in regional development cannot be underestimated, but their future development is limited. They should transform into high-tech ones, otherwise they will quickly lose their competitiveness due to the simplicity to copy their technologies [Hirsch-Kreinsen, Jacobson, Robertson, 2006].
What determines the specifics of the medium-term economic development of a territory - industry or spatial factors? The importance of regions' proximity should be included in the study of their socioeconomic development [Kotov, 2021]. According to the results of cluster analysis, the study sample included metals industry regions, and among them only the Sverdlovsk oblast is a neighbour of the Chelyabinsk oblast. A comparative analysis of the trends in the economic development of the selected regions will make it possible to understand which factor predetermines the generality of this development to a greater extent - industrial or spatial.
Materials and methods
Using the methods given in Table 1, it was possible to achieve the stated objectives. The statistics was taken from an information and analytics system FIRA PRO1 on large and medium-sized enterprises in the regions by type of economic activity in 2006-2021.
The first objective of the study is identifying the Russian Federation's subjects whose specialisation of manufacturing industries is similar to the Chelyabinsk oblast. The method of cluster analysis was applied to achieve this objective, which allowed grouping objects based on the proximity of their properties and determining clusters (groups) of regions that are similar to one another and different from other subjects of the Russian Federation. Cluster analysis is widely and effectively used for typology of territories within a single region [Anselin, Rey, 2010], when assessing the unevenness of the economic space of a country as a whole [Oku-nev, Lopatina, 2022], as well as for identifying regions based on a set of statistical characteristics [Sirenko, Rychkova, 2020] and time series [Piskun, Khokhlov, 2019; Aralbaeva, Berikbolova, 2021].
Two criteria were used to identify regions with a specialisation of manufacturing industry similar to the Chelyabinsk oblast.
The first criterion is the general technological intensity of the manufacturing industries located in the region. In Table 2 we aggregated the types of the manufacturing industries in accordance with the technological intensity based
1 FIRA PRO. https://pro.fira.ru/search/index.html#company.
Table 1. Description of the research method
No. Research objective Indicators Statistics Methods and materials
1 To identify regions close to the Chelyabinsk oblast's specialisation 1. The average number of employees of large and medium-sized enterprises. 2. The cost of own produced goods shipped by large and medium-sized enterprises. 3. The value of fixed assets of large and medium-sized enterprises* 1-4. The share of the average number of employees in various types of the manufacturing industry by the technological intensity in the total number of employees in the manufacturing industry. 5. The share of the average number of employees in manufacture of basic metals (24) and manufacture of fabricated metal products, except machinery and equipment (25) **, in the total number of employees in all sectors of the economy. 6. The share of the cost of shipped own production metals goods in the total cost of own produced goods shipped in all sectors of the economy. 7. The share of fixed assets of metals production in the total fixed assets in all sectors of the economy Cluster analysis. Sample is all regions of Russia, 2021
2 To determine the technological intensity of the metals industry regions Average number of employees 1. Share of the average number of employees in high-tech manufacturing industry in the total number of employees in the manufacturing industry. 2. Share of the average number of employees in medium-high-tech activities in the manufacturing industry in the total number of employees in the manufacturing industry. 3. Share of the average number of employees in medium-low-tech manufacturing activities in the total number of employees in the manufacturing industry. 4. Share of the average number of employees in low-tech manufacturing activities in the total number of employees in the manufacturing industry Comparative analysis of the metals industry regions. Sample is metals industry regions, 2017-2021
Table 1 (concluded)
No. Research objective Indicators Statistics Methods and materials
3 To reveal trends in the technological intensity of the metals industry regions Average number of employees 1. Share of the average number of employees in high-tech manufacturing industry in the total number of employees in the manufacturing industry 2. Share of the average number of employees in medium-high-tech activities of the manufacturing industry in the total number of employees in the manufacturing industry Building trends. Comparison with all-Russian trends. Sample is metals industry regions, 2006-2021
4 To position the metals industry regions in the all-Russian space 1. Average number of employees (ANE) 2. Cost of fixed assets (FA) 3. Cost of own produced goods shipped (SOPG) 1-3. Share of the corresponding indicator of the metals industry region in the corresponding all-Russian indicator Building trends. Sample is metals industry regions, 2005-2021
5 To search for the synchronisation processes of the metals industry regions' economic development Growth rate of the corresponding indicator calculated for the period 2005-2021 for each studied metals industry region Correlation analysis. Sample is metals industry regions, 2006-2021
6 To answer the question whether universal laws of economic development operate Correlation of the growth rates of input and output indicators for 2006-2021. Sample is all studied metals industry.
