Household expenditures are one of the indicators of individual and social welfare. Analysis of household expenditure dynamic has showed a significant change in the period of economic crisis: during the global economic crisis and the recession in Europe. Change of household expenditures occurs under the influence of such macroeconomic factors as disposable income of households, government spending, inflation, interest rate that should be considered in public policies. The population and the education level of population are also significant demographic factors that influence consumer spending and household spending on health

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.

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.

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.

Economic behavior is analyzed in terms of household propensity to save and of their accumulated social capital. In this article the comparison of countries according to a rate of household savings in years 2005-2010 and social capital index based on cluster approach is made. Main trends in household saving behavior in some countries are connected with reduction of savings share in disposable income. For most countries social capital tends to decrease due to the transition to a new household type, so the socio-economic policy should be aimed at improving their economic activity

   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.