Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-61-72
V. V. Medyanik, A. Kurbatskii, P. I. Nagorny, D. Goryashin
The paper analyses available foreign and domestic expertise in the treatment and methods for measuring human capital. The authors identify weaknesses of a common approach to measuring human capital based on international statistical standards not adapted to national features of information and statistical support for regional analysis in the face of prominent interregional differences in socio-economic development.A methodological approach to assessing human capital is substantiated using an integral indicator that combines subindices that reflect the accumulation and reproduction of human capital. Guided by this approach and statistics on the demographic situation, upgrading knowledge, skills and strengthening the public health, including that of the working population, and based on actual data for the regions of Russia (for the year 2020), a unified summary indicator of human capital for the constituent entities of the Russian Federation was calculated and the regions were ranked by a level of human capital development.According to the authors, the developed methodological approaches to measuring the human capital index shall make it possible to take more specific action on implementing the Programme for Socio-economic Development as applied to individual Russian regions
{"title":"Methodological Approaches to Constructing Human Capital Index: Regional Dimension","authors":"V. V. Medyanik, A. Kurbatskii, P. I. Nagorny, D. Goryashin","doi":"10.34023/2313-6383-2024-31-2-61-72","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-61-72","url":null,"abstract":"The paper analyses available foreign and domestic expertise in the treatment and methods for measuring human capital. The authors identify weaknesses of a common approach to measuring human capital based on international statistical standards not adapted to national features of information and statistical support for regional analysis in the face of prominent interregional differences in socio-economic development.A methodological approach to assessing human capital is substantiated using an integral indicator that combines subindices that reflect the accumulation and reproduction of human capital. Guided by this approach and statistics on the demographic situation, upgrading knowledge, skills and strengthening the public health, including that of the working population, and based on actual data for the regions of Russia (for the year 2020), a unified summary indicator of human capital for the constituent entities of the Russian Federation was calculated and the regions were ranked by a level of human capital development.According to the authors, the developed methodological approaches to measuring the human capital index shall make it possible to take more specific action on implementing the Programme for Socio-economic Development as applied to individual Russian regions","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-39-51
S. V. Arzhenovskiy
The subject of the article is the study of inequality and poverty, taking into account under-reporting household income. The purpose of the research was to estimate the under-reporting incomes of households and their impact on the smoothing income inequality and poverty based on representative data of the All-Russian Survey of Consumer Finance (2013, 2015, 2018, 2020 and 2022). It is relevant to analyze changes in inequality for individual population groups, as well as the contribution to inequality of individual sources of income, including those hidden from statistical observation. The novelty of the study is the obtained estimates of under-reporting household income, as well as the results of an analysis of their impact on smoothing income inequality and poverty based on the specified dataThe methodology included the use of econometric modeling according to the Pissarides – Weber approach, as well as decomposition of the Gini elasticity by income sources based on the Lerman – Yitzhaki algorithm. The results of estimations differ from those previously obtained according to the data of the Russia Longitudinal Monitoring Survey – Higher School of Economics. The resulting share of under- reporting income in the disposable resources of households averaged about 20% for 2013 to 2022 and decreased by 2,3% on average per year. Decomposition of the Gini index by income sources led to the conclusion that the main contribution to inequality was made by the non-poor population. Calculation of Gini income elasticities by income sources for poor and non-poor households (taking into account the imputed component of under-reporting income) revealed their differently directed impact on inequality. Wages and social transfers increase inequality among the poor and do not increase inequality among non-poor households. Estimating under-reporting income was higher for the group of poor households. At the same time, this component increased inequality in the post-crisis year 2015 and in the pandemic year 2020 for the group of non-poor households. The obtained results will provide important information to social policy makers, taking into account the influence of under-reporting income` on the poverty reduction and equalization of the standard of living of the Russian population
{"title":"Statistical Analysis of Ineqality and Poverty with Under-Reporting Incomes of Russian Households","authors":"S. V. Arzhenovskiy","doi":"10.34023/2313-6383-2024-31-2-39-51","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-39-51","url":null,"abstract":"The subject of the article is the study of inequality and poverty, taking into account under-reporting household income. The purpose of the research was to estimate the under-reporting incomes of households and their impact on the smoothing income inequality and poverty based on representative data of the All-Russian Survey of Consumer Finance (2013, 2015, 2018, 2020 and 2022). It is relevant to analyze changes in inequality for individual population groups, as well as the contribution to inequality of individual sources of income, including those hidden from statistical observation. The novelty of the study is the obtained estimates of under-reporting household income, as