Ekaterina Ivanovna Kadochnikova

Endogenous growth theory explains the asymmetric dynamic relationship between economic and social development through human capital accumulation and innovation, institutional quality, and demand channels. The objective of this paper is to assess the dynamic relationship between social and economic development in developing countries, where institutional imperfections and development instability create the most pronounced asymmetries. A composite social development index, obtained using the entropy method, operationalizes social development as the expansion of human capabilities in three dimensions: health, education, and material security. A panel vector error correction model (PVECM), estimated using the generalized method of moments (GMM) on panel data from 18 countries in Central Asia, the Middle East, and North Africa for the period 2001–2023, revealed asymmetric dynamic relationships: improved social indicators are associated with a short-term slowdown in economic indicators and more favorable economic dynamics in the medium term. In contrast, economic growth is accompanied by a positive lagged response in social development, although the short-term response may reflect the costs of social adjustment. The influence of control variables confirms the positive role of agglomeration for economic development, revealing the social costs of rapid urbanization and demographic pressure on social development. Estimates of the error correction coefficients indicate a slow adaptation of the system to long-term equilibrium, high inertia, and institutional rigidity of macrosocial processes. Impulse response functions confirm the dynamic and delayed nature of the interaction between economic and social development and positive shocks in the medium term. The obtained empirical results substantiate the need for institutional regulation of policy decisions on human capital accumulation and innovation, as well as social reforms.

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.

Objective: to measure the short- and long-term effects of currency exchange rate transfer on consumer prices in modern Russian realities with the analysis of the effect asymmetry.Methods: the study is based on the Federal State Statistics Agency and the Russian Central Bank data on the consumer price index, dollar exchange rate, and gross domestic product for 2000-2024. The study uses the autoregressive distributed lags (ARDL) model similar to error correction model (ECM).Results: the article shows cointegration relationship between the consumer price index, the exchange rate, the GDP gap, and the global consumer price trend. The research found excess of the short-term effect of the transfer over the long-term one, the strengthening of the short-term effect of the exchange rate transfer and the weakening of the long-term effect after 2020. In comparison with the results of previous studies over the past time periods, there is a decrease in the incomplete effect of the currency exchange rate transfer on consumer prices and a reduction in its volatility. To explain the results obtained, the author concludes that the direct and indirect mechanisms of exchange rate transfer play a combined role in increasing the short-term effect regardless of the monetary policy regime. It is shown that the inflation targeting regime, the new configuration of foreign economic relations and weakened traditional channels of economic structural changes transfer influence the reduction of the transfer long-term effect after 2020. The asymmetry of the exchange rate in the long term is proved, when the effect of lower prices exceeds the effect of rising prices.Scientific novelty: for the first time, the article assesses the effect of currency exchange rate transfer and its asymmetry in the Russian economy under the sanctions and economic structural changes after 2020.Practical significance: the presented results can be useful in developing monetary policy regimes, managing the structure of foreign economic relations, and developing effective economic policy in modern conditions.

   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.

According to the theory of endogenous growth, technologies play a key role in economic development. Digital technologies, as innovations that create new innovations, change economic activity and create new opportunities for economic growth. Digitalization helps to reduce specific economic costs, changes the nature of work. It also causes the substitution of labor with capital. The study is devoted to characterizing the features of regional development and evaluate digital predictors of economic growth. We used a sample of data from 83 regions in the period 2009-2019, excluding from the sample years 2020 and 2021 because of changes in economic dynamics under the influence of pandemic shocks. Econometric estimates are obtained using least squares and a feasible generalized least squares method based on unidirectional panel data models. A statistically significant influence of the number of university students, innovation costs on the growth rate of gross regional product per capita was found. The impact of the number of granted patents and the number of Internet users of the organization on the regional development was not confirmed. The results of the study emphasize the necessity of state science and innovation policy to reduce technological inequality, strengthen macroeconomic stability, high level of qualifications and technologies. The direction of further research can be the creation of indicator system measuring the quality of political and economic institutions.

The significant role of knowledge in ensuring productivity, competitiveness and technological sovereignty places renewed emphasis on the concept of the knowledge economy. The purpose of the article is to develop a methodological approach to assessing the three components of the knowledge economy in the regions based on integral latent variables. The values of knowledge economy indicators obtained by the authors based on minimax normalization formed the basis of three integral latent variables for 85 Russian regions in 2022: knowledge workers, R&D, and innovativeness. To analyze the concentration of the knowledge economy, the Gini and Herfindahl-Hirschman coefficients were used. The findings about the uneven concentration of knowledge economy indicators in Russian regions were demonstrated by cartograms of integral latent variables. In conclusion, practical recommendations are presented for overcoming the technological gap by integrating resources related to the development of the innovation sphere and eliminating a number of barriers.

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.

Цифровые технологии в экономике становятся ключевым фактором обеспечения благополучия и ресурсов будущих поколений как экономической составляющей концепции устойчивого развития. Преимущества цифровых технологий в условиях санкционных ограничений могут стать драйвером развития инноваций в регионах. На региональных данных с 2010 по 2021 годы исследованы динамика инновационной активности и территориальный разрыв в инновациях. Для актуализации роли цифровизации в решении проблемы сбалансированного инновационного развития авторы анализируют и обнаруживают σ-конвергенцию и β-конвергенцию доли инновационных товаров, работ, услуг в объеме отгруженных товаров, работ, услуг. Не подтвержденный моделями анализа панельных данных когнитивный эффект цифровизации в экономике регионов, отсутствие взаимосвязи между человеческим капиталом, внутренними затратами на НИР на душу населения и темпом роста доли инновационных товаров, работ, услуг в объеме отгруженных товаров, работ, услуг позволяют обосновать вывод о необходимости проведения мероприятий научно-технологической политики, направленных на рост инновационной активности в регионах. Статья будет интересна институтам и органам государственной власти в управлении преобразованием приоритетных отраслей экономики и региональным развитием.

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 current trends in the labor market under digitalization and the growing risks and global instability in the world and national markets in the EAEU countries.Methods: panel data analysis models, graphical method, least squares method, generalized feasible least squares method.Results: the data of the EAEU countries from 2016 to 2020 show the synchronization of unemployment indicators; a slight increase in the share of unemployed aged 55 and older in all countries except Russia; a decrease in value added produced within the “Information and Communication” type of activity, with the exception of Kazakhstan; during the pandemic, a decrease in the integral indicator of unemployment and the number of unemployed aged 55 and older was found under the influence of the gross value added within the “Information and Communication” type of activity, as well as a decrease in unemployment among elder citizens under the influence of the share of the population using the Internet.Scientific novelty: the current trends of unemployment are reflected, under digitalization and the growing risks and global instability in the world and national markets in the EAEU countries.Practical significance: the main conclusions of the article can be an argument in favor of the competitive advantages of the older population in the labor market under digitalization and uncertainty of economy.