Pub Date : 2022-01-01DOI: 10.17059/ekon.reg.2022-4-6
E. Lyaskovskaya, T. Khudyakova, A. Shmidt
Digitalisation and urbanisation have led to smart city becoming a key element of the concept of sustainable development, acting as an object of sustainable development management. Thus, a smart city must become a sustainable smart city. The first step is the creation of the system of current state diagnostics or smart city ranking. This ranking should consider international standards, the specificity of Russian urban development, as well as various components of sustainable development. It is hypothesised that the ranking methodology reflecting these components can be used to analyse the dynamics of sustainable development based on the chosen criteria and factors. To assess social, environmental, economic and management components of cities with a population of more than 100,000 people, the proposed technique relies on the theory of multiple comparisons and the distance method. The comprehensive assessment of smart cities includes 71 indicators grouped into 8 criteria: people, social cohesion, economy, management, ecology and the environment, transport, urban planning, technology. The study analysed Russian and international statistical databases, as well as data from industry agencies. The ranking of 171 cities was developed; regional features of Russian smart cities were identified using the selected criteria. The cities of Moscow, St. Petersburg, Balashikha, Krasnodar and Kazan are at the top of the ranking. The leadership of the Central Federal District and a significant differentiation by the criteria “urban planning” and “technology” are the key regional features. Annual comprehensive assessment can be used to track the dynamics of the development of smart cities, evaluate the effectiveness of decision-making and implementation, plan urban development based on federal and regional digitalisation projects, as well as create strategies to enhance the sustainability of Russian smart cities.
{"title":"Improving the Ranking of Russian Smart Cities","authors":"E. Lyaskovskaya, T. Khudyakova, A. Shmidt","doi":"10.17059/ekon.reg.2022-4-6","DOIUrl":"https://doi.org/10.17059/ekon.reg.2022-4-6","url":null,"abstract":"Digitalisation and urbanisation have led to smart city becoming a key element of the concept of sustainable development, acting as an object of sustainable development management. Thus, a smart city must become a sustainable smart city. The first step is the creation of the system of current state diagnostics or smart city ranking. This ranking should consider international standards, the specificity of Russian urban development, as well as various components of sustainable development. It is hypothesised that the ranking methodology reflecting these components can be used to analyse the dynamics of sustainable development based on the chosen criteria and factors. To assess social, environmental, economic and management components of cities with a population of more than 100,000 people, the proposed technique relies on the theory of multiple comparisons and the distance method. The comprehensive assessment of smart cities includes 71 indicators grouped into 8 criteria: people, social cohesion, economy, management, ecology and the environment, transport, urban planning, technology. The study analysed Russian and international statistical databases, as well as data from industry agencies. The ranking of 171 cities was developed; regional features of Russian smart cities were identified using the selected criteria. The cities of Moscow, St. Petersburg, Balashikha, Krasnodar and Kazan are at the top of the ranking. The leadership of the Central Federal District and a significant differentiation by the criteria “urban planning” and “technology” are the key regional features. Annual comprehensive assessment can be used to track the dynamics of the development of smart cities, evaluate the effectiveness of decision-making and implementation, plan urban development based on federal and regional digitalisation projects, as well as create strategies to enhance the sustainability of Russian smart cities.","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":"83468388","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-5
Y. Myslyakova, S. Kotlyarova, N. Matushkina
In the context of external shocks, socio-economic coherence of an industrial region reflects its ability to function successfully. Many researchers examine economic space, focusing on interaction between entities (including infrastructure) while ignoring territorial boundaries. However, it is necessary to consider a region’s endogenous core, whose historically connected elements generate evolutionary changes under the influence of external factors. This study develops tools to assess regional infrastructure coherence, taking into account the endogenous determinants of its socio-economic development. The research methodology includes: a comparison of absolute and relative territorial characteristics and infrastructure development parameters; statistical, economic and mathematical methods for determining and evaluating the resulting indicators; an expert assessment of the infrastructure potential; a matrix method for identifying the depth of infrastructure gaps. An analysis of Sverdlovsk oblast and neighbouring regions revealed infrastructure gaps of the first level of depth (insignificant, significant, stably significant), violating the integrity of the regional core, as well as gaps of the second level of depth (forming, potentially forming), requiring serious transformations of the core elements. The con ducted research determined the infrastructure coherence characteristics of the regional core. Thus, the most favourable situation is in Sverdlovsk oblast, whose core has strong integrity. The most unfavourable situation is observed in Perm Krai and Khanty- Mansi Autonomous Okrug. Perm Krai’s core is characterised by minor gaps of the first level of depth and potential second level gaps. In Khanty-Mansi Autonomous Okrug, significant first level gaps are already established, while second level gaps are still forming. This situation occurred due to the industrial specificity of these regions, as well as the discrepancy between high economic activity (increasing the demand for transport services) and infrastructure development. Further research will focus on the ways to improve the regional connectivity at the intra- and inter-regional levels.
