CC BY
2DOI: 10.29141/2658-5081-2022-23-4-4 EDN: PGYYUB JEL classification: R58, R50, B41
Tatyana V. Mirolyubova Perm State University, Perm, Russia
Dmitry A. Koshcheev HSE University; Perm State University, Perm, Russia
System spatial method for assessing an industrial cluster's impact on the regional socioeconomic development
Abstract. New economic circumstances developed by the COVID-19 pandemic fallout, the sanctions, and the special military operation have paved the way for intensifying the development of Russia's domestic industrial production. An efficient method to deal with this task can be the cluster approach. However, the operation of an industrial cluster brings about both positive and negative effects for the regional socioeconomic development. The problem of assessment and adjustment of these effects has not yet received the definitive answer. In the study, we aim to design a system spatial method for analysing the impact exerted by industrial clusters on the regional socioeconomic development. The combination of the theories of regional and spatial economics constitutes the methodological basis of the research. The methods include content analysis, critical and comparative analysis. Based on the review of the scientific publications indexed in the Web of Science, Scopus and eLibrary databases we structure the tools used to research the mutual influence of a region and a cluster in 1990-2022. In line with the review findings, we identify six theoretical approaches (system, network, institutional, agglomeration, classical, administrative) and four methods (statistical, regionalistic, marketing, situational). Theoretical analysis indicates some limitations of existing combinations of these methods and approaches. To overcome them, we suggest a system spatial method for examining the mutual influence of a region and an industrial cluster that eliminates the major weaknesses inherent in similar tools. Its application allows formulating recommendations for the implementation of the regional cluster policy that favour a positive impact of an industrial cluster on a region under a predominantly positive impact of a region on an industrial cluster.
Keywords: socioeconomic development; industrial cluster; system spatial approach; cluster policy; region.
For citation: Mirolyubova T. V., Koshcheev D. A. (2022). System spatial method for assessing an industrial cluster's impact on the regional socioeconomic development.
Journal of New Economy, vol. 23, no. 4, pp. 69-86. DOI: 10.29141/2658-5081-202223-4-4. EDN: PGYYUB.
Article info: received August 12, 2022; received in revised form September 2, 2022; accepted September 15, 2022
Introduction
In 2020-2021, the COVID-19 pandemic and anti-epidemic restrictions imposed
by most countries became the new critical factor in the development of the global economy [Kuznetsova, 2021]. This stimulated the growth of competition between the territories, increasing interest in clustering as an effective tool for regional development. In 2022, the unfolding of the sanctions crisis and the start of the special military operation necessitated the intensification of the industrial production development in Russia. To address this problem, the clustering tool can also be used, which eventually incentivised the preparation of relevant programmes in a number of regions.
At the same time, studies of the last decade reflected the ambiguity of a cluster's impact on the socioeconomic development of a region. This ambiguity manifested itself in the system of negative effects [Kolchinskaya, Limonov, Stepanova, 2019; Li, Lin, Lyu, 2022] due to errors in the design of the regional cluster policy and poor elaboration on the mechanism of mutual influence of the two territorial and economic systems [Chen et al., 2020]. To settle with this issue, a method for analysing this mutual influence is needed.
The purpose of the study is to develop a universal method for assessing the impact of a cluster on the socioeconomic development of a region - a subject of the federation.
To achieve this goal, we pursued the following objectives:
• to develop a conceptual mechanism illustrating the mutual influence of a region and an industrial cluster;
• to systematise and structure the tools for studying the region - cluster mutual influence, used in scientific works of 1990-2022;
• to identify and hold a comparative analysis of approaches to the study of the region - industrial cluster mutual influence, as well as to find out advantages and disadvantages of the methods used in the system of these approaches.
The study is based on a combination of general scientific methods of analysis and synthesis, induction and deduction used to develop a model of the region - cluster mutual influence. To study the literature reflecting the problems of this mutual influence, we applied the own system criterial approach to the theoretical analysis and
methods of content analysis and comparison; to develop the own method for assessing the region - cluster mutual influence we employed a method of critical analysis of the strengths and weaknesses of existing methods, comparative analysis and elements of the grounded theory.
