CC BY
4DOI: 10.29141/2658-5081-2022-23-3-5 EDN: GZNFED JEL classification: L60, L61
Evgeny N. Starikova,b Ural State University of Economics, Institute of Economics
(Ural branch of RAS), Ekaterinburg, Russia Marina V. Evseevaa Ural State University of Economics, Ekaterinburg, Russia Ilya V. Naumovb Institute of Economics (Ural branch of RAS), Ekaterinburg,
Russia
Industrial growth and specialisation: The impact of the government support tools
Abstract. In the past decade, the concept of smart specialisation has become the basis for incorporating the principle of selectivity while using the tools of industrial policy. The paper studies the impact of the selective government support on industrial growth and assesses its qualitative correspondence with the industrial specialisation of Russian regions. Against this backdrop, considering the activity of the Industrial Development Fund (IDF) is at the center of the study. Modern concepts of industrial growth and the policy of new priorities constitute the methodological basis of the research. The methods include correlation analysis and ARIMA modelling. The research uses the information about 924 projects implemented with the funding from the IDF, panel data on the industrial production index (IPI) for 2015-2021, expert opinions on industrial specialisation of the Russian regions to identify three groups of Russian regions depending on the support provided by the IDF. The first group is characterised by the highest amount of support and displays an inverse relationship between IPI and the share of allocated funding, whereas other groups demonstrate a direct relationship. Presumably, high degree of interconnectedness of industries in the regions belonging to the first group leads to the network effects: these territories are self-developing and therefore, the government support does not much influence their industrial growth. On the contrary, other regions feature narrow specialisation and a small degree of interconnectedness of industries. The paper concludes that the IDF should prioritise loans for projects aimed at developing new production in the regions of the second and third groups based on the model of smart specialisation.
Keywords: industrial growth; industrial production index; Industrial Development Fund; industrial specialisation; policy tools; regions; industrial development projects.
Acknowledgements: The research is prepared awith the financial support of the Russian Foundation for Basic Research (RFFI) and Sverdlovsk oblast within the framework of
the research project no. 20-410-660032 r_a "Innovation-technological development of regional industry in the context of the transformation of business architecture and management technologies that produce knowledge and common values: Institutional and stakeholder aspects"; bin accordance with the state assignment for the Institute of Economics (Ural branch of RAS) for 2022.
For citation: Starikov E. N., Evseeva M. V., Naumov I. V. (2022). Industrial growth and specialisation: The impact of the government support tools. Journal of New Economy, vol. 23, no. 3, pp. 86-108. DOI: 10.29141/2658-5081-2022-23-3-5. EDN: GZNFED. Article info: received April 24, 2022; received in revised form June 7, 2022; accepted June 16, 2022
Introduction
In recent years, the role of the state in the processes of formulation and implementation of industrial policy has been increasing, including through the development and improvement of tools related to supporting investment projects for the technological modernisation of enterprises and the setting up of the new production in the country. Such a strategy provides the regions of the Russian Federation with new opportunities to attract state resources of the federal level into the sphere of industrial development in order to handle specific territorially localised tasks, as well as allows them to accordingly modernise and improve the mechanisms of regional industrial policy.
One of the strategic national objectives is to reduce interregional differences in the level and quality of the population life, accelerate the pace of economic and technological growth in the regions, including by creating conditions for the development of production of goods and services in the sectors of promising economic specialisations of the constituent entities of the Russian Federation1. The practice of applying the smart specialisation strategy (regional innovation strategies of smart specialisation, RIS3) has shown its effectiveness in boosting economic activity in lagging regions in the EU countries [D'Adda, Iacobucci, Perugini, 2022].
The idea of this specialisation is that each region must select a number of priority sectors in which it can gain a sustainable competitive advantage through the development and implementation of innovations [Audretsch, 1998]. In rare cases, such specialisation may be formed spontaneously [Boschma, 2013], but in most cases it is the result of state policy to modernise the material and production base, support innovation, improve technology and create new areas of activity, often similar to existing ones in the region [Balland et al., 2018].
1 Spatial development strategy of the Russian Federation for the period before 2025: Decree of the Government of the Russian Federation of February 13, 2019 no. 207-r. (In Russ.)
Researchers also point out that RIS3 is essentially a region-centric economic model that reflects the contribution of government policy to stimulating private investment in innovation and development of new industries [McCann, Ortega-Argiles, 2013].
According to some research findings, in Russian practice less than a half of the regions are ready to implement smart specialisation strategies [Zemtsov, Barinova, 2016; Kutsenko, Islankina, Kindras, 2018], and in most cases goals and objectives set are of a formal and declarative nature [Repichev et al., 2018; Gasford, 2019]. In addition, domestic strategic documents do not contain approaches to identifying promising regional specialisations. Too broad and rather vague definition of specialisations makes them useless for practical implementation in regional strategies [Kolomak et al., 2018; Kalyuzhnova, Violin, 2020].
In 2021, the Higher School of Economics presented the Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021]. Its distinguishing feature is that specialisation was identified on the basis of 55 industry groupings formed applying a cluster approach, but not traditional types of activities specified in the Russian Classification of Economic Activities (OKVED-2). The assessment of the degree of interconnectedness of industry groupings provided in the Atlas also turns out to be a significant contribution, because it allows judging about the strength of the manifestation of network effects in a region.
This article continues a series of our scientific research devoted to the evaluation of such a tool of industrial policy as the Industrial Development Fund (hereinafter referred to as the IDF, the Fund) [Evseeva, Starikov, Voronov, 2021; Starikov, Evseeva, Naumov, 2022].
The purpose of the study is to find out to what extent the objectives of the smart specialisation strategy of the regions of the Russian Federation are provided with state support in the form of financing allocated by the Fund for the implementation of investment projects by enterprises and how this can affect the industrial growth of the territory. We consider the Fund's activity as a mechanism of selective state support.
To achieve this purpose, two main objectives had to be met:
• comparing the industries composition of industrial specialisation in Russian regions and the sectoral structure of projects implemented in these regions with the use of IDF financing;
• assessing the impact of the Fund's activities on industrial growth, which is characterised by the industrial production index.