{"title":"Genetic Approach to Assessing the Infrastructure Coherence of an Industrial Region","authors":"Y. Myslyakova, S. Kotlyarova, N. Matushkina","doi":"10.17059/ekon.reg.2021-3-5","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-5","url":null,"abstract":"In the context of external shocks, socio-economic coherence of an industrial region reflects its ability to function successfully. Many researchers examine economic space, focusing on interaction between entities (including infrastructure) while ignoring territorial boundaries. However, it is necessary to consider a region’s endogenous core, whose historically connected elements generate evolutionary changes under the influence of external factors. This study develops tools to assess regional infrastructure coherence, taking into account the endogenous determinants of its socio-economic development. The research methodology includes: a comparison of absolute and relative territorial characteristics and infrastructure development parameters; statistical, economic and mathematical methods for determining and evaluating the resulting indicators; an expert assessment of the infrastructure potential; a matrix method for identifying the depth of infrastructure gaps. An analysis of Sverdlovsk oblast and neighbouring regions revealed infrastructure gaps of the first level of depth (insignificant, significant, stably significant), violating the integrity of the regional core, as well as gaps of the second level of depth (forming, potentially forming), requiring serious transformations of the core elements. The con ducted research determined the infrastructure coherence characteristics of the regional core. Thus, the most favourable situation is in Sverdlovsk oblast, whose core has strong integrity. The most unfavourable situation is observed in Perm Krai and Khanty- Mansi Autonomous Okrug. Perm Krai’s core is characterised by minor gaps of the first level of depth and potential second level gaps. In Khanty-Mansi Autonomous Okrug, significant first level gaps are already established, while second level gaps are still forming. This situation occurred due to the industrial specificity of these regions, as well as the discrepancy between high economic activity (increasing the demand for transport services) and infrastructure development. Further research will focus on the ways to improve the regional connectivity at the intra- and inter-regional levels.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81385299","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 : 2021-10-05DOI: 10.17059/EKON.REG.2021-3-17
Hilmola Olli-Pekka, Panova Yulia
{"title":"SUSTAINABILITY OF THE EU-28 TRADE WITH CHINA AND THE USA","authors":"Hilmola Olli-Pekka, Panova Yulia","doi":"10.17059/EKON.REG.2021-3-17","DOIUrl":"https://doi.org/10.17059/EKON.REG.2021-3-17","url":null,"abstract":"","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82918105","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-19
D. Ciołek, Anna Golejewska, Adriana Zabłocka-Abi Yaghi
The literature emphasises the role of regional and local innovation environment. Regional Innovation Systems show differences in innovation outputs determined by different inputs. Understanding these relationships can have important implications for regional and innovation policy. The research aims to classify Regional Innovation Systems in Poland according to their innovation capacity and performance. The analysis covers 72 subregions (classified as NUTS 3 in the Nomenclature of Territorial Units for Statistics) in 2004–2016. Classes of Regional Innovation Systems in Poland were identified based on a combination of linear and functional approaches and data from published and unpublished sources. It was assumed that innovation systems in Poland differ due to their location in metropolitan and non-metropolitan regions, thus, the Eurostat NUTS 3 metro/non-metro typology was applied for this purpose. Panel data regressions as models with individual random effects were estimated separately for metropolitan and non-metropolitan groups of subregions. The study identified common determinants of innovation outputs in both NUTS 3 types: share of innovative industrial enterprises, industry share, unemployment rate, and employment in research and development. Next, NUTS 3 were classified within each of two analysed types in line with output- and input-indices, the latter being calculated as non-weighted average of significant inputs. Last, the subregions were clustered based on individual inputs to enable a more detailed assessment of their innovation potential. The cluster analysis using k-means method with maximum cluster distance was applied. The results showed that the composition of the classes identified within metropolitan and non-metropolitan systems in 2004– 2016 remains unstable, similarly to the composition of clusters identified by inputs. The latter confirms the changes in components of the capacity within both Regional Innovation System types. The observed situation allows us to assume that Regional Innovation Systems in Poland are evolving. In further research, the efficiency of Regional Innovation Systems should be assessed, taking into account the differences between metropolitan and non-metropolitan regions as well as other environmental factors that may determine the efficiency of innovative processes.
