The PRC and European Union Countries
In: Problemy Dalnego Vostoka, Heft 5(1)
In: Problemy Dalnego Vostoka, Heft 5(1)
In: Wiadomości statystyczne / Glówny Urza̜d Statystyczny, Polskie Towarzystwo Statystyczne: czasopismo Głównego Urze̜du Statystycznego i Polskiego Towarzystwa = The Polish statistician, Band 62, Heft 5, S. 79-99
ISSN: 2543-8476
The aim of this paper is to analyse tax revenues and examine similarities of selected tax revenues (mainly VAT, CIT, PIT and excise duty) in the European Union countries. The analysis of the EU members concerns the period between 2003 (i.e. the year preceding the biggest enlargement of the EU) and 2012 (due to data completeness). Tax rates and the structure of tax revenues in the EU countries were compared and then the cluster analysis was applied to assess the similarity of tax revenues. The analysis suggests that the process of tax harmonization, which took place in the period considered, did not exert a significant impact on the similarity of the structure of tax revenues in the EU countries. The structure seems to be still determined by e.g. social, economic or historical factors, which influenced the tax systems creation in particular EU countries.
In: European integration studies: research and topicalities, Band 0, Heft 9
ISSN: 2335-8831
In: OECD journal: economic studies, Heft 1/34, S. 91-151
ISSN: 1995-2848, 0255-0822
World Affairs Online
In: OECD economic studies, Band 2002, Heft 1, S. 91-151
ISSN: 1609-7491
In: Wiadomości statystyczne / Glówny Urza̜d Statystyczny, Polskie Towarzystwo Statystyczne: czasopismo Głównego Urze̜du Statystycznego i Polskiego Towarzystwa = The Polish statistician, Band 62, Heft 11, S. 17-28
ISSN: 2543-8476
he following research aims at comparing GDP seasonality and its components in the European Union countries. An attempt was made to determine the size of seasonal fluctuations, their share in short-term variability and the differences depending on the country and economic category. The analysis, based on the Eurostat data for the years 2002—2015, relies on a model of deterministic seasonality.
The obtained results show large but varied seasonal fluctuations depending on the country and economic category. Gross fixed capital formation was subject to the largest seasonal fluctuations, whereas imports was exposed to the minor ones. A visible negative correlation between GDP seasonality and GDP per capita was found.
In: Journal of international studies, Band 16, Heft 4
ISSN: 2306-3483
In the European Union (EU), as a global economic community, there are countries with very different levels of economic development. Therefore, it takes place unevenly – some countries develop intensively, but with significant changes in it, others – stably, without significant changes, but not intensively, etc. In such a situation, it is important to determine adequately the current state of economic development. It is an integral quantity that combines both development changes and intensity. These two components of development form two of its partial indicators. Combined into one size, they comprehensively reflect the current situation of the country's economic development. Development refers to a process, so it must be assessed not on the basis of the state at the end of the period under consideration, but on the whole of this period. The presentation of development in the sense of recent years does not reflect the overall development process, based on the economic development of countries 2018–2022. Their ranks are determined by the results of the comprehensive assessment. Since they do not adequately reflect the current situation, the countries were combined into homogeneous groups, where countries with close values of the economic development indicator were located. This allows to highlight the economic development trends of EU countries.
In: Studies in educational evaluation: SEE, Band 37, Heft 2-3
ISSN: 0191-491X
In: Journal transition studies review: JTSR, Band 17, Heft 1, S. 22-38
ISSN: 1614-4015
In: Zagadnienia Ekonomiki Rolnej / Problems of Agricultural Economics. No. 1, p. 5-21, 2022
SSRN
In: Wiadomości statystyczne / Glówny Urza̜d Statystyczny, Polskie Towarzystwo Statystyczne: czasopismo Głównego Urze̜du Statystycznego i Polskiego Towarzystwa = The Polish statistician, Band 63, Heft 11, S. 21-40
ISSN: 2543-8476
The aim of the study is to verify the hypothesis of a tendency towards levelling of household income in the EU countries in the years 2007—2015. Convergence process of the level and the distribution of household disposable income was analysed. The basic source of information were Eurostat's data from EU-SILC database. The convergence analysis of income levels was carried out for the mean, median and first decile of annual equivalised household disposable incomes, weighted by country population. To examine their convergence, regression models used in economic convergence analyses were adapted and the concept of absolute ß convergence was used. In the study of income distributions convergence the income distribution in households by decile groups was used, and the degree of their discrepancy was assessed using the generalised form of the Jensen-Shannon divergence measure. The study was conducted for 27 EU countries, 15 of the old EU states and 12 new member states.
