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This paper proposes the application of taxonomic tools to study the differentiation of standard of living in the European Union countries. The aggregate distance between given countries is the basis for grouping member states in terms of their internal structure of the studied characteristics. The analysis is based on two chosen methods–the Ward's and k-means method. The study included 24 member states of the European Union in 1995-2010. Depending on the distance between the object, the countries were divided into two or four clusters. Similar configuration of each group obtained using both methods has led to the conclusion that these methods can be used both complementarily and separately. ; This paper proposes the application of taxonomic tools to study the differentiation of standard of living in the European Union countries. The aggregate distance between given countries is the basis for grouping member states in terms of their internal structure of the studied characteristics. The analysis is based on two chosen methods–the Ward's and k-means method. The study included 24 member states of the European Union in 1995-2010. Depending on the distance between the object, the countries were divided into two or four clusters. Similar configuration of each group obtained using both methods has led to the conclusion that these methods can be used both complementarily and separately.

This paper proposes the application of taxonomic tools to study the differentiation of standard of living in the European Union countries. The aggregate distance between given countries is the basis for grouping member states in terms of their internal structure of the studied characteristics. The analysis is based on two chosen methods–the Ward's and k-means method. The study included 24 member states of the European Union in 1995-2010. Depending on the distance between the object, the countries were divided into two or four clusters. Similar configuration of each group obtained using both methods has led to the conclusion that these methods can be used both complementarily and separately. ; This paper proposes the application of taxonomic tools to study the differentiation of standard of living in the European Union countries. The aggregate distance between given countries is the basis for grouping member states in terms of their internal structure of the studied characteristics. The analysis is based on two chosen methods–the Ward's and k-means method. The study included 24 member states of the European Union in 1995-2010. Depending on the distance between the object, the countries were divided into two or four clusters. Similar configuration of each group obtained using both methods has led to the conclusion that these methods can be used both complementarily and separately.

The aim of the article is to present the spatial diversity of tourism in the countries of the European Union (EU). The main objective of the article can be divided into three immediate goals, each of which is to determine countries that are similar by means of: (1) accommodation base ; (2) tourism traffic ; and (3) tourism-related expenditures and revenues. In order to group countries, Ward&rsquo ; s cluster analysis method is used. The aim is verified with the use of 2017 United Nations World Tourism Organization (UNWTO) and Eurostat data. The analysis covers all EU member states. The research conducted confirms, inter alia, the key role of the accommodation base in the development of tourism in those countries.

The aging of European societies is reshaping their population pyramids. The increase in life expectancy and the decrease in the fertility rate lead to an increasing share of the elderly population. This leads to rising age-related expenditures, especially public pension expenditures to GDP. Consequently, economies are reforming their pension systems to make them more sustainable. Next to the aging-related challenges, the new EU members, eleven post-socialist economies: Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia share a similar history of restructuring of their pension systems and establishment of a multi-pillar system. The objective of this article is to examine the similarities and differences between the pension systems of the selected post-transition economies of the European Union to establish the basis for further research, simulations, and assumptions on the impact of future pension reforms. For that purpose, we apply Ward's clustering methodology on three variable groups in three selected years: 1996, 2006, and 2016. The idea of clustering economies in three years with a 10-year gap is relevant since it reveals how the cluster structure is changing over time. Additionally, three periods represent three different phases in the pension systems' development. Three groups of variables were used for cluster analysis. First, pension systems' characteristics include average effective retirement age, pension expenditure, and replacement rate. Second, demographics encompass fertility rate, life expectancy at the age of 65, net migration rate, and old-age dependency ratio. Third, the macroeconomics and labour market variables refer to the GDP growth rate, real labour productivity, labour force participation rate, and the unemployment rate. Results of cluster analysis show that the composition of the countries in the extracted clusters changes significantly, both throughout the observed period and when looking at different variable groups. Our results revealed that the pension systems of economies with later retirement proved to be more sustainable, during the three observed periods, due to lower pension expenditures that are in turn positively reflected in the favourable economic conditions and their labour market.