well as the results of an analysis of their impact on smoothing income inequality and poverty based on the specified dataThe methodology included the use of econometric modeling according to the Pissarides – Weber approach, as well as decomposition of the Gini elasticity by income sources based on the Lerman – Yitzhaki algorithm. The results of estimations differ from those previously obtained according to the data of the Russia Longitudinal Monitoring Survey – Higher School of Economics. The resulting share of under- reporting income in the disposable resources of households averaged about 20% for 2013 to 2022 and decreased by 2,3% on average per year. Decomposition of the Gini index by income sources led to the conclusion that the main contribution to inequality was made by the non-poor population. Calculation of Gini income elasticities by income sources for poor and non-poor households (taking into account the imputed component of under-reporting income) revealed their differently directed impact on inequality. Wages and social transfers increase inequality among the poor and do not increase inequality among non-poor households. Estimating under-reporting income was higher for the group of poor households. At the same time, this component increased inequality in the post-crisis year 2015 and in the pandemic year 2020 for the group of non-poor households. The obtained results will provide important information to social policy makers, taking into account the influence of under-reporting income` on the poverty reduction and equalization of the standard of living of the Russian population","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140658182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-84-100
A. Dmitriev, I. I. Eliseeva
The paper covers the 300 years of the history of the Russian Academy of Sciences, which embodies the intellectual potential of Russia. The authors focus on the figures of academic scientists who have left their mark on the history of Russian statistics. It is stressed that the choice of personalities – members and corresponding members of the Academy of Sciences – was relative due to the complex structure of the Academy itself when а branch or a department of the Academy nominates a scientist to the rank of an academician or a corresponding member, as well as an ambiguity of a scientist’s contribution if the area of subsequent activity of the elected member of the RAS strained from the profile of the nominating division of the Academy. When discussing personal achievements, the authors adhered only to chronology, consistently moving from one historical phase to another, from one system of government to another. It is difficult to review the three centuries of the Academy of Sciences in any detail; this explains the lapidary style and the lack of description of the features of Russian history in each period and their impact on scientific research priorities. The article may be of interest to researchers, teachers and post-graduate students of economic universities, as well as employees of state statistics institutions in Russia.
{"title":"Statistical Scientists in the Academy of Sciences: To Mark the 300th Anniversary of the Russian Academy of Sciences (RAS)","authors":"A. Dmitriev, I. I. Eliseeva","doi":"10.34023/2313-6383-2024-31-2-84-100","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-84-100","url":null,"abstract":"The paper covers the 300 years of the history of the Russian Academy of Sciences, which embodies the intellectual potential of Russia. The authors focus on the figures of academic scientists who have left their mark on the history of Russian statistics. It is stressed that the choice of personalities – members and corresponding members of the Academy of Sciences – was relative due to the complex structure of the Academy itself when а branch or a department of the Academy nominates a scientist to the rank of an academician or a corresponding member, as well as an ambiguity of a scientist’s contribution if the area of subsequent activity of the elected member of the RAS strained from the profile of the nominating division of the Academy. When discussing personal achievements, the authors adhered only to chronology, consistently moving from one historical phase to another, from one system of government to another. It is difficult to review the three centuries of the Academy of Sciences in any detail; this explains the lapidary style and the lack of description of the features of Russian history in each period and their impact on scientific research priorities. The article may be of interest to researchers, teachers and post-graduate students of economic universities, as well as employees of state statistics institutions in Russia.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-5-22
M. Klevakina, T. V. Guren, I. A. Repin
The article discusses the main characteristics of the international statistical standard «System of Environmental-Economic Accounting 2012 – Central Framework» (SEEA) in terms of the Environmental Goods and Services Sector (EGSS) developing as well as the provisions of the Eurostat methodological guidelines which are designed to facilitate the monitoring of progress in the EU policy priorities implementation in the field of environmental protection, rational resource management and «green» growth economy. The authors presented a brief overview of publications made by Rosstat specialists and Russian scientists in the field of environmental protection.The body of article defines practical recommendations on the EGSS implementation into statistical practice in Russia: both the justified approach for compilation the list of environmental goods and services, and the identified statistics sources for calculation the EGSS indicators, as well as an algorithm for their calculation developed by the authors. The recommendations provided in this article are the methodological framework for environmental goods and services accounting.According to the authors, the study findings will permit further development of methodological guidelines for the EGSS compilation accordance with the Action Plan («Roadmap») for the Implementation of Priority Accounts of the System of Environmental-Economic Accounting approved by the Government of the Russian Federation.
