Modern trends in social development transform environmental pollution risk into a fundamental category without which economic activity is impossible. In essence, the environmental risk indicator is on a par with such important economic categories as profit, cost, profitability, gross domestic or regional product, etc. At the same time, let us pay attention to a variety of approaches existing in the world practice for assessing environmental risks and their components (contamination likelihood and environmental harm) together with multifaceted specifics of economic entities activities. All these require a systemic consideration of methodological principles and methodological support, identifying the problems and areas in improving the applied calculation tools. The purpose of the study is to review the world experience in terms of methodology and methodological support of environmental risk assessment. The research methods incorporate environmental and economic analysis, content analysis, information modeling. The key findings of the study highlight the shortcomings and contradictions in understanding and assessing environmental risk, develop a broad systemological classification of the types and characteristics of environmental risks methodology, streamline and methodological support of these risks and show the directions to improve the assessment approaches. The scope of application encompasses environmental audit and expertise, eco-insurance, as well as regulatory documents of ministries (RF Ministry of Natural Resources and Environment, RF Ministry of Health, the Moscow Region Ministry of Ecology and Nature Management) and departments (Rostekhnadzor, Rosprirodnadzor, Rospotrebnadzor, Rosvodresursy, Rosleskhoz, Rosnedra, etc.), scientific and educational publications on environmental risk assessment.

The article reviews publications on the impact of epidemics, including the coronavirus pandemic, on economic development. The author first explores the mechanisms of such influence, including both direct and indirect effects. He then treats the channels of influence for direct effects — through the factors of the Solow model of productivity and the volume of production, demand and consumption, the volume of savings and investment; for indirect effects — through the influence on investors and consumers sentiment through media news, Internet, social networks. Drawing on the works focused on the coronavirus pandemic impact on markets, mainly financial markets, the authors summarize and structure the types of models used as well as dependent and independent variables. The findings contribute to identifying the ways of minimizing the pandemic negative impact on economies in general and financial markets in particular.

The article provides an empirical study of factors which make a significant impact on the yield spread of corporate bonds on Russia’s market. Unlike most studies based only on IPO data, the current study covers primary and secondary markets data of 2010–2019. The aim of the study is to test the method allowing to build a model capable of predicting the yield spread with maximum approximation to actual value. The author develops a regression model applying a cross-validation method, a procedure for empirical assessment of the model generalizing ability. An important prerequisite is the inclusion of a limited number of regressors with high explanatory power in the model, stability over time, and explicit economic interpretation. The paper confirms the key hypothesis on high degree of issuer's rating on the yield spread which is a new step in the study of the Russian market. The findings prove that the yield spread is determined mainly by the level of risk corresponding to the degree of the issuer's reliability. The issuer's industry affiliation, the size of the company, the MSCI stock index has also a significant impact on the spread. Among the advantages of the proposed model is its relative simplicity, explicit economic interpretation and stable response to various data which determine the practical significance of the work. The approaches to building and testing the model applied in this work can be useful in further studies aimed at developing predictive models of this class.

This article addresses the development and testing of ‘Return-Risk’ model aimed at designing the investment strategies suitable for practical needs. The model rests on fundamental principles of portfolio analysis and incorporates the following properties: expected return and risk are derived from historical data while learning sample size and time span are set by practice requirements in such a way as to maintain efficiency and regular monitoring of investment portfolio. The model tested on independent material (data of 2017 were not used for the model) shows: the suggested method of moving verification results in higher forecast accuracy for return and risk of investment portfolio and, consequently, in higher quality of investment decisions. The model annual yeild in moving verification mode is 25%, whereas S&P500 index shows only 15% of the annual gain, i.e. the ‘Return-Risk’ model significantly beats the market. The win-loss ratio of deals (time spans) is 11:1.

