The external shocks from 2020 to 2021 and again from 2022 to 2023 induced an economic crisis that required the Russian government not only to ease tax, administrative, and other burdens so that businesses could quickly adapt, but also prompted greater financial support for small and mediumsized enterprises (SMEs). However, direct subsidies may not always produce the desired effect. This study examines the specifics of state support for SMEs and assesses how well it sustained the number of SMEs in the regions during these shocks. Official data from the register of SMEs receiving support indicates that assistance totaling 931.4 billion rubles was provided over the eighteen months from 2022 to the first half of 2023; that amount was 1.7 times greater than pandemic assistance (issued from 2020 to the first half of 2021). Guarantees and sureties accounted for 83% of the later rounds of support, whereas those instruments made up less than 50% of previous pandemic support. The coverage of SMEs by state assistance nevertheless decreased from 26.6% in 2020 to 6.3% in 2022. The federal government had shifted its focus from providing a mass of small subsidies and grants to more targeted guaranteed support for manufacturing and technology companies in order to stimulate import substitution. Overall, state support became more concentrated; approximately 2.1 million rubles per supported SME were allocated in 2022, which was 6.4 times more than during the pandemic. The proportion of indirect measures, such as those provided by development institutions, also increased, which could be attributed to a more ecosystem-based entrepreneurship policy. The study employed a system-GMM (system Generalized Method of Moments) approach to assess the effectiveness of various government policy approaches. The econometric results show that indirect assistance with increased volume per SME is effective for maintaining the number of SMEs, while increasing the coverage of SMEs has a beneficial effect when coupled with such direct measures as subsidies and grants. Going forward, the government should differentiate its approach to providing SME assistance using various instruments and build a support system that takes into account the shift from traditional strategies to the development of regional and local entrepreneurial ecosystems.

Economic sanctions and countersanctions are expanding worldwide, posing spatially heterogeneous threats to most countries. The study aims to develop and test a methodology for assessing regional exposure to sanctions risks using Russian data. The share of foreign trade with the countries that introduced restrictions can be used to evaluate the exposure to new trade barriers. In several cases, this share exceeded 50 %, necessitating a rapid reorientation of product flows in Nenets, Khanty-Mansiysk Autonomous Areas, Komi, and Murmansk region. The Kaliningrad, Kaluga, and Leningrad regions exhibit high import dependence in the production sector, particularly in the automotive industry, due to their active involvement in global supply chains. Sanctions against large legal entities created risks for the stability of regional economies but the increase in demand for domestic products offset this impact. Foreign enterprises exiting the market posed risks of disrupting production chains but also provided opportunities for local business development. Before some countries introduced sanctions, their companies had held more than 20 % of the market share in Kaluga, Moscow region, and the city of Moscow. However, the share of foreign firms that announced complete withdrawal exceeded 5 % of the market only in the Komi, Samara, Leningrad, and Moscow regions. An integral index of exposure was proposed based on the mentioned indicators. Its value is lower for the regions with a more diversified economy and foreign trade. The greatest risks were observed in the closely connected to the European Union northwestern territories of Russia: Karelia, Komi, Kaliningrad, Leningrad, and Arkhangelsk regions. In 2022, regions with a high index value were more likely to experience a decline in economic activity, but in 2023, this impact was less explicit due to economic adaptation and transformation. Based on the results of the study, some recommendations can be formulated.

Geography of innovation allows us to understand the spatial patterns for creation, diffusion, and support of new technologies, although with the development of communications there is a delusion of insignificance of space in these processes. The development of one of the breakthrough technologies – artificial intelligence (AI) – cannot be widespread but must be concentrated in centers with high innovation potential, where the intensity of knowledge spillovers is high. In Russia, education in the field of AI can be obtained in 21 regions, research is conducted in 35, and technology is being developed in 40. We proposed a rating of the regional potential to create AI technologies based on scientific and technological development and the main elements of the regional innovation ecosystem in the field of AI. It shows a high concentration of potential in Moscow and several creative core regions: Moscow region, St. Petersburg, Tatarstan and Novosibirsk region. 16 creative-acceptor centers have also been identified, capable of both creating and implementing AI technologies, mainly acceptor centers (23 regions) and 40 regions with minimal potential. Leading regions can receive priority attention and funding in Russia. In acceptor regions, advantage may be given to AI production technologies, and in lagging regions, increasing the population’s receptivity to digital technologies in general.