The aging of European societies is reshaping their population pyramids. The increase in life expectancy and the decrease in the fertility rate lead to an increasing share of the elderly population. This leads to rising age-related expenditures, especially public pension expenditures to GDP. Consequently, economies are reforming their pension systems to make them more sustainable. Next to the aging-related challenges, the new EU members, eleven post-socialist economies: Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia share a similar history of restructuring of their pension systems and establishment of a multi-pillar system. The objective of this article is to examine the similarities and differences between the pension systems of the selected post-transition economies of the European Union to establish the basis for further research, simulations, and assumptions on the impact of future pension reforms. For that purpose, we apply Ward's clustering methodology on three variable groups in three selected years: 1996, 2006, and 2016. The idea of clustering economies in three years with a 10-year gap is relevant since it reveals how the cluster structure is changing over time. Additionally, three periods represent three different phases in the pension systems' development. Three groups of variables were used for cluster analysis. First, pension systems' characteristics include average effective retirement age, pension expenditure, and replacement rate. Second, demographics encompass fertility rate, life expectancy at the age of 65, net migration rate, and old-age dependency ratio. Third, the macroeconomics and labour market variables refer to the GDP growth rate, real labour productivity, labour force participation rate, and the unemployment rate. Results of cluster analysis show that the composition of the countries in the extracted clusters changes significantly, both throughout the observed period and when looking at different variable groups. Our results revealed that the pension systems of economies with later retirement proved to be more sustainable, during the three observed periods, due to lower pension expenditures that are in turn positively reflected in the favourable economic conditions and their labour market.

The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is important, too. The higher the quality of life level, the citizens' happiness and satisfaction levels are higher, which has positive impact on the development and operating of an economy. The goal of this paper is to identify groups of European countries, using statistical hierarchical cluster analysis, by using the quality of life indicators, and to recognise differences in quality of life levels. The quality of life is measured by using seven different indicators. The conducted statistical hierarchical cluster analysis is based on the Ward's clustering method, and squared Euclidean distances. The results of conducted statistical hierarchical cluster analysis enabled recognizing of three different groups of European countries: old European Union member states, new European Union members, and non-European Union member states. The analysis has revealed that the old European Union member states seem to have in average higher quality of life level than the new European Union member states. Furthermore, the European Union member states have in average higher quality of live level than non-European Union members do. The results indicate that quality of life levels and economic development levels are connected.

The European Union strives to create sustainable, low-carbon economies; therefore, energy policies of all member states should move towards renewable energy sources (RES). That concerns also the so-called new EU member states. These countries, on the one hand, are characterized by significant historical similarities in terms of post-communist legacy and adopted development strategies linked with the EU membership, and on the other hand, by significant social, economic and environmental differences resulting from different transformation and development paths and conditions. The question remains how the selected countries should cope with actions in the field of national energy transformations to confront the multiple challenges linked to assuring a significant level of sustainable development. In order to be successful, it is necessary to conduct an effective and rapid changes in the energy industry, which should be preceded by an analysis of the differentiation of countries in terms of their potentials. The results of such analyses should be helpful in selecting the most appropriate strategies for transformation of the described industry. Therefore, the purpose of the article is to assess the new EU member states for RES diversification and identify similar subgroups of countries using cluster analysis, taking into account the percentage share of individual renewable energy sources in total renewable energy production. This was done for the years 2010, 2015 and 2019 which should allow us to demonstrate the differences between them as a group and also reveal changes recorded over time for a single country. Ward's method was used for the analysis. The presented approach to the analysis of energy production enabled the acquisition of new knowledge in this field and supported the assessment of the current state of RES. The results obtained can be used in countries of comparable specificity to undertake activities of similar nature in relation to internal energy production, technological development or common energy policy.

