Pub Date : 2023-03-07DOI: 10.21686/2500-3925-2023-1-26-36
V. Korzhak
Competitive functioning at the micro and macro levels of businesses in different industries creates the need to make changes in the assessment of the impact on the performance of companies of tangible and intangible assets, leading to an increase in the value of both products and companies. In this context, it is appropriate to measure and evaluate intellectual capital, which includes not only goodwill, shares and intellectual property, but also organizational aspects (strategy, database, network data), and relationships with customers, partners, etc. Scientists and economists have studied the issues of defining and measuring intellectual capital for more than 30 years, however, there is no unified approach yet.This study presents the results of the analysis of approaches to the definition of the concept of “intellectual capital”, its components and methods of calculating the indexes of intellectual capital of organizations.The purpose of the study is to determine the direction of development of research concerning the calculation of indexes of intellectual capital on the value of organizations and indexes reflecting the state of individual elements and subelements of intellectual capital.Empirical base and research methods. The issues of measuring intellectual capital are reflected in the works of Edvinsson (1997), Sweibi (2010), assessing its impact on the value of companies and the effectiveness of their activities operating in the developed countries - Zegala (2010), Clark (2011), Liu (2017) - and developing countries - Garanina (2010), Bayburina, Bykova (2012), Andreeva (2016) [1, 5] and other scientists. The works of these researchers formed the basis of the research conducted in this article. This study used general scientific methods such as comparison, deduction, induction and analysis.Results. The paper presents the results of the analysis and systematization of approaches to the definition of “intellectual capital” and its components, methods for calculating its indexes and assessing their impact on the financial results of companies. Currently, there is also a need for research to assess the mutual influence of individual elements of intellectual capital. In order to identify the industry specifics of the impact of intellectual capital components on the value of organizations, the methods given in the article should be tested separately using data from companies from different industries. When conducting an analysis, it is important to take into account factors related to the external environment of the company, such as the level of economic development of the country in which the company operates, as well as fluctuations in economic activity.Conclusion. The practical value of the paper lies in the possibility of applying its results in the process of making managerial decisions of organizations in various fields. The proposed options for identifying and calculating indexes can significantly affect the financial growth of organization
{"title":"Methods of Calculating the Indexes of Intellectual Capital of Organizations","authors":"V. Korzhak","doi":"10.21686/2500-3925-2023-1-26-36","DOIUrl":"https://doi.org/10.21686/2500-3925-2023-1-26-36","url":null,"abstract":"Competitive functioning at the micro and macro levels of businesses in different industries creates the need to make changes in the assessment of the impact on the performance of companies of tangible and intangible assets, leading to an increase in the value of both products and companies. In this context, it is appropriate to measure and evaluate intellectual capital, which includes not only goodwill, shares and intellectual property, but also organizational aspects (strategy, database, network data), and relationships with customers, partners, etc. Scientists and economists have studied the issues of defining and measuring intellectual capital for more than 30 years, however, there is no unified approach yet.This study presents the results of the analysis of approaches to the definition of the concept of “intellectual capital”, its components and methods of calculating the indexes of intellectual capital of organizations.The purpose of the study is to determine the direction of development of research concerning the calculation of indexes of intellectual capital on the value of organizations and indexes reflecting the state of individual elements and subelements of intellectual capital.Empirical base and research methods. The issues of measuring intellectual capital are reflected in the works of Edvinsson (1997), Sweibi (2010), assessing its impact on the value of companies and the effectiveness of their activities operating in the developed countries - Zegala (2010), Clark (2011), Liu (2017) - and developing countries - Garanina (2010), Bayburina, Bykova (2012), Andreeva (2016) [1, 5] and other scientists. The works of these researchers formed the basis of the research conducted in this article. This study used general scientific methods such as comparison, deduction, induction and analysis.Results. The paper presents the results of the analysis and systematization of approaches to the definition of “intellectual capital” and its components, methods for calculating its indexes and assessing their impact on the financial results of companies. Currently, there is also a need for research to assess the mutual influence of individual elements of intellectual capital. In order to identify the industry specifics of the impact of intellectual capital components on the value of organizations, the methods given in the article should be tested separately using data from companies from different industries. When conducting an analysis, it is important to take into account factors related to the external environment of the company, such as the level of economic development of the country in which the company operates, as well as fluctuations in economic activity.Conclusion. The practical value of the paper lies in the possibility of applying its results in the process of making managerial decisions of organizations in various fields. The proposed options for identifying and calculating indexes can significantly affect the financial growth of organization","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73993913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-07DOI: 10.21686/2500-3925-2023-1-64-72
