Abstract
The relevance of the topic is determined by a pioneering attempt to generalize, based on world experience and Russian realities of the last three decades, new patterns in the location of Russia’s productive forces arising in connection with innovative modernization of the national economy. This research objective has identified the solution to three main problems: (1) identification and characterization of urban agglomerations, economic clusters, and new industrial areas as new forms of the location of Russia’s productive forces; (2) analysis of the phenomenon of center–periphery dichotomy of the Russian space using the tools of the gravity model; (3) study of differences in the factors involved in placing new industrial facilities in Russia, depending on the degree of their production efficiency. The main methods for solving these problems were multiscale analysis, the Clark–Medvedkov potential model, and comparative analysis. Analysis showed that the new location patterns of Russia’s productive forces are associated with the rise in importance of urban agglomerations, and economic clusters, grassroots (small) economic districts in the form of localized areas of increased economic concentration: innovation valleys, technology parks, industrial parks, special economic zones, etc., as modern forms of the spatial organization of productive forces. These forms of compact placement yield better conditions for the learning processes between employees and firms, the flow of knowledge, and generation of innovations than former, larger, and more spatially dispersed ones. The Russia’s placements specifics include low population density of the country’s space, extreme cold, significant underurbanization, and landlocked territory. These features of the Russian space are manifested in the particular nature of Russia’s center–periphery: about half the country’s territory does not have “distinct” nearby center, the role of which in innovative modernization and spatial reorganization is therefore under force carried out by Moscow. The main modern watershed in placement processes is not along the industrial-nonindustrial axis; rather, it involves routine–creative types of economic activity. Industrial facilities themselves can fall into the categories of routine and creative. The tendency for innovative (new) industries to cluster in space is related to the role of knowledge flows: closer to the places of development of new knowledge, technical, and technological innovations. The high-tech branches of the electronic and biotechnological industries, which rely on the intangible assets of new knowledge and new technical solutions, are maximally concentrated in terms of location. The traditional industrial sectors of the light and food industries are much more dispersed. The location of high-tech types of economic activity is particularly sensitive to the factors of the institutional environment, the agglomeration effect, and the presence of innovation infrastructure facilities. Modernization of the spatial organization of Russia’s productive forces, in the presence of general innovation trends, proceeds with significant specifics in large metropolitan agglomerations, million-plus cities, old industrial districts, single-industry cities, and industrial–agrarian and agrarian territories.
Keywords: location of productive forces, urban agglomerations, economic clusters, grassroots economic districts, center–periphery dichotomy, routine and creative industrial activities
INTRODUCTION
The relevance of the topic is determined by the need, based on world experience and Russian realities of the last two decades, to generalize the new patterns of location of Russia’s productive forces, which arise in connection with the innovative modernization of the national economy.
The logic of the study is as follows: first, based on generalization of materials from the strategies of federal districts, new patterns (forms) of location are formulated: urban agglomerations, economic clusters and new industrial areas. Then, the phenomenon of the center–periphery of the Russian space is analyzed, which is clearly manifested in the modern distribution of Russia’s productive forces. Next comes sectoral analysis of the location factors of new industrial facilities created in the country in 2006–2010, in the context of differences in their technological level. All three sections of the analysis are united by a common desire to show the role of new drivers of the location of Russia’s productive forces: knowledge and innovation.
In the classical theory, land (resources), labor, and capital were recognized as location factors. Where does knowledge place here? There are two possibilities for taking into account the increased role of knowledge in the modern location of productive forces. The first is that knowledge has deeply penetrated the traditional factors of location: namely, it radically changes their nature and the location patterns of economic assets in space. For example, the combination of knowledge and capital leads to venture finance; the link between knowledge and the labor market leads to a specific talent market, and so on. The second possibility proceeds from acknowledgment of the changes introduced by knowledge factors in the social division of labor so radical that it proposes to single out a special (new) fourth factor of location.
In all cases, recalling the lessons of classical political economy, it is important to understand that knowledge and innovation affect the location of productive forces via changes in the primary forms of the social division of labor. And the deepening of the social division of labor taking place today and the emergence of subtler, narrower, intellectual competencies and specializations is reflected in the spatial development of these processes, i.e., the location of productive forces.
STRATEGIES OF FEDERAL DISTRICTS: GENERALIZATION OF NEW FORMS OF LOCATION OF PRODUCTIVE FORCES
It is at Russia’s federal district level that new location trends become the most distinct: on the one hand, something that may not manifest itself in every region is necessarily present at the federal district level; at the same time, what may be hidden by scale at the country level is identified as an important new trend/shift in location processes at the federal district level. Generalization of the materials of the strategies of the federal districts carried out by us in 2011–2012 made it possible to identify the forms of location of productive forces that manifest themselves almost everywhere. This is a phenomenon of large urban agglomerations; economic associations (territorial clusters); a grassroots economic district in the form of localized areas of increased economic concentration—innovation valleys, technology parks, industrial parks, special economic zones, etc.; innovation infrastructure and its basic elements in the form of federal and research universities, research institutes, intellectual platforms, science cities; transit transport corridors integrating local and global; heterogeneous assets in new resource projects in the North and the Arctic, the development of which is characterized by the significant specifics involved in the organization and location of productive forces.2
A natural question arises: what is common in all these structures? All of them can ensure the effects of increasing returns for the economy due to the constant (spontaneous) generation of new knowledge and innovation in the form of new types of economic activity, new technologies, new business processes, new product groups owing to diversification and heterogeneity of assets. Even more precisely, they are better suited to this than other forms of location that dominated the industrial era (e.g., single-industry cities, territorial production complexes and industrial hubs, large energy-economic districts) and mainly actuated the effects of economies of scale with large volumes from the same types of operations. The current forms of location, on the contrary, are to a much greater extent tailored to small and medium-sized businesses, to obtain effects on variety and diversification (Kotov and Pilyasov, 2012).
