Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-4-7
I. Golova
Ensuring technological independence is one of the key challenges for modern Russia. Solution of this problem requires the consideration of the development of the scientific and technical capacity of Russian regions. The study aims to establish theoretical and methodological bases of the scientific and technical capacity of Russian regions as the foundation for technological independence. To this end, the following tasks were set: to present the definition of the scientific and technical capacity of regions; to create a methodology for managing capacity building; to develop methodological approaches to increase the ability of the scientific and technical capacity of regions to solve the problems of technological independence. The research used statistical data from the Organisation for Economic Co-operation and Development (OECD), Federal Customs Service of Russia, Federal State Statistics Service (Rosstat), and other sources. Analysis of the import dependence of the Russian Federation shows that the rate of coverage of imports by exports for the majority of high-tech goods does not exceed 5–10 %. The genesis of the concept of the scientific and technical capacity of regions was examined taking into account the innovation theory and changes in knowledge flows in the context of the digital society. The study identified barriers to the development of the scientific and technical capacity of Russian regions: technological backwardness and financial instability of the high-tech sector, low willingness to update, imbalance in the structure of the scientific and technical capacity, etc. A methodology for overcoming these barriers based on an integrated approach to regional innovation processes was presented. Additionally, a methodological approach to enhancing the interaction between science and business relying on the open innovation model was proposed. Calculations performed using the hierarchical cluster analysis revealed a group of the most promising regions for the establishment of innovative development centres. Three clusters were identified (in descending order of priority): leading (4 constituent entities), advanced (6 constituent entities) and developed (3 constituent entities) regions. The obtained results can be used to manage regional scientific and technological development and create the methodology for innovative transformation of the Russian economy.
{"title":"Scientific and Technical Capacity of Regions as the Foundation for Technological Independence of the Russian Federation","authors":"I. Golova","doi":"10.17059/ekon.reg.2022-4-7","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-4-7","url":null,"abstract":"Ensuring technological independence is one of the key challenges for modern Russia. Solution of this problem requires the consideration of the development of the scientific and technical capacity of Russian regions. The study aims to establish theoretical and methodological bases of the scientific and technical capacity of Russian regions as the foundation for technological independence. To this end, the following tasks were set: to present the definition of the scientific and technical capacity of regions; to create a methodology for managing capacity building; to develop methodological approaches to increase the ability of the scientific and technical capacity of regions to solve the problems of technological independence. The research used statistical data from the Organisation for Economic Co-operation and Development (OECD), Federal Customs Service of Russia, Federal State Statistics Service (Rosstat), and other sources. Analysis of the import dependence of the Russian Federation shows that the rate of coverage of imports by exports for the majority of high-tech goods does not exceed 5–10 %. The genesis of the concept of the scientific and technical capacity of regions was examined taking into account the innovation theory and changes in knowledge flows in the context of the digital society. The study identified barriers to the development of the scientific and technical capacity of Russian regions: technological backwardness and financial instability of the high-tech sector, low willingness to update, imbalance in the structure of the scientific and technical capacity, etc. A methodology for overcoming these barriers based on an integrated approach to regional innovation processes was presented. Additionally, a methodological approach to enhancing the interaction between science and business relying on the open innovation model was proposed. Calculations performed using the hierarchical cluster analysis revealed a group of the most promising regions for the establishment of innovative development centres. Three clusters were identified (in descending order of priority): leading (4 constituent entities), advanced (6 constituent entities) and developed (3 constituent entities) regions. The obtained results can be used to manage regional scientific and technological development and create the methodology for innovative transformation of the Russian economy.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81806842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-3-9
G. Konat, M. F. Coşkun
Hysteresis is a dominant feature of unemployment in numerous countries. According to the hysteresis hypothesis, it is a well-known fact that high unemployment may persist and remain an economic threat in the long run if policy measures are not taken. In this study, it is tested whether the unemployment rates for 10 selected countries of the Organisation for Economic Co-operation and Development (OECD) (Belgium, Canada, Czech Republic, Estonia, France, Japan, Netherlands, Spain, Britain and the USA) contain unit root or not, in other words, whether the hysteresis effect is valid for these countries. For this purpose, this study utilises the concept of the multi-factor panel unit root test proposed by Pesaran, Smith and Yamagata. This method measures cross-section dependence through factors. The test analyses whether the unit root is valid or not, using information about a sufficient number of additional explanatory variables. The characteristic of these additional variables is that they must share a common factor with the variable whose stationarity is tested. It is accepted that this common factor causes cross-sectional dependence. We have taken tax wedge, trade union density and minimum wage as factors that cause cross-sectional dependency and affect unemployment hysteresis. In this test developed by the authors, in the case of a multi-factor error structure, the test procedure is completed by using the information contained in 3 additional variables. The study explores not only the validity of unemployment hysteresis but also the factors that affect the rigidity of the unemployment rate. However, the research was unable to encompass the entire OECD countries and all times because of the lack of data. The results showed that the hysteresis is valid for 10 selected OECD countries.
