Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-2-15
Е. В. В. iD, . М.В.КозловаiD, О. В. Куур, Г. Б. Пестунова, Е. В. Варавин, М. В. Козлова, Yevgeniy V. Varavin iD, . MarinaV.KozlovaiD, O. Kuur, Galina B. Pestunova
Considering current trends in the development of green economy and introduction of ESG-principles, the issues of investment attractiveness of enterprises, industries and regions are gaining attention. The literature review has shown that not all available methodologies for assessing regional investment attractiveness take into account the determinants of negative anthropogenic impacts on the environment. The present study aims to assess the investment appeal of the basic industries of the East Kazakhstan region in the context of green development and outline ways to attract more green investment in the region. The research methodology involves comparing the indicators of investment attractiveness of regional basic industries with their green attractiveness, characterised by investment in environmental protection. Additionally, a decoupling index was included in the model in order to examine a possible mismatch between the economic growth of regional industries and their pollution rates. Official statistical data for 2015-2019 were analysed. The study concluded that manufacturing is the only industry with a high green attractiveness, although it has a medium investment attractiveness. Given the need for industrialisation and diversification of the economy in East Kazakhstan, local authorities are recommended to focus on improving the investment climate in this sector. Agriculture and construction have high investment attractiveness, while mining and electricity supply are characterised by above average attractiveness. However, all these sectors remain unattractive in terms of environmental investment. To increase green attractiveness of the aforementioned industries, the study suggests to develop an effective mechanism for financing green projects, as well as to apply government regulation tools aimed at improving the efficiency of environmental investment. Further research may be related to the substantiation of such regulatory measures.
{"title":"Assessment of Investment Attractiveness of Regional Industries in the Context of Green Development","authors":"Е. В. В. iD, . М.В.КозловаiD, О. В. Куур, Г. Б. Пестунова, Е. В. Варавин, М. В. Козлова, Yevgeniy V. Varavin iD, . MarinaV.KozlovaiD, O. Kuur, Galina B. Pestunova","doi":"10.17059/ekon.reg.2023-2-15","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-2-15","url":null,"abstract":"Considering current trends in the development of green economy and introduction of ESG-principles, the issues of investment attractiveness of enterprises, industries and regions are gaining attention. The literature review has shown that not all available methodologies for assessing regional investment attractiveness take into account the determinants of negative anthropogenic impacts on the environment. The present study aims to assess the investment appeal of the basic industries of the East Kazakhstan region in the context of green development and outline ways to attract more green investment in the region. The research methodology involves comparing the indicators of investment attractiveness of regional basic industries with their green attractiveness, characterised by investment in environmental protection. Additionally, a decoupling index was included in the model in order to examine a possible mismatch between the economic growth of regional industries and their pollution rates. Official statistical data for 2015-2019 were analysed. The study concluded that manufacturing is the only industry with a high green attractiveness, although it has a medium investment attractiveness. Given the need for industrialisation and diversification of the economy in East Kazakhstan, local authorities are recommended to focus on improving the investment climate in this sector. Agriculture and construction have high investment attractiveness, while mining and electricity supply are characterised by above average attractiveness. However, all these sectors remain unattractive in terms of environmental investment. To increase green attractiveness of the aforementioned industries, the study suggests to develop an effective mechanism for financing green projects, as well as to apply government regulation tools aimed at improving the efficiency of environmental investment. Further research may be related to the substantiation of such regulatory measures.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"24 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81908024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-1-12
E. Skvortsov
Spatial aspects, including remoteness as one of the most important characteristics, signifi-cantly affect the socio-economic development of regions, in particular, the introduction of innovations by business. The present study aims to analyse the impact of distance to large cities and regional centres on the use of robotics in agriculture. At the first stage, the Google Maps application was used to determine the distances between robot farms and district and regional centres; at the second stage, a cluster analy-sis of the obtained data was performed. The study involved 81 farms located in 32 Russian regions, which use 371 robot units (85.2 % of their total number in the country). The greatest distance from the robot farm to the regional centre is 470 km, to the district centre — 73 km. The cluster analysis revealed an in-verse correlation between distances to regional centres and the average number of robots on farms. On average, there are 32.5 robots in a cluster with an average distance of 35.0 km between a farm and a re-gional centre, 3.6 robots in a cluster with a distance of 114.7 km, and 3.0 robots in a cluster of extremely remote farms with a distance of 227.5 km. Farms with the largest number of robots are located near ma-jor urban agglomerations. Accordingly, the introduction of robotics in remote areas will be slower due to underdeveloped transport and other infrastructure. At the same time, rural population commuting to large cities additionally stimulates the robotisation of agriculture. To reduce the technological backward-ness of remote rural areas, it is proposed to implement measures of innovation stimulation, including ag-ricultural growth corridors, agriculture clusters, agro-industrial parks, special economic zones and agri-business incubators.