On the basis of the obtained results, it was concluded that there was no convergence of the distributions of income by quantiles. However, the convergence of the considered characteristics of income distributions (mean, median, first decile) was observed.
In: Studies in educational evaluation, Band 37, Heft 2-3, S. 108-122
ISSN: 0191-491X
In: Universal Journal of Accounting and Finance Vol. 9(4), pp. 841 - 851, 2021 DOI: 10.13189/ujaf.2021.090430
SSRN
In: Notitia: časopis za održivi razvoj : journal for sustainable development, Band 6, Heft 1, S. 1-11
ISSN: 1849-9066
This paper aims to examine the innovation performance of 28 European Union countries. Hypothesis of the paper states there is a significant difference of innovation performance between the old and the new EU members. Furthermore, the role of SMEs regarding innovation capacity may not be the same across EU. Using K-means clustering results indicated Germany, Ireland, France, Luxemburg and Austria as the most innovative countries and Bulgaria, Estonia, Latvia, Hungary, Poland and Slovakia as the least innovative countries. Czech Republic, Croatia, Cyprus, Denmark, Finland, Greece, Italy, Lithuania, Malta, Netherlands, Portugal, Slovenia, Sweden, United Kingdom and Spain were found to have a medium level of innovation performance. Furthermore, United Kingdom surpassed the average innovation level of the cluster for the small sized enterprises. Croatia was below the average level of the cluster regardless of the size of the enterprise. Romania was the outlier with the least innovation. In order to facilitate more innovation these findings may be valuable in creating more country specific recommendations for entrepreneurial policy.
In: Wiadomości statystyczne / Glówny Urza̜d Statystyczny, Polskie Towarzystwo Statystyczne: czasopismo Głównego Urze̜du Statystycznego i Polskiego Towarzystwa = The Polish statistician, Band 66, Heft 8, S. 24-45
ISSN: 2543-8476
Innovation is one of the main determinants of economic development. Innovative activity is very complex, thus difficult to measure. The complexity of the phenomenon poses a great challenge for researchers to understand its determinants. The article focuses on the problem of innovation-related geographical disparities among European Union countries. Moreover, it analyses the principal components of innovation determined on the basis of the European Innovation Scoreboard (EIS) dimensions. The aim of the paper is to identify the principal components of the innovation index which differentiate countries by analysing the structure of the correlation between its components. All calculations were based on indicators included in the EIS 2020 Database, containing data from the years 2012–2019. A comparative analysis of the studied countries' innovation performance was carried out, based on the principal component analysis (PCA) method, with the purpose of finding the uncorrelated principal components of innovation which differentiate the studied countries. The results were achieved by reducing a 10-dimensional data set to a 2-dimensional one, for a simpler interpretation. The first principal component (PC1) consisted of the human resources, attractive research systems, and finance and support dimensions (understood as academia and finance). The second principal component (PC2), involving the employment impacts and linkages dimensions, was interpreted as business-related. PC1 and PC2 jointly explained 68% of the observed variance, and similar results were obtained for the 27 detailed indicators outlined in the EIS. We can therefore assume that we have an accurate representation of the information contained in the EIS data, which allows for an alternative assessment and ranking of innovation performance. The proposed simplified index, described in a 2-dimensional space, based on PC1 and PC2, makes it possible to group countries in a new way, according to their level of innovation, which offers a wide range of application, e.g. PC1 captures geographic disparities in innovation corresponding to the division between the old and new EU member states.