{"title":"Testing Unemployment Hysteresis with Multi-Factor Panel Unit Root: Evidence from OECD Countries","authors":"G. Konat, M. F. Coşkun","doi":"10.17059/ekon.reg.2022-3-9","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-3-9","url":null,"abstract":"Hysteresis is a dominant feature of unemployment in numerous countries. According to the hysteresis hypothesis, it is a well-known fact that high unemployment may persist and remain an economic threat in the long run if policy measures are not taken. In this study, it is tested whether the unemployment rates for 10 selected countries of the Organisation for Economic Co-operation and Development (OECD) (Belgium, Canada, Czech Republic, Estonia, France, Japan, Netherlands, Spain, Britain and the USA) contain unit root or not, in other words, whether the hysteresis effect is valid for these countries. For this purpose, this study utilises the concept of the multi-factor panel unit root test proposed by Pesaran, Smith and Yamagata. This method measures cross-section dependence through factors. The test analyses whether the unit root is valid or not, using information about a sufficient number of additional explanatory variables. The characteristic of these additional variables is that they must share a common factor with the variable whose stationarity is tested. It is accepted that this common factor causes cross-sectional dependence. We have taken tax wedge, trade union density and minimum wage as factors that cause cross-sectional dependency and affect unemployment hysteresis. In this test developed by the authors, in the case of a multi-factor error structure, the test procedure is completed by using the information contained in 3 additional variables. The study explores not only the validity of unemployment hysteresis but also the factors that affect the rigidity of the unemployment rate. However, the research was unable to encompass the entire OECD countries and all times because of the lack of data. The results showed that the hysteresis is valid for 10 selected OECD countries.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83189227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-4-22
N. Akbulaev, F. Ahmadov, M. R. Mammadova
The present paper investigates the impact of the COVID-19 pandemic on the prices of the Italian stock exchange indices. During the pandemic, the global economy as well as financial markets suffered due to isolation and social distancing. Paired models of the dependence of the key indices of the Italian stock exchange on the number of patients, recovered and died were analysed using the least squares method. Further, various tests were performed to verify the feasibility of the Gauss-Markov conditions by applying Gretl tools: White Test for heteroskedasticity of residues, Durbin-Watson test for autocorrelation of residuals and normality of distribution of residuals. Statistically significant regression models were constructed that characterise the impact of morbidity and mortality in the Italian population during the COVID-19 pandemic on the price of 11 key stock exchange indices. Based on this, the study examined the COVID-19 pandemic period in the spring of 2020 in Italy, the results of which revealed a loss in stock returns and high volatility in stock returns during this period compared to the normal study period. The econometric model shows that COVID-19 had a negative impact on stock returns and a number of other stock market indicators in Italy. It was revealed that the number of deaths from coronavirus is statistically significantly interconnected with all key stock exchange indices.