{"title":"Regional Innovation Systems in Poland: How to classify them?","authors":"D. Ciołek, Anna Golejewska, Adriana Zabłocka-Abi Yaghi","doi":"10.17059/ekon.reg.2021-3-19","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-19","url":null,"abstract":"The literature emphasises the role of regional and local innovation environment. Regional Innovation Systems show differences in innovation outputs determined by different inputs. Understanding these relationships can have important implications for regional and innovation policy. The research aims to classify Regional Innovation Systems in Poland according to their innovation capacity and performance. The analysis covers 72 subregions (classified as NUTS 3 in the Nomenclature of Territorial Units for Statistics) in 2004–2016. Classes of Regional Innovation Systems in Poland were identified based on a combination of linear and functional approaches and data from published and unpublished sources. It was assumed that innovation systems in Poland differ due to their location in metropolitan and non-metropolitan regions, thus, the Eurostat NUTS 3 metro/non-metro typology was applied for this purpose. Panel data regressions as models with individual random effects were estimated separately for metropolitan and non-metropolitan groups of subregions. The study identified common determinants of innovation outputs in both NUTS 3 types: share of innovative industrial enterprises, industry share, unemployment rate, and employment in research and development. Next, NUTS 3 were classified within each of two analysed types in line with output- and input-indices, the latter being calculated as non-weighted average of significant inputs. Last, the subregions were clustered based on individual inputs to enable a more detailed assessment of their innovation potential. The cluster analysis using k-means method with maximum cluster distance was applied. The results showed that the composition of the classes identified within metropolitan and non-metropolitan systems in 2004– 2016 remains unstable, similarly to the composition of clusters identified by inputs. The latter confirms the changes in components of the capacity within both Regional Innovation System types. The observed situation allows us to assume that Regional Innovation Systems in Poland are evolving. In further research, the efficiency of Regional Innovation Systems should be assessed, taking into account the differences between metropolitan and non-metropolitan regions as well as other environmental factors that may determine the efficiency of innovative processes.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90829309","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-23
I. Naumov, N. Nikulina
The issue of increasing budgetary independence and security is relevant for the majority of territorial systems, both at the regional and municipal levels. It was hypothesised that changes in the structure of regional public debt have a negative impact on their budgetary security. According to this hypothesis, an increase in the proportion of bank borrowing and corresponding decrease in the issue of debt securities by the constituent entities of the Russian Federation leads to a greater overall debt burden on the regional budget. In order to study transformation processes affecting budgetary independence and regional security. We developed a methodology to permit a separate assessment of these concepts. According to this approach, we propose to evaluate the budgetary independence of regional systems in terms of: (1) the balance of the budget (ratio of internal tax and non-tax revenues to budget expenditures); (2) financial dependence on transfers and subsidies from budgets at other levels; (3) budget security, taking into account gratuitous and non-gratuitous transfers. Thus, budget ary security can be assessed in accordance with the public debt dynamics, as well as the level of budgetary debt covered by the region’s own tax and non-tax revenues. The novelty of the presented methodological approach consists in its systematic use of Moran’s I for various spatial weight matrices combined with regression analysis methods based on panel data. Testing this methodology demonstrated the spatial heterogeneity of regional fiscal capacity, highlighting the financial dependence of most regions on federal and other gratuitous transfers. Autocorrelation analysis carried out according to Moran’s I using various spatial weight matrices confirmed the increasing tendency of the budgetary debt of Russian regions towards spatial heterogeneity. Future studies will focus on simulating the influence of various factors on regional budgetary security in order to predict the dynamics of its change.
