Stepan Zemtsov

Integrated assessment of socio-economic risks of hazardous hydrological phenomena in Slavyansk municipal district

In 2012, the damage costs of floods in Russia amounted to about €300m, and these floods have caused nearly 200 fatalities (Kotlyakov et al. in Reg Res Rus 3(1):32–39, 2013). Risk assessment is one of the most pressing scientific topics in Russia, but most of the works are devoted to natural hazards assessment. The purpose of this work is to estimate the influence of hazardous hydrological phenomena on society. The field research was conducted in the Slavyansk municipal district in the Krasnodar region (the south-western part of Russia), which is a highly populated coastal territory with a high frequency of hazardous hydrological events. Modified methods of the Ministry of the Russian Federation for Affairs for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM) were used for potential economic damage calculation. The paper did not only focus on direct, tangible risks, but also included social risk (i.e. risk to life and health). Social vulnerability has been calculated directly as a percentage of vulnerable people, estimated in opinion polls, while in many recent papers the social vulnerability index was calculated as a combination of several statistical indicators. The resulting percentage of vulnerable people was converted to numbers of potential victims. Finally, the social risk was expressed by financial indicators in terms of the cost of the value of statistical life lost (Mrozek and Taylor in J Policy Anal Manag 21(2):253–270, 2002; Viscusi and Aldy in J Risk Uncertain 27(1):5–76, 2003). Social risk can be underestimated in comparison with economic risk because of a low “value of life” in Russia (no life insurance, neglecting of basic safety rules, etc.) (Guriev in Myths of economics, Alpina Business Books, Moscow, 2009).

Robots and potential technological unemployment in the Russian regions: Review and preliminary results

The use of unmanned technologies can cause a decrease in the level of employment. The article discusses the compensation mechanisms and conflicting results of empirical studies. On the basis of internationally comparable methods, it was estimated that about 44% of the workers in Russia can be replaced, which is lower than in most developed countries. In the regions, specializing in the manufacturing industry, this value is higher, the least values are in the least developed regions. Some people will not be ready for life-long learning, competition with robots, and accordingly there is a possibility of their social exclusion in the future.

Specifics of Cluster Policy in Russia

The article presents the results of management quality survey in Russian clusters that reveals specifics of cluster support policy in Russia. We compare 22 Russian clusters, supported by the Government, using series of indicators measuring cooperation intensity of cluster participants and activity of cluster management teams. We introduce a description of the typical Russian innovative territorial cluster, based on the average values of the indicators. Our analysis revealed that international communications, information about funding and training courses are highly useful tools to improve collaborations among cluster participants. This paper proposes a methodology for measuring cluster performance by the cluster scale index, cluster development index and cluster management efficiency index. In conclusion, we formulate recommendations for cluster policy improvement in Russia, based on our analysis of indicators’ correlations and comparison between the results of our research and the similar researches in other countries. This analysis will be useful for researchers and policymakers from countries, where cluster policy has recently become a popular topic.

A Paradigm Change in Russia's Regional Innovation Policy: From Equalization to Smart Specialization

The article substantiates the need for regionally differentiated innovation policies based on smart specialization principles. Having reviewed the international experiences and literature, we propose guidelines for a new policy aimed at human capital preservation, creation of innovative businesses and intensification of horizontal ties. Using cluster analysis, we developed a typology of regions and cities to be used in developing innovation policy goals. We offer appropriate support tools for each region type, which differ in their potential for creating and implementing new technologies, and their scientific-technological and industrial specialization.

Какие Факторы Влияют На Концентрацию Быстрорастущих Компаний в Регионах России? (What Factors Affect Concentration of Fast Growing Companies in Russian Regions?)

Russian Abstract: В условиях стагнации экономики России необходим поиск региональных факторов, влияющих на концентрацию быстрорастущих фирм («газелей»). Авторы проверяли гипотезу о значимости инновационного потенциала регионов как положительного фактора концентрации «газелей» обрабатывающей промышленности. Проведенный эконометрический анализ подтвердил, что концентрация «газелей» на региональном уровне зависит от качества человеческого капитала и доли занятых в научно-исследовательском секторе, но наиболее важное значение играет наличие и близость крупных рынков.English Abstract: In a stagnant economy, Russia needs the search for regional factors influencing the concentration of fast-growing manufacturing firms (“gazelles”). We assumed that regional innovation performance might have a significant influence. Econometric analysis confirmed the importance of the human capital quality and R & D costs, but market potential is the most important factor.

Does economic-geographical position affect innovation processes in Russian regions?

A favourable economic-geographical position (EGP) of regions and cities is one of the factors of their socio-economic development. Economic agents can take advantages of their proximity to the major markets of goods and services, thereby reducing their transport costs and increasing their profitability. In the sphere of innovation, proximity to the innovation centres may also significantly affect the creation of new knowledge and technologies, due to the existence of tacit knowledge and knowledge spillovers. The authors propose the term ‘innovation-geographical position’ by analogy with EGP. It has been demonstrated that location matters to regional innovation output. If there is 1 % more new technologies in neighbouring regions, there are approximately 0.35–0.58 % more newly created technologies in the target region. Proximity to the world centres of new technologies has even greater impact.

Assessing the Potential of Economic-Geographical Position for Russian Regions

On the basis of the review of the scientific literature, the category of economic-geographical position (EGP) is formalized. The developed method of international and interregional EGP potential assessment is based on the use of gravity models; in the future, it can be widely used in regional studies to explore the benefits of the spatial location of objects (countries, regions, cities, etc.). These calculations for Russian regions have showed a significant spatial differentiation. The regions located near Moscow and St. Petersburg agglomerations have the maximum potential of interregional EGP, the potential decreases uniformly to the east. The maximum international EGP potential is concentrated in the regions on the coast of the Black Sea, the Baltic Sea and the Sea of Japan. The potential of the Kaliningrad region is in 5.6 times higher than it is for the Tyva Republic. In addition, it is 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-Pacific economies. The regions with a high and low efficiency of EGP use are revealed. The results are used to identify the connections between the EGP potential and the indicators of socio-economic development. It is found that a favorable EGP is one of the factors for gross regional product growth, the growth of investment and foreign trade, migration growth and spread of new technologies. Formalizing EGP as a category allows to use it to predict the spatial changes in the socioeconomic development of Russia.

