Economy of Regions

Dear authors

Yuliya G. Lavrikova — 2024-12-29

From 2025, the journal Economy of Regions will switch to gold
open access model. Authors of all articles submitted after January
10, 2025 will pay APC. The quoted APC will only be payable after
the article is accepted for publication following peer review.

Exploring the Link Between Provincial Exports and Economic Growth: Evidence from Türkiye

Ahmed Yusuf SARIHAN — 2024-12-29

Exports play a vital role, particularly for developing countries, and economic growth remains a central goal for all nations. Over time, numerous approaches have sought to understand and explain the causal relationship between exports and macroeconomic indicators, with extensive studies conducted on the subject. This research examines the relationship between exports and economic growth using panel data analysis at the provincial level in Türkiye, offering a unique perspective compared to traditional country-level analyses. Given that international trade is often studied at the national or enterprise level, this province-focused approach provides distinctive insights. The study covers the period from 2004 to 2020 and employs the Westerlund ECM Cointegration Test, Panel ARDL, and Dumitrescu & Hurlin Causality Test as analytical methods. The findings reveal both cointegration and bidirectional causality between provincial exports and economic growth. Furthermore, increases in exports positively impact economic growth in both the short and long term. Notably, the effect is more pronounced in provinces with well-developed tourism and industrial sectors.

Asymmetric Effects of Trade Openness and National Income on Government Size in BRICS Countries: New Evidence for Wagner’s Law

Dhyani Mehta — 2024-12-29

The growing economic prominence of BRICS nations (Brazil, Russia, India, China, and South Africa) has attracted considerable attention to the macroeconomic dynamics driving their development. As these economies grow rapidly and become more integrated into global markets, it becomes increasingly difficult to balance economic growth, trade liberalization, and sustainable fiscal policies. Government size, a key factor in fiscal management, tends to increase with national income (as suggested by Wagner’s Law) and in response to trade openness (as outlined by the Compensation Hypothesis). Understanding these dynamics is crucial due to the unique fiscal pressures and global competitiveness faced by BRICS countries. This study investigates the validity of Wagner’s law and the Compensation Hypothesis in the context of BRICS. Using a panel nonlinear autoregressive distributed lag model on annual panel data from 1999 to 2023, our findings confirm Wagner’s law, showing a positive relationship between economic growth and government size. Additionally, the results support the Compensation Hypothesis, indicating that trade openness enhances government size. This study underscores the potential trade-offs between promoting economic growth and trade liberalization, as these strategies may inadvertently expand the government sector and affect fiscal stability. As BRICS economies continue to integrate into global markets, this research contributes to the discussion on Wagner’s law and trade openness, offering new insights into sustainable fiscal policies, government expenditure optimization, and the pursuit of global competitiveness and economic growth within the BRICS framework.

The Impact of China’s Environmental Protection Tax on Regional Economic Effects

Ye Chenghao — 2024-12-29

In 2018, China adopted the Environmental Protection Tax Law, transitioning from administrative fees to statutory taxes. The law aimed to incentivize enterprises to reduce pollution emissions through economic means, improve environmental quality, and promote the optimization and upgrading of industrial structures for economic development. This study seeks to reveal the mechanisms of the impact of environmental protection tax on regional economic effects, providing policy recommendations for achieving high-quality economic development and ecological environmental protection. The study analyses four key variables—environmental protection tax revenue, regional industrial output value, regional GDP, and regional industrial pollution control investment—from 31 regions in China between 2018 and 2022, forming a sample of 30 observations. A random effects model is constructed and empirically analysed using Python 3.12. The empirical results show that for every additional unit of environmental protection tax, the average expected growth of regional GDP is 0.1043 units. There are significant differences in the economic effects of China’s environmental protection tax on regions, and these differences have random effects. This study provides new insights and empirical evidence for understanding and evaluating the impact of environmental protection taxes on regional economic outcomes, helping policymakers assess current impacts and continue encouraging enterprises to adopt clean production technologies, improve energy efficiency, and promote economic structure optimization and industrial upgrading to support high-quality economic development.