As a basis for our work, we used the Atlas of economic specialisation of Russian regions mentioned above [Gokhberg, Kutsenko, 2021], while limiting the scope of consideration only to industrial specialisation, since the Fund's activities exclude other types of economic activity.
Theoretical review
In the modern theory and practice of regional industrial development, three concepts have received the most attention: regional industrial identity, smart specialisation and regional industrial path (for more details, see the review: [Akberdina, Romanova, 2021]).
The concept of regional industrial identity is based on the idea that the attractiveness of a region for investment and skilled labour is determined by how it is perceived externally. Romanelli and Khessina note that regions with a positively perceived growth potential can be attractive even in the absence of unique resources or their limited supply [Romanelli, Khessina, 2005]. The growth potential, in its turn, is perceived on the basis of the industrial specialisation of a territory [Dvoryadkina, Dzhalilov, 2021] and the prospects for the development of this configuration of industries in terms of competitiveness in world markets [Khessina, Romanelli, 2007].
According to the concept of smart regional specialisation, any region can find its own unique path of industrial development, avoiding simply copying the competencies and priorities of other more successful territories [Giannitsis, 2009]. This task can be fulfilled by concentrating resources and efforts on a limited set of areas, and clearly defining in which area of the economy it is likely to achieve leadership [Foray, 2017; McCann, Ortega-Argiles, 2016].
In its original conceptual version, smart specialisation meant searching for hidden opportunities and concentrating resources on developing the strengths of a region [Foray, David, Hall, 2009]. This concept was put in contrast to the catch-up development strategy, which involves copying the strategies of the leading regions, which, as the experience of European countries has shown, only keeps the unsuccessful regions lagging behind [Foray, Goenaga, 2013].
Later, the concept evolved into exact strategies and models of smart specialisation. According to Foray, the main strategic goals are stimulating the development of new types of activities with innovative potential and expanding opportunities for production and diversification of regional economies [Foray, 2017]. This transformation is carried out through the development of new technologies, competencies and resources [Asheim, 2018]. Radosevic notes that the success of the smart specialiation strategy is determined by the ability of a region to create new production and services industries [Radosevic, 2017]. Bosch and Vonortas point out that the strategy is designed to transform the structure of the regional economy through the development of new specialised areas [Bosch, Vonortas, 2019].
Finally, the concept of a regional industrial path is a continuation of the idea of path dependence within the theory of evolutionary economic geography [Martin, Sun-ley, 2006]. "The regional industrial path is the trajectory of the regional industrial
development, due to the existing industrial structure and the multidimensional set of technological solutions created based on accumulated human knowledge and existing in the same information field, which includes economic relations and the institutional environment" [Akberdina, Romanova, 2021, p. 722]. There are two scenarios for the transformation of the regional industrial path: strengthening the industrial diversification of a territory by increasing the range of applied technologies [Isaksen et al., 2019; Frangenheim, Trippl, Chlebna, 2020] and deepening the region's specialisation in a narrow segment of high-tech industries [Grillitsch, 2016].
Certainly, these concepts cannot be taken in isolation. Moodysson notes that "the ultimate goal of the "smart specialisation concept" is to plan a new regional industrial path to ensure fundamental structural changes in the regional industry" [Moodysson, Trippl, Zukauskaite, 2017, p. 388]. In this regard, Foray focuses on observing the principle of priority and selectivity in the application of industrial and innovation policy tools, thus assigning a secondary role to vertical models and leaving horizontal models [Foray, 2018].
The main source of state support for manufacturing industries in Russia is the Industrial Development Fund, which acts as a development institution, in this case, a financial tool of the state industrial policy designed to stimulate the inflow of direct investment in the technological modernisation of the manufacturing sector [Ivanter et al., 2017]. The fund positions itself as a catalyst for private investment in priority industrial sectors and sectors of the economy1. The mechanism of its functioning is the basic principle of the state policy of new priorities - selective support for strong and efficient industries that have growth potential and are likely to be leaders in the industry.
At the same time, our previous studies have shown that the Fund's activities do not have a significant effect on the development of the material base of manufacturing enterprises [Evseeva, Starikov, Voronov, 2021], and in the most industrialised regions it has a slight negative impact on the dynamics of the manufacturing sector output [Starikov, Evseeva, Naumov, 2022].
According to the primary hypothesis of our study, one reason for this situation is not paying attention to a region's industrial specialisation during the selection of enterprises and their projects for state support, which does not allow obtaining a systemic effect of growth.
Materials and methods
In accordance with the objectives set, we have identified the following stages of work:
1) grouping the regions according to the size of the average annual cost of investment projects supported by the IDF. It is necessary to take into account the spatial
1 Industrial Development Fund: official website. https://frprf.ru/.
heterogeneity of the support provided by the Fund and to build correct, reliable regression models with robust estimates that are resistant to various kinds of bursts and interference;
2) qualitatively assessing the correspondence between the industrial structure of the projects supported by the Fund and the industrial specialisation of the constituent entities of the Russian Federation;
3) examining the correlation and the degree of influence of the financing allocated by the Fund on regional industrial growth, expressed in the dynamics of the industrial production index (IPI); forecasting industrial production indices for the period up to 2024 by autoregression based on the ARIMA modeling method.
To group the regions of the Russian Federation within the first stage of the study we used the empirical database formed from panel databases1 which include:
• cost indicators for 924 projects included in the IDF portfolio in 2015-2021 for receiving financial support and grouped regionally;
• indicators of the investments in fixed assets (all sources of financing) for the full range of enterprises in the manufacturing industries in 2015-2021 grouped regionally for 73 constituent entities of the Russian Federation.
12 constituent entities of the Russian Federation were excluded from the analysis, where not a single project with the Fund's participation2 was implemented in the period under consideration, as well as another 23 regions where the Fund's activities were occasional, since individual projects completed fragmentarily with little investment potential are unlikely to influence industrial growth in the region. We should note that this group of 35 regions is of interest for studying the factors of industrial growth in the absence of the impact of investments supported by the IDF, but this is outside the focus of our study.