Notes: * Further, we will omit the mention of large and medium-sized enterprises, but only they are considered. * * Further, the sum of sections (24) and (25) of OKVED 2 will be called metals production or metals industry.
Table 2. Types of the manufacturing industry by the technological intensity
Technological intensity Types of economic activity according to OKVED 2
High-tech manufacture (21) Manufacture of basic pharmaceutical products and pharmaceutical preparations. (26) Manufacture of computer, electronic and optical products. (30.3) Manufacture of air and spacecraft and related machinery
Medium-high-tech manufacture (20) Manufacture of chemicals and chemical products. (25.4) Manufacture of weapons and ammunition. (27) Manufacture of electrical equipment. (28) Manufacture of machinery and equipment n.e.c. (29) Manufacture of motor vehicles, trailers and semi-trailers. (30) Manufacture of other transport equipment, excluding (30.1) Building of ships and boats and excluding (30.3) Manufacture of air and spacecraft and related machinery. (32.5) Manufacture of medical and dental instruments and supplies
Medium-low-tech manufacture (18.2) Reproduction of recorded media. (19) Manufacture of coke and refined petroleum products. (22) Manufacture of rubber and plastic products. (23) Manufacture of other non-metallic mineral products. (24) Manufacture of basic metals. (25) Manufacture of fabricated metal products, except machinery and equipment, excluding (25.4) Manufacture of weapons and ammunition. (30.1) Building of ships and boats. (33) Repair and installation of machinery and equipment
Low-tech manufacture (10) Manufacture of food products. (11) Manufacture of beverages. (12) Manufacture of tobacco products. (13) Manufacture of textiles. (14) Manufacture of wearing apparel. (15) Manufacture of leather and related products. (16) Manufacture of wood and products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials. (17) Manufacture of paper and paper products. (18) Printing and reproduction of recorded media excluding (18.2) Reproduction of recorded media. (31) Manufacture of furniture. (32) Other manufacturing excluding (32.5) Manufacture of medical and dental instruments and supplies
Source: Eurostat. High-tech classification of manufacturing industries. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Hightech_classification of_manufacturing_industries.
on two-digit OKVED 2 codes and using the Eurostat methodology, which allows distributing manufacturing industries according to four levels of technological intensity.
In our opinion, it is appropriate to use the share of the average number of employees in various types of the manufacturing industry in the total number of people employed in the manufacturing industry as the main indicator of the technological intensity of the regions' manufacturing industry. This indicator does not fully reflect the differences in the level of workforce productivity in various types of activities (which are common in all regions), but it allows leveling the impact of changes in the exchange rate and the dynamics of world prices for manufacturing industry products (which is associated with the level of export orientation and dependence on imports of regional economy).
To take into account the hierarchy of technological intensity of manufacture, we propose the calculation of cumulative indicators:
T1 is the share of the average number of employees in high-tech manufacturing activities (in pure OKVED) in the average number of employees in the manufacturing industry, %;
T2 is the share of the average number of employees in high-tech and medium-hightech types of manufacturing activities (in pure OKVED) in the average number of employees in the manufacturing industry, %;
T3 is the share of the average number of employees in high-tech, medium-hightech and medium-low-tech types of manufacturing activities (in pure OKVED) in the average number of employees in the manufacturing industry, %.
The second criterion is the specialisation level of the regional economy in the types of manufacturing industry traditional for the Chelyabinsk oblast - (24) Manufacture of basic metals; (25) Manufacture of fabricated metal products, except machinery and equipment.
