The attitude to statistics, to the published data at all times was difficult, regardless of the type of state, socio-economic situation, etc. There are always diametrically opposed groups – those who defend and those who accuse statistics of the accuracy or distortion of data. Statistics cannot accurately reflect reality, because even during the period of data calculation, products can be produced, people can be born or die, sales increase or decrease, etc. And, as life shows, the less state bodies interfere with the statistical methodology and results, the higher the quality of statistical indicators. We invite scientists, employees of Rosstat, all persons interested in the development of statistics to take part in the discussion of the place of statistics in our life.

Purpose of the study. Analysis of inflationary factors associated with the labor market and employment is usually limited to the study of the relationship between the consumer price index and the Phillips curve. Therefore, the study examines the potential impact of a wider range of labor and employment market indicators on inflationary processes in the Russian economy. The purpose of the paper is to identify and assess the links between unemployment, on the one hand, and indicators of the labor market, employment, and incomes of the population in the national economy of Russia.

Materials and methods. The study used the author’s hypothesis about the possibility of influ-encing inflation not only by unemployment, but also by other indicators of the labor market, such as the share of informal employment or the average working hours per week. The research also studied the impact of the labor market not only on the consumer price index, but also on the basic consumer price index (cleared of the influence of seasonal and administrative factors). The monthly data of the Federal State Statistics Service of the Russian Federation for 2016-2020 were used in Russia as a whole. We useda standard apparatus for searching and measuring cause-and-effect relationships (ma-trices of paired correlation coefficients, regression analysis).

Results. In the short term, the level of labor force participation and economic activity have a positive relationship with inflation, as they are even lower than the level that could cause inflationary pressure (according to the second order polynomial). In 2017-2018 inflation was positively influ-enced by the size of the nominal accrued wages and the average number of hours worked per week. The traditional impact of the population income and aggregate demand on inflation has manifested itself. But it was insignificant (up to 10 % of inflation variance). This effect occurs only in those years when there are no more powerful inflationary factors. Consequently, cost inflation was fairly limited. In the short term, in some years, there is also a certain positive relationship between the share of people employed in the informal sector and the consumer price index. The rise in the infla-tionary tax on businesses without market power is forcing the majority of workers to be hired infor-mally. In the long term, an increase in the level of labor force participation explains part of the vari-ance of the basic consumer price index (but not related to the general consumer price index). With an increase in economic activity and income, the population acquires a wider range of goods, prices for which are not seasonal and are not administratively regulated.

Conclusion. In general, the factors of the labor market and the population’s income are not de-cisive for inflation in the Russian economy, but they explain some of the changes. In the future, it is possible to build more accurate models in which indicators such as the level of labor supply can take a certain place next to the main inflationary factors. The findings of the study can be used when mak-ing decisions in the field of labor market regulation in conjunction with monetary policy.

Factoring is a fairly new way for Russia to finance the accounts payable and receivable of business structures by specialized companies or credit institutions and their divisions (Factors) against the assignment of claims against one of the parties (buyer or seller) of a sale and purchase transaction of products or property to a third party (Factor) is currently developing at a high pace. On average, according to sample data for 2011 -2019 the annual growth rate of the volumes of financing of accounts payable and receivable of companies in Russia due to factoring operations increased by 20%, which in absolute terms amounted to 303.3 billion rubles, and reached by 2019 - 3.5 trillion rubles.