В мире расширяется применение экономических санкций и контрсанкций, что несет пространственно неоднородные угрозы для большинства стран. Цель исследования — разработать и апробировать методику оценки подобных рисков на примере регионов России. Для оценки потенциального влияния торговых санкций рассчитывалась доля внешней торговли, приходившаяся на страны, вводившие санкционные ограничения. В ряде случаев она превышала 50 % (Ненецкий, Ханты-Мансийский автономные округа, Коми, Мурманская область), что потребовало быстрой переориентации потоков. Производственная импортозависимость высока для Калининградской, Калужской и Ленинградской областей, активно вовлеченных в глобальные цепочки, в частности в автомобилестроении. Санкции против юридических лиц могли создать риски для стабильности экономик домашних регионов, но рост спроса на отечественную продукцию нивелировал это влияние. Уход иностранных предприятий с рынка создавал риски разрыва производственных цепочек, но и предоставлял возможности для развития местного бизнеса: доля компаний из недружественных стран до введения санкций занимала более 20 % рынка в Калужской, Московской областях, в Москве, но лишь в некоторых регионах доля иностранных фирм, заявивших о своем полном уходе, превышала 5 % рынка: Коми, Самарская, Ленинградская и Московская области. Для комплексной оценки потенциальной подверженности экономики регионов внешним ограничениям рассчитан интегральный индекс на основе вышеупомянутых составляющих. Его значение ниже для регионов с более диверсифицированной отраслевой структурой экономики и внешнеторговых потоков, а наибольшие риски наблюдались для северо-западных территорий России, ранее тесно связанных со странами Европейского союза: Республики Карелия, Коми, Калининградская, Ленинградская, Архангельская области. В 2022 г. в регионах с высоким значением индекса была выше вероятность снижения экономической активности, но в 2023 г. это влияние прослеживается в меньшей мере. По результатам исследования сформулированы некоторые рекомендации.

After external trade restrictions were imposed on Russia and China in recent years, their access to foreign technologies decreased. This created new incentives for scientific and technological partnership between the two countries. In addition, an analysis of strategic documents indicates that the US and the EU are likely to increase economic and technological pressure in the future. This article analyzes China’s experience in shoring up technological sovereignty under sanctions, an exercise which can be instructive for many countries. China’s long-term scientific and technological policy follows a catch-up development model, which has enabled a transition from borrowing simple technologies via specialized institutions (joint ventures, special economic zones, etc.) to global leadership in R&D and technologies that has been made possible through developing human capital and applying preferential treatment to local innovative businesses. Although Russia is one of the world’s leading scientific and technological centers, to some extent it lags in developing high-tech businesses and exports. Moreover, for some time it has been importing advanced products and technologies, some of which are now being quickly replaced by Chinese versions as Western companies have exited. The article applies a SWOT analysis to Russian-Chinese scientific and technological cooperation in order to highlight the benefits of this collaboration, especially concerning machine tools, microelectronics, and aerospace. The benefits from such partnership will materialize for most high-tech industries over the long run by means of joint scientific research. However, one cannot ignore the risks for Russia due to increased technological dependence on a single partner and the potential outflow of personnel and technology, as well as risks for China related to potential secondary sanctions.

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.

Studies of employment growth factors are more relevant during crises. Review of foreign studies and analysis of Russian data in 2005—2018 using a distributed lag model based on the Almon method shows that there are multidirectional short-term direct and longer-term indirect effects of starting a business on employment growth. The regional context is important; and the prevalence of one effect over another and the direction of influence of additional factors depend on the type of region. Thus; for large agglomerations with high labor productivity and an active SME sector; an S-shaped lag structure of the dependence of employment on the creation of new firms was revealed: with short-term positive; medium-term negative; and further positive effects. For regions with low urbanization; labor productivity and a less active SME sector; the most striking is the short-term positive impact on employment from the opening of firms; which is replaced by a negative one after 2—3 years. At the same time; in the latter regions; the total impact may be higher than in the former; and on average; a new firm (per 1;000 people in the workforce) leads to an increase in employment by 0.56 p.p. This provides grounds for some policy recommendations.

