This article explores the risks and opportunities of using generative artificial intelligence (GenAI) in the activities of statistical agencies. In the context of digitalization and the growing volume of data, GenAI is becoming a key tool for automating data collection and processing, optimizing analytics and increasing the accuracy of forecasts. However, implementing these technologies poses several significant risks, such as data leakage, cybersecurity threats, reduced trust in official statistics, and the lack of unified standards for data quality assessment.
The purpose of the study is to identify potential risks of using GenAI and to propose recommendations for minimizing them by analyzing the use of artificial intelligence technologies in the work of statistical agencies. The results show that the most significant threats are related to data leakage, possibility of receiving inaccurate information, and cyberattacks on GenAI models. The article suggests risk management strategies, including developing AI usage policies, increasing algorithm transparency, and creating security monitoring systems.
The novelty of this work lies in the comprehensive analysis of GenAI risks in the activities of statistical agencies, which has not been sufficiently covered in the scientific literature before. Unlike previous publications, the emphasis is placed on institutional and cybersecurity aspects, as well as on the need to develop international standards for data validation and risk management

The element-wise reconciliation of quarterly macroeconomic matrices with the annual matrix in the process of operational balancing of quarterly national accounts is considered in the article as a universal way for quarterly decomposing the annual output matrix. A generalized formulation of the problem of such decomposing is given in terms of mathematical programming. The basis of the proposed approach is the weighted least squares method applied in the linear space of the quarterly coefficients vectors of output distribution by products and industries with weights characterizing a priori relative importance or reliability for every summand of the problem quadratic objective function.
It is shown that the problem of the annual matrix quarterly decomposition in its generalized formulation does not have an optimal solution and is of practical interest only as a source of its two operational versions – the «product» one and the «industry» one. Both versions are quadratic programming problems with linear constraints; their solutions are obtained in analytical form using the Lagrange multiplier method.
The advantages of the developed methods for reconciling quarterly matrices of the products and industries outputs with the annual matrix are the simplicity of practical calculations using compact formulas and a very moderate need for computing resources even with a huge amount of initial data. The proposed optimization approach demonstrates a high degree of flexibility and adaptability in solving problems of reconciling quarterly matrices with annual data on the output of goods and services. High flexibility is provided by the dependence of the considered problems on sets of exogenous parameters that could vary purposefully in the course of performing practical calculations.

reputational capital of higher education institutions becomes extremely important given the growing competition in the market of educational services, which influences the attraction of applicants, highly qualified teachers, and scientists. Building reputation capital has recently been significantly influenced by the digital environment, which is now starting to play a key role. Assessing a university's reputation is a multifaceted issue that includes analyzing information resources, determining its main criteria and indicators, and developing a methodology.
The article considers the problems of measuring the reputation of Russian universities offering engineering training programs based on databases compiled from various sources. The study aims to identify reputational indicators and develop an approach to assessing the reputation of higher education institutions in Russia.
The author analyzed data from the Ministry of Science and Higher Education of the Russian Federation and Yandex search queries.
The top Russian universities providing training of engineering personnel were identified through a comparative analysis of the evaluated parameters. When processing statistical data on the number of search queries, data quality was assessed with regard to aberrations and logical coincidences with other established word forms.
The application of normalized data processing methods made it possible to compare the results of reputation assessments of higher education institutions from different regions of the country. Universities in Moscow and St. Petersburg lead in the majority of indicators, emphasizing their status as leading centers for education. At the same time, certain regional universities have high rankings in international and digital reputation, which indicates recognition of their role in training engineering specialists.
The differences in the reputation assessment of Russian universities were found to arise from the specifics of the methodologies used in data collection and processing. The scientific novelty of the study lies in determining the composition of indicators characterizing the reputation of Russian universities, a detailed analysis of existing methodologies for its assessment, and the proposal of new approaches that expand the theoretical and methodological bases of the analysis. The findings can be used both in theoretical terms – to improve approaches to assessing university performance – and to optimize the management of higher education institutions

