This article presents an overview of the self-reported health status of the population of the European Union Member States (EU-28) in the year 2018 based on Eurostat data. The self-reported health status of the inhabitants of the Slovak Republic has been analyzed in more detail with regard to the availability of individual data of the survey results from the European Statistics of Income and Living Condition (EU-SILC). The aim of the article is to analyse the relationship between social and demographic characteristics and the self-perceived health of the population in the EU-28 countries and their comparison as well as a comparison with the results found in the Slovak Republic. The characteristics gender, age, educational level, income, employment, and place of residence have been considered as the determinants of the self-reported health status. The obtained results of self-reported health status by selected demographics and social indicators in the European Union Member States have been compared in visual form using tables and graphs. For assessment of impact selected socio-economic and demographic characteristics on the self-perceived health by inhabitants in the Slovak Republic has been used the logistic regression model based on data extracted from the EU SILC 2016 cross-sectional component provided by the Statistical Office of the Slovak Republic. The obtained results can provide valuable information for health protection policy in EU countries and especially in the Slovak Republic. It could also be used to compare self-reported health status in the EU countries and the health status established based on the official health data published by European institutions.
Working Poverty and Its Dimensions The importance of working poverty increased in 2003, owing to the European strategy in which one of the goals was to reduce the number of working people in poverty in the EU. Despite its seriousness, experts have not achieved a uniform theoretical model or definition. The situation is the same for methodology and empirics, which are determined by availability of statistical data. For these reasons, knowledge of working poverty is in its infancy. Therefore, we set a goal to take into account findings from past research into working poverty in the world, focusing primarily on the situation in Slovakia. Based on EU SILC 2016 data, we created our own model of variables in which we assumed that they determine the genesis and manifestations of working poverty in its particular structural components and we measured their intensity using the logistic regression and the decision tree model.
The article contains the results of empirical analysis of data on one percent of employees with the highest salaries in the Slovak Republic in 2020. The starting point for the analysis there is 11,570 anonymized individual values of average gross monthly wage and also personal data of the employees whose wage exceeded the 99th percentile of the sample survey The Informational System on Labour Costs, implemented in the Slovak Republic since 1992 by the company Trexima Bratislava. The aim of the article is to assess the gender pay gap for the best-earning men and women and assess the significance of the impact of selected factors that contribute it. Given the availability of data the monitored factors of the gender pay gap there are education, region of residence, the type of occupation, and the categorized age of employees. To achieve the objective, selected quantitative methods were used, namely methods of descriptive statistics and statistical inference, as goodness-of-fit tests, chi-squared tests of independence and machine learning methods, as normalized Shannon entropy and regression decision tree models. The results of analyses by these methods have been preferably presented in a graphical form. Based on the application of the above methods the significant wage differences by gender at the highest wages (over the 99th percentile of the sample) and significant impact of monitored factors has been confirmed not only on the gender pay gap, but also on the structure of their employment. The results of the analyses lead to the conclusion that the significant wage differences by gender at the highest wages are caused precisely by unequal representation of men and women on the different levels of the monitored factors. The obtained results are partially compared with the results of a similar analysis based on data from 2010 (Pacáková et al., 2012).
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