Relevance. COVID-19 is an extremely dangerous disease that not only spreads quickly, but is also characterized by a high mortality rate. Therefore, prediction of the number of deaths from the new coronavirus is an urgent task. Research objective. The aim of the study is to provide a more accurate estimate of the real number of coronavirus-related deaths in Russian regions. Data and methods. The main research method is econometric modeling. Comparison of various data was also applied. The authors’ calculations were based on Rosstat data, the data of the World Bank and specialized sites with coronavirus statistics in Russia and in the world. Results. We identified the factors affecting the COVID-19 mortality rates in various countries were identified, assessed how much the official Russian statistics underestimated mortality in Russian regions, and provided predictive estimates of mortality as a result of the pandemic. We also determined the number of additional coronavirus-induced deaths. Conclusions. The official data on COVID-19 mortality in Russia underestimate the actual numbers more than twofold. The number of direct and indirect victims of the pandemic in Russia at the end of July was approximately 43 thousand people.
This study’s relevance lies in the need to assess the role of socioeconomic, medical, and demographic factors on working-age population mortality in Russia. The purpose of this study is to substantiate the methodological tools for the assessment of the partial contribution of the most important factors that determine the dynamics of the mortality of the working-age population. Our hypothesis is that the factors determining the socioeconomic situation in the country affect the level and dynamics of mortality of the working-age population, but to a different extent in each separate period. To analyse the impact of the factors, we used official Rosstat data for the period from 2005 to 2021. We used the data that reflect the dynamics of socioeconomic and demographic indicators, including the dynamics of mortality of the working-age population in Russia as a whole and in its 85 regions. First, we selected 52 indicators of socioeconomic development and then grouped them into four factor blocks (working conditions, health care, life security, living standards). To reduce the level of statistical noise, we carried out a correlation analysis, which allowed us to narrow down the list to 15 key indicators with the strongest association with the mortality rate of the working-age population. The total period of 2005–2021 was divided into five segments of 3–4 years each, characterising the picture of the socioeconomic state of the country during the period under consideration. The socioeconomic approach used in the study made it possible to assess the extent to which the mortality rate was influenced by the indicators adopted for analysis. The results of this study show that over the whole period, life security (48%) and working conditions (29%) contributed most to the level and dynamics of mortality in the working-age population, while factors determining living standards and the state of the healthcare system accounted for much smaller shares (14% and 9%, respectively). The methodological apparatus of this study is based on the application of methods of machine learning and intelligent data analysis, which allowed us to identify the main factors and their share in the total influence on the mortality rate of the working-age population. The results of this study show the need to monitor the impact of socioeconomic factors on the dynamics and mortality rate of the working-age population in order to improve the effectiveness of social programme. When developing and adjusting government programmes to reduce mortality in the working-age population, the degree of influence of these factors should be taken into account.
The article analyses static data that show the deterioration of the situation of SMEs around the world, both in developed and developing countries. Measures taken by governments to support SMEs and overcome the crisis were considered in the analysis. In most cases, they turned out to be insufficient to help reach the pre-pandemic economic level. The article also confirms that the self-employed and female entrepreneurs have suffered most. Industries such as tourism, construction industry, wholesale and retail trade, real estate, and services have suffered major loss.
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