|Elena Zhogova||Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street, 29, St. Petersburg 195251, Russia|
|Olga Zaborovskaia||State Institute of Economics, Finance, Law and Technologies, Roschinskaya Street, 5, Gatchina 188309, Russia|
|Olga Nadezhina||Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street, 29, St. Petersburg 195251, Russia|
The unevenness of regional spatial development is a relevant subject of research in Russia and internationally. This study is intended to substantiate a modified methodology based on the cluster approach for identifying regional cluster groups and the methods for determining priority development areas for clusters of small and medium-sized enterprises. This study identifies the priority sectors for the development of the Leningrad region, including manufacturing, construction, wholesale and retail trade, electricity, gas and water production, transport and communications, agriculture, hunting and forestry, operations with real estate, and rental services. These industries are ranked according to their degree of importance (with high and maximum degree of importance). The results of the study enable the identification of sectors and enterprises that have the greatest potential and require government support. The proposed algorithm can be applied to any territorial entity and makes it possible to modify the set of statistical data for the required adjustments, thus contributing to the spatial development of the region
Cluster approach; Indicators; Region; Spatial development
The unevenness of regions’ spatial development has been the subject of research in many international and Russian studies (Kasala, 1996; Argüelles et al., 2000). This unevenness is reflected in different levels of industrial development and must be taken into account when working on regional industrial policy (Maier, 1998; Bruszt and Palestini, 2016; Polyanin et al., 2020). When choosing priority areas for industrial development, it is important to consider the ongoing processes of agglomeration and digitalization, the positions of major industrial enterprises, growth poles, and the industries that have the greatest economic potential (Lyakin, 2014). Consequently, a factor analysis of regional economy spatial development makes it possible to identify the uneven development of various industries and to highlight the priority ones.
One common theory that takes into account significant factors and obtains results correlated with economic realities is the cluster theory. Based on the modification of methods for assessing clusters’ efficiency and the effects of production localization (agglomeration), the cluster theory makes it possible to determine the uneven development of the branches of the regional economy and identify the priority areas. The purpose of this study is to analyze the indicators of regional spatial development and identify the priority sectors of the regional economy in the Leningrad region of the Russian Federation.
There are many scientific works in the framework of the territorial-sector approach that aim to determine the causes of uneven spatial distribution of production resources and economic activities (Maier, 1998; Karayel, 2017). In the age of globalization, some socio-economic factors affecting the development of the regions have undergone changes and new factors have emerged, such as digitalization, virtual space, and integration into the global economy. There are certain contradictions between the global expansion of sales markets and the increased unevenness of regional development. Some researchers propose the idea of inclusive social development of territories and communication infrastructure (Glukhov and Korobko, 2003).
One of the key theories explaining the patterns of spatial development of territories is Thünen's theory. It is based on the concept of an economy as an isolated system that considers two major factors—the rent and the distance to the place of sale (Limonov et al., 2017). In Launhardt's model, Thünen's ideas are represented in graphs. Launhardt’s triangle is based on the location of the factory, the location of the resources, and the location of outlets. Weber complemented Launhardt's model with a description of the influence of labor market on the location of the factory. Hotelling's model adds the factor of competitiveness to Weber's model. Hotelling's model adds the following parameter: prices are related to the level of constant marginal costs, which are the same for all companies. Cristalller’s theory of central places contributes to the theories discussed above. The theory is focused on the formation of “central places” in cities in the form of hexagons that cover the entire territory (Limonov et al., 2017). This theory is an attempt to formulate a hypothesis about the regular location of the cities relative to each other, but it does not take into account the real situation and has a large number of inaccuracies. Lesch continued to develop these theories based on the location of cities (Limonov et al., 2017). His model outlines the conditions for continuous industrial expansion and free competition, with an increasing number of highly profitable factories.
Alonso’s spatial model of the city introduced the profitability function for companies and the utility function for households, as well as the other factors such as the size of the enterprise and its distance from the center. Alonso used his model to analyze the external economic environment in terms of location and population density in the city (Limonov et al., 2017). A significant contribution to the understanding of spatial development was made by the authors of regional growth models based on demand: The Harrod-Domar model, Thirlwall's, model by V.V. Leontiev.
Theories of absolute and relative advantages are focused on the issue of regional specialization and trade; one example is the model developed by David Ricardo (Kistanov and Kopylov, 2003; Limonov et al., 2017). A modified version of this model introduced an indicator of scientific and technological progress in the form of a new knowledge factor. This approach is reflected in the production model of Robert Solow, in which technological progress is considered independent of capital and labor (Limonov et al., 2017). The theory of competitive advantages is based on the cluster approach and determines the productivity of production factors and their returns (capital, labor, natural resources), as well as the possibility of support measures for firms to develop competitive advantages (Limonov et al., 2017).
Within the framework of the cluster approach, Michael Potrer identified two competing forces affecting the regional economy at the regional level: convergence and agglomeration. Convergence leads to a decrease in the industry’s growth rat, despite stable or high economic activity. Agglomeration gives an increasing return on activity, either from one industry (localization) or from diversification at the regional level (urbanization). Both competing forces influence the regional economy; convergence is reflected at the level of an individual industry or within a narrow group of industries, while agglomeration affects the entire complex of clusters. These studies confirmed the hypothesis that within the framework of strong cluster groups, there is a higher rate of employment and wages and a greater number of firms not only in the region but also in adjacent territories (Ferova et al., 2018).
study is based on the approach regarding agglomeration influence (Maskell and Malmberg, 2002). It
also takes into account the activity of cluster groups and the priority areas
for their development. The authors aim to identify priority sectors of the
regional economy for the Leningrad region.
Spatial development theories focus on the factors of the spatial economic development of specific regions (Theodoropoulou et al., 2009). Originally, spatial development models described the relationship between the location of entities and various types of costs (time costs, financial costs, transportation costs, etc.). However, with globalization processes, the impact of these factors on regional economies has changed, and new factors of influence have emerged. The most productive method for the analysis of spatial development is the cluster approach, which takes into account the location of enterprises and their economic efficiency. With strongly clustered connections, there is a high level of employment and wages and a greater number of businesses; therefore, it is advisable for regional industrial policy to focus on the support of such industrial groups.
For the analysis of spatial development in the Leningrad region, we have applied a method that identifies the priority areas for the development of clusters of small and medium-sized enterprises with the relevant modification of the indicators used. Using this technique made it possible to identify the most significant industries of the region. The priority sectors of the region are the following: manufacturing; construction; wholesale and retail trade; production and distribution of electricity, gas and water; transport and communications; agriculture, hunting and forestry; real estate transactions; and rental services.
So, the development of the infrastructure of industrial enterprises belonging to priority sectors can act as a significant contribution to the regional economy and industrial policy, as is argued in many works (Gradov et al., 2003; Granberg, 2004; Rodionov et al., 2018a). Expanding the number of industrial companies and providing them with the opportunities for development will contribute to the growth of human capital (Shabunina et al., 2018; Rodionov et al., 2018b) and increase the number of jobs, which will consequently reduce the unevenness of regional development.
In conclusion, it should be noted that all spatial
models considered in this study need to be further updated and should take into
account additional factors for effective regional development (?rešnar et al., 2020). Factors such as the
effectiveness of industrial sectors and the number of people employed are
important for economic feasibility analysis and can ensure the development of
the most efficient industrial policy.
research work was supported by the Academic Excellence Project 5-100 implemented
by Peter the Great St. Petersburg Polytechnic University.
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