The article studies the features of internal migration in St. Petersburg, one of the largest metropolises in Russia, from 2015 to 2023. The reason for selecting this time interval is that since 2015, Rosstat has provided access to detailed data on migration, which significantly increases the completeness and accuracy of the analysis of migration processes and includes the most recent published data for 2023.
The study presents an extended gravity model developed by the author, which considers socio-economic factors influencing migration processes, including population size, average monthly wages, employment levels, and geographic remoteness of regions. Unlike traditional gravity models that primarily rely on demographic variables, the work considers economic factors that significantly affect migration in a country with pronounced regional differences.
Graphical methods were used to visualize regional migration trends, including the structure and characteristics of arrivals by gender, age, and level of education, while also identifying donor regions for St. Petersburg.
Using the developed model, a forecast of internal Russian migration flows to St. Petersburg was made for the period up to 2026. The forecast serves as a starting point for further research, considering the limited time data set and the negative consequences of the COVID-19 pandemic. This study reveals regional characteristics of internal migration in the largest metropolis in Russia and offers insights into the long-term impact of migration processes, emphasizing the significance of both economic factors and regional development strategies in shaping migration flows.

The article highlights the key role of official statistics in monitoring a country's socio-economic development which is the basis for managerial decision-making. The role of international organizations in regulating information exchange between countries is discussed. International approaches developed by the World Bank and the non-profit organization Open Data Watch to assessing the statistical capacity of the countries are presented, including their proposed indicator systems. Based on a study of informational resources and methodological materials prepared by experts from these organizations, differences in approaches to measuring the capacity of national statistical organizations have been identified.
The article provides assessments of the statistical capacity of the Russian Federation and its global ranking positions based on internationally recognized indicators, as well as factors influencing the resulting assessment.
The results of the analysis of information sources used by the World Bank and Open Data Watch experts during the Open Data Inventory (ODIN) process are presented, along with verification outcomes of Russia’s statistical capacity indicators based on updating the source information and applying a rational approach to its selection, ensuring complete data coverage for established indicators.
This study has substantiated the potential for enhancing the statistical capacity of the Russian Federation.
The relevance and novelty of this study compared to similar works are emphasized, and solutions for more objectively reflecting Russia’s positions in international statistical capacity rankings are proposed.
The practical significance of the work lies in formulating well-grounded proposals and specific adjustments for engagement with international organizations.

The article offers a comprehensive analysis of modern methodological approaches to measuring multidimensional poverty in CIS countries. The study is based on data obtained from a survey conducted in 2024 as part of the «Development of CIS Statistics» project.
It examines the practices of national statistical agencies and methodological approaches of international organizations like UNDP, Eurostat, and the World Bank. Particular emphasis is placed on the application of the Alkire – Foster method for calculating the Multidimensional Poverty Index, the evaluation of the At Risk of Poverty or Social Exclusion (AROPE) indicator following European standards, and the World Bank’s approach (MPM), which integrates both monetary and non-monetary dimensions.
The analysis reveals significant discrepancies in the selection of indicators, criteria, and threshold values, reflecting the socio-economic conditions and developmental priorities of the region's states. The findings contribute to a deeper understanding of the multifaceted nature of poverty and identify potential avenues for harmonizing approaches to poverty measurement, which is essential for improving public policy and developing strategic socio-economic development programs.
The article underscores the need to further integrate multidimensional poverty assessments into frameworks for targeted social support measures for the population.

The article analyses the methods for conducting the 2020 round of national population censuses in the CIS countries and the motivation for transitioning from the traditional model to the combined census. Some key aspects of the applied technical solutions in the population censuses are considered. It is emphasized that the chosen path for transitioning to a combined census in the CIS countries, despite the existing difficulties, will be a crucial factor in determining the method for conducting the population census in 2030. The results of the past round provided the basis for further work in this direction.