Today, the correct forms of location are those that promote, facilitate, and ensure the accumulation of human capital. The correctness of those forms that are able to establish the interaction of corporations, small and medium-sized firms, households with each other in such a way that external positive externalities of experience, knowledge, information are as easy and strong as possible. This is both a supergoal and a criterion for success of the modern location of productive forces: it ensures the collective learning of participants in the production process from each other and helps them absorb new knowledge, which means that it has viability in the new era.
And this becomes the key for us in explaining the rise of new, differently named forms of location, but possessing an essential unity: economic clusters, special economic zones, industrial and technological parks, business incubators and other island platforms; these forms of compact, localized, concentrated placement provide better conditions for the learning by workers and firms from each other and flow of knowledge than the former, spatially more dispersed, larger ones. They seem to replace the prior industrial forms of location: large centers (urban agglomerations)—metropolitan areas—single-industry industrial cities; flat network clusters—vertically integrated territorial production complexes; small (grassroots) economic districts—former large energy-economic districts; transit transport corridors that provide access to global networks—former routes of penetration into undeveloped areas, etc.
Even the behavior of large foreign investors is also subject to new patterns due to the increased role of knowledge. How else can one explain their “herd instinct”—when, after the first automobile corporation, an entire group of other automotive giants followed in Leningrad and Kaluga oblasts? Competitors in all other markets, in Russia they suddenly settled down extremely compactly, neighborlike. The first investor spends significant resources and efforts to take root in new soil and acquire the social capital of connections and acquaintances with the regional and local authorities who make decisions on preparing the industrial site, passing the necessary approvals, etc. On the other hand, subsequent investors can already economize significantly on these transaction costs, using the accumulated experience of the first, learning from him the practice of social embeddedness (Zamyatina and Pilyasov, 2013).
However, it is possible to understand the role of knowledge as a new factor of location and more literally: location of economic entities concerned with maintaining their competitiveness gravitates towards places where new knowledge is generated. In studies by M. Feldman, D. Audretsch, and their colleagues (2012), this is proved by numerous examples of the location of small innovative firms within a radius of daily physical accessibility to research universities, research laboratories, R&D departments of corporations, and other innovation infrastructure facilities.
However, even in Russian conditions, the factors of proximity to the source of new knowledge, objects of innovation infrastructure begin to operate as factors of location. For example, proximity to Novosibirsk University and research institutes of Novosibirsk Akademgorodok became a factor in the creation of innovative firms of the first Russian technopark.3 A.V. Suvorova (2014) defines a 100 km radius of accessibility as the distance from the innovation infrastructure facility, which has the effects of proximity, ease of location of start-ups, and small innovative entrepreneurship in Sverdlovsk oblast. In the science city of Biysk, a factor in the emergence of the entire network of small innovative biotechnological firms in the 1990s–2000s was the presence of the parent company “Altai” as a generator of new knowledge.
As our surveys of small and medium-sized (up to 50 000 people) Russian cities have shown, very often they do not have canonical objects of innovation infrastructure in the form of universities and research institutes. Frequently in this case, the role of such facilities for small innovative businesses and urban start-ups is played by the local business support infrastructure—technology parks, business incubators, venture funds, etc.
The nature of knowledge has an impact on the migration routes of Russian university graduates. For example, graduates of the departments of Chemistry, Physics and Technology, and Applied Mathematics and Cybernetics at Tomsk University have a much narrower location zone than those of the Department of Humanities. More than three-quarters of the graduates of the Department of Higher Mathematics and Cybernetics settle in Tomsk itself. Graduates of the Department of Physics and Technology are to a large extent oriented towards by “intellectual cities” (Snezhinsk, Sarov, Chernogolovka), including Tomsk itself (two-thirds of graduates), as well as St. Petersburg. Conversely, graduates of the Department of Arts and Culture have a very wide range of employment in large cities of the country (Novosibirsk, Krasnoyarsk, Omsk, Kemerovo, Samara, Voronezh, Yekaterinburg, etc.) (Zamyatina, 2011).
URBAN AGGLOMERATIONS, CLUSTERS, AND NEW INDUSTRIAL AREAS: THE TRIAD OF MODERN LOCATION OF PRODUCTIVE FORCES
The processes of metropolitanization, which everywhere in the world involve the increased economic role of global cities, city-regions, with a vast suburban area in their zone of influence, began to work actively in Russia since the 1990s. The phenomenon of urban agglomerations was discussed in the location of productive forces in the Soviet era, but back then, no one saw them as the main drivers of the national economy. They were in the shadow of large single-industry cities generating the main industrial output of the industrial economy and were often perceived as “parasite cities” that did produce nothing.
However, due to the significant diversification and flexibility of their economies, under the conditions of radical economic reform, urban agglomerations became significantly more stable, comfortable, and therefore attractive to energetic and talented migrants from the periphery than previously prestigious industrial centers. Unsurprisingly, compared with industrial cities, large urban agglomerations in Russia either increased in number or suffered minimal losses due to the relocation of energetic labor migrants here from the Russian periphery and CIS countries.