In an era of aging of the European Union population, it is crucial to take care of human resources in various spheres of their life. The potential of young people is particularly important, as their economic activity creates the basis of maintaining the European welfare state model. However, the labour market situation of young people is difficult. Moreover, the phenomena, which have recently attracted increasing attention, are remaining for young people without employment, education or training (NEET). The occurrence of NEET's resources is harmful at micro level - due to pauperization of European households as well as for the whole economy due to insufficient usage of human resources. The paper aim is to compare how the situation of young people differs in the European Union labour markets.The paper was based on both the desk-research of literature as well as the analysis of selected economic indicators of young people (aged 15-29 years). The indicator analysis was made through the usage of cluster analysis (Ward's method and k-means method). The data was gathered from the databases of Eurostat. The selected indicators determine the labour market situation of young people in the EU countries and they are derived from two years – 2006 and 2014.Ward's and k-means methods allowed for dividing the EU countries into three groups. It occurred that the groups in 2006 have a completely different composition of countries than in 2014, which was mainly determined by crisis influences on the labour markets as well as directions of conducted reforms. Additionally, the k-means method allowed for comparison of selected groups on the basis of chosen variables and determination of countries with the best and the worst situation of young people.

Abstrak Kendala utama yang dialami masyarakat berekonomi lemah adalah dalam memenuhi kebutuhan pangan dengan jumlah dan kualitas yang cukup bagi anggota keluarganya. Pada masa pertumbuhan dan perkembangan manusia, masa balita merupakan periode terpenting dalam pertumbuhan dan perkembangan anak. Oleh karena itu pangan menjadi faktor utama dalam masalah kekurangan gizi yang berakibat pada gizi buruk pada anak. Selain itu faktor tidak langsung lainnya seperti tingkat pendidikan orang tua, pendapatan orang tua, pola asuh, pelayanan kesehatan, serta faktor lingkungan memberikan pengaruh. Salah satu metode analisis multivariat yang digunakan adalah analisis cluster. Penelitian ini bertujuan mengelompokkan kabupaten/kota berdasarkan pada variabel faktor-faktor yang mempengaruhi gizi buruk balita di Jawa Timur menggunakan metode Ward's. Data penelitian yang digunakan diambil dari BPS tahun 2011. Sebelumnya dua atau lebih variabel bebas yang mengalami multkolinearitas akan dihilangkan terlebih dahulu dengan analisis komponen utama. Dari hasil analisis komponen utama akan digunakan sebagai variabel bebas baru untuk diproses ke analisis cluster. Hasil analisis cluster diperoleh 3 kelompok dimana kelompok 1 terdiri dari 13 kabupaten, kelompok 2 terdiri dari 17 kabupaten, dan kelompok 3 terdiri dari 8 kabupaten. Kelompok 1 variabel yang paling mempengaruhi adalah sumber air minum bukan berasal dari air kemasan dan fasilitas penggunaan kloset yang tidak dipakai sendiri, kelompok 2 variabel yang paling mempengaruhi adalah tempat pembuangan akhir (tinja) selain tanki septitank, kelompok 3 variabel yang paling mempengaruhi adalah jenis dinding tempat tinggal yang bukan berupa tembok, jarak sumber air minum ke penampungan akhir (tinja) kurang dari 10 meter dan fasilitas penggunaan kloset tidak dipakai sendiri. Dengan adanya penelitian ini pemerintah daerah dapat lebih teliti lagi dalam meningkatkan gizi balita dengan lebih memperhatikan faktor tidak langsung yang mempengaruhinya. Kata Kunci: Gizi Buruk, Analisis Komponen Utama, Analisis Cluster Abstract The hardest difficulty had been concerned by poor people is food needs which quality and quantity enough for relative members. At growth and development phase, toddler is the most important phase in human growth and development phases. Therefore, food becomes the main factor of malnutrition problems that will effect for children. In other hand, another factors such as parental education, income, parenting, health care, and environmental factors also give effect. Cluster analysis is one of multivariate methods. The purpose of this research to agglomerate area/city based on malnutrition factors effect in East Java by using Ward's method. Research data which has taken from BPS 2011. Previously, the multicolinearity between two or more independent variables must be vanish at first by using Principal Component Analysis (PCA). Then, the result of PCA is going to be use as new independent variables to process into cluster analysis. Cluster analysis obtained 3 groups which is the first group consist 13 areas/cities, the second group consist 17 areas/cities, and the thitd group consist 8 areas/cities. Water of source and using toilet facility are the most effect variables from the first group. Defecation place except septitank is the most effect variable from the second group. Type of wall, the distance of water of source less than 10 meters from defecation, and using toilet facility do not use by own are the most effect variables from the third group. By this research the local government should be more thorough to improve the nutritional with more attention indirect factors. Keywords: Malnutritition, Principal Component Analysis (PCA), Cluster Analysis