The remaining 50 regions of the Russian Federation were grouped using standard statistical methods: by calculating the average Russian median level of the average annual cost of projects in the manufacturing industry completed over the specified period with the participation of the IDF (Vt is 731.6 million rubles) and calculating the standard deviation to obtain the upper limit of the scatter in the sample data relative to the average (Vmax is 1,774.9 million rubles):
1 These databases were formed by students of the Department of Regional Economics, Innovative Entre-preneurship and Security of the School of Public Administration and Entrepreneurship of the Institute of Economics and Management of the Ural Federal University Yu. V. Gracheva (group EU-203830), P. V. Bannikova (EU-393805), A. A. Barysheva (EU-393805), T. I. Suslova (EU-393805) and A. N. Belokur (EU-393805) in the second semester of the 2021/2022 academic year as part of a research workshop.
2 Amur oblast, Kabardino-Balkaria, Kalmykia, Murmansk oblast, Nenets Autonomous Okrug, Republic of Sakha (Yakutia), Sakhalin oblast, North Ossetia-Alania, Tyva, Khakassia, Chechnya, Chukotka Autonomous Okrug.
V™ = Vt + J^, (1)
where Vmax is the upper limit of the average annual cost of investment projects implemented with the support of the IDF relative to the average Russian median level, million rubles; Vt is the size of the average annual cost of investment projects implemented with the support of the IDF in the manufacturing industry of the i-th region, million rubles; Vj is the size of the average annual cost of investment projects implemented with the support of the IDF, million rubles.
As a result, three groups of regions were obtained for the analysis (Table 1):
1) regions with a high average annual cost of investment projects implemented with the participation of the IDF exceeding the upper limit (Vj > Vmax);
2) regions in which the size of the average annual cost of investment projects implemented with the participation of the IDF exceeds the average Russian median level (Vj > Vd;
3) regions with a low average annual cost of investment projects implemented with the participation of the IDF, which is below the average Russian median level (Vj < V^).
Table 1. Grouping of regions by the amount of the average annual cost of investment projects implemented by the IDF in 2015-2021
Average annual Share of attracted investments
Region cost of implemented investment projects, million rubles in the region in the total volume of project financing in the Russian Federation, %
The first group
Moscow oblast 5,177.7 9.47
Perm krai 3,431.4 6.28
Republic of Tatarstan 3,153.1 5.77
Saint Petersburg 2,797.2 5.12
Moscow city 2,706.8 4.95
Irkutsk oblast 2,215.9 4.05
Nizhny Novgorod oblast 2,198.8 4.02
Chelyabinsk oblast 2,186.0 4.00
Republic of Bashkortostan 2,122.8 3.88
Sverdlovsk oblast 1,861.6 3.41
Tula oblast 1,823.3 3.34
The second group
Leningrad oblast 1,702.3 3.11
Kaluga oblast 1,621.1 2.97
Stavropol krai 1,380.0 2.52
Table 1 (concluded)
Average annual Share of attracted investments
Region cost of implemented investment projects, million rubles in the region in the total volume of project financing in the Russian Federation, %
Yaroslavl oblast 1,298.7 2.38
Chuvash Republic 1,292.6 2.36
Rostov oblast 1,178.2 2.16
Samara oblast 1,133.7 2.07
Voronezh oblast 1,086.6 1.99
Krasnoyarsk krai 996.8 1.82
Khanty-Mansi Autonomous Okrug 960.5 1.76
-Yugra
Omsk oblast 931.6 1.70
Novosibirsk oblast 871.7 1.59
Vladimir oblast 871.0 1.59
Ivanovo oblast 784.2 1.43
The third group
Bryansk oblast 678.9 1,24
Penza oblast 665.3 1,22
Republic of Mordovia 642.1 1.17
Ulyanovsk oblast 615.0 1.13
Saratov oblast 561.4 1.03
Kurgan oblast 522.1 0.96
Udmurt Republic 506.9 0.93
Kursk oblast 438.7 0.80
Belgorod oblast 407.3 0.75
Tyumen oblast 404.9 0.74
Volgograd oblast 384.6 0.70
Ryazan oblast 345.8 0.63
Tver oblast 342.2 0.63
Republic of Buryatia 339.1 0.62
Smolensk oblast 323.2 0.59
Kirov oblast 309.4 0.57
Khabarovsk krai 306,0 0.56
Novgorod oblast 275.4 0.50
Arkhangelsk oblast 166.9 0.31
Tomsk oblast 152.6 0.28
Krasnodar krai 141.8 0.26
Altai krai 123.3 0.23
Lipetsk oblast 91.3 0.17
Kemerovo oblast 88.2 0.16
Kaliningrad oblast 41.2 0.08
The Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021] and the annual reports of the Fund were used as an information base for the implementation of the second stage. The Atlas uses an approach that implies dividing all types of economic activities into two large groups: tradable (for example, aircraft manufacturing, pharmaceuticals, metalworking, etc.) and non-tradable (for example, housing and communal services, cinemas) industries. The tradable sector included 55 functionally related production and services activities (industry groupings). Since the object of our consideration is the manufacturing industry, we have identified those groupings, the core of which belongs to the manufacturing sector, in total, 21 groups. To assess the correspondence between the sectoral focus of the projects supported by the Fund and the regions' industrial specialisation, we structured them in a similar way.
Within the third stage the panel data were analysed by applying the least squares regression using the Gretl environment. The industrial production index for 201520211 served as a dependent variable being the key composite indicator of the Consolidated strategy for the development of the manufacturing industry of the Russian Federation (hereinafter referred to as the Strategy)2. The independent variable was the share of investment projects supported by the Fund in the total volume of attracted investments in fixed capital in the manufacturing industry, showing the relative contribution of the totality of the Fund's projects to the accumulated investment potential of the region over the period:
Xt = yt 100, (2)
where Xi is an independent variable, %; Pi is the total cost of investment projects in the manufacturing industry of a region implemented with the support of the IDF for a year, million rubles; V is the volume of investments in fixed capital in the manufacturing industry of a region (all sources of financing) for a year, million rubles.