{"title":"Transformation of Regional Budgetary Independence and Security: Spatial Analysis","authors":"I. Naumov, N. Nikulina","doi":"10.17059/ekon.reg.2021-3-23","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-23","url":null,"abstract":"The issue of increasing budgetary independence and security is relevant for the majority of territorial systems, both at the regional and municipal levels. It was hypothesised that changes in the structure of regional public debt have a negative impact on their budgetary security. According to this hypothesis, an increase in the proportion of bank borrowing and corresponding decrease in the issue of debt securities by the constituent entities of the Russian Federation leads to a greater overall debt burden on the regional budget. In order to study transformation processes affecting budgetary independence and regional security. We developed a methodology to permit a separate assessment of these concepts. According to this approach, we propose to evaluate the budgetary independence of regional systems in terms of: (1) the balance of the budget (ratio of internal tax and non-tax revenues to budget expenditures); (2) financial dependence on transfers and subsidies from budgets at other levels; (3) budget security, taking into account gratuitous and non-gratuitous transfers. Thus, budget ary security can be assessed in accordance with the public debt dynamics, as well as the level of budgetary debt covered by the region’s own tax and non-tax revenues. The novelty of the presented methodological approach consists in its systematic use of Moran’s I for various spatial weight matrices combined with regression analysis methods based on panel data. Testing this methodology demonstrated the spatial heterogeneity of regional fiscal capacity, highlighting the financial dependence of most regions on federal and other gratuitous transfers. Autocorrelation analysis carried out according to Moran’s I using various spatial weight matrices confirmed the increasing tendency of the budgetary debt of Russian regions towards spatial heterogeneity. Future studies will focus on simulating the influence of various factors on regional budgetary security in order to predict the dynamics of its change.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84377255","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-21
Еvegenii N. Smirnov, S. Lukyanov
The current crisis, as well as complicated economic relations between countries, sustainable development risks of global value chains (GVCs), and international trade protectionism cause changes in the modern system of global foreign direct investment (FDI). Due to the complexity of these risks and the vulnerability of the world economy to future global recessions, it is necessary to develop a new concept of cross-border capital flows in the form of FDI. The research aims to identify structural changes in global FDI in the context of international trade and capital market challenges. Structural and dynamic analysis and a descriptive assessment were conducted to examine global foreign direct investment in the system of international capital movement, taking into account the unstable economic environment. A test of the impact of the current coronavirus crisis revealed that the retrospective transformation of global FDI occurred due to changes in the internationalisation of companies, cross-border mergers and acquisitions, and regional structure of FDI. Corporate disinvestment and the growing importance of intangible assets also played an important role. As a result, the slowdown in global FDI led to a decline in reinvested earnings in many economic sectors. As the long-term recovery of the world economy will be largely determined by the dynamics of global FDI, transnational companies (TNCs) should consider local crises and strive to geographically distribute capital investment. The research revealed that the differentiation of national GVC strategies and new approaches to foreign outsourcing of TNCs are the main risks of regionalisation or nationalisation of global value chains and a corresponding decrease in FDI. The findings can be used to modify well-known FDI concepts, taking into account their impact on modern international economic relations.