K. Kuznetsov
The available methods for calculating the age level of consumption focus on determining the level of consumption by children. At the same time, previous studies show a decrease in the level of consumption after retirement. From a research point of view, it is of practical interest to estimate the distortion of one-year age profile of consumption depending on the age difference between the adult members of the household. Among common episodic households, the paper compares the age profiles of consumption for households with two adults aged 18 to 80 years. The paper compares indexes of average per capita consumption, and estimates the level of age difference in households for the age level of consumption. The author introduces such a concept as “net consumption”, under the influence of which sensitivity to age is absorbed. To create “net consumption” in the general chronology, only households with a minimum age difference are examined.The purpose of the study. Estimation of the age level of consumption depending on the demographic characteristics of the household is little studied in modern literature. As a rule, the papers provide methods for assessing the level of children consumption. However, when creating age profiles of consumption, the age characteristics of households are not taken into account. The aim of the study is to assess the impact of the difference in the age of people living together in a household on the age profiles of consumption. Both in Russia and in the EU countries, the average difference in age between the first marriage is on average 2.5 years, and the largest difference can reach more than 30 years. From a statistical point of view, it is of practical interest to study the average per capita age level of consumption depending on the difference in the age of a married couple.Materials and methods. The source of data for the study is the Household Budget Survey (HBS) conducted by Rosstat for 2020. The paper compares age profiles of consumption created by the minimum age of the household, by the maximum, by the average, and by the classical method. In addition, the paper compares the age profiles created by the classical method, taking into account the limitation of the age difference between household members.Results. The results obtained from the 2020 HBS survey allow conclusions to be drawn about different age profiles depending on the proposed calculation methods. The results of the study showed that among households consisting of two people, almost a third of households (34.1%) have an age difference of more than 5 years. In the context of calculating one-year age profiles of consumption, a significant age difference in age will lead to a distortion of age profiles of consumption. Among households consisting of two people, the largest difference in the level of average per capita consumption between all households of two people and households of two people with an age difference of no more than 1 year is observed in ea
{"title":"Estimation of the Impact of the Age Difference in Households of the Same Generation on the Age Level of Consumption","authors":"K. Kuznetsov","doi":"10.21686/2500-3925-2023-1-64-72","DOIUrl":"https://doi.org/10.21686/2500-3925-2023-1-64-72","url":null,"abstract":"The available methods for calculating the age level of consumption focus on determining the level of consumption by children. At the same time, previous studies show a decrease in the level of consumption after retirement. From a research point of view, it is of practical interest to estimate the distortion of one-year age profile of consumption depending on the age difference between the adult members of the household. Among common episodic households, the paper compares the age profiles of consumption for households with two adults aged 18 to 80 years. The paper compares indexes of average per capita consumption, and estimates the level of age difference in households for the age level of consumption. The author introduces such a concept as “net consumption”, under the influence of which sensitivity to age is absorbed. To create “net consumption” in the general chronology, only households with a minimum age difference are examined.The purpose of the study. Estimation of the age level of consumption depending on the demographic characteristics of the household is little studied in modern literature. As a rule, the papers provide methods for assessing the level of children consumption. However, when creating age profiles of consumption, the age characteristics of households are not taken into account. The aim of the study is to assess the impact of the difference in the age of people living together in a household on the age profiles of consumption. Both in Russia and in the EU countries, the average difference in age between the first marriage is on average 2.5 years, and the largest difference can reach more than 30 years. From a statistical point of view, it is of practical interest to study the average per capita age level of consumption depending on the difference in the age of a married couple.Materials and methods. The source of data for the study is the Household Budget Survey (HBS) conducted by Rosstat for 2020. The paper compares age profiles of consumption created by the minimum age of the household, by the maximum, by the average, and by the classical method. In addition, the paper compares the age profiles created by the classical method, taking into account