To comprehensively assess the metals industry, we offer to calculate indicators characterising the volume of shipped products, as well as the industrial specialisation of labour and capital resources:
M1 is the share of the average number of employees in the "Manufacture of basic metals" and "Manufacture of fabricated metal products, except machinery and equipment" (in pure OKVED), in the total average number of employees in all sectors of the economy, %;
M2 is the share of fixed assets cost by types of activity "Manufacture of basic metals" and "Manufacture of fabricated metal products, except machinery and equipment", in the total cost of fixed assets in all sectors of the economy, %;
M3 is share of the cost of own produced goods shipped by types of activity "Manufacture of basic metals" and "Manufacture of fabricated metal products, except
machinery and equipment", in the total cost of own produced goods shipped in all sectors of the economy, %.
The subjects of the Russian Federation were classified by the method of hierarchical cluster analysis using the IBM SPSS Statistics software, the metric is the squared Euclidean distance, the distance between clusters was determined using the Ward method. Clustering criteria are indicators T1, T2, T3, M1, M2, M3.
Additionally, based on the metric of the squared Euclidean distance, a composite estimate of the differences in the specialisation of the manufacturing industry for each pair of regions was determined:
L(x,y) = Z (x - yd2 , (1)
where x = (x1, x2 ... x6 ) and y = (y1, y2 ... y6) are vectors of values of criteria for clustering regions X and Y; i = (T1, T2, T3, M1, M2, M3) is clustering criteria.
The next objectives of the study were to determine the technological intensity of metals industry regions and build trends for technological intensity. The technological intensity was determined based on the above Eurostat methodology only for 2017-2021, because the FIRA PRO system lacks some industrial data for the period before 2016. In this regard, it is not possible to correctly identify the share of the number of employees in medium-low-tech and low-tech activities in the manufacturing industry. On the contrary, it seems possible to determine the share of the number of employees in high-tech and medium-high-tech activities. These trends will be shown in the next section of the work.
Research results and discussion
The cluster analysis of statistical data on the activities of large and medium-sized enterprises in 2021 was held for 82 subjects of the Russian Federation. According to its findings, four regions have a manufacturing industries' specialisation similar to the Chelyabinsk oblast's one, namely the Vologda, Lipetsk, Sverdlovsk and Tula oblasts. Figure 1 shows a dendrogram (Ward's method) built on a matrix of proximity measures and reflecting the mutual relationships between the specialisations of the regions' manufacturing industries.
The regions included in the same cluster with the Chelyabinsk oblast (marked with a box in Figure 1) differ significantly from other Russian Federation's subjects. The same conclusion can be drawn from a complex assessment of the differences in the specialisation of the manufacturing industry of Russia's regions and the specialisation of the Chelyabinsk oblast. Some results of the assessing differences for the 20 most similar subjects of the Russian Federation are presented in Figure 2. The composite estimate of differences is defined as the squared Euclidean distance according to formula (1) and is a dimensionless quantity. The Sverdlovsk oblast has the industry's
Republic of Sakha (Yakutia) 74
Jewish autonomous oblast 81
Chechen Republic 42
Tyumen oblast 60
Republic of Ingushetia 38
Kursk oblast 8
Karachay-Cherkessia 40
Republic of Karelia 19
Krasnodar krai 32
Ivanovo oblast 5
Kabardino-Balkar Republic 39
Kamchatka krai 75
Chukotka autonomous okrug 82
Altai Republic 62
Republic of Tyva 63
Komi Republic 20
Sakhalin oblast 80
Republic of Adygea 29
Voronezh oblast 4
Stavropol krai 43