Russian abstract: В малом и среднем предпринимательстве (МСП) работает более 50% населения развитых стран, поэтому его поддержка – одна из значимых мер в сохранении занятости. Актуальность этого направления выросла в кризис 2020 г., который нанес заметный ущерб сфере МСП. Целью исследования является систематизация и обобщение опыта развитых стран в области поддержки МСП в кризисные и некризисные периоды и определение инструментов поддержки, необходимых в России. Среди задач – проанализировать как опыт становления политики по поддержке занятости, так и инструменты поддержки и развития предпринимательства на примере разных кризисов. В работе использовались общенаучные методы (анализ, синтез, индукция и дедукция, сравнения, обобщения и др.), проводился анализ литературы и открытых источников, актуальных на 2021 год, в том числе отчетов международных организаций, нормативно-правовых актов. Результаты исследования – это выводы относительно зарубежного опыта поддержки занятости и возможности его применения в России. Основное направление политики по поддержке занятости в некризисный период за рубежом – создание мотивации у среднесрочных и долгосрочных безработных искать работу, привлечение к работе незащищенных социальных групп населения; для поддержки МСП используется снижение административного давления и повышение прозрачности регулирования, акселерационные программы. В ходе текущего кризиса государственная поддержка бизнеса за рубежом активизировалась в двух направлениях: поддержка ликвидности компаний и содействие максимальному переводу деятельности на удалённый, цифровой режим. Россия за последнее десятилетие активно продвинулась в области некризисной поддержки МСП, но в условиях текущего кризиса ей необходимо расширить направления льготного кредитования или софинансирования, активизировать содействие по цифровизации деятельности МСП. Главным направлением дальнейшей работы является анализ применения описанных зарубежных инструментов в России, сопоставление их эффективности и корректировка с учётом российской специфики. English abstract: Small and medium-sized enterprises (SMEs) employ more than 50% of the population of developed countries, so their support is one of the significant measures in maintaining employment. The relevance of this direction has grown since the crisis of 2020, which caused significant damage to the SME sector. The aim of the work was systematization and generalization of the developed countries’ experience in SME support in crisis and non-crisis periods, identification of support tools needed in Russia. The tasks of the study included analyzing both the experience of the development of employment support policies and tools for supporting and developing entrepreneurship using various crises as an example. The research was carried out using general scientific methods (analysis, synthesis, induction and deduction, comparisons, generalizations, etc.). The authors reviewed the literature and open sources relevant for 2021, including reports by international organizations, regulatory legal acts. The results of the research are conclusions about the foreign experience of employment support and the possibility of its application in Russia. The main direction of the employment support policy in non-crisis periods abroad is to create motivation for medium and long-term unemployed to look for work, to attract vulnerable social groups to work; acceleration programs are used to support SMEs by reducing administrative pressure and increasing regulatory transparency. During the current crisis, the government support for business abroad intensified in two directions: supporting the liquidity of companies and facilitating the maximum transfer of activities to a remote, digital mode. Over the past decade, Russia has actively advanced in non-crisis support for SMEs, but in the current crisis, it needs to expand the areas of preferential lending or co-financing, and step up assistance to digitalize the activities of SMEs. The main direction of further research is to analyze the use of said foreign instruments in Russia, compare their effectiveness and adjust them taking into consideration Russian specifics.

2022 demonstrated intensification of sanction pressure on the Russian Federation. Consumer demand contracted against the backdrop of inflation coupled with the rising cost of borrowing, many entrepreneurs faced the risk of bankruptcy. Under pressure from unfriendly countries, many large foreign companies left Russia, and established supply chains were severed. The release of jobs, caused by the closure of a number of industries, created the preconditions for the development of forced entrepreneurship, mainly for people who lost their jobs. Small and medium-sized enterprises suffered both from the imposed restrictions and the ensuing decline in consumer demand, and therefore became one of the objects of the anti-crisis state policy.