the limitation of the age difference between household members.Results. The results obtained from the 2020 HBS survey allow conclusions to be drawn about different age profiles depending on the proposed calculation methods. The results of the study showed that among households consisting of two people, almost a third of households (34.1%) have an age difference of more than 5 years. In the context of calculating one-year age profiles of consumption, a significant age difference in age will lead to a distortion of age profiles of consumption. Among households consisting of two people, the largest difference in the level of average per capita consumption between all households of two people and households of two people with an age difference of no more than 1 year is observed in ea","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72452457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-07DOI: 10.21686/2500-3925-2023-1-4-23
V. Kapitanov, O. S. Osipova, A. U. Maksimova
The purpose of the study is to demonstrate the possibility of adequate mathematical modeling of property inequality in a stratified society based on an agent approach, taking into account the unequal (rank) exchange between agents with different socio-economic status. To achieve this goal, it was necessary to solve the following tasks:1. To develop minimum criteria for the adequacy of mathematical models of socio-economic inequality.2. To substantiate the advantages of mathematical models based on an agent approach using rank exchange.3. To present the author’s version of the mathematical model of the goods’ movement in society based on an agent approach, taking into account rank exchange.4. To check the author’s mathematical model for compliance with the minimum qualitative criteria of adequacy and to create a quantitatively coinciding with the real distribution of the country’s population of the Russian Federation by income.5. Determine the limitations of the developed mathematical model by the authors.Materials and methods. The paper used statistical data from Rosstat, the Federal Tax Service of the Russian Federation, the World Bank, the US Bureau of the Census, the Edelman Trust Barometer, as well as data published by domestic and foreign researchers of inequality. These data were compared with the results of inequality calculations obtained using the mathematical model of the goods’ movement in society based on an agent approach, taking into account rank (nonequivalent) exchange.Results. The minimum criteria that any adequate model of stratification of society must meet are defined: 1. reveals a lognormal distribution of the population by income with a heavy tail; 2. reflects in long-term historical retrospect the trend of inequality growth; 3. demonstrates a short-term reduction of inequality during periods of socio-economic crises.The proposed model meets these requirements, it demonstrates not only qualitative, but also quantitative adequacy – reproduces by calculation the curve of the actual distribution of Russian society by income.Common concepts of inequality, in particular, the theory of superstars or skill-based technological change do not allow achieving such a result. The limitations of the created mathematical model are shown:– the impossibility of creating an adequate Lorentz curve with insufficient computing power;– inability to describe changes in cross-country inequality, since countries are not subjects of rank exchange, although interstate agreements certainly have an impact on the exchange between economic subjects of social interaction;– inability to describe situations of absolute poverty, i.e. long-term decline in income, although in the real economy such situations are sometimes observed;– the endless growth of inequality over time, whereas in reality economic processes of inequality growth are always opposed by social processes of counteraction to this growth.Conclusion. The mechanism of spontaneous emergence and gro
{"title":"The Model of Socio-Economic Stratification of Society Based on An Agent Approach, Taking Into Account Rank Exchange","authors":"V. Kapitanov, O. S. Osipova, A. U. Maksimova","doi":"10.21686/2500-3925-2023-1-4-23","DOIUrl":"https://doi.org/10.21686/2500-3925-2023-1-4-23","url":null,"abstract":"The purpose of the study is to demonstrate the possibility of adequate mathematical modeling of property inequality in a stratified society based on an agent approach, taking into account the unequal (rank) exchange between agents with different socio-economic status. To achieve this goal, it was necessary to solve the following tasks:1. To develop minimum criteria for the adequacy of mathematical models of socio-economic inequality.2. To substantiate the advantages of mathematical models based on an agent approach using rank exchange.3. To present the author’s version of the mathematical model of the goods’ movement in society based on an agent approach, taking into account rank exchange.4. To check the author’s mathematical model for compliance with the minimum qualitative criteria of adequacy and to create a quantitatively coinciding with the real distribution of the country’s population of the Russian Federation by income.5. Determine the limitations of the developed mathematical model by the authors.Materials and methods. The paper used statistical data from Rosstat, the Federal Tax Service of the Russian Federation, the World Bank, the US Bureau of the Census, the Edelman Trust Barometer, as well as data published by domestic and foreign researchers of inequality. These data were compared with the results of inequality calculations obtained using the mathematical model of the goods’ movement in society based on an agent approach, taking into account rank (nonequivalent) exchange.Results. The minimum criteria that any adequate model of stratification of society must meet are defined: 1. reveals a lognormal distribution of the population by income