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.
Russia’s agricultural sector faces growing challenges from external economic restrictions, sanctions, and disrupted supply chains. The traditional sustainable development framework, encompassing environmental, economic, and social components, does not account for risks stemming from geoeconomic instability, creating a gap in theory and methodology. This study develops a concept of geoeconomic sustainability in agriculture and proposes an assessment methodology that enables a quantitative evaluation of the sector’s capacity to adapt to external constraints and maintain export potential. The research employs a systems approach, along with comparative and dynamic analysis. The empirical base includes data from international organizations (ITC, WTO) and the Federal Centre for Agricultural Export Development of the Russian Ministry of Agriculture for 2014–2024. Subindices are calculated using formalized models with weighting coefficients reflecting sectoral characteristics. The results reveal a paradoxical trend: despite a decline in the number of importing countries (from 126 in 2018 to 85 in 2024), export value increased (from 562.1 to 3,383.6 million roubles). The geoeconomic sustainability index decreased from 54.0 to 38.4 points, indicating rising systemic risks, primarily due to reduced export diversification. This reflects a pattern of successful but risky adaptation, characterized by concentration on key markets (China, Turkey, Egypt) and reduced logistics diversification. The proposed methodology can support policymakers and businesses in managing external economic risks, diversifying exports, and developing adaptive logistics systems. Limitations include reduced statistical detail after 2022. Future research should focus on sectoral and regional models and integration into strategic planning frameworks.
This study investigates the size, determinants, and fiscal impact of Ethiopia’s shadow economy from 1990 to 2023, addressing a critical gap in understanding how pervasive informality constrains tax policy and revenue mobilization in developing economies. The research employs a sequential three-stage econometric methodology. First, an Enhanced Multiple Indicators Multiple Causes (EMIMIC) model, estimated within a Vector Error Correction Model (VECM) framework, is used to quantify the latent shadow economy, analysing seven cause variables (e. g., tax burden, GDP per capita, government expenditure) and four indicator variables (e. g., self-employment, electricity consumption gap). Second, these estimates are calibrated to construct a shadow-adjusted GDP series. Third, the fiscal implications are rigorously assessed through comparative Autoregressive Distributed Lag (ARDL) models and Diebold-Mariano tests to evaluate differences in tax elasticity and revenue forecasting performance between conventional and shadow-adjusted specifications. The results reveal a dramatic expansion of the shadow economy from 24.79 % to 61.69 % of official GDP over the period. The analysis identifies a paradoxical positive association with GDP per capita (+0.581) and a significant negative relationship with government expenditure (-0.350), while the direct tax burden is statistically insignificant. Fiscal impact analysis demonstrates that accounting for informality alters the long-run tax elasticity estimate by 13.8 %. The recommendations include integrating shadow economy estimates into national accounts and fiscal planning, simplifying tax systems through broad-based digital presumptive regimes, and using public procurement to encourage business formalization. Together, these measures can support a more sustainable and inclusive fiscal framework.
Urban agglomerations in Russia concentrate population and economic activity, yet a unified approach to assessing their socio-economic dynamics remains undeveloped, due to complex inter-municipal interactions and limitations in municipal statistics. This study develops and tests a methodological approach for assessing the socio-economic potential and outcomes of urban development through an integral indicator of total territorial capital. Current approaches, including coefficient-index, sectoral, and integrated methods, are reviewed and found to offer limited analytical capacity for assessing agglomeration resource base dynamics over the medium and long term. In response, total territorial capital is proposed as a system of five interrelated types: produced, human, financial, natural, and innovative capital. The approach was tested on data from the Barnaul agglomeration for 2011–2024 at constant 2024 prices. Cost calculations cover produced, human, natural, and financial capital, while the innovative component is identified as a direction for further methodological development. Over the study period, total capital grew at an average of 1.18 % per year, reaching 1,762.2 billion roubles in 2024, with human capital dominant at 1,151.7 billion roubles and concentrated in the core. The analysis reveals stable core-periphery differences: human, financial, and produced capital are relatively more concentrated in the core, while natural capital plays a growing role in some peripheral municipalities. The proposed approach is applicable for monitoring strategic planning documents and informing inter-municipal priorities, complementing flow and local indicators with an assessment of resource base dynamics. Key limitations are related to data gaps and reliance on proxy indicators; future work will focus on assessing the contribution of individual projects to capital change.
