This article is based on the materials of the analytical report «Analysis of the results obtained on the basis of data from a sample observation of the employment of graduates in 2016–2020 who received secondary vocational and higher education».

The introductory part explains the relevance of improving statistical tools for studying the economic activity of young people at the present stage of the socio-economic development of Russia.

The body of the article introduces a brief description of the main methodological provisions of the observation, and also covers main differences between the second round of observation (VTR-2021) and the first round (VTR-2016). The paper presents results of the economic and statistical analysis of the employment of graduates according to the data of VTR-2021. As part of the analysis of educational migration, the article reveals the orientation of graduates to educational institutions within the borders of the subject of the Russian Federation they reside in, as well as the relationship between the level of education of graduates and the intensity of their mobility. The distance education «withers away» and the part-time education starts to play a special role as it allows one to combine study with work. There is an increase in the proportion of those who prefer to combine work and study. The authors highlight two main reasons for combining work and study: 1) the desire to have a personal income; 2) the desire to gain work experience in the future specialty. Based on the structural analysis, the conclusion is made about the dominance of various motives depending on the level of education.

It is shown that the main difficulties the graduates face when finding employment are the lack or insufficiency of work experience, low wages, as well as the lack or absence of suitable vacancies. It is noted that the acquired knowledge of employed graduates meets the requirements of employers and is sufficient to perform basic duties. The results of the assessment of retraining (overtraining) in the first year of work are reviewed.

Using mathematical and statistical methods, the authors made an attempt to estimate the losses in the domestic economy under the impact of the imposed sanctions. As a measurement tool is proposed a global vector autoregression model. In this model, the dynamics of GDP is considered as the main performance characteristic, and the rate of change in foreign trade turnover with key partners is used as the key prerequisites for the forecast. The use of this model building scheme is explained by the fact that the economic sanctions imposed on Russia have a negative impact on domestic macroeconomic indicators, and this happens through a variety of channels, key ones being the restriction of external demand and supply, leading to a decrease in trade turnover with the main trading partners, and change in oil revenues. This approach takes into account the relationship between the economies of different countries and their dependence on global markets. To obtain quantitative estimates, conditional forecasts are used as part of scenario analysis.

According to the authors’ approach the losses from economic sanctions are measured by the decrease in the volume of Russia's foreign trade with key foreign trade partners, as well as by the discount for Russian oil compared to its global price. In addition to the forecast estimates, the validity of the approach is proofed with the forecast for the crisis period of 2014–2015 as an example. Based on the analysis of the current situation in foreign trade, Russian GDP in 2022 in the baseline scenario may fall by 5.4%, but with some reorientation of trade a decrease may be around 3.7%.

The system of statistical indicators, which is necessary for the construction of mathematical and statistical models that reflect modern domestic trends in the development of the residential real estate market is explained. The official data from the Federal State Statistics Service (Rosstat), the Unified Interdepartmental Information and Statistical System (EMISS), the Central Bank of the Russian Federation (CBR), and the Unified Housing Construction Information System (UIIS) served as information sources for the empirical component of the study.

Based on quarterly data for 2010–2021 using ARIMA and SARIMA models, a time series of residential real estate commissions in the Russian Federation was modeled and predicted for 2022. Both models make it possible to account for the influence of the seasonal component. Based on results of the time series regression analysis, the authors selected a mathematical and statistical model with the best approximating characteristics. To model the volume of commissioning of residential real estate in the Russian market, with due regard to the influence of macroeconomic factors, the ARMAX model was used, which has significant explanatory power.

The results of the study presented in the article may be of interest to analytical agencies, developers, banking professionals, financiers, economists, analysts of the real estate market or related areas, as well as authorities for strategic planning of the development of the real estate market.

This article aims to investigate economic factors that determine the mortality rate from cardiovascular diseases in Russian regions. Independent variables are socio-economic factors, including the level of well-being, characteristics of the health care system in the region, level of alcohol consumption as a characteristic of the lifestyle of the population, as well as the directions of state policy for prevention and treatment of cardiovascular diseases, and alcohol and tobacco control initiatives. These factors are the focus of several social programs currently being implemented in Russia. The objective of this study is to identify the degree of sensitivity of the mortality rate from circulatory system diseases to changes in each of the factors considered. This will help to guide public policies aimed at improving population health.

The empirical component of the study is based on the data from the Federal State Statistics Service of Russia (for the period from 2005 to 2019), as well as regional budgets and territorial compulsory health insurance funds. Panel data model with fixed effects was evaluated, and the elasticity of mortality from circulatory system diseases to changes in the formed set of factors. The paper shows that an increase in health care costs leads to a decrease in mortality from circulatory system diseases. With that, the sensitivity of this indicator to a one percent change in factors of well-being and lifestyle, including the volume of alcohol consumption, is higher than the sensitivity to a one percent change in health care expenditures. The article revealed the presence of a statistically significant relationship between changes in the legislation, which led to a significant increase in the availability of expensive medical care, and a decrease in mortality from circulatory system diseases. The findings are relevant for those reforming the health care system and can serve as a guideline for developing parameters of government healthcare programs.