with a heavy tail; 2. reflects in long-term historical retrospect the trend of inequality growth; 3. demonstrates a short-term reduction of inequality during periods of socio-economic crises.The proposed model meets these requirements, it demonstrates not only qualitative, but also quantitative adequacy – reproduces by calculation the curve of the actual distribution of Russian society by income.Common concepts of inequality, in particular, the theory of superstars or skill-based technological change do not allow achieving such a result. The limitations of the created mathematical model are shown:– the impossibility of creating an adequate Lorentz curve with insufficient computing power;– inability to describe changes in cross-country inequality, since countries are not subjects of rank exchange, although interstate agreements certainly have an impact on the exchange between economic subjects of social interaction;– inability to describe situations of absolute poverty, i.e. long-term decline in income, although in the real economy such situations are sometimes observed;– the endless growth of inequality over time, whereas in reality economic processes of inequality growth are always opposed by social processes of counteraction to this growth.Conclusion. The mechanism of spontaneous emergence and gro","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73308235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-07DOI: 10.21686/2500-3925-2023-1-53-63
M. Karyshev
Purpose of the study. The process of qualitative transformation of the economy under the influence of information and communication technologies is called digital transformation. The change of the economic paradigm in the technocratic context raises the question of whether the existing statistical methodology can still be applied objectively and effectively to the study of the subject area in the new conditions. The purpose of the article is to assess the relevance of statistics in the field of information and communication technologies as an information source for analyzing the digital transformation of the economy, taking into account its industry specifics.Materials and methods. To clarify the depth of this problem, there seems to be no better way than to carry out an analysis using statistical data collected and published by the Federal State Statistics Service. As statistical tools, descriptive statistics indexes are used to describe particular indicators, one of the methods of multivariate statistical analysis for obtaining a classification according to a number of characteristics (cluster hierarchical analysis) and, finally, a method for calculating an integral index that can rank the units of the analyzed population formed according to the type of economic activities (18 units), simultaneously for all of its constituent indicators.Results. The system of statistical indexes formed during the analysis included four thematic groups of indexes: a) IT infrastructure and access to it; b) the level and direction of use of IT services; c) availability and qualifications of IT specialists; d) information security. The calculation of descriptive statistics showed that all groups (with the exception of indexes of the presence and qualifications of IT specialists) are homogeneous; comparison of the values of the arithmetic mean and the median does not make it possible to declare any significant asymmetry in their distribution. Cluster hierarchical analysis was carried out by the Ward method using the Minkowski metric, which made it possible to obtain two approximately equal in size industry clusters and one mono-cluster, consisting of a type of activity in the field of information and communication. The resulting grouping, however, could not definite answer the question of the priority of some industries over others in the digital transformation of their constituent organizations. To solve this problem, an integral index was developed, which included the most significant indexes of each of the groups (selected by experts). As a result of calculating the values of this integral index based on the arithmetic weighted average, a ranked series was obtained, transformed into a typological grouping, the leader of which is activity in the field of information and communication, the outsider is agriculture. An analysis of this grouping made it possible to draw a very curious conclusion: in general, in the analyzed set of types of economic activity, there is
{"title":"To the Question of the Relevance of Statistics of Information and Communication Technologies in the Context of the Digital Transformation of the Economy","authors":"M. Karyshev","doi":"10.21686/2500-3925-2023-1-53-63","DOIUrl":"https://doi.org/10.21686/2500-3925-2023-1-53-63","url":null,"abstract":"Purpose of the study. The process of qualitative transformation of the economy under the influence of information and communication technologies is called digital transformation. The change of the economic paradigm in the technocratic context raises the question of whether the existing statistical methodology can still be applied objectively and effectively to the study of the subject area in the new conditions. The purpose of the article is to assess the relevance of statistics in the field of information and communication technologies as an information source for analyzing the digital transformation of the economy, taking into account its industry specifics.Materials and methods. To clarify the depth of this problem, there seems to be no better way than to carry out an analysis using statistical data collected and published by the Federal State Statistics Service. As statistical tools, descriptive statistics indexes are used to describe particular indicators, one of the methods of multivariate statistical analysis for obtaining a classification according to a number of characteristics (cluster hierarchical analysis) and, finally, a method for calculating