Characteristics of Pendulum Labour Migration in Russian Agglomerations

Natalia M. Logacheva — 2024-12-29

Pendulum migration, which involves regular commuting between home and work locations, supports personal self-realization and provides individuals with the flexibility to choose optimal work environments. However, it may also hinder the socio-economic growth of certain regions. Managing pendulum migration within territorial development plans proves challenging, primarily due to the lack of detailed municipal-level statistical data. This article aims to explore the distinctive characteristics of pendulum labour migration in Russian agglomerations, with a particular focus on the gender and age demographics of migrants. We hypothesize that pendulum labour migration exhibits unique patterns in agglomeration centres and their satellite areas, including surrounding cities and villages. The study relies on social network analysis as the primary research method, complemented by age pyramid analysis and comparative methods. The empirical data, collected by the authors in 2023, comprises anonymized information from approximately 396,000 VKontakte users, aged 14 to 73, across 14 major cities and 92 satellite cities. Our findings reveal that, with the exception of the Voronezh and Ufa agglomerations, a significant proportion of pendulum migrants traveling from satellite cities to agglomeration centres are men. The most active age group for both genders is between 29 and 43 years. Conversely, migration flows from urban centres to satellite areas show a higher proportion of women (52.8 %), with women commuting to satellite villages more frequently than men. In satellite cities, the age distribution of migrants is more balanced across genders compared to satellite villages. The primary age groups of residents from major cities engaging in pendulum migration are 24-28 years and 44-48 years. These insights can inform urban development strategies related to population migration and enhance employment support programs, taking into account the gender and age patterns of pendulum labour migration in various agglomerations and their satellite areas.

Measurement and Assessment of Precarious Employment of University Graduates for the Regional Labour Market

Anastasia D. Melnik — 2024-12-29

In rapidly changing societies, where economic integration and the digitalization of labor relations are accelerating, the socio-economic position of young professionals in the labor market is increasingly viewed through the lens of their human capital. The pace of technological advancement highlights the need for innovative tools to evaluate the stability of youth employment in the face of external influences. This study examines university graduates’ adaptation to the evolving labor market. The study aims to assess the risk of university graduates entering precarious employment, with a focus on their educational background. Sociological data were collected six months after respondents’ graduation from a major Russian university (n = 7,706), based on six annual cross-sectional surveys conducted from 2017 to 2022 using a unified methodology. Results revealed a rising trend in precarious employment among young workers in the Ural Federal District, reaching 38.7 % in 2022. An innovative algorithm, developed by using an indicative approach and mathematical methods such as the Cobb-Douglas production function, was applied to calculate precarious employment risks based on data from 2017–2021 (n=6,500). These risks were found to vary depending on graduates’ educational levels and majors. The study offers practical tools to expand the understanding of precarious employment characteristics and to identify such risks across various professions, providing valuable insights for policy-making in the sphere of labor market regulation and employment strategies.

Building a Rating of Russian Regions According to their Level of Financial Market Development

Piotr S. Kirichenko — 2024-12-29

The use of indicators characterizing the regional level of its functioning is relevant for monitoring the development of the financial market. The purpose of the article is to develop an approach to build a rating of Russian regions according their level of the financial market development. The authors’ methodology includes integral indicators calculating, which characterize the development of the financial market in terms of the territorial entities using the method of principal components. As a result, we have built a composite index, and on its basis a rating of Russian regions is compiled. The components of the composite index are subindexes, calculated using the method of principal components for each of the five market sectors: banking, insurance, microfinance, non-state pension funds and stock market. On the one hand, this approach allows to aggregate heterogeneous initial indicators of financial market sectors. On the other hand, it helps making a comparative analysis of regions in intersectoral terms. The research is based on the statistical data of Rosstat, the Bank of Russia and the Federal Tax Service for 2020–2022. The authors conclude that the constructed rating allows to track changes in the positions of the constituent entities of regions on the financial market and to detail the development areas of its specific sectors. For example, in 2022, the rating leaders were Moscow (1st place), Tyumen (2nd place) and Novosibirsk Oblasts (3rd place). In general, over 2020–2022, the Kamchatka Kray (banking sector), Chukotka Autonomous Okrug (banking sector), Kostroma Oblast (banking and stock sectors) significantly improved their position in the rating. Growth zones for the Tver Oblast are the insurance, microfinance and stock sectors, for the Bryansk and Volgograd Oblasts the insurance and microfinance sectors. The approach proposed by the authors allows expanding the composition of the index components, which characterizes a high potential of its application in solving the problems of the financial market assessment.

Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function

Anton S. Strokov — 2024-12-29

Land degradation is a widely discussed and pressing global issue, as highlighted in the UN Sustainable Development Goals (SDGs). Understanding the extent of land degradation and its impact on agriculture requires precise research and an interdisciplinary approach due to the complexity of factors and indicators that characterize the issue. This paper focuses on one of Russia’s key agricultural regions, Samara Oblast, to examine how land degradation of agricultural soils affects crop production at the farm level. The dataset used in the study includes farm inputs (costs, land, and labour) and land quality variables, such as organic content (humus), levels of land degradation and soil erosion, as well as climate indicators, at the municipal level. To analyse the relationship between land degradation and agricultural output, the stochastic frontier analysis (SFA) was employed. This method not only estimates the parameters of a classic production function but also accounts for errors in the model by evaluating parameters related to risk and technical inefficiency. The results indicate that the proportion of degraded land in a district of the given region moderately reduces the maximum potential for crop production. In contrast, most inputs—such as production costs, cropland area, and labour—contribute positively to output. The study suggests that both the method and the estimates could be refined if data on land degradation, alongside other economic and environmental indicators, were collected and published annually.

Regional Variations in the Role of Dairy Farming in Agriculture of Russia’s Non-Black Earth Zone

Tatiana V. Yurchenko — 2024-12-29

Dairy farming plays a pivotal role in agricultural production as it helps optimize the use of farmland. This study aims to evaluate the impact of dairy farming on the growth of agricultural output and changes in cultivated land areas in Russia’s Non-Black Earth Zone. Methodologically, the research relies on econometric analysis of panel data, Moran’s spatial autocorrelation methods, and statistical grouping techniques. The study employs a dataset from the Russian State Statistics Service, covering agricultural and dairy farming trends in 29 regions of the Non-Black Earth Zone from 1991 to 2021. Using Moran’s spatial autocorrelation, regions were grouped into three categories: “northern,” “central,” and “southern.” The findings reveal significant regional variations in the influence of dairy farming on agricultural output. The contribution of dairy farming to the agricultural gross product varied over time, with notable positive impacts observed in the period after 2012. In the northern and central regions, dairy farming is a major factor, with its importance increasing the further north the region is located. On average, the elasticity of gross agricultural output with respect to dairy production is 0.707 in the northern, 0.583 in the central, and 0.482 in the southern regions. The latter, being more suited to beef cattle farming and crop production, show no statistically significant impact from dairy farming. The number of cows remains a crucial factor for dairy production in the northern and central regions, influencing both the intensity of arable land use and the size of cultivated areas.

Risks and Prospects for Russian Regional Export Development in the Face of the Global Energy Transition

Yulia D. Sokolova — 2024-12-29

Export development is a priority for the Russian economy, as it plays a crucial role in ensuring sustainable economic growth. In this context, understanding the determinants of regional export development is essential. In their export activities, Russian companies face a range of limiting factors, many of which have been thoroughly examined, with corresponding mitigation strategies incorporated into export plans. However, the role of the global climate agenda and the energy transition in shaping export development remains largely unexplored for Russian regions. The shift of focus to fulfilling environmental goals creates a new type of economic risk for exporters — transitional climate risks, which intensified after February 2022. This study investigates the comprehensive impact of the global energy transition on export flows in Russian regions and identifies region-specific factors that influence how the energy shift affects export levels. The hypothesis is that the global energy transition creates both risks and opportunities for Russian regions, with varying effects depending on the specific components of the energy shift and the socio-economic and environmental characteristics of each region. Using the gravity equation with the Poisson Pseudo-Maximum Likelihood (PPML) technique, the study finds that the impact of the global energy transition on Russian regional exports is multidirectional. First, environmental regulations in partner countries reduce exports from many Russian regions by 0.3 %, though regions with favorable socio-economic conditions for innovation and active regional environmental policies see an increase in exports—by 0.3 % and 0.7 %, respectively. Second, the production of alternative energy in partner countries decreases Russian exports by 0.2 %. Finally, exports from mineral-abundant Russian regions benefit from the global energy transition. These findings contribute to the literature on Russian export promotion and offer valuable policy insights for addressing the challenges and opportunities posed by the global energy transition.

