What Affects The Ability To Accumulate The Best Applicants By Russian Universities? The Application Of Quantile Regression Model

Our findings might be useful for the governmental authorities during the universities’ assessment as well as for the higher education institutions themselves - in order to define their strategic development and attract better students

Diffussion of ICT-products and "five Russias"

The authors explored the potential of new information and communications technologies (ICT) absorption in Russian regions primarily on an example of mobile communication. ICT-sector is rapidly growing, especially in consumer market, and it is an ideal object for diffusion research because it is fast spreading, and it can be obtained by almost all parts of a social system. The purpose was to classify regions by the rate of innovativeness. The saturation rate for mobile phone usage (active SIM cards per 100 people) was used as a proper indicator on the first stage of the research. All regions were classified according to rates of diffusion from 1999 to 2011, and five clusters were identified, corresponding to diffusion stages, identified by E. Rogers: innovators, early adopters, early majority, late majority and laggards. There were four stages of spatial diffusion, according to the theory of T. Hagerstrand. Each stage were determined by several factors. The most influential factors were income, price of services and competition. Mobile phone usage in most Russian regions reached 100% saturation (one active SIM card per capita) in 2006-2007. Later development was determined not by demand for phone connection, but by the demand for internet connection, which was easily provided by mobile systems in smartphones, tablets, and other devices. To assess the innovativeness of regional communities, or their ability to absorb new products, cluster analysis, based on the threshold values of Bass model parameters, was performed. The results were similar to those obtained earlier, but the early appearance of innovators in several regions did not increase the total number of users. Both previous methods of classification could be biased regarding special features of mobile communication diffusion. That is why, on the last stage an integral index of innovativeness was introduced, including rate of diffusion for several ICT-products on the early period of their introduction. The analysis proved that hierarchical model of diffusion from the main centres to secondary prevailed in Russia. Factor of geographical location also played an important role. The research showed the significant difference in the rate of diffusion between Russian regions. Five stable clusters were identified, which were corresponding with idea of “five Russias” existence. Moscow and Saint Petersburg’s rate of diffusion was higher than in most countries, but there was a widespread periphery

Diffussion of ICT-products and "five Russias"

The authors explored the potential of new information and communications technologies (ICT) absorption in Russian regions primarily on an example of mobile communication. ICT-sector is rapidly growing, especially in consumer market, and it is an ideal object for diffusion research because it is fast spreading, and it can be obtained by almost all parts of a social system. The purpose was to classify regions by the rate of innovativeness. The saturation rate for mobile phone usage (active SIM cards per 100 people) was used as a proper indicator on the first stage of the research. All regions were classified according to rates of diffusion from 1999 to 2011, and five clusters were identified, corresponding to diffusion stages, identified by E. Rogers: innovators, early adopters, early majority, late majority and laggards. There were four stages of spatial diffusion, according to the theory of T. Hagerstrand. Each stage were determined by several factors. The most influential factors were income, price of services and competition. Mobile phone usage in most Russian regions reached 100% saturation (one active SIM card per capita) in 2006-2007. Later development was determined not by demand for phone connection, but by the demand for internet connection, which was easily provided by mobile systems in smartphones, tablets, and other devices. To assess the innovativeness of regional communities, or their ability to absorb new products, cluster analysis, based on the threshold values of Bass model parameters, was performed. The results were similar to those obtained earlier, but the early appearance of innovators in several regions did not increase the total number of users. Both previous methods of classification could be biased regarding special features of mobile communication diffusion. That is why, on the last stage an integral index of innovativeness was introduced, including rate of diffusion for several ICT-products on the early period of their introduction. The analysis proved that hierarchical model of diffusion from the main centres to secondary prevailed in Russia. Factor of geographical location also played an important role. The research showed the significant difference in the rate of diffusion between Russian regions. Five stable clusters were identified, which were corresponding with idea of “five Russias” existence. Moscow and Saint Petersburg’s rate of diffusion was higher than in most countries, but there was a widespread periphery

Internet diffusion and interregional digital divide in Russia: trends, factors, and the influence of the pandemic

The demand for digital technologies has been growing due to a shift in the technological and economic paradigm. The need for online services has increased since the beginning of the COVID pandemic. There are significant disparities between Russian regions in the digital technology accessibility and the development of computer skills. In 2020, the Internet diffused rapidly in most regions, although previously, there had been a slowdown. As markets got saturated with digital services, the digital divide between Russian regions narrowed. Overall, the Internet use patterns are consistent with those of the spatial diffusion of innovations. Amongst the leaders, there are regions home to the largest agglomerations and northern territories of Russia, whereas those having a high proportion of rural population lag behind. Coastal and border regions (St. Petersburg, the Kaliningrad region, Karelia, Primorsky Krai, etc.) have better access to the Internet due to their proximity to the centres of technological innovations as well as the high intensity of external relations. Leading regions have an impact on their neighbours through spatial diffusion. Econometrically, access to the Internet depends on income, the average age and level of education, and its use depends on the business climate and Internet accessibility factors. Regional markets are gradually getting more saturated with digital services and technologies. The difference between regions in terms of access to the Internet is twofold, whereas, in terms of digital technology use, the gap is manifold. In many regions, the share of online commerce, which became the driver of economic development during the lockdown, is minimal. Based on the results of the study, several recommendations have been formulated

How to assess advantages of economic-geographical position for Russian regions?