{"title":"Analysis of the Impact of the COVID-19 Pandemic on Stock Exchange Indices in Italy","authors":"N. Akbulaev, F. Ahmadov, M. R. Mammadova","doi":"10.17059/ekon.reg.2022-4-22","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-4-22","url":null,"abstract":"The present paper investigates the impact of the COVID-19 pandemic on the prices of the Italian stock exchange indices. During the pandemic, the global economy as well as financial markets suffered due to isolation and social distancing. Paired models of the dependence of the key indices of the Italian stock exchange on the number of patients, recovered and died were analysed using the least squares method. Further, various tests were performed to verify the feasibility of the Gauss-Markov conditions by applying Gretl tools: White Test for heteroskedasticity of residues, Durbin-Watson test for autocorrelation of residuals and normality of distribution of residuals. Statistically significant regression models were constructed that characterise the impact of morbidity and mortality in the Italian population during the COVID-19 pandemic on the price of 11 key stock exchange indices. Based on this, the study examined the COVID-19 pandemic period in the spring of 2020 in Italy, the results of which revealed a loss in stock returns and high volatility in stock returns during this period compared to the normal study period. The econometric model shows that COVID-19 had a negative impact on stock returns and a number of other stock market indicators in Italy. It was revealed that the number of deaths from coronavirus is statistically significantly interconnected with all key stock exchange indices.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87581136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-3-19
R. Vasilyeva, O. Mariev, V. Voytenkov, A. Urazbaeva
Oil and gas dependence and the volatility of their prices is currently a serious challenge for the Russian economy. Coincidently, export revenues from oil and gas products are the main source of the federal budget. Export diversification can contribute to risk reduction for the Russian economy by increasing the share of products from other industries in the export structure. In this regard, the present study examines the determinants of export diversification in Russian industrial regions using econometric modelling methods. To this end, the Herfindahl and Theil indices for 97 export groups were calculated. It is hypothesised that the development of small and medium-sized enterprises, as well as the sanctions imposed by Western states against Russia are the main factors of export diversification in industrial regions. Simultaneously, natural resource extraction is assumed to significantly increase the concentration of exports in the regions. To test this hypothesis, panel data of 50 Russian industrial regions for the period 2001-2019 were analysed. The quantile regression approach was applied to solve the heteroscedasticity problem. Three groups of regions were distinguished according to their level of diversification: regions with a high level of export diversification (Q10-Q30), with an average level of diversification (Q40-Q60), with a low level of export diversification (Q70-Q90). The research findings show that the development of small and medium-sized enterprises contributes to export diversification in Russian industrial regions. While the sanctions did not have a significant impact on export diversification, regional potential and natural resource extraction increase the concentration of exports. The obtained study results complement the existing literature on export diversification in Russia, and contribute to the development of policy implications.
{"title":"Factors of Export Diversification: Empirical Analysis of Russian Industrial Regions","authors":"R. Vasilyeva, O. Mariev, V. Voytenkov, A. Urazbaeva","doi":"10.17059/ekon.reg.2022-3-19","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-3-19","url":null,"abstract":"Oil and gas dependence and the volatility of their prices is currently a serious challenge for the Russian economy. Coincidently, export revenues from oil and gas products are the main source of the federal budget. Export diversification can contribute to risk reduction for the Russian economy by increasing the share of products from other industries in the export structure. In this regard, the present study examines the determinants of export diversification in Russian industrial regions using econometric modelling methods. To this end, the Herfindahl and Theil indices for 97 export groups were calculated. It is hypothesised that the development of small and medium-sized enterprises, as well as the sanctions imposed by Western states against Russia are the main factors of export diversification in industrial regions. Simultaneously, natural resource extraction is assumed to significantly increase the concentration of exports in the regions. To test this hypothesis, panel data of 50 Russian industrial regions for the period 2001-2019 were analysed. The quantile regression approach was applied to solve the heteroscedasticity problem. Three groups of regions were distinguished according to their level of diversification: regions with a high level of export diversification (Q10-Q30), with an average level of diversification (Q40-Q60), with a low level of export diversification (Q70-Q90). The research findings show that the development of small and medium-sized enterprises contributes to export diversification in Russian industrial regions. While the sanctions did not have a significant impact on export diversification, regional potential and natural resource extraction increase the concentration of exports. The obtained study results complement the existing literature on export diversification in Russia, and contribute to the development of policy implications.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87608310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-3-4
T. Maleva, M. Kartseva, P. Kuznetsova
Income inequality driven by inequality of opportunity can lead to slower economic growth and social instability. The present paper analyses inequality of opportunity in three Russian regions, namely, Moscow, Stavropol krai and Chelyabinsk oblast. For each region, the contribution of unequal opportunities to income inequality (objective estimates), as well as the population perception of inequality of opportunity (subjective estimates) were examined. The existing estimates of inequality of opportunities in Russia at the regional and national levels were compared. Additionally, the correspondence between the subjective perception of inequality of opportunities by the population of the region and its objective estimates was considered. The research is based on the data of а sociological survey conducted by the Russian Presidential Academy of National Economy and Public Administration in 2019. Methods ensuring the comparability of the obtained estimates with the results presented in the works of Russian and foreign scientists were utilised. The contribution of inequality of opportunity at the regional level ranges from 9 to 12 % for labour income and from 7 to 13 % for average per capita income; these values are considerably lower than the national estimates reported in earlier studies. This situation can be explained, among other things, by significant interregional inequality in Russia. The composite index of subjective inequality of opportunities, constructed in accordance with people’s perception of various success factors, shows that 14–20 % of the population in the above regions consider the problem of inequality of opportunity as important or very important, and 6–11 % as very important. The conducted regression analysis shows that higher levels of education and well-being correspond to less acute perception of inequality of opportunity. No significant regional differences were found, since the perception of inequality mostly depends on individual characteristics of the respondents rather than on their place of residence. The considerable difference between the objective estimates of regional inequality of opportunity and its perception is consistent with the results of international studies. For example, the perception of inequality of opportunity in Stavropol krai is higher than in other regions, while its objective estimate, on the contrary, is lower. To form a complete picture of inequality of opportunity in Russian regions, it is necessary to conduct a survey designed to be representative at the regional level.