an integral index that can rank the units of the analyzed population formed according to the type of economic activities (18 units), simultaneously for all of its constituent indicators.Results. The system of statistical indexes formed during the analysis included four thematic groups of indexes: a) IT infrastructure and access to it; b) the level and direction of use of IT services; c) availability and qualifications of IT specialists; d) information security. The calculation of descriptive statistics showed that all groups (with the exception of indexes of the presence and qualifications of IT specialists) are homogeneous; comparison of the values of the arithmetic mean and the median does not make it possible to declare any significant asymmetry in their distribution. Cluster hierarchical analysis was carried out by the Ward method using the Minkowski metric, which made it possible to obtain two approximately equal in size industry clusters and one mono-cluster, consisting of a type of activity in the field of information and communication. The resulting grouping, however, could not definite answer the question of the priority of some industries over others in the digital transformation of their constituent organizations. To solve this problem, an integral index was developed, which included the most significant indexes of each of the groups (selected by experts). As a result of calculating the values of this integral index based on the arithmetic weighted average, a ranked series was obtained, transformed into a typological grouping, the leader of which is activity in the field of information and communication, the outsider is agriculture. An analysis of this grouping made it possible to draw a very curious conclusion: in general, in the analyzed set of types of economic activity, there is ","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79725930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01Epub Date: 2021-07-09DOI: 10.1162/rest_a_01070
Michael Geruso, Timothy J Layton, Grace McCormack, Mark Shepard
Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data from Massachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.
{"title":"The Two-Margin Problem in Insurance Markets.","authors":"Michael Geruso, Timothy J Layton, Grace McCormack, Mark Shepard","doi":"10.1162/rest_a_01070","DOIUrl":"10.1162/rest_a_01070","url":null,"abstract":"<p><p>Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data from Massachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.</p>","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9488275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.21686/2500-3925-2022-6-53-62
A. A. Bryzgalov, Yury F. Telnov
{"title":"An Economic Model for Creating a Network Enterprise Architecture","authors":"A. A. Bryzgalov, Yury F. Telnov","doi":"10.21686/2500-3925-2022-6-53-62","DOIUrl":"https://doi.org/10.21686/2500-3925-2022-6-53-62","url":null,"abstract":"","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86271666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.21686/2500-3925-2022-6-40-52
A. Vasilieva
One of the stages of the statistical study of the competitiveness of a region is the selection of competing regions.Purpose of the study. The purpose of the article is to form a statistical set of regions-competitors based on the sectoral structure of the economy.Materials and methods. As research methods in this article, the method of the main array, factorial, cluster methods, statistical methods are chosen. The statistical data of Rosstat were used for the study. To perform the calculations, the GVA was considered in the structure of Russian National Classifier of Types of Economic Activity2 for 2019. Results. With the help of factor analysis, 19 types of economic activity of the regions were grouped according to similarities and differences. As a result, six factors were formed, each of which collected dependent types of economic activity. The use of cluster analysis made it possible to form groups of regions with a similar sectoral structure of the economy. The study involved 85 regions of the Russian Federation. Cluster analysis made it possible to solve the methodological problem of determining the boundaries of GVA intervals for certain types of economic activity in the selection of competing regions.The paper shows that for the Amur Region, nine regions of the Russian Federation should be considered as competing regions. The regions of this cluster are united by a high share of gross value added by the types of activity “Transportation and storage”, “Public administration”, “Trade”. At the same time, competitors are regions from different federal districts: 70% of the regions of the Far Eastern Federal District, 20% of the Southern Federal District, 10% of the Siberian Federal District. The main results of the study are the following: 1) a high variation of the regions of the Russian Federation in 2019 was revealed by the type of economic activity “Mining” and “Manufacturing”; 2) a grouping of 19 types of economic activity of the regions was carried out using the factor analysis method; 3) a cluster analysis of the regions of the Russian Federation was carried out according to the sectoral structure of gross value added for 2019; five clusters were received. Conclusion. This paper shows that the selection of competing regions must be carried out using the sectoral structure of the region’s economy. Consideration of the region’ specialization is an important requirement of the selection methodology. The advantage of the author’s methodology is its universality, objectivity and reflection of the specialization of the region. As a direction for further research, one should consider determining the specialization of regions using localization coefficients and, on its basis, the formation of a statistical set of competing regions. The presented sample of regions is necessary for assessing their competitiveness.
