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Article ; Online: New technologies, potential unemployment and 'nescience economy' during and after the 2020 economic crisis.

Zemtsov, Stepan

Regional science policy & practice

2020 Volume 12, Issue 4, Page(s) 723–743

Abstract: 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 ... ...

Abstract	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. Using the internationally comparable Frey-Osborne methodology, we estimated that less than a third of employees in Russia work in professions with a high automation probability. Some of these professions can suffer the most during quarantine measures; employment in traditional services can be significantly reduced. By 2030, about half of the jobs in the world and a little less in Russia will need to adapt during the fourth industrial revolution because they are engaged in routine, potentially automated activities. In the regions, specializing in manufacturing, this value is higher; the lowest risk is in the largest agglomerations with a high share of digital economy, greater and diverse labour markets. Accelerating technological change can lead to a long-term mismatch between the exponential increase in automation rate and compensating effects of retraining, new jobs creation and other labour market adaptation mechanisms. Some people will not be ready for a life-long learning and competition with robots, and accordingly there is a possibility of their technological exclusion. The term "nescience economy" and corresponding assessment method were proposed. Using an econometric model, we identified factors that reduce these risks: human capital concentration, favourable business climate, high quality of life and ICT development. Based on these factors, some recommendations for authorities were proposed in the conclusion.
Language	English
Publishing date	2020-05-22
Publishing country	England
Document type	Journal Article
ISSN	1757-7802
ISSN (online)	1757-7802
DOI	10.1111/rsp3.12286
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

Zemtsov, Stepan

Reg. Sci. Policy Pract.

Abstract	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. Using internationally comparable Frey-Osborne methodology, we estimated that less than a third of employees in Russia work in professions with a high automation probability. These professions can suffer the most during quarantine measures; employment in traditional services can be significantly reduced. Till 2030, about half of the jobs in the world and a little less in Russia need to adapt during fourth industrial revolution because they are engaged in routine, potentially automated activities. In the regions, specializing in manufacturing, this value is higher; the lowest risk is in the largest agglomerations with a high share of digital economy, greater and diverse labour markets. Accelerating technological change can lead to a long-term mismatch between the exponential increase in automation rate, compensating effects of retraining and new jobs creation. Some people will not be ready for a life-long learning and competition with robots, and accordingly there is a possibility of their technological exclusion. The term ‘nescience economy’ and corresponding assessment method were proposed. Using an econometric model, we identified factors that reduce these risks: human capital, business climate, quality of life and ICT development. Based on these factors, some recommendations for authorities were proposed in the conclusion.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #116220
Database	COVID19

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Article ; Online: Risks of morbidity and mortality during the COVID-19 pandemic in Russian regions

Zemtsov, Stepan / Baburin, Vyacheslav

Population and Economics 4(2): 158-181

2020

Abstract: 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 ... ...

Abstract	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.
Keywords	coronavirus ; morbidity ; mortality ; Russian regions ; risks ; consequences ; covid19
Language	English
Publisher	Faculty of Economics, Lomonosov Moscow State University
Publishing country	bg
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Risks of morbidity and mortality during the COVID-19 pandemic in Russian regions

Zemtsov, Stepan P. / Baburin, Vyacheslav L.

Population and Economics 4((2)) 158-181

2020

Abstract	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.
Keywords	coronavirus morbidity mortality Russian regions risks consequences ; covid19
Publishing date	2020-06-16
Publisher	Faculty of Economics, Lomonosov Moscow State University
Publishing country	eu
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Risks of morbidity and mortality during the COVID-19 pandemic in Russian regions

Stepan P. Zemtsov / Vyacheslav L. Baburin

Население и экономика, Vol 4, Iss 2, Pp 158-

2020 Volume 181

Abstract	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.
Keywords	Economic theory. Demography ; HB1-3840 ; covid19
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	Moscow State University, Faculty of Economics
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Conference proceedings ; Online: Economic-geographical position as a factor of regional development in Russia

Zemtsov, Stepan / Baburin, Vyacheslav

2016

Abstract: The category of economic-geographical position (EGP) is one of the most common in regional science in Russia. N. Baranskiy firstly proposed the concept: 'EGP is an attitude of any place to other outside lying givens that have a particular economic ... ...