{"title":"Implementation of the Environmental Goods and Services Accounting into the Statistical Practice of the Russian Federation","authors":"M. Klevakina, T. V. Guren, I. A. Repin","doi":"10.34023/2313-6383-2024-31-2-5-22","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-5-22","url":null,"abstract":"The article discusses the main characteristics of the international statistical standard «System of Environmental-Economic Accounting 2012 – Central Framework» (SEEA) in terms of the Environmental Goods and Services Sector (EGSS) developing as well as the provisions of the Eurostat methodological guidelines which are designed to facilitate the monitoring of progress in the EU policy priorities implementation in the field of environmental protection, rational resource management and «green» growth economy. The authors presented a brief overview of publications made by Rosstat specialists and Russian scientists in the field of environmental protection.The body of article defines practical recommendations on the EGSS implementation into statistical practice in Russia: both the justified approach for compilation the list of environmental goods and services, and the identified statistics sources for calculation the EGSS indicators, as well as an algorithm for their calculation developed by the authors. The recommendations provided in this article are the methodological framework for environmental goods and services accounting.According to the authors, the study findings will permit further development of methodological guidelines for the EGSS compilation accordance with the Action Plan («Roadmap») for the Implementation of Priority Accounts of the System of Environmental-Economic Accounting approved by the Government of the Russian Federation.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-52-60
V. Glinskiy, L. Serga, K. S. Yushina
Development of the digital transformation of the state, the economy in general, business and society creates the need to quantify its results and assess the level of impact on the economy. The paper explains a methodological approach to calculating digital economy multipliers for territories based on the calculation of total income (direct and indirect) from digital activity. The multiplier measures the impact of the digital economy on macroeconomic and regional indicators, helps assess the extent of this impact and study its dynamics. The authors calculated digital economy multipliers for the Russian Federation and at the regional level (using the example of the Novosibirsk region) and estimated total digital sector income with a multiplier effect. The paper concludes with the contribution of digital transformation to the economic growth of the Russian Federation and the development of the Novosibirsk region about the multiplier dynamics and the ratio of direct and indirect effects in the total income of the digital sector. Rosstat data for 2017–2021 served as the information base for empirical calculations.
