The coronavirus pandemic and the economic crisis in 2020 are accelerating digital transformation. During and after the crisis, there are opportunities and needs for remote work facilities, online services, delivery drones, etc. We discuss how unmanned technologies can cause a long-term employment decrease, and why compensation mechanisms may not work.

T he 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 Кeywords: digital economy; robots; STEAM; automation risks; technological exclusion; nescience economy; human capital; entrepreneurship; ICT 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.

The main aim of this study is to compare Russian regions according to their ability to create new technologies efficiently and to identify factors that determine these differences over a long period of time. We apply data envelopment analysis (DEA) to assess the relationship between the results of patenting and resources of a regional innovation system (RIS). Unlike previous studies, we apply the DEA method over a long period, comparing regions to one another and over time. In general, RIS efficiency in Russia increased during the period, especially in the least developed territories. There was significant regional differentiation. The most efficient RIS were formed in the largest agglomerations with leading universities and research centers: the cities Moscow and Saint Petersburg and the Novosibirsk, Voronezh, and Tomsk regions. Econometric calculations show that RIS efficiency was higher in technologically more developed regions with the oldest universities and larger patent stock. Time is a crucial factor for knowledge accumulation and creating links between innovative agents within RIS. Entrepreneurial activity was also a significant factor because it helps to convert ideas and research into inventions and new technologies and it enhances the interaction between innovative agents. It is advantageous to be located near major innovation centres because of more intensive interregional knowledge spillovers. Public support of more efficient regions can lead to a more productive regional innovation policy.

Despite many governmental support programs, the entrepreneurship development in Russia is still very uneven. In this article we analyze numerous studies on entrepreneurship and find out that the institutional background in general and in certain regions is very important for the development of entrepreneurship. The risks of doing business, the complexity and duration of administrative procedures, access to capital, regulation and informal community norms are of extreme importance. The aim of this paper is to identify regional institutional factors for the development of small enterprises in Russia. With the help of the proposed econometric model we show that high investment risks and large number of economic crimes are significant deterrents for the entrepreneurial activity in Russia. The banking services’ availability and the proximity of large markets, combined with the human capital concentration, contribute to the entrepreneurship development. The impact of state support turned out to be not significant. We formulate some policy advice for entrepreneurship support in Russia.

I n the current climate of sanctions imposed against Russia by several countries in 2014, special attention should be given to high-tech sectors of the economy as a key source of import substitution on the domestic market. One of the important policy measures is to support the development of high-tech, specialized clusters by forming new linkages and strengthening existing ones between small and medium-sized businesses, large enterprises, and research organizations. The starting point for an effective cluster policy is to define areas with high potential for Keywords: clusters; small and medium enterprises; location quotients; pilot innovative clusters; regions; Russia; hightech industries clustering of these industries. The paper presents an original method to identify potential clusters and tests the method on Russian regions. We show that most of the state-supported pilot innovative territorial clusters are being developed in regions and sectors that have a high level of cluster potential. A typology of existing clusters depends on the index of clustering potential. We identified regions that have similar or comparatively favourable conditions for creating clusters in the pilot sectors.

The observed spread of coronavirus infection across Russian regions, as a first approximation, obeys the classic laws of diffusion of innovations. The article describes in detail theoretical approaches to the analysis of the spread of social diseases and discusses methodological limitations that reduce the possibility of predicting such phenomena and affect decision-making by the authorities. At the same time, we believe that for most regions, including Moscow, until May 12, 2020, the dynamics of confirmed cases are a reduced and delayed reflection of actual processes. Thus, the introduced self-isolation regime in Moscow and other agglomerations affected the decrease in the number of newly confirmed cases two weeks after its introduction. In accordance with our model, at the first stage, carriers infected abroad were concentrated in regions with large agglomerations and in coastal and border areas with a high intensity of internal and external links. Unfortunately, the infection could not be contained, and it started growing exponentially across the country. By mid-April 2020, cases of the disease were observed in all Russian regions; however, the remotest regions least connected with other parts of Russia and other countries had only isolated cases. By mid-May, at least in Moscow, the number of new cases began to decline, which created the prerequisites for reducing restrictions on the movement of residents. However, the decrease in the number of new cases after passing the peak of the epidemic in May is slower than the increase at the beginning. These facts contradict the diffusion model; thus, the model is not applicable for epidemiological forecasts based on empirical data. Using econometric methods, it is shown that for different periods of diffusion, various characteristics of the regions affect the spread of the disease. Among these features we note the high population density in cities, proximity to the largest metropolitan areas, higher proportion of the most active and frequently traveling part of the population (innovators, migrants), and intensive ties within the community, as well as with other regions and countries. The virus has spread faster in regions where the population has a higher susceptibility to diseases, which confirms the importance of the region's health capital. The initial stage was dominated by random factors. We conclude this paper with directions for further research.

The article explains the uneven development of small and medium enterprises in Russia within the framework of the ‘entrepreneurial ecosystems’ concept. A corresponding typology of the Russian regions was carried out according to the proposed model. The most developed ecosystems with highdensity and sectoral diversity of SMEs are formed in regions with access to large consumer markets, capital, and low risks for investors. The least developed SMEs sector is in areas with high costs of doing business: the North Caucasus, the Far East and the Arctic zone, which requires special measures of state policy. The level of development of ecosystems determines their ability to withstand external shocks. The effect of the FIFA World Cup is positive in the hosting regions and in its neighbors. Based on the typology, we proposed differentiated support measures.

Аннотация. Уровень предпринимательской активности в России характеризуется высокой изменчивостью и территориальной неоднородностью. Ряд регионов способны сохранять определенный уровень развития предпринимательства продолжительное время, в других же регионах он может сильно варьироваться даже в течение нескольких лет. При этом растущие регионы могут располагаться рядом с регионами-лидерами, а слабые регионы вблизи друг друга. В работе дана оценка подобных временных и пространственных эффектов, которые часто игнорируются при принятии политических решений. Выявлена группа регионов с крупнейшими агломерациями и выгодным экономико-географическим положением, обладавших высокими показателями предпринимательской активности за период 1998-2014 гг.: Санкт-Петербург, Москва, Калининградская, Новосибирская, Самарская, Ярославская, Свердловская, Белгородская и Омская области. Также были определены межрегиональные кластеры с концентрацией регионов-лидеров (Новосибирская и Томская области) и регионов-аутсайдеров (Северный Кавказ). Как следует из эконометрических расчетов, предпринимательская активность в регионе существенно зависит от ее уровня в предшествующие два года и находится под влиянием активности в соседних регионах, удаленных на расстояние не более 300 км. При этом также были учтены уровень развития региона, институциональная среда и структура экономики. Результаты работы являются обоснованием необходимости проведения территориально дифференцированной политики в сфере малого и среднего предпринимательства. Ключевые слова: зависимость от пройденного пути, малые и средние предприятия, регионы России, перетоки знаний, укорененность, региональная политика. Классификация JEL: L26, C23, R12. ской области в XX в., связанные с полной заменой местных жителей, а соответственно, и институтов, муниципалитеты, обладавшие высокой предпринимательской активностью в начале XX в., обладают ею и сейчас.