Pub Date : 2022-01-01DOI: 10.14530/se.2022.4.128-157
N. Dzhurka, O. Dyomina
This paper assesses the effects of the investment shock to the fuel and energy sectors of the Far East within the framework of the Eastern vector of Russia’s energy policy. We systemize the basic investment projects implemented in these sectors in order to increase export supplies of fuel and energy resources to the Asia-Pacific countries and assess the shifts in the structure of fuel and energy resources production in the region over the period from 2012 to 2019. In calculations, we use the classical and spatial versions of the method of structural decomposition of the growth rates of the sectoral indicators (shift-share analysis). On the basis of structural decomposition of the changes on the average annual number of employees and the GVA of the fuel and energy complex of the Far East at different spatial levels (national, macroregional (interregional) and regional) we draw the following conclusions: a strong impulse for development in the framework of the Eastern vector of Russia’s energy policy was given to extractive industries, their competitive effects were positive and overrode the negative shocks, which were destabilizing the national economy; an important factor in the economic dynamics of the extractive industries of the fuel and energy complex were gains/losses in inter-regional competition, but not spatial externalities; in the oil and gas industry, unlike in the coal industry, there is potential for formation of spatial externalities; developing faster than others, extractive industries remain enclaves in regional economies
{"title":"Structural Shifts in the Fuel and Energy Sectors of the Far East: A Spatial Analysis","authors":"N. Dzhurka, O. Dyomina","doi":"10.14530/se.2022.4.128-157","DOIUrl":"https://doi.org/10.14530/se.2022.4.128-157","url":null,"abstract":"This paper assesses the effects of the investment shock to the fuel and energy sectors of the Far East within the framework of the Eastern vector of Russia’s energy policy. We systemize the basic investment projects implemented in these sectors in order to increase export supplies of fuel and energy resources to the Asia-Pacific countries and assess the shifts in the structure of fuel and energy resources production in the region over the period from 2012 to 2019. In calculations, we use the classical and spatial versions of the method of structural decomposition of the growth rates of the sectoral indicators (shift-share analysis). On the basis of structural decomposition of the changes on the average annual number of employees and the GVA of the fuel and energy complex of the Far East at different spatial levels (national, macroregional (interregional) and regional) we draw the following conclusions: a strong impulse for development in the framework of the Eastern vector of Russia’s energy policy was given to extractive industries, their competitive effects were positive and overrode the negative shocks, which were destabilizing the national economy; an important factor in the economic dynamics of the extractive industries of the fuel and energy complex were gains/losses in inter-regional competition, but not spatial externalities; in the oil and gas industry, unlike in the coal industry, there is potential for formation of spatial externalities; developing faster than others, extractive industries remain enclaves in regional economies","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82336213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14530/se.2022.4.068-092
A. Polbin, T. Ivakhnenko
In this paper we test convergence of income inequality in Russia’s regions for the period 1995–2020. To do this, conditional and unconditional beta convergence models for the regional Gini index are evaluated on cross-sectional and panel data using time and spatial effects. Estimates of the models show that both conditional and unconditional convergence of income inequality takes place in Russia’s regions. It is shown that the rate of convergence varies significantly within the considered period: the levels of income inequality in the regions converged most strongly at the beginning of the period with a gradual slowdown in the rate of convergence in subsequent periods. This result may be related to the recovery growth and redistribution policy in the 2000s, as well as the consequences of the 2014 crisis. The use of the same initial characteristics, such as GRP per capita, level of education and population, accelerates convergence. Spatial effects are statistically significant for models of unconditional, but not conditional convergence, but do not affect the estimates obtained. When considering a panel data structure with the inclusion of fixed time effects, convergence estimates increase for both unconditional and conditional convergence
{"title":"Convergence of Income Inequality in Russia’s Regions","authors":"A. Polbin, T. Ivakhnenko","doi":"10.14530/se.2022.4.068-092","DOIUrl":"https://doi.org/10.14530/se.2022.4.068-092","url":null,"abstract":"In this paper we test convergence of income inequality in Russia’s regions for the period 1995–2020. To do this, conditional and unconditional beta convergence models for the regional Gini index are evaluated on cross-sectional and panel data using time and spatial effects. Estimates of the models show that both conditional and unconditional convergence of income inequality takes place in Russia’s regions. It is shown that the rate of convergence varies significantly within the considered period: the levels of income inequality in the regions converged most strongly at the beginning of the period with a gradual slowdown in the rate of convergence in subsequent periods. This result may be related to the recovery growth and redistribution policy in the 2000s, as well as the consequences of the 2014 crisis. The use of the same initial characteristics, such as GRP per capita, level of education and population, accelerates convergence. Spatial effects are statistically significant for models of unconditional, but not conditional convergence, but do not affect the estimates obtained. When considering a panel data structure with the inclusion of fixed time effects, convergence estimates increase for both unconditional and conditional convergence","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"222 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73200103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14530/se.2022.4.009-035