The issues of determining and assessing the effectiveness of national economic (macroeconomic) industries’ integration into global value chains (GVC) are of great importance in the context of globalization processes revision. The paper provides the criterion basis for assessing comprehensive national economic (macroeconomic) efficiency of high-tech industries participation in GVCs based on the comparison of indicators of downward (Backward GVC) and upward (Forward GVC) industry links in GVCs and the indicator of revealed comparative advantage in trade (RCA). The author proposes and substantiates the criterion of effective macroeconomic integration — a combination in the dynamics of positive RCA and the excess of positive Forward GVC over Backward GVC. Drawing on the analysis of the Trade in Value Added indicators of the OECD database, the author argues that with such combinations the high-tech industry expands in foreign markets due to the outstripping growth of technological conversions with high added value in its territory, which has a beneficial effect on the growth rate of the national value-added indicator in the gross export of manufacturing industry. This expresses the effectiveness of national economic structural shifts, which indicates an increase in high-tech redistributions on the country territory, aimed at exporting products of manufacturing industries. The study reveals the success of the country ‘import substitution’ integration model of high-tech industries into GVCs. The analysis concludes with empirical cases from Kazakhstan pharmaceutical industry and digitalization of chemical global TNCs business flows providing a valid criterion for effective macroeconomic integration in GVCs.

The article analyzes existing barriers for innovative entrepreneurship in Russian markets that raise obstacles to market efficiency. The purpose of the study is to rethink the role of the main barriers to creating competitive Russian startups, to identify the basic reasons for the failure of Russian new high-tech companies in the fields of information technology, biotechnology, medicine and pharmaceuticals, and to provide an insight in relative significance and sectoral characteristics of these factors. To identify the conditions and circumstances underlying unsuccessful start-up, the authors developed the methodology, conducted interviews with start-up participants, provided a comparative quantitative assessment and compiled the rating of existing barriers. All these made it possible to formulate the main reasons for Russian startups failures that relate primarily to product and market problems: difficulties in forming an adequate supply of in-demand goods and services, wrong choice of promotion strategy, limited marketing communications with a potential client audience, lack of sales channels, narrow target consumer segment. Other barriers are associated with the lack of financial resources (inadequate financial planning, errors in investment design, underestimation of costs), alongside managerial and organizational issues (building cohesive professional teams and lack of leadership qualities among managers). The findings show that to minimize the risk of adverse outcomes, executives and employees of high-tech startups should pay special attention to target markets, marketing and strategic management. The unique approach of the research, together with the identification and classification of relevant barriers, contributes to understanding the specifics of high-tech startups in preventing their collapse.

This article addresses current tools for assessing the level of dominance in industries (Concentration Index (CR), Lind Index (L), Hall-Tideman Index (HT), Herfindahl– Hirschman Index (HHI)), identifies the limitations of the existing toolkit, and proposes its modification based on the developed SV Matrix, which allows analyzing the competitive situation on different markets. Drawing on the RAEX ranking of 2020 for four industries (consulting, audit, outsourcing, information technology), the authors propose classical indices modification algorithms to conduct a comparative analysis of the dominant level on different markets. To conduct a strategic competitive analysis based on modified indices, the article proposes to use the SV Matrix, allowing to reflect the comparative dominant level for different markets, as well as market shares controlled by leading companies. The technique is tested on the sample of 90 Russian industries (according to 2020 official reports) and allows to identify the presence of dominant groups in 31 sectors, assess their size and inner differentiation that resulted in comparative analysis of industries. The SV Matrix expands the toolkit for strategic analysis, allowing to assess not only the competition level in the market and the presence of dominant players in the industry, but also the differentiation level for companies within the dominant group. Applying this matrix help analyze the characteristics of the market (both for the companies already in the market and those just planning to enter it) and draw conclusions regarding the strategic behavior of companies.

The article examines the directions in forming digital competencies of the personnel in big businesses in conditions of digitalization. Digital technologies influence labor processes and labor markets, raise the requirements for intellectual, creative approaches in solving situational production problems. The aim of the study is to develop a list of digital competencies necessary for large companies’ specialists performing various managerial and production functions characterized by multitasking in digital environment. Transformation of approaches to personnel management in conditions of technological advance is the new role of the individual, expansion of the potential, formation of new conditions and technologies for performing labor functions, flexibility and compromise in interaction with the employer. The methodological basis of the study are scientific and theoretical concepts of personnel management in conditions of digitalization, developed by national and foreign scholars and research teams. The empirical component rests on industry and production features of big businesses in Russia. The author identifies the trends in professional market expansion, the increasing role of competence approach to the development of human resources, the significance of digital competencies. Evidence from big businesses reveals the priority of individualization in training, the adjustment of acquired working skills in digital environment to the strategic and tactical needs of business. Effective interaction between an employee and the company implies the flexibility in mutual requirements and opportunities, striving for the end result, constant exchange of knowledge, skills and suggestions for improving the labor function. The findings offer the list of digital competencies necessary for big businesses’ specialists performing in novel digital environment. These competencies may lay the foundation of legal framework governing labor relations in narrow professional fields.

