Research background: The positive relationship between the availability of intellectual capital and the ability of the state, region or firm to develop economically stimulates an increase in the intellectual capital. In order to manage intellectual capital, it is necessary to have a clear idea of its availability, capacity, features, growth reserves, as well as concentration in certain territories and ability to spread. Many studies are devoted to the measurement of intellectual capital, its diffusion and impact on the economic efficiency of the organization, region, and nation. However, in the case of the Russian Federation there is a gap in the study of the spread of intellectual capital over the country. Purpose of the article: The purpose of the article is to evaluate intellectual capital in the federal districts of the Russian Federation and to model the spread of intellectual capital. Methods: Data on 8 Russian federal districts for the 2017 year from Unified Inter-departmental Information and Statistical System (EMISS) of the Russian Federation were taken as a basis for the research. Based on three-component model (human capital, structural capital, and relational capital), we formed a set of indicators for assessing regional intellectual capital, relevant to the Russian Federation. This allowed us to evaluate the integrated indicators of intellectual capital in federal districts and to determine the probability of intellectual capital spreading from each federal district to neighboring federal districts. We used percolation theory methods to model the spread of intellectual capital. Findings & Value added: The study contributes to the Russian regional knowledge on intellectual capital. Intellectual capital in the Russian Federation is disproportionately distributed, concentrating closer to the capital, and has a lower level in remote territories. It spreads unevenly, flowing from the Central Federal District to neighboring federal districts, however, other federal districts develop almost in isolation.
Efficiency, optimization, speed and timeliness have always played a decisive role in logistics and supply chain management. Covid-19 has triggered supply chain disruptions, increased logistics costs and decreased productivity. The purpose of the article is to show the role of new digital solutions and modern information technologies in the digitalization of labor activity in the field of logistics as a key factor in reducing and neutralizing negative trends. The objectives of the study are to identify tendencies and trends in the use of digital solutions (as one of the aspects of the fourth industrial revolution) to increase labor productivity and efficiency of logistics activities in the postpandemic period. The promising areas of application of digital technologies in logistics for increasing labor productivity, eliminating existing gaps in the management of logistics systems, as well as overcoming the consequences of the pandemic, have been identified. Funding: The reported study was funded by RFBR, project number 20-010-00877.
Purpose: The paper deals with the analysis of the influence of interregional labor migration in the Russian Federation on regional labor productivity. Design/Methodology/Approach: Empirical analysis was conducted on the statistical data collected from the Federal State Statistics Service of the Russian Federation. The sample includes data on 85 subjects of the Russian Federation for the period 2011-2016. The study substantiates the impact of interregional labor migration in the Russian Federation on regional labor productivity and to form the tools for managing migration processes, ensuring its improvement. Findings: The study showed that interregional differences in wages, the differentiation of the characteristics of labor markets in the region of residence and the potential region of employment, different transport accessibility and additional employee costs associated with staying in another region are the main economic reasons for interregional labor migration in the Russian Federation. The regression analysis confirmed hypotheses that higher level of labor migration from the region leads to a decrease in labor productivity in the region. Practical Implications: Based on the empirically derived relationships, authors created a set of tools for managing migration processes, ensuring their improvement, which can be used for the development of program documents at the regional and interregional levels. Originality/Value: The main contribution of this study is the combination of deep statistical analysis and migration factors' analysis to provide valuable conclusions in interregional labor migrations.
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