The field of STEAM (Science, Technology, Engineering, Arts and Mathematics) is one of the most promising in education, and these professionals are employed in high-tech and creative industries that determine the future of global economy. The paper presents a detailed analysis of this sphere based on original approaches, both in the Russian Federation at large and in its regions. In Russia more than half a million people are annually admitted to STEAM training courses for higher education programs. Contrary to the all-Russian trend of reducing the number of students, enrollment for these programs has been steadily growing. According to the latest data, about 36% of university graduates in Russia have a specialty within STEAM. For comparison, this share in South Korea, Singapore, and Germany is above 45%. The largest share of specialists is trained in Tomsk region, St. Petersburg, and Moscow. At the same time, the number of STEAM graduates decreased in 41 Russian regions. In 2022, therisks for STEAM industry in Russia increased markedly: there is a migration outflow of most qualified personnel, with declining domestic demand for them. Over six months from February to August 2022, the total number of vacancies in the Russian labor market decreased by 9%, while STEM - by 23% (IT - by 32.5%). This may affect the long-term scientific and technological development of the country: our calculations show the importance of STEAM for creating start-ups, increasing publication and innovative activity. The article concludeswith some measures for developing STEAM as a tool for long-term development and a way to adapt the country to external shocks.

Some institutions can restrict or stimulate the business activity, which affects long­term economic growth. To assess this influence on regional level, we have collected and processed historical data on the distribution of serfs, the creation of universities, and business activity over more than a century. By business activity, we mean various direct and indirect assessments of the involvement of the population in entrepreneurial activity: merchants, NEPmen, cooperatives, small businesses, etc. Although the geography of business activity has constantly changed, we can identify relatively stable centers (Moscow, St. Petersburg, the south of the Far Eastern Russia) and the periphery (some regions of the North Caucasus, the Central Black Earth and the Volga regions). Econometric calculations confirm the existence of a relationship between the current density of small businesses in the Russian regions and the density of cooperatives in the late Soviet period; the relationship with the density of retail enterprises disappears by the 1970s as the planned economy strengthens. But the relationship with the merchant class is ambiguous: only in some regions did the entrepreneurial culture manage to survive the Soviet period. We distinguish three main channels of influence of the historical level of business activity on the modern one: geographical, functional, and socio­cultural. According to the calculations, the earlier emergence of universities in the regions contributed to the spread of business culture and could stimulate the emergence of more inclusive institutions, but serfdom, as an extractive institution, on the contrary, could limit incentives for entrepreneurship. Even after a radical change in the political and economic regime, the influence of extractive institutions on business activity may persist, and inclusive institutions take a significant amount of time to take root.

Traditional approaches to entrepreneurship support prevail in Russia. Significant amounts of direct federal support are allocated to small and medium enterprises (SMEs), but their role in the economy is consistently low. Public support imposes a weak impact on the SMEs development in comparison with macroeconomic and institutional factors. However, the support has increased significantly during the pandemic. We show that direct financial support is not always efficient, especially in developing countries. The aim of the article is to propose an alternative entrepreneurship policy for Russia and other countries, based on creation of favourable business environment in regions and cities, networks of business agents and proactive public support system considering specifics of regional entrepreneurial ecosystems. The article formulates the principles and goals of the ecosystem approach. The approach involves cooperation of local stakeholders and evaluating the support measures’ efficiency to reduce transaction costs for business agents and creating the motivation for growth and diversification. The work identifies the best practices in Russia and abroad. Previous policy in Russia does not fully comply with the principles and goals of the ecosystem approach: local authorities and business agents are not sufficiently involved, do not always consider regional specific, some measures create the wrong incentives for business agents, and some support institutions are not opened enough to public control. We proposed an appropriate typology of regional entrepreneurial ecosystems, based on the mentioned goals and principles, for a more differentiated entrepreneurship policy. There are several successful regional practices of SMEs’ development (Belgorod, Voronezh, Kaliningrad, Leningrad, Lipetsk, Novosibirsk, Samara, and Tyumen oblasts, Moscow, St. Petersburg, the Republic of Tatarstan), that can be distributed within similar types of ecosystems.

