The article outlines the international standard, System of Environmental-Economic Accounting (SEEA) – Central Framework, adopted by the UN in 2015 as the basis for developing 23 indicators of the Sustainable Development Goals. Statistical data from SEEA accounts provide the comprehensive assessment of environment, help identify trends and consequences of human impact on nature, serve as a tool for the formation and implementation of environmental policy in general, and help in monitoring the implementation of international obligations and recommendations including sustainable development areas.
A separate section of the article covers specific features of SEEA implementation in selected countries of the world. There is an elaborate reflection of the work on the introduction of this standard to the Russian Federation, carried out by the Federal State Statistics Service. The authors believe that the need for a certain sequence of actions associated with the development and implementation of SEEA accounts gives impetus (in the short term) for the application of priority accounts, guided by the roadmap for the SEEA priority accounts implementation, approved by the order of the Government of the Russian Federation. The information base built using these accounts is necessary for conducting international comparisons, calculations of SDG indicators and green economy indicators.

The article proposes a theoretical rationale and procedure for constructing new composite indicators that combine the results of quarterly consumer expectations surveys conducted by the Federal State Statistics Service (Rosstat). The information capabilities of surveys for the period 2005–2021 were studied to find the best alternative to the traditional consumer confidence index in the context of early estimates of the growth in household final consumption. The experimental Consumer Behavior Index reflects the «common profile» in the changes in the key parameters of consumer activity. The index is based on time series of primary survey indicators with high cross-correlation coefficients and a confirmed statistically significant causal relationship with a statistical referent – the growth rate of the final consumption expenditure of households. The new Consumer Anxiety Index with a counter-directional profile assesses the scale and tendencies of household response to market shocks based on short-term gaps between the indicator components and their long-term average.
In accordance with the proposed procedure for statistical testing of the time series, a cross-correlation analysis was carried out. Granger causality was studied, principal component analyses applied, unobservable cyclical components were identified in the dynamics of the Consumer Behavior Index and the referent using Hodrick – Prescott, Christian – Fitzgerald and Kalman statistical filters, and phase-by-phase movement of these components is demonstrated. A statistically significant cyclical correspondence and a close relationship of leading and coinciding nature between the composite survey-based index and the growth rate of the final consumption expenditure of households were confirmed.

In the introductory part of the article, the authors substantiate the relevance of developing methodological tools for analyzing job vacancies in the labor market in the context of the modern technological revolution, which significantly increases requirements for professional knowledge and experience of working personnel and changes the ratio between traditional and new professions.
To assess the current situation on the labor market and the demand for currently existing professions, the main section of the published results of the study presents the algorithm for analyzing vacancies using large data arrays from open sources using mathematical and statistical tools and machine learning methods using the Python programming language and the IBM SPSS modeler analytical platform. The algorithm includes: parsing data on vacancies, analyzing vacancies by the main criteria, clustering vacancies by salary level and building a neural network model – a multilayer perceptron of the dependence of salary on a number of predictors. It should be noted that the developed algorithm is universal, because it can be used to analyze big data from any open source at a certain point in time.
The results of the analysis will allow researchers and specialists of management structures to more realistically assess the current situation on the labor market, educational institutions will be able to adjust training programs in accordance with the modern requirements of employers, employers will make decisions on the development of competencies in their field of activity and conduct a comparative analysis of demanded vacancies in terms of quantitative and qualitative characteristics, and for the applicant it will be easier to see the demand for vacancies in the labor market and develop new skills.

The study aims to substantiate statistical and methodological approaches that allow the most accurate measurement of the contribution of the COVID-19 pandemic to the level and dynamics of the total mortality in the Russian Federation (in 2019–2020), and to assess the regional differentiation of mortality from the pandemic. The relevance of the study stems from revealing the role of some factors in the rise of mortality rates in Russia (according to data for 2020).
In the study, the authors used regression analysis with a set of factors determining pandemic-induced differences in changes in mortality rates across regions of the Russian Federation as a basic analytical tool. The information base of the study is a set of official statistics data, as well as regional representative results of sample socio-demographic surveys of Rosstat.
The authors used a set of regression models to test the hypotheses about the influence of a combination of demographic and socioeconomic factors on the increase in the total mortality rate. A repeating set of factors affecting the increase in mortality in different models may indicate the stability of the influence of the following factors: the share of people employed in the service sector, migration turnover, the presence in a region of a city of 500 thou. inhabitants or more, and the death rate from COVID-19. The set of factors influencing the increase in mortality differs by type of settlement and by gender.
The article argues for the possibility of using the crude mortality rate as a dependent variable in assessing the causes of mortality growth. A significant part of the regional variation in the increase in the total mortality rate in 2020 in Russian regions is explained by the characteristics of the demographic structure of the region (the share of elderly, the size of the household, the share of people with cancer), as well as the high population density and frequency of social contacts (especially in cities). Rise of COVID-19 related mortality rate had impact on the increase of the total urban mortality rate but did not lead to any significant growth in rural mortality rate.
The problem of the need to improve the information base for the selection of objective indicators and methods for analyzing the contribution of the COVID-19 epidemic to the level and dynamics of mortality is emphasized. An important methodological conclusion relevant for further research is the need to search for instrumental variables for COVID-19 related mortality indicators, due to the correlation of factors with the total mortality rate and with the mortality rate from COVID-19, as well as the need for further analysis of changes in the national health care system and its funding amid the pandemic.

