The potential for the development of digital data and their infrastructure creates new opportunities for economic growth. The purpose of this study was to develop an approach to identify a set of indicators to quantify the data economy and model its impact on economic growth. The cumulative index and Gini coefficient indicated differentiation and disparity in the digital data infrastructure of 85 regions for 2016–2021. In the presence of a positive spatial correlation, digital development does not indicate clear spatial clubs. Selected according to the calculation of Lagrange multipliers and likelihood ratios, panel econometric models with spatial lags, using SAR, SEM and SAC, showed a short-term negative effect and a long-term positive effect of the digital data economy on economic growth, confirmed by the calculation of marginal effects. During the pandemic, the data economy had a positive impact on regional economic growth. The positive spatial effect of interactions between regions detected by the models in the framework of economic growth indicates the synergistic nature of digitalization. The main conclusions of this study provide evidence-based support for the digital transformation of regions and can help create information infrastructure and accumulate human capital to eliminate disparities in the digital development of regions.

   Objective: to identify industry trends in aggregate factor productivity, labor productivity and capital productivity in economic sectors. The hypotheses are: there is a continuing slowdown in aggregate factor productivity in the Russian economy sectors; there is a gap in aggregate factor productivity in various complexes of industries.   Methods: Cobb–Douglas production function, multiple regression, Domar aggregation, Gini coefficient, graphical visualization.   Results: the study revealed a more efficient use of labor resources in contrast to capital, the latter being accumulated everywhere. The highest increase in labor productivity was noted in financial and insurance activities, manufacturing industries, professional, scientific and technical activities. There is a cyclical dynamics of aggregate factor productivity, its most stable growth and a positive cumulative technological shift marked from 2017 until 2021. They are observed in the manufacturing and investment complexes of the Russian economy (financial and insurance activities), and in agriculture.A decrease in total factor productivity was found in the mining industry, transportation and storage, hotels and catering, construction, education, and healthcare.  Scientific novelty: it consists in a change in the total factor productivity and the total technological shift in the context of sectors and complexes of sectors of the Russian economy from 2011 to 2021.   Practical significance: the study results can be used for the policy of stimulating internal sources of economic growth, to improve sectoral recommendations of innovation policy.

A scientific discussion is unfolding around data as a new factor of production that contributes to the transformation of traditional sectors of the economy, industrial integration, and ensures interregional interaction. At the same time, the question of the relationship with such traditional production factors as labor and capital needs to be answered. The study aims to identify the determinants of organizations' use of Big Data at a regional level. The main hypothesis of the study suggests that the key determinants of organizations' use of big data technologies are digital labor, digital capital and the socio-economic characteristics of regions. In the study we proposed a modified knowledge production function which was tested on open data from the Federal State Statistics Service for 85 regions of Russia in 2021-2022. Panel models were constructed using the method of least squares, generalized feasible least squares. The study presents illustrative material made using cartograms and graphical methods. The results of the study distinguished the spatial heterogeneity in the use of Big Data technologies in Russia's regions and differentiation of the regions by volume of digital capital and digital labor. Panel data models with random effects confirmed the positive impact of digital labor and digital capital on organizations' use of Big Data. Among the socio-economic characteristics of regions as determinants of the use of big data technologies, significant effects were obtained for the share of urban population, gross regional product and share of innovation costs. The study identifies the determinants of the development of the data economy in Russian regions, considering geographic, technological, and economic differentiation. The theoretical significance of the study lies in the proposal of the author's concept of a modified knowledge production function, which can be used as a fundamental basis for the development of the theory of data economics. The practical significance of the study lies in the validity of the value of Big Data, the use of which can help institutions and government authorities find new opportunities for the development of the data economy, taking into account regional differentiation, improving the methodology for monitoring the use of digital technologies by organizations, and identifying the key factors influencing the use of Big Data technologies by organizations.

The subject of the study is the dynamics and structure of revenues and expenditures of the budget of the Kazan City Duma in 1871–1885. The object of the study is the budget of the Kazan City Duma in the post-reform period. The authors analyze income items (19 items) and expenses (22 items) of the budget of the Kazan City Duma in 1871–1880 to calculate descriptive statistics and identify the nature of the distribution and variation of the initial data. Special attention is paid to counterfactual forecasts of the revenue and expenditure dynamics of the Kazan City Duma budget for 1881–1885, which are modeled and compared with the actual values of budget indicators during this period. The purpose of the work is to identify structural changes and dynamic patterns in the formation of the municipal budget in the context of institutional transformations in the 1870s and 1880s. The empirical basis of the study is the reports of the Kazan City Public Administration for 1871–1885. The research methodology is based on a combination of structural shift analysis (A. Salai, V. Ryabtsev indexes, K. Gatev coefficient) and counterfactual modeling of time series using ARIMA and ARIMAX models. The counterfactual approach is implemented in relation to the conditions for comparing the actual budget dynamics in 1881–1885 with the inertial trajectory formed on the basis of trends in 1871–1880. The scientific novelty of the research lies in the integration of methods for analyzing structural shifts and counterfactual modeling to study the budgetary practice of urban thought in post-reform Russia. It has been established that the revenue side of the budget in 1871–1880 was characterized by relative structural stability and inertia in the formation of revenue sources. The expenditure side demonstrated a higher degree of structural mobility and redistribution of funds between urban areas. Counterfactual analysis showed no signs of a radical transformation of the budget model in the early 1880s; the revealed deviations are in the nature of parameter adjustments within the established system of municipal finance. The findings make it possible to clarify the understanding of the mechanisms of adaptation of municipal finances to institutional changes and expand the possibilities of inter-regional comparisons. The scope of the results is related to the research of economic history, the history of urban self-government and municipal finance of the Russian Empire.