Voprosy statistiki (Moscow, Russia : 1994)最新文献

{"title":"Provision of Housing for Residents of the Far East: Statistical Analysis","authors":"I. Schneiderman, A. Yarasheva, S. Makar","doi":"10.34023/2313-6383-2023-30-3-53-63","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-3-53-63","url":null,"abstract":"Building on state statistics, the authors analyzed the situation with providing housing opportunities for residents of the Far Eastern Federal District (FEFD). The objective of the study is to define the housing stock characteristics of the macroregion. Based on the methods of statistical analysis applied by the authors, the following parameters of the FEFD housing stock are identified: differentiation of regions – constituent entities of the Russian Federation in terms of housing provision and housing improvement, the degree of housing satisfaction according toresident surveys, debt on housing loans and affordability of rental housing. The paper demonstrates differences in the positions of FEFD regions. The article concludes that additional funding of the FEFD regions through state programs since 2014 has not affected the growth of the FEFD housing stock, which in terms of specific indicators of housing provision, is below the Russian average (2018–2020). The authors argue that for several FEFD regions, there is a dire need to improve housing quality (this applies particularly to the Republic of Buryatia and the Trans-Baikal Territory).","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81701853","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}

{"title":"Russian Economy in 2022–2024: Results, Trends, Forecasts","authors":"A. Frenkel, B. I. Tikhomirov, A. Surkov","doi":"10.34023/2313-6383-2023-30-3-33-52","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-3-33-52","url":null,"abstract":"The article discusses the results of the socio-economic development of Russia in 2022 and highlights trends and forecasts of the dy namics of macroeconomic indicators for 2023–2024. At the same time, the main challenges and threats are analyzed, including production, trade, economic, financial, monetary, infrastructural, logistical and other restrictions that have become the main instruments of the West’s sanctions wars against Russia in the new geopolitical reality associated with Russia’s conducting a special military operation  (SMO).The main objects of analysis are the current state, trends, risks and threats to the Russian economy, the dynamics of industrial production, prices, inflation and foreign trade. It is noted that, despite the toughening of challenges, sanctions and threats in 2022, the macroeconomic results of 2022 turned out to be higher than official forecasts and experts' expectations. This made it possible to substantiate the main conclusion of the study that the Russian economy managed to prevent a collapse and find additional opportunities to better unlock the potential of economic growth and social progress in the current three-year period of 2023–2025 and in the long term. According to the authors, the need to further transform and improvethe efficiency of the Russian economy is predetermined not only by current but also, possibly, future challenges.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80365990","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}

{"title":"Social and Property Losses from Fires in Russia: Statistical Analysis","authors":"M. V. Zagumennova, A. Firsov, E. N. Malemina, T. A. Chechetina","doi":"10.34023/2313-6383-2023-30-2-98-108","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-98-108","url":null,"abstract":"   The article analyzes the general situation in the Russian Federation related to fires (based on data for 2021 and 2022). Characteristics of social damage are given along with the data on the distribution of those killed in fires according to their social status, which allows for suppositions about the strong influence of a social component in revealing the basic factors forming a fire death rate indicator. Considerable attention in the publication is given to the characteristics of the fire property damage statistics. Taking into account the advance research into different approaches to assessing the subject of the study, the authors explain the preference of calculation methods for assessing the economic damage from fires. Based on statistical data on fires and their consequences, calculations of direct fire property damage were carried out. To raise analytical level of the study, statistical groupings were built, which, according to the authors, made it possible to make more adequate management decisions to minimize social and material damage from fires.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"35 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79771865","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}

{"title":"Assessment of the Prevalence of Behavioral Risk Factors and Their Impact on the Health of the Adult Population in the Russian Federation","authors":"O. Salagay, G. Sakharova, N. Antonov, S. Y. Nikitina, N. M. Stadnik, V. Starodubov","doi":"10.34023/2313-6383-2023-30-2-72-86","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-72-86","url":null,"abstract":"   The objective of this study is to substantiate the system of indicators for assessing behavioral risk factors (tobacco use, alcohol consumption, detrimental diet patterns, lack of physical activity) and their impact on public health in the Russian Federation based on the authors’ methodology for assessing the impact of behavioral risk factors on the health of the adult population using the materials of the statistical survey of the health status of the population (conducted by Rosstat in 2021).   For each risk factor, two main indicators were calculated: the prevalence of the risk factor and its impact on public health according to demographic characteristics. The scale of such a risk factor as smoking was assessed in terms of consumption of various types of tobacco and nicotine-containing products and the prevalence of nicotine addiction of varying severity. Alcohol consumption as a behavioral risk factor was assessed in the context of the relationship between the amount of consumption of various alcoholic products and the degree of health risk. The impact of a detrimental diet was estimated by the body mass index for overweight and obesity, with the account to the physical activity of Russian citizens. According to the authors, the proposed set of indicators and specific calculation results can be used to organize continuous monitoring of the prevalence of behavioral risk factors and their impact on the health of the population of the Russian Federation. As a result, a more effective response of the healthcare system and strengthening of interagency cooperation to reduce non-communicable diseases is possible.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"626 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77025891","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}

