Medical insurance is critical for state labour efficiency. In many countries (including in the United States of America), it is tightly connected to labour, which makes workers have valid insurance policies for free and constant access to medical aid. That strongly secures workers’ health and their high performance. In state-supporting insurance cases, citizens have a common access to medical services (regardless of their employment type). Here, people can be provided with medical aid without worrying about any prices, which keeps their strong health and high productivity skills. Within employment-related medical insurance, it is employers who are fully responsible for their employees’ insurance. As a tangible financial business burden, it may keep workers close to their employment place itself: if resigned, they can lose good medical insurance at all. The medical insurance system is a key and decisive factor to raise labour efficiency. To achieve and secure it, governments should permanently develop affordable and reliable insurance systems. In our research, we chose the following indexes: coverage of state and private insurances, labour efficiency levels, national employment levels, life expectancy, healthcare costs (% of gross domestic product), healthcare costs by volume. We conducted the given study via data normalisation and regression modelling (backward data selection). We applied Multivariate Adaptive Regression Splines (MARS) as a regression-based method to describe non-linear variable relations. Among our engaged methods, there were also bibliography analysis, data processing, systematisation, comparison and logical generalisation. The current research results are relevant for politics and business. Politicians may use them in developing social-economic principles to improve medical insurance and labour efficiency. Enterprises can involve such information to define medical insurance payments for the health and labour efficiency increase among all types of employees in any countries.

The global reduction of carbon dioxide emissions is one of the critical priorities for implementing the Sustainable Development Goals by 2030 and the Paris Agreement 2015. Therefore, it stimulates and increases the ability of countries to implement green imperatives in policies to force the anthropogenic environment, reduce use of fossil fuels, and simultaneously develop alternative energy. Thus, it is crucial to understand the impact of renewable energy development on the dynamic of CO2 pollution. Countries can increase or decrease the development of renewable energy depending on the effectiveness of its impact on the level of CO2 pollution. This paper aims to analyze the influence of the growth dynamics of renewable energy production in countries on CO2 emissions. The article uses Ward’s method to test the research hypothesis. Empirical results allowed us to conclude the interdependence of renewable energy production and CO2 emissions. The results indicate a strong relationship between the level of renewable energy production and carbon emissions in countries. For the global development of renewable energy technologies, governments must understand their impact on changing the scale of environmental pollution and expand the awareness of state leadership, the business sector, and society.

Data clustering is one of the most popular methods of search based on machine learning in the blind, the similarity of statistics in one data cluster and at the same time the differences in data in other data clusters. The use of this method is due to the amount of statistics used in the research process and the high speed of such analysis. Finding similar countries by type of development will make it possible to identify those statistics that give intra-cluster similarity of data, the difference between the data between clusters. The aim of the study is to find similar groups of countries that can be attributed to each other and determine the strength of the impact of each statistical indicator on the creation of a group of countries. The research methodology is based on the use of open-source data analysis techniques using software such as Statgraphics Centurion and Microsoft Excel. The research used methods of comparative analysis, systematization, logical generalization, bibliometric analysis (using ScientoPy tools), cluster and discriminant analysis (using Statgraphics Centurion tools). Results. Work on the analysis of recent publications on cluster analysis methods according to the Scopus scientometric database has generated a cloud of keywords that help to see the scope of cluster analysis methods in the scientific world. Analyzed the data of Human Development Index (HDI) statistical databases and took the 10 most relevant indicators in the opinion of the authors. The required number of clusters for the data was identified using the Sturgess formula. Lists of countries included in each cluster have been created. The determined regression formula of the discriminant analysis function with its help is determined in the influence of each indicator on the created data cluster. The significance of discriminant functions is substantiated by Lambd Wilks and the significance level of P-value calculated using the Statgraphics Centurion toolkit. The results of cluster distribution can be used in the process of state development to find the optimal static values to which the development of the state should be directed. To make the transition of underdeveloped countries to more developed groups. The obtained data will be used for further in-depth analysis of data and finding new patterns in the development of the world.