{"title":"Global Foreign Direct Investment: Structural Changes in the Current Crisis","authors":"Еvegenii N. Smirnov, S. Lukyanov","doi":"10.17059/ekon.reg.2021-3-21","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-21","url":null,"abstract":"The current crisis, as well as complicated economic relations between countries, sustainable development risks of global value chains (GVCs), and international trade protectionism cause changes in the modern system of global foreign direct investment (FDI). Due to the complexity of these risks and the vulnerability of the world economy to future global recessions, it is necessary to develop a new concept of cross-border capital flows in the form of FDI. The research aims to identify structural changes in global FDI in the context of international trade and capital market challenges. Structural and dynamic analysis and a descriptive assessment were conducted to examine global foreign direct investment in the system of international capital movement, taking into account the unstable economic environment. A test of the impact of the current coronavirus crisis revealed that the retrospective transformation of global FDI occurred due to changes in the internationalisation of companies, cross-border mergers and acquisitions, and regional structure of FDI. Corporate disinvestment and the growing importance of intangible assets also played an important role. As a result, the slowdown in global FDI led to a decline in reinvested earnings in many economic sectors. As the long-term recovery of the world economy will be largely determined by the dynamics of global FDI, transnational companies (TNCs) should consider local crises and strive to geographically distribute capital investment. The research revealed that the differentiation of national GVC strategies and new approaches to foreign outsourcing of TNCs are the main risks of regionalisation or nationalisation of global value chains and a corresponding decrease in FDI. The findings can be used to modify well-known FDI concepts, taking into account their impact on modern international economic relations.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81261939","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-15
O. Gritsova, E. Tissen
The quality of online learning mechanisms, widely implemented due to the COVID-19 pandemic, is a significant issue for regional higher education systems. The research aims to assess student satisfaction with the quality of online education by identifying discrepancies between their requirements and the actual learning process. In order to examine the gaps between students’ expectations and perceptions, a new approach was proposed based on the integrated use of Gap analysis and SERVQUAL methodology, combining qualitative and quantitative aspects. SERVQUAL questionnaires for measuring student satisfaction with online learning include the following criteria: tangibles, reliability, responsiveness, assurance, empathy. Full- and part-time undergraduates of humanitarian and socio-economic departments of two universities participated in the study. Ural Federal University bachelors, learning via Moodle and Microsoft Teams platforms, could directly communicate with their peers and professors, while students of National Research Nuclear University MEPhI were engaged in massive open online courses (MOOC). As a result, all five criteria were analysed in the proposed model for quality assessment of online learning to reveal the gaps between students’ expectations and perceptions of the educational process. Significant discrepancies in the «empathy» and «responsiveness» criteria in both groups demonstrate low student satisfaction with the quality of communication and individualisation of learning. The research findings can be used to construct resource allocation models for implementing educational programmes and developing support measures for regional higher education institutions.
{"title":"Quality Assessment of Online Learning in Regional Higher Education Systems","authors":"O. Gritsova, E. Tissen","doi":"10.17059/ekon.reg.2021-3-15","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-15","url":null,"abstract":"The quality of online learning mechanisms, widely implemented due to the COVID-19 pandemic, is a significant issue for regional higher education systems. The research aims to assess student satisfaction with the quality of online education by identifying discrepancies between their requirements and the actual learning process. In order to examine the gaps between students’ expectations and perceptions, a new approach was proposed based on the integrated use of Gap analysis and SERVQUAL methodology, combining qualitative and quantitative aspects. SERVQUAL questionnaires for measuring student satisfaction with online learning include the following criteria: tangibles, reliability, responsiveness, assurance, empathy. Full- and part-time undergraduates of humanitarian and socio-economic departments of two universities participated in the study. Ural Federal University bachelors, learning via Moodle and Microsoft Teams platforms, could directly communicate with their peers and professors, while students of National Research Nuclear University MEPhI were engaged in massive open online courses (MOOC). As a result, all five criteria were analysed in the proposed model for quality assessment of online learning to reveal the gaps between students’ expectations and perceptions of the educational process. Significant discrepancies in the «empathy» and «responsiveness» criteria in both groups demonstrate low student satisfaction with the quality of communication and individualisation of learning. The research findings can be used to construct resource allocation models for implementing educational programmes and developing support measures for regional higher education institutions.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76130168","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-16
M. Petrov, L. Serkov, Кonstantin B. Kozhov
As factors affecting interregional interactions play an important role in regional economic development. Thus, developing a methodology for assessing these interactions is becoming urgent. The article proposes a methodological approach to analyse the factors influencing possible interactions between Sverdlovsk oblast and other constituent entities of the Russian Federation in the manufacturing industry. It is hypothesised that the elements of an interregional interaction matrix are proxy variables characterising the degree of this interaction. An economic analysis of relations and production chains between Sverdlovsk oblast and other constituent entitles confirmed this hypothesis. First, based on the spatial distribution of manufacturing output in the examined regions, values of an indicator showing the strength of their mutual influence were determined. Second, the impact of economic, infrastructural and institutional factors on the obtained indicator, characterising the inter action between Sverdlovsk oblast and other regions, was assessed using quantile regression. In this case, such a technique was chosen instead of the classical ordinary least squares (OLS) regression that incorrectly estimates the dependencies between the studied variables. This is expressed in the fact that the regression coefficients de pend on q-quantile of the dependent variable. We have revealed that price levels of the examined regions do not affect their possible interactions with Sverdlovsk oblast. Simultaneously, the dissemination of knowledge acts a driver of interaction between the considered regional manufacturing industries. The research findings can be used to prepare strategies, programmes and schemes for the placement and development of industries, considering the potential of Sverdlovsk oblast and other Russian regions.
