The article discusses the use of microdata in the transition to a detailed representation of the households sector in the System of National Accounts (SNA), in accordance with the objectives of developing methods for measuring well-being at the macroeconomic level. Implementing distributed macroeconomic indicators of household income, expenses, and savings in the core sequence of national accounts allows for a more complete picture of intersectoral cooperation in the national economy. However, it largely depends on the completeness and methodological compatibility of microeconomic and macroeconomic statistical indicators.

The first part of the paper examines the problems of using data from various surveys to evaluate income, expenditure, and savings indicators distributed by income groups. It proposes compiling harmonized sets of detailed information for the construction of distributive information based on the statistical integration of microdata from several sources. This allows the formation of the so-called synthetic sets of microdata with additional characteristics without the need for additional surveys.

One example of such data harmonization is the statistical integration of the results of two Rosstat surveys – Sample Observation of Income of Population and Participation in Social Programs (SOIP) and Households Budget Sample Survey (HBS). Since the SOIP data are used to calculate the Gini coefficient, which characterizes income inequality, they were accepted as the primary data set, while the HBS served as a donor (source) of information on final consumption expenditures. The second part of the paper presents an algorithm for the statistical integration of two surveys to obtain a set of microdata characterizing household incomes and expenditures.

In the third part of the paper, the income and expenditure balances of each quintile group of households are presented and compiled by the authors following the methodology of the System of National Accounts using microeconomic data obtained during sample surveys conducted by Rosstat. The estimated distributed indicators of household income and expenses are integrated into the experimental social accounts matrix (SAM) for the Russian Federation for 2020. This made it possible to combine flows between subsectors (quintile groups) of the household sector and other institutional sectors of the economy in a single macroeconomic model. This approach significantly increases the analytical value of the SAM.

As global digital transformation unfolds, analyzing the effects of digital technologies on economics is increasingly important. The choice of better methods and tools for assessing the contribution of digitalization requires further in-depth research. This paper focuses on developing a methodology for measuring the following key indicators of the digital economy, such as development level, dynamics, and linkages between sectors in the context of structural changes in production and consumption at the industry level. The basis of the proposed solution lies in using matrices of coefficients of direct and full material costs obtained based on the Input-Output tables.

Using the data from 2016 to 2020 for the Russian Federation, the authors conducted calculations and identified the economic activities most affected by digital transformation. The developed methodology makes it possible to measure the contribution of the digital economy based on the inter-industry balance, identify key sectors, and propose measures to stimulate digitalization processes. The results of the study can inform managerial decision-making at the country, regional, and industry levels. Open data from the Federal State Statistics Service (Rosstat) served as the information basis of the study.

The article covers the main results of the statistical study of the insurance market. It presents the structure and dynamics of bancassurance indicators in Russia for 2010–2023. The authors explore the benefi of cooperation between banks and insurance companies while identifying the challenges and development opportunities in today’s market. Modelling and forecasting banking processes in insurance are important aspects of statistical methodology. The application of statistical models and time series analysis enabled forecasting of key trends in insurance activities, allowing for a quick response to market changes and prompting suitable risk management actions. Based on the purpose of the study, the paper analyzes the current status of the insurance market and bancassurance in Russia, examines their dynamics and structure and forecasts bancassurance trends using the trend equation for 2024–2026.

It is noted that, for the eff ctive development of bancassurance in the coming years, it is necessary to take several actions to foster sales growth. The implementation of new high-tech solutions by insurers and the enhancement of sales and after-sales services can signifi antly infl e the development of this sector. The main factor for growth in bancassurance remains the recovery of consumer demand and an increase in economic activity.

This paper examines the impact of Body Mass Index (BMI) on individual earnings. The study aims to determine whether there is weight–wage discrimination against individuals with higher BMI levels in the Russian labor market, resulting in lower wages, all other things being equal. The distinctive feature of this study is that the author analyzed the results of representative annual population surveys using regression analysis methods, allowing control for potential reverse causality between BMI and wages. The research used a panel dataset of officially employed respondents from the Russian Longitudinal Monitoring Survey collected by HSE University (RLMS–HSE) from 2013 to 2022.

To address the endogeneity problem in studying the relationship between BMI and wages and to obtain consistent estimates of regression coefficients, the paper employs the instrumental variable (IV) method, which relies on the heteroscedasticity of residuals.

The findings indicate that the wages of overweight or obese workers are significantly lower than those of normal-weight employees. Thus, an individual's BMI, in addition to other socio-economic and demographic characteristics, can be considered a factor affecting the wages of Russians.

Purchasing power parities (PPP) provide a unique tool for macroeconomic analysis. Multilateral computations of purchasing power parities for participating economies are currently carried out under the global International Comparison Programme (Global ICP), guided by the World Bank. The article contains the main features of the Global ICP and the results from the recently completed cycle conducted using 2021 data. The author presents the main directions of PPP-based international comparative analysis and addresses its future development prospects.

Among the great names of representatives of the St. Petersburg school of probability theory – P.L. Chebyshev, A.M. Lyapunov, A.A. Markov – Alexander Alexandrovich Chuprov stands on a par, but apart. However, his contribution to world statistics is no less valuable. The paper offers a brief overview of A.A. Chuprov’s life path and the influence that his father, Alexander Ivanovich Chuprov, had on him, helping his son obtain a double education – both in mathematics and the humanities, and introducing him to zemstvo statistics. The authors highlight the significance of A.A. Chuprov’s book «Essays on the Theory of Statistics» which evoked a keen response from leading Russian scientists and brought him to the forefront of Russian statistical science. In this work, A.A. Chuprov substantiated the need for a statistical method in both social and natural sciences. He consistently advanced the idea of a stochastic world, arguing that all data statisticians work with are nothing more than a sample: they necessarily contain a random component. All of A.A. Chuprov's works focus on finding a priori (true) characteristics based on a posteriori (observed) data. The authors of the article draw attention to A.A. Chuprov's characteristic approach to considering the logic of the statistical method in studying the stability of statistical series, the nature of the correlation relationship, and his broad outlook and understanding of the laws of development of statistical science. The cognitive potential of A.A. Chuprov was formed by a combination of Russian and European education and later by diverse scientific contacts with the foreign scientific community.

The article identifies A.A. Chuprov's place as a teacher who managed to create his school of teaching statistics. His contribution to demographic research is emphasized, especially in establishing proportions in the sex ratio and the effect of war on marriage rates. Alexander Chuprov's expertise in philosophy and mathematics, combined with his understanding of the problems of practical statistics, enabled him to foresee the future development of the theory of statistics as a mathematical statistics, i.e. a science in the centre of which are the problems of statistical estimates of general distributions and parameters.