The use of autoregressive ARIMA modeling at this stage allowed predicting the most accurately the dynamics of the manufacturing industry production index in the regions of Russia given the trends observed in the past. Comparing the obtained forecast of this indicator with the indicators defined for the regions in the Strategy helped to assess the contribution of the projects supported by the Fund into the achievement of the target values by 2024. In addition, the obtained regression models made it
1 The time period takes into account the beginning of the functioning of the IDF - since 2015.
2 On approval of the Consolidated strategy for the development of the manufacturing industry of the Russian Federation until 2024 and for the period up to 2035: Decree of the Government of the Russian Federation of June 6, 2020 no. 1512-r. https://www.garant.ru/products/ipo/prime/doc/74142592/. (In Russ.)
possible to identify the regions where industrial enterprises need investment support from the Fund the most.
Results and discussion
According to the results of the grouping (Table 1), the first group includes 11 constituent entities of the Russian Federation amounting to about 54 % of the total cost of all investment projects of manufacturing enterprises supported by the Fund in the period under study. At the same time, the highest cost of projects was recorded in the Moscow oblast (9.5 % of the total cost of the projects supported by the IDF), the Perm krai (6.3 %), the Republic of Tatarstan (5.8 %), Saint Petersburg (5.1 %) and the Moscow city (4.9 %).
The second group of regions includes 14 regions, while the total amount of financing for industrial development projects in them amounted to only 29.5 % of the total of all investment projects supported by the Fund.
Finally, the third group consists of 25 constituent entities of the Russian Federation, which accounted for the remaining 16.2 %. In most regions of this group, the share of attracted investments for the implementation of the Fund's projects did not exceed 1 % of their total funding.
At the second stage of the study, we identified the industrial specialisation of the regions with regard to manufacturing industries (Table 2). The colours indicate the industries of industrial specialisation of the regions; the numbers indicate the quantity of projects supported by the Fund in a particular industry. The composition of industries is detailed in Appendix 2 of the Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021].
In the overwhelming majority of regions from the first group (10 out of 11), the number of projects supported by the IDF and implemented in industries of industrial specialisation exceeds the number of projects implemented in industries that are not related to regional industrial specialisation. Moreover, in the two constituent entities - Moscow oblast (52 projects) and Saint Petersburg (30 projects) - all projects were implemented in the industries of industrial specialisation. The only exception is the Irkutsk oblast, where 8 out of 25 industrial development projects were completed in the sectors of industrial specialisation of the region, and 17 were undertaken in non-core sectors of regional industry.
The situation with the projects implemented in the regions of the second group differs significantly. In this group, two equal subgroups can be distinguished with 7 regions each. Thus, in the Leningrad, Kaluga, Omsk, Novosibirsk, Samara, Yaroslavl oblasts and the Chuvash Republic, most projects (over a half, the lowest figure is in Samara oblast which is 56 %) were completed in industries of regional industrial
No. Subject of the RF Industry groupings
Aerospace industry and space Automotive industry Household appliances Secondary metal products Wood products and timber industry Chemicals and chemical products Pulp and paper products Furniture Electrical equipment and lighting devices Metalworking industry Primary metal products Refractory materials and rubber products Office equipment and goods for leisure Microelectronics and instrumentation Plastic products Manufacture of communication equipment Production of building materials Shipbuilding and water transport Heavy engineering Pharmaceutical products Medical equipment
1 Moscow oblast 1 2 1 3 2 1 5 3 4 5 25
2 Perm krai 4 1 1 2 7 1 4 10 4 1 3 5 1 1 2 4 2 2
3 Republic of Tatarstan 2 5 2 2 1 6 3 2 2 1 3 3 2 1 4 1 2 2 2
4 Saint Petersburg 2 1 1 1 4 2 1 2 2 4 2 3 2 1 2
5 Moscow city 1 2 1 4 1 3 2 5 1 3
6 Irkutsk oblast 1 4 12 3 5
7 Nizhny Novgorod oblast 1 1 2 2 2 1 4 1 4 2 4
8 Chelyabinsk oblast 1 2 1 1 5 2 2 1 3 3 2 1
9 Republic of Bashkortostan 1 4 2 1 2 2 3 1 1 2
10 Sverdlovsk oblast 1 2 1 1 4 3 2 2 3 3 2 2 4 3 2 4 5 3 2
11 Tula oblast 2 5 2 2 2 4 4
12 Leningrad oblast 4 1 1 2 1
13 Kaluga oblast 1 1 2 1 3 3
14 Stavropol krai 2 2 5 1 1 1
15 Yaroslavl oblast 1 2 1 1 1 2 1 2 1 5
16 Chuvash Republic 1 3 1
17 Rostov oblast 2 4 1 1
18 Samara oblast 2 2 1 2 1 2 2 1 1 2 1 4 2 2
19 Voronezh oblast 1 1 3 2 1 1 3
20 Krasnodar krai 1 4 1 1 1 4
21 Khanty-Mansi Autonomous Okrug 2 7
22 Omsk oblast 9 1 1
23 Novosibirsk oblast 1 1 2 1 3 1 2 3 2
24 Vladimir oblast 1 2 2 1 2 2 3
25 Ivanovo oblast 3 2 3 3
26 Bryansk oblast 1 1 2 1 2 1 1 1
27 Penza oblast 1 1 4 2 2
28 Republic of Mordovia 4 2 1 1
29 Ulyanovsk oblast 2 3 1 1
30 Saratov oblast 1 1 1 2 1 4 1 1
31 Kurgan oblast 1 2 2 3
32 Udmurt Republic 1 3 2 2 3 2 1 3 1 1
33 Kursk oblast 2 5 1 1
34 Belgorod oblast 3 2 1 1 3
35 Tyumen oblast 1 1 4
36 Volgograd oblast 1 3 3 2
37 Ryazan oblast 1 3 1 1 1 2
38 Tver oblast 1 2 2 1 2 4
39 Republic of Buryatia 1 1 1 1
40 Smolensk oblast 1 6 1
41 Kirov oblast 3 3 1 1 4
42 Khabarovsk krai 4 2
43 Novgorod oblast 3
44 Arkhangelsk oblast 2 1
45 Tomsk oblast 1 1 1 1
46 Krasnodar krai 2 2 1 2
47 Altai krai 1 1 1
48 Lipetsk oblast 3 2 2
49 Kemerovo oblast 2 2
50 Kaliningrad oblast 1 1 1
specialisation, and in the Chuvash Republic 100 % of projects were in such industries (though the total the number of projects is small, only 5). In the rest of the regions from this group, the projects supported by the IDF are undertaken in the industries not belonging to regional industrial specialisation, while in two regions - the Khanty-Mansi Autonomous Okrug - Yugra (9 projects) and the Ivanovo oblast (11 projects) all of the projects are implemented in non-core industries.