The article focuses the attention of the civil and professional scientific community on the content, features, and terrible risks of modern mental warfare, conducted using methods and techniques of information and statistical impact on both individual socio-demographic groups and the general population.

The author covers the role, place, and possible use of statistics and statistical data in the process of implementing mental special operations aimed at changing the worldview, breaking down traditional values, and destroying national sovereignty of actual and potential enemies without the use of conventional weapons. The article concludes that there is a need to provide mental security, especially for the younger generations, in which the critical role belongs to the further development of different levels of statistical education and the improvement of statistical literacy of the population, forming the prerequisites for successfully combatting information sabotage aimed at destroying the historical bonds and traditional values of the Russian society.

The relevance of international comparisons of the level of digitalization of countries based on rating assessments is argued, due to the lack of uniformity in the methods and results of using information and communication technologies. The information base of the study is data from Rosstat and Eurostat on a number of digitalization variables for the period 2016–2020. The methodological basis of the analysis is the principal component method and k-means cluster analysis. The importance of using various statistical tools to assess the scale of digital inequality in the context of countries and regions is argued.

Scientific and methodological approaches to studying the problem of digital inequality were generalized. Three levels of digital gaps and statistical indicators reflecting them were allocated. The role of indices as generalizing indicators in assessing the level of digitalization of the economy and society was determined. A rating of European countries was built (in descending order of the calculated digitalization index). A grouping of countries that differ in the level of digital development is proposed, estimates of digital gaps are given.

The results of the statistical analysis showed that the leaders of the rating are Finland, Denmark, and Norway; outsiders are Greece, Bulgaria, Romania. All these countries occupy a stable position in the ranking. Three clusters of countries were formed in terms of the level (high, medium, low) of digitalization of the economy. It is shown that the digital gaps between European countries fluctuate in the range of 42–44%. Russia's place in the ranking of 20 countries in the period from 2016 to 2020 was determined.

The objective of the study, selected results of which are presented in the article, is to find the main determinants of life expectancy (LE) using regression analysis methods for four groups of countries classified by income. The criterion for assigning the country to the income group is the indicator of GDP per capita. The paper identifies four income groups: the so-called poor countries, upper middle- and lower middle-income countries, and wealthy countries. The first part of the article analyzes theoretical and applied research in health care, demography, and the relationship of life expectancy with economic development. At the same time, special attention is paid to scientific publications that address the impact on macroeconomic growth in individual countries of such factors as the development of healthcare systems, environmental factors, and life expectancy.

The second part of the article highlights issues of testing empirical hypotheses about the direction of the influence of certain groups of factors on life expectancy and the degree of their influence on different income groups of countries using data from the World Bank and the World Health Organization. The paper uses panel data for 2011–2019, based on which regression models are built for each income group of countries, taking into account time lags to correct for endogeneity.

The results of the study show the importance of both health system characteristics and socioeconomic factors in most income groups. However, it turns out that the influence of individual factors on life expectancy differs depending on the value of GDP per capita of the country. Thus, for poor countries with low life expectancy, the problems of food availability, health care costs, the share of Internet users, unemployment, and population density are significant. At the same time, in middle-income countries, life expectancy is influenced not only by the above factors but also by the proportion of the urban population, the prevalence of tobacco, the number of hospital beds, and carbon dioxide emissions. For wealthy countries, however, bad habits (both the prevalence of tobacco and alcohol consumption) that are commonly called "diseases of civilization", turn out to be especially significant.

The study aimed to analyze the impact of several economic and social factors on the course of the COVID-19 pandemic by comparing statistical data in large and representative samples and to assess the critical factors influencing the development of the infectious process of the SARS-CoV-2 virus.

To create this review, the necessary publications were found on the Internet for the selected keywords both in one tag and different tag combinations. Statistics of economic and social factors were based on data available on the Internet. The Pearson correlation coefficient (r) to determine the statistical relationship was used.

The relationship between economic or social factors and the impact of the 15-month COVID-19 pandemic in different regions was investigated using various available statistics for five continents and 52 countries for the first time. A positive relationship between the consequences of viral epidemic and GDP per capita or the type of human diet was found with correlation coefficients in the range of 0.42–0.87. The development of the viral epidemic showed a less clear correlation with population density from r = -0.18 to r = -0.28, depending on the selected group of countries. For island nations, geographic isolation was the dominant defense against SARS-CoV-2 infection.

The comparison of the development of COVID-19 according to statistical data in different regions and the study of economic or social aspects, performed on large representative samples, showed that the productive infection and pathogenicity of SARS-CoV-2 increased with a high standard of living and excessive consumption of staple foods. In countries with low GDP and adequate protein or fat intake, the rates of SARS-CoV-2 infection, and death did not exceed the minimum epidemic threshold. The study of the influence of consumed macronutrients on the dynamics of the infectious cycle of the SARS-CoV-2 virus will help explain the reason for such resistance to the pathogen. Such a study would require further comparative analysis of COVID-19 pandemic statistics.