Tambov oblast 14
Republic of North Ossetia-Alania 41
Saratov oblast 56
Novosibirsk oblast 69
Moscow oblast 10
Irkutsk oblast 67
Mari El Republic 45
Penza oblast 54
Saint Petersburg city 28
Republic of Bashkortostan 44
Yaroslavl oblast 17
Samara oblast 55
Ryazan oblast 12
Omsk oblast 70
Republic of Dagestan 37
Moscow city 18
Ulyanovsk oblast 57
Khabarovsk krai 77
Republic of Buryatia 72
Pskov oblast 27
Republic of Crimea 31
Sevastopol city 36
Magadan oblast 79
Leningrad oblast 24
Altai krai 65
Amur oblast 78
Kostroma oblast 7
Astrakhan oblast 33
Primorky krai 76
Kaliningrad oblast 23
Novgorod oblast 26
Bryansk oblast 2
Tver oblast 15
Oryol oblast 11
Republic of Mordovia 46
Smolensk oblast 13
Tomsk oblast 71
Arkhangelsk oblast 21
Zabaykalsky krai 73
Republic of Kalmykia 30
Republic of Tatarstan 47
Chuvash Republic 49
Perm krai 50
Kurgan oblast 58
Kaluga oblast 6
Nizhny Novgorod oblast 52
Rostov oblast 35
Kirov oblast 51
Vladimir oblast 3
Udmurt Republic 48
Volgograd oblast 34
Kemerovo oblast 68
Orenburg oblast 53
Republic of Khakassia 64
Belgorod oblast 1
Murmansk oblast 25
Krasnoyarsk krai 66
Sverdlovsk oblast Chelyabinsk oblast Lipetsk oblast Volgograd oblast Tula oblast
59 61 9 22 16
5 10 15 20
Rescaled distance cluster combine
Fig. 1. Cluster analysis results of Russian regions
Sverdlovsk oblast Lipetsk oblast Vologda oblast Tula oblast Krasnoyarsk krai Republic of Khakassia Murmansk oblast Volgograd oblast Udmurt Republic Nizhny Novgorod Vladimir oblast Kemerovo oblast Sverdlovsk oblast Lipetsk oblast Moscow oblast Perm krai Vologda oblast Republic of Mordovia Tomsk oblast Saratov oblast
■ 0.038 H 0.101 0.196 ^^ 0.243 0.440 )4 0.964 0.999
izr
1.956 1.968 1.975
0.0
0.5
1.0
1.5
2.0
Fig. 2. Composite assessment of differences between the manufacturing industry's specialisations of Russian regions and the Chelyabinsk oblast
specialisation closest to the Chelyabinsk oblast, followed by the Lipetsk, Vologda and Tula oblasts. Then there is a value jump of the composite estimate of the difference, and the next region in terms of proximity, the Krasnoyarsk krai, falls into another cluster.
In terms of industrial specialisation, the Krasnoyarsk krai belongs to a typical mono-industrial metals region and is considered in comparison with the Vologda, Lipetsk and Chelyabinsk oblasts [Danilova, Pravdina, 2022]. However, taking into account the factor of technological intensity allows identifying differences in the manufacturing industry's specialisation of these regions.
Figure 3 presents a comparative assessment of regional specialisation according to the standardised values of the clustering criteria. The maximum value for each indicator among the studied regions is 100 %. Generally, the Krasnoyarsk krai's indicators of specialisation in metal production (M1, M2, M3) are lower compared to the Chelyabinsk oblast. At the same time, in the Krasnoyarsk krai, the share of high-tech products (T1) is much higher, and the share of medium-high-tech products (T2) is much lower than in the Chelyabinsk oblast, which indicates a different structure of the manufacturing industry.
-Chelyabinsk oblast — Chelyabinsk oblast
-Sverdlovsk oblast — Krasnoyarsk krai
Fig. 3. Comparative assessment of the manufacturing industry's specialisations in regions, % The results of comparing the metals industry regions are shown in Table 3.
Table 3. Comparative characteristics of the metals industry regions
Subject of the Russian Federation Technological intensity indicators of manufacturing industries, % Indicators of specialisation in metal production, %
Ti T2 T3 M1 M2 M3
Chelyabinsk oblast 6.1 30.9 86.1 9.9 52.5 51.4
Sverdlovsk oblast 7.0 35.5 90.4 9.1 45.8 45.1
Lipetsk oblast 0.9 17.2 71.7 11.5 53.5 56.8
Vologda oblast 0.2 14.8 67.7 8.5 54.7 35.8
Tula oblast 7.6 29.0 79.3 11.7 35.5 32.1
Cluster-average value 4.4 25.5 79.0 10.2 48.4 44.3
Average value forl Russia's subjects 14.0 40.7 74.3 2.6 10.0 4.5
Source: Own compilation based on the data from FIRA PRO. https://pro.fira.ru/search/index. html#company.
