This paper provides an analysis of the debt burden of Russian companies and raises the issue of debt-level heterogeneity across economic sectors. To identify the causes of this heterogeneity, it estimates a regression model that includes both fundamental exfully explain the variation in the debt levels of companies in different sectors. According between fundamental factors and companies' debt levels. An understanding of the formation process and structure of debt burden in individual industries is extremely important reserved. debt burden, capital structure, sector analysis, microdata of Russian companies, emerging
This paper evaluates the impact of anti-coronavirus measures on the dynamics of economic activity. In addition to primary shocks directly caused by restrictive measures, we assess their secondary effects through inter-industry relationships. Our assessments show that secondary effects impact more industries than primary effects do. The overall impact of secondary effects on the economy proves to be of a larger scale than the impact of primary effects with high heterogeneity of dynamics by industry.
The current status of ITS deployment around Europe is characterized by lack of Standards for ITS implementation at EU level, limited cooperation among the various related IT Stakeholders and more importantly lack of wide awareness of the results and outcomes deriving from numerous and significant investments in ITS throughout Europe. This knowledge fragmentation can be reduced through the development of the ITS Observatory, a flexible and user-friendly, open source of knowledge for ITS Deployment in EU and abroad. The current study presents the user needs and requirements for this unique ITS Observatory.
The access to credits for companies with high productivity is an important factor for the economic recovery after the shock. In this paper, we analyze changes in banks’ lending to Russian companies’ in 2020. Our analysis shows that in 2020 the volume of new ruble credits increased relative to the level of the previous year. At the same time, there were changes in loans’ structure, which are explained by the effect of government lending support programs that began in May—June 2020. This fact indicates that a large number of firms made use of these programs last year, partially or fully covering temporary liquidity needs in the period of significant decrease in demand and revenue. Outside of the government support programs, the structure of market lending did not change significantly in 2020 compared to 2019. Banks prefer to lend to more productive companies: we see that the volume of credits to high productive firms was at the same level as in 2019. This means that efficient firms that should be drivers of economic recovery did not have problems with access to credit in 2020.
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