However, there is still no established definition of this economic category in the Civil Code of the Russian Federation. In the scientific and educational foreign and domestic literature there are somewhat contradictory interpretations of the classifications of the types of factoring, there is no legislatively established system of statistical indicators characterizing this segment of the financial intermediation services market. The lack of a developed regulatory framework for regulating relations in this market segment slows down its development, necessitates improving both Russian legislation and methodological support for a comprehensive statistical analysis of the state and development of this market segment. In this regard, the purpose of this study is to develop a methodology for a comprehensive statistical analysis of the market segment of financial intermediation services, to determine the prevailing sectoral, territorial and types of proportions related to the scale of business of the parties to the factoring agreement in this market segment using the statistical methodology for analyzing the series of dynamics and attributive groupings of the main indicators characterizing the state and development of the Russian factoring market. Based on the theoretical analysis, the author’s interpretation of the content of the economic category “factoring” is given, a system of indicators is proposed that characterizes factoring as a type of financial intermediation services (object of research), based on the development of the Association of factoring companies, Rosstat, expert agencies (for example, “Expert RA”); the features of the formation of statistical groupings (series: attributive, variation, dynamics) for various purposes of analyzing the market of factoring services are revealed, the author’s method of complex statistical analysis of any segment of the financial intermediation services market, which is the subject of research, is presented. The results of approbation of the methodology for a comprehensive statistical analysis of the Russian factoring market based on sample data for 2011 – 2019 are presented, conclusions are drawn about the dynamics of the main indicators of the factoring market development, structural shifts and changes in proportions in this market segment, a forecast of expected changes in the Russian factoring market for  2021 is made, incl. and influenced by the Covid-19 pandemic.

The results of this study are aimed at developing a methodology for a comprehensive statistical analysis of factoring as a segment of the financial intermediation services market, including for the purposes of international comparisons of indicators of the state and development of the Factors and their clients.

They can be useful to the professional community of factoring companies (Factors), business structures - consumers of factoring services, and also be used in educational activities in the preparation of financial specialists in economic universities of the country.

The paper presents method of clustering and grouping the university performance indicators to prepare an integral rating of Russian regions by the level of university involvement in innovative development of Russian regions. The following problems that require the management influence of state structures are considered: the role of the research base of regional universities in strengthening the innovative potential of regions, the degree of involvement of universities in the innovative regional space.

The aim of the study is to develop a rating system for Russian regions according to the degree of university involvement in innovative development using mathematical tools and an intelligent data processing system. The main hypothesis of the article is the existence of a link between regional innovative development and the effectiveness of university contributions. The mathematical approach involves the consideration and multidimensional ranking of the main groups of university performance indicators in order to obtain cluster classifications within each of the problems posed. The solution to the problems is to build a barometer of innovation activity in the form of a multi-purpose problem-oriented rating.

Materials and methods. An approach is applied that includes the assessment and multidimensional ranking of the contribution of universities using groups of integral indices created on the basis of aggregation of several important indicators of the university’s activity. Computational experiments were conducted according to the data of the Ministry of Education and Science for the regions of Russia and regional universities. 16 indicators of Russian regions for 2016 are considered.

Results and discussion. The ranking and comparison of universities according to the degree of involvement in the innovative development of the region was performed using an indicator model and aggregation depending on the target problem. Quantitative indicators of the quality of university’s activities by regions are obtained, which allow us to obtain an integral rating of Russian regions by the level of university involvement. Unlike other approaches, the author’s method includes three components, which are independent integral indexes and indicate the level of involvement of universities in regional innovative development. Statistical data on the leading indicators of Russian universities for 2016 were processed. The methodology Science-model with three factors - the model with regrouping, named by the author A-B-C, showed the high potential of Russian universities to balance their regional demand as management research centers.

Conclusion. The results of the study are compared with the ratings of the well-known agencies. The author hopes that soon Russia will have a reliable scientometric system at the level of rating universities on their involvement in the innovative development of Russia, such a rating will be an indisputable argument in favor of financing regional universities. The author laid down a high requirement: compliance with the three models, only in this case, regional universities can receive funding from the municipality, after redistribution from the center. At the same time, it is necessary to carefully choose universities in which projects will receive development and perspective. Regional authorities must meet the requirements to receive the necessary investments in promising projects. The scientific potential and demand for theoretical research for their full application at all enterprises, the combination of theoretical science and practical implementation will reduce the cost of stabilizing outdated technologies in all areas of knowledge and use the experience of older generations and the strength of young people for high-tech production growth in Russia. Therefore, the results of the study will be useful to federal authorities and financial and credit organizations that provide financing.

