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The implementation of new automation technologies together with the development of artificial intelligence can free up a significant amount of labor. This sharply increases the risks of digital transformation. At the same time, certain regions and cities differ greatly in their ability to adapt to future changes. In this article, we seek to determine the capabilities of Russian regions to reduce risks and adapt to digital transformation. The literature stipulates that there are several factors able to reduce these risks. First of all, they are associated with retraining, ICT and STEAM-technologies’ development, the promotion of economic activities that are less subject to automation. As a result of econometric calculations, we identified several factors that contribute to the new industries’ development (in our case, ICT development), and, accordingly, increase regional adaptivity. These factors  include diversification, the concentration of human capital, favorable entrepreneurship conditions, the creative potential of residents, and the development of ICT infrastructure. We identified several regions with high social risks and low adaptivity, which are mainly the poorly developed regions of southern Russia, where entrepreneurial risks are high, STEAM specialists are not trained, shadow economy is large. This work contributes policy tools for adaptation to digital transformation.

Technological startups help to adapt to the global risks and allow one to track future trends. This paper identifies the main trends and birth factors of new high-tech companies in the Russian regions during 2013-2020. In 2020, fewer than 10,000 startups were created, this number has been steadily declining (by 40% since 2015), especially during the pandemic (-21%). Most of the startups are concentrated in Moscow, the Moscow region, St Petersburg, and the largest metropolitan areas. The share of the Leningrad, Belgorod, Kaliningrad, Lipetsk, Ulyanovsk, and Kaluga regions is growing due to the proactive policies of local authorities. Most startups are associated with knowledge-intensive services for business (B2B) and digital technologies. In 2020, their number increased in pharmaceuticals (about 100%) and in the production of medical devices (by about 30%).Based on the results of econometric analysis, start-up activity in Russia, analogous to countries with an established market economy, depends upon human capital concentration, market access, and a favorable business climate. Universities, through attracting students, especially those in STEM specialties, stimulate startup creation; although the share of university startups does not exceed one third of a percent. Budgetary and university expenditures on R&D are ineffective in terms of creating new companies. The influence of development institutions on start-up activity was not found, while clusters and technology parks have a weak effect. The growth of startups is lower in regions with a predominance of large organizations, as well as in resource centers. The latter may be one of the manifestations of the “resource curse”. Startup activity is stable over time and depends on the situation in neighboring regions, which limits the chances to change the situation by means of entrepreneurship support policy. During the pandemic, start-up activity decreased minimally in regions with large metropolitan areas and a high level of education. Recommendations include tools for establishing a more balanced cross-regional situation by implementing the model of an entrepreneurial university, an expansion of start-ups’ access to capital and markets, and the regionalization of entrepreneurship policies.

The COVID-19 pandemic has covered all Russian regions. As of May 8, 2020, about 190 thousand cases have been identified, more than 1600 people with the corresponding diagnosis have died. The values of the indicators are expected to rise. However, the statistics of confirmed cases and deaths may underestimate their actual extent due to testing peculiarities, lagging reporting and other factors. The article identifies and describes the characteristics of the regions in which the incidence and mortality of COVID-19 is higher. Migration of potential carriers of the virus: summer workers and migrant workers from Moscow and large agglomerations, as well as return of labour migrants to the North increase the risks of the disease spread. The risk of mortality is higher in regions with high proportions of the poor and aged residents, for whom it is difficult to adapt to the pandemic, and lower in regions with greater health infrastructure. Based on the revealed patterns, a typology of regions on possible risks is proposed. Above all the risks in and near the largest agglomerations (the cities of Moscow and Saint Petersburg, Moscow and Leningrad Oblasts), in the northern regions where the share of labour migrants is high (Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs), in southern underdeveloped regions (Ingushetia, Karachay-Cherkess, Kabardino-Balkarian Republics, Dagestan, North Ossetia). For the latter, the consequences may be most significant due to the limited capacity to adapt to the pandemic and self-isolation regime, and additional support measures may be required in these regions.

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 ).

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.

The 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.