In the third group of regions, the subgroup with projects in non-core industries significantly exceeds the subgroup of regions where the Fund supported projects in the sectors of industrial specialisation. Most of the projects supported by the Fund in 17 regions are implemented in non-core industries and only in 7 regions they belong to the industrial specialisation. In the Kemerovo oblast, out of the total number of implemented projects (4), 2 projects relate to the core industries of the region and 2 projects - to the non-core heavy engineering industry. In four regions (Tyumen (6 projects), Tver (12 projects), Smolensk (8 projects) and Kaliningrad (3 projects) oblasts), all projects are implemented in non-core industries, and in three regions (Volgograd (9 projects), Novgorod (3 projects) and Arkhangelsk (3 projects) oblasts), all projects, on the contrary, belong to the industrial specialisation of the region.
Analysis of the data presented in Table 3 reveals the high spatial heterogeneity of investments in industrial development projects implemented with the Fund's participation. In this regard, in order to correctly assess the impact of the IDF activity on the dynamics of the industrial production index in the Russian regions, it is advisable to build regression models within the selected groups of regions, which makes it possible to increase the uniformity of the distribution of data and the reliability of models that have robust estimates and resistance to various kinds of outliers1.
The regression models that we have obtained are the following2:
• for regions of the first group (66 observations):
1 The non-linearity test of the dependence of variables in each group of regions showed that their distribution is non-linear. Therefore, in order to obtain the coefficients of elasticity of changes in the manufacturing industry production index from the share of investment projects supported by the Fund in the total volume of attracted investments in fixed capital in the manufacturing industry in various groups of regions, it was decided to convert the initial data using natural logarithms.
2 These fixed-effects and random-effects regression models are pooled least squares and adjusted for het-eroscedasticity. To select the optimal model, a panel analysis was performed using the Hausman test and Schwartz, Akaike, and Hennan - Quinn information criteria, the reliability of the main model parameters was assessed using standard errors and P-values, and the reliability of the regression parameters was assessed using the Chow test to check for the presence of structural breaks in the sample of observations. The reliability of the models was also assessed using the coefficient of determination and the probability of fulfilling the null hypothesis of its insignificance (F-value). The presence of heteroscedasticity in the model was analysed using the White test, autocorrelation between the residuals - using the Wooldridge and Durbin - Watson tests, as well as the normality of the distribution of model errors.
Table 3. The summary of the obtained regression models
Indicator of a model Regression model
for the first group of regions for the second group of regions for the third group of regions
const 4.638328455 (1.54E-109***) 4.638232 (0.0000***) 4.63092 (0.00***)
X -0.006870279 (0.042495***) 0.000408 (0.0939*) 0.003823 (0.0584*)
^-squared 0.408431186 - -
F-value 0.001291*** - -
Schwartz criterion - 219.9247345 -204.845 - 477.618
Akaike criterion - 246.201 -209.706 - 483.6393959
Hennan - Quinn criterion - 235,818 -207.752 - 481.193145
Durbin - Watson statistic 1.508114 1.492779 1.483669308
P-value of the Chow test (null-hypothesis is presence of structural shifts) 0.440525 0.924231 0.26933
P-value of the non-linearity test (presence of non-linearity under null hypothesis) 0.201211406 0.559037 0.002138
P-value of the Wooldridge test (presence of autocorrelation of residuals under null hypothesis) 0.35684602 0.1349 0.08841
P-value of the White test (presence of hetero-scedasticity under null hypothesis) 0.55808 0.726263 0.46308
P- value of the Chi-square test (normal distribution under null hypothesis) 0.667526 0.85024 3.22E-10
Note: P-values of the regression coefficients are given in brackets.
7 = e4>638 x X -0,0069; (3)
• for regions of the second group (84 observations):
Y = e4>638 x X °>00041; (4)
• for regions of the third group (150 observations):
Y = e4>631 x X ^ (5)
where Y is the manufacturing industry production index in the region in the current period compared to the previous one, %; X is the ratio of the cost of investment projects completed in the region with the support of the IDF to the total volume of attracted investments in fixed assets in the region, %
Thus, the modeling results (Table 3, formulas 3-5) show an inverse relationship between the studied indicators for the regions of the first group and a positive correlation between them for the regions of the second and third groups, which is less significant for the regions of the second group.
The results of ARIMA modeling and forecasting the dynamics of the industrial production index are presented in Table 4.