It was here that positive transformations in the comfortization of the environment, improvement in the quality of life, expansion of the leisure complex unfolded with the maximum dynamism. And not only this. Due to the agglomeration effect, localized associations of cities began to spur the development of neighboring territories within 1.5 h transport accessibility.
Hourly transport accessibility to a large city center has become a positive factor in the location of many types of industrial, service, and agricultural activities. High-speed rail connections has not negated, but only expanded the zone of attractive influence of metropolitan cities on the location of productive forces. Both the population and material assets have increasingly become concentrated around large urban centers. The hinterland of large metropolitan agglomerations, their periphery within a 1.5-h range, has become a new attractive place for location. Here, the reserves of free space have worked positively to accommodate retail and warehouse facilities of large chains and branches of global industrial companies.
There are now 22 major agglomerations in Russia in which more than 1 mln people reside (according to expert estimates,4 the country has more than 50 urban agglomerations with populations over a quarter of a million).
The most prominent role of urban agglomerations as generators of shifts in the location of productive forces (primarily the peripheral–central migration of qualified personnel), as can be seen from the 25-year dynamics of the share of urban agglomerations in the population of a federal district or region, manifests itself precisely in the sparse, low-density Russian space: in the Siberian Federal District it is more powerful than in the Volga region, while in the Republic of Sakha (Yakutia), it is more powerful than, e.g., in Leningrad oblast.
In recent years, many studies have been devoted to the new role of large urban agglomerations in the innovative development and location of productive forces (Aksenov, 2011; Iglovskaya, 2011; Kirsanova, 2010; Selivanova, 2011; Zotova, 2007). These authors note the hybrid nature of the new spatial structure of Russian urban agglomerations, which combine the features of industrial and modern times and elements of western urban agglomerations (e.g., the classic features of world suburbanization begin to appear). The ability of urban agglomerations to expand their zone of influence on the adjacent territory and their attractiveness for qualified personnel and talents have been noted. Successful performance of the function of an innovation leader directly depends on the dynamics (rate) of transformation of their internal structure. In various Russian cities, this transformation follows either a service or a reindustrialization scenario, but it is always accompanied by a significant change in the functional structure of utilized urban lands, extensive relocation of assets (e.g., partial removal of the main production assets of industry beyond the city limits), transformation of part of the territory of industrial zones into objects of leisure, trade, business, or innovation infrastructure.
Another factor determining the strength of influence of an urban agglomeration on the surrounding territory is related to its status in the unified network of large Russian city centers. This is significantly influenced by the degree of development of knowledge-intensive business services (KIBS). With maximum ease, the status of an urban agglomeration within the Russian network is determined by the number of direct flights between the city and other major agglomeration centers of Russia.
Many strategies of federal districts deal with creating new economic clusters: biomedical, logistical, pharmaceutical, etc. It is absolutely no coincidence that the formation of clusters in many research papers is directly linked to the regional innovation process (Dmitrieva, 2013; Kutsenko, 2012; Markushina, 2011; Sinergiya …, 2012). The fact is that clusters imply localized (territorially concentrated) location of intensively cooperating firms and other participants in cluster cooperation, which allows them to very quickly adopt the best practices from each other, interact on a regular basis with carriers of unique new knowledge and competencies, and ultimately, constantly generate and innovate. It is no coincidence that cluster policy is integrated into innovation policy in most EU countries.
However, in Russian regions, as shown by materials of the strategies of federal districts and regulatory legal documents of federal subjects, innovation and cluster policy are being linked. For example, in the Republic of Tatarstan, the priority areas of cluster development are development of innovation activities and mechanisms for commercializing innovation projects; in the Chuvash Republic, regional innovation development programs are based on the cluster approach, e.g., the Program of Industrial Development up to 2020.5
Researchers note that cluster activity affects the level of innovative activity of firms included in the cluster (Kutsenko, 2012), i.e., localized placement and cooperation of business entities increases their innovation ability. However, the opposite is also true: the same author, using the example of the Moscow city economy, shows in his dissertation research that it is precisely high- and medium-tech activity (measuring and research equipment, educational and research activity, information technology, biopharmaceuticals, communication equipment, aerospace industry, financial services, publishing and printing) that is primarily prone to clustering.
Providing conditions for the colocation of cooperating economic entities can be recognized as a new effective state policy tool that takes into account the positive externalities of knowledge flows and is implemented as technoparks, business incubators, special economic zones, in which location, cluster policy, and innovative imperatives of the modern economy are interconnected.
A number of strategies of federal districts foresee the formation of new industrial districts—often in rural areas, where no industrial layer was created during the previous period of economic development, or in the hinterland of large urban agglomerations that were previously suburban rural areas, but as a result of urban sprawl, have acquired an urbanized appearance. Our research in recent years has made it possible to identify several types of new industrial districts in Russia, most of which were described by foreign researchers as early as the mid-1990s (Markusen, 1996). These are new industrial areas with resource specialization (e.g., oil and natural gas in the Nenets Autonomous Okrug and on the Arctic shelf), the classical Marshallian Rostov industrial area for the specialization of small and medium-sized businesses in footwear, the Kaluga–Borovsk automotive industry area similar to the satellite platform of large global corporations, the center–periphery high-tech Upper Salda Industrial District Titanium Valley of Sverdlovsk oblast, and Vladivostok Industrial District of Localized Large-Scale Attraction of State Investment Resources (Kotov and Pilyasov, 2013).
