The purpose of the article is to present the main results of the study on the development of methods and testing of tools necessary for continuous monitoring of the business environment in Russia by measuring the level of its uncertainty and identifying the degree of its influence on the dynamics of economic activity based on the analysis of business tendency surveys. This methodology was tested for the first time to assess the level of uncertainty in the business environment and its impact on the digital and technological development of domestic industrial enterprises.

The source of data for the study is regular sample surveys of business leaders for the period from 2009 to 2022. Two independent databases are used: quarterly surveys of business activity of organizations of various types of economic activity to build national and industry indicators of uncertainty in the business environment and annual pilot surveys of digital activity of industrial enterprises to construct an indicator of uncertainty in the field of digital and technological development of industry.

All calculated composite indicators were able to identify significant features of the economic situation for the Russian economy associated with shock events. When analyzing the fluctuations of the indicators under study, «spikes» are clearly detected in response to the shocks of the business tendencies in 2020 and 2022. In general, the period from 2020 to the present is characterized as a period of prolonged crisis associated with growing uncertainty in the Russian economic environment, the unprecedented scale of which was demonstrated by the constructed indicators.

According to the authors, the proposed composite indicators expand the analytical capabilities of business tendency surveys data, creating the prerequisites for regular monitoring of changes in the level of uncertainty in the business environment and increasing the operational value of the information provided by companies. This allows to develop more accurate socio-economic scenario forecasts.

When constructing estimates of gross domestic product (GDP) and gross value added (GVA) at constant prices, three approaches or a combination of them are used: extrapolation, simple deflation and double deflation. The latter approach is considered preferable, but is not yet used in Russian statistics. The article is devoted to the issues of constructing estimates of GDP and GVA dynamics for the Russian Federation using double deflation. The interest in constructing such estimates is due to the possibility of taking into account the dependence of production on domestic and import supplies, which the other two approaches lack. Received for the period from 2011 to 2016 estimates were compared with official indicators for the overall economy, the service sector, and for mining and manufacturing.

From 2011 to 2016 alternative estimates of the physical volume indices of GDP and GVA showed lower growth rates compared to official indicators. This is due to the faster growth of alternative estimates of intermediate consumption compared to official ones. In the structure of intermediate consumption, domestic intermediate products were replacing imported analogues. The current dynamics of indicators, to a first approximation, shows signs of localization of production, which may be associated with the transition of enterprises to domestic outsourcing and the implementation of an import substitution policy.

At the same time, the presence of measurement problems in the use of double deflation is obvious, most clearly manifested when moving to the analysis of indicators of lower levels of aggregation. The analysis shows in favor of conducting additional research related to the introduction of the double deflation technique in the official methodology.

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.

The paper presents the core findings of a study on improving methodological approaches to adequate assessment of dynamics of the quality of life of older people in Russia. The author has identified challenges in implementing some international recommendations on the topic thereon. These challenges resulted from differences in the socio-economic development of the Russian Federation and its ethnocultural traditions. The article defines domestic information and statistical base for studying the quality of life of the population, in particular, the older population.

The author describes a system of basic indicators that includes key measures of health status of older people, their financial security and economic activity. Based on the proposed approach and official statistics, the situation with the older generation in Russia in 2010–2020 is analyzed. An assessment is made of the significance of both positive trends in improving the quality of life in the older generation (increased life expectancy, improved health, increased economic activity of the population aged 60 years and older), as well as unresolved problems, primarily, relatively low level of pensions compared to the subsistence level and high degree of interregional differentiation of pensions.

The paper concludes with proposals concerning directions and sequence of organizational and financial measures aimed at improving the quality of life of pensioners, proposed by the author, based on specific results of statistical analysis. Among the top priorities are: indexation of pensions for non-working pensioners ahead of the inflation rate, indexation of pensions for working pensioners at 1/2 of the inflation rate, registration and, if necessary, organization of medical and social services for older citizens living alone.

The results of a statistical analysis of the factors determining the decisions of families regarding the choice of the month of child birth are presented in the article. A review of the literature shows that a wide range of factors – cultural, biological, climatic and socio-economic – can influence these decisions. In the article, the authors focus on socio-economic determinants.

 In the Russian Federation, as well as throughout the world, there is a steady seasonality of births: between 2000 and 2022 most children were born in July and August. The article formulates a number of hypotheses regarding possible seasonality determinants, which were tested on the basis of Rosstat data from a sample survey of households «Comprehensive observation of living conditions of the population in 2020» and Rosstat data on registered births by month in Russian regions for 2019–2022. Using unordered multiple choice logit model estimates, cluster analysis and correlation analysis the authors obtained results demonstrating that the choice of summer months for the child birth is determined primarily by household, with the increase of which the birth of a child is more often planned for July. In regions characterized by high rates of quality-of-life indicators, predominantly urban population and a high level of contraceptive use, family preferences in choosing the summer months for child birth are also increasing. The influence of mother's age and education was not identified in the work.

The results of the study can be used by the authorities to develop an effective strategy in the field of demographic policy.

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.

The article is devoted to the problems of transformation of the educational system in the context of lifelong learning and Industry 4.0 in government decision-making for the development of the investment ecosystem of the Republic of Uzbekistan.

The article outlines characteristics of information and statistical resources used in the public administration system in modern conditions on the basis of analysis, and presents directions of transformation of the system of statistical training and retraining of statisticians.

The issues of introducing digital competencies in the field of statistics into training programs and the need to develop modern knowledge and skills related to the introduction of digital technologies were highlighted.

The author emphasizes the relevance of adapting the educational system in the field of statistics to the evolving requirements of the modern world and concludes that the development of professional competencies of statisticians should be continuous and start with secondary education, followed by retraining, and advanced training received throughout their professional careers.

The recommendations presented in the author’s studycan be used by the authorities and other interested organizations to improve the educational system in the field of statistics.