The transition to a circular economy and the rise of Industry 5.0 are creating an increasing demand for comprehensive tools to assess industrial transformation. This study develops a methodological approach for evaluating the levels of industrial development in Russia under these twin conditions and applies it to the period 2015–2024. Empirically, the study draws on official statistical data on Russia’s industrial production, selected according to criteria of comparability and completeness. The methodology integrates structural and logical analysis, a comparative economic approach, min–max normalization of indicators, and integral assessment, complemented by a systematic review of relevant theoretical and empirical literature. The principal output is a CE × I5.0 matrix that enables the classification of industrial development levels across nine quadrants. Analysis of positioning dynamics reveals an uneven trajectory of change. In 2015, industry was situated in Quadrant VI, reflecting moderate circular practices alongside low technological development. Between 2016 and 2018, incremental gains shifted positioning to Quadrant VIII, indicating evolutionary progress without substantial qualitative transformation. By 2019, improvements in both CE and I5.0 indicators signalled a transition to Quadrant V, suggesting gradual adaptation to emerging technological and institutional conditions. Regressions were recorded in 2020 and 2022, attributable to systemic constraints and declining investment activity, while recovery phases in 2021 and 2024 returned industry to Quadrant IV, consolidating a foundation for sustainable development. The empirical findings confirm a robust relationship between the adoption of circular economy principles and the level of industrial technological development. Their joint implementation produces a synergistic effect, manifested in improved economic efficiency, reduced resource dependence, and enhanced long-term resilience of industrial production. The findings offer practical value for the design of industrial and technological modernization programs.
Low innovation performance despite significant potential remains a pressing issue in Russia, particularly under conditions of strong regional differentiation. At the same time, empirical evidence on how infrastructure factors influence the effectiveness of regional innovation policy remains limited. This study aims to identify constraints in innovation policy through the quality of supporting infrastructure, taking regional heterogeneity into account. The sample includes only regions that are comparable in terms of development level and policy orientation. The methodological framework is based on infrastructure analysis, where policy effectiveness is linked to the quality of resource, research and educational, human capital, financial, and regulatory infrastructure. We analyse panel data (1,870 observations) using the partial least squares method. By integrating differentiated regional analysis with an infrastructure-based approach, the study provides a novel framework for assessing innovation policy effectiveness in Russia. The results indicate that the main infrastructure constraints are concentrated in the financial, resource, and research and educational domains. We identify several statistically significant negative relationships, including the relationship between the share of business funding in R&D and technology exports, as well as between federal project funding and the share of innovative products based on Russian intellectual property. In resource infrastructure, machinery and equipment renewal appears to act as a constraining factor. In research and educational infrastructure, limitations include weak commercialization of research output and a long lag between publication activity and practical implementation. No statistically significant effect is found for human capital infrastructure. These findings can support regional authorities in Russia in adjusting innovation policy and prioritizing resource allocation. The main limitation of the study is its focus on linear relationships only.
The study defines two core regional categories of «resource» and «extractive industry» regions and describes their place and role in Russia’s national economy. Using quantitative indicators, such as area, population, gross regional product, and number of employed, a ranking of these regions is constructed. Key factors shaping their socio-economic development are identified, encompassing geographical location, natural and labour resource endowment, and economic and social potential. Building on the proposed «continent–island» framework, the study establishes key criteria for delineating resource territories, maps their spatial differentiation across the country, and assesses their natural, economic, and social potential over a multi-year period. This analysis yields five continental clusters — North-West, Volga, Ural, Siberian, and Far Eastern — and three island territories: Central (Belgorod, Kursk, and Lipetsk regions), Southern (Astrakhan Oblast), and Eastern (Sakhalin Oblast), each separated from the continental clusters by non-resource zones referred to as straits. Applying an economic-geographical approach across a unified array of resource and extractive industry territories, which occupy predominantly the northern and eastern parts of the country, the study assigns 32 constituent entities to these categories, comprising autonomous republics (6), territories (5), regions (17), and autonomous okrugs (4), with 16 resource and 16 extractive industry territories among them. Together, these territories account for 80 % of the country’s total area, 30 % of its population, and over 90 % of gross value added in the mining and minerals sector. The study identifies the key drivers of spatial differentiation among resource regions and confirms the critical role of commodity territories in Russia’s national economy and export structure.