The category of economic-geographical position (EGP) was formalized based on a review of the scientific literature. The developed method of international and interregional EGP potential assessment was based on the use of gravity models; it can further be widely used in regional studies to explore the benefits of the spatial location of objects (countries, regions, cities, etc.). These calculations for Russia’s regions showed significant spatial differentiation. The maximum potential of interregional EGP potential have the regions located near Moscow and St. Petersburg agglomerations, the potential decreases uniformly to the east. The maximum international EGP potential concentrated in regions on the coast of the Black Sea, the Baltic Sea and the Sea of Japan. The potential of the Kaliningrad region 5.6 times higher than it is for the Tyva Republic. In addition, it was revealed a significant increase in the total EGP potential in the 2000s, and its shift to the southern regions of the Far East due to the growth of the Asia-Paci c economies. The results were also used to identify connections between the EGP potential and indicators of socio-economic development. It was found that favourable EGP is one of the factors for GRP growth, investment, foreign trade, migration growth and spread of new technologies. Formalizing EGP as a category allows using it to predict the spatial changes in the socio-economic development of Russia

Факторы и практики развития стартапов

The main goal of the work was to identify the best practices for supporting new technology companies (startups). The first chapter summarizes the best practices and approaches to supporting start-ups in developed countries, and the second chapter provides examples of developing countries of the world. The third chapter contains conclusions and main recommendations for the Russian authorities based on the analysis carried out taking into account the current situation

Geography and persistence of entrepreneurship in Russia

Can entrepreneurial activity be stronger and more persistent than the continuity of socialist institutions? The answer to this question is overwhelmingly positive. Using the historical data on entrepreneurship, retail trade and cooperatives in Russian regions, this study shows a strong persistence of entrepreneurship activity in Russia during the period 1926–2018, while we also evidence that the restructuring of the Soviet economy resulted in a structural break in the 1970s. By distinguishing three periods of 1998–99, 2000–07 and 2008–18 since the transition started, we demonstrate that the historical persistence of entrepreneurship is not constant and may change from one period to another

Uncovering New Economy Potential of Russian Regions on the Basis of the Last 20 Years Dynamics’ Analysis

Recent global events have accelerated new technologies implementation worldwide. This process can likely lead to a future increase in regional disparities, especially in large developing countries such as Russia. Resource-based growth, which prevailed in the last 20 years in Russia, could slow down technological change in most regions. We aimed to assess regional potential for new economy formation based on its previous dynamics in 2000–2020. For that purpose, we developed a complex index that evaluates regional ability to create, use and disseminate new knowledge and technologies. There were long-term upward trends of most of the indicators in Russian regions due to intensive interregional alignment policy and a rapid spread of information and communication technologies. Economic growth, according to the Granger test results, contributed to the new economy formation. However, many research and development (R&D) indicators did not achieve higher values in comparison with 2000, when the oil prices started to grow. The growth rates in recent years have been low, and the share of R&D employees and R&D expenditures as well as entrepreneurial activity have declined especially in 2020. A significant but decreasing divide remains between leading and lagging regions. In accordance with the identified types of regions, it is necessary to pursue a diversified regional policy. Our results can be used to justify smart specialisation principles in Russia. Indirectly the study measures the resilience, or adaptability of regions to crises

Uncovering New Economy Potential of Russian Regions on the Basis of the Last 20 Years Dynamics’ Analysis

Recent global events have accelerated new technologies implementation worldwide. This process can likely lead to a future increase in regional disparities, especially in large developing countries such as Russia. Resource-based growth, which prevailed in the last 20 years in Russia, could slow down technological change in most regions. We aimed to assess regional potential for new economy formation based on its previous dynamics in 2000–2020. For that purpose, we developed a complex index that evaluates regional ability to create, use and disseminate new knowledge and technologies. There were long-term upward trends of most of the indicators in Russian regions due to intensive interregional alignment policy and a rapid spread of information and communication technologies. Economic growth, according to the Granger test results, contributed to the new economy formation. However, many research and development (R&D) indicators did not achieve higher values in comparison with 2000, when the oil prices started to grow. The growth rates in recent years have been low, and the share of R&D employees and R&D expenditures as well as entrepreneurial activity have declined especially in 2020. A significant but decreasing divide remains between leading and lagging regions. In accordance with the identified types of regions, it is necessary to pursue a diversified regional policy. Our results can be used to justify smart specialisation principles in Russia. Indirectly the study measures the resilience, or adaptability of regions to crises

Social risk and vulnerability assessment of the hazardous hydrological phenomena in Russia

Methods and results of social vulnerability and risk assessment are presented in the article. It is explored if modified methodology of the United Nations University (World risk index) can be used on different scale levels: regional, municipal and settlement. It was estimated that, despite the low value of the World risk index for Russia, southern coastal and mountain regions have high values of the risk index for hydrological phenomena because of higher frequency of the hazardous events, higher population density, and high social vulnerability. The Krasnodar region (in the south-western part of Russia) was chosen for a detailed analysis. A municipal risk index was developed, and municipal districts in the Kuban river mouth were identified as territories with the highest risk. For verification of the index results, the percentage of vulnerable people was estimated based on opinion polls. The results can be used in further risk calculation for other hazardous phenomena