The Impact of Climate Change on Agricultural Outcomes of Climate Policy: A Regional Perspective

Nilolai M. Svetlov — 2024-12-29

Model estimates of the impact of climate policy on agriculture typically do not account for the fact that it will be implemented in a changing climate. There only a few studies free from this simplification that analyse the subnational level. To address this gap, the study tests the hypothesis that climate has little impact on the outcomes of climate policy, using a scenario approach focused on the Altai, Krasnodar, Krasnoyarsk, and Moscow regions. Climate is represented by the geographical location of natural and agricultural zones, productivity levels, production outcome uncertainty, and rising global prices for agricultural products. Policy is represented by the guaranteed reduction of greenhouse gas emissions. The state of agriculture in each scenario is assessed by using a spatial partial equilibrium model for nine types of agricultural products across Russian regions (the VIAPI model). The model is based on data from the Federal State Statistics Service and the Russian Ministry of Agriculture for all Russian regions from 2015 to 2019. The hypothesis is tested across various indicators in each of the four regions and for Russia as a whole, with most results rejecting it, suggesting that ignoring climate change may result in an inaccurate understanding of the consequences of climate policy. Limiting greenhouse gas emissions in agriculture worsens the situation for both producers and consumers of agricultural products. Climate change, if not accompanied by rising global prices, alleviates this effect. These findings are useful for investors, as they reveal the advantages and risks for agriculture in the given regions, and for regional authorities, as they help prevent potential losses. This study also provides momentum for applied research that explores the causal relationships between regional markets’ responses to the combination of climate and policy factors.

Decoupling as a Tool for Assessing the Sustainable Development of Industrial Complexes

Anna A. Urasova — 2024-12-29

Accelerated industrial growth and technologisation bring to the foreground issues of environmental impact, making it crucial to assess how industrial production affects the environment. This assessment is crucial for measuring progress toward the sustainable development of regional industrial complexes. The study posits that the decoupling effect is a key indicator for evaluating these prospects. The study proposes a theoretical approach that links the sustainable development of industrial complexes with environmental conditions, alongside a methodology for assessing the decoupling effect. This methodology combines correlation analysis, an audit system of environmental and economic metrics, and the OECD’s decoupling framework. Through this lens, the study examines industrial complexes in the Ural Economic Region (Russia) from 2016 to 2022, revealing a stable relationship between economic growth and environmental conditions in these areas. The combined use of these methods is a key advantage, enabling the positioning of industrial complexes within a sustainable development matrix. This matrix evaluates both the magnitude of the decoupling effect and environmental impact. The analysis identifies the industrial complexes of Perm Krai, Orenburg, and Sverdlovsk regions as dynamically sustainable; the Kurgan Oblast, and the Republics of Bashkortostan and Udmurtia as relatively sustainable; and the Chelyabinsk Oblast as transitioning toward sustainability. The findings open pathways for future research on the decoupling effect as both an indicator and a tool for achieving sustainable industrial development. Moreover, they provide a basis for defining strategic directions and limitations for sustainable industrial growth in an environmentally conscious economy, while positioning specific complexes within the broader framework of Russia’s environmental and economic industrial development.

A New Role for Unregulated Activities in the Regional Economic Power Industry

Lazar D. Gitelman — 2024-12-29

A potential solution to the problem of attracting investment in the electric power industry, resulting from economic growth and regional restructuring, is to foster industrial entrepreneurship and create a more conducive environment for investment. This article examines the economic prerequisites, key areas of opportunity, and organizational conditions required to develop energy businesses in unregulated sectors as a means of diversification. The research draws on Russian and international studies on diversification and entrepreneurship in the energy sector, as well as expert insights from surveys and interviews with specialists from major Ural energy companies, including Rosseti Ural, T Plus, and Chelyabloblkommunenergo. This study explores various forms of sectoral diversification, focusing on integration strategies that broaden the scope of energy services in emerging market segments. Key areas include small-scale power generation, electrification, and energy demand management, all driven by cutting-edge scientific and technological advancements. The research highlights that state support for entrepreneurship is most effective in areas closely aligned with the core activities of energy companies, as well as in sectors that have a strong regional socio-economic impact. Theoretically, the study contributes to the field by identifying entrepreneurial opportunities in the electric power industry and examining the organizational and economic impacts of non-regulated activities. Practically, the study offers recommendations for industry stakeholders and regional authorities on how to regulate and stimulate entrepreneurship in the sector.