In both developed and developing countries, occupational injury and workplace safety remain significant issues for economic activity. The purpose of the study is to determine the differentiation of regions by the level of occupational injuries and labour efficiency, the relationship between injuries and labour productivity based on regional data of the Russian Federation in 2010–2023. The objectives set to achieve the goal are: a) to characterize the dynamics of industrial injuries in the Russian Federation in the years under review; b) to identify regional inequality in labour productivity and industrial injuries; c) to substantiate the relationship between the latter and regional labour productivity in Russia. The research methods include descriptive statistics and ranking, the cluster approach (k-means clustering), and econometric modelling: pooled OLS regression, the fixed effects model, the random effects model, and their modifications. Despite the overall trend toward a decline in the occupational injury rate (from 2.2 to 1.02 accidents per 1,000 workers) and an increase in labour productivity (by 1.36 times), the number of regions with injury rates above the Russian average remains constant (49–50). Four groups of regions were identified that differed in the rates of change in labour productivity and occupational injury rates from 2010 to 2023. They include regions of all types of specialization. The hypothesis of a negative relationship between the rate of occupational injuries and labour productivity was confirmed. An increase in the injury rate by 1 case per 1000 workers is accompanied by a decrease in labour productivity in the following year by an average of 6.3 %. The hypothesis of significant interregional variation in the strength of this relationship was not supported; the effect is homogeneous across regions. The analysis also indicates a weak countervailing effect of labour productivity on injuries. The findings can be used to adjust socioeconomic policies in the area of occupational safety. Research on occupational injuries across industries and comparative cross-country analysis appear promising.
In light of the UN 2030 Agenda for Sustainable Development, it has become increasingly important to integrate the Sustainable Development Goals (SDGs) into regional strategies. Although this process is often fragmented, little is known about how regional authorities’ approaches to sustainable development produce systemic imbalances in strategic documents. This study examines patterns of SDG integration and the resulting imbalances using a two-level qualitative content analysis of 11 regional development strategies in Russia’s Northwestern Federal Okrug. The main finding is a phenomenon we call “implicit localization”: although the term “sustainable development” is widely used in strategies, it is rarely linked in a systematic way to the international SDG framework. The paper argues that national projects function as a systemic “filter,” allowing only those SDGs that directly align with federal priorities to enter the regional agenda, thereby shaping the observed imbalance. A detailed mapping of all 17 SDGs using a heat map method reveals a clear hierarchy of priorities: economic goals (SDGs 8 and 9), infrastructure development (SDG 11), and core social objectives (SDGs 3, 4, and 16) are strongly integrated. In contrast, long-term environmental challenges (SDGs 12, 13, and 14) and specific social goals (SDGs 2 and 5) consistently remain marginal in regional planning. To account for these imbalances, the paper proposes a typology of conceptual models (socio-economic, resource-based economic, eco-economic, and comprehensive). This framework demonstrates that the observed economic bias is a logical consequence of dominant goal-setting approaches. The study contributes to the theory of regional strategic planning, and its findings can be applied by public authorities to audit and adjust development strategies, as well as to identify gaps in goal-setting.
Russia’s national target of raising life expectancy to 78 years by 2030 faces a key challenge: significant territorial disparities driven by the combined influence of socioeconomic, environmental, and behavioural factors. This study hypothesizes that life expectancy in each of Russia’s macroregions is persistently shaped by a distinct combination of such factors. Using macroregional panel data, we apply correlation and regression analysis alongside tree-based machine learning algorithms to quantify the contributions of socioeconomic conditions, healthcare accessibility, and population behaviour, accounting for regional specificity and nonlinear relationships. The analysis covers 12 macroregions, delineated by combining principles of administrative division with economic zoning as used in spatial and socioeconomic development strategies and territorial planning. The results reveal clear macroregional patterns. In low-urbanization macroregions (Central Chernozem, Southern), life expectancy is driven primarily by healthcare accessibility and socioeconomic conditions. In industrial macroregions (Volga-Ural, Ural-Siberian, South Siberian), behavioural risks are the dominant influence, with the prevention of deviant behaviour and promotion of health-preserving practices consistently ranking among the top three factors. We identify clusters of macroregions with similar factor-influence profiles, with differences attributable to established behavioural patterns, institutional environment, and income levels. These findings can inform federal and regional policy-makers in tailoring socioeconomic strategies to promote health-preserving behaviours and advance national life expectancy goals.