Recent macroeconomic and demographic trends have resulted in new challenges for pension systems. One of these challenges is to create a sustainable pension system while simultaneously providing adequate pension benefits for current and future pensioners. This research explores how similar are pension systems of eleven European Union countries by using hierarchical cluster analysis for year 2016. Variables representing pension systems, as well as demographic, macroeconomic and labour market data were used to cluster these economies. Three clustering solutions were generated using hierarchical clustering approach, one for each variable group. Given the number of observed countries, only two cluster solutions were considered. According to the characteristics of the pension systems, countries that have greater problems of unsustainability are recognized. A similar group of countries also forms the cluster characterized by unfavourable demographic trends that make it more difficult to maintain sustainability. Romania stands out from other economies, based on macroeconomic indicators, as it recorded faster economic growth, greater labour productivity growth and lower unemployment rate in 2016. The findings of this study provide a guideline for future pension reforms, since they indicate which countries' experience could be valuable in defining certain policy measures. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.

The purpose of the article is to present the method of constructing the GTI index used for a multidimensional assessment of a terrorist threat. Next, using Ward's method, groups of EU countries similar in terms of the terrorism threat level will be identified. Particular attention was paid to the position of Poland, which due to its active participation in military operations in Afghanistan and Iraq, may be the target of a terrorist attack. The analyses show that Poland is in the group of countries with the lowest terrorism threat level. However, this threat cannot be underestimated. ; Celem artykułu była prezentacja sposobu konstruowania miernika GTI wykorzystywanego do wielowymiarowej oceny zagrożenia terroryzmem. Następnie przy użyciu metody Warda zidentyfikowano grupę krajów UE podobnych pod względem zagrożenia terroryzmem. Szczególną uwagę zwrócono na pozycję Polski, która z powodu aktywnego udziału w działaniach militarnych w Afganistanie i Iraku czy rozmieszczenia wojsk amerykańskich może być celem ataku terrorystycznego. Z przeprowadzonych analiz wynika, że Polska znajduje się w grupie krajów o najmniejszym zagrożeniu terroryzmem, jednak zagrożenia tego nie można lekceważyć.

The article addresses problems of population aging in Poland and the selected environmental hazards exerting a negative impact on seniors&rsquo ; health. The introduction presents the reasons underlying the above-mentioned aging process and provides the characteristics of the primary environmental threats. The next part covers the most important trends and indicators related to the demographic situation in the country. The core of the study is focused on presenting and interpreting the results of empirical research on the periodization of the population aging process in Poland, in the years 2004&ndash ; 2019, using the multidimensional statistical analysis method including, in particular, the data classification method. The key demographic factors differentiating the development phases of the population aging process include, in order of their significance: longer life expectancy of the population, narrowed gross reproduction rate, declining birth rate, and total migration balance. In addition, the article provides the analysis of the selected environmental threats&rsquo ; impact on population aging in Poland, among which the following were indicated: high temperatures, solar conditions, heavy rainfall, strong winds, droughts, and fires. Moreover, the process of longer life expectancy in fine health is essentially related to two issues: the level of medical care, with particular attention paid to check-ups and preventive measures, and the promotion of a healthy lifestyle. In summary, it should be highlighted that the elimination of all pollutants or the reasons of environmental hazards is not possible ; however, the actions primarily focused on reducing the emission of harmful gases into the atmosphere and other forms of environmental pollution should definitely be taken.