{"title":"Using the Sectoral Structure of the Economy to Select Competing Regions (on the Example of the Amur Region)","authors":"A. Vasilieva","doi":"10.21686/2500-3925-2022-6-40-52","DOIUrl":"https://doi.org/10.21686/2500-3925-2022-6-40-52","url":null,"abstract":"One of the stages of the statistical study of the competitiveness of a region is the selection of competing regions.Purpose of the study. The purpose of the article is to form a statistical set of regions-competitors based on the sectoral structure of the economy.Materials and methods. As research methods in this article, the method of the main array, factorial, cluster methods, statistical methods are chosen. The statistical data of Rosstat were used for the study. To perform the calculations, the GVA was considered in the structure of Russian National Classifier of Types of Economic Activity2 for 2019. Results. With the help of factor analysis, 19 types of economic activity of the regions were grouped according to similarities and differences. As a result, six factors were formed, each of which collected dependent types of economic activity. The use of cluster analysis made it possible to form groups of regions with a similar sectoral structure of the economy. The study involved 85 regions of the Russian Federation. Cluster analysis made it possible to solve the methodological problem of determining the boundaries of GVA intervals for certain types of economic activity in the selection of competing regions.The paper shows that for the Amur Region, nine regions of the Russian Federation should be considered as competing regions. The regions of this cluster are united by a high share of gross value added by the types of activity “Transportation and storage”, “Public administration”, “Trade”. At the same time, competitors are regions from different federal districts: 70% of the regions of the Far Eastern Federal District, 20% of the Southern Federal District, 10% of the Siberian Federal District. The main results of the study are the following: 1) a high variation of the regions of the Russian Federation in 2019 was revealed by the type of economic activity “Mining” and “Manufacturing”; 2) a grouping of 19 types of economic activity of the regions was carried out using the factor analysis method; 3) a cluster analysis of the regions of the Russian Federation was carried out according to the sectoral structure of gross value added for 2019; five clusters were received. Conclusion. This paper shows that the selection of competing regions must be carried out using the sectoral structure of the region’s economy. Consideration of the region’ specialization is an important requirement of the selection methodology. The advantage of the author’s methodology is its universality, objectivity and reflection of the specialization of the region. As a direction for further research, one should consider determining the specialization of regions using localization coefficients and, on its basis, the formation of a statistical set of competing regions. The presented sample of regions is necessary for assessing their competitiveness.","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82601882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.21686/2500-3925-2022-6-28-39
O. Soboleva
{"title":"Development of Departmental Statistical Accounting of the Federal Penitentiary Service in the Conditions of Digital Transformation","authors":"O. Soboleva","doi":"10.21686/2500-3925-2022-6-28-39","DOIUrl":"https://doi.org/10.21686/2500-3925-2022-6-28-39","url":null,"abstract":"","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72418851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.21686/2500-3925-2022-6-21-27
V. Vrublevskaya, A. Mamaeva
{"title":"Assessment of the State of the Meat Market and the Reproductive Process in Terms of Ensuring Food Security","authors":"V. Vrublevskaya, A. Mamaeva","doi":"10.21686/2500-3925-2022-6-21-27","DOIUrl":"https://doi.org/10.21686/2500-3925-2022-6-21-27","url":null,"abstract":"","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75634223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.21686/2500-3925-2022-6-63-71
D. Vlasov, P. Karasev, A. Sinchukov
{"title":"Game Simulation of Choosing the Optimal Strategy for Providing a Tourist Product, Taking Into Account the Preferences of Consumers in the Tourist Market","authors":"D. Vlasov, P. Karasev, A. Sinchukov","doi":"10.21686/2500-3925-2022-6-63-71","DOIUrl":"https://doi.org/10.21686/2500-3925-2022-6-63-71","url":null,"abstract":"","PeriodicalId":48456,"journal":{"name":"Review of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76735220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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