This paper is organised as follows. In the first part of the work, the state of the theory of the region - cluster mutual influence is described and the approaches used within its framework are distinguished: system, network, institutional, agglomeration, classical, administrative, as well as the own system spatial approach, first presented in 2020 [Koshcheev, Tretyakova, 2020]. Based on the results of their analysis, we propose a conceptual model of the mutual influence of the two considered territorial economic systems (a cluster and a region).
In the second part of the article, the tools for studying the region - cluster mutual influence, which appeared in scientific papers in 1990-2022, are structured. These tools include four universal methods (statistical, regionalistic, marketing and situ-ational) applied within the framework of the above-mentioned approaches to the analysis of the considered mutual influence. The strengths and weaknesses of the specifications of all methods are noted, and the need to overcome the existing limitations is substantiated. To eliminate this problem, a new method for assessing the industrial region - cluster mutual influence is created based on the adaptation of the statistical method.
The conclusion summarises the main advantages of using the system spatial approach to design the regional cluster policy taking into account the data on the advantages and disadvantages of the existing methods.
Theoretical approaches to the analysis of interaction between a region
and a cluster and mutual influence model of territorial economic systems
The impact of the industrial cluster on the socioeconomic development of the region in the scientific literature is considered within the framework of the industrial clusters concept, which includes six key approaches to this phenomenon.
The classical approach looks at an industrial cluster as a localised group of interconnected and interdependent enterprises operating within the framework of a common direction of economic activity [Porter, 2003; Rychikhina, 2012]. Technically, it connects the influence of a cluster on a region with the synergy effect induced by the joint functioning of organisations in a cluster Based on this, the main focus of analysis according to this approach is shifted to the study of cluster elements and their interaction with each other.
The agglomeration approach understands a cluster as a special form of spatial agglomeration in which clustered organisations, resource bases, infrastructure
connecting them, and a special socioeconomic environment are localised [Bingham, 1992; Saadatyar et al., 2020]. The interaction between a region and a cluster in this case is described through the prism of the agglomeration effect connected with the dynamics of the intracluster environment.
The administrative approach interprets a cluster as a special form of organising economic activity within a certain administrative-territorial unit (or a group of administrative-territorial units) [Gao, Liu, Jin, 2012; Klepikova, 2013]. Perceiving the cluster as an artificial structure, the approach allows for the possibility of programming its influence on the socioeconomic development of a region.
The network approach describes a cluster as a special form of a network formed by autonomous companies with a common value chain [§engun, 2015; Mobedi, Tanyeri, 2019]. The influence of a cluster on a region within the framework of this approach is explained by the dynamics of intra-network processes and the actions of individual companies that are a part of it.
The institutional approach considers a cluster as a special form of institutional agreement [Feser, 1998] or a sustainable institutional partnership based on a common system for introducing innovations [Beisekova, 2019]. According to this approach, the influence of a cluster on a region is associated with the effect of an intracluster system of norms and mechanisms.
The system approach emerged as an independent direction in the early 2000s from the classical approach. From the viewpoint of the system direction, an industrial cluster is a geographically localised innovation system based on a common information environment and joint economic activity [Zimmer et al., 2014; Li, Guo, 2020]. The mutual influence of a region and a cluster in accordance with this approach is seen through the prism of the system effect. At the same time, unlike the classical direction, the main analysis is focused not on the elements of the internal environment of a cluster, but on the very effect and channels of its impact on the socioeconomic development of the region.
The afore-mentioned approaches have a number of imperfections that distort the analysis results of the industrial cluster's impact on a region.
First, they project a one-sided view of a cluster, looking at it either as a territorial-geographical structure (agglomeration, administrative and classical approaches) or as a socioeconomic construct (system, institutional and network approaches). At the same time, the mainstream of spatial economics emphasises the duality of industrial cluster as a territorial-geographical and socioeconomic system.
Second, most modern approaches to examining the impact of an industrial cluster on a region do not take into account the integration of a cluster into the regional socioeconomic system, where any action stimulates a reciprocal effect.
Third, studies of a cluster influence on a region are distinguished by 'point' logic -they view changes in individual parameters of the socioeconomic development of a territory, while the overall picture of the corresponding impact remains unclear.
