This paper provides a comprehensive analysis of the territorial differentiation of private household plots (PHPs) in Russia based on the 2016 and 2021 agricultural censusesdata. The studyaimsto assess the spatial distribution and development key indicators (number of farms, land and crop areas, livestock population, and resource structure) in 18 municipal districts of 9 pilot regions of the Russian Federation. Particular attention is paid to intraregional heterogeneity, farm concentration, and changes in agricultural activity between censuses.

The relevance of the study stems from the key role of PHPs in Russia's agricultural sector – to ensure food security, support rural incomes, and preserve traditional land use patterns. The study is based on spatial statistics and economic-mathematical modelingtheory.

Evidence-based recommendations stemming directly from the study findingswere developed to streamline the 2027 All-Russian Agricultural Census. These recommendations can be used for regional adaptation of survey methodology, thereby increasing the accuracy of the future census data.

The article analyzes the sources of information for building the Digital Economy Satellite Account (DESA) in Russia. It examines the existing statistical accounting system and identifies key institutional and methodological barriers to integrating digital phenomena into the SNA structure. Particular attention is paid to studying the approaches of international organizations and various countries to accounting for digital products, free digital services, and the performance of digital platforms, as well as analyzing the possibilities of obtaining information from both existing statistical reporting forms and alternative data sources.

The aim of the study is to define a source of information and statistics for developing the DESA in Russia, ensuring its compliance with international standards. Key objectives include identifying gaps in the statistical accounting of digital resources and their use and developing proposals for improvement.

The study contributes to scientific systematizing data sources, which can subsequently be used to calculate DESA indicators in compliance with international methodology.

The recommendations presented in the paper are of practical significance for identifying new sectors of the digital economy and identifying key areas for implementing international standards for constructing the DESA in the context of the SNA modernization.

This paper presents methodology for estimating the cyclical component of the Russian economy with a mixed-frequency unobserved components model. The key advantage of the approach is the ability to construct a rapid assessment of the cycle using monthly indicators, which allows for up-to-date estimates to be obtained approximately 3–5 months earlier than when using only quarterly GDP data. The use of monthly dynamics indicators makes it possible to construct a business cycle at a monthly frequency, which is especially relevant for obtaining insights into the effects of the economic policy. Also, seasonality is specified within the model itself which eliminates the need for preliminary seasonal adjustment of the time series.

The study presents an estimate of the monthly business cycle dynamics based on the proposed model. Results are then compared with estimates from a quarterly-frequency unobserved components model and the classical Hodrick-Prescott filter. The comparison covers several sub-periods between 2001 and 2025. The most recent observations suggest that the rather aggressive monetary policy of the Bank of Russia at the end of 2024 has undoubtedly contributed to mitigating the overheating of the Russian economy.

The article proposes a new digital technology of inflation targeting based on daily monitoring of inflation processes in the country. The purpose of the study is to identify potential opportunities for implementation of neural networks in collecting and processing large volumes of data by statistical services. It will lead to improvements in the efficiency and accuracy of statistics provided, which will be shown on the example of the CPI. Thisresearch is based on the analysis of the use of artificial intelligence technologies applied in foreign countries in the financial market. The novelty of the study lies in the development of a neural network structure that will allow targeting both demand-pull inflation and supply-pull inflation simultaneously. It is assumed that the core of this technology will be a modular controlled recurrent neural network, the architecture of which was developed to solve this specific problem. Taking as input and processing in hidden layers daily values of macro- and microeconomic variables that can affect inflation dynamics, the neural network will calculate as output the daily value of the CPI. If this value turns out to be higher than the target inflation level set by the Bank of Russia, then the neural network, during backpropagation of the error, will issue recommendations for adjusting some of the neural network weighting coefficients and the values of the macro- and microeconomic variables themselves to reduce the inflation level. The paper concludes that only a comprehensive approach to combating such a complex economic phenomenon as inflation, which consists of taking into account not only demand-pull inflation but also supply-pull inflation, will bring tangible results.

The article provides a comprehensive economic and statistical analysis of the employment of persons with disabilities in the Russian labor market based on microdata from the 2023 Labor Force Survey. Unlike previous studies, which treat persons with disabilities as a homogeneous group, this article analyzes the intragroup characteristics of employment among persons with disabilities. The relevance of this study is conditioned by demographic challenges, labor shortages, and the need to address the socio-economic integration of persons with disabilities.  The employment rate of persons with disabilities of working age is low (19.8%) and significantly lower (by 60 percentage points) compared to the rest of the working population. The stagnation of this indicator over the past five years is an unfavorable trend.  It has been shown that the sectoral and occupational structure of employment among persons with disabilities is skewed toward low-skilled activities with low entry barriers. Persons with disabilities are significantly more likely to be employed in agriculture and in unskilled positions than employees without disabilities, and less likely to be employed in positions requiring high qualifications. They are characterized by higher levels of non-specialized work and educational mismatch, as well as a high concentration of employment in the informal sector.  Four internally heterogeneous groups of employed persons with disabilities have been identified based on their employment status. The highest quality jobs are held by persons with disabilities who are employed in the corporate sector, while the least protected groups are home-based agricultural producers and informal sector workers, who typically have informal employment relationships and whose jobs do not match their education and specialization. It is shown that the level of education is a key factor determining entry into a more prosperous employment group.  The results obtained indicate that the current task is not only to increase the level of employment of persons with disabilities, but also to improve the quality of their jobs.

The article substantiates the relevance of developing a unified methodological framework for sustainable tourism statistics in the countries of the Commonwealth of Independent States (CIS), which is necessary to ensure international comparability of data. The aim of the work is to systematize approaches to building a comprehensive system of indicators that allows assessing the contribution of tourism to achieving the Sustainable Development Goals (SDGs).

Based on the analysis of the international recommendations of the United Nations World Tourism Organization (UNWTO), the United Nations Statistics Division and the best practices of national statistical services, the evolution from traditional tourism satellite accounts to integrated sustainability indicators is traced. Special attention is paid to methods of comparing economic data with social and environmental indicators.

The article discusses modern types of statistical data, including administrative records and big data, as well as methods of their integration to assess the sustainability of tourist destinations. The generalization of international experience serves as a basis for developing practical recommendations for adaptation of the considered approaches in the CIS countries. The authors conclude that development of such a system of indicators is a key condition for making informed decisions on industry regulation. The development of this area of statistics will allow for not only monitoring the current state, but also modeling the strategic development of tourism, minimizing its negative impact and maximizing the socio-economic effect.

The article examines publications by World Bank experts on methodology, comparability and revision of macroeconomic statistics of the former USSR countries during the transition period of the 1990s. The beginning of that period was marked by tremendous changes in the state, economic, and social structure of the former USSR countries. These changes have fully affected macroeconomic statistics. Today's researchers may find it difficult to understand the terminology, methodology, and content of transition statistics. For a better understanding, it might be helpful to study the views of international experts who have studied this subject with regard to international statistical methodology, which was generally accepted at that time.

The article examines three landmark statistical works by experts of the World Bank. The first and the earliest work provides indicators calculated according both to the Soviet and international methodologies, which makes them comparable. The second work by a well-known World Bank expert examines, which of the former Soviet republics were «subsidized» by others due to low prices for fuel and energy resources and other goods traded between them. Finally, the third paper presents the results of the published GDP estimates revision due to some changes in methodology. It was the first revision in the history of officially published GDP estimates in Russia.

Some of the conclusions drawn by the authors of these works may seem simplistic and even somewhat naive today, thirty years later, however, the works also contain very useful explanations and conclusions regarding Russian statistics of this period and, as such, they can be interesting.