This study investigates how capital markets evaluate energy sector companies across different global regional associations. It aims to identify the regional distribution of marginality in the energy sector and assess whether operating conditions are comparable for single-industry companies located in various global economic regions. The analysis is based on market capitalization data and marginality indicators derived from official corporate reports of leading publicly listed energy companies. The assessment covers four and nine global regional associations, as defined by World Bank methodology. Multivariate regression analysis reveals statistically significant differences in marginality levels (six levels on a ten-point scale, with probabilities of 0.92 for the upper bound and 0.31 for the lower bound) among industry participants in the CIS, China, the Middle East, and OECD core economies. The findings support the hypothesis of a weak relationship between marginality and market capitalization. Companies in non-Western economies are also found to exhibit higher marginality compared to their Western counterparts. The prevalence of stock market income in OECD core economies ensures high sectoral capitalization for firms in these regions, regardless of actual performance. Investors tend to expect operational efficiency and dividend payouts from companies in developed non-Western economies, while capital gains or high capitalization are expected from Western economies. These differing expectations create varying financial pressures on firms. In conclusion, sector-specific companies in the CIS and the Middle East demonstrate higher efficiency and investment attractiveness. These results highlight the need to revise criteria for peer-group comparisons and to strengthen regulatory oversight of global institutional investors operating in non-Western economies.
When regional economies vary significantly in structure and budgetary capacity, state agricultural support can easily fall out of step with actual production outcomes. Most existing studies examine overall funding volumes and spending trends, but say little about how support is distributed relative to production performance across different regional groups. This gap hinders fair assessment of resource distribution and obscures underlying disproportions. This study focuses on key imbalances in the distribution of state agricultural support by evaluating its proportionality to the economic role and fiscal conditions of agricultural regions, and offers recommendations for improving the targeting and validity of support measures. The analysis draws on official data for Russian regions over the period 2012–2023. K-means clustering was applied to identify a leading cluster by average agricultural output. Findings show that the value of agricultural production within this cluster grew by 160.5 %, reaching RUB 4,195.8 billion, thus outpacing the national growth rate of 149.8 %. Meanwhile, state support grew by only 47.0 %, reaching RUB 81.6 billion. The support concentration coefficient remained stable at 1.06–1.08, suggesting that the relative distribution of support across regions was broadly preserved. Nevertheless, there is a deepening disproportion between agriculture’s economic significance and its budgetary priority: the sector’s share in gross value added rose from 11.6 % to 12.3 %, while the share of support expenditures declined from 3.2 % to 2.1 %, driving the proportionality coefficient from 3.59 to 5.95. Dependence on federal financing also declined, with the dependency coefficient falling from 4.16 to 2.43. These findings have practical implications for calibrating intergovernmental fiscal mechanisms and enhancing the targeting of state support for the agro-industrial sector.
Local economic activity forms the foundation of regional and national economies by generating employment, producing goods and services, and supporting local development processes. In Russia, however, research on this topic remains limited due to the scarcity of municipal-level statistical data, which constrains the analysis of small towns and rural settlements and underscores the need to develop more robust analytical tools for studying local economies. The aim of this article is to develop and test a methodological framework for obtaining reliable data on local economic activity. The proposed framework is based on web scraping techniques and automated information extraction via APIs. Its application enabled the creation of a dataset on the activities of legal entities and individual entrepreneurs in Vologda Oblast for 2025 (n = 49,766 records across more than 200 settlements). The main selection criteria included taxpayer identification numbers, current operational status, and affiliation with specific municipal units. The analysis of legal entity data reveals significant sectoral differences between two small towns: Sokol, an industrial centre dominated by the woodworking and pulp-and-paper industries, and Veliky Ustyug, a peripheral town with a predominance of agriculture, handicrafts, and tourism services. Financial indicators by type of economic activity demonstrate the substantial fiscal importance of individual large enterprises. In contrast, data on individual entrepreneurs are considerably less informative, being largely limited to sectoral classification and taxation regime. The findings may support regional and municipal policy planning, contribute to the development of small and medium-sized enterprises, and improve understanding of local economic structures. Key limitations of the study include the labour-intensive nature of data collection and the difficulty of constructing dynamic time series, which points to the need for further integration with digital platforms and the expansion of periodic monitoring.
Current geopolitical and demographic challenges in Russia have heightened the importance of economic security and population health, especially among the working-age population. Despite extensive research, the impact of healthcare on regional economic security remains underexplored, which determines the relevance of this study. The study examines how changes in healthcare indicators relate to regional economic security and compares their dynamics across Russian regions for 2020–2023. It develops an applied methodological toolkit that uses index and point-rating methods with color-coded visualization to compare healthcare and economic security indicators, identify patterns and clusters, and calculate a smoothed regression between the “Average Comprehensive Rating of Health Indicators” and the “Economic Security” indicator. In addition, a comprehensive expert assessment framework was developed to evaluate economic security and healthcare indicators across regions using color-coded zones in a Cartesian coordinate system. This framework enables the forecasting of changes in the state of economic security while accounting for changes in healthcare indicators of Russian regions. The findings demonstrate the absence of a direct linear relationship between healthcare and economic security indicators. However, they reveal the significant influence of the healthcare system on reducing mortality among the working-age population and improving the economic security of Russian regions. The proposed approach can be used to identify and forecast changes in regional economic security while accounting for healthcare indicators based on expert regional ratings. The results can support socio-economic and healthcare policy-making in Russian regions, contributing to the preservation of labour potential and the strengthening of regional economic security.
In Russia, sanctions-related constraints and the strategic goal of technological sovereignty have increased the need for evidence-based regional investment priorities. This study develops the methodological foundations of a niche-based approach to regional investment policy as an alternative to the traditional sectoral framework. The proposed methodology integrates three analytical dimensions: a demand-based dimension, which identifies market gaps through the analysis of interregional trade flows and procurement activity; an institutional dimension, which aligns investment priorities with national technological development objectives; and a competitive dimension, which evaluates niche occupancy and opportunities for interregional cooperation. The empirical analysis relies on a comprehensive examination of data on interregional trade (Rosstat BI Portal), foreign trade activity (Ural Customs Administration), and procurement activity (MARKER-Interfax system). Using Sverdlovsk Oblast as a case study, the results reveal a 3.2-fold excess of imports over exports, with the largest trade deficit concentrated in medium-technology industries, which account for 37 % of imports but only 18 % of exports. Applying the proposed selection criteria, 14 product categories characterized by unmet demand were identified and classified into a typology of investment niches according to technological intensity. The resulting typology comprises four groups: high-technology industries (pharmaceuticals, CNC machine tools, unmanned aerial vehicles, and batteries) as strategic priorities of industrial policy; medium-technology industries (tires and stainless-steel products) as drivers of technological value chains; low-technology industries (dairy products, particleboard, paper, and ceramics) as instruments of import substitution and food security; and infrastructure and logistics sectors (data centres and thermal greenhouse complexes) representing emerging business models. The findings can support regional authorities in designing investment strategies and assist investors in identifying promising investment opportunities.