Table 3 presents the indicators of technological intensity calculated using the cumulative method. If to consider the technological intensity, focusing on its four types without the use of cumulative curves, we will get the following result (Figure 4).
2021
Chelyabinsk oblast | 6.14
Sverdlovsk oblast | 6.98
Lipetsk oblast | 0.88
Vologda oblast 0.23
Tula oblast | 7.53
Russia H 14,00
24.78 28.50 16.29 14.60 21.40 26.73
2017
Chelyabinsk oblast | 6.61
Sverdlovsk oblast | 6.95
Lipetsk oblast | 0.53
Vologda oblast | 0.34
Tula oblast | 9.10
Russia ■ 13.63
25.12 27.75 18.02 17.39 21.55 26.62
Types of activity
■ High-tech Medium-high-tech Medium-low-tech l Low-tech Fig. 4. Technological intensity of the metals industry regions in 2017 and 2021, %
On average, across the country the share of high-tech production increased slightly in the past five years, however, in the Chelyabinsk, Vologda and Tula oblasts it decreased. In the Chelyabinsk, Lipetsk, Vologda and Tula oblasts, the share of mediumhigh-tech industries dropped as well. Yet these dynamics are so insignificant that it would be incorrect to announce any changes.
In the general sense, the data in Figure 4 show that the alignment of forces has not changed over the period under consideration. In the five metals industry regions, the absolute majority of employees of large and medium-sized enterprises work in medium-low-tech industries. And the share of these industries in almost all metals industry regions, with the exception of the Sverdlovsk oblast, has grown in five years. Thus, according to the criterion of high-tech production, two pairs of close regions can be distinguished: the Chelyabinsk and Sverdlovsk oblasts, as well as the Lipetsk and Vologda oblasts.
In order to assess the positioning of the metals industry regions in the medium term, we will show the dynamics of the share of employees in high-tech and mediumhigh-tech industries over the period 2006-2021 (Figures 5, 6).
Compared to the five-year period, the trends in the medium term differ (Figure 5). In all studied regions, there is a steady increase in the share of people employed in high-tech industries. However, it is not as significant as the one in the country. With the medium-tech manufacture, the situation is not so definite
(Figure 6). The all-Russian value increased insignificantly - from 24.2 to 26.73 %. The largest growth over the 15 years is demonstrated by the Lipetsk oblast, where the share of employees in medium-high-tech industries increased by 2.8 times. In the Chelyabinsk, Sverdlovsk and Vologda oblasts the growth was 168, 149 and 131 %, respectively. Only the Tula oblast saw a fall of 43 %. However, as of 2021, this region is not an outsider among the studied regions, it significantly surpasses the
Lipetsk and Vologda oblasts. %
13.6 13.7 14-0
0 o»o
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
-Chelyabinsk oblast Sverdlovsk oblast — Lipetsk oblast
Vologda oblast —Tula oblast -Russia
Fig. 5. Share of average number of employees in the high-tech manufacture in the total number of people employed in manufacturing industry, 2006-2021
11.0 11.4 10.5 8.1 8.1 15.4 14.6
11.1
5.8 5.3 6.3 6.0 7.3 7.4
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
-Chelyabinsk oblast Sverdlovsk oblast
Vologda oblast —Tula oblast
-Lipetsk oblast
-Russia
Fig. 6. Share of the average number of employees in the medium-high-tech manufacture in the total number of people employed in manufacturing industry, 2005-2021
Let us position the metals industry regions in the all-Russian space by showing how the share of own produced goods shipped in the corresponding region has changed in the all-Russian value. We will do this for all industries (Figure 7), and for the metals industry in particular (Figure 8).