{"title":"Interregional Differentiation and Heterogeneity of the Consequences of Pandemic Restrictions in Russia","authors":"N. Bozo, O. Shipkova","doi":"10.34023/2313-6383-2023-30-2-43-52","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-43-52","url":null,"abstract":"   The purpose of the study is to analyze the features of the economic development of Russian regions under the influence of various socio-economic factors and the differentiation of region groups depending on the implementation of state regulation of economic activity measures in them during the pandemic period.   The analysis was carried out using statistical methods (primarily methods of statistical groupings and correlation-regression analysis). The conceptual and methodological principles for assessing changes in economic activity in Russian regions after the introduction of restrictive measures of different levels of severity are outlined based on the study of Russian and foreign scientific publications on the issues under consideration. A regression model uses to identify the influence of the studied factors. To identify the impact of the studied factors, the authors built a regression model that included the following statistical indicators: index of industrial production, unemployment rate, share of the urban population, share of the region's gross value added (GVA), number of small and medium-sized enterprises (excluding micro-enterprises) per 10,000 population, share of the region in the production of all Russian mining enterprises, region openness coefficient, average per capita monetary income of the population, poverty level. The final part of the article presents the results of the study indicating that pandemic restrictions, depending on the degree of their severity, had a different impact on the economy of the regions. The authors emphasize the heterogeneity of changes in the factors determining the economic activity of regions under the influence of restrictive measures on the part of governments, particularly regional authorities.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"63 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89715845","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}

{"title":"Demographic Policy in Russia: Performance Evaluation","authors":"A. Ageev, O. Zolotareva","doi":"10.34023/2313-6383-2023-30-2-53-71","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-53-71","url":null,"abstract":"   One of the reasons for the heavy hindering of the socio-economic development of modern Russia – is a new protracted wave of depopulation. In this regard, it is relevant and practically significant to analyze basic problems in the strategic management of demographic development and impartially assess the effectiveness of the tasks solved in this area, primarily in the field of birth rate growth. The article focuses on reviewing the core regulatory document in the field of demography – the Concept of the Demographic Policy of the Russian Federation for the period up to 2025. A number of its imperfections admitted at the development stage are identified, and directions for improving the document are proposed. The article assesses the dynamics of several core target demographic indicators and attempts to identify problems and factors affecting the birth rate in the country. The correlations assessment confirmed the importance of social support measures and regional maternity capital for achieving the increase in the birth rate, which is essential for overcoming depopulation processes in society. A serious barrier to demographic development has been identified, caused by the widespread practice of postponing childbearing, which has demonstrated stability in the younger generation, regardless of the self-assessment of their standard of living. Against this background, the transformation of the fertility age model was determined – the modal age of mothers for fifteen years has shifted by more than five years. There is a positive confirmation of the connection between the intention of young people to marry and their desire to have a child, which seems essential for family well-being and forms the prerequisites for further demographic development, based on the calculated Pearson and Chuprov's mutual contingency coefficients.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86211327","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}

{"title":"Big Data in Official Statistics: A View of the Problem","authors":"A. Surinov","doi":"10.34023/2313-6383-2023-30-2-5-22","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-5-22","url":null,"abstract":"   This article addresses the use of Big Data in official statistics, a much-discussed topic in the statistical community. The idea of replacing official statistics' indicators with a new information source, Big Data, was actively promoted in an overwhelming number of publications by foreign and domestic researchers several years ago. A more realistic perspective of this issue has gained ground in recent years; the issue is how to incorporate this unconventional source into statistical production and use Big Data for official statistical evaluations. The UN Statistical Commission's proactive stance greatly contributed to the discussion's shift in focus. The UN Statistical Commission was able to make the conversation about issues with Big Data application in official statistics a constructive process, leveraging its authority and opportunities. This process included all regional statistical platforms of the UN, specialized organizations and programs and other international agencies. The creation, testing, and implementation of novel approaches in official statistical surveys also involved national statisticians working for the government in universities, research centers and business. The article analyzes the practical application of Big Data in national statisticians' line of work, outlines the benefits and drawbacks of Big Data regarding its potential application in official statistics, outlines the directions of this activity, and formulates suggestions for the involvement of the Russian statistical community in this process. The author argues the need to avoid contrasting Big Data as information on society and economy versus official statistics based on traditional sources. In his opinion, Big Data is currently not a complete alternative to data collected on the basis of classical natural phenomena but can be built into national systems for collecting information on specific characteristics of socio-economic phenomena. The expansion of their use largely depends on the level of development of information and communication technologies as well as ICT integration in public life.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74816104","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}