{"title":"Inequality of Opportunity in Russian Regions: Objective Estimates and Population Perception","authors":"T. Maleva, M. Kartseva, P. Kuznetsova","doi":"10.17059/ekon.reg.2022-3-4","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-3-4","url":null,"abstract":"Income inequality driven by inequality of opportunity can lead to slower economic growth and social instability. The present paper analyses inequality of opportunity in three Russian regions, namely, Moscow, Stavropol krai and Chelyabinsk oblast. For each region, the contribution of unequal opportunities to income inequality (objective estimates), as well as the population perception of inequality of opportunity (subjective estimates) were examined. The existing estimates of inequality of opportunities in Russia at the regional and national levels were compared. Additionally, the correspondence between the subjective perception of inequality of opportunities by the population of the region and its objective estimates was considered. The research is based on the data of а sociological survey conducted by the Russian Presidential Academy of National Economy and Public Administration in 2019. Methods ensuring the comparability of the obtained estimates with the results presented in the works of Russian and foreign scientists were utilised. The contribution of inequality of opportunity at the regional level ranges from 9 to 12 % for labour income and from 7 to 13 % for average per capita income; these values are considerably lower than the national estimates reported in earlier studies. This situation can be explained, among other things, by significant interregional inequality in Russia. The composite index of subjective inequality of opportunities, constructed in accordance with people’s perception of various success factors, shows that 14–20 % of the population in the above regions consider the problem of inequality of opportunity as important or very important, and 6–11 % as very important. The conducted regression analysis shows that higher levels of education and well-being correspond to less acute perception of inequality of opportunity. No significant regional differences were found, since the perception of inequality mostly depends on individual characteristics of the respondents rather than on their place of residence. The considerable difference between the objective estimates of regional inequality of opportunity and its perception is consistent with the results of international studies. For example, the perception of inequality of opportunity in Stavropol krai is higher than in other regions, while its objective estimate, on the contrary, is lower. To form a complete picture of inequality of opportunity in Russian regions, it is necessary to conduct a survey designed to be representative at the regional level.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86627669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-4-2
A. Fedyunina, N. Gorodnyi, Y. Simachev, I. Drapkin
The retail and wholesale sector has been hit hard by the coronavirus pandemic, leading to a major sector transformation. In this study, we analyse the factors of firm-level e-commerce adoption and expansion in response to the COVID-19 pandemic and pay special attention to the regional level determinants of e-commerce. We use the data provided by the EBRD-EIB-WB Enterprise Survey that includes about 18,000 observations for firms in Central and Eastern Europe (CEE) and Central Asia (CA) and approximately 1000 observations in Russia. We use the probit and weighted probit estimation techniques. Our central hypothesis states that while large cities are usually seen as drivers of the expansion of e-commerce, lagging regions are catching up with the leading regions in the adoption of e-commerce. The study shows that firms in regions with lower levels of e-commerce before COVID-19 and firms in large cities were more likely to adopt e-commerce during the pandemic, which evidences a convergence in e-commerce between Russian regions. In contrast to the firms in CEE and CA countries, export market orientation and supply chain signals do not foster e-commerce adoption in Russia. This can be explained by weak development of subcontracting networks and low participation of small and medium-sized firms in cooperative relationships in Russia. Regarding policy implications, we argue that policy measures should focus on the distribution of low-cost solutions aiming to decrease entry barriers, liberalise domestic markets for entrance of foreign platforms in Russia, and support the development of domestic platforms.