-Chelyabinsk oblast Sverdlovsk oblast - Lipetsk oblast
Vologda oblast -Tula oblast
Fig. 7. Share of metals industry regions' own produced goods shipped in Russia's total (all industries), 2005-2021
-Chelyabinsk oblast Sverdlovsk oblast -Lipetsk oblast
Vologda oblast -Tula oblast
Fig. 8. Share of metals industry regions' own produced goods shipped in Russia's total (metals industry), 2005-2021
The Sverdlovsk oblast is a stable leader as shown by the data in Figure 7 (with the exception of only 2005) and in Figure 8. However, this trend is gradually declining (Figure 7). The data in Figure 8 cannot be considered positively. Despite the differences in terms of industry, the grouping of regions is clearly visible: the geometry of the graphs of the Chelyabinsk and Sverdlovsk oblasts almost coincides. In addition, the lines themselves are closer to each other (especially in Figure 8), which indicates the economies' proximity, and first and foremost, of the trends in the metals industry. In the medium term, the share of almost all regions under consideration in the total Russian metal production is falling (Figure 8). Only the Tula oblast went up from 2.02 % in 2005 to 3.61 % in 2021, though the share of this region over the entire period is the lowest in comparison with other metals industry regions.
The indicator "cost of own produced goods shipped" is an output indicator, that is, it allows assessing the state of economic development of a region. By contrast, such indicators as "cost of fixed assets" and "average number of employees" can be considered as input indicators. Let us analyse how the shares of input indicators changed over the same period (Figures 9-12).
The positions of all the regions in terms of the share of fixed assets in the all-Russian value have weakened, which is quite logical for we see the same trends in terms of the share of own produced goods shipped. If we consider only metal production but not all industries (Figure 10), we can conclude that the positions of the regions in relation to one another were not always the same. From 2006 to 2010, the Vologda oblast showed the largest share of fixed capital in the country's total, yet we cannot say the same about the cost of own produced goods shipped (Figure 8). The trends shown in Figure 10 are not linear. Only the Tula oblast demonstrates a steady upward trend, and even then a non-linear one - a significant surge in value begins in 2016. The Lipetsk, Chelyabinsk and Sverdlovsk oblasts have parabolic dynamics. A continuous decline in the indicator is typical of the Vologda oblast (if not to take into account the year 2005). An assessment of the medium-term perspective allows us to state that over the past five years, the positions of the regions in relation to one another have not changed.
With respect to personnel, there is a steady, albeit insignificant, decrease in the share of the regions' employees (all industries) in Russia's total (Figure 11). However, comparatively larger fluctuations in values are peculiar for metals industry. The Sverdlovsk oblast experienced the most significant drop in the share of the number of employees (metals industry) in Russia's total. And this decline has been steady since 2005. If we compare 2021 and 2005, this indicator is also decreasing in the Chelyabinsk oblast. However, the trend here was not that sustainable. Until 2015,
%
-Chelyabinsk oblast Sverdlovsk oblast -Lipetsk oblast
Vologda oblast -Tula oblast
Fig. 9. Share of the metals industry regions' fixed assets in Russia's total cost of fixed assets (all industries), 2005-2021
%
-Chelyabinsk oblast Sverdlovsk oblast -Lipetsk oblast
Vologda oblast -Tula oblast
Fig. 10. Share of the metals industry regions' fixed assets in Russia's total cost of fixed assets (metals industry), 2005-2021
2.0 1.5 1.0 0.5
0.90
0.88
0.85
0.82
0.79
0.78
0.77
0.77
1.01
0.81
0.76
0.0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
-Chelyabinsk oblast Sverdlovsk oblast
Vologda oblast -Tula oblast
Lipetsk oblast
Fig. 11. Share of the metals industry regions' average number of employees in Russia's average number of employees (all industries), 2005-2021
%
-Chelyabinsk oblast Sverdlovsk oblast -Lipetsk oblast
Vologda oblast -Tula oblast
Fig. 12. Share of the metals industry regions' average number of employees in Russia's average number of employees (metals industry), 2005-2021
the region, on the contrary, showed a rise in the share of metals industry's employees in the all-Russian value.
As for the objectives that we proposed addressing with the use of correlation analysis, we can determine the economic development synchronisation of the metals industry regions by assessing the correlation between the growth rates of the above indicators (SOPG, FA, ANE) in all metals industry regions.