Almost all strategies of federal districts mention various new compact forms of the distribution of productive forces: special economic zone, industrial district, intellectual valley, technoecopolis, local growth pole, etc. The abundance of titles gives rise to the illusion that we are talking about very different phenomena. In fact, both in Russia and other countries, the same phenomenon of modern spatial development takes place—the universal natural rise in the significance of a grassroots (small) economic district (Pilyasov, 2013). And this spatial phenomenon is directly related to new features, the peculiarities of innovative development. Dynamically developing grassroots economic districts bring new specialization, a new tone for the development of the entire regional and national economy.
The key features of the grassroots economic district can be clearly understood by comparison with a “large” economic district (Table 1).
Table 1. .
Economic districts of two different eras
| Characteristic | “Large” district of Kolosovsky | “Small” area of Alayev–Becattini |
|---|---|---|
| Economic era | Early industrialization | Beginning of postindustrial transformation, new industrialization |
| Organizational structure | Large conveyor-type production plant | Conglomerate district (network) of small, medium, and large firms |
| Regional basis | Energy production | Communication, labor market |
| Borders | By energy districts | By local labor markets |
| Size | Thous. km2 | Hundreds km2 |
| Basis of district | Localized selection of sectoral combinations | Localized selection of combinations of skills and competencies |
| Purpose of existence | Export of products by sectors with allied specialization | Sustainable development of local community through global competitiveness of local firms and companies |
| Specialization | By industry, stable | By type of activity and competencies (professions), highly variable |
| Sources of development | Exogenous—state capital investments and local natural resources | Endogenous— local community, its human, creative, social capital, global markets and investors |
| District core | Territorial–production complex, based on a steadily recurring type of production process | Territorial cluster, constantly changing specialization |
| Economic effects | Economies of scale, externalities within enterprise | Economy on diversity (diversification), external externalities |
| Nature of main tangible and natural assets | Homogeneous | Heterogeneous |
| Life cycle | Energy production cycle (matter, energy fluxes) | Innovation and production (creative) cycle: knowledge and information flows |
| Key problem | Departmentality versus parochialism (conflict of interests of departments and territory) | Globalism versus local identity (conflict of interest between global companies and local community, small businesses) |
| Atomic (primary) element—catalyst for regional development | Nuclear manufacturing enterprise development program (program approach) | Investment/social project (project approach) |
| Internal territorial structure | Linearly nodal | Network |
The small economic district is the key spatial cell in which the most important modern issues of the new location of productive forces, a new territorial organization of the Russian economy, are linked. On this site, the size of the local labor market (urban agglomeration, metropolitan area, etc.), the possibility of regular personal communication and knowledge exchange between agents of the local economy is realized, which is important for the development of new knowledge, the formation of new ideas and competencies. The phenomenon of the grassroots economic district reflects the fact that the modern process of innovative modernization of the Russian economy is much more localized in space than the more areal process of Soviet industrialization.
NEW CENTER–PERIPHERY STRUCTURE OF THE RUSSIAN SPACE AND THE LOCATION OF PRODUCTIVE FORCES
The vastness of Russian spaces has always been acknowledged as a generator of significant specifics in the development and location of productive forces. That is why many researchers of these processes, e.g., in state planning institutes, in Soviet times concluded that the general world patterns of location are manifested in Russia with significant peculiarities.
However, it does not suffice to simply name the vastness of Russian spaces. The Russian specifics of placing economic assets stems from the low population density (e.g., specialization of the Ural–Kuznetsk Combine was akin to the German Ruhr, but the population density was an order of magnitude lower and the distances between coal and metallurgical enterprises many times bigger); the extreme cold, characteristic of most of the country’s territory, is unprecedented by the criteria of Europe; significant underurbanization—the fundamental shortage of cities exerts limiting pressure on any deployment of productive forces; landlocked territory—a significant part of the country’s territory is far from sea coasts and ports (Bezrukov, 2008). All these factors led to significant friction of space and therefore a strong state involvement in any major project for the reconstruction of the spatial organization of productive forces.
Folded up, all these specific features of the Russian space are manifested in the peculiar nature of the Russian center–periphery: compared to European countries, the United States, and even a close analogue, another northern federation, Canada. The Russian Federation has always been exclusively centralized in terms of the nature of social processes occurring in its spaces (including the location of productive forces).
The phenomenon of center–periphery contrasts further intensified with the start of the radical market reforms in the 1990s. As soon as the leveling power of state intervention in the location of productive forces in space weakened, regions and cities with concentrated objective economic advantages of location immediately underwent economic separation: metropolitan, central, border, agglomeration, and export-oriented.
Russian economic reform has led to a new center–periphery features in the denationalized, now open to global markets, economic space of the country. The former Soviet industrial space had a main center–periphery dichotomy along the city–village axis, which reflected the main constructive contradiction of that industrial era. The new Russian economic space has a main center–periphery polarization along the metropolitan and peripheral Russia axis, which reflects the very rapid penetration of elements of the new postindustrial economy into the largest urban agglomerations and a significant lag in these processes in the Russian rural and urban periphery.
