Stress Index of the Tax System of the Russian Federation in Terms of Tax Revenues

Economic systems are increasingly exposed to external shocks of various naturewhich test their resilience. The tax system, which is directly linked to the level of business activity, is one of the first to experience stress, so the ways it reacts to shock are of particular research interest. The 2020–2021 coronavirus pandemic made such studies more acute. The purpose of the paper is to develop and test new approaches in studying the resilience of the tax system in terms of tax revenues by analyzing the dynamics and structure of the tax system stress index in the Russian Federation in pre-pandemic, pandemic and recovery periods. The tax system stress index for tax revenues is calculated as the difference between the moving standard deviation and the moving average growth rate of tax revenues. We have developed a method for decomposing the stress index by source with determining the contribution of each tax to the average growth rate and to the standard deviation of the growth rate. We have also calculated the Russian Federation tax revenue stress index from December 2015 to March 2022 and identified its sources. It was found that the stress indices for almost all taxes (except for excises and state duties) are significantly positively correlated with each other. The main contribution to the growth of the stress index during the crisis and its decline within the recovery period is made by profit tax and a group of taxes on natural rent, which significantly negatively correlate with oil pricesUnder the pandemic crisis in Russia, the stress index on revenue form special tax regimes also increased significantly. It was found that the personal income tax has a stabilizing effect on the tax system stress index in the crisis and post-crisis periods. During the pandemic in Russia, the damping role of excises also came to the fore, which is explained by institutional factors and changes in tax rates. The research findings can be advantageous for the authorities to make an impact on the most vulnerable components of the tax system of the Russian Federation in order to increase its resilience to crises. © Malkina M.Yu., Balakin R.V., 2022.

On the effectiveness of COVID-19 restrictions and lockdowns: Pan metron ariston.

BACKGROUND: Early evaluations of the effectiveness of non-pharmaceutical intervention (NPI) mandates were constrained by the lack of empirical data, thereby also limiting model sophistication (e.g., models did not take into account the endogeneity of key variables). METHODS: Observational analysis using a behavioral four-equation structural model that accounts for the endogeneity of many variables and correlated unobservable country characteristics. The dataset includes information from 132 countries from February 15, 2020, to April 14, 2021, with data on confirmed cases and deaths, mobility, vaccination and testing rates, and NPI stringency. The main outcomes of interest are the growth rates of confirmed cases and deaths. RESULTS: There were strongly decreasing returns to more stringent NPI mandates. No additional impact was found for NPI mandates beyond a Stringency Index range of 51-60 for cases and 41-50 for deaths. A nonrestrictive policy of extensive and open testing constituted 51% [27% to 76%] of the impact on pandemic dynamics of the optimal NPIs. Reductions in mobility were found to increase, not decrease, both case [Formula: see text] and death growth rates [Formula: see text]. More stringent restrictions on gatherings and international movement were found to be effective. Governments conditioned policy choices on recent pandemic dynamics, and were found to be more hesitant in de-escalating NPIs than they were in imposing them. CONCLUSION: At least 90% of the maximum effectiveness of NPI mandates is attainable with interventions associated with a Stringency Index in the range of 31-40, which impose minimal negative social externalities. This was significantly less than the average stringency level of implemented policies around the world during the same time period.

A Study on Impact of International Container Vessels during and Post Pandemic

The international trade is totally relying on shipping, more than 90% of the international trade is dependent on shipping. The dependence of shipping in the international trade is due to its low-cost transportation. The container ships are more popular in the international trade, as the containers are easy to handle at ports, reducing the turnaround time for ship, reducing the dwell time of cargo, containerization provides better security for the cargo, also used in multimodal and intermodal transportation and also facilitating liner trade. The outbreak of COVID virus has put the entire world shipping industry in mess, causing great havoc to all economies. This paper analysis the impact of COVID on the traffic of international container vessels and also, how the impact has affected the growth of world container port throughput. If the world container port throughput is affected, then the traffic of container vessels is also affected. The traffic of the world container port throughput is linked with number of container ships around the globe and with container ships in dead weight. The Study is analysed, with 10 years’ data (2011-2020) taken from secondary sources. The data is analysed using DEA-Efficient Frontier. The efficient frontier signifies an efficiency mark in the entire set of decision-making units (DMU). Each year is considered as a decision-making unit, to find out the efficiency year-on-year, in the first model comparison was made keeping world container port throughput as output (O) and number of ships globally is assigned as input (I). In the second model efficiency was compared with world container port throughput as output (O) and container ships dead weight as input (I). The Relative efficiency is also calculated, that is, by dividing efficiency of DMU’s by the best efficient DMU. Before applying DEA, it is ascertained that the world container port throughput is correlated with number of container ships around the globe and similarly world container port throughput is correlated with container ships in dead weight. To study the impact of growth of world container port throughput, compound annual growth rate (CAGR) is calculated on a year-on-year basis. The traffic of the world container port throughput has declined in 2020, the efficiency and the relative efficiency calculated also shows a downward trend. The compound annual growth rate also shows a negative growth in 2020. This reflects that the world container port throughput has declined in 2020, which means the traffic of container vessels throughout the word has suffered enormously, essentially due to the impact of COVID virus. © 2021 21st Annual General Assembly, IAMU AGA 2021 - Proceedings of the International Association of Maritime Universities ,IAMU Conference. All rights reserved.

Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis.

AIMS: By associating features with orthonormal bases, we analyse the values of the extracted features for the daily biweekly growth rates of COVID-19 confirmed cases and deaths on national and continental levels. METHODS: By adopting the concept of Fourier coefficients, we analyse the inner products with respect to temporal and spatial frequencies on national and continental levels. The input data are the global time series data with 117 countries over 109 days on a national level; and 6 continents over 447 days on a continental level. Next, we calculate the Euclidean distance matrices and their average variabilities, which measure the average discrepancy between one feature vector and all others. Then we analyse the temporal and spatial variabilities on a national level. By calculating the temporal inner products on a continental level, we derive and analyse the similarities between the continents. RESULTS: On the national level, the daily biweekly growth rates bear higher similarities in the time dimension than the ones in the space dimension. Furthermore, there exists a strong concurrency between the features for biweekly growth rates of cases and deaths. As far as the trends of the features are concerned, the features are stabler on the continental level, and less predictive on the national level. In addition, there are very high similarities between all the continents, except Asia. CONCLUSIONS: The features for daily biweekly growth rates of cases and deaths are extracted via orthonormal frequencies. By tracking the inner products for the input data and the orthonormal features, we could decompose the evolutionary results of COVID-19 into some fundamental frequencies. Though the frequency-based techniques are applied, the interpretation of the features should resort to other methods. By analysing the spectrum of the frequencies, we reveal hidden patterns of the COVID-19 pandemic. This would provide some preliminary research merits for further insightful investigations. It could also be used to predict future trends of daily biweekly growth rates of COVID-19 cases and deaths.