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Relevance. Inter-municipal cooperation is an effective tool for addressing resource deficits faced by municipalities, and its importance has grown in today’s context of socio-economic instability. However, the success of such cooperation largely depends on a careful selection of partners, which raises the question of how the strength of ties between municipalities impacts their ability to collaborate effectively. Research objective. The aim of this study is to explore how the spatial characteristics of interdependence between municipalities influence their interactions and cooperation. Data and methods. The research draws on official statistical data from Russia’s Federal State Statistics Service (Rosstat), as well as information from investment passports and municipal socio-economic development strategies. The study employs spatial correlation methods, cartographic analysis, and general research techniques, including analysis and synthesis. Results. The inter-municipal relationships in Sverdlovsk Region are highly uneven, with significant disparities in the level of involvement across different areas. These relationships are predominantly concentrated around the region’s administrative center and its neighboring municipalities, while the northern and eastern parts exhibit the weakest connectivity. Municipalities in the Ekaterinburg urban agglomeration are the most active participants in joint projects, whereas those in the southwestern part of the region show less involvement. The northern and eastern areas, in particular, demonstrate minimal engagement in forming partnerships with other municipalities, highlighting a stark regional imbalance. Conclusions. The study confirms a strong link between the interdependence of municipalities and the extent of their cooperation. Factors such as territorial and socio-economic proximity play a key role, but additional drivers, such as national or regional policies, also significantly influence inter-municipal collaboration. Interestingly, a lack of resources among potential partners does not appear to impede cooperation.
Relevance. Intergovernmental budgetary regulation is pivotal for promoting territorial equalization and bridging socio-economic gaps among regions. It ensures equitable access to public goods for all citizens, irrespective of their location. The effective distribution of federal financial assistance, guided by the principles of budget federalism, is essential for maintaining macroeconomic stability and fostering sustainable socio-economic development on the national and regional levels. Research Objective. The study aims to assess the competitive positions of Russian regions within the distribution system of federal intergovernmental transfers. Method and Data. To investigate Russia’s system of intergovernmental transfers, the research employs statistical methods, including structural-dynamic, grouping, and graphical analysis, along with novel analytical tools like the dependence coefficient and income coefficient. The study encompasses data from 2015 to 2022, sourced from the Federal Treasury and Federal State Statistics Service (Rosstat). Results. The study analyzes the dynamics of transfers from the federal budget to regional budgets and shows significant transformations in the transfer structure, revealing a decrease in non-targeted transfers and an increase in targeted ones. The study also examines the differences between regions in the distribution of these transfers. Russian regions were grouped depending on the share of federal transfers in their total budget income and the average per capita income in their consolidated budget. The study introduces a novel methodological approach through the use of newly developed tools - the dependence coefficient and income coefficient - to evaluate regional competitiveness. Conclusion. Despite an overall increase in federal transfers, persistent regional disparities intensify competition for financial assistance. These findings can be of interest to policymakers at federal and regional levels, researchers, and educators in relevant fields.