The new center–periphery contrasts of the Russian open space are enormous even compared with the Soviet era and reflect the features of the new social and geographical division of labor. Researchers (e.g., (Zubarevich, 2012)) note a significant rise in the economic role of the largest Russian agglomerations, primarily Moscow and St. Petersburg, in generating GDP, housing construction, and wholesale trade. The location of many assets of industrial enterprises is concentrated in the inner circle of large urban agglomerations and in small and medium-sized satellite cities. It is there that a fundamentally new industry is emerging: branches of foreign companies, new small and medium-sized enterprises, financial and organizational power, and management elements (headquarters of the main Russian corporations).
In the new location of productive forces, the availability and proximity not only to the main urban agglomerations, but precisely to large air hubs, acquires a huge role. As before, port and river cities were command posts that organized the Russian space; now these functions are being performed by Russian air hubs.
Researchers call the phenomenon of new center–periphery contrasts of the Russian space its contraction (Szhatie …, 2010)—retreat from previously populated and developed territories, which occurs simultaneously with densification in large urban agglomerations, triggering the processes of positive Jacobs externalities on urbanization (diversification).
For a visual assessment of the new center–periphery processes unfolding in the Russian space, a cartographic model was constructed based on identification of zones of influence of the centers of the settlement system of different size.6 For calculations, the Clark–Medvedkov7 gravitation model was used with the difference that instead of the total potential for each point j, the maximum (Wj) was calculated according to the following formula8:
 |
where Pi is the population of each “center” (as of 2013), Rij is the distance between it and point j, and a is the space friction value.
Each point was assigned the identifier of the corresponding center with the maximum impact potential. All Russian cities and urban-type settlements were considered as probable centers of the settlement system (except for cases when the latter, together with a hierarchically superior city, are part of municipal urban districts or settlements)—more than 2000 objects.
With the help of geographic information systems (GIS) for the entire territory of Russia, a regular network of points with a fixed step was constructed (about 300 thousand in total). Each of them, according to the above scheme, was assigned to a particular center. As a result of GIS analysis, the zones of influence of all probable centers of the settlement system were identified, after which, using the statistical analysis methods, the smallest were excluded (the zones of influence of which were less than the empirically derived threshold). After recalculating the model for a smaller number of centers (476 population centers were included in the final list), their gravity zones were identified (Fig. 1).
Fig. 1.
Estimated zones of influence of centers of settlement system in Russian Federation. Note: The borders of Russia in the article are considered in accordance with the Constitution of the Russian Federation adopted by popular vote on December 12, 1993, with amendments approved during the All-Russian vote on July 1, 2020 and are relevant as of January 1, 2021..
The most important result of this cartographic model is the clear discovery of the fact that half the country’s territory consists of “unanchored” local centers, which are not fulfilling their functions of stabilizing population and developing productive forces. That is why these spaces fall into Moscow’s zone of influence, forcing it to play the role of a local center for them due to underdevelopment.
This is precisely the most important modern (in Soviet times, local urban centers served as stabilizers for their then rural periphery) specific feature of the Russian space and modern features of the location of productive forces: there are local centers, but they cannot fulfill their functions (e.g., the most important for the modern era—generating and broadcasting innovations for the subordinate territory) and are forced to yield them to Moscow. In practical terms, this is manifested in labor migrations oriented towards Moscow by residents of Russia’s unanchored territories.
As a result, innovative modernization of the productive forces at these territories is significantly slowed. One solution to this problem may be the rollout here of new projects of various scale and significance (local, regional, federal), capable of concentrating qualified, creative personnel around themselves and becoming catalysts for the innovative modernization of productive forces.
It is precisely owing to the vastness of the unanchored territories that there are no straightforward analogies when designating modern trends in the location of productive forces, even with the northern federation of Canada, which has many features in common with Russia in the resource profile of the national economy, location zoning, and low population density. Canada has significantly more large urban agglomeration centers per unit area than Russia.
In the 2000s, a team of Canadian authors led by Mario Polese completed a series of studies on the new location patterns of productive forces in Canada, manifested in the 1980s–1990s. Despite the significant differences in the agglomeration organization of the Russian and Canadian economic space, it is helpful to generalize them. The most important conclusion by Canadian authors is the promotion of metropolitanization as a key trend in the new location of productive forces (Polese and Shearmur, 2003; Shearmur and Polese, 2004). The proximity to major metropolitan areas and the agglomeration economy (city size) have played an increasing role in attracting employment in recent decades.
Canada’s population is increasingly concentrated in large metropolitan areas (urban agglomerations) with more than half a million people. Already up to 75% of the total population of the country lives either directly in metropolises or their “shadow,” i.e., within a 100–150 km zone of transport accessibility. Canada’s large urban agglomerations have set a new metric for the country’s space and reorder all other elements of the settlement system as the most dynamically developing element of the spatial structure. The benefits they offer are high creativity, quality of life, and density of information flows. Settlements and placed types economic activity within an hour’s reach of major metropolitan areas benefit from more dynamic development.
In the past three decades, Canada has been characterized by three main trends in the distribution of productive forces. First, traditional industry is moving out of large urban agglomerations into nearby small and medium-sized cities. Second, business services (financial, manufacturing, wholesale trade, etc.) are subject to hierarchical distribution in terms of location (the higher the status of the city, the greater the share of employees). Printing and publishing activity in its location has a structure close to business services: the higher the city in the hierarchy, the greater the share of employees in it, e.g., in publishing: in metropolitan cities, it is higher than in central ones, where it is higher than in peripheral ones. Third, primary and resource activities are subject to an “antihierarchical” distribution, with both distance and size pushing firms to more distant locations.