N. Dzhurka
In this paper we discuss the capabilities of input-output tables for obtaining the estimates of system effects generated by interregional interactions in the hierarchically organized space. Two options of integrating the concepts of interregional interactions and central place are presented, one of which implies the a priori, the other – the a posteriori solution to the problem of identifying (constructing) a market hierarchy. While the first one is used only in situations when the system effects are reduced to the spill-over of economic activity from the periphery to the center, the second one is used in more general situations when the system effects include not only spill-over effects but also the feedback effects. We consider the feedback loop input-output analysis, which allows us to get a posteriori estimates of regions distribution by the levels of spatial hierarchy. And determine that it had varying effectiveness for the cases of Japan and Russia. In accordance with the existing methods of decomposition of spatial multipliers the system effects of interregional interactions are determined, on the one hand, as a residual multiplier effect obtained after identifying the effects of intra-regional interactions, on the other hand, as a result of superposition of the effects of interregional interactions within the framework of dyads, triads, tetrads, etc. composed of regions. In order to obtain estimates of the system effects generated by interactions on markets of different levels (provided that these levels are identified), we propose the method of localizable partition, organizing the calculation of the structural blocks of spatial multipliers in the ‘from the general to the particular’ logic (from the system effects of interactions on the national market to the effects of interactions on local markets). On the basis of this method, we estimate the size and structure of the system effects absorbed by the economies of the three central regions of Japan (Kanto, Chubu, Kinki), which form the core of the national economic space
{"title":"Estimating the Effects of Economic Interactions in a Hierarchically Organized Space: Possibilities of the Balance Method","authors":"N. Dzhurka","doi":"10.14530/se.2022.4.009-035","DOIUrl":"https://doi.org/10.14530/se.2022.4.009-035","url":null,"abstract":"In this paper we discuss the capabilities of input-output tables for obtaining the estimates of system effects generated by interregional interactions in the hierarchically organized space. Two options of integrating the concepts of interregional interactions and central place are presented, one of which implies the a priori, the other – the a posteriori solution to the problem of identifying (constructing) a market hierarchy. While the first one is used only in situations when the system effects are reduced to the spill-over of economic activity from the periphery to the center, the second one is used in more general situations when the system effects include not only spill-over effects but also the feedback effects. We consider the feedback loop input-output analysis, which allows us to get a posteriori estimates of regions distribution by the levels of spatial hierarchy. And determine that it had varying effectiveness for the cases of Japan and Russia. In accordance with the existing methods of decomposition of spatial multipliers the system effects of interregional interactions are determined, on the one hand, as a residual multiplier effect obtained after identifying the effects of intra-regional interactions, on the other hand, as a result of superposition of the effects of interregional interactions within the framework of dyads, triads, tetrads, etc. composed of regions. In order to obtain estimates of the system effects generated by interactions on markets of different levels (provided that these levels are identified), we propose the method of localizable partition, organizing the calculation of the structural blocks of spatial multipliers in the ‘from the general to the particular’ logic (from the system effects of interactions on the national market to the effects of interactions on local markets). On the basis of this method, we estimate the size and structure of the system effects absorbed by the economies of the three central regions of Japan (Kanto, Chubu, Kinki), which form the core of the national economic space","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"66 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74485726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-319-20565-6_7
M. Barthelemy
{"title":"Measuring the Time Evolution of Spatial Networks","authors":"M. Barthelemy","doi":"10.1007/978-3-319-20565-6_7","DOIUrl":"https://doi.org/10.1007/978-3-319-20565-6_7","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"33 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79184618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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