Purpose of the study. On the basis of the construction of a multifactorial econometric model, it is necessary to identify the factors of income differentiation of the population. In accordance with the goal, the following tasks are set: 1) to propose a typology of factors of household income differentiation; 2) on the basis of correlation analysis, to assess the closeness of the relationship between the average income of the population and those statistical indicators that maximally reflect the level of formation, the content and nature of the factors’ influence of household income differentiation; 3) using a step-by-step regression analysis algorithm to construct an econometric model to quantify the relationship between the factors of income differentiation and the income of the population.

Materials and methods. In the process of preparing the article, the authors used information from the website of the Federal State Statistics Service, analytical statistical materials, scientific works of Russian and foreign scientists. The following methods were used in the paper: system analysis method (to develop a typology of factors for differentiating household income); the method of economic and mathematical modeling (when building an econometric model to quantify the relationship between the factors of income differentiation and the income of the population).

Results. The classification of the factors of differentiation of household incomes was carried out according to three criteria: the level of formation, the content and nature of the influence of the factors. Four groups of statistical indicators have been formed, which, to the maximum extent, are the essence of the factors of income differentiation. An analysis of the correlation coefficients indicates a close relationship between the average income of the population of the Russian Federation regions and the overwhelming majority of statistical indicators. Assessment of the statistical significance of the regression coefficients made it possible to identify those indicators with which the indicator of the average income of the population has a significant quantitative dependence, namely: retail trade turnover per capita; the volume of personal services per capita; average monthly nominal accrued wages; the value of the subsistence minimum. This made it possible to build a four-factor econometric model.

Conclusion. A typology of factors of household incomes’ differentiation is proposed, which combines such classification features as: the level of formation, the content and nature of the influence of factors. Those statistical indicators that reflect to the maximum extent the level of formation, content and nature of the influence of the previously considered factors of income differentiation on the level of income of the population are selected and grouped according to the corresponding criterion. Based on the correlation analysis, an assessment of the closeness of the relationship between the average income of the population and statistical indicators reflecting the factors of income differentiation was carried out. Using the algorithm of stepby-step regression analysis, a multivariate econometric model was built, which made it possible to identify a quantitative relationship between the factors of income differentiation and the average income of the population.

Analysis of the use of information and communication technologies in organizations makes it possible to determine the speed and intensity of the digital transformation of the economy. In previous studies, specialists evaluated the dynamics of ICT use in organizations using a small range of indicators in the short term. In this study, the analysis of the dynamics of the use of ICT in organizations was carried out for a wide range of indicators in the long term. Based on this approach, I have identified the patterns of digital transformation and the stage of digital maturity of the economy.

Purpose of the study. The purpose of this study is to analyze trends in the digital transformation of the Russian economy. To achieve this goal, I calculated the indicators of the speed and intensity of the level of ICT use in the activities of organizations for certain types of technologies.

Materials and methods. I analyzed the dynamics of indicators that characterize: the use of Internet access, business process automation systems, websites and EDI systems, and e-commerce. To analyze the dynamics, I used information from surveys of the Federal State Statistics Service. Indicators are presented in annual frequency for 2005-2019 for the Russian Federation.

Results. I found that the peculiarity of the current stage of digital maturity of organizations lies in reaching the limits of the spread of the Internet. For 2010-2019 the average annual growth rate of the share of organizations using the Internet was 1.1%. One of the main directions of digital transformation is the automation of the organization’s business processes. However, the potential for automation has been unevenly realized in a number of areas. The share of organizations using CRM, ERP, SCM - systems increased 2.7 times in 2010-2019, to 20.5%. At the same time, the share of organizations using electronic document management increased by 13% over the same period, to 70% in 2019. At the current stage of digital maturity, organizations are showing a high level of participation in e-commerce. The increase in the share of organizations placing orders for goods and services was 23.7%, and the increase in the share of organizations that make settlements with suppliers of ordered goods and services - up to 93%. Also digital economic activity has expanded, the value of data has increased, and this has been reflected in the use of the Internet for the exchange of electronic products.

Conclusion. The results of the analysis can be used in studies on modeling and forecasting trends in digital transformation. The findings of the study on trends in the use of ICT in organizations can be useful for a deeper and more comprehensive study of the nature of the dynamics and patterns of development of digital transformation.