{"title":"Impact of the Remoteness of Farms on the use of Robotics in Regional Agriculture","authors":"E. Skvortsov","doi":"10.17059/ekon.reg.2023-1-12","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-1-12","url":null,"abstract":"Spatial aspects, including remoteness as one of the most important characteristics, signifi-cantly affect the socio-economic development of regions, in particular, the introduction of innovations by business. The present study aims to analyse the impact of distance to large cities and regional centres on the use of robotics in agriculture. At the first stage, the Google Maps application was used to determine the distances between robot farms and district and regional centres; at the second stage, a cluster analy-sis of the obtained data was performed. The study involved 81 farms located in 32 Russian regions, which use 371 robot units (85.2 % of their total number in the country). The greatest distance from the robot farm to the regional centre is 470 km, to the district centre — 73 km. The cluster analysis revealed an in-verse correlation between distances to regional centres and the average number of robots on farms. On average, there are 32.5 robots in a cluster with an average distance of 35.0 km between a farm and a re-gional centre, 3.6 robots in a cluster with a distance of 114.7 km, and 3.0 robots in a cluster of extremely remote farms with a distance of 227.5 km. Farms with the largest number of robots are located near ma-jor urban agglomerations. Accordingly, the introduction of robotics in remote areas will be slower due to underdeveloped transport and other infrastructure. At the same time, rural population commuting to large cities additionally stimulates the robotisation of agriculture. To reduce the technological backward-ness of remote rural areas, it is proposed to implement measures of innovation stimulation, including ag-ricultural growth corridors, agriculture clusters, agro-industrial parks, special economic zones and agri-business incubators.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"181 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72916718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-2-20
İbrahim Halil, Sugözü, Sema Yaşar, Мировая экономика
In the study, panel data analysis was conducted on 32 OECD countries covering the period 1990-2018. To analyse the effect of energy consumption on economic growth, first, a cross-section dependence test of the variables was carried out, then CADF Test, which is the most suitable unit root test based on the obtained results results, was applied. According to the findings of the Hausman, autocorrelation, and heteroscedasticity tests, it has been decided to use the Driscoll-Kraay test for the model’s forecast. The forecast results demonstrate that energy consumption positively affects economic growth. Westerlund ECM Panel Cointegration Test was conducted to determine the long-term relationship, and it concluded that the variables acted together in the long term. Emirmahmutoglu & Kose and Dumitrescu & Hurlin tests were used to determine the direction of the relationship between energy consumption and growth. Through the results of both tests, a maximum number of countries emerged respectively in the null hypothesis with no causality relationship and then in the growth hypothesis explaining the causality relationship from energy to growth. Along with the panel fisher and panel Z_NT test results of both causality tests, a causality relationship has been detected from energy to growth.