{"title":"How Has the COVID-19 Pandemic Accelerated E-Commerce in Russia: Evidence from Firm-Level Data with Spatial Factors","authors":"A. Fedyunina, N. Gorodnyi, Y. Simachev, I. Drapkin","doi":"10.17059/ekon.reg.2022-4-2","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-4-2","url":null,"abstract":"The retail and wholesale sector has been hit hard by the coronavirus pandemic, leading to a major sector transformation. In this study, we analyse the factors of firm-level e-commerce adoption and expansion in response to the COVID-19 pandemic and pay special attention to the regional level determinants of e-commerce. We use the data provided by the EBRD-EIB-WB Enterprise Survey that includes about 18,000 observations for firms in Central and Eastern Europe (CEE) and Central Asia (CA) and approximately 1000 observations in Russia. We use the probit and weighted probit estimation techniques. Our central hypothesis states that while large cities are usually seen as drivers of the expansion of e-commerce, lagging regions are catching up with the leading regions in the adoption of e-commerce. The study shows that firms in regions with lower levels of e-commerce before COVID-19 and firms in large cities were more likely to adopt e-commerce during the pandemic, which evidences a convergence in e-commerce between Russian regions. In contrast to the firms in CEE and CA countries, export market orientation and supply chain signals do not foster e-commerce adoption in Russia. This can be explained by weak development of subcontracting networks and low participation of small and medium-sized firms in cooperative relationships in Russia. Regarding policy implications, we argue that policy measures should focus on the distribution of low-cost solutions aiming to decrease entry barriers, liberalise domestic markets for entrance of foreign platforms in Russia, and support the development of domestic platforms.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78178890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-3-10
V. Kryukov, A. Tokarev
Russia has great potential in terms of developing hard-to-recover oil reserves (HRR), which account for more than two thirds of the total reserves. However, relevant scientific studies mostly focus on geological and technical problems, while the issues of creating conditions for the effective development of HRR are often limited to recommendations for tax incentives. At the same time, little attention is paid to the problems of creating institutional conditions aimed at transforming the resource potential into real socio-economic effects. The interests of resource regions are also not taken into consideration. In this regard, the present study assesses potential socio-economic effects of the development of HRR at the regional level (on the case of Khanty-Mansi Autonomous Okrug — Yugra, KhMAO) and provides recommendations to create institutional conditions for the development of such resources. In order to establish an approach for analysing potential socio-economic effects from the implementation of HRR development projects, methods for evaluating investment projects and examining inter-industry relations were utilised. Dynamics of socio-economic development indicators of KhMAO, production projections of hard-to-recover oil reserves, as well as available technical and economic parameters of HRR development projects in Russia and abroad were considered. The calculations show that the development of hard-to-recover oil reserves will help stabilise production volumes in KhMAO and generate significant direct and indirect effects associated with tax revenues increase, maintenance of related industries and employment. The study results can be used to develop strategic documents for oil and gas regions. Future research will analyse interregional relationships aimed at ensuring the extraction of hard-to-recover oil using innovative equipment, and providing high-tech services.