Persistent regional disparities and limited developmental progress in Indonesia's special autonomy regions, despite substantial fiscal transfers, raise important questions about the effectiveness of Special Autonomy Funds as an instrument for accelerating regional economic convergence and growth. This study examines the role of these funds in reducing regional disparities and stimulating economic growth in special autonomy regions. Although designed as an affirmative fiscal instrument, their capacity to achieve long-term macroeconomic goals remains in question. Prior research has largely focused on development indicators such as poverty reduction and infrastructure improvement, with few studies explicitly examining whether Special Autonomy Funds contribute to faster regional economic convergence. This study addresses that gap by evaluating both the convergence process and the direct impact of Special Autonomy Funds on growth. Drawing on unbalanced panel data from 496 cities and regencies in Indonesia, including 59 special autonomy areas, over the period 2007–2020, the study employs two econometric models: the Hausman–Taylor Estimator for convergence analysis and the Fixed Effects Model for growth estimation. Both models incorporate fiscal transfer variables and control for sectoral economic structure. The results indicate that while interregional economic convergence exists at the national level, convergence in special autonomy regions proceeds more slowly than in other provinces. Moreover, the positive effects of Special Autonomy Funds appear limited to the short term and do not generate a sustained growth stimulus, suggesting that fund utilization has yet to translate into productive capacity. The study concludes that long-term effectiveness requires both improved fund management and a reorientation of allocations toward strategic infrastructure, education, and health. Future research should examine micro-level impacts and governance mechanisms to further strengthen the developmental impact of these funds.
The paper proposes a methodology for calculating the Statistical Index of Child Well-being, developed for Rosstat and tested with the support of the Timchenko Foundation using 2018–2022 data. Based on 27 Rosstat indicators, the index introduces a dual regional classification by domains of child well-being and by observed improvement outcomes. The aim of the study is to identify types of Russian regions according to the level and dynamics of child well-being in 2018–2022 using the integrated Child Well-being Index and its component sub-indices. Correlation analysis is conducted to examine the relationship between the Child Well-being Index (and its sub-indices) and regional socio-economic conditions, including gross regional product (GRP) per capita and income inequality. In addition, cluster analysis is applied to classify Russian regions based on child well-being indicators. The results reveal significant regional disparities in both the composite child well-being index and its sub-indices. Several stable regional groups are identified: regions with consistently high levels of child well-being, which may serve as benchmarks for effective policy practices; regions with persistently low levels; regions with a sustained decline in the index; regions demonstrating steady improvement; and regions characterized by unstable dynamics. The analysis shows that the dynamics of child well-being are associated with the level of regional economic development, with higher GRP per capita corresponding to higher index values and more favourable dynamics. Income inequality primarily affects sub-indices related to the financial well-being of families and public expenditure levels. Future research directions include identifying determinants of fluctuations in the Child Well-being Index and developing a reduced “mini-index” based on a smaller set of indicators to enable quarterly monitoring of child well-being dynamics.
The digital transformation of smart territories significantly enhances the level of economic development and the investment climate of economic entities in Russia, alongside the growth of the highly skilled labour market. Russia is actively investing in its digital infrastructure, guided by a state-level commitment to achieving global leadership in this domain. Yet the uneven pace of territorial development calls for dedicated assessment tools that account for varying levels of digitalization and identify growth points. This study addresses that gap by introducing the PROBLEM-POTENTIALS model — a systematic framework for evaluating smart territories across four core categories: Challenges, Risks, Effects, and Abilities (CREA). This structure constitutes the study’s primary scientific contribution. To validate the model, experts are surveyed using purpose-designed questionnaires. The results are used to compile a ranking of economic entities both within individual categories and across the model as a whole. A comparative approach was applied in the pilot study to evaluate selected territories. The factor systems underpinning each category are open, allowing factors to be added, removed, or aggregated in accordance with the model’s mathematical apparatus. The model is fully compatible with Russia’s current regulatory framework for digital transformation, which emphasizes baseline digitalization assessment and the identification of growth points. It is equally applicable to other domains of economic, social, and managerial activity.