{"title":"Which Hypothesis is Valid for OECD Countries in the Context of the Relationship between Energy Consumption and Economic Growth? A Panel Data Analysis","authors":"İbrahim Halil, Sugözü, Sema Yaşar, Мировая экономика","doi":"10.17059/ekon.reg.2023-2-20","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-2-20","url":null,"abstract":"In the study, panel data analysis was conducted on 32 OECD countries covering the period 1990-2018. To analyse the effect of energy consumption on economic growth, first, a cross-section dependence test of the variables was carried out, then CADF Test, which is the most suitable unit root test based on the obtained results results, was applied. According to the findings of the Hausman, autocorrelation, and heteroscedasticity tests, it has been decided to use the Driscoll-Kraay test for the model’s forecast. The forecast results demonstrate that energy consumption positively affects economic growth. Westerlund ECM Panel Cointegration Test was conducted to determine the long-term relationship, and it concluded that the variables acted together in the long term. Emirmahmutoglu & Kose and Dumitrescu & Hurlin tests were used to determine the direction of the relationship between energy consumption and growth. Through the results of both tests, a maximum number of countries emerged respectively in the null hypothesis with no causality relationship and then in the growth hypothesis explaining the causality relationship from energy to growth. Along with the panel fisher and panel Z_NT test results of both causality tests, a causality relationship has been detected from energy to growth.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"38 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82865502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-1-4
S. Borodin
Assessment of development opportunities for socio-economic systems is particularly relevant in the context of constantly changing macroeconomic conditions. A retrospective analysis is an important step in assessing development opportunities at the regional level. Based on a presented model for assess-ing regional sustainable development, the article analyses statistical data of the regions included in the Central, Northwestern and Southern Federal Districts for the period 2005-2019. According to the concept of sustainable development, the indicators were divided into three subgroups: social, economic and environ-mental. The following results were obtained. Social indicators revealed positive dynamics in the number of regions developing sustainably until 2014; later, the number changed erratically every year, ranging from 21 to 38. Economic indicators demonstrated negative dynamics in the number of sustainable regions un-til 2014. In the period 2014-2019, an abrupt fluctuation from 13 to 32 units was observed. Environmental indicators show that, on average, half of the examined regions managed to develop sustainably. After de-termining the overall index, the rate of change of the index was identified. Then, a sustainable develop-ment matrix was constructed, where 1 means that the index value increased year-on-year or remained the same, and 0 means that the index value decreased year-on-year. The findings can be used for ranking re-gions by summing up values in the region’s row of the sustainability matrix. The study may also serve as a basis for identifying the relationship between various large-scale phenomena such as the economic crisis, pandemic, the development of digital currency markets and changes in regional sustainability indicators.
{"title":"A Model for Assessing Regional Sustainable Development Based on the Index Method","authors":"S. Borodin","doi":"10.17059/ekon.reg.2023-1-4","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-1-4","url":null,"abstract":"Assessment of development opportunities for socio-economic systems is particularly relevant in the context of constantly changing macroeconomic conditions. A retrospective analysis is an important step in assessing development opportunities at the regional level. Based on a presented model for assess-ing regional sustainable development, the article analyses statistical data of the regions included in the Central, Northwestern and Southern Federal Districts for the period 2005-2019. According to the concept of sustainable development, the indicators were divided into three subgroups: social, economic and environ-mental. The following results were obtained. Social indicators revealed positive dynamics in the number of regions developing sustainably until 2014; later, the number changed erratically every year, ranging from 21 to 38. Economic indicators demonstrated negative dynamics in the number of sustainable regions un-til 2014. In the period 2014-2019, an abrupt fluctuation from 13 to 32 units was observed. Environmental indicators show that, on average, half of the examined regions managed to develop sustainably. After de-termining the overall index, the rate of change of the index was identified. Then, a sustainable develop-ment matrix was constructed, where 1 means that the index value increased year-on-year or remained the same, and 0 means that the index value decreased year-on-year. The findings can be used for ranking re-gions by summing up values in the region’s row of the sustainability matrix. The study may also serve as a basis for identifying the relationship between various large-scale phenomena such as the economic crisis, pandemic, the development of digital currency markets and changes in regional sustainability indicators.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"86 5 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89361037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-2-4
Nailya K. Nurlanova iD, . FaridaG.AlzhanovaiD, Z. Satpayeva, Н. К. Н. iD, . Ф.Г.АльжановаiD, З. Т. Сатпаева
World experience shows that in the context of the increase in urbanisation, the achievement of the Sustainable Development Goals largely depends on the sustainability of cities. It was hypothesised that big cities in Kazakhstan are more stable than medium-sized cities and single-industry towns. The study aims to develop a modified rating assessment methodology for sustainable development of cities and test it using cities in Kazakhstan as an example in order to develop tools for planning and monitoring the achievement of the Sustainable Development Goals taking into account country specifics. To this end, such methods as generalisation, concretisation, economic and statistical, factorial and comparative analysis, ranking, and mapping were used. A modified methodology for rating assessment of sustainable development of cities based on social, economic, environmental factors was proposed. The method for the mapping of sustainable development risks was utilised. The research substantiated the criteria and typology of risks of sustainable urban development, which can be adapted to country-specific circumstances. The possibility of its use was demonstrated on the example of different types and categories of cities in Kazakhstan. The study was limited due to the inaccessibility of statistical data, especially for small towns and single-industry towns. The obtained results can be used to simulate and monitor the implementation of socio-economic programmes in cities of Kazakhstan and other countries. The research findings can be used as the basis for mechanisms and tools intended to make decisions by authorities to achieve the Sustainable Development Goals and develop sustainable cities.