Spending on innovation increased annually in the 2000s in Russia’s regions, but innovation productivity varies greatly between regions. In the current climate of sanctions between Russia and Western countries and limitations on international technology transfer, there is a growing need to analyse the factors influencing regional innovation. Previous empirical studies using a knowledge production function approach have found that the main factor of the growth of regional innovation is increasing spending on research and development (R&D). Our econometric analyses show that the quality of human capital, a product of the number of economically active urban citizens with a higher education (the so-called creative class) has the greatest influence on the number of potentially commercializable patents. Other significant factors were buying equipment, which indicates a high rate of wear and tear of Russian machinery, and spending on basic research. The ‘centre-periphery’ structure of Russia’s innovation system favours the migration of highly qualified researchers to leading regions, which weakens the potential of the ‘donor regions’. However, at the same time, we see significantly fewer limitations on knowledge spillovers in the form of patents and - in this case - proximity to the ‘centres’ is a positive factor.

В условиях экономических санкций, введенных в отношении России рядом зарубежных партнеров в 2014 г., особое значение приобретают высокотехнологичные отрасли хозяйства как важнейший источник замещения импортной продукции на внутреннем рынке. Одной из ключевых мер поддержки таких отраслей служит развитие специализированных кластеров за счет установления новых и укрепления существующих связей между субъектами малого и среднего бизнеса, крупными предприятиями и научными организациями. Отправной точной эффективной кластерной политики служит идентификация регионов с высоким потенциалом кластеризации указанных отраслей.В работе представлена оригинальная методика выявления потенциальных кластеров и приведены результаты ее апробации в регионах России. Авторы демонстрируют, что большинство поддерживаемых государством пилотных инновационных проектов реализуются в регионах и отраслях, обладающих высоким кластерным потенциалом. Произведена типологизация пилотных инновационных территориальных кластеров в зависимости от потенциала кластеризации регионов их расположения, выраженного в соответствующем индексе. Определены регионы со сходными или более благоприятными условиями для формирования кластеров в инновационных отраслях, отобранных в качестве пилотных.Примечание: Уважаемые читатели, обращаем Ваше внимание, что в печатной версии статьи на картосхемах (рис. 1-7) цвета в легенде ошибочно приведены в обратном порядке. Следует читать: красным показаны наибольшие значения индекса, синим - наименьшие.

Затраты на поддержку инновационной деятельности в российских регионах в 2000-е гг. планомерно росли одновременно с сильной дифференциацией ее результатов. Внешнеэкономические санкции и ограничения по технологическому импорту придали актуальность исследованию факторов региональной изобретательской активности. Эмпирические работы в этой области подтвердили основные положения теоретической модели производственной функции знаний, определив ключевым фактором развития инноваций увеличение затрат на научные исследования.Как показано в статье, количество потенциально коммерциализируемых патентов в наибольшей степени зависит от качества человеческого капитала, производного от численности экономически активных горожан с высшим образованием (так называемый креативный класс). Значимым фактором выступают также затраты на приобретение оборудования вследствие его высокого износа и на фундаментальные исследования, закладывающие основу для новых разработок. Центр-периферийная структура российской инновационной системы способствует миграции высококвалифицированных исследователей в регионы-лидеры, ослабляя потенциал регионов-доноров. Вместе с тем ограничения на переток знаний в форме патентов существенно меньше, а потому близость к «центру» в этом случае рассматривается как положительный фактор.

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 thefuture, 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 Moscowand St. Petersburg agglomerations have the maximum potential of interregional EGP, the potential decreases uniformly to theeast. The maximum international EGP potential is concentrated in the regions on the coast of the Black Sea, the Baltic Sea andthe 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 isrevealed a significant increase in the total EGP potential in the 2000s, and its shift to the southern regions of the Far East dueto the growth of the Asia-Pacific economies. The regions with a high and low efficiency of EGP use are revealed. The results areused to identify the connections between the EGP potential and the indicators of socio-economic development. It is found thata favorable EGP is one of the factors for gross regional productgrowth, the growth of investment and foreign trade, migrationgrowth and spread of new technologies. Formalizing EGP as a category allows to use it to predict the spatial changes in the socioeconomicdevelopment of Russia.