Julia Varlamova

The development of the digital economy and society aims to ensure equitable access to digital resources for all residents, regardless of geographic location, and to reduce the digital divide between regions. This study examines whether Russian regions are converging in terms of digitalization levels. The analysis uses open data from the Federal State Statistics Service (Rosstat) for 2003–2023, covering 80 federal subjects. The methodology employs sigma, gamma, and delta convergence approaches, focusing on two key economic agents—households and organizations. Digitalization indicators are categorized into three dimensions: access, use, and economic benefits derived from digitalization. Unlike aggregate index-based methods, this classification captures regional heterogeneity and provides a more nuanced understanding of the digital convergence process. The results indicate convergence across regions in local telephone service digitalization and household Internet use. A similar pattern is observed in the use of digital channels to access public services, reflecting progress in government–citizen digital interaction. However, divergent trends emerged during the pandemic: the digital divide widened in terms of access but narrowed in household usage. For organizations, convergence was identified in the use of servers, websites, and electronic data interchange. Based on these findings, the study concludes that expanding high-speed digital infrastructure should be a priority. Additionally, government efforts should focus on scaling best practices through digital platforms and promoting standardized IT solutions to support adoption by households and public authorities. Future research should investigate the factors that facilitate or hinder regional digital convergence.

   Objective: to provide a comparative assessment of the use of artificial intelligence technologies by organizations in the context of Russian regions and to identify determinants of their dynamics.   Methods: descriptive statistics, histogram, grouping, principal component method, panel data models.   Results: an absolute trend of recent years is to study and implement artificial intelligence technologies in many economic, industrial processes and social life. The article analyzes the trends in the application of artificial intelligence technologies in the Russian regions. The comparative analysis of regions by the level and growth rate of artificial intelligence technologiesuse by organizations showed that the regions were heterogenous by the dynamics of this indicator in 2020-2022. The regions were divided into four groups: above average and below average level in Russia. Econometric modeling based on the method of principal components gave grounds to unite the determinants of the use of artificial intelligence technologies into four components. Panel data fixed-effects models showed a significant impact of the component, characterizing the state of human capital, the level of economic development, and innovation activity of organizations in the region.   Scientific novelty: for the first time an attempt was made to provide a comparative analysis of Russian regions by the level of artificial intelligence technologies use by organizations and to find the determinants of its change.   Practical significance: the heterogeneity of regions in terms of the artificial intelligence technologies use by organizations was substantiated, as well as a great impact of the specific characteristics of regions, which should be taken into account when building a national policy of artificial intelligence development.

The formation of a data economy and the spread of advanced digital technologies raises the question of how the sectoral structure of regional economies can promote or hinder the use of new technological solutions. The purpose of the study is to assess the relationship between the share of economic activities in the structure of the regional economy and the share of organizations using data economy technologies: Big Data, Artificial Intelligence, cloud technologies, Internet of things based on calculations of correlation coefficients. The findings of the analysis of data from the Federal State Statistics Service of the Russian Federation in 2020-2022 for 85 regions of Russia demonstrate the presence of stable correlations between sectors of regional economies and data economy technologies, differing depending on the type of technology: an inverse relationship between the high share of agriculture and the prevalence of Big Data technologies and the Internet of things, a direct relationship between the manufacturing industry, trading and the use of cloud tech-nologies and Artificial Intelligence. Moreover, multidirectional relationships were identified, when a direct relationship can be traced between the share of a particular industry in the structure of the regional economy and one technology of the data economy, and the reverse –for another. The prac-tical significance of the study lies in the empirical substantiation of the nexus between the sectoral structure of regional economies and the spread of Big Data technologies, Artificial Intelligence, cloud technologies and the Internet of things during the pandemic and sanctions, which can serve as the basis for developing recommendations for stimulating the spread of data economy technolo-gies, considering the regional industry specifics.

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.

Objective: to assess the impact of cross-border data flows on imports and exports.Methods: static panel data models, graphical method.Results: the paper shows that currently the issues of the digitalization impact on economic processes are insufficiently studied from an empirical point of view, and the conclusions obtained by the researchers are quite contradictory. To assess the impact of digitalization, the authors formed a model with fixed effects, the variables of which are indicators of exports and imports of goods and services of geographical macro-regions, the use of international bandwidth, oil rents, investment indicators, etc. The main conclusion is that the use of international data transmission channels has a statistically significant effect on export and import flows. Modeling in terms of geographical macro-regions showed that the impact of international data flows on export and import flows was uneven.Scientific novelty: the negative impact of international data flows on exports in Latin America and the Caribbean and imports in Europe and Central Asia, East Asia and the Pacific is shown. A quantitative assessment of the impact of cross-border data flows on international trade flows gives grounds to assert the ambiguous consequences of digital transformation in relation to international trade for certain geographical macro-regions.Practical significance: conclusions based on the study results can become the basis for assessing the consequences of measures aimed at increasing the country’s involvement in the globalization and digitalization processes. The study results can also be used to further assess the impact of international data flows on international trade indicators during external shocks and to determine the specifics of this relationship during increased macroeconomic instability and risk.

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.