{"title":"Sustainable Development of Cities: Rating Assessment Methodology and Risk Analysis (Using Kazakhstan as an Example)","authors":"Nailya K. Nurlanova iD, . FaridaG.AlzhanovaiD, Z. Satpayeva, Н. К. Н. iD, . Ф.Г.АльжановаiD, З. Т. Сатпаева","doi":"10.17059/ekon.reg.2023-2-4","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-2-4","url":null,"abstract":"World experience shows that in the context of the increase in urbanisation, the achievement of the Sustainable Development Goals largely depends on the sustainability of cities. It was hypothesised that big cities in Kazakhstan are more stable than medium-sized cities and single-industry towns. The study aims to develop a modified rating assessment methodology for sustainable development of cities and test it using cities in Kazakhstan as an example in order to develop tools for planning and monitoring the achievement of the Sustainable Development Goals taking into account country specifics. To this end, such methods as generalisation, concretisation, economic and statistical, factorial and comparative analysis, ranking, and mapping were used. A modified methodology for rating assessment of sustainable development of cities based on social, economic, environmental factors was proposed. The method for the mapping of sustainable development risks was utilised. The research substantiated the criteria and typology of risks of sustainable urban development, which can be adapted to country-specific circumstances. The possibility of its use was demonstrated on the example of different types and categories of cities in Kazakhstan. The study was limited due to the inaccessibility of statistical data, especially for small towns and single-industry towns. The obtained results can be used to simulate and monitor the implementation of socio-economic programmes in cities of Kazakhstan and other countries. The research findings can be used as the basis for mechanisms and tools intended to make decisions by authorities to achieve the Sustainable Development Goals and develop sustainable cities.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"14 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88446148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-2-18
L. Agheli, Levin, P. Im
The overall economic performance is summarised in the economic growth. It occurs when resources are combined technically in an effective way. While advanced countries have no reliance on natural resources, they experience steady growth compared to natural resource-abundant countries. The Caspian Sea basin countries (Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan) and Central Asia states (Kyrgyz Republic, Tajikistan, and Uzbekistan) own considerable mineral and ecological resources. This paper aims to examine the relationship between economic growth and natural resource depletion in the region during 1997–2019. Due to abundance of natural resources, this region trades fossil fuels and minerals with other economic blocs. Hence, foreign direct investment is added into the regression model in order to account for economic openness. In addition, the share of industry value added in gross domestic product is included to embody the industrialisation impact on economic growth. Finally, the tertiary enrolment is entered into the regression to measure the effect of human capital on economic growth. After specifying the econometric model, variables under study were tested for unit root. Due to difference in order of integration among variables, panel fully modified least squares method was used to estimate the model. The estimation results indicate the significant and positive effects of natural resource depletion, foreign direct investment, the share of industry value added and tertiary enrolment on economic growth. These findings imply that natural resource depletion contributes to economic growth much greater than foreign direct investment and tertiary enrolment. Thus, the resource curse is not confirmed across the examined countries.