In order to overcome the indicated imperfections, we have proposed a system spatial approach to studying the influence of an industrial cluster on a region. It was developed on the basis of integrating ideas of the system direction and individual elements of the administrative, agglomeration and classical approaches, focusing on the territorial and geographical dimension of an industrial cluster [Koshcheev, Tretyakova, 2020].
According to our interpretation, an industrial cluster is a localised territorial and economic structure that combines the properties of an industrial agglomeration and a socioeconomic system, which is based on a polyfactorial geographical space formed by a multitude of interactions between its constituent organisations.
Therefore, it is relevant to describe the structure of an industrial cluster using Mi-rolyubova's concept [Mirolyubova, Karlina, Kovaleva, 2013] in our modification. According to this model, the industrial cluster includes three cores.
The first core is represented by the "main production" - companies that create the main part of added value produced by a cluster and determine the vector of its development. Additionally, the core includes "cluster management structures" formed by cluster member companies or (in Russian practice) by regional authorities. The second core is made up of organisations of "related industries" that ensure the functioning of the cluster's key production chain through the supply of raw materials, specialised services and technologies. The third core is formed by companies of "supporting industries", which create conditions for the work of the first and second cores, but they are not part of the main production chain of a cluster. This core may include service organisations, not related to the profile of a cluster, as well as scientific and educational organisations.
The proposed three-core model makes it possible to develop a conceptual mechanism for the influence of an industrial cluster on the socioeconomic development of a region. At the same time, the following two aspects are to be taken into account.
First, since the industrial cluster is embedded in the regional socioeconomic system, any of its impact on the region entails a response, which, in its turn, determines the subsequent impact of a cluster on a region. Given this pattern, to ensure the study is complete, it is relevant to consider the entire chain of interactions in the context of the mutual influence mechanism of a region and an industrial cluster.
Second, although the scientific literature mentions the effects of cluster influence on a region (see, for example: [Delgado, Porter, Stern, 2010; Azhar, Adil, 2016]), most of them are not associated with complex formation process in the regional economy. Therefore, they illustrate the impact not of a cluster, but of an industrial complex of a
region on the socioeconomic development of a territory. In our opinion, the exception to this rule is a small group of publications following the principles of the system approach and linking the functioning of a cluster in the regional economy with the emergence of special effects, called "cluster multiplier effects" (see, for example: [Masyuk, Busheva, 2012]. At the same time, a complete and generally recognised list of such effects has not been formed in the scientific literature at the moment.
According to our previous studies, the impact of a cluster on a region is most often correlated with changes in regional exports, employment and household incomes [Koshcheev, Tretyakova, 2020]. Based on this, the impact of an industrial cluster on the socioeconomic development of a region should be described through the prism of three cluster multipliers: employment, income and exports.
In modern studies, multiplicative effects are regarded as one of two elements of a complex dynamic system, the second component of which is accelerating effects [Polyakov, 2000]. Guided by this provision, in the projected mechanism of mutual influence, a region's impact on the functioning of an industrial cluster can be illustrated by three cluster accelerators: employment, income and export. In this case, a cluster accelerator should be understood as a numerical coefficient reflecting the measure of the multiplying impact of changes in the main indicators of the socioeconomic environment of a region on the performance of an industrial cluster.
The resulting model of the industrial cluster - region mutual influence is shown in Figure 1. Let us start its description with the impact of a cluster on the socioeconomic development of a region.
The main catalyst for the action of the cluster employment multiplier is the change in the number of employees in organisations that are the core part of the territorial economic system under consideration. The growth of their number consistently determines the creation of new jobs in organisations of the second, and then the third core. Due to this, unemployment in a region nominally decreases, household incomes grow, and as a result, the volume of tax revenues to the regional budget goes up. In the conditions where newly created jobs are cost-effective, it is possible to increase the added value created by a cluster, which ensures the growth of regional exports, and, as a result, cargo turnover.