The results of the correlation analysis, which was performed for 2006-2021, are given in Tables 4-9 (values of correlation coefficients showing a strong statistical dependence are highlighted). For all types of activities, most pairs of regions feature a strong positive correlation between the dynamics of the own produced goods shipped (Table 4), while the same strong correlation between the dynamics of the shipped metals industry goods is typical only of a pair of regions that are similar in terms of the technological structure of production (Figure 4), namely Vologda and Lipetsk oblasts (Table 5).
Table 4. Assessment of the relationship between the growth rates of the own produced goods shipped in all industries
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.93 1 - - - -
Sverdlovsk 0.93 0.88 1 - - -
Vologda 0.94 0.89 0.84 1 - -
Tula 0.77 0.80 0.67 0.76 1 -
Russia 0.92 0.86 0.89 0.89 0.69 1
Table 5. Assessment of the relationship between the growth rates of the own produced goods shipped in the metals industry
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.40 1 - - - -
Sverdlovsk 0.50 0.63 1 - - -
Vologda 0.43 0.95 0.46 1 - -
Tula -0.025 0.04 -0.10 0.09 1 -
Russia 0.56 0.46 0.41 0.46 0.32 1
The correlation between the growth rate of the fixed assets cost in all industries (Table 6) and in the metals industry (Table 7) is strong only in pairs of the regions with a similar technological structure of production - Vologda and Lipetsk oblasts, as well as in Sverdlovsk and Chelyabinsk oblasts.
Table 6. Assessment of the relationship between the growth rates of fixed assets cost in all industries
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.27 1 - - - -
Sverdlovsk 0.71 0.47 1 - - -
Vologda 0.05 0.78 0.18 1 - -
Tula 0.56 -0.41 0.37 -0.50 1 -
Russia 0.70 0.17 0.55 -0.03 0.45 1
Table 7. Assessment of the relationship between the growth rates of fixed assets cost in the metals industry
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.40 1 - - - -
Sverdlovsk 0.50 0.63 1 - - -
Vologda 0.43 0.95 0.46 1 - -
Tula -0.03 0.04 -0.10 0.09 1 -
Russia 0.56 0.46 0.41 0.46 0.32 1
Most pairs of regions have a strong positive correlation between the dynamics of the number of employees in all sectors (Table 8), while the most similar dynamics of the number of employees in the metals industry is observed only in pairs of regions with the closest technological structure (Table 9).
In order to determine whether the universal laws of economic development operate, we again turn to correlation analysis. The results of calculating the correlation coefficients between the growth rates of input indicators (FA, ANE) and the growth rates of the output indicator (SOPG) are given in Table 10.
Table 8. Assessment of the relationship between the growth rates of the number of employees in all industries
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.86 1 - - - -
Sverdlovsk 0.82 0.83 1 - - -
Vologda 0.79 0.80 0.76 1 - -
Tula 0.61 0.82 0.62 0.69 1 -
Russia 0.89 0.90 0.90 0.89 0.77 1
Table 9. Assessment of the relationship between the growth rates of the number of
employees in the metals industries
Oblast Chelyabinsk Lipetsk Sverdlovsk Vologda Tula Russia
Chelyabinsk 1 - - - - -
Lipetsk 0.25 1 - - - -
Sverdlovsk 0.79 0.54 1 - - -
Vologda 0.48 0.37 0.70 1 - -
Tula 0.41 0.43 0.67 0.42 1 -
Russia 0.61 0.50 0.84 0.59 0.93 1
Table 10. Results of the correlation analysis
Correlation Oblast Russia
Chelyabinsk Sverdlovsk Lipetsk Vologda Tula
between SOPG and FA growth ates All industries 0.06 0.32 0.22 0.42 0.27 0.15
Metals industry -0.03 0.35 0.07 0.02 0.07 0.13
between SOPG and ANE growth rate All industries 0.60* 0.63* 0.24 0.35 -0.04 0.33
Metals industry 0.63* 0.54* 0.21 0.34 0.89** 0.57*
Note: The superscripts *, ** denote moderate and strong correlation, respectively.