{"title":"The Nexus between Economic Growth, Natural Resource Depletion and Foreign Direct Investment","authors":"L. Agheli, Levin, P. Im","doi":"10.17059/ekon.reg.2023-2-18","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-2-18","url":null,"abstract":"The overall economic performance is summarised in the economic growth. It occurs when resources are combined technically in an effective way. While advanced countries have no reliance on natural resources, they experience steady growth compared to natural resource-abundant countries. The Caspian Sea basin countries (Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan) and Central Asia states (Kyrgyz Republic, Tajikistan, and Uzbekistan) own considerable mineral and ecological resources. This paper aims to examine the relationship between economic growth and natural resource depletion in the region during 1997–2019. Due to abundance of natural resources, this region trades fossil fuels and minerals with other economic blocs. Hence, foreign direct investment is added into the regression model in order to account for economic openness. In addition, the share of industry value added in gross domestic product is included to embody the industrialisation impact on economic growth. Finally, the tertiary enrolment is entered into the regression to measure the effect of human capital on economic growth. After specifying the econometric model, variables under study were tested for unit root. Due to difference in order of integration among variables, panel fully modified least squares method was used to estimate the model. The estimation results indicate the significant and positive effects of natural resource depletion, foreign direct investment, the share of industry value added and tertiary enrolment on economic growth. These findings imply that natural resource depletion contributes to economic growth much greater than foreign direct investment and tertiary enrolment. Thus, the resource curse is not confirmed across the examined countries.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"42 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79116204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-1-14
R. Shestakov, E. I. Lovchikova
The diversity of natural, climatic, and economic conditions of Russian regions implies a wide range of approaches to their classification. Simultaneously, the task of creating an abstract methodology for any branch of the national economy becomes more complicated. Effective clustering plays an important role in the establishment and implementation of agricultural and economic policies. The paper explores the potential of basic agricultural and economic regional clustering based on time series of main eco-nomic and agricultural development indicators. The dynamic segmentation technique was applied in order to monitor and predict the direction of meso-economic changes. Official Russian statistics were analysed to identify groups of indicators on production, production and institutional, and production and structural criteria. The k-means clustering algorithm was chosen as the key research method. Based on the three simulated regional segments, baseline average values were calculated. Then, the segments were classified according to the obtained characteristics. The outliers, significantly differing from the main data sets, were considered separately. The findings confirmed a wide spatial distribution of regions included in certain agricultural and economic segments. The presented classification can be applied to justify the directions and choice of instruments of agricultural and economic policy and a strategy for creating production clusters. Moreover, it can be used to plan the activities of regional agri-businesses and reduce their devel-opment imbalances. To improve the dynamic segmentation technique in the field of agricultural and economic development, the analysis can be expanded by changing the examined time interval, increasing the number of factors included in the model and their interactions, and introducing new clustering algorithms. Additionally, this model can be used to forecast structural changes and production dynamics.
{"title":"Clustering of Regions Using Basic Agricultural and Economic Criteria","authors":"R. Shestakov, E. I. Lovchikova","doi":"10.17059/ekon.reg.2023-1-14","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-1-14","url":null,"abstract":"The diversity of natural, climatic, and economic conditions of Russian regions implies a wide range of approaches to their classification. Simultaneously, the task of creating an abstract methodology for any branch of the national economy becomes more complicated. Effective clustering plays an important role in the establishment and implementation of agricultural and economic policies. The paper explores the potential of basic agricultural and economic regional clustering based on time series of main eco-nomic and agricultural development indicators. The dynamic segmentation technique was applied in order to monitor and predict the direction of meso-economic changes. Official Russian statistics were analysed to identify groups of indicators on production, production and institutional, and production and structural criteria. The k-means clustering algorithm was chosen as the key research method. Based on the three simulated regional segments, baseline average values were calculated. Then, the segments were classified according to the obtained characteristics. The outliers, significantly differing from the main data sets, were considered separately. The findings confirmed a wide spatial distribution of regions included in certain agricultural and economic segments. The presented classification can be applied to justify the directions and choice of instruments of agricultural and economic policy and a strategy for creating production clusters. Moreover, it can be used to plan the activities of regional agri-businesses and reduce their devel-opment imbalances. To improve the dynamic segmentation technique in the field of agricultural and economic development, the analysis can be expanded by changing the examined time interval, increasing the number of factors included in the model and their interactions, and introducing new clustering algorithms. Additionally, this model can be used to forecast structural changes and production dynamics.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"58 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79780779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-1-7