Abstract	The category of economic-geographical position (EGP) is one of the most common in regional science in Russia. N. Baranskiy firstly proposed the concept: 'EGP is an attitude of any place to other outside lying givens that have a particular economic significance. It is extremely important to a particular region to be in a short distance to main routes, markets and large centres'. The concept has become an important tool for regional and cross-country analysis, but it was suffering from a lack of formalization. An EGP may have a significant impact on a modern regional development. New automobile factories in Russia are located close to the largest and growing in 2000th consumer markets (Moscow and St. Petersburg, respectively). Poor development of distant Russian regions, as the Republic of Altai and the Tuva Republic, is related to their unfavourable landlocked position away from the main traffic flows and major economic centres. Thus, EGP is a probabilistic category, and its potential benefits can be realized depending on the regional policy, development of infrastructure and other factors. The aim of this work is to formalize the EGP category and assess the benefits (potential) of economic-geographical position in its relation to regional development in Russia. We formalized the concept in the paper according to a review of the corresponding scientific literature. We developed a method of assessment of international (EGP2) and interregional EGP (EGP1) potential based on the use of gravity models: EGPi = EGP1 + EGP2 = sum MVj/Rij^a where MVj -is gross regional product of a region j, or gross domestic product of a country j; Rij is an actual distance between regional and country capitals; a is an empirical coefficient, showing a speed of potential socio-economic interaction decrease between regions in accordance with increasing distance between them. The higher the EGP potential is, the more intensive interactions can be and the higher benefits for regions will be. These calculations for Russia's regions showed a ...
Keywords	ddc:330 ; O18 ; economic-geographical position ; market potential ; the Russian regions ; gravity models ; regional development
Subject code	910
Language	English
Publisher	Louvain-la-Neuve: European Regional Science Association (ERSA)
Publishing country	de
Document type	Conference proceedings ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Intraurban social risk and mortality patterns during extreme heat events: A case study of Moscow, 2010-2017.

Zemtsov, Stepan / Shartova, Natalia / Varentsov, Mikhail / Konstantinov, Pavel / Kidyaeva, Vera / Shchur, Aleksey / Timonin, Sergey / Grischchenko, Mikhail

Health & place

2020 Volume 66, Page(s) 102429

Abstract: There is currently an increase in the number of heat waves occurring worldwide. Moscow experienced the effects of an extreme heat wave in 2010, which resulted in more than 10,000 extra deaths and significant economic damage. This study conducted a ... ...

Abstract	There is currently an increase in the number of heat waves occurring worldwide. Moscow experienced the effects of an extreme heat wave in 2010, which resulted in more than 10,000 extra deaths and significant economic damage. This study conducted a comprehensive assessment of the social risks existing during the occurrence of heat waves and allowed us to identify the spatial heterogeneity of the city in terms of thermal risk and the consequences for public health. Using a detailed simulation of the meteorological regime based on the COSMO-CLM regional climate model and the physiologically equivalent temperature (PET), a spatial assessment of thermal stress in the summer of 2010 was carried out. Based on statistical data, the components of social risk (vulnerabilities and adaptive capacity of the population) were calculated and mapped. We also performed an analysis of their changes in 2010-2017. A significant differentiation of the territory of Moscow has been revealed in terms of the thermal stress and vulnerability of the population to heat waves. The spatial pattern of thermal stress agrees quite well with the excess deaths observed during the period from July to August 2010. The identified negative trend of increasing vulnerability of the population has grown in most districts of Moscow. The adaptive capacity has been reduced in most of Moscow. The growth of adaptive capacity mainly affects the most prosperous areas of the city.
MeSH term(s)	Cities ; Climate ; Extreme Heat/adverse effects ; Hot Temperature ; Humans ; Mortality ; Moscow/epidemiology ; Seasons
Language	English
Publishing date	2020-09-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1262540-1
ISSN	1873-2054 ; 1353-8292
ISSN (online)	1873-2054
ISSN	1353-8292
DOI	10.1016/j.healthplace.2020.102429
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DOES ECONOMIC-GEOGRAPHICAL POSITION AFFECT INNOVATION PROCESSES IN RUSSIAN REGIONS?

Stepan P. Zemtsov / Vyacheslav L. Baburin

Geography, Environment, Sustainability, Vol 9, Iss 4, Pp 14-

2016 Volume 32

Abstract: 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 ... ...

Abstract	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.
Keywords	innovation-geographical position ; knowledge spillovers ; russian regions ; innovation ; r&d ; market access ; Geography (General) ; G1-922
Subject code	339
Language	English
Publishing date	2016-12-01T00:00:00Z
Publisher	Lomonosov Moscow State University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SME's cluster identification in Russia

Zemtsov, Stepan / Barinova, Vera / Bukov, Denis

(World renaissance ; : changing roles for people and places : programme and list of participations : ERSA 55th Congress : in conjunction with the 21th APDR Congress : 25-28 August 2015 - Lisbon, Portugal / Regional Science Association International; Lisbon School of Economics & Management, Universidade de Lisboa; Associação Portuguesa papa o Desenvolvimento Regional[...])