Диффузия технологий способствует технологическому и экономическому сближению стран. Стремительное распространение интернет-технологий дает основания выдвинуть гипотезу о конвергенции стран по уровню проникновения данных технологий. Целью исследования является выявление тенденции к конвергенции или дивергенции в фиксированном и в мобильном широкополосном доступе в Интернет для стран мира в целом и для групп стран, выделенных на основе следующих критериев: географического (по географическим макрорегионам), экономического (по уровню национального дохода на душу населения) и институционального (по членству в международной организации). Источниками данных послужили открытые данные Международного союза электросвязи. Методология исследования включает в себя построение динамических моделей на панельных данных для проверки гипотезы о наличии безусловной бета-конвергенции. Кроме того, был оценен абсолютный масштаб цифрового разрыва между группами стран по широкополосному доступу в Интернет с помощью модели сигма-конвергенции. Результаты исследования свидетельствуют о наличии бета-конвергенции как для фиксированной, так и для мобильной широкополосной связи. Данная закономерность наблюдается и по полной выборке стран, и по географическим регионам, за исключением Океании. Также бета-конвергенция характерна для групп стран, выделенных по уровню дохода на душу населения и по участию в Организации экономического сотрудничества и развития. В то же время результаты расчетов свидетельствуют о сохраняющемся цифровом разрыве между странами по уровню проникновения широкополосного Интернета. Практическую значимость имеют разработанные на основе полученных результатов рекомендации, направленные на реализацию стратегии ускоренного технологического развития с целью стимулирования распространения мобильного широкополосного доступа, расширения телекоммуникационной инфраструктуры. цифровая конвергенция, широкополосный доступ в Интернет, диффузия ИКТ, инфраструктура для данных, мобильный широкополосный доступ, фиксированный широкополосный доступ, бета-конвергенция, телекоммуникационная инфраструктура.

The article examines the impact of changes in the number of employees on wages and housing prices in Russian regions. According to the local labor market model higher nominal wages in a particular region can attract workers from other regions. Due to the limited supply of housing an increase in the number of people employed in a certain area leads to an increase in demand for housing and an increase in prices for it, which can cause workers to lose in terms of real wages. In the presented work we tried to answer the question of whether the change in the number of employees in the region affects wages and housing prices in the subjects of the Russian Federation. The estimates were carried out on aggregated regional Rosstat data using panel models with fixed and spatial effects (SEM and SAR models). The study showed that there are spatial correlations both between regional labor markets and between local housing markets. Changes in the number of employees in the region make a significant positive impact on housing prices and a significant negative impact on real wages. In terms of significance and direction of impact all the evaluated models are similar to each other. The results of the analysis can be used to carry out social, regional and migration policies.

Различия между поколениями в использовании цифровых технологий приобретают особое значение в свете старения населения, увеличения продолжительности жизни и поиска путей включенности старшего поколения в активную социальную жизнь. Фокус исследования сосредоточен на использовании Интернета в России. Цель исследования ‒ выявить различия в доступе и использовании Интернета представителями разных поколений. Исследование проводилось на основе выделения трех уровней цифрового разрыва: первый уровень ‒ доступ к Интернету, второй ‒ использование Интернета, третий ‒ выгоды от использования Интернета. В работе использованы данные Российского мониторинга экономического положения и здоровья населения НИУ ВШЭ. Основными методами исследования являются сравнительный анализ, анализ динамики показателей, метод расстояния во времени. Результаты исследования позволили: (1) выявить специфические черты во владении гаджетами, имеющими выход в Интернет, среди поколений; (2) проследить динамику межпоколенческого разрыва первого и второго уровня в использовании Интернета на протяжении 2012–2019 годов; (3) оценить межпоколенческие разрывы в доступе и использовании Интернета с использованием метода расстояния во времени. Результаты исследования позволяют констатировать существование различий в доступе и использовании Интернета между поколениями как с точки зрения владения цифровыми гаджетами, так и с точки зрения целей использования Интернета. Наличие цифрового разрыва между молчаливым поколением и более молодыми поколениями позволяет сделать вывод о необходимости учета особенностей использования интернет-услуг и интернет-сервисов представителями старших поколений, оказания им помощи по инклюзии в цифровую среду. При этом отмечена положительная динамика среди представителей всех поколений в освоении Интернета. Оценка цифрового разрыва третьего уровня позволила сделать вывод о наличии выгод для всех поколений в России от использования Интернета.

This study examined the role of information and communication technologies (ICT) in labor productivity dynamics. The following ICT components were analyzed: the share of organizations using computers and the share of organizations using the internet. The purpose of the study was to analyze labor productivity in the Russian economy in the context of digital transformation, taking into account two dimensions: temporal and spatial. We investigated the impact of organizations’ use of ICT on labor productivity, the relationship between labor productivity and high-tech production, and spatial effects in the dynamics of labor productivity. We used a spatial autoregressive model (SAR) and a panel vector autoregressive model (PVAR) to analyze data for the period 2010–2018. The findings show that digitalization of business processes and an increase in the share of organizations using internet technologies leads to an increase in labor productivity. Certain socioeconomic indicators were also found to be significant, namely, real wages and the percentage of people with higher education in the workforce. Exogenous variables acting as external shocks did not exert significant effects. The results have important implications for managers who develop strategies to increase labor productivity and production efficiency. Such strategies should focus on internetization, business digitalization, and e-commerce.