The genesis of the cluster income multiplier results from the change in the level of wages at the enterprises that make up the core of an industrial cluster. The growth of this indicator stimulates a co-directional change in wages in organisations of the second, and then the third core. Due to this, a trigger is formed that boosts the growth of wages in the system of related industries, and then in the entire region. This process determines the change in household incomes, the volume of tax revenues to the regional budget and the number of the unemployed.
Effects produced
by a region on the functioning of an industrial cluster
Changes in the costs of organisations participating in a cluster
Changes in the number of employees in organisations participating in a cluster
Changes in the availability of resources
Changes in the turnover of organisations participating in a cluster
CLUSTER
Influence of an industrial cluster on the socioeconomic development of a region
Cluster employment multiplier
Cluster revenue multiplier
Cluster export multiplier
Cluster employment accelerator
Cluster income accelerator
/ Cluster
\ export accelerator
Influence of a region on the functioning of an industrial cluster
Effects produced by an industrial cluster on the socioeconomic development of a region
Changes in household incomes
Changes in regional budget revenues
Changes in employment
Changes in the amount of value added created by a region
Changes in the volume of cargo transportation in a region
Changes in regional export volumes
REGION
Fig. 1. Model of the region - industrial cluster mutual influence
In the medium term, the growth of household incomes has a positive effect on the purchasing power of the population, which may result in a change in the volume of added value created by the region and the cargo turnover across the territory of a subject of the federation. The transformation of the situation in the domestic market inevitably affects the model of economic behaviour of local producers and may lead to a partial reorientation to the domestic market and a subsequent change in the volume of regional exports.
The cluster export multiplier performs the role of an indicator reflecting the changes in the total contribution of clustered enterprises to regional exports. The change in the volume of total exports of the core of the industrial cluster determines the change in the similar indicator of enterprises of the second and (in some cases) the third level. The consequence of this is changes in the unemployment rate, household incomes, tax revenues of the regional budget, cargo turnover of the territory, and, as a result, the volume of added value created by the economy of the territory under consideration.
The joint action of three cluster multipliers determines the change in the socioeconomic environment of a region and stimulates the response effect, which, in the framework of the said model, is represented by the action of three cluster accelerators.
The cluster employment accelerator establishes a relationship between the number of employees in clustered organisations and the overall dynamics of the employment rate in a region. Ceteris paribus, the growth of employment in a region determines the decrease in the level of influence of an industrial cluster on the labour market of a subject of the federation. This process is accompanied by a change in the costs of clustered enterprises (primarily the costs associated with wages) and, as a consequence, a change in the number of their staff. This, in its turn, determines the change in the turnover indicators of the entire territorial economic system under consideration.
The spillover effect of the employment accelerator influences internal and external suppliers, which impacts on the availability of resources and supplies for cluster member organisations.
The cluster income accelerator correlates the change in wages in clustered enterprises with the dynamics of wages within the boundaries of a subject the federation. The growth of this indicator at the regional level stimulates cluster member organisations to also increase the wages of employees in order to retain human resources potential, primarily leading specialists. At the same time, to reduce costs for individual positions, staff can be released or hiring restrictions can be imposed. The reaction of employees of clustered organisations to such conditions determines the change in performance indicators and, as a result, in the turnover indicators of the territorial economic system under consideration.
The dynamics of the general wages in a subject of the federation also affects the suppliers of an industrial cluster, which impacts on the price of their products and, accordingly, their availability for clustered enterprises.
The cluster export accelerator illustrates the change in the export of an industrial cluster in the context of a change in a similar regional indicator. In general, the positive dynamics of regional exports can be regarded as a reflection of the growth in the production potential of a regional economy. Such conditions open wide opportunities for clustered organisations to increase their staff and output. The timely use of these opportunities allows a cluster to strengthen its influence in the manufacturing sector of a region and the region as a whole. This, in turn, can simplify access to resources and, in some cases, reduce the cost of purchasing raw materials.
The joint action of the three cluster accelerators contributes to changing the mode of operation of the industrial cluster, and therefore, determines its impact on the socioeconomic development of a region.
The presented conceptual mechanism of the mutual influence of a region and a cluster formed the basis of our system spatial method for studying the influence of an industrial cluster on the socioeconomic development of a region.