A. V. Topilin, O. D. Vorobyova
The imbalance between labour supply and demand, both by types of economic activity and by professional groups, differs in Russian regional labour markets, causing long-term unemployment and im-poverishment of the population. The article examines the transformation of the labour market, regional characteristics of market failures and its recovery during the COVID-19 pandemic. Based on sample surveys of the labour force conducted by the Federal State Statistics Service, we determined monthly unemploy-ment dynamics and, subsequently, the vulnerability and instability of regional labour markets. It is hypoth-esised that the stronger the contraction of employment and the greater the unemployment, the longer the process of labour market recovery during the pandemic; regions recover from the crisis at different speeds. Indicators of the intensity of labour market failures and its recovery are proposed. Since the pandemic is a peculiar phenomenon that affected the economy and society, human behaviour in the labour market, the concept of excessive unemployment was used (the difference between actual unemployment and its pre-pandemic level). We performed a correlation analysis of the relations between labour market failures and its recovery in four groups of regions characterised by different labour market fluctuations. The cal-culated Spearman’s coefficients showed a positive relationship between the indicators. The depth of la-bour market failures and its recovery rate in regions with developed infrastructure, attracting labour mi-grants, are revealed. A positive relationship was established between the unemployment dynamics and the increase in vacancy rate reported by employers to employment agencies, increase in the average monthly salary. This article presents the results of the first research stage. Further studies will expand the time se-ries of employment and unemployment in order to identify long-term trends and build a forecasting model.
{"title":"Dynamics and Regional Features of Labour Market Recovery During COVID-19","authors":"A. V. Topilin, O. D. Vorobyova","doi":"10.17059/ekon.reg.2023-1-7","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-1-7","url":null,"abstract":"The imbalance between labour supply and demand, both by types of economic activity and by professional groups, differs in Russian regional labour markets, causing long-term unemployment and im-poverishment of the population. The article examines the transformation of the labour market, regional characteristics of market failures and its recovery during the COVID-19 pandemic. Based on sample surveys of the labour force conducted by the Federal State Statistics Service, we determined monthly unemploy-ment dynamics and, subsequently, the vulnerability and instability of regional labour markets. It is hypoth-esised that the stronger the contraction of employment and the greater the unemployment, the longer the process of labour market recovery during the pandemic; regions recover from the crisis at different speeds. Indicators of the intensity of labour market failures and its recovery are proposed. Since the pandemic is a peculiar phenomenon that affected the economy and society, human behaviour in the labour market, the concept of excessive unemployment was used (the difference between actual unemployment and its pre-pandemic level). We performed a correlation analysis of the relations between labour market failures and its recovery in four groups of regions characterised by different labour market fluctuations. The cal-culated Spearman’s coefficients showed a positive relationship between the indicators. The depth of la-bour market failures and its recovery rate in regions with developed infrastructure, attracting labour mi-grants, are revealed. A positive relationship was established between the unemployment dynamics and the increase in vacancy rate reported by employers to employment agencies, increase in the average monthly salary. This article presents the results of the first research stage. Further studies will expand the time se-ries of employment and unemployment in order to identify long-term trends and build a forecasting model.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"27 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86455685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-1-5
G. Korovin
Technological and organisational opportunities provided by digitalisation to the society and economy can help improve the efficiency of industry and advance the development of industrial regions. The study aims to assess the digitalisation level and rate of industrial regions in comparison with the av-erage Russian level. For this purpose, structural and dynamic analysis, as well as the method of grouping of various indicators from the official Russian statistics in the field of ICT were applied. It was hypothe-sised and confirmed that digital technologies are used more intensively in industrial regions. In terms of the use of basic information technologies, the values are higher by 1-7 %. Organisations in industrial re-gions are 3 % more likely to use global networks to interact with counterparts. There are also more en-terprises (by 4 %) that have implemented automated data exchange with partners. Industrial regions have been using special design, production management and product lifecycle software 15 % more often since 2018. However, a hypothesis of a larger-scale implementation of advanced digital technologies in indus-trial regions has not been unequivocally confirmed. The values are higher only for indicators of the use of industrial robots (by 25 %), artificial intelligence technologies (by 12.4 %), digital platforms (by 3.4 %), geo-information systems (by 4.7 %), the Internet of Things (by 4.3 %). The findings can be used to develop digi-talisation strategies at the regional and federal levels. Variability of the regulatory framework for collecting statistics and underdeveloped terminology in the field of digital technologies can be considered as limita-tions to the application of the results. Further research may focus on building econometric and other models for implementing digitalisation in regions.