Kuzbass in Transition: Development Strategies for Revitalizing an Old Industrial Region

Valery A. Kryukov — 2024-12-29

This article explores the challenges of revitalizing Kuzbass (Kemerovo Oblast), an old-industrial region, within the context of global and national challenges, including economic fragmentation, Russia’s evolving economic model amidst sanctions, and the global energy transition. This study continues a series of research projects implemented by the Institute of Economics and Industrial Engineering of the Siberian Branch of the Russian Academy of Sciences and analyses Kuzbass’s revitalization model from the 1990s and 2000s and its impact on current development. The research uses chronological, historical, and structural analysis, as well as strategic documents and statistical data from the 1990s to the present. The study shows that Kuzbass represents an exceptional case in global revitalization practices, where, unlike other resource regions that diversify away from raw material specialization, the region strengthened its focus on coal production. In the 1990s, Kuzbass began laying the foundation for sustainable development, but by the 2000s, a focus on coal exports slowed diversification, leading the region into a “raw materials trap.” As a result, Kuzbass must now restart revitalization efforts and seek new growth drivers through economic diversification. The findings and conclusions presented in this article are relevant to both regional policy-makers in the economic sphere and researchers studying sustainable development in resource-dependent regions.

Assessment of Path Dependence Parameters in Technological Innovation across Russia’s Regions

Yegor L. Domnich — 2024-12-29

The dynamics of technological innovation in regional industries across Russia show significant spatial and temporal disparities. This variation underscores the importance of studying the parameters of innovation path dependence. This study evaluates the role of innovation path dependence and the current level of economic development in driving industrial innovation across various regions of Russia. Using a dynamic autoregressive model, this study examined the current innovation costs and outputs of industrial enterprises as functions of both innovation path dependence (measured by previous values) and the current economic development level (measured by gross regional product) across a panel of 70 Russian regions from 2000 to 2020, with detailed analyses for federal districts and periods 2000–2005, 2006–2010, 2011–2015, and 2016–2020. Results show that most Russian regions manifested a positive innovation path dependence only between 2011 and 2020. Conversely, from 2000 to 2005, some regions exhibited a negative path dependence, which hindered innovation growth. Throughout the entire 2000–2020 period, a region’s current economic development level was found to be a more influential factor in driving innovation than path dependence. The study concludes that the influence of innovation path dependence and regional economic development on innovation output was mainly a compromise—only one factor had a significant impact at a given time. This indicates that innovative enterprises across Russia are vulnerable. However, industries in the Urals and Siberia are an exception; in these regions, the factors at play consistently work together positively, making a substantial contribution to the success of innovation projects. These findings can provide insights into the spatial and temporal economic mechanisms driving innovative development in Russian regions.

Potential of Digital Transformation: Ranking of Russian Regions

Alexey O. Verenikin — 2024-12-29

This study posits a positive correlation between the level of socio-economic development, accumulated experience in digitalizing regional economies, and the potential for regions to achieve digital transformation targets set out in their 2021 digital transformation strategies. To test this hypothesis, Russian regions were ranked according to their potential to meet these targets, using the Adaptive Automated Method of Principal Component Analysis, supplemented by Data Envelopment Analysis (PCA-DEA). Two data sets were used as inputs in the model: the level of ICT sector development in each region (18 indicators) and regional socio-economic development levels for 2022 (20 indicators). Model outputs include indicators for which the regions had set measurable targets for 2023 (43 indicators). The sample included all regions of the Russian Federation, with the exception of the Donetsk and Luhansk People’s Republics, Zaporozhye and Kherson oblasts (due to the lack of digital transformation strategies as of July 1, 2023), the city of Moscow (which follows the Smart City strategy for digital transformation), and Chukotka Autonomous Okrug (due to the lack of data for over 70 % of the indicators). The analysis identified five groups of regions, each with differing levels of potential to achieve planned targets. Ranking positions were influenced by the degree of digitalization, socio-economic development, and the scope of strategic indicators incorporated in each region’s digital transformation strategy. Notably, considerable discrepancies were observed between the indicators proposed by regional authorities and those recommended by the relevant ministries. Using the decomposition of the composite indicator and calculating correlation coefficients, the authors identified several key factors affecting regional rankings.

