The main goal of this paper is to provide an analysis of the relationships among selected indicators of economic activity of regions (GDP, unemployment) and regional tax revenues obtained from taxes imposed on economic activity (VAT, income taxation) in the Czech Republic, with a detailed analysis of VAT. Our methodology is based on correlation analysis (both the Pearson's and the Spearman's correlation coefficients) using data from the official statistics of the Czech Republic. The main idea of the paper is that regional tax revenues should give us a picture of the economic activity of companies, residents and entrepreneurs in these regions. Based on the results, we can say that there is a statistically significant positive relationship between regional VAT revenues and the value of regional GDP, and a statistically significant negative relationship between regional income taxes revenues and regional unemployment.

There are several reasons for environmental taxation implementation. Besides its environmental impact, the main reason for such taxation is its fiscal impact, particularly in generating revenues of public budgets. The main goal of this paper is to observe possible spatial patterns in fiscal impacts of environmental taxation in the EU countries, and to depict the groups of countries with the same (or similar) fiscal impact of these instruments on public budget revenues, including environmental and economic characteristics. Two methods of cluster analysis are used, Ward linkage and K-nearest neighbors (spatial) cluster analysis to observe potential geographical links or implication of fiscal impact. The study is performed for the years 2008 and 2017. Based on the results, we can say that in the year 2008, the EU countries were divided into “the west” and “the east”, with some exceptions. The western countries were characterized by high environmental tax revenues, the eastern countries by low environmental tax revenues. For 2017, the situation is different. The border between old and new EU member states is not so abrupt and clear. The results show higher diversification between EU countries concerning the fiscal impacts of environmental taxation.

Location decision-making constitutes one of pivotal tasks for banking managements, which applies to both banking headquarters and affiliates. The main objective of this article consists in analysis and interpretation of location factors that stay behind location-decision making of banking managements and subsequent collocation of banking head offices in the Czech Republic. In doing so, cluster analysis was utilised appropriately and it turned out, initial 18 location factors can be decreased to final three mutually similar groups, which satisfactorily embody location criteria banking managements take into account. As it has been proven banking landscape in the Czech Republic is still influenced by traditional hard factors of location, nonetheless their soft counterparts should be increasingly considered.

The success of a national economy and the economic development of a country are mainly assessed based on economic indicators. The Gross Domestic Product (GDP) is the most commonly used indicator at national and regional levels. The informational power of GDP is limited, so alternative ways of measuring economic development and well-being have begun to be used, of which the Human Development Index (HDI) is the best known and the most accessible. The aim of this research is to highlight the areas that are to be considered when assessing economic development and well-being, especially social and environmental factors. One of the objectives is to compare the GDP and the HDI in the V4 countries. There was a gradual, slight increase in HDI without regard to economic cycles and changes in the GDP in the V4 countries between 2007 and 2017.

The paper focuses on the issue of income inequality, which is an integral part of currently developed economies. Accesses to mitigation provided by the government to a large extent by means of transfer payments may be different. The aim of this paper is to find out whether there are mutual links between selected indicators of income inequality and how they change after the inclusion of transfer payments. The data contains the following indicators: the Gini index before and after a transfer payment and the coefficient S80/S20. For the purposes of achieving the main objective of the paper, there is the correlation analysis uses. Based on the results there is very strong dependence between the Gini index and the coefficient S80/S20. Their ability to describe the system is almost the same. The relationship between these indicators of income inequality and the Gini index before transfer payments is only middle positive dependency because a transfer payment is different in more aspects.