{"title":"Modelling the Heterogeneity of the Mutual Influence between Russian Regions in the Manufacturing Industry","authors":"M. Petrov, L. Serkov, Кonstantin B. Kozhov","doi":"10.17059/ekon.reg.2021-3-16","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-16","url":null,"abstract":"As factors affecting interregional interactions play an important role in regional economic development. Thus, developing a methodology for assessing these interactions is becoming urgent. The article proposes a methodological approach to analyse the factors influencing possible interactions between Sverdlovsk oblast and other constituent entities of the Russian Federation in the manufacturing industry. It is hypothesised that the elements of an interregional interaction matrix are proxy variables characterising the degree of this interaction. An economic analysis of relations and production chains between Sverdlovsk oblast and other constituent entitles confirmed this hypothesis. First, based on the spatial distribution of manufacturing output in the examined regions, values of an indicator showing the strength of their mutual influence were determined. Second, the impact of economic, infrastructural and institutional factors on the obtained indicator, characterising the inter action between Sverdlovsk oblast and other regions, was assessed using quantile regression. In this case, such a technique was chosen instead of the classical ordinary least squares (OLS) regression that incorrectly estimates the dependencies between the studied variables. This is expressed in the fact that the regression coefficients de pend on q-quantile of the dependent variable. We have revealed that price levels of the examined regions do not affect their possible interactions with Sverdlovsk oblast. Simultaneously, the dissemination of knowledge acts a driver of interaction between the considered regional manufacturing industries. The research findings can be used to prepare strategies, programmes and schemes for the placement and development of industries, considering the potential of Sverdlovsk oblast and other Russian regions.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78765557","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-12
S. Shulgin, Yu V Zinkina
A quantitative assessment of human capital is necessary for both understanding society and implementing effective socio-economic policies. In the present paper, a new approach — the Human Life Indicator (HLI) — was implemented to measure inequality in life expectancy. The Human Development Index (HDI), proposed by the United Nations, does not take into account significant internal inequalities of countries with the same or similar life expectancy. On the contrary, HLI reflects the well-being in terms of years of life, additionally considering the inequality in life expectancy. Presented calculations were based on federal mortality statistics. This study estimated human development of Russian federal districts by comparing HDI and HLI. The analysis revealed that high HDI values, achieved, for example, due to a high gross regional product (GRP) per capita, do not translate into an improvement in the quality of life for the majority of the population. Such situation is observed in the Far Eastern Federal District. The regions that are relatively prosperous in terms of HLI are concentrated in the European part of Russia and the North Caucasus Federal District. Simultaneously, most Siberian and the Far Eastern regions, characterised by high inequality in life expectancy, require the attention of federal and regional authorities. The presented approach to assessing the success of regional development can be used to estimate how the ongoing socio-economic policy and health care reforms influence the quality of life in the regions. This method can also be applied to compare inter-regional indicators of human capital and monitor changes in well-being of the population.