Traditional industries were relocated from large centers to the surrounding territories, to small and medium-sized cities. The strongest concentration of industry (excluding high-tech) is found in small and medium-sized cities within a 100-kilometer radius of the main metropolitan areas. The 100–150 km limit (hourly accessibility) serves as the limit for placing industrial plants. This milestone has not changed with new technologies (Polese and Shearmur, 2001).
The highest concentration of the automotive industry, e.g., is found in large cities with populations of 100 000–500 000 within a 100-km zone from major metropolitan areas. The highest concentration of the textile industry is in smaller towns (up to 100 000 people) within a 100-km zone (Shearmur and Polese, 2004). The more demanding the industry is to an area, to the space, the farther from the city core it is located: e.g., automotive production and the chemical industry are placed in satellite towns, while less land-intensive publishing is closer to the center.
The high-tech industry, sensitive to the culturally diverse urban environment, with an agglomeration effect, a wide range of KIBS, a large and diversified pool of talent and qualified personnel, has concentrated in urban agglomerations. In large cities, there are also those industries for which proximity to the market is critical, such as information processing and publishing activity.
The more distant the cities and towns of peripheral Canada are from regional capitals, the greater the role of natural resources and resource employment in the local economy. Peripheral (often single-industry resource) cities and towns face the combined action of several trends: the growth of labor productivity in capital-intensive resource sectors versus depletion of many types and deposits of natural resources, the growth of international competition in the context of globalization, the introduction of new technologies–substitutes for natural resources (e.g., in wood processing is increasingly dominated by composite materials: plastics, synthetics; the main factor in the location of such industries is proximity to markets) and loss of population as a result of migration outflow and often natural causes.
For these reasons, resource settlements are closed or they have switched to seasonal operation. At the same time, peripheral resource cities in “small central” urban zones, as well as in rural central areas, acquire a chance for development by creating resource industries with low added value (pulp production, food, textile, light industry, etc.).
The most important conclusion from the generalization of the new Russian realities in the center–periphery organization of space and Canadian trends in the location of productive forces over the past three decades is the concept of a new nature of the economic and geographical position as a factor in the location of productive forces. Currently, it is not the status of location of economic assets that becomes more important, but its economic and geographical position in relation to the nearest center of national or subregional significance. Cities and villages having one status develop completely differently near the main urban centers and in peripheral Russia.
FACTORS OF LOCATION OF THE NEW “POSTINDUSTRIAL” INDUSTRY
Our initial understanding of the 1990s was to recognize differences in the location patterns between industrial and nonindustrial (postindustrial) types of economic activity. However, already in the 2000s, it became obvious that the main modern watershed in location processes is not along the industrial axis–nonindustrial axis, but a totally different axis: routine–creative types of economic activity, because industrial facilities themselves can fall into the category of routine and creative ones, while the laws of location in both cases will differ for the objects of the “old” and “new” industries. Thus, A.P. Gorkin (2012) first introduced the concept of postindustrial industry.
The propensity to locate in large urban agglomerations will be higher in those industries and types of industrial activity in which new knowledge plays a greater role: since at first it cannot be formalized, it is better to be closer to its sources, to objects of innovation infrastructure in the form of universities, research centers, etc.; such are the laws of location of young high-tech industries such as biotechnology and genetic engineering. The tendency of innovative (new) industries to cluster in space is related to the role of knowledge flows: closer to the places of development of new knowledge and technical and technological innovations. After all, their competitiveness directly depends on the speed at which new product groups, series, and product releases are brough to the market.
However, as soon as an industry ages, its degree of concentration decreases; it becomes more dispersed, because the share of implicit knowledge in the general knowledge pool is already declining. This is how the “cognitive” laws of location of new industrial sectors work. For example, the high-tech sectors of the electronic and biotechnological industries, which rely on the intangible assets of new knowledge and new technical solutions are maximally concentrated in space: it is as though these firms learn from each other. And the traditional industrial sectors of the light and food industries are much more dispersed.
In view of modern processes of deepening social division of labor and specialization, the watershed often no longer passes between high- and low-tech industries, but within one industry, between high-tech (creative core) and routine types of economic activity. For example, in the global music industry, several music networks are closely linked to each other through record labels. The creative networks of the birth of new music, new songs, and melodies are very concentrated; conversely, networks of industrial music production are spatially diffuse (Leyshon, 2001): for mass music production, the former industrial laws of location are typical.
Similar trends apply, e.g., in the automotive industry. On the one hand, the most creative types of activity and competencies (e.g., industrial design) are fully or partially stored in the “root directory” of a global company, its headquarters, or head office. All routine procedures for the manufacture of components and assembly production are moved as a result of production outsourcing to places with low production costs and/or large new sales markets.
The laws of location begin to act differently not between sectors of the economy, but even within the same industry in different types of economic activity, and even deeper—even within one type of economic activity, the laws of location may differ for people of different professions. The laws of location of particular specialists—highly talented personnel—are one thing; location of just highly qualified personnel is another. This was very well expressed by R. Florida (2002), who substantiated the need for a transition in modern economic dimensions from the concept of human capital, which is differentiated by the education level of workers, to the concept of a creative class, which is singled out by professions (specialties). In our studies, the differences in the directions of migration of qualified and talented personnel are clearly distinguished (Zamyatina and Pilyasov, 2013): the former move to work in the largest and most attractive labor markets of the capital Russian agglomerations, while the latter answer the call of large national economic projects, which require particular directors of tasks in narrow fields of knowledge, with very “pointed” competencies—these may number several people throughout the country.