{"title":"The Use of Multipliers in Assessing the Economic Effects of Digital Transformation of Territories (Case Study: Novosibirsk Region)","authors":"V. Glinskiy, L. Serga, K. S. Yushina","doi":"10.34023/2313-6383-2024-31-2-52-60","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-52-60","url":null,"abstract":"Development of the digital transformation of the state, the economy in general, business and society creates the need to quantify its results and assess the level of impact on the economy. The paper explains a methodological approach to calculating digital economy multipliers for territories based on the calculation of total income (direct and indirect) from digital activity. The multiplier measures the impact of the digital economy on macroeconomic and regional indicators, helps assess the extent of this impact and study its dynamics. The authors calculated digital economy multipliers for the Russian Federation and at the regional level (using the example of the Novosibirsk region) and estimated total digital sector income with a multiplier effect. The paper concludes with the contribution of digital transformation to the economic growth of the Russian Federation and the development of the Novosibirsk region about the multiplier dynamics and the ratio of direct and indirect effects in the total income of the digital sector. Rosstat data for 2017–2021 served as the information base for empirical calculations.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140655288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-101-109
N. Burova, M. P. Dekina, Y. Neradovskaya
The article contains an overview of reports presented at the Whole-Russian scientific and practical conference «Well-being Measuring and Analyzing», held on 25–27 January 2024 at St. Petersburg State Economic University (UNECON). The program of the conference included 142 reports in face-to-face and online formats. Approximately a quarter of all the conference papers were presented by young researchers – students, undergraduates, and postgraduate students – who co-authored work with academic supervisors. Scientists from educational and research institutions from 19 constituent entities of the Russian Federation and seven foreign countries delivered their presentations.The conference participants discussed the accumulated domestic and international experience in measuring well-being, highlighting specific features and regional characteristics of this socio-economic phenomenon. Aspects of the application of statistical and econometric methods of analysis and forecasting in studying issues of welfare and social inequality were considered, including measuring the level of poverty, the impact of pension system reform, changes in consumer behavior of the population in the housing market, ways to solve social problems in certain regions, including, Arctic zone. The participants identified directions for improving the methodology for measuring well-being and measures to address social inequality at the federal and regional levels.
{"title":"Whole-Russian Scientific and Practical Conference «Well-Being Measuring and Analyzing»","authors":"N. Burova, M. P. Dekina, Y. Neradovskaya","doi":"10.34023/2313-6383-2024-31-2-101-109","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-101-109","url":null,"abstract":"The article contains an overview of reports presented at the Whole-Russian scientific and practical conference «Well-being Measuring and Analyzing», held on 25–27 January 2024 at St. Petersburg State Economic University (UNECON). The program of the conference included 142 reports in face-to-face and online formats. Approximately a quarter of all the conference papers were presented by young researchers – students, undergraduates, and postgraduate students – who co-authored work with academic supervisors. Scientists from educational and research institutions from 19 constituent entities of the Russian Federation and seven foreign countries delivered their presentations.The conference participants discussed the accumulated domestic and international experience in measuring well-being, highlighting specific features and regional characteristics of this socio-economic phenomenon. Aspects of the application of statistical and econometric methods of analysis and forecasting in studying issues of welfare and social inequality were considered, including measuring the level of poverty, the impact of pension system reform, changes in consumer behavior of the population in the housing market, ways to solve social problems in certain regions, including, Arctic zone. The participants identified directions for improving the methodology for measuring well-being and measures to address social inequality at the federal and regional levels.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-23-38
L. Strizhkova
The article presents the results of the author's research devoted to the improvements in domestic developments of Input-Output Tables (IO Tables), significantly increasing their analytical potential. According to the author, the level of development of Russian statistics in the field of Input-Output Tables is quite high – the basic accounts contain not only the symmetrical IO Tables, but also symmetrical tables of domestic products and imports that they are derived from. This is the main condition for implementing the methodology and methodological solutions described and elaborated in the article for compiling the tables with the coefficients of total costs of domestic products and the total need for import of intermediate imports estimates.In the first part of the article, the author characterizes conceptually different versions of the intersectoral model with the features of the technological matrix corresponding to these versions – the basis for estimating the values of total costs coefficients in the national economy. Three «extreme» variants of the economy model representation of indicative country are studied. They define the economy as a part of a global economy within the framework of an inter-country model; in the form of a national model without the allocation of the import matrix in a symmetrical IO Tables (traditional method) and in the form of a national model with the possibility of such allocation (alternative method). The substantial differences in the values of total costs coefficients for the options are described and their preference for study of the country's economy is emphasizedThe second part of the article presents the results of a quantitative analysis based on the basic IO Tables for 2016 data. It contains the estimates of the measure of discrepancy in total costs coefficients values according to traditional and alternative methods; the differences in the associated indicators of intermediate products total costs distribution by specific dimensions of final demand. The analytical possibilities of the alternative method are described, the estimates for the decomposition of final products cost into domestic and imported value added are provided.The author explains the analytical relevance of total costs coefficients using an alternative method in forecasting practice and in the macroeconomic effects of management decisions assessment and proposes to supply the basic IO Tables for 2021 with tables with these coefficients and with the necessary methodological explanations
{"title":"Total Costs Coefficients in the Basic Input-Output Tables","authors":"L. Strizhkova","doi":"10.34023/2313-6383-2024-31-2-23-38","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-23-38","url":null,"abstract":"The article presents the results of the author's research devoted to the improvements in domestic developments of Input-Output Tables (IO Tables), significantly increasing their analytical potential. According to the author, the level of development of Russian statistics in the field of Input-Output Tables is quite high – the basic accounts contain not only the symmetrical IO Tables, but also symmetrical tables of domestic products and imports that they are derived from. This is the main condition for implementing the methodology and methodological solutions described and elaborated in the article for compiling the tables with the coefficients of total costs of domestic products and the total need for import of intermediate imports estimates.In the first part of the article, the author characterizes conceptually different versions of the intersectoral model with the features of the technological matrix corresponding to these versions – the basis for estimating the values of total costs coefficients in the national economy. Three «extreme» variants of the economy model representation of indicative country are studied. They define the economy as a part of a global economy within the framework of an inter-country model; in the form of a national model without the allocation of the import matrix in a symmetrical IO Tables (traditional method) and in the form of a national model with the possibility of such allocation (alternative method). The substantial differences in the values of total costs coefficients for the options are described and their preference for study of the country's economy is emphasizedThe second part of the article presents the results of a quantitative analysis based on the basic IO Tables for 2016 data. It contains the estimates of the measure of discrepancy in total costs coefficients values according to traditional and alternative methods; the differences in the associated indicators of intermediate products total costs distribution by specific dimensions of final demand. The analytical possibilities of the alternative method are described, the estimates for the decomposition of final products cost into domestic and imported value added are provided.The author explains the analytical relevance of total costs coefficients using an alternative method in forecasting practice and in the macroeconomic effects of management decisions assessment and proposes to supply the basic IO Tables for 2021 with tables with these coefficients and with the necessary methodological explanations","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.34023/2313-6383-2024-31-2-73-83
M. A. Klupt
Based on an analysis of the time series of the ratios of export, import, and foreign trade turnover to GDP, the article periodizes the dynamics of world trade from 1970 to 2023. The features of these indices that require attention when interpreting their changes are shown. The growth in the intensity of foreign trade was followed in the 1970s by its stagnation in the 1980s and then by a new growth which, in turn, was stopped by the global financial and economic crisis of 2008–2009. After its end and until now, the growth in the intensity of international trade in the world as a whole has not resumed. Simultaneously, since 2014 there has been an increase in geopolitical fragmentation of the world trade. Nowadays, it manifests itself primarily in the rapid growth of turnover between Russia and China, further contraction of Russia’s turnover with EU countries, and a dramatic fall of China's share in the total U.S. import in 2023.Comparison of the ratios of export, import and foreign trade turnover to GDP with the KOF globalization index reveals synchroneity of changes in international trade and globalization as a whole. The common factor guiding the dynamics of both processes is a change in the world order, the breaking points of which are the crises generated by this change. The movement toward a multipolar world order is accompanied today by new strengthening of the barrier functions of state borders, resulting in a slowdown or reverse of globalization in its most vital domains, including international trade.