In all metals industry regions, the correlation between the dynamics of production and the cost of fixed assets is weak both for all industries and in metal production, which indirectly indicates the presence of the above-mentioned regional and sectoral specifics of investment activity. For the metals industry regions (except the Tula oblast), the level of correlation between the dynamics of production and employment coincides in all economic sectors and in the metals sector, which is not typical for the country's economy as a whole where metals industry is not predominant. At the same time, for regions with the most similar technological intensity, the values of the correlation coefficients are close: the Sverdlovsk and Chelyabinsk oblasts have a moderate correlation, while the Vologda and Lipetsk oblasts have a weak one. The Tula oblast, which has a distinctive technological specialisation, has a very low correlation between the dynamics of production and the number of employees in all sectors of the economy, while a similar dependence is very strong in metal production.
Thus, the dynamics of economic development in terms of production in all industries is almost the same in all studied regions. This is confirmed by the calculations of the correlation coefficient given in Table 4. Almost all paired values demonstrate the synchronising effect of the economic development of the studied territories. In addition, the synchronising effect of the metals industry's development is observed in a pair of oblasts "Lipetsk - Vologda" in terms of "growth rates of SOPG" (Table 5) and "growth rates of FA" (Table 7). The Chelyabinsk and Sverdlovsk oblasts are synchronised in terms of the "growth rate of the ANE" (Table 9).
We can assume that the dynamics of the metals industry regions' development to a certain extent depend on the technological specialisation of the manufacturing industry and the proximity of the regions in terms of technological intensity (Figure 4). The closest synchronising effect of growth in metal production (Table 5) is typical only for a pair of regions "Lipetsk - Vologda". They are close in terms of high technological intensity of the manufacturing industries (Figure 4).
Conclusion
In our study, we performed a cluster analysis of the Russian regions according to the criteria of the manufacturing industries' technological intensity and degree of the regional economy specialisation in the metal production. It identified metals industry regions similar to the Chelyabinsk oblast: the Sverdlovsk, Lipetsk, Vologda and Tula oblasts.
To detect noticeable changes in the production structure of manufacturing industries in terms of technological intensity, we built medium-term development trends and found the following.
First, in the medium term, there is a steady increase in the share of people employed in high-tech industries in all regions studied. The Vologda and Lipetsk oblasts saw an increase in the share of the average number of employees in medium-hightech activities. In the Tula oblast, the level of technological intensity declined.
Second, the contribution of the metals industry regions to the total volume of products shipped in the national economy is going down both by all industries (10.17 % in 2005, 8.36 % in 2021) and by the metal production (47.24 % in 2005, 40.37 % in 2021). Only the Tula oblast experienced a rise in the localisation of metal production (primarily due to low-tech products) from 2.02 % in 2005 to 3.61 % in 2021. This growth is accompanied by an increase in the localisation of resources (an increase in the share of fixed assets and the share of the average number of employees in the metal production in the region's territory).
Third, the localisation of capital and labour resources of metal production decreased in the Vologda and Lipetsk oblasts. The most rigid dynamics of the input and output indicators of this production is typical of the Chelyabinsk and Sverdlovsk oblasts. Differences in the technological intensity of the manufacturing industries can explain this situation.
The correlation analysis found that the dynamic characteristics of the metals industry regions' development largely depend on the technological intensity of manufacturing industries.
The paper outlines medium-term trends in the economic and technological development of the metals industry regions, as well as points to the dependence of the economic development's dynamics on the technological intensity of the manufacturing industries. However, a wide range of regional and sectoral factors that determine the specifics of the trends in the regions' economic development remained beyond the scope of the study, and should be considered in future research.
The results obtained are of interest to government bodies that handle strategic management of the regions' industrial development, and offer the prospects for studying the mechanisms for adapting regional industrial complexes to changing conditions of external environment.
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Information about the authors
Darya S. Bents, Cand. Sc. (Econ.), Associate Prof., Prof. of Industries and Markets Dept. Chelyabinsk State University, Chelyabinsk, Russia. E-mail: benz@csu.ru Aleksandr V. Rezepin, Cand. Sc. (Econ.), Associate Prof., Associate Prof. of Economic Theory, Regional Economy, State and Municipal Governance Dept. South Ural State University, Chelyabinsk, Russia. E-mail: avrezepin@susu.ru
© Bents D. S., Rezepin A. V., 2023