The article from the perspective of interregional comparative analysis methodology substantiates the system of labor productivity factors and presents the results of statistical modeling of labor productivity in the regions of Russia in 2020.
The shortcomings of the existing measures of labor productivity in international and domestic practice are noted, namely, the ambiguity and inconsistency of the assessment results. The authors propose a technique for constructing an integral index of regional labor productivity and substantiate a four-factor econometric model of the dependence of the integral index of regional labor productivity, which allowed for an improved system of labour productivity factors to be applied in regional economic analysis. Estimates are given of the relative impact of both traditional and new factors of regional labor productivity, which are the increasing incidence rate and the growing share of unprofitable enterprises (organizations), investments and industrial output per capita in the regions of Russia in 2020.
The need to monitor regional labor productivity factors annually to help bring forward problems in the regions is justified. The reasons that led to the insufficient level of labour productivity (compared to Russian average) were identified. According to the authors (following the results of socio-economic development in 2020), they should include low growth rates of investment and production volumes, as well as an increase in the incidence and in the share of unprofitable enterprises and organizations.

The articleexamines a number of urgent problems of spatial development of seven regional centers of Siberia (cities of Abakan, Gorno- Altaysk, Kemerovo, Kyzyl, Tomsk, Ulan-Ude, Chita) based on data of the Russian state statistics and the results of sociological research conducted in 2021 by the staff of the Department of Geo-Urban Studies and Spatial Demography of the Institute for Demographic Research (IDR FCTAS RAS). The aim of the study was to identify socio-economic and demographic conditions for the development of the centers of the regions of Siberia as nodes of the supporting framework of the country's settlement. Questionnaire data from seven regional centers were analyzed using statistical methods, methods of comparative analysis, as well as graphical and cartographic presentation.
The authors identified demographic trends in the development of agglomerations of regional centers of the regions of Western and Eastern Siberia. The territories with a greater degree of regionalization of business revealed a more active involvement of industrial enterprises in the implementation of social and economic development projects. The article evaluates development dynamics of small and medium-sized businesses in the centers of the regions, as well as of higher and secondary vocational education. It reveals general patterns and individual features in the socio-economic development of the centers of the territories under consideration.
The article conducts ranking of cities by the degree of influence of negative factors on their development, analyses subjective assessments of respondents of problems of cities of residence with attitudes to emigration. The most vulnerable to depopulation regions, in terms of their geographical location (remote regions of Eastern Siberia), as well as the cities – large centers of high-tech engineering were identified. The paper proposes organizational and economic solutions to overcome these negative factors. The results of the study, according to the authors, can be used to formulate priority measures of regional and economic policy of Siberia and develop its spatial potential.

In the introduction, the authors argue the relevance of the researched problem of improving the algorithm for estimating transaction cost of bank’s purchase and sale in the Russian financial services market. It is emphasized that the need to manage the value of a commercial enterprise arises not only when planning the purchase and sale transaction of the entire business or part of it. The valuation is taken into account, first of all, when corporatizing an organization, attracting new shareholders, insuring its property, obtaining a loan secured by property, calculating the fair value of taxes.
The first part of the paper substantiates the business valuation algorithm intended for a potential investor and adapted to the conditions of the Russian financial market. This takes into account the specific features of each bank in question. Based on the data from open financial statements (for 2017–2022), the parameters required in the procedure for obtaining estimates are calculated.
The second part of the paper presents the result of selecting indicators of the external and internal economic environment of banks that have a significant statistical relationship with the resulting valuation. The selected factors are under the control of managers and can be used to guide the proposed cost estimate. Data were obtained on the direction and strength of influence of selected factors on the value of business. The selection algorithm is based on mathematical modeling using bank data for the period under consideration.
The valuation is based on a comparative approach, where the values of the multiples are calculated using a linear regression model. To organize cost management, a panel regression model is built, which allows to select significant financial indicators and determine the nature of their impact on the bank's value.

The article develops methods for constructing analytical («economic») indices, and their application in the analysis of consumer demand in Russia based on official statistics on the consumption of goods and services (468 items for the period 2012–2017). The «economic» direction of indexology, which is based on the use of consumer demand theory, when constructing prices indices and measuring consumption dynamics, takes into account consumer preferences instead of the subjective biases of statisticians or authors of various index formulas. This direction has a long history, dating back to the 1924 work of the Soviet economist A.A. Konüs. The development of this concept in the works of Western researchers refers only to individual or household demand due to the absence of market demand theory in neoclassical Economics. Such a holistic theory of market demand was developed in recent years in the works of V.K. Gorbunov, and on this basis were developed methods for constructing analytical indices, using the ambiguity of restoring the utility function from a finite set of data for the variant determination of indices with characteristics, namely optimistic, pessimistic, and objective.
To compare the traditional and «economic» directions of indexology, the authors constructed Laspeyres, Paasche and Fischer price and quantity indices. The indices were calculated both for the summary statistics of prices and consumption of the population, and for the main groups of consumer goods: food products, non-food products and services. General (two-stage) indices of all types were constructed for group indices and the consistency in aggregation property was tested. The obtained results are a new example of a successful verification of the holistic market demand theory.