To determine the features of location of new high-tech industry facilities, a study of investment projects was undertaken, which were reported (as plans or programs of Russian or foreign companies for the development of production in Russia) in articles of the journal “Expert” and regional issues thereof and in the newsfeed of www.expert.ru from September 2006 to August 2010. During monitoring, about 900 informational announcements were collected, from which a database was created on 754 industrial investment objects. At the next stage, projects were excluded from the total set, which, despite announcement, had still not been implemented. Also excluded were projects the total cost of which was less than RUB 200 mln due to insufficient information on them (Table 2). The basis of the compiled information base was 290 production facilities. About half of them accounted for new facilities; the remainder, for the reconstruction of existing industries, as well as expansion of their capacity.
Table 2. .
Number of projects processed during study
| Total number of projects for which information was collected | 754 |
| Projects for creation of new factory, | 299 |
| including without infrastructure projects | 204 |
| Projects for reconstruction and expansion of capacities, | 123 |
| including without infrastructure projects | 86 |
| Projects announced for implementation, but not implemented | 332 |
| Total projects covered by study | 290 |
The first group included raw materials for the extraction and enrichment of raw materials and agricultural enterprises. The second group is represented by low-tech primary processing enterprises, in particular, metallurgical enterprises, oil refineries, or sawmills. The third group includes medium-tech industries, primarily machine-building, chemical, and metalworking enterprises. The fourth group included high-tech industries (Table 3).
Table 3. .
Distribution of projects by technology groups
| Company type | New construction projects | Reconstruction and expansion projects |
|---|---|---|
| Mining (raw material) enterprises | 49 | 11 |
| Low-tech enterprises | 75 | 38 |
| Medium-tech enterprises | 70 | 34 |
| High-tech enterprises | 10 | 3 |
Location factors were divided into four groups according to their degree of intellectual level. The first group included intellectual factors, such as labor, agglomeration, innovation and institutional factors. Transport and consumer factors were divided into two separate groups. The fourth group includes the natural resource group of factors, such as raw materials, resource and energy, and environmental factors.
The labor resource factor of location determines the choice of location of the object being created in accordance with the need for qualified personnel. The innovation factor plays a role when an enterprise needs constant scientific support in the development and production of products: that is, for its normal functioning, a sufficiently “dense” research environment must be formed on the territory. The agglomeration factor is defined broadly and includes the presence of a well-formed industry environment, operating enterprises in this industry, and manufacturers from related industries. The concentration of enterprises in one industry stimulates the more active introduction of innovations. The influence of the agglomeration factor was also considered for the situation when the enterprise is located near the existing enterprises of the investor company, which allows the latter to achieve savings in the organizational and managerial sphere.
The institutional factor includes state policy and actions on the part of the authorities to develop the economy. As well, actions at the federal, regional, or municipal level can be considered to form a special tax regime and purposefully create special spatial forms (including the preparation of sites for the development of industrial parks), as well as other actions of the authorities to attract industries to the territory under their jurisdiction. The institutional factor is expressed in the provision of tax incentives, subsidies, the creation of infrastructure-prepared sites, lobbying and work with investors.
The resource and energy factor of location is understood as the dependence of an enterprise on natural conditions and the availability of land and energy resources. The environmental factor determines the process of location of industrial facilities depending on the environmental situation, the operation of environmental programs, and legislation.
Let us dwell in more detail on the most interesting case of location of new industrial facilities in industries of varying degrees of science intensity. In low-tech industries and types of industrial activities, mining enterprises, traditional, classic raw materials and consumer factors of placement dominate (Table 4).
Table 4. .
Importance of location factors in creating new production capacities
| Company type | Intellectual factors | Transport | Consumer | Resource and raw materials factors | ||||
|---|---|---|---|---|---|---|---|---|
| labor-resource | agglome-ration | innovative | insti-tutional | raw material | resource and energy |
|||
| Mining enterprises | + | + | ++ | +++ | ++ | |||
| Low-tech enterprises | + | + | + | +++ | +++ | ++ | ||
| Medium-tech enterprises | + | ++ | + | ++ | + | +++ | + | |
| High-tech enterprises | ++ | ++ | ++ | +++ | + | |||
The most interesting are the factors that determine the location of medium-tech enterprises. For the objects of this group, one can distinguish the general dominance of the consumer factor and the importance of labor and agglomeration factors. Due to the large heterogeneity of this group, the set of leading factors can vary significantly depending on the industry. Thus, in the automotive industry, the institutional factor and agglomeration factors stand out sharply. This is clearly seen in the example of two automotive clusters in Kaluga oblast, where Volkswagen, Volvo, and the alliance of PSA Peugeot Citroen and Mitsubishi Motors Corporation have located their production facilities, and in St. Petersburg, in the vicinity of which the General Motors, Toyota, Nissan, and Hyundai plants operate. In both cases, one of the key factors for companies was the active position of the authorities to attract investors, the availability of sites with prepared infrastructure, and the possibility of obtaining economic benefits from the concentration of these enterprises.
For electrical engineering enterprises, the labor resource factor is significant. For example, enterprises for the production of household appliances by Bosch, Samsung, and Beko were built, respectively, in St. Petersburg and Kaluga and Vladimir oblasts, i.e., regions with specialists from the engineering and electrical industries.