Materials and methods
The development of the system spatial method requires the generalisation and systematisation of the tools already existing in the cluster theory. The results of the theoretical review show that such tools have been formed since the 1950s within the spatial economic schools of thought, and after 1990 in the context of the modern cluster theory [Koshcheev, Tretyakova, 2020]. We propose to apply our own system criterial approach for their analysis (Figure 2).
Fig. 2. Algorithm for developing the system spatial method
The preliminary stage was the scoping review. Using the Semantria software we identified a set of keywords that characterises the conceptual and methodological area. Then, using the search engines of Web of Science, Scopus, eLibrary, Google Scholar in two chronological ranges (1950-1990 and 1990-2022), we selected publications for the review.
After that, we applied formal and substantive criteria to this set of scientific publications, which allowed determining the final sample.
This study used the following formal criteria:
1) a quality criterion that determines the choice of works, the conclusions of which are regarded by the academic community as relevant. For English-language publications, this criterion is associated with inclusion in the Web of Science and Scopus databases, for Russian-language works, relates to the inclusion of a publication in the List of the Higher Attestation Commission in a particular year;
2) a chronological criterion indicating the period for the examination of scientific works (for this study, it is 1950-2022);
3) a criterion of compliance with the research topic (in this work, it is performed automatically through a system of keywords);
4) an accessibility criterion, which is related to the access to the full texts of the selected articles. This study examines papers included in JSTOR, Science Direct, Pro-Quest, AEA Journals, Emerald, EBSCO, Wiley Online Library, Taylor Francis, Cambridge Journals Online, Springer Link, Google Scholar, Oxford Journals, eLibrary, Grebennikon and East View.
Substantive criteria relate to the elements of the theory that must be presented in publications in order to perform an analysis relevant to the topic. The elements of the concept "industrial cluster" that are significant for this study and act as substantive selection criteria are reflected in Table 1.
Table 1. Elements of the concept "industrial cluster" acting as essential criteria
for selecting publications
Element Its essence
Interpretation of the concept "industrial cluster" Definition describing what an industrial cluster is
Cluster structure Description of the cluster structure and the main elements that make it up
Cluster formation path Reflection in the article of how a cluster arises: in a natural, artificial or mixed way
Object examined by a method Part of the reality that the method considers when analysing the influence of a region on a cluster and a cluster on a region
Basic tools and techniques of analysis Research tools and techniques that the method uses to analyse the influence of a region on a cluster and a cluster on a region
Result of the influence Result of the influence of a cluster on a region and vice versa
Should these elements be present in a scientific publication and it meet the formal criteria, it is included in the sample for conceptual and theoretical analysis.
At the analytical stage, the sample was studied using the tools of content, comparative and conceptual analysis. The above system of formal and substantive criteria was used for comparison.
On this basis, we distinguished the methods for analysing the influence of a cluster on a region and a region on a cluster, which appeared in scientific research in 1950-2022. Further, these methods were correlated with the conceptual approaches that form the mainstream of cluster theory.
The stage of methodological design included a comparative analysis of the identified conceptual systems "theoretical approach to an industrial cluster - a method for assessing the impact" and the development on this basis of the own system spatial method for assessing the impact of an industrial cluster on the socioeconomic development of a region.
Research results
The application of the system criterial method of theoretical analysis made it possible to form a sample that combined 150 publications from the period 1950-1990 and 937 papers published between 1990 and September 1, 2022. The analysis of this sample allowed identifying four methods for assessing the influence of an industrial cluster on a region and a region on an industrial cluster (Table 2).