{"title":"Creation of Conditions for the Development of Hard-to-Recover Oil Reserves: Regional Aspects","authors":"V. Kryukov, A. Tokarev","doi":"10.17059/ekon.reg.2022-3-10","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-3-10","url":null,"abstract":"Russia has great potential in terms of developing hard-to-recover oil reserves (HRR), which account for more than two thirds of the total reserves. However, relevant scientific studies mostly focus on geological and technical problems, while the issues of creating conditions for the effective development of HRR are often limited to recommendations for tax incentives. At the same time, little attention is paid to the problems of creating institutional conditions aimed at transforming the resource potential into real socio-economic effects. The interests of resource regions are also not taken into consideration. In this regard, the present study assesses potential socio-economic effects of the development of HRR at the regional level (on the case of Khanty-Mansi Autonomous Okrug — Yugra, KhMAO) and provides recommendations to create institutional conditions for the development of such resources. In order to establish an approach for analysing potential socio-economic effects from the implementation of HRR development projects, methods for evaluating investment projects and examining inter-industry relations were utilised. Dynamics of socio-economic development indicators of KhMAO, production projections of hard-to-recover oil reserves, as well as available technical and economic parameters of HRR development projects in Russia and abroad were considered. The calculations show that the development of hard-to-recover oil reserves will help stabilise production volumes in KhMAO and generate significant direct and indirect effects associated with tax revenues increase, maintenance of related industries and employment. The study results can be used to develop strategic documents for oil and gas regions. Future research will analyse interregional relationships aimed at ensuring the extraction of hard-to-recover oil using innovative equipment, and providing high-tech services.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73685517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-2-17
V. Lesnykh, T. B. Timofeeva
Urbanisation led to the establishment of infrastructure-complex territories (ICTs). The growing interaction between critical infrastructures in such territories, combined with an increase in the frequency and scale of natural disasters, caused a surge in intersystem accidents (ISA). ISAs are characterised by cascading processes and catastrophic consequences for regional socio-economic development, since they affect both the critical infrastructure and environment. The paper aims to classify intersystem accidents in infrastructure-complex territories, as well as to assess the adaptive resilience of these areas to external influences. An examination of available statistics on domestic and foreign intersystem accidents demonstrated the importance of the issue and allowed us to identify common features of ISAs. The research analysed various approaches to the classification of territories and their adaptive resilience to external influences, showing that the existing classifications mostly do not consider infrastructure-complex territories and the possibility of intersystem accidents. Based on the analysis of statistical data and simulation of cascade failures and emergencies, the article proposes a new approach to the classification of intersystem accidents in infrastructure-complex territories. The scale of economic and social consequences, location of the accident, structure of the development of emergency processes, and other classification features were used. The proposed classification will help simulate emergencies, develop methods for assessing the consequences and resistance of infrastructure-complex territories to external influences, and, subsequently, increase adaptive resilience and economic efficiency of regional development. Further research will be aimed at predicting the development of ISAs and assessing the resulting damage in accordance with the proposed classification.
{"title":"Classification of Intersystem Accidents in Infrastructure-Complex Territories","authors":"V. Lesnykh, T. B. Timofeeva","doi":"10.17059/ekon.reg.2022-2-17","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-2-17","url":null,"abstract":"Urbanisation led to the establishment of infrastructure-complex territories (ICTs). The growing interaction between critical infrastructures in such territories, combined with an increase in the frequency and scale of natural disasters, caused a surge in intersystem accidents (ISA). ISAs are characterised by cascading processes and catastrophic consequences for regional socio-economic development, since they affect both the critical infrastructure and environment. The paper aims to classify intersystem accidents in infrastructure-complex territories, as well as to assess the adaptive resilience of these areas to external influences. An examination of available statistics on domestic and foreign intersystem accidents demonstrated the importance of the issue and allowed us to identify common features of ISAs. The research analysed various approaches to the classification of territories and their adaptive resilience to external influences, showing that the existing classifications mostly do not consider infrastructure-complex territories and the possibility of intersystem accidents. Based on the analysis of statistical data and simulation of cascade failures and emergencies, the article proposes a new approach to the classification of intersystem accidents in infrastructure-complex territories. The scale of economic and social consequences, location of the accident, structure of the development of emergency processes, and other classification features were used. The proposed classification will help simulate emergencies, develop methods for assessing the consequences and resistance of infrastructure-complex territories to external influences, and, subsequently, increase adaptive resilience and economic efficiency of regional development. Further research will be aimed at predicting the development of ISAs and assessing the resulting damage in accordance with the proposed classification.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89776486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-2-20
M. Arkhipova
The article examines new methodologies for modelling crop yield in agricultural regions of Russia based on the use of remote capabilities to get information on the field state. The proposed approach can be applied to develop indicator systems and create methodological platforms and models necessary to obtain more accurate estimates. In comparison with the traditional regression model, this method uses computer vision technology to gather additional data. Statistical hypothesis testing confirmed the significance of satellite photographs of fields for improving the accuracy of crop yield forecasting models. Traditional econometric tools were compared with various neural networks in order to discover the optimal model. The proposed tools were tested using data from 100 agricultural fields located in municipalities of 43 Russian regions, selected in proportion to the volume of crop production in this region. The conducted analysis showed the advantage of the mixed data neural network in comparison with other neural (multilayer perceptron and convolutional neural network) and regression models. In conditions of uncertainty and a large amount of data, the mixed data neural network can help obtain more accurate estimates. Additionally, while environmental factors have different effects on crop yields, they must be considered along with socio-economic characteristics. The use of new models and data types differing from table information can significantly improve the forecasting accuracy and interpretation. The analysis results can be used for examining and monitoring agricultural production in regional municipalities, determining farm resource requirements, as well as for creating sectoral and comprehensive projects and programmes for the development of the agricultural industry.
