This chapter models industrial, tech, and financial cooperation between Russia and the countries of the Asia-Pacific region. We use several complex methods of economic and…
This chapter models industrial, tech, and financial cooperation between Russia and the countries of the Asia-Pacific region. We use several complex methods of economic and mathematical modeling to analyze specific features of such cooperation and determine critical factors in industrial, technological, and financial development. The preferable choice for the Asia-Pacific region is cooperation with Russia, which is ready for an increase in imports of industrial and high-tech products as well as joint industrial innovational entrepreneurship. Investments would lead to synergetic effects, ensuring simultaneous industrial, technological, and financial development.
This chapter elaborates on Industry 4.0 and the Internet of Things to develop a strategy of optimization and achieve sustainable industrial development of Russia. We…
This chapter elaborates on Industry 4.0 and the Internet of Things to develop a strategy of optimization and achieve sustainable industrial development of Russia. We propose a framework strategy for perfecting the process of management of Industry 4.0 in Russia. The provision of the development strategy with financial resources is to be ensured by a public–private partnership and implementation of transnational initiatives for cooperation in the sphere of Industry 4.0. The critical result of this chapter is that Industry 4.0 must become a top-priority direction of modernization and sustainable growth.
This chapter analyzes the technological growth of the modern Russian economy through the prism of evaluating its effectiveness from the position of costs and profits. It…
This chapter analyzes the technological growth of the modern Russian economy through the prism of evaluating its effectiveness from the position of costs and profits. It also determines the factors of this growth and develops recommendations for managing them in the interests of acceleration of rate and increase of effectiveness of technological growth of the modern Russian economy. While technological growth in Russia shows high effectiveness, the model of technological growth in Russia has its specifics, connected to the unconventional influence of a standard set of the factors on technological growth. The influence of the factor of the number of organizations that implement R&D is insignificant, and the influence of the number of personnel involved with R&D, volume of private expenditures for R&D, and the number of used leading production technologies is negative (reverse). We offer recommendations for increasing the effectiveness of technological growth of the modern Russian economy, which are connected to increase of the volume of state expenditures for R&D (recommended volume – USD 35.09, its growth as compared to 2018 constitutes 191.76%) and increase of the number of developed leading production technologies (recommended number of these technologies – 2350.05, its growth as compared to 2018 constitutes 62.34%).
The purpose of the article is to study the recent tendencies of growth of Russia’s agro-industrial complex (AIC), determine the optimal scenario of its development, and…
The purpose of the article is to study the recent tendencies of growth of Russia’s agro-industrial complex (AIC), determine the optimal scenario of its development, and develop recommendations in the sphere of state regulation for its practical implementation. While there are tendencies of growing production and increase in Russia’s export, against this background, there is a tendency of quicker increase of import of food – if it continues, positive balance of foreign trade of food products in 2018 will turn into negative balance in 2020–2024. Though efficiency of crop farming is peculiar for a tendency of quick growth, efficiency of animal breeding is stable, which does not allow overcoming the growing deficit of food in Russia, which grows under the influence of the tendency of wear of fixed funds and slow implementation of new fixed funds due to insufficient financing. Scenarios of mid-term (i.e., until 2024) growth of Russia’s AIC are compiled, of which the most optimal is scenario that requires technological advancements, due to which increase in the value of index of food security up to 85.00 points (27%) will be achieved and the set goals of growth and development of Russia’s AIC will be reached. For a successful optimal scenario of the growth of Russia’s AIC, we offer recommendations in the sphere of state regulation of its digital modernization: adoption of the national strategy of transition to AIC 4.0 within the program “Digital economy of the RF,” development of import substitution in the AIC with emphasis on B2B markets, preparation of the technological platform for transition to AIC 4.0, and sufficient financing for digital modernization of the AIC.
This chapter examines the effects of financial deepening on the sources of economic growth in Russia. Previous empirical literature based on cross-country studies presented the evidence that in developing countries financial development affects capital accumulation more than productivity growth. We tested this proposition with panel data from 75 regions of Russia’s regions between 2008 and 2015 using system generalized method of moments techniques. Our results are not consistent with this proposition: the effect of finance on output growth occurs primarily through productivity; the positive influence of finance on capital accumulation is less significant, which is more typical for developed countries. This outcome can be explained by the fact that structural problems in Russia and developed countries are somewhat similar. More helpful for Russian economy are tools that would help business get a more profound effect from efforts to promote innovation and boost productivity than from increasing investment by expanding credit.
This chapter identifies the determinants of economic growth for the newly formed countries of the former Soviet Union and the Socialist Bloc. Starting from a neoclassical…
This chapter identifies the determinants of economic growth for the newly formed countries of the former Soviet Union and the Socialist Bloc. Starting from a neoclassical theory of growth this chapter adds recently identified contributing factors such as human capital, economic freedom, and financial developments to find the determinant of the growth in the region. Other control variables such as population are also included. There are numerous variables that can measure any of the theoretically suggested factors, most of which are correlated. The study checks for multicollinearity among variables. It also accounts for differences in development stages of the countries under study. The data are measured in levels. Tests of normality and randomness are performed to assure compliance with theoretical requirements. This study utilizes panel data analysis using both fixed and random effect models, tested for relevance using the Breusch–Pagan method. The chapter identifies relevant factors and concludes that there are differences among per capita gross domestic products of the countries even after controlling for contributing variables.
Smart cities boost economic growth around the world by changing to the smart environment, smart economy, smart technologies, smart human capital, smart infrastructure, and…
Smart cities boost economic growth around the world by changing to the smart environment, smart economy, smart technologies, smart human capital, smart infrastructure, and smart governance to improve economic growth and better use of limited resources. We justify the development model of the “smart city” concept in Russia within a green economy approach and the managerial relations of innovative smart projects implemented within the public–private partnership (PPP). Our toolkit allows us to assess the effectiveness of management decisions in the implementation of the “smart city” concept and to carry out continuous public–private monitoring of the effectiveness of innovations in implementing smart PPP projects. To this end, we propose and justify a new indicator of the effectiveness of managerial decision-making, taking into account the quality of the information received during the implementation of innovative PPP projects, which allows calculating the degree of utilization of the potential capabilities of a company participating in a PPP.