{"title":"Assessment of Human Capital in Russian Macroregions","authors":"S. Shulgin, Yu V Zinkina","doi":"10.17059/ekon.reg.2021-3-12","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-12","url":null,"abstract":"A quantitative assessment of human capital is necessary for both understanding society and implementing effective socio-economic policies. In the present paper, a new approach — the Human Life Indicator (HLI) — was implemented to measure inequality in life expectancy. The Human Development Index (HDI), proposed by the United Nations, does not take into account significant internal inequalities of countries with the same or similar life expectancy. On the contrary, HLI reflects the well-being in terms of years of life, additionally considering the inequality in life expectancy. Presented calculations were based on federal mortality statistics. This study estimated human development of Russian federal districts by comparing HDI and HLI. The analysis revealed that high HDI values, achieved, for example, due to a high gross regional product (GRP) per capita, do not translate into an improvement in the quality of life for the majority of the population. Such situation is observed in the Far Eastern Federal District. The regions that are relatively prosperous in terms of HLI are concentrated in the European part of Russia and the North Caucasus Federal District. Simultaneously, most Siberian and the Far Eastern regions, characterised by high inequality in life expectancy, require the attention of federal and regional authorities. The presented approach to assessing the success of regional development can be used to estimate how the ongoing socio-economic policy and health care reforms influence the quality of life in the regions. This method can also be applied to compare inter-regional indicators of human capital and monitor changes in well-being of the population.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85519501","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 : 2021-10-05DOI: 10.17059/ekon.reg.2021-3-14
Micael Queiroga dos Santos, A. Marta-Costa, X. Rodríguez
While scientific studies have not reached a consensus on the methodology for examining Technical Efficiency (or Inefficiency), the influence of regions appears to be important for efficiency scores. Therefore, this research aims to investigate the empirical procedures for the achievement of more robust results in the analysis of productive efficiency, as well as to evaluate the effect of the location of farms on such efficiency. The goal was to check whether the most developed regions are the most efficient. Meta-regression analysis provides an adequate method for an accurate assessment of both situations. This technique was applied based on a database of 166 observations on the agricultural sector from countries around the world, published in the period 2010–2017. The criteria used for the database collection and for the conceived model were not previously used and, thereby, enrich the discussion on the topic. The procedure aims to check the variation in the Mean of Technical Inefficiency and conduct an analysis using Quasi-Maximum Likelihood Estimation. The regressions showed that the Mean of Technical Inefficiency could be mainly explained by data, variables, employed empirical models and the region of study. The studies that focus on farms of developed countries present the lowest Mean of Technical Inefficiency, while studies for developing or low-income countries exhibit the opposite. Therefore, for future research on productive analysis, we suggest empirical procedures aimed at achieving robust results that take into account specific regional characteristics of farms.
{"title":"Meta-regression Analysis of Technical (In)Efficiency in Agriculture: a Regional Approach","authors":"Micael Queiroga dos Santos, A. Marta-Costa, X. Rodríguez","doi":"10.17059/ekon.reg.2021-3-14","DOIUrl":"https://doi.org/10.17059/ekon.reg.2021-3-14","url":null,"abstract":"While scientific studies have not reached a consensus on the methodology for examining Technical Efficiency (or Inefficiency), the influence of regions appears to be important for efficiency scores. Therefore, this research aims to investigate the empirical procedures for the achievement of more robust results in the analysis of productive efficiency, as well as to evaluate the effect of the location of farms on such efficiency. The goal was to check whether the most developed regions are the most efficient. Meta-regression analysis provides an adequate method for an accurate assessment of both situations. This technique was applied based on a database of 166 observations on the agricultural sector from countries around the world, published in the period 2010–2017. The criteria used for the database collection and for the conceived model were not previously used and, thereby, enrich the discussion on the topic. The procedure aims to check the variation in the Mean of Technical Inefficiency and conduct an analysis using Quasi-Maximum Likelihood Estimation. The regressions showed that the Mean of Technical Inefficiency could be mainly explained by data, variables, employed empirical models and the region of study. The studies that focus on farms of developed countries present the lowest Mean of Technical Inefficiency, while studies for developing or low-income countries exhibit the opposite. Therefore, for future research on productive analysis, we suggest empirical procedures aimed at achieving robust results that take into account specific regional characteristics of farms.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74593524","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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