Russian-Kazakhstan Border Territories: On The Way To Form Sibir Cross-Border Macroregions

Oleg M. Roy — 2024-12-29

Border regions occupy a significant part of the territory of Russia. All of them are adapted to the conditions of cross-border interactions to varying degrees. This article examines the economic and institutional conditions for interaction between the regions of Russia and Kazakhstan on the Siberian section of the state border within the so-called “cross-border macroregions”. The list of studied border regions of both countries includes regions that have shared borders. The purpose of this article is to select an optimal set of compatibility features between the border regions in order to form a cross-border macroregion in the alignment of the Russian-Kazakh border. The study assesses the readiness of the border regions to cooperate with each other and determines a list of indicators for the monitoring of their development. A hypothesis suggests equal readiness of the border regions of Russia and Kazakhstan to establish cross-border cooperation and sustainable economic relations within the macroregion. The article presents a conceptual model of a transboundary macroregion, which includes the common borders of adjacent regions, their economic specialisation and infrastructure provision. A typology of border regions is based on an assessment of a region’s infrastructure provision and its economic activity. The results show the leadership of the Novosibirsk and Pavlodar Regions. A comparative analysis made it possible to identify three options for the formation of transboundary macroregions based on the correlation of the indicators of geographically close and open to each other border regions of both countries by the leading Russian region within each macroregion – Omsk, Novosibirsk and Altai. The leading role of the Novosibirsk direction in the formation of a transboundary macroregion is determined.

). Between Culture and Economy: Factors Motivating Regions to Support Creative Industries

Victoria O. Boos — 2024-12-29

Due to the lack of a unified approach to supporting creative industries and to the differentiation of initial socioeconomic conditions in Russian regions, different government participation models in the development of creative industries are emerging. Understanding the balance of factors influencing the creative industries is key to more effective goal-setting and support for the creative sector in Russian regions. Using the polynomial logistic regression method, this study defines objective factors that influence the quality of policies in the field of creative industries in the regions of Russia and, using k-means clustering, determines groups of regions with similar profiles of implemented creative policies. As a result, three groups of policies were identified: “organized”, “independent” and “unmanifested”. It was revealed that eight “organized” regions were ahead of other groups in terms of the level of organizational support (by 5 and 7 times), by federal support measures attracted (by 1.5 and 1.8 times), regulatory legal and strategic support (by 4 % and 8 %) and were approximately on the same level as twenty “independent” regions in terms of the scale of regional support measures. “Organized” regions were 1.4 times ahead of “independent” regions in terms of the number of students, and 1.9 times ahead of “unmanifested” regions. In terms of the number of patent applications “organized” regions were 2.2 and 2.5 times ahead, respectively, and in terms of attendance at cultural institutions 22 % and 18 %. Modelling showed that government authorities’ support for creative industries at the regional level is facilitated by a combination of innovative and cultural development factors.