Despite the limited markets now available to entrepreneurs and the disruption of supply chains, many indicators of business activity in Russia did not decrease in 2022. However, there are significant dif-ferences between regions in their response to these external shocks. The article evaluates factors that brought about changes in the most pertinent indicator of business activity - the growth in the number of new small and medium-sized businesses. First, in regions where economic ties with the countries that imposed sanctions were weaker, there were more such new businesses. In some regions that produce raw materials, those countries annually accounted for more than 80% of exports and imports. Second, the withdrawal of companies from Russia may open up certain market niches (in trade, IT, other services, and processing). The revenue of enterprises from countries deemed unfriendly by the Russian government was about 16 trillion rubles, or about one tenth of the market. The hypothesis that this amount of revenue had a positive effect on the number of newly created small and medium-sized enterprises because of less competition in certain market niches was confirmed provisionally and with a low degree of significance. Third, just as the online sector provided one way of adapting to a crisis during the pandemic, the ubiq-uitous reach of online trading platforms along with access to parallel imports came into play. In regions where businesses and the public placed more orders for goods and services via the internet, more new enterprises were created. The hypothesis that proximity to unfriendly countries had a negative impact on business activity in the regions at their borders was confirmed. And vice versa, the proximity to Georgia and Azerbaijan had a positive effect as the flow of goods and tourists was redirected toward the North Caucasus. The scale and diversity of markets due to large economic agglomerations in the regions was also beneficial. These observations bear on a number of recommendations.

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.

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.

In this paper we use an institutional approach and apply a regional perspective to explore how market potential, formal institutions, taxes, access to finance, regional policy instruments, and digitalization have affected small business activity in 83 Russian regions during 2008-2018. We use various regional data sources and official statistics to study the effects of regional business environments on entrepreneurship. Our results suggest that Russia's business environment, including policy measures in taxation, is important in explaining small business activity, however digital transformation and the role of market potential can be better controlled by entrepreneurs in terms of what skills to learn and where to locate their businesses. In addition, we discuss the effect of exogenous shocks and changes in the business environment, along with dynamics, challenges, and perspectives of entrepreneurship in Russia.

Technological entrepreneurship is a potential driver of Russia’s socio-economic development. But an optimal combination of environment and business networks (entrepreneurial ecosystem) to be formed is very rare and depends on many factors. The article discusses the potential role of technology start-ups in diversification, economic and employment growth, and adaptation to technological changes. The number of start-ups in Russia had decreased since 2015, and there is a low entrepreneurial activity in comparison with other countries. In general, the change in the industry structure of startups in Russia is consistent with the global trends; the role of knowledge-intensive business services and ICT is high and growing, the share of manufacturing is declining. We revealed the determining role of socio-cultural factors, human capital and universities, business climate and access to foreign markets in the creation and success of technology companies, as well as the contradictory impact of state support. Significant and increasing role of immigrants and diaspora abroad is underestimated for Russia. We noted the inability of the widespread development of successful technology companies (“gazelles”, “unicorns”); identified regional and sectoral priorities for public policy. We examined the main elements and models of national entrepreneurial ecosystems, limitations and prospects for their application in Russia.

We proposed an approach to evaluate ecological efficiency of an economy as the ratio of the created output of non-primary goods and services to the input of consumed resources (labor, capital, raw materials, environmental costs) using the DEA. The eco-efficiency of an average Russian region has been growing since 2003. Using econometric calculations, we have established it grew faster in densely populated areas with a high share of high-tech services, investment attractiveness, and intensive technology implementation; it decreased in most northern and Siberian regions. The simultaneous growth of GRP per capita and ecological efficiency in a region was considered as a model of sustainable development. We observed this pattern more than half of the period 1998–2017 in most Russian regions although the Russian economy mainly developed due to the extraction of raw materials.

High-tech startups play a leading role in new technological revolution after pandemic, but regional environment for them (entrepreneurial ecosystems) in 83 Russian regions that differs significantly. Our econometric results show that startup activity was higher in regions with more educated population and university students, as many startups are created by young entrepreneurs and in need for high-qualified personnel. Although commercialization of R&D is low in Russia, a greater number of new firms appeared in scientific and technological centres due to the knowledge spillovers. Digitalization, improved access to the Internet, the larger domestic consumer markets also encouraged the new high-tech firms' formation. Agglomeration effects increase the concentration of startups in large cities. New companies are rare in regions with higher share of informal sector and in mining centres. According to these factors, we propose some recommendations for Russian regional authorities.