The introductory part of the article formulates its objective – to characterize development conditions in Russia and main structural changes in the Russian economy (according to 2014–2019 data) from the standpoint of their compliance with the tasks of increasing the stability and quality of the country's economic growth during the transition of the world system to a new technological order. Аccording to the authors, this period is of particular interest in assessing and studying the experience associated with structural transformation in a highly unfavorable external condition amid tight monetary policy and restrictive income policy.

The article sequentially addresses certain aspects of the structural transformation of the Russian economy. The authors, relying on macroeconomic statistics, assess shifts in production and use of products, investment, import intensity, the balance of payments, and international investment position, considering external and internal conditions.

The paper focuses on analyzing the proportions of the economy according to the data of the Rosstat input-output tables (IOTs). The article provides estimates of structural changes in the dependence of the economy and its segments on types of imports, signifcant changes in the direction of the use of fnal products, including considering the dynamics of changes in the total input of intermediate imports. For the frst time, the characteristics of the concentration of gross value added (GVA) and gross domestic product (GDP) by areas of fnal demand are given. A methodological approach is proposed for monitoring shifts in import consumption by compiling annual summary tables with a minimum set of data obtained during the analytical processing of IOT information. Based on the results of testing this approach on data for 2016 and 2018, the authors rated cost product balances by the share of imports in the resources of products. Conclusions were drawn about the change in the import intensity of the economy for the selected types of products, the import intensity of Russian industries, and shifts in the distribution structure of types of imported products. The article formulates proposals for the development of measures necessary to consolidate and expand positive results of this stage in the development of the Russian economy and statistical support of macroeconomic analysis.

The growing interest both in our country and throughout the world in agriculture as an economic activity of strategic importance for ensuring food independence of the country determines the relevance of research on devising approaches to the analysis of its development using statistical methods. The authors’ approaches to assessing the development of agriculture in Russia based on data from the system of national accounts, including input-output tables, presented in this paper, make it possible to characterize the dynamics of the formation and use of output and gross value added (GVA) of the agricultural sector of the economy (including in the institutional context); assess the demand for each of the presented products within the industry and in the intermediate consumption of other industries; to identify structural changes in the composition of intermediate consumption in the production of goods; determine the trends and prospects for the development of agriculture on their basis.

As part of the study, the authors disclosed additional analytical capabilities of statistical databases that include agricultural censuses and current accounting data in the system of macroeconomic indicators and allow for a more accurate assessment of the effectiveness of agricultural development. The calculated coefcients of direct costs ranged in the form of time series, analysis of the structure of intermediate consumption made it possible to identify the features of the development of the Russian agricultural sector and the directions for improving its economic and statistical analysis. The authors argue for the need to take into account the phenomenon of multistructure of agriculture in Russia to calculate the gross value added produced in the sector of peasant farms, subsidiary farms, separately small and large agricultural organizations, including holdings, with the obligatory comparison of the selling prices of their products used when calculating output and GVA. A proposal was made to include a table with additional information on labor costs and consumption of feed, fertilizers, electricity, and other basic resources in physical terms in the system of input-output tables by industry.

The study focuses on analyzing regional features of the decline in the birth rate in Russia in 2016–2019. Taking into account regional specifcs is crucial when perfecting the implemented measures for improving the general demographic situation in the Russian Federation.

The information base of the study contained time series of the total fertility rate in selected Russian regions. The author used methods of descriptive statistics and assessed convergent trends based on the sigma-, beta- and gamma-convergence methods. Spatial effects in regional differentiation of fertility were assessed based on Moran's I.

As a result of the analysis, the following features were established. Firstly, in recent years in Russia, there has been a high degree of differentiation in the recorded declining birth rates. Secondly, the processes of falling fertility in the regions have specifc characteristics, the absence of typical trajectories in those subjects where it fell most or least of all. Thirdly, in Russia, there are no pronounced territo rial localizations of the processes of fertility decline. And fourthly, based on a comparison of the birth rate dynamics in Russian regions, no convergent trends have been identifed, i. e., there is no convergence of territorial entities in terms of the birth rate.

According to the author, the demographic policy of recent years has not yet responded positively either in terms of birth rate growth or leveling of regional differences. The results obtained indicate that unifed approaches are unsuited to solving the demographic problems of Russian territories, and there is a need for demographic policy measures that take into account regional variability and are aimed at smoothing regional disproportions. Consequently, it is necessary to conduct regular statistical and demographic studies of the specificity of regional situations using methods of convergence and spatial autocorrelation analysis, rarely used in demography

The article reflects the results of a statistical analysis of signifcant changes in the Russian labour market in 2020 amidst the spread of coronavirus infection. The study presents the results of a labour force survey organized for the frst time in Russia (following the ILO recommendations) through telephone interviews of respondents based on a representative sample.

The author analyzed the transformation of the Russian labour market in both the Russian Federation as a whole and regions using different group forming criteria, in particular, types of economic activity, sectors of the economy, various socio-demographic characteristics of the population (gender, age, place of residence, etc.). The paper outlines the dynamics and structure of employment (including part-time work) and unemployment, as well as the results of a comparative analysis of the unemployment rate in Russia and a number of foreign countries. Other indicators of labour underutilization, including the potential labour force, are considered.