Amid global instability and supply chain disruptions, sectoral industrial ecosystems (SIEs) are becoming vital for technological sovereignty and reduced import dependence. This study aims to develop a methodological framework for assessing the effectiveness of SIEs. The research is based on an ecosystem approach, which considers industrial enterprises not as isolated entities but as dynamic networks of interconnected actors united by a common platform and oriented toward the joint production of goods and services. The proposed unified system of indicators comprises 18 metrics grouped into six dimensions: production, human capital, finance, ecology, innovation, and digitalization. The study applies the methodology to three high-tech Russian sectors that showed the highest growth rates in 2024: pharmaceuticals and medical materials; computers, electronic and optical products; and other transport equipment. The results confirm the hypothesis that these sectors exhibit the key characteristics of sectoral industrial ecosystems. The analysis also identified several challenges, including low return on sales, insufficient investment in environmental protection, and lagging digitalization indicators. The toolkit presented in this study for monitoring and managing SIEs allows identifying structural imbalances, and informing targeted state and corporate policies. Future research may focus on refining the indicator system, deepening the analysis of inter-industry linkages, and developing management algorithms to support the sustainable development of sectoral industrial ecosystems.
This article examines the production and consumption of patronized goods in the humanities and social sector, including science, education, healthcare, and culture, as critical domains for social investment in human capital. These goods often do not align with standard market mechanisms, and existing theoretical approaches offer limited guidance on the nature and economic mechanisms of government support. We propose a new theoretical and methodological approach to financing the humanities and social sector, grounded in the characteristics of creative labour and the principles of W. Baumol's "cost disease" theory. A parametric model of budget subsidies is proposed, grounded in the concept of "consolidated income" and incorporating a novel composite price index. In this model, state subsidies are treated as social investments designed to offset income losses of organizations due to their societal significance. The model incorporates two key normative conditions: N-1, reflecting the alignment of total labour productivity with macroeconomic indicators of a Russian region, and N-2, regulating the average monthly wages of employees. Testing the model on statistical data from 80 regions of the Russian Federation for 2015-2019 confirmed a persistent lag in total labour productivity in most theatrical organizations. Only 20 of the 80 regions saw productivity in the theatre sector exceed the regional average, while the average underfunding of the sector exceeded 50 % over the given period. These results underscore the need to increase the share of budget funds in the normative consolidated income to satisfy conditions N-1 and N-2, ensuring the effective operation of organizations. Growth in budget subsidies is shown to be crucial for preventing hyperinflationary ticket price increases and maintaining the affordability of publicly supported goods. The study concludes that normative subsidies aimed at supporting labour productivity growth effectively standardize wages and cover essential non-wage expenses in the humanities and social sector.
Contemporary empirical studies document statistically significant bilateral relationships between population health and labour market participation. Health and employment are dynamically interrelated: health promotion measures enhance productivity and economic participation, while adverse employment conditions and job loss negatively affect health, increasing social vulnerability and limiting employment stability. Understanding how these relationships are reflected in employment promotion and health protection policies in Russia is increasingly important. This study aims to clarify the ways in which employment influences health and, in turn, how health affects employment, and to assess the extent to which these relationships are incorporated into federal and regional policies. The methodological framework combines a systematic analysis of regulatory documents and government programs with a comparative review of international practices in the EU, the UK, and Finland. Empirical evidence is drawn from the authors' previously published quasi-experimental analysis using data from the Russian Longitudinal Monitoring Surveyfor 2015-2022,which demonstrated a statistically significant positive effect of employment on self-rated health and a negative effect of health on the likelihood of employment. The analysis identifies two pathways of interaction between the labour market and health: the direct effect of employment on health and the inverse effect of health on labour market outcomes. Within the direct effect, four key channels are highlighted: employment availability and support, consequences of job loss, informal and underemployment, and working conditions. Current policies in Russia address these mechanisms only partially and lack systematic coordination with the healthcare system. The findings underscore the need for stronger intersectoral collaboration in developing and implementing measures to promote employment and protect health, as well as practical recommendations for their integration.