Prerequisites and Consequences of Territorial Servitisation in Russian Regions

Andrey A. Pushkarev — 2024-12-29

The need to find new sources of economic growth in industrial and old industrial areas determines the importance of research into the relationship between knowledge-intensive business services and industrial development in regions. This paper aims to explore how Russian regions participate in territorial servitisation, focusing on the growth of knowledge-intensive business service activities. The study also seeks to determine whether this participation helps boost productivity in manufacturing companies by implementing a servitisation strategy—specifically, by adding services to their products. The proposed analytical framework addresses the gap between research on the effects of servitisation strategies and the potential for enhancing a region’s industrial capacity through the knowledge-intensive business services sector. This study is the first to use Russian data to explore the concept of territorial servitisation, thereby contributing to the advancement of this topic in economic literature through its novel methodology. The research employs a three-step CDM model with fixed effects for panel data. Based on the econometric analysis of manufacturing companies across 56 Russian regions, the findings indicate that the integration of knowledge-intensive business services into the regional economy is positively influenced by market size and an increase in the number of manufacturing firms. Moreover, the anticipated growth rate of knowledge-intensive services is positively associated with the servitisation of manufacturing firms in the region. In addition, the study shows a positive correlation between the projected share of employment in servitised firms and productivity in manufacturing firms, as measured by output per employee. The results of this study can inform policy-making in regional economic and industrial development. However, the study faces limitations related to the challenges of accurately capturing the actual use of service classifications (OKVEDs) by Russian manufacturing companies.

The Impact of Regional Integration in GVCs on Subsidy Allocation Among Manufacturing Firms in Russia

Yuri Simachev — 2024-12-29

While global demand for industrial policy-making continues to rise, increasing attention is being given to how these policies are shaped by participation in global value chains (GVCs), both in developed and developing countries. However, much of the research overlooks the regional dimension of support allocation, particularly the integration of regional economies into GVCs. This study aims to address this gap by examining the factors influencing state support at the regional level, with a focus on backward and forward linkages within GVCs in the manufacturing sector. The analysis is based on a survey of 1,900 Russian manufacturing firms conducted between August and November 2022, using data from 2019 to 2022 across various sectors and firm sizes. The findings show that Russian regional governments generally adopt conservative strategies when allocating financial support, focusing on a core group of companies crucial for maintaining regional economic stability. This support is primarily directed at exporters and firms fulfilling government contracts, with state-affiliated companies becoming the primary beneficiaries due to shifts in external conditions. Additionally, regions with greater integration into the global economy tend to adopt a more vertical policy approach, favoring large, GVC-integrated firms, while less integrated regions prioritize smaller firms, especially SMEs. Regions with stronger downstream linkages focus on supporting innovation-active firms to advance localization, import substitution, and technological independence goals. These findings highlight emerging priorities in Russia’s industrial policy, suggesting that regional initiatives are needed to strategically reposition the country’s regional economies in the global landscape amidst changing global dynamics.

Assessment of the Sustainability of Border Ecosystems under Geopolitical Challenges

Vilena A. Yakimova — 2024-12-29

Interest in the problems of studying border ecosystems is caused by the fact that they function under the influence of both internal environmental factors and external macro – and meso-environment. Most studies in the field of the ecosystem approach in regional economics are theoretical in nature and are not supported by empirical analysis. The purpose of the article is to assess the influence of internal and external environmental factors on the sustainability of business ecosystems located in border regions. The methodology is based on the selection of ecosystems under study and the identification of their distinctive characteristics – emergence and cooperative competition. For assessment and grouping, methods of cluster analysis and principal components were used using machine learning in the Phyton programming language. As a result of the study, system-forming factors that are important both from the perspective of ecosystem theory and specific to the border state were identified and grouped; indicators have been formed that allow a comprehensive assessment of the influence of environmental factors on the sustainability of ecosystems; Four clusters were proposed and ecosystems were ranked by type of sustainability. Two classes of the most stable ecosystems with a high level of joint activity, located in favourable border conditions, have been identified. Most ecosystems are classified as unstable for two groups of reasons: the influence of geopolitical factors (66 ecosystems) and instability of the internal environment (65 ecosystems), which hinders their development. During the analysis of the principal components, ecosystems were grouped according to the integral sustainability score and key factors were identified (presence of large enterprises, cooperative ties between actors, foreign investments, import operations with friendly countries, institutional environment and border infrastructure). 47 stable ecosystems and 43 most unstable ones were identified. The results of the study are of practical importance for the development of strategies for the development of regional entrepreneurship, taking into account changes and adaptation to environmental challenges.