Table 4. Forecast of the manufacturing industry production indices in the regions of the Russian Federation, %*
Subjects of the RF 2022 2023 2024
Regions of the first group
Irkutsk oblast 101.61 102.04 103.73
Moscow city 102.96 103.28 102.87
Moscow oblast 102.75 102.36 102.64
Nizhny Novgorod oblast 101.84 101.62 101.40
Perm krai 101.57 101.09 101.13
Republic of Bashkortostan 101.38 101.02 100.97
Republic of Tatarstan 103.09 102.43 102.36
Saint Petersburg 102.75 102.19 102.69
Sverdlovsk oblast 103.36 103.47 103.61
Tula oblast 103.09 103.21 102.73
Chelyabinsk oblast 103.47 102.86 103.14
Regions of the second group
Vladimir oblast 105.90 110.43 98.46
Voronezh oblast 113.52 113.97 114.52
Ivanovo oblast 103.04 100.51 99.27
Kaluga oblast 103.43 103.40 103.39
Krasnoyarsk krai 103.36 103.39 103.37
Leningrad oblast 94.13 97.14 100.26
Novosibirsk oblast 103.39 103.40 103.40
Omsk oblast 103.42 103.34 103.38
Rostov oblast 103.42 103.40 103.36
Samara oblast 100.82 99.48 99.12
Stavropol krai 107.74 101.22 95.10
Khanty-Mansi Autonomous Okrug 97.56 94.40 100.83
Chuvash Republic 101.00 103.73 97.02
Yaroslavl oblast 103.46 103.43 103.32
Regions of the third group
Republic of Mordovia 110.5 112.1 113.4
Bryansk oblast 106.5 109.1 108.8
Table 3 (concluded)
Subjects of the RF 2022 2023 2024
Novgorod oblast 103.5 103.6 103.7
Penza oblast 103.5 103.6 103.7
Altai krai 101.4 100.4 101.1
Volgograd oblast 101.8 102.3 102.9
Kaliningrad oblast 102.7 102.9 102.9
Kirov oblast 102.5 102.6 102.7
Tomsk oblast 102.8 103,5 103,1
Tver oblast 102.6 102,7 102,9
Belgorod oblast 102.6 103.3 102.7
Arkhangelsk oblast 102.6 102.4 102.6
Kursk oblast 103.7 103.2 102.4
Tyumen oblast 102.1 102.6 102.8
Ulyanovsk oblast 102.7 102.6 102.7
Khabarovsk krai 103.2 102.6 102.6
Republic of Buryatia 96.4 84.0 96.4
Kemerovo oblast 99.9 102.0 103.1
Krasnodar krai 95.9 95.8 95.6
Kurgan oblast 99.0 101.8 100.4
Lipetsk oblast 101.7 99.7 99.7
Ryazan oblast 98.9 103.4 101.1
Saratov oblast 105.9 102.9 100.4
Smolensk oblast 102.3 104.2 99.0
Udmurt Republic 96.3 93.7 104.5
Forecast values of IPI in Russia according to the Strategy 101.7 103.5 106.5
* In order to correctly compare the forecast indices of industrial production, which are calculated as relative indicators with a variable base of comparison, with those indicated in the Strategy, which have a constant base of comparison (2019), the latter were adjusted by converting them into chain indices.
Our estimates show that the forecast values of IPI indicated in the Strategy for the regions of the first group will not be achieved either in 2023 or in 2024. The strategic targets in some regions of the first group may be achieved only by the end of 2022. However, given the sanctions under which Russia's industrial complex has to operate today, the impossibility of achieving the parameters of the manufacturing industry development specified in the Strategy is obvious.
At the same time, most regions of the second group will be able to meet the target values for the industrial production index specified in the Strategy both in 2022 and 2023. Only in some territories of this group, namely the Leningrad, Ivanovo, Samara
oblasts and Khanty-Mansi Autonomous Okrug - Yugra the targets are unlikely to be met. Industrial enterprises in the regions of this group carry out investment projects, the cost of which slightly exceeds the average Russian median level, only 29.5 % of the total investment is attracted for their implementation within the framework of the Fund's project portfolio. In this regard, we consider it important to organise work to attract additional financial resources for the development of enterprises and the implementation of additional industrial development projects in the regions of this group.
In the third group, not all regions will be able to meet the forecast values of the industrial production index established by the Strategy in 2023 and 2024. Such territories include the republics of Buryatia and Udmurtia, Kemerovo, Kurgan, Lipetsk, Ryazan oblasts and the Krasnodar krai. In general, the regions of this group attract an insignificant amount of investments for the implementation of projects to develop the manufacturing industry (only 16.2 % of the total investment in the IDF project portfolio). In this regard, it is likely that the growth rate of the industrial production index set by the Strategy will also not be achieved. At the same time, in this group, a number of subjects of the Russian Federation can be distinguished, where the investment activity in the manufacturing sector will contribute to the accelerated development of enterprises. These include, in particular, the Republic of Mordovia, Bryansk, Novgorod, Penza and Smolensk oblasts. The values of the industrial production index predicted on the basis of ARIMA modeling in these regions both for 2022 and 2023 significantly exceed the target indicators of the Strategy. In this regard, in order to enhance industrial development and increase the rate of industrial growth in the regions of the third group, it is advisable to attract additional investment resources and use loans for financing industrial development projects of the IDF.
Thus, the situation is as follows. The regions that are traditionally industrialised with a diversified industry (the first group, 11 regions) receive 54 % of the total amount of the Fund's financing. At the same time, we found a negative correlation with the industrial development index, i.e., in this case, government support does not have any effect on industrial growth. The second and third groups of regions are territories with less developed industry. These groups (39 regions) account for 46 % of the financing allocated by the Fund, and we observe a positive correlation in them: state support for enterprises in these regions stimulates the growth of industrial production. Moreover, the correlation is stronger in the third group, which includes regions with the amount of the average annual cost of investment projects implemented with the participation of the IDF below the average Russian median level.
The regional profiles presented in the Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021] demonstrate significant differences in the
degree of interconnectedness between the types of economic activity in the regions of the first group and two other groups that we have identified (cf. examples in Figures 1, 2). This suggests that the regions of the first group are self-developing1, their development and industrial growth occur due to network effects [Orekhova, Zarutskaya, Kislitsyn, 2021], generated by multiple links between industry groupings. Therefore, it is likely that state support in the form of the Fund's financing allocated to individual enterprises to implement investment projects does not give the expected effect.
Agricultural
services and fertilizer production
Livestock and mixed agriculture
Meat products
Mining of non-metallic ores
Wood products
Timber industry
Medical services
Airspace industry and space M
Electrical equipment and lighting devices
Office equipment and leisure goods
Film industry Culture
Microelectronics and instrumentation
Refractory materials and rubber products
Construction and building materials
Medical equipment
Tourism
Secondary
metal products ^^
Plastic products
products
Water treatment and distribution, waste treatment
W Furniture
Metal processing industry
Heavy engineering
Publishing, desii and marketing
Insurance
Automotive industry
Household appliances
Financial services
Clothing
Textile production
Pulp and paper products
Shoes
Production and transmission of electricity
# Industry of national and local importance # Industry of national importance
High level of interconnectedness of industries
Fig. 1. Interconnectedness of industry groupings: the Moscow oblast2
1 Self-development is understood as the ability of a region, in the conditions of the macroenvironment that has developed in society, to ensure expanded reproduction of the gross regional product at the expense of its own profitable sources [Tatarkin, 2013; Tatarkin, Doroshenko, 2011].