{"title":"International Trade in the Changing World: Economic and Statistical Analysis","authors":"M. A. Klupt","doi":"10.34023/2313-6383-2024-31-2-73-83","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-2-73-83","url":null,"abstract":"Based on an analysis of the time series of the ratios of export, import, and foreign trade turnover to GDP, the article periodizes the dynamics of world trade from 1970 to 2023. The features of these indices that require attention when interpreting their changes are shown. The growth in the intensity of foreign trade was followed in the 1970s by its stagnation in the 1980s and then by a new growth which, in turn, was stopped by the global financial and economic crisis of 2008–2009. After its end and until now, the growth in the intensity of international trade in the world as a whole has not resumed. Simultaneously, since 2014 there has been an increase in geopolitical fragmentation of the world trade. Nowadays, it manifests itself primarily in the rapid growth of turnover between Russia and China, further contraction of Russia’s turnover with EU countries, and a dramatic fall of China's share in the total U.S. import in 2023.Comparison of the ratios of export, import and foreign trade turnover to GDP with the KOF globalization index reveals synchroneity of changes in international trade and globalization as a whole. The common factor guiding the dynamics of both processes is a change in the world order, the breaking points of which are the crises generated by this change. The movement toward a multipolar world order is accompanied today by new strengthening of the barrier functions of state borders, resulting in a slowdown or reverse of globalization in its most vital domains, including international trade.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140655819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.34023/2313-6383-2024-31-1-83-97
Е. V. Zarova, G. К. Abdurakhmanova, В. О. Tursunov
The article substantiates the problem of measuring and analyzing the «response» of the employment level to the introduction of artificial intelligence (AI) in the economic and social spheres. The authors propose methods for studying the interdependence of integral and component assessments of the development of artificial intelligence and the level of employment for a set of countries representing different continents and economic groups. An assessment was made based on the first ever Global AI Index (GAII) published by Tortoise Media in 2023 for 62 countries and cluster analysis methods, including differentiation of countries by general level and components of artificial intelligence. The values of AI sub-indices were taken into account, characterizing such components as the presence of a state strategy for the implementation of AI, its commercial basis, use for scientific research and development, the formation of an operating environment, infrastructure development, support for «talents» - intellectual leaders (including institutional ones) in the field of AI. Based on the results of cluster analysis, the Russian Federation’s place in the group of countries characterized by a relatively average overall assessment of the development of artificial intelligence and leading in the implementation of statestrategic programs for the introduction of AI into public life has been established.The results of the analysis and modeling of trends in scatter diagrams constructed for selected clusters of countries show the multidirectionality and ambiguous strength of the existing relationship between the development of artificial intelligence for individual components of the Global Index and the level of employment. At the same time, the existing relationship between the level of employment and the integral assessment of the Global AI Index was assessed as statistically weak for all clusters of countries. Conclusions were drawn about the need to take into account the identified differences in statistical estimates (both by country and by AI components) when predicting the impact of AI on changes in the level and structure of employment.As this topic is filled with statistical research, the conclusions drawn from the results of the study will be deepened and continued by the authors. At the same time, according to the authors, the formulated conclusions, which are preliminary at this stage, indicate the relevance, theoretical and practical significance of the problem of assessing the impact of AI on employment, as well as the ambiguity of its solution in different countries.
{"title":"The Relationship of the Global Al Index and the Level of Employment: A Cluster Approach in Assessing Cross-Country Differences","authors":"Е. V. Zarova, G. К. Abdurakhmanova, В. О. Tursunov","doi":"10.34023/2313-6383-2024-31-1-83-97","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-1-83-97","url":null,"abstract":"The article substantiates the problem of measuring and analyzing the «response» of the employment level to the introduction of artificial intelligence (AI) in the economic and social spheres. The authors propose methods for studying the interdependence of integral and component assessments of the development of artificial intelligence and the level of employment for a set of countries representing different continents and economic groups. An assessment was made based on the first ever Global AI Index (GAII) published by Tortoise Media in 2023 for 62 countries and cluster analysis methods, including differentiation of countries by general level and components of artificial intelligence. The values of AI sub-indices were taken into account, characterizing such components as the presence of a state strategy for the implementation of AI, its commercial basis, use for scientific research and development, the formation of an operating environment, infrastructure development, support for «talents» - intellectual leaders (including institutional ones) in the field of AI. Based on the results of cluster analysis, the Russian Federation’s place in the group of countries characterized by a relatively average overall assessment of the development of artificial intelligence and leading in the implementation