Table 2. Comparative analysis of methods used to research the region - cluster mutual influence
Parameter for comparison Method
Statistical Regionalistic Marketing Situational
Origin of the method Economic statistics Regional economics, urban studies Marketing analysis Situational approach in management
Result of the influence Formation of statistical dependencies Changes in the characteristics of the territorial economic system Socioeconomic effects Changes in situational variables
Object of analysis Statistical dependencies between variables reflecting factors of the regional environment and parameters of cluster activity Change in indicators illustrating the properties of two interconnected territorial and economic systems (regional environment and cluster) Opinions (of experts, consumers, business representatives, regional authorities, etc.) Situation (case)
Table 2 (concluded)
Parameter for comparison Method
Statistical Regionalistic Marketing Situational
Basic methods 1) regression analysis 2) correlation analysis 3) factor analysis 1) input-output matrices 2) analysis of shifts 3) evaluation of target indicators 1) method of expert assessments 2) survey method 3) interview method Case study
Uses [Nicolini, 2001; Azhar, 2017; Basile, Pittiglio, Reganati, 2017] [Sakharova, 2015; Pizengolts, Savelyeva, Korobeynikova, 2018; Michaud, Jolley, 2019] [Nishimura, Okamuro, 2011; Akhunzhanova, Tomashevskaya, Osipov, 2020] [Irawati, 2007; Akhtarieva, 2009]
The statistical method studies the region - cluster mutual influence using statistical dependencies. Within the framework of this method, a three-stage algorithm was developed.
The first stage is the theoretical analysis and development of a conceptual model of the influence of one territorial economic system on another. Within this model, channels of influence are identified and then given the corresponding indicators. The second stage of the method involves the selection and justification of techniques for studying statistical dependencies. The third stage is based on the use of mathematical and statistical tools, which allows obtaining quantitative parameters of statistical dependencies between changes in indicators characterising the state of each of the two territorial economic systems.
This method, on the one hand, opens up wide possibilities for mathematical modeling and forecasting, on the other hand, it in fact adjusts the model of mutual influence to the available empirical data. As a result, it is characterised by a certain subjectivism and a relatively weak explanatory function.
The regionalistic method is close to the 'black box' strategy. It is based on registering the data on the state of the territorial economic system at the beginning and end of a certain chronological period or at regular intervals. The first stage of this method involves determining the parameters of the territorial economic system and the period of registering indicators. The second stage is relates to the selection of techniques. The third stage is the empirical analysis of the data on changes in the parameters of the territorial economic system in a certain chronological period and a system of factors that caused these changes.
The considered method allows predicting the consequences of cluster activity in a region and makes it possible to choose relevant management actions. At the same time, registering the changes, this method does not reveal their mechanism. Consequently, many of the effects identified with its help are simply attributed to cluster activity, although technically they may have a different genesis.
The marketing method is based on working with indirect information received from the primary observer. At the first stage of its implementation, the type of required information and the potential sample of respondents are modeled; at the second stage, techniques are determined and field research is carried out; at the third stage, the collected data are processed and interpreted. The result of applying this method is a certain aggregated opinion (experts, consumers, business representatives, regional authorities, etc.) about the impact of a cluster on a region or vice versa.
Based on the analysis of opinions, this method elaborates on the social dimension of the mutual influence of the two territorial economic systems. However, by its nature, it is quite subjective and has poor forecasting capabilities.
The situational method takes a specific situation (case) as the basis of the analysis and subjects it to a comprehensive study. At the first stage, cases are selected and a set of situational variables is created to ensure their comparability. At the second stage, data is collected and systematised for each case. The third stage involves a comparative analysis of cases and the formation on this basis of a conceptual mechanism for the influence of one territorial economic system on another.
The advantages of this method are the depth of analysis and an individual approach to each case. However, working with different types of data (quantitative and qualitative) significantly limits the possibilities of mathematical modeling and forecasting. In addition, the situational method does not eliminate the problem of data extrapolation.
As the analysis revealed, four identified methods with various degrees of frequency appear in studies based on each of the six modern approaches to industrial clusters (Table 3). The "frequently used" category means that the method or its elements were identified in at least 40 % of the papers included in the sample for theoretical analysis. The "used" category includes methods present in 10-40 % of the publications. "Rarely used" means that the method or its elements appear in less than 10 % of the selected publications. The "not detected" marker indicates that the analysis of a sample of studies did not record the use of a certain method within a certain approach.
As Table 3 shows, the statistical method is the most demanded in cluster studies. Accordingly, in the modern cluster theory, the view of the mutual influence of a region and a cluster as the formation of statistical dependencies prevails. This concept that should be used in the development of new research tools.