2015

Abstract: ... that did not declare their interaction. In the last step, the authors conducted field research - a survey ...

Author's details	Stepan Zemtsov, Vera Barinova, Denis Bukov
Series title	World renaissance ; : changing roles for people and places : programme and list of participations : ERSA 55th Congress : in conjunction with the 21th APDR Congress : 25-28 August 2015 - Lisbon, Portugal / Regional Science Association International; Lisbon School of Economics & Management, Universidade de Lisboa; Associação Portuguesa papa o Desenvolvimento Regional[...]
Abstract	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.
Keywords	cluster identification ; localization ; SME ; Russian regions
Language	English
Size	1 Online-Ressource (circa 18 Seiten), Illustrationen
Publisher	European Regional Science Association
Publishing place	Louvain-la-Neuve
Document type	Book ; Online
Database	ECONomics Information System

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Book ; Online: Knowledge economy formation in Russian regions in 2000th

Zemtsov, Stepan / Baburin, Vyacheslav / Komarov, Vladimir

2015

Abstract: Knowledge economy? as a concept describes a stage of socio-economic development, when knowledge become a major growth factor. In modern economy, it can be associated with processes of knowledge acquiring, creation and dissemination; its main agents are ... ...

Author's details	Stepan Zemtsov (Senior Researcher, the Laboratory for studies of corporate strategy and firms' behaviour, Institute of applied economic research, Ranepa), Vladimir Komarov (Head of the Laboratory of Knowledge Economy, Institute of applied economic research,, Ranepa), Vyacheslav Baburin (Professor, head of the department of economic and social geography of Russia, Lomonosov Moscow State University)
Series title	World renaissance ; : changing roles for people and places : programme and list of participations : ERSA 55th Congress : in conjunction with the 21th APDR Congress : 25-28 August 2015 - Lisbon, Portugal / Regional Science Association International; Lisbon School of Economics & Management, Universidade de Lisboa; Associação Portuguesa papa o Desenvolvimento Regional[...]
Abstract	?Knowledge economy? as a concept describes a stage of socio-economic development, when knowledge become a major growth factor. In modern economy, it can be associated with processes of knowledge acquiring, creation and dissemination; its main agents are educational, scientific organizations and innovative business. In conditions of oil prices falling and sanctions, it is important to identify the Russian regions, where knowledge economy is forming, as new areas of growth. Another aim was to estimate whether economic growth of 2000s promote knowledge economy formation. We used methodology of World Bank with some modifications according to available statistics. The Russian knowledge economy index (RKEI) consisted of four blocks: the level of economic development (GRP growth rate and GRP per capita), education and human capital (number of students per capita and the average number of education years for employees), science and innovation (number of researchers and PCT-applications per capita) and information infrastructure (number of cell phones and computers with Internet access per capita). Since the performance of education and science are relatively stable for the Russian regions, characteristics of GRP and information infrastructure, which grew throughout the 2000s, hold the largest share in the variation of the RKEI. We used calculation of the average rank index (measured from to 10), according to the formula: Ri = 10*(Rlow / R), where Ri is a desired figure, Rlow is a number of regions with a lower rank, R is the total number of regions. The calculation was carried out for the whole period from 1998 to 2012 to review the dynamics. The highest level of the RKEI in 1998 (in descending order) was observed in Moscow (6.5) and St. Petersburg (5.9), Tomsk (4.5), Moscow (4.5), Samara (3.6), Khabarovsk (3.48), Primorsky (3.4) and Novosibirsk (3.3) regions; in 2012 the leaders were St. Petersburg (8.8), Moscow (8.7), Tomsk (8.3), Samara (8.1) regions, Tatarstan (8) and Novosibirsk (7.95) region. The areas with the most diversified economy are among the leaders; monospecialized regions (agriculture, mining) are among the laggards. The RKEI increased for all regions during the period, especially for low rank regions (<4: the North Caucasus and the Far East). Voronezh and Tyumen regions, Tatarstan and Bashkortostan have the highest RKEI growth rates (2012/1998) among high rank regions (>5). These regions established more innovative infrastructure and form a better investment climate. All regions have experienced the negative effects of the crisis in 2009, especially Moscow agglomeration. As a result, Moscow gave the leadership in the RKEI to St. Petersburg in 2010 and Moscow region left the top ten leading regions in 2011. The methodology allows us to track the RKEI framework for every region via the radar chart to identify problem areas and competitive advantages.
Keywords	knowledge economy ; index ; Russian regions ; education ; human capital ; information
Language	English
Size	1 Online-Ressource (circa Seiten), Illustrationen
Publisher	European Regional Science Association (ERSA)
Publishing place	Louvain-la-Neuve
Document type	Book ; Online
Database	ECONomics Information System

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