{"title":"Modelling Crop Yield in Agricultural Regions Using Computer Vision Technology","authors":"M. Arkhipova","doi":"10.17059/ekon.reg.2022-2-20","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-2-20","url":null,"abstract":"The article examines new methodologies for modelling crop yield in agricultural regions of Russia based on the use of remote capabilities to get information on the field state. The proposed approach can be applied to develop indicator systems and create methodological platforms and models necessary to obtain more accurate estimates. In comparison with the traditional regression model, this method uses computer vision technology to gather additional data. Statistical hypothesis testing confirmed the significance of satellite photographs of fields for improving the accuracy of crop yield forecasting models. Traditional econometric tools were compared with various neural networks in order to discover the optimal model. The proposed tools were tested using data from 100 agricultural fields located in municipalities of 43 Russian regions, selected in proportion to the volume of crop production in this region. The conducted analysis showed the advantage of the mixed data neural network in comparison with other neural (multilayer perceptron and convolutional neural network) and regression models. In conditions of uncertainty and a large amount of data, the mixed data neural network can help obtain more accurate estimates. Additionally, while environmental factors have different effects on crop yields, they must be considered along with socio-economic characteristics. The use of new models and data types differing from table information can significantly improve the forecasting accuracy and interpretation. The analysis results can be used for examining and monitoring agricultural production in regional municipalities, determining farm resource requirements, as well as for creating sectoral and comprehensive projects and programmes for the development of the agricultural industry.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81279382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-3-20
M. M. Islam, M. Tareque, M. Moniruzzaman, M. I. Ali
The Asian countries, particularly Bangladesh, China, India and Myanmar, have been witnessing impressive economic growth rates due to their trade performance in the international market. Although export-led growth assumption is functional in these economies, existing pieces of literature hardly considered them in their studies. Against this backdrop, the present study investigates the export-led growth hypothesis for four South Asian countries — Bangladesh, China, India, and Myanmar — covering country-specific different time ranges. This research employs the autoregressive distributed lag (ARDL) bounds testing approach to co-integration and the MWALD Granger causality test to determine the causal relationship between variables. The results obtained from the autoregressive distributed lag method confirm the co-integration among the variables. In addition, the Granger causality test explores both the export-led and growth-led export hypotheses in Bangladesh and India as per the bidirectional causation between exports and economic development. Only the export-led growth theorem is relevant to China, and the growth-led export hypothesis is valid in the case of Myanmar based on the unidirectional causation between these variables. Therefore, any joint footstep of BCIM countries is critical to promoting exports by penetrating new destinations with diversified export goods and services. The obtained findings also indicate the potential for utilising these countries’ unused resources to encourage exports to uplift the existing growth trajectory.
{"title":"Assessment of Export-Led Growth Hypothesis: The Case of Bangladesh, China, India and Myanmar","authors":"M. M. Islam, M. Tareque, M. Moniruzzaman, M. I. Ali","doi":"10.17059/ekon.reg.2022-3-20","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-3-20","url":null,"abstract":"The Asian countries, particularly Bangladesh, China, India and Myanmar, have been witnessing impressive economic growth rates due to their trade performance in the international market. Although export-led growth assumption is functional in these economies, existing pieces of literature hardly considered them in their studies. Against this backdrop, the present study investigates the export-led growth hypothesis for four South Asian countries — Bangladesh, China, India, and Myanmar — covering country-specific different time ranges. This research employs the autoregressive distributed lag (ARDL) bounds testing approach to co-integration and the MWALD Granger causality test to determine the causal relationship between variables. The results obtained from the autoregressive distributed lag method confirm the co-integration among the variables. In addition, the Granger causality test explores both the export-led and growth-led export hypotheses in Bangladesh and India as per the bidirectional causation between exports and economic development. Only the export-led growth theorem is relevant to China, and the growth-led export hypothesis is valid in the case of Myanmar based on the unidirectional causation between these variables. Therefore, any joint footstep of BCIM countries is critical to promoting exports by penetrating new destinations with diversified export goods and services. The obtained findings also indicate the potential for utilising these countries’ unused resources to encourage exports to uplift the existing growth trajectory.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85328913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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