Table 3. Implication of the methods for researching the region - industrial cluster mutual
influence within the modern cluster theory
Approach Methods
Statistical Regionalistic Marketing Situational
Classical Frequently used Used Rarely used Rarely used
Agglomeration Frequently used Not detected Rarely used Not detected
Administrative Frequently used Used Rarely used Not detected
Institutional Frequently used Rarely used Rarely used Rarely used
System Frequently used Used Rarely used Rarely used
Network Frequently used Used Not detected Not detected
A comparative analysis of the practices of applying statistical and other methods within the framework of those six theoretical approaches made it possible to develop the own system spatial method for assessing the impact of an industrial cluster on the socioeconomic development of a region (Figure 3).
In accordance with the proposed method, at the first stage, the industrial clusters operating within a certain type of economic activity are identified. For this purpose, it is recommended to take a territory of a country, a group of countries or a functional economic area with clearly defined borders.
Clusters are identified on the basis of two attributes proposed by the team of authors led by Mirolyubova: the value of the localisation coefficient above 1.25 and the presence of exports from the region for the analysed type of economic activity. If these signs appear for six or more years within a 10-year period, it is concluded that an industrial cluster was functioning in a region at that time.
At the second and third stages, the influence of a cluster on the socioeconomic development of a region and the influence of a region on the functioning of a cluster are assessed based on regression analysis.
The fourth stage involves comparing the results of assessing the impact of a cluster on a region and a region on a cluster. This data is accumulated in a conceptual model that illustrates general patterns of the considered mutual influence emerged within the territories for which the analysis was performed (a country, a group of countries, a functional economic area, etc.).
The fifth stage is grouping the analysed regions according to the parameters of intra-regional patterns of interaction between a region and a cluster. Technically, in accordance with the ideas of the system spatial approach, these regularities are reduced to the values of variables in the regression analysis equations built at the first and second stages of this method.
Such a grouping can be done based on the index method or cluster analysis. Additionally, a comprehensive analysis of the socioeconomic space of a region is made in
Fig. 3. System spatial method for assessing the impact of industrial clusters on regional socioeconomic development
order to identify the local development specifics of a cluster due to the demographic, infrastructural, resource, investment and geographical features of a territory.
In line with the results obtained, for each group of regions, a set of recommendations is formulated to manage the impact of a cluster on the socioeconomic development of a region, taking into account the response impact of a region on an industrial cluster.
Conclusion
In our study, we performed a comprehensive analysis and structuring of sources to find out about modern tools for studying the region - industrial cluster mutual influence, and identified four main methods of analysis. The correlation of these methods with the conceptual approaches that form the mainstream of the cluster theory has shown the predominance of a statistical view of the nature of the interaction between a region and a cluster in modern academic discourse. Proceeding from this concept and comparing the practices of applying these methods, we have developed a system spatial method for assessing the impact of an industrial cluster on the socioeconomic development of a region.
First of all, its advantages include an equivalent focus on the territorial-geographical and socioeconomic dimensions of an industrial cluster, which ensures the completeness and comprehensive nature of the analysis. Then, we consider a cluster as a part of the regional socioeconomic system, in which the impact of a cluster on the socioeconomic development of the region entails the impact of the region on the functioning of a cluster. In the medium term, the change in the parameters of the functioning of a cluster, in its turn, determines the impact of a cluster on a region. Accordingly, the proposed analytical model provides accurate results that offer an opportunity to foresee the consequences of the administrative measures taken.
The application of the system spatial method can deepen the understanding of the influence mechanism of industrial clusters operating in different industries on the socioeconomic development of a region. In addition, the use of this method allows developing a system of measures that will ensure a positive impact of an industrial cluster on the socioeconomic development of a region, with a predominantly positive impact of a region on the functioning of a cluster.
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Information about the authors
Tatyana V. Mirolyubova, Dr. Sc. (Econ.), Prof., Dean of the Economics Faculty. Perm State University, Perm, Russia. E-mail: mirolubov@list.ru
Dmitry A. Koshcheev, Sr. Lecturer of Management Dept. HSE University, Perm, Russia; Sr. Lecturer of World and Regional Economy, Economic Theory Dept. Perm State University, Perm, Russia. E-mail: dakoshcheev@hse.ru
© Mirolyubova T. V., Koshcheev D. A., 2022