To conduct a comprehensive analysis of the effects of the pandemic on the Russian labour market transformation indicators of dynamics of employees' wages are considered along with indicators of employment. At the same time, special attention is given to the analysis of the situation of personnel remuneration during the most severe period of the viral pandemic – in April 2020

The article presents the results of a study, the aim of which is to investigate the dynamics of the development of Russian higher education institutions based on an analysis of their performance indicators. The sample includes 535 universities from 80 constituent entities of the Russian Federation. It presents the results of the clustering of universities on the basis of changes in indicators of six variables characterizing the key areas of activity of universities for the period from 2013/2014 to 2017/2018 academic years. The characteristics of each cluster are described, an inter-cluster comparison of quantitative indicators of the activities of universities is presented.

As a result of the calculations of the average annual growth rates of six key indicators characterizing the activities of universities, fve clusters were identifed that differ in their development trajectories. Thus, the universities that belong to Cluster 1 over a fve-year period retained or improved their positions in the main areas of educational activity and managed to maintain income growth at an average level.

However, their indicators in such areas as international and research activities, as well as infrastructure development (provision of educational and laboratory facilities) have decreased. In universities from clusters 2 and 3, all performance indicators changed in a fairly balanced way. At the same time, the indicator of an increase in the internationalization of the students’ body was signifcantly higher in Cluster 2, while educational organizations from Cluster 3 showed a signifcant increase in the publication activity of academic staff.

The results of the activities of universities in Cluster 4 were positive in all considered areas. This cluster had the highest average annual growth rates in the average Unifed State Exam (USE) score and publication activity of the academic staff. Educational organizations from Cluster 5, on the one hand, achieved the best results in attracting foreign students, increasing the proftability of their activities and the provision of teaching and laboratory facilities but, on the other hand, their average annual growth rate of the average USE score has signifcantly decreased.

The study represents one of the frst attempts to cluster Russian universities based on the analysis of changes in their performance indicators. Previous studies on the clustering of universities were mainly based on the analysis of one-time indicators. The approach proposed by the author makes it possible to compare indicators of the dynamics of development of higher educational institutions of different size and scope. Further research in this area could be aimed at analyzing a larger number of performance indicators of universities and studying in detail their strategies for a deeper understanding of the reasons for the differences in their effectiveness.

The author describes the key characteristics of domestic and foreign experience in the development of conceptual and methodological foundations for the construction of subnational purchasing power parities. The relevance of the topic is explained, in particular, by the fact that subnational purchasing power parities are one of the key tools of the interarea comparison analysis. This causes the expanding attention paid by the national statistical ofces to its compilation and the development of the corresponding methodology. Thus, in July 2021, the World Bank as the Global ICP coordinator published two important guides presenting the international standards for producing purchasing power parities – for developing subnational PPPs and for integrating PPPs and CPI production activities.

The international standards recommend considering the sum of expenditures on fnal consumption and gross fxed capital formation as the aggregated regional indicator in interarea PPP-based comparisons. A narrower indicator – household fnal consumption – can also be used depending on the purposes of further analysis; household budget survey data provide weights in comparing the consumer prices. Producing subnational PPPs is closely linked with assessing the spatial adjustment factors, which can provide an important tool in computing the national PPPs.

Bearing in mind that producing subnational PPPs is a labor-intensive process, which includes all work stages (price data collection and validation, forming the weights system, computations, ensuring a balanced presentation of regions), the international standards suggest focusing on the annual frequency of works.

Integrating the production of subnational PPPs with the CPI calculations system is one of the key factors for ensuring the efciency of the whole work.

The article touches upon the topical issues of the residential real estate market, which are proposed to be solved by means of time series cointegration. The study aims to assess the structure of the housing market by types of apartments using price dynamics per one square meter of apartments' total area. The objectives of the study are to develop a methodology of determination of time series cointegration for the data with structural relationships; to analyze the average prices for the types of apartments on the primary and secondary housing market; to study the housing market in the Russian Federation by quarterly data of state statistics for the period 2000–2020 based on the developed methodology.

The results of the research showed that the prices at the primary and secondary housing market by types of apartments do not always represent an integrated process of the frst order and cannot be used for building a co-integration equation. This necessitated additional analysis and, as a consequence, the correction of the time period. It was proposed to ensure stationarity of linear combination of nonstationary data corresponding to the integrated process of the frst order by using the generalized least squares method (GLS). The sum of the elements of the cointegrating vector obtained this way tends to unity, and the elements themselves are estimates of the relative indi cators of the structure by types of apartments on the primary and secondary housing markets respectively. Thus, the suggested methodology allows estimating, on average, the share of the sold apartments of each type in the period under consideration, both in the regional context and in the country as a whole.

The proposed methodology can be used for the estimation of relative indicators of the structure according to temporal data in different applications.