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Pub Date : 2023-01-01DOI: 10.17059/ekon.reg.2023-2-11
. Н.Н.КуницынаiD, А. В. Д. I. С. Ф. университет, Российская Ставрополь, Федерация, СОЦиА льНОе РАЗВитие РеГиОНА, . NataliaN.KunitsynaiD, Aleksandr V. Dzhioev
The coronavirus spread transformed the economy and social order, and dealt a crushing blow to the labour market. Considering the worsening unemployment, it becomes important to reduce informal employment, which leads to an increase in the shadow economy. It is hypothesised that the decline in official income is accompanied by an increase in informal employment differentiated across Russian region. The study aims to theoretically justify and empirically confirm the relationship between the consequences of the pandemic, decline in population income and dynamics of informal employment in regions, as well as to develop ways to reduce their negative impact on the labour market. The study utilised an approach of the Federal State Statistics Service (Rosstat) to determining employment criteria; additionally, expert and analytical methods, analysis of statistical series, clustering and cartography were applied. The regions were clustered according to Ward’s hierarchical method based on weighted standardised data. To this end, official data from Rosstat, the United Nations, and the World Bank were examined. As a result, the analysis of informal employment in Russian regions during the pandemic did not confirm the hypothesis, showing that informal employment actually decreased in most constituent entities; the largest decrease was observed in the North Caucasus republics. The performed clustering revealed groups of Russian regions in terms of the dependence of informal employment on average per capita income and gross regional product per capita. The obtained findings can be used to develop standard solutions for establishing long- and short-term support measures for employees at the national, regional and micro-level aimed at reducing the negative impact of the identified reasons for the growth of informal employment.
{"title":"Dependence of Informal Employment on Population Income in Russian Regions: Lessons from the Pandemic","authors":". Н.Н.КуницынаiD, А. В. Д. I. С. Ф. университет, Российская Ставрополь, Федерация, СОЦиА льНОе РАЗВитие РеГиОНА, . NataliaN.KunitsynaiD, Aleksandr V. Dzhioev","doi":"10.17059/ekon.reg.2023-2-11","DOIUrl":"https://doi.org/10.17059/ekon.reg.2023-2-11","url":null,"abstract":"The coronavirus spread transformed the economy and social order, and dealt a crushing blow to the labour market. Considering the worsening unemployment, it becomes important to reduce informal employment, which leads to an increase in the shadow economy. It is hypothesised that the decline in official income is accompanied by an increase in informal employment differentiated across Russian region. The study aims to theoretically justify and empirically confirm the relationship between the consequences of the pandemic, decline in population income and dynamics of informal employment in regions, as well as to develop ways to reduce their negative impact on the labour market. The study utilised an approach of the Federal State Statistics Service (Rosstat) to determining employment criteria; additionally, expert and analytical methods, analysis of statistical series, clustering and cartography were applied. The regions were clustered according to Ward’s hierarchical method based on weighted standardised data. To this end, official data from Rosstat, the United Nations, and the World Bank were examined. As a result, the analysis of informal employment in Russian regions during the pandemic did not confirm the hypothesis, showing that informal employment actually decreased in most constituent entities; the largest decrease was observed in the North Caucasus republics. The performed clustering revealed groups of Russian regions in terms of the dependence of informal employment on average per capita income and gross regional product per capita. The obtained findings can be used to develop standard solutions for establishing long- and short-term support measures for employees at the national, regional and micro-level aimed at reducing the negative impact of the identified reasons for the growth of informal employment.","PeriodicalId":47897,"journal":{"name":"Cambridge Journal of Regions Economy and Society","volume":"57 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90587158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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