2 Source: Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021].
Airspace industry and space
Refractory materials Jewelry an<^ rubber products
Shipbuilding and water transport
Tourism
Tobacco products
<
Electrical equipment and lighting devices
Science
• Printing industry
Shoes
Business and IT Services
Heavy engineering
Automotive industry
Industry of national and local importance Industry of national importance
Industry of local importance
High level of interconnectedness of industries
Fig. 2. Interconnectedness of industry groupings: the Yaroslavl oblast1
On the contrary, in the regions of the second and third groups, the interconnectedness of industries is often absent or insignificant. In addition, in half of the cases, projects supported by the Fund were completed in non-core industries for these territories. And the fact that a positive impact of state support on the IPI indicator in these regions is recorded, confirms that the development initiation of something new for the region, non-core type of activity makes it possible to convert the resources of this territory into industrial growth.
We come to the conclusion that the priority direction for the Fund is the selective support of projects in regions with a low level of industrial development in order to form new industry groupings on the territory and develop interconnectedness between them. This will foster the emergence of network effects and give impetus to the
1 Source: Atlas of economic specialisation of Russian regions [Gokhberg, Kutsenko, 2021].
economic development of the territory in general. And in this case, the smart specialisation model will make it possible to determine the reference points for the use of state support in the form of IDF loans.
Conclusion
While strengthening its role in the implementation of industrial policy, the state is objectively interested in efficient distribution of the support resources in order to obtain systemic effects of industrial growth and structurally transform the country's industry. To achieve this goal, over the past few years, industrial development strategies have been designed and adopted to direct and set targets for some industries and sectors, including the Strategy, the target indicators of which guided our study while assessing the potential of industrial growth. This dictates the need to determine the appropriateness and sufficiency of the industrial policy tools used to achieve the goals of industrial development stated in the strategic documents.
However, the analysis of these documents shows that the aspect of spatial heterogeneity of industrial growth, including the specialisation of regions, is not taken into account by their developers. As a result, the implementation of industrial policy is getting more about declaring than doing, and pouring the state support resources becomes fragmented and discrete, which is also confirmed by the results of this study.
To eliminate these disadvantages, approaches to forming the IDF portfolio of industrial development investment projects should be corrected taking into account the spatial specificities of the Russian industrial landscape.
References
Akberdina V. V., Romanova O. A. (2021). Regional industrial development: review of approaches to regulation and determining of priorities. Ekonomika regiona = Economy of Region, vol. 17, issue 3, pp. 714-736. https://doi.org/10.17059/ekon.reg.2021-3-1. (In Russ.)
Bosch A., Vonortas N. (2019). Smart specialization as a tool to foster innovation in emerging economies: Lessons from Brazil. Forsayt = Foresight and STI Governance, no. 1, pp. 32-47. DOI: 10.17323/2500-2597.2019.1.32.47. (In Russ.)
Gasford A. O. (2019). Principles of the application of "smart specialization" to regional development of Russia. Ekonomika: vchera, segodnya, zavtra = Economics: Yesterday, Today and Tomorrow, no. 3-1, pp. 441-447. DOI: 10.34670/AR.2019.89.3.048. (In Russ.)
Gokhberg L. M., Kutsenko E. S. (eds.). (2021). Atlas of economic specialisation of Russian regions. Moscow: Higher School of Economics. 264 p. DOI: 10.17323/978-5-7598-2379-7. (In Russ.)
Evseeva M. V., Starikov E. N., Voronov M. P. (2021). Technological development of industrial regions: The ecosystem approach. Upravlenets = The Manager, vol. 12, no. 3, pp. 13-30. DOI: 10.29141/2218-5003-2021-12-3-2. (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.)
Ivanter V. V., Porfiryev B. N., Shirov A. A., Shokin I. N. (2017). Basis of structural-investment policy in modern conditions of Russian economy. Vestnik finansovogo universiteta = Bulletin of the Financial University, no. 1, pp. 6-15. (In Russ.)
Kalyuzhnova N. Ya., Violin S. I. (2020). Smart specialization of Russian regions: Prospects and limitations. Ekonomika, predprinimatel'stvo i parvo = Economy, Entrepreneurship and Law, vol. 10, no. 10, pp. 2457-2472. DOI: 10.18334/epp.10.10.111061. (In Russ.)
Kolomak E.A., Kryukov V.A., Melnikova L.V., Seliverstov V.E., Suslov V.I., Suslov N.I. (2018). Spatial development strategy of Russia: Expectation and reality. Region: ekonomika i sotsiologiya = Region: Economics and Sociology, no. 2 (98), pp. 264-287. DOI: 10.15372/REG20180212. (In Russ.)
Kutsenko E. S., Islankina E. A., Kindras A. A. (2018). Smart by oneself? An analysis of Russian regional innovation strategies within the RIS3 framework. Forsayt = Foresight and STI Governance, no. 1, pp. 25-45. DOI: 10.17323/2500-2597.2018.1.25.45. (In Russ.)
Orekhova S. V., Zarutskaya V. S., Kislitsyn E. V. (2021). An empirical investigation of network relationships in the market. Upravlenets = The Manager, vol. 12, no. 1, pp. 32-46. DOI: 10.29141/22185003-2021-12-1-3. (In Russ.)
Repichev A. I., Tugacheva L. V., Vorobyeva A. V., Avdeeva D. A. (2018). Opportunities for the development of regional innovation strategies based on the principles of "smart specialization". Voprosy upravleniya = Management Issues, no. 2 (51), pp. 37-45. (In Russ.)
Starikov E. N., Evseeva M. V., Naumov I. V. (2022). Managing industrial growth: The role of development funds. Upravlenets = The Manager, vol. 13, no. 3, pp. 16-29. DOI: 10.29141/2218-50032022-13-3-2. (In Russ.)
