Suchergebnisse

Russia inherited pattern of economic activity location from the Soviet Union, where the main forms of industry organization were territorial-production complexes (TPC) - networks of industrial organizations united by a single technological process or the chain of raw materials processing. In a market economy in the 90s, economic ties within the TPC were destroyed, leading to a drop in the level of production, fragmentation of large enterprises and the formation of a set of independent and often competing firms. Some scientists believe that this situation over the last 20 years could serve as a necessary foundation for the formation of industrial clusters (in interpretation of modern regional science). Today, interest in clusters in Russia rises again due to the need to find new mechanisms to support production and innovation in a stagnant economy. Ministry of Economic Development of Russia has developed a project to support the pilot territorial innovative clusters by providing funding for infrastructure formation. The selection of cluster initiatives was based on applications from regional governments, interested in attracting of additional investment. Most of the clusters, formed in Russia, are not in innovative sectors, as shown by studies of the Russian Cluster Observatory. But a lot of potential clusters in Russia is not formed due to the high level of distrust between firms, due to lack of understanding of the potential benefits, etc., although these clusters can develop due to geographical proximity (high concentration) of firms. The aim of our work is to identify clusters as areas of geographical concentration of small and medium businesses (SME). We also wanted to check whether the existing cluster initiatives correspond to the concentration of economic activity and whether there is potential for increasing the cluster initiatives. In our work, we use the analysis based on the localization index, but on three geographical levels for verification reasons: regions, districts and cities. Most of the data were collected from RUSLANA database, consisting information of Russian firms. After identifying a high degree of localization of a particular industry or a group of industries, we analyze the location of enterprises, based on distance-oriented methods in specific regions or between regions. The result is a map of the high concentration and localization of small and medium businesses in certain areas in a number of industries. The authors confirmed the existence of traditional and well-known clusters and identified previously unknown concentration of firms that did not declare their interaction. In the last step, the authors conducted field research - a survey of firms in areas of concentration, where clusters today are not formed, for determining the reasons for the lack of interaction.

AbstractThe study aims at identifying the role of traditional and new factors that contribute to attracting highly educated workers. We summarized the key literature facts and performed econometric analyses on previously unused data on both internal and external migration with higher education in the Russian regions from 2008 to 2019. Our methodology differs from traditional models based on migration flows between destinations and focuses on characteristics of receiving regions. We showed that densely populated metropolitan areas with broader labour markets opportunities stimulate highly skilled mobility; higher income, new vacancies and housing availability are among significant traditional factors. However, migrants with higher education also chose educated, healthy communities and favourable business environment as such regions provided wider career and other opportunities. It is shown for the first time for Russia that improving the business climate helps to attract highly skilled human capital. Mild climate and comfortable environment turned out to be preferable, although the richest centres of oil and gas production in the north are actively attracting migrants. Improved access to the Internet and further digitalization can reduce migration, which may be related to the prospects of remote work. High scientific and educational potential is significant, but only attracting students is not enough, as they will leave a region after graduation. In conclusion, we offered some non‐trivial policy recommendations based on the identified factors and considering the new pandemic reality: high‐tech cluster development, proactive scientific and entrepreneurial policy, and measures to improve urban environment in the largest agglomerations and southern regions.

The aim of this paper is to evaluate which university's characteristics have the greatest impact on the competitiveness of universities in their ability to attract better students in Russia. We examined the impact of three groups of factors,related to teaching, research and entrepreneurial activities of universities. The quantile regression model was applied for the subsample of public and private higher education institutions localized in Russia. The results prove that not only traditional, teaching-related factors affect the attractiveness of the universities. We found that the research quality and entrepreneurial experience both increase the ability to accumulate the best applicants by Russian universities. However, the synergy between training, research and business activities is not always achieved. The importance of science and business-oriented activities varies between public and private institutions. According to the results from the quantile regression the importance of the certain factors differs between the quantiles of the dependent variable distribution. 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. ; Celem artykułu jest identyfikacja czynników, determinujących konkurencyjność uczelni wyższych w zakresie pozyskiwania najlepszych studentów. Główna uwaga położona została na weryfikacji trzech grup czynników –związanych z procesem kształcenia, reprezentujących jakość badań naukowych oraz wskazujących na powiązania biznesowe uczelni. W badaniu wykorzystano model regresji kwantylowej, którego parametry oszacowano oddzielnie na próbie publicznych i prywatnych szkół wyższych, zlokalizowanych w Rosji.| Uzyskane wyniki wskazują, że nie tylko tradycyjne czynniki, związane z procesem kształcenia, wpływają na atrakcyjność edukacyjną szkół wyższych. Istotny wpływ na zdolność do akumulacji najlepszych studentów ma jakość prowadzonych badań naukowych i powiązania uczelni z biznesem. Należy przy tym zauważyć, że osiągnięcie efektu synergii między działalnością naukową, edukacyjną i biznesową szkół wyższych nie jest łatwe i nie zawsze się udaje. Siła z jaką wspomniane czynniki determinują atrakcyjność edukacyjną różni się w zależności od typu uczelni (prywatna lub publiczna) oraz jest funkcją aktualnego potencjału jednostki. Zawarte w pracy spostrzeżenia mogą być potencjalnie wykorzystane przez szkoły wyższe oraz władze w procesie ewaluacji orientacji strategicznej uczelni oraz do sformułowania rekomendacji w zakresie działań sprzyjających poprawie atrakcyjności szkół wyższych w oczach przyszłych studentów.

The aim of this paper is to evaluate which university's characteristics have the greatest impact on the competitiveness of universities in their ability to attract better students in Russia. We examined the impact of three groups of factors,related to teaching, research and entrepreneurial activities of universities. The quantile regression model was applied for the subsample of public and private higher education institutions localized in Russia. The results prove that not only traditional, teaching-related factors affect the attractiveness of the universities. We found that the research quality and entrepreneurial experience both increase the ability to accumulate the best applicants by Russian universities. However, the synergy between training, research and business activities is not always achieved. The importance of science and business-oriented activities varies between public and private institutions. According to the results from the quantile regression the importance of the certain factors differs between the quantiles of the dependent variable distribution. 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.