of statestrategic programs for the introduction of AI into public life has been established.The results of the analysis and modeling of trends in scatter diagrams constructed for selected clusters of countries show the multidirectionality and ambiguous strength of the existing relationship between the development of artificial intelligence for individual components of the Global Index and the level of employment. At the same time, the existing relationship between the level of employment and the integral assessment of the Global AI Index was assessed as statistically weak for all clusters of countries. Conclusions were drawn about the need to take into account the identified differences in statistical estimates (both by country and by AI components) when predicting the impact of AI on changes in the level and structure of employment.As this topic is filled with statistical research, the conclusions drawn from the results of the study will be deepened and continued by the authors. At the same time, according to the authors, the formulated conclusions, which are preliminary at this stage, indicate the relevance, theoretical and practical significance of the problem of assessing the impact of AI on employment, as well as the ambiguity of its solution in different countries.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140439013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.34023/2313-6383-2024-31-1-43-54
О. V. Sitnikova, Y. N. Filimonova, S. Р. Vorobyov, V. Vorobyova
Currently observed significant differentiation of organizations involved in dairy products production in the Altai Territory in terms of financial results and the amount of property determines their development prospects, sources and cost of attracted financial resources and opportunities for formation of an optimal capital structure. Under these conditions it is important to identify the main trends in the formation and use of financial resources of dairy production organizations, taking into account market conditions in the dairy products market.The purpose of the study is a statistical assessment of the formation of financial results of organizations specializing in the production of dairy products, considering the observed processes of capital concentration. The authors specify that the main contribution to the formation of the balanced financial result of the dairy industry organizations in the Altai Territory is made by entities specializing in the production of cheese and cheese products, drinking milk and cream. The results of the statistical grouping made it possible to determine that for all types of economic activity in the groups of organizations there were both profitable and unprofitable entities with a fairly high differentiation in terms of revenue level. At the same time decrease in profitability of all types of products is the main factor in reducing products profitability level in 2019–2021. The impact of the change of cost of sales structure by entities of various types of economic activity on profitability was positive but not significant. Entities differ significantly in terms of current assets turnover, and the general slowdown in the current assets turnover in 2019–2021 led to additional involvement of funds in turnover. The government bodies taking into account the results of the study in their activities will allow them to increase the effectiveness of monitoring the efficiency of the use of financial resources of economic entities, as well as to justify the feasibility of preferential lending to dairy industry enterprises depending on their specialization.
{"title":"Statistical Analysis of Financial Results of Dairy Production Organizations of the Altai Territory","authors":"О. V. Sitnikova, Y. N. Filimonova, S. Р. Vorobyov, V. Vorobyova","doi":"10.34023/2313-6383-2024-31-1-43-54","DOIUrl":"https://doi.org/10.34023/2313-6383-2024-31-1-43-54","url":null,"abstract":"Currently observed significant differentiation of organizations involved in dairy products production in the Altai Territory in terms of financial results and the amount of property determines their development prospects, sources and cost of attracted financial resources and opportunities for formation of an optimal capital structure. Under these conditions it is important to identify the main trends in the formation and use of financial resources of dairy production organizations, taking into account market conditions in the dairy products market.The purpose of the study is a statistical assessment of the formation of financial results of organizations specializing in the production of dairy products, considering the observed processes of capital concentration. The authors specify that the main contribution to the formation of the balanced financial result of the dairy industry organizations in the Altai Territory is made by entities specializing in the production of cheese and cheese products, drinking milk and cream. The results of the statistical grouping made it possible to determine that for all types of economic activity in the groups of organizations there were both profitable and unprofitable entities with a fairly high differentiation in terms of revenue level. At the same time decrease in profitability of all types of products is the main factor in reducing products profitability level in 2019–2021. The impact of the change of cost of sales structure by entities of various types of economic activity on profitability was positive but not significant. Entities differ significantly in terms of current assets turnover, and the general slowdown in the current assets turnover in 2019–2021 led to additional involvement of funds in turnover. The government bodies taking into account the results of the study in their activities will allow them to increase the effectiveness of monitoring the efficiency of the use of financial resources of economic entities, as well as to justify the feasibility of preferential lending to dairy industry enterprises depending on their specialization.","PeriodicalId":509234,"journal":{"name":"Voprosy statistiki","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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