{"title":"The Role of Innovation in the Economic Development of the Eastern Regions of the Russian Federation: Economic and Statistical Analysis","authors":"L. Faleychik, I. Zabelina","doi":"10.34023/2313-6383-2023-30-2-23-42","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-23-42","url":null,"abstract":"   The article evaluates an innovative component of economic growth in the eastern regions of the Russian Federation using statistical analysis methods. The relevance of the matter under consideration is increasing dramatically due to the introduction of economic sanctions against Russia, which significantly restrict the import of high-tech and innovative products into our country. Based on Rosstat data for 2016–2019, a comparative analysis of the development of several areas of innovation and economic activity in the regions of the Russian Federation as part of the Far East Federal District (Far East – FE) and the Baikal Region (BR) was carried out. According to the authors, currently, in the regions under consideration, there is an imbalance in the individual components of the innovation sphere, significant heterogeneity, and uneven development of innovation and technological processes in general. The effectiveness of innovation activity in the regions under review in comparison with the indicators for Russia as a whole is low. The revealed multidirectional dynamics of innovation activity indicators in the regions and the lag of most of them from the average Russian level are connected, in the authors’ opinion, with the problems of organizational and financial support of innovation activity and insufficient budget funding allocated to stimulate it. Innovations in the FE and the BR regions were carried out mainly at the expense of the organizations' own funds. In the structure of their costs, most of the funds were allocated for the purchase of machinery and equipment, in 2019 the share of research and development costs significantly increased. The export of innovative products was carried out in less than half of the considered group of regions. The intensity of exports of innovative products in the economy as a whole, as well as in the field of industrial production, exceeds the national level and accounts for about half or more of the total export volume. In terms of the effectiveness of innovation activity among the analyzed regions, the Khabarovsk Territory is the undisputed leader, and among the outsiders are the Magadan Region and the Kamchatka Territory in 2016, the Trans-Baikal Territory in 2019. In most regions, there is an increase in the raw materials orientation of economic systems, and a decrease in the share of the manufacturing sector in the gross regional product (GRP). The results of the analysis of the role of innovation activity in the economic development of the eastern regions of the country and interregional comparative statistics, according to the authors, can be used in the development of updated and upgraded programs for the socio-economic development of the subjects of the FE and the BR of the Russian Federation, concerning present-day foreign policy and foreign economic realities.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82305988","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}

{"title":"Development of Dairy Cattle Breeding in the Regions of the Russian Federation: Economic and Statistical Study","authors":"N. A. Vladimirov","doi":"10.34023/2313-6383-2023-30-2-87-97","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-2-87-97","url":null,"abstract":"   The article reflects the main results of the statistical analysis of the development of dairy cattle breeding in the Russian regions. The relevance of the topic is due to the strategic value of the industry, serving the needs of the population of the country's regions in demanded food products. Based on statistical data compiled by the Federal State Statistics Service, the dynamics of milk production and the number of live-stock and cattle of dairy breeds are analyzed. In addition, an assessment is made of the impact of climate on the industry in question, as well as a number of other factors on milk yields in individual regions. To deepen the inter-regional comparative analysis of the problems of the development of dairy cattle breeding, the author used the hierarchical clustering method. To do this, the author built a system of indicators that characterizes the industry in question, the effectiveness of its functioning, and the factors of its development, while the calculations used the average annual values of each indicator for the period from 2018 to 2021. The preference for the 5-cluster structural model is substantiated, which reflects the differentiation of the constituent entities of the Russian Federation in terms of the development of dairy cattle breeding. The conclusion summarizes the results of an interregional comparative analysis of the development of the considered segment of the economy, which, in the author's opinion, should be taken into account when improving and optimizing both the federal program and regional plans for socio-economic development, increasing the welfare of the Russian population, equalizing the levels of economic development of individual regions of the country.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90255713","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}

{"title":"Building a Country Rating of Digitalization of the Economy Based on the Principal Component Analysis","authors":"A. Ovcharov, A. Terekhov","doi":"10.34023/2313-6383-2023-30-1-58-69","DOIUrl":"https://doi.org/10.34023/2313-6383-2023-30-1-58-69","url":null,"abstract":"The relevance of international comparisons of the level of digitalization of countries based on rating assessments is argued, due to the lack of uniformity in the methods and results of using information and communication technologies. The information base of the study is data from Rosstat and Eurostat on a number of digitalization variables for the period 2016–2020. The methodological basis of the analysis is the principal component method and k-means cluster analysis. The importance of using various statistical tools to assess the scale of digital inequality in the context of countries and regions is argued.Scientific and methodological approaches to studying the problem of digital inequality were generalized. Three levels of digital gaps and statistical indicators reflecting them were allocated. The role of indices as generalizing indicators in assessing the level of digitalization of the economy and society was determined. A rating of European countries was built (in descending order of the calculated digitalization index). A grouping of countries that differ in the level of digital development is proposed, estimates of digital gaps are given.The results of the statistical analysis showed that the leaders of the rating are Finland, Denmark, and Norway; outsiders are Greece, Bulgaria, Romania. All these countries occupy a stable position in the ranking. Three clusters of countries were formed in terms of the level (high, medium, low) of digitalization of the economy. It is shown that the digital gaps between European countries fluctuate in the range of 42–44%. Russia's place in the ranking of 20 countries in the period from 2016 to 2020 was determined.","PeriodicalId":85843,"journal":{"name":"Voprosy statistiki (Moscow, Russia : 1994)","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81894448","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}