Tatarkin A. I. (2013). Self-development of regional socioeconomic systems as the need for Russia's federal development. Ekonomika regiona = Economy of Region, no. 4, pp. 9-26. (In Russ.)
Tatarkin A. I., Doroshenko S. V. (2011). Region as a self-developing socio-economic system: Crossing the crisis. Ekonomika regiona = Economy of Region, no. 1, pp. 15-23. (In Russ.)
Asheim B. T. (2018). Smart specialisation, innovation policy and regional innovation systems: What about new path development in less innovative regions? Innovation: The European Journal of Social Science Research, vol. 32, no. 1, pp. 8-25. https://doi.org/10.1080/13511610.2018.1491001.
Audretsch D. B. (1998). Agglomeration and the location of innovative activity. Oxford Review of Economic Policy, vol. 14, no. 2, pp. 18-29.
Balland P.-A., Boschma R., Crespo J., Rigby D. L. (2018). Smart specialization policy in the European Union: Relatedness, knowledge complexity and regional diversification. Regional Studies, vol. 35, no. 4, pp. 1-17. https://doi.org/10.1080/00343404.2018.1437900.
Boschma R. (2013). Constructing regional advantage and smart specialization: Comparison of two European policy concepts (Papers in Evolutionary Economic Geography no. 13.22). Utrecht: Utrecht University. 16 p.
D'Adda D., Iacobucci D., Perugini F. (2022). Smart Specialisation Strategy in practice: Have regions changed the allocation of Structural Funds? Regional Studies, vol. 56, issue 1, pp. 155-170. DOI: 10.1080/00343404.2021.1890326.
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.
Foray D. (2017). The economic fundamentals of smart specialization strategies. In: Radosevicet S., Curaj A., Gheorghiu R., Andreescu L., Wade I. (eds.) Advances in the theory and practice of smart specialization (pp. 38-50). Amsterdam: Academic Press.
Foray D. (2018). Smart specialisation strategies and industrial modernisation in European regions - theory and practice. Cambridge Journal of Economics, no. 42, issue 6, pp. 1505-1520. DOI: 10.1093/cje/bey022.
Foray D., David P. A., Hall B. H. (2009). Smart specialization - the concept (Knowledge Economists Policy Brief no. 9). Brussels: European Commission. 5 p.
Foray D., Goenaga X. (2013). The goals of smart specialisation (S3 Policy Brief Series no. 01/2013). Luxembourg: Publications Office of the European Union. 18 p. DOI:10.2791/20158.
Frangenheim A., Trippl M., Chlebna C. (2020). Beyond the Single Path View: Inter-path dynamics in regional contexts. Economic Geography, no. 96, issue 1, pp. 31-51. https://doi.org/10.1080/00 130095.2019.1685378.
Giannitsis T. (2009). Technology and specialisation: Strategies, options and risks (Knowledge Economists Policy Brief no. 8). Brussels: European Commission. 7p.
Grillitsch M. (2016). Institutions, smart specialisation dynamics and policy. Environment and Planning C: Government and Policy, vol. 34, issue 1, pp. 22-37. https://doi.org/10.1177/0263774X15614694
Isaksen A., Jakobsen S.-E., Nj0s R., Normann R. (2019). Regional industrial restructuring resulting from individual and system agency. Innovation: The European Journal of Social Science Research, no. 32, issue 1, pp. 48-65. DOI: 10.1080/13511610.2018.1496322.
Khessina O., Romanelli E. (2007). Regional industrial identity and spatial arrangements in the U.S. Biotherapeutics industry, 1976-2004 (Johnson School Research Paper Series no. 07-07). 56 p. http:// dx.doi.org/10.2139/ssrn.1019307.
Martin R., Sunley P. (2006). Path dependence and regional economic evolution. Journal of Economic Geography, vol. 6, issue 4, pp. 395-437. https://doi.org/10.1093/jeg/lbl012.
McCann P., Ortega-Argiles R. (2013). Smart specialization, regional growth and applications to European Union cohesion policy. Regional Studies, vol. 49, no. 8, pp. 1291-1302. https://doi.org/10 .1080/00343404.2013.799769.
McCann P., Ortega-Argiles R. (2016). The early experience of smart specialization implementation in EU cohesion policy. European Planning Studies, vol. 24, no. 8, pp. 1407-1427. https://doi.org /10.1080/09654313.2016.1166177.
Moodysson J., Trippl M., Zukauskaite E. (2017). Policy learning and smart specialization: Balancing policy change and continuity for new regional industrial paths. Science and Public Policy, vol. 44, no. 3, pp. 382-391. DOI: 10.1093/scipol/scw071.
Radosevic S. (2017). Advancing theory and practice of smart specialization: Key messages. In: Radosevicet S., Curaj A., Gheorghiu R., Andreescu L., Wade I. (eds.) Advances in the theory and practice of smart specialization (pp. 345-355). Amsterdam: Academic Press.
Romanelli E., Khessina O. (2005). Regional industrial identity: Cluster configurations and economic development. Organization Science, vol. 16, no. 4, pp. 344-358. DOI: 10.1287/orsc.1050.0131.
Information about the authors
Evgeny N. Starikov, Cand. Sc. (Econ.), Associate Prof., Associate Prof. of Chess and Computer Mathematics Dept. Ural State University of Economics, Ekaterinburg, Russia; Sr. Researcher of Regional Industrial Policy and Economic Security Dept. Institute of Economics (Ural branch of RAS), Ekaterinburg, Russia. E-mail: starik1705@ yandex.ru
Marina V. Evseeva, Cand. Sc. (Econ.), Associate Prof. of Economic Theory and Corporate Governance Dept. Ural State University of Economics, Ekaterinburg, Russia. E-mail: m.evseeva@inbox.ru
Ilya V. Naumov, Cand. Sc. (Econ.), Head of the Laboratory of Territories' Spatial Development Modelling. Institute of Economics (Ural branch of RAS), Ekaterinburg, Russia. E-mail: ilia_naumov@list.ru
© Starikov E. N., Evseeva M. V., Naumov I. V., 2022