Uncovering Regional Clustering of High Technology SMEs: Russian Case

The Soviet Union predetermined Russia’s economic activity’s location patterns. While the main forms of industry organization were territorial production complexes (TPC) - networks of industrial organizations united by a single technological process, - switch to the market economy in the early 90s destroyed economic ties within the TPC, leading to fragmentation of large enterprises and formation of a number of independent firms. Some scientists believe that this situation over the last 20 years could serve as a necessary foundation for clusters’ formation. Nowadays interest in clusters in Russia is rekindled due to the need to find new support mechanisms for production and innovation in a stagnating economy. The Ministry of Economic Development of the Russian Federation has initiated a project to support pilot territorial innovation clusters with infrastructure formation funding. The aim of this work is to identify clusters as areas of geographical concentration of small and medium enterprises (SMEs) in high technology sector. Authors also try to check, whether existing cluster initiatives comply with the actual concentration of high-tech SMEs and whether there is any potential for new cluster initiatives. The present paper analysis exploits modified methodology, based on localization index. The study provides tables and maps, reflecting small and medium businesses concentration in Russian regions using evidence from high and medium-high technology industries. The authors empirically confirm the existence of traditional and well-known clusters and identify new concentrations of firms in Russia. This useful information can be used for policy advice.

Factors of Attractiveness of the Leading Russian Universities Overview of Literature and Econometric Analysis of the Leading Universities

DOI: 10.17323/1814-9545-2015-4-201-233Stepan Zemtsov - PhD in Geography, Senior Researcher, Institute of Applied Economic Research, RANEPA. Email: spzemtsov@gmail.comVladimir Eremkin - Researcher, Institute of Applied Economic Research, RANEPA. Email: eremkin@ranepa.ruVera Barinova - PhD in Economics, Head of the Corporate Strategies and Behavior Laboratory, Institute of Applied Economic Research, RANEPA. Email: barinova-va@ranepa.ruAddress: 82/1, Vernadskogo pr., 119571 Moscow, Russian Federation.Attractiveness of a university means being in demand and reflects its ability to attract the best students. The aim of this research is to identify key factors influencing the best students choice in Russia. Average unified state examination (USE) scores of successful candidates for admission to the universit y, are used as a dependent variable. The paper provides a review of theoretical models and empirical studies of high school entrants decision criteria. Our econometric analyses show that the key factors of university selection are universitys brand, success of its graduates, quality of teaching staff and colleges location. In some cases research activity is also a significant factor. Those technical universities, which retained their specialization, are among the most in-demand, and demand for technical universities, which increased the share of students in economics, is below the average. The achieved results are useful for the heads of higher education institutions while developing evidence-based measures to increase demand for universities. First of all, there is a need for reputation improvement, which includes dissemination of information about achievements of graduates and scientific achievements, etc. on the internet.DOI: 10.17323/1814-9545-2015-4-201-233

Оценка Эффективности Региональных Инновационных Систем в России (Efficiency Assessment of Russian Regional Innovation Systems)

Russian Abstract: Затраты на поддержку инновационной активности на региональном уровне в России ежегодно растут. Фактически в ведущих регионах создана необходимая рыночная инфраструктура поддержки. Наблюдается сильная дифференциация между регионами по результатам инновационной деятельности. Появляется необходимость в оценке эффективности региональных инновационных систем (РИС). Одним из индикаторов являются патенты на изобретения. В работе с помощью производственной функции знаний (ПФЗ) исследуются факторы, оказывающие воздействие на патентную активность. Для оценки использовались как данные о заявках на патенты Роспатента, так и зарубежные заявки. Основная гипотеза состояла в том, что патентная активность в России слабо поддается моделированию, так как сильно зависит от деятельности местных властей и интересов местной научной элиты. Эконометрический анализ показал, что существует возможность выделить некоторые ведущие факторы (человеческий капитал, затраты на научные исследования и др.), а поэтому возможна оценка эффективности РИС на основе соотношения этих факторов и патентной активности. Авторами разработана и предложена методика оценки. Среди лидеров преобладают регионы с крупнейшими агломерациями. В работе показано, что РИС большинства ведущих регионов с середины 2000-х гг. теряют свою эффективность.English Abstract: The cost for supporting of innovation activities at the regional level in Russia is growing. In fact, the necessary infrastructure in the leading regions has been created. But there is a strong differentiation between the regions in their results of innovation. It is important to assess the effectiveness of regional innovation systems (RIS). One of the indicators is patents. In this paper, factors affecting patent activity were investigated, using the production function of knowledge (PFK). For verification reasons, the data on Russian patent applications (as a dependent variable) was supplemented by PCT-applications. The main hypothesis was that the patent activity in Russia couldn’t be modelled, because it greatly depends on the activities of local authorities and the interests of the local scientific community. The econometric analysis shows that it is possible to highlight some of the major factors (human capital, the costs of research, etc.). Therefore, it was useful RIS efficiency estimation, based on the relationship of the identified factors and patent activity. The authors have developed and proposed an assessment methodology. The regions with the largest agglomerations (Moscow, Saint Petersburg, etc.) are among the leaders. It is shown that most of the RIS of the leading regions lose their effectiveness since the mid-2000s.