The paper examines labor market patterns in the light of digital economy development. The aim is to analyze labor market conjuncture in terms of registered unemployment dynamics. The degree of digital economy development is assessed through three indicators: the share of organizations using (1) the Internet, (2) local area networks, (3) number of personal computers per 100 employees. In addition to technological development indicators, the article also makes account of certain demographic factors. Since early 2000s, the degree of Internet penetration in organizations has doubled, while the number of computers increased threefold. The share of elderly population and life expectancy rate have increased by 5 percent and 7 years respectively. Drawing on econometric panel data models regarding federal districts in Russia for 2003–2019, the author provides the following findings. First, the proliferation of technological advances reduces unemployment rate. Second, an increase in the proportion of population above working age and life expectancy entails the reduction in unemployment rate. Third, combined effect of technological and demographic factors increases unemployment rate. The magnitude of the reduction exceeds the degree of increase in the unemployment rate due to technological advances. Thus, technological unemployment is more likely a theoretical possibility. Fourth, the determinants taken into consideration explain from 38 to 43 percent of unemployment rate change. The proposed approach can be applied in designing regulatory policies regarding employment with regard to the implementation of digital technologies in organizations and population aging in Russia’s regions.

The object of the research is human-oriented economy. The purpose of the study is to develop a model for the development of human-centered economy through its gradual complication. The author puts forward a hypothesis that the development of an hierarchy of objectively necessary human needs will provide a basis for identifying the structure of a new economic system with targets focused on gradual development of each economic entity and the economy as a whole. The study applies the methods of content analysis and the «Final information flow» method. The author first performs identification and cognition of qualitative characteristics of human-oriented economy based on the «Finite information flow» method using such parameters as logical level, logical limit and transformability. He then formulates the logical levels of human-centered economy development on the basis of human needs allocation, determines the presence of diverse structures within the framework of a humancentered economy, and presents human-oriented economy as a object gradually becoming more complex. The practical and theoretical findings of the study prove that the comprehension of human-centered economy through «Final information flow» method may serve as a basis for further research aimed at building a scientifically grounded theory of the phenomenon under study.

The paper analyzes political decisions in the field of personnel and educational policy as a response of four German administrations, with Angela Merkel as the Federal Chancellor, to challenges of digitalization. Since challenges in the development of labor market induced by demographic and migration factors are similar for Russia and Germany, German experience may be useful for Russia as a large federal state with significant risks of rising social inequality. Challenges in the development of the labor market induced by demographic and migration factors are common for Russia and Germany. The author addresses the key challenge for the German government — the organization of humane working conditions in a new digital reality presented in the White Paper “Work 4.0” after careful examination and consultation with main actors: associations, trade unions, companies, academia and civil society. He sees the solution to the problem in improving qualifications and developing digital skills, as well as ensuring attractive working conditions, in the first place for specialists in engineering, technical and natural science specialties (STEM). The analysis concludes with recommendations for policymakers which include such measures as improving gender equality in STEM professions and selective migration policy that mitigates unnecessary barriers for highly qualified immigrants.

The article contains a commentary on the publication of G. A. Maslov «Scientific and technological progress and the advantages of socialism: the evolution of Soviet Economic thought» (Bulletin of the Moscow State University, Series 6. Economics, No. 3, 2021). Against the current difficulties in implementing national projects (criticism of their implementation was voiced at SPIEF-2019 and continues today), the publication of materials praising the organization of scientific and technological progress during the construction of the material and technical base of communism seems a nostalgic appeal. The glorification of real achievements does not raise protest. What is unacceptable is to silence equally evident failures. Here we mean the resolution «On the development of computer hardware production» (#1180-420) adopted at the meeting of the Communist party Politburo and the USSR Council of Ministers on December 30, 1967, which entailed the destruction of the Soviet industry that produced computer equipment for civil needs. This decision resulted in the replacement of Soviet-made computers with obsolete models of the largest US companies. We are still trying to correct the effect of this strategic mistake, but to recognize and, most importantly, draw necessary lessons both from the wrong decision and the accompanying processes is vital for successful implementation of today's national projects, primarily the Digital Economy Agenda.