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T he global nature of the COVID-19 pandemic outlined new challenges for the economic studies aimed to define the factors measuring the difference in the scope of the coronavirus-induced crisis consequences for the national social economic systems. The purpose of this research is to develop the tools to define the COVID-19 pandemic impact on the social economic development of the Russian regions and the resilience of the regional systems to the pandemic in terms of demographic factors. The methodology of the research includes statistical analysis and econometric modeling. The authors defined the economy resilience to the pandemic and developed a resilience index of the regional economy to the COVID-19 pandemic. The resilience index includes groups of homogeneous indicators characterizing the factors of the regional economic growth. Resilience of the regional economy to the COVID-19 pandemic is measured to reveal a negligible positive impact of the population density on the resilience of the RF regions’ economy to the coronavirus-induced crisis. The regions were clustered by the resilience index of their economies to the COVID-19 pandemic, and the leaders-regions, the regions with a moderate level and outsiders-regions were defined. A higher level of the regional economic development is found not to guarantee a more resilient economy to the COVID-19 pandemic. The obtained scientific results could be used to choose customized tools for the recovery of the regional social economic systems with due regard to the area with the worst dynamics of the indicators. Further scientific research is seen to be in analyzing the spatial non-homogeneity during the pandemic compared with pre-pandemic and post-pandemic periods, as well as in measuring the detrimental effects of the coronavirus and other external shocks on the RF regions’ economies in the context of demographic factors.

Introduction. The scientific problem under consideration is of particular relevance due to the need to assess the impact of the factors in the digital transformation of the regional economy and in the economic growth on the economic development of the regions of the Russian Federation. Based on the research conducted, the article presents an econometric assessment of the dependence of the level of the gross regional product per capita in the regions of Russia on such factors as digital labor and digital capital. Materials and Methods. The authors analyzed panel data from the Federal State Statistics Service covering 87 regions of Russia for the period from 2010 to 2018. The research methodology is based on the use of the Cobb–Douglas production function, statistical and correlation data analysis, as well as on econometric methods for studying panel data. Results. To analyze the impact of the digital transformation of the economy on the regional economic growth of the regions of Russia, various models based on panel data have been considered, such as the pooled model, fixed effects models, random effects models, as well as time-varying effects models using dummy variables. Based on statistical criteria, the best model has been chosen and conclusions have been drawn about the nature of the impact of the digital transformation indicators on the gross regional product per capita in the regions of Russia. Discussion and Conclusion. The results of econometric modeling have demonstrated that digital factors in economic growth (digital labor, digital capital), along with common factors in economic growth (labor and capital), affect the regional economic growth. According to the regional data for the period from 2010 to 2018, the time fixed effects model has proved to be the best model of the impact of the factors in economic growth and digital transformation on the economic development of the regions of the Russian Federation. The research results can be used when developing a public policy aimed at stimulating the digital transformation of the regional economy.

Abstract. Theoretical principles of neoindustrial transformation and basic assets reproduction have been studied in the article. It has been reviewed historical approaches to the disclosure of the examined conceptions for the purpose of defining the extent of every presented concept's reflection of the most important aspects in the analyzed categories. The authors considered one of the internal factors being an obstacle in the new way of Russian development -reproductive process of basic assets in industry. There have been determined the rates of investment growth in capital asset, the growth rates of capital renewals, and the growth rates of basic assets depreciation according to the types of industrial economic activity. There have been revealed general problems of regional industry development connected with reproduction of basic assets. The authors of the article have made a conclusion about the essential improvement of reproductive processes in the regional industry by means of parallel processes' stimulation: elimination of worn assets from the economic turnover and increase of investment in capital assets aimed at renewal of conventional production and conferring high-tech character to the fields identifying basic competencies of industrial region.

An industrial cluster as an integrated tool for an area development is extensively applied all over the world. The purpose is to retranslate the practices of some successful cluster initiatives. Meanwhile, actual outcomes of industrial clustering are often negative or neutral. This can be explained with insufficient knowledge about the interaction of two territorial economic systems: a region and a cluster. Here, we take one of the first attempts to comprehensively evaluate this process and to develop some conceptual mechanism which can simulate the implications of a cluster policy both for the region and for an industrial cluster. To do this, the literature was reviewed. This reflected quite a complicated structure of theoretical and methodological background covering the interaction between a region and a cluster and methods of its study. The analyzed background describes six approaches to the interpretation of an industrial cluster (system, institutional, network, agglomeration, classical, and administrative ones) and four evaluative methodologies for a cluster impact on a region and a region impact on a cluster (statistical, regional, marketing, and case ones). In their practical studies, researchers combine methodologies with the approaches and develop a kind of conceptual systems (a theoretical approach + methodology). However, these systems are focused on either a territorial geographical aspect of an industrial cluster or its social economic field. As a result, the comprehensive assessment which is based on these conceptual systems doesn’t show the broad picture. To solve this problem, the authors offer a conceptual system which includes a system spatial approach and a statistical methodology with the cluster’s binary nature problem solved. In this case, the assessment of region and cluster interaction helped develop a visual model which predicts changes in the region’s impact on a cluster under the changes of cluster’s influence on a region and vice versa. The application of the model decreases the detrimental effects of a cluster policy and maximizes positive externalities both for a cluster and a region.