This paper summarizes the arguments and counterarguments within the scientific discussion on defining the essence of health as an economic category. Systematization of the scientific works to defining health as an economic category requires a clear formation and a detailed description of the health determinants. For achieving the research goal, the study was carried out in the following logical sequence: 1) defining the general research problem; 2) theoretical analysis on the relevant publications; 3) classifying the health determinants by item functioning content; 4) defining areas of public policy concerning the investigated issue; 5) determining the global changes due to COVID-19 impact. The methodological base of this study was the methods of systematization, comparison, structural analysis, logical generalization, and bibliometric analysis. The study involved the VOSviewer 1.6.15 software in visualizing the obtained results. The study sample consists of 610 documents indexed in the Scopus database from 2010 to May 2021. The paper presents the classification of the health determinants by the item functioning content as follows: 1) medical and demographic orientation determinants; 2) morbidity determinants, their composition, a list of the number of appeals to medical institutions, reports of medical examinations; 3) disability determinants; 4) determinants of physical development such as somatometric (average height, weight, chest volume, geometric shape of organs and body tissues), somatotopic (geometry of the spine, legs, arms, skeletal development, etc.), and psychometric (arm strength, respiratory rate, blood pressure, etc.); 5) determinants of natural population movement (births, deaths, natural population growth, life expectancy). The authors noted that quantitative determinants mentioned above allowed determining in detail and systematically the level of health as an economic category. The findings showed that the global COVID-19 pandemic changes the legislation support of health care. Besides, the healthcare guarantees program ensures transparent government support in the medical sector of Ukraine. The authors indicated that using an «Analytical panels» (dashboards) service on the website of the National Health Service allowed the analytical data processing on medical costs. The authors highlighted the improvement in healthcare institutions’ performance and financial transparency. The obtained results could be helpful for scientists and students interested in this research issue.

Today, humanity has one of the oldest unresolved problems – human inequality. It is precisely because of inequality in the modern world that a sharp differentiation is possible, such that people can live below the poverty line and not have the opportunity to change the situation, when on the other side there are people who are always enriched. Inequality is caused by such systems as the labor market, education and its accessibility, health and life expectancy, and the environment. The strongest negative effects of human inequality can be seen in terms of people's health status, life expectancy, economic and social well-being, and social mobility. The purpose of the study is to form a feature space of determinants that determine and influence the value of the indicator of human inequality, as well as to develop a statistically significant regression model for an in-depth analysis of the degree of influence of socio-economic factors, health determinants on the value of the effective indicator of human inequality, to identify opportunities for reducing gaps in the values of the effective indicator in the context of Research countries. The research methodology is based on a meaningful and logical generalization of the essence of socio-economic indicators and determinants of Health that influence the indicator of human inequality, descriptive analysis of the quality of the formed feature space. The research used methods of comparative analysis, content analysis, systematization, logical generalization, bibliometric analysis (using the Vosviewer toolkit), correlation and regression analysis (using the Statgraphics Centurion toolkit). Results. A statistically significant econometric regression model has been developed that characterizes the impact of independent indicators determined by the Gini coefficient, inequality in life expectancy, gross national income, and the inequality – adjusted life expectancy index on the performance indicator-the human inequality coefficient. The significance of the model is justified by statistical criteria for checking the student, Fischer, Darbin-Watson, the value of the coefficient of determination and the significance level of P-value. Practical calculations were performed using the state-of-the-art Statgraphics Centurion application software. The results of the study can be used by state economic agencies for the development of society in order to develop a system of measures aimed at in-depth analysis of influential indicators, taking into account the direction of influence (direct or inversely proportional) to reduce the gap in the value of the indicator of human inequality. The implementation of the developed system of comprehensive measures will contribute to increasing economic growth for any country.