For the timber processing and chemical industries, the raw material factor is of great importance. For example, the location of Pfleiderer’s MDF plant was linked to the acquisition of a suitable forest area.
For high-tech industries, intellectual factors are the most significant. In particular, for this group, the role of labor, agglomeration, innovation, and institutional factors is most pronounced.
The availability of a factory for the production of Sitronics microcircuits in Zelenograd is connected with the availability of qualified labor resources and a scientific base; the same factors predetermined the creation by the Rosnano corporation of the production of LEDs in St. Petersburg. The availability of a production and research base, qualified personnel, and great interest on the part of local authorities predetermined the development of pharmaceutical enterprises in Novouralsk (Sverdlovsk oblast), in particular, enterprises for the production of genetically engineered human insulin.
In high-tech industries and types of economic activity, new location factors are starting to work—proximity to sources of knowledge and innovation (e.g., universities), sensitivity to the quality of the local environment and characteristics of the local community, etc. New industries are more mobile, freer in their location, but also more demanding on the state of the environment, infrastructure, and human resources. To accommodate high-tech firms, the number of educational institutions in the city, region, number of students and university graduates is important.
CONCLUSIONS
New location patterns of Russia’s productive forces are associated with the rise in importance of urban agglomerations, economic clusters, new industrial areas as modern forms of spatial organization of productive forces. Many Russian regions manifest the phenomenon of a small economic district, which has different names (special economic zone, intellectual valley, industrial district, etc.) but an essential unity.
The most important feature of the new center–periphery structure of the Russian space is that about half the country’s territory is unanchored, i.e. lacking a “distinct” nearby center, whose role in innovative modernization, spatial reorganization, therefore, is per force carried out by Moscow.
The specifics of the location of high-tech types of economic activity lie in the special sensitivity to factors of the institutional environment, the agglomeration effect, and the availability of innovation infrastructure facilities. Modernization of the spatial organization of Russia’s productive forces—an economy large in space, but medium in size, with insufficient density and agglomeration, gradually unfolding in the new Asia-Pacific center of global economic power—in the presence of common innovation trends, proceeds with significant specifics in different parts of the Russian space.
For the high-density environment of large metropolitan agglomerations, one can realistica
lly talk about the manifestation of postindustrial placement patterns, expressed in the rise of the role of creative industries, the creative class, knowledge in the dislocation of new economic assets in the microspace of million-plus cities and their immediate suburbs. In old industrial regions and single-industry cities of Russia, the distribution of productive forces significantly depends on the “past path” for several decades and can be seen as a response to the challenge of restructuring the past industrial heritage. For industrial–agrarian and agrarian territories and zones of advanced (pioneer) development, we are talking about “new industrialization” and the formation of new industrial regions in previously absolutely nonindustrial territories—agrarian republics, on the Arctic shelf, and in recreational (resort) zones.
FUNDING
The study was carried out in accordance with Fundamental Research Programs of the Presidium of the Russian Academy of Sciences.
CONFLICT OF INTEREST
The authors declare that they have no conflicts of interest.
Footnotes
Now, many new northern and Siberian deposits are condensate with complex compositions. For their effective development, it is necessary to create nearby combine–processing facilities, simultaneously with the construction of mining complexes. To ensure extraction of all useful components, processing should be as close as possible to the place of production. These make up the new patterns in the location of productive forces. In Soviet times, when the main exploited deposits were homogeneous in terms of raw materials, there was no such problem: oil and gas resources were transported for processing thousands of kilometers from the place of production.
D. Verkhovod—Director of JSC Technopark of Novosibirsk Akademgorodok, personal communication.
G.M. Lappo, V.Ya. Lyubovny, P.M. Polyan, N.I. Naimark, and others.
The program was approved by Decree of the Cabinet of Ministers of the Chuvash Republic no. 178 of May 29, 2009; currently, the Program for Industrial Development and an Innovation Economy has in effect been approved. Decree of the Cabinet of Ministers of the Chuvash Republic of December 14, 2018 no. 522, amended from Decree no. 382 of July 8, 2020.
An example of this calculation is given in (Zamyatina et al., 2020).
The Clark–Medvedkov gravitation model is an analogue of the principal potential model (developed by L.V. Smirnyagin (2011)). This method calculates the value of the potential of each city for any point in a locality (in the model of the main potentials for each city, the zone of influence is calculated with respect to all other cities). Both models are used to delimit the gravity zones of settlements. According to the Clark–Medvedkov model, the total potential (Vj) of any point j is equal to the sum of the population ratios of each city (Pi) to the distance from it to the given point (Rij): 
An advantage of this indicator is that it makes it possible to evaluate the settlement system over vast territories, while simplifying computational processes. The original Clark–Medvedkov formula did not take into account the friction of space (a), but in subsequent works it was already taken into account (see Distance Decay Models, Geospatial Analysis Portal, http://www.spatialanalysisonline.com/HTML/?distance_decay_models.htm). In the calculations for this work, as in other Russian studies (Vasilenko, 2013), there were two degrees.
The calculation method was developed by A.D. Yashunsky.
The article was written in 2020 based on the results of research carried out in 2009–2019 (see Glezer, O.B., Shvetsov, A.N., and Kotlyakov, V.M., Reg. Res. Russ., 2023, vol. 13, no. 1, pp. 1–5) and does not reflect the impact on the subject matter considered therein from the latest events stemming from two global crises that erupted suddenly: the COVID-19 pandemic and aggravation of the military–political situation with Ukraine in 2022.
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