The article presents methodological approaches to organizing and conducting comprehensive statistical monitoring of the development and application of artificial intelligence (AI) technologies. The relevance of this topic is driven by the high significance of AI technologies for the economy and society, and their recognition as one of the leading technologies of the current decade, both globally and in Russia. A comprehensive assessment of AI development requires a robust and well-developed research methodology.

Based on the analysis of existing approaches to AI observation, including research topics in this field, available data sources, and data collection methods used in international and domestic practices, the study has developed a terminological framework, classifiers for AI technologies and related goods and services, a conceptual model of monitoring, and a system of indicators. The proposed monitoring approach was tested in 2023–2024 during specialized surveys of organizations and universities. The new AI data collection toolkit has been officially implemented in the federal statistical observation system.

According to the authors, the implementation of monitoring will enable the acquisition of quantitative and qualitative characteristics regarding the creation, diffusion, and future prospects of AI technologies across economic sectors and social domains, as well as facilitate the assessment of the effects of their implementation.

This paper analyses the dynamics of performance of Russian banks from 2012 to 2021. To assess the efficiency of financial institutions, the authors used parametric Stochastic Frontier Analysis (SFA) method. The study examines various aspects of bank operations, including lending, deposit-taking, and cost management.

For an in-depth analysis and to obtain estimates from more homogeneous samples, all banks were divided into five groups based on asset size. The efficiency models were estimated separately for each group and for each year, enabling the tracking of changes both across groups and over time. The study’s key conclusion is that an overall decline in banking efficiency was observed over the period under review. This fact may be linked to the dynamics of the key interest rate, specifically to the periods of monetary policy easing (during times of higher interest rates, banks are forced to operate with greater efficiency). Only the group of the five largest banks managed to maintain their deposit-taking efficiency, which is likely due to their high level of customer trust and these institutions’ relatively easier access to retail funds.

Cost management efficiency was generally found to be higher, with the largest banks demonstrating the most successful performance. The findings of the study and the resulting estimates can be valuable for both government bodies providing banking supervision and regulation, and for the banks themselves and their counterparties.

The article is devoted to the analysis of salary predictors based on data on vacancies published in the public domain on the Internet portal of the federal state information system of the Federal Service for Labor and Employment «Work in Russia». This article examines the determinants of job openings that determine salary levels and make them the highest-paying in the labor market. To identify these determinants, the author uses the random forest machine learning method.

The forecast quality of the random forest method is assessed using the mean absolute error metric, which is more easily interpreted in the context of the scale of the dependent variable used. In addition, the values of the mean square error and estimates of the coefficient of determination, which show the proportion of the variance of the dependent variable explained by the predictors, were calculated. To improve the accuracy of the model, hyperparameters were tuned using the RandomizedSearchCV algorithm, which allows selecting the best options from a large number of possibilities.

The results of the study showed that the characteristics that have the greatest impact on salary are the region in which the vacancy is open, and to a relatively lesser extent (but also significantly) the professional field and work schedule. The findings may be useful for understanding the factors that shape competitive salaries and for implementing effective support measures in the labor market.

The article presents the results of a study of the disparities in the standard of living between the urban and rural population, as one ofthedrivers of internal and interregional migration. The authors argue that this is a crucial aspect for understanding migration patterns and serves as an indirect indicator of the level of socio-economic development of a region and its potential for future growth.

To examine the differences in the standard of living, a comparative analysis of indicators for two time periods (2016 and 2022) was conducted using the Tambov Region as a case study. This region is notable within the Central Federal District for its significant share of rural population and its specialization in agricultural production.

The first stage of the study involved assessing socio-economic situation in cities and villages of the region. Subsequently, the authors developed a system of indicators reflecting the population's standard of living, and calculated an integral index of the standard of living for the region's municipalities. This index provides an objective measure of the degree of territorial disparity in living standards.

Based on the developed system of indicators, a multivariate statistical analysis was performed. It led to the conclusion that the standard of living of the rural population in the Tambov Region rose over the 2016–2022 period. This improvement is largely attributed to measures implemented under the Russian Federation's Food Security Doctrine, which has stimulated the active development of agriculture, thereby creating preconditions for the rise in the standard of living across the region.

This article examines the prospects for the development of digital and IT entrepreneurship in Russia. It identifies key success factors and risks, and proposes recommendations to support innovation initiatives and increase the industry's contribution to the national eco nomy.

The study investigates the impact of Information and Communication Technologies (ICT) on the formation of Gross Domestic Product (GDP) through factor analysis. The use of this analytical approach ensures the creation of an effective predictive model, which is crucial for making informed management decisions that support and further develop the digital economy of the Russian Federation.

The study established that the contribution of ICT to GDP is dependent on the level of informatization and digitalization within organizations, the spread and use of digital and telecommunication technologies, the digital transformation, and innovative development of theeconomy and society. Based on the analysis, a forecast was developed according to which the contribution of ICT to GDP will continue to grow, albeit ata slowing pace, emphasizing the need for regular monitoring and timely responses to changes in the external environment and intra-industry factors. The results demonstrate that the primary sources of growth in the ICT sector's contribution to GDP are high levels of investment in thedigital economy and the active development of innovations.

The most critical directions for future work include improving the system of statistical data collection, refining methods for evaluating the results of state program implementation, and enhancing the transparency of funding for innovative projects.

The article examines the production function with Constant Elasticity of Substitution (CES), particularly its special case – the Cobb-Douglas production function – which assumes an elasticity of substitution equal to unity. In many works, the assumption of a unitary elasticity of substitution is challenged, and it is noted that estimates vary depending on the characteristics of the economy under study and the analysis horizon, and can be both significantly lower and higher than unity.

The purpose of the study is to test the hypothesis about the possibility of using the Cobb-Douglas production function to model economic processes at the regional level in the Russian Federation. The CES production function, which depends on two factors – labor and capital (accounting for Hicks-neutral technological progress) – is examined. The assumption that the constant elasticity of substitution of production factors equals unity is tested using statistical data from the Rosstat website for 80 regions of the Russian Federation for the period from 2010 to 2022. The methodology includes panel data analysis to determine the elasticity of substitution between capital and labor, as well as the application of spatial econometrics models that allow spatial effects to be taken into account to minimize bias in estimates due to interregional heterogeneity.

The results of the evaluation of panel data models with the inclusion of spatial effects and control variables do not reject the proposed hypothesis and indicate the applicability of the Cobb-Douglas production function for modeling the economic development of Russian regions.

The paper argues the feasibility and applicability of power models, including three-parameter rank-size models, for analyzing the distribution of the number of employees in organizations and related socio-economic indicators across cities of the Russian Federation. Using municipal statistics for urban districts, the authors estimated the corresponding three-parameter Zipf – Pareto – Mandelbrot models for each year from 2021 to 2023. This approach made it possible to analyze the dynamics of competition between cities as elements of an urban system for limited resources: population (including the working-age population), labor force, and investments. A comparison of the estimated model parameters revealed varying degrees of balance within Russia's urban system in the distribution of employees, population, and investments across cities of different size, as well as key factors that determine imbalances in the distribution of key macroeconomic indicators among cities.