Assessing Spatial Development Priorities in Sverdlovsk Oblast (Russia) Through SAR Modelling and Autocorrelation Analysis

Natalia L. Nikulina — 2024-12-29

Disparities in the socio-economic development of regions necessitate clearer priority-setting for their spatial growth and sustainable economic progress. The hypothesis of this study is that the primary goal for the region’s spatial development should be to establish “growth poles” in municipalities with a high concentration of resources and strong connections to surrounding areas. The study aims to develop a method to identify and justify these spatial development priorities, using municipalities in Sverdlovsk oblast as a case study. Within the proposed methodology, territories were divided into two subgroups within the quadrants of P. Moran’s scatter plot, based on spatial interaction levels, to identify both existing and emerging growth poles, as well as areas of strong and moderate influence. The effectiveness of selected priorities was assessed by applying a differentiated approach to building spatial SAR models. As a result, two groups of territories were categorized: spatially interconnected areas, which include growth poles and regions forming spatial clusters along with their influence zones, and spatially remote areas. Findings indicate that the spatial development priorities of Sverdlovsk oblast should focus on establishing growth poles in municipalities with a high concentration of enterprises, labour, and investment resources (e.g., Nizhny Tagil and Kamensk-Uralsky), as well as in municipalities within the strong influence zone of an active growth pole (e.g., Berezovsky, Verkhnyaya Pyshma, and others). It is essential to strengthen cooperative relationships between municipalities, particularly in the influence zone of Ekaterinburg agglomeration, as highlighted by P. Moran’s spatial autocorrelation analysis.

Disparities in the Spatial Organization of Regions in the Ural Federal Okrug

Светлана Котлярова — 2024-12-29

The heterogeneity of regions in terms of labour supply, natural and other resources, as well as production and social infrastructure, brings to the fore the need to assess the quality of economic space. To identify the strengths and weaknesses of specific areas and address regional disparities while fostering a more unified economic space, it is necessary to analyse the spatial organization of economic territories. The article discusses the spatial organization of regions in the Ural Federal Okrug. The federal okrug is analysed by looking at its constituent parts, using data from the Federal Municipal Statistics Service. The proposed methodology employs indicators across three key dimensions: economic space capacity, economic activity, and spatial connectivity. An integrated index is calculated for each dimension to assess the quality of economic space in 200 municipalities over two years (2012 and 2022). The results reveal pronounced spatial heterogeneity in population distribution, economic activity, and spatial connectivity across the Ural Federal Okrug. The analysis highlights significant variations in spatial organization between industrially developed regions (Sverdlovsk and Chelyabinsk Oblasts), the less industrialized Tyumen Oblast (excluding autonomous okrugs), the agrarian-industrial Kurgan Oblast, and the resource-driven Khanty-Mansiysk and Yamalo-Nenets Autonomous Okrugs, where economic development is shaped by raw material specialization and geographical conditions in the Far North. The findings highlight the need for differentiated spatial development strategies aligned with regional goals, while emphasizing the promotion of inter-municipal cooperation mechanisms to address the challenges of regional disparities. The study lays the groundwork for more informed prioritization of spatial development initiatives and updates to regional policies in the federal okrug.

Research Connectivity of Russian Regions: A Bibliometric Analysis

Yuliya G. Mysliakova — 2024-12-29

Improving connectivity between Russian regions to enhance knowledge sharing and business collaboration on national technological challenges is a key strategic goal for economic development. This study examines the research connectivity of Russian regions by looking at the publication activity of local authors, more specifically, geographic proximity, the number of co-authored articles, and the similarity of research topics shaping economic trends. The study employs bibliometric analysis of 1,846 articles published in 2023 across 53 Russian peer-reviewed economic journals, authored by 3,102 researchers. A textual analysis of article abstracts, using TF-IDF and cosine similarity measures, was conducted to determine the similarity of research topics. Key findings show that the distance between regions has minimal impact on the connectivity of the scientific economic space and researcher collaboration. Instead, connectivity is more strongly influenced by the level of collaboration, especially at the interregional level. Drawing on these findings, the study outlines strategies for improving regional connectivity, for example, developing research networks that align with regional industrial, technological, and scientific specializations. It also highlights the role of major “scientific” regions (based on their publication volume) and the importance of supporting collaboration between smaller regions and these leaders. These hubs can help develop regional research potential and facilitate the spatial implementation of joint project outcomes.