scholarly journals Study of the Tax Wedge in EU and other OECD Countries, Using Cluster Analysis

2018 ◽  
Vol 238 ◽  
pp. 687-696
Author(s):  
Claudia Florina Radu ◽  
Cristina Fenişer ◽  
Klaus Bruno Schebesch ◽  
Florin Fenişer ◽  
Florin Marian Dobrea
Keyword(s):  
Cluster Analysis ◽  
Oecd Countries

The influence of health on economic growth from the perspective of sustainable development: a case of OECD countries

2020 ◽  
Vol 16 (3) ◽  
pp. 181-194 ◽  
Author(s):  
Seda Yıldırım ◽  
Durmus Cagri Yildirim ◽  
Hande Calıskan
Keyword(s):  
Economic Growth ◽  
Cluster Analysis ◽  
Sustainable Development ◽  
Health Status ◽  
Life Expectancy ◽  
Oecd Countries ◽  
Content Type ◽  
Life Expectancy At Birth ◽  
Lower Health

PurposeThis study aims to explain the role of health on economic growth for OECD countries in the context of sustainable development. Accordingly, the study investigates the relationship between health and economic growth in OECD countries.Design/methodology/approachThis study employed cluster analysis and econometric methods. By cluster analysis, 12 OECD countries (France, Germany, Finland, Slovenia, Belgium, Portugal, Estonia, Czech Republic, Hungary, South Korea, Poland and Slovakia) were classified into two clusters as high and low health status through health indicators. For panel threshold analysis, the data included growth rates, life expectancy at birth, export rates, population data, fixed capital investments, inflation and foreign direct investment for the period of 1999–2016.FindingsThe study determined two main clusters as countries with high health status (level) and low health status (level), but there was no threshold effect in clusters. It was concluded that an increase in the life expectancy at birth of countries with higher health status had no significant impact on economic growth. However, the increase in the life expectancy at birth of countries with lower health status influenced economic growth positively.Research limitations/implicationsThis study used data that including period of 1999–2016 for OECD countries. In addition, the study used cluster analysis to determine health status of countries, and then panel threshold analysis was preferred to explain significant relations.Originality/valueThis study showed that the role of health on economic growth can change toward country groups as higher and lower health status. It was proved that higher life expectancy can influence economic growth positively in countries with worse or low health status. In this context, developing countries, which try to achieve sustainable development, should improve their health status to achieve economic and social development at the same time.

The Assessment of Economic Performance of OECD Countries—Factor Analysis and Cluster Analysis

1994 ◽  
Vol 5 (2) ◽  
pp. 65-78
Author(s):  
Peijie Wang
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Comparative Study ◽  
Economic Performance ◽  
Oecd Countries ◽  
Factor Scores ◽  
Economic Variables ◽  
Two Factors ◽  
The Common ◽  
And Cluster Analysis

This paper studies the economic performance of 19 OECD countries. Eight economic variables are selected to describe the economic performance which are incorporated into two factors afterwards by factor analysis. The relations among the variables and that between factors and variables are analysed. A comparative study is made, based on the factor scores of these 19 countries; and the countries are classified into 5 categories using cluster analysis, according to their similarities in the variables and the extracted factors. The common characteristics of the countries with similar economic performances on the two factors are discussed. The paper presents an outline of the nations’ economic performance during this period. It is quite interesting, as a by-product finding, that the countries sharing the economy similarities also have the geographical communalities.

scholarly journals Forms of Providing and Financing Long-Term Care in OECD Countries

2017 ◽  
Vol 17 (2) ◽  
pp. 159-178 ◽  
Author(s):  
Renáta Halásková ◽  
Pavel Bednář ◽  
Martina Halásková
Keyword(s):  
Cluster Analysis ◽  
Long Term Care ◽  
Formal System ◽  
Principal Component ◽  
Oecd Countries ◽  
Population Ageing ◽  
Term Care ◽  
Care Systems ◽  
Provision Of Care

Abstract Long-term care is being prioritised due to population ageing, and hand in hand with the development of professional provision of long-term care, public expendi-tures will be increasing. Mainly countries with a sharp increase in the number of people aged 80+ will have to address the sustainability of long-term care systems and the pro-curement of relevant services. This paper aims to evaluate the forms of provision and financing of long-term care in selected OECD countries. Provision and funding of long-term care in terms of a formal system are assessed based on selected criteria using analytical methods (principal component analysis and TwoStep cluster analysis). Results of the evaluation carried out in 2008 and 2013 by means of the selected indicators of long-term care, using TwoStep cluster analysis, confirmed both similar as well as different approaches to the provision and financing of long-term care in the analysed countries. The most marked differences in the provision of care based on indicators LTC recipients aged 65+ and LTC recipients in institutions as a percentage of total LTC recipients were found between the first cluster (Australia and Korea with the highest share of LTC recipients) and the second cluster (Czech Republic, Estonia, with the lowest share of LTC recipients). In financing of long-term care (LTC expenditures on institutions as a percentage of total LTC expenditures), the most significant differences were observed between the first (Australia, Korea, with the largest share of LTC expenditures on institutions) and third cluster (mainly Nordic countries, with the lowest share of LTC expenditures on institutions of total LTC expenditures).

scholarly journals Evaluating the High-Tech Industries in the Progressive Structure of the Economy of the World Countries and Ukraine

2020 ◽  
Vol 9 (512) ◽  
pp. 81-98
Author(s):  
V. Y. Khaustova ◽  
◽  
O. I. Reshetnyak ◽  
H. V. Kramarev ◽  
Y. M. Kriachko ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Structural Analysis ◽  
Pharmaceutical Industry ◽  
Service Sector ◽  
Oecd Countries ◽  
Export Market ◽  
High Tech ◽  
The World ◽  
Per Capita ◽  
Structural Shifts

The article is aimed at defining and evaluating the place of high-tech industries in the progressive structure of the economy of the world countries and Ukraine. Research methods: structural analysis, graphical analysis, analysis of structural shifts, cluster analysis. A structural analysis of the economy of Ukraine and OECD countries is carried out in terms of gross output, gross value added and the share of GVA in terms of output in four sectors: agriculture, hunting, forestry and fisheries; industry, including energy industry; construction; in general with regard to the service sector. A further structural analysis of the processing industry of Ukraine compared to OECD countries is carried out. A rating of the world countries is carried out by the share of costs for R&D in the field of production of basic pharmaceutical products and pharmaceuticals; production of computers, electronic and optical products; production of aerospace equipment. The positioning of the the world countries is made in the quadrants of the matrix in the coordinate plane of «Share of costs for the R&D of high-tech sectors of the economy and GVA per capita». Structural shifts in research and development costs in high-tech sectors of both the OECD and Ukrainian economies are computed. The structure of the export market of high-tech sectors of the economy is analyzed and Ukraine’s place in this market is evaluated. The carried out cluster analysis allowed to divide the world countries into groups taking into account the data by the following indicators: share of costs for the R&D in the pharmaceutical industry; share of costs for the R&D in the computer, electronic and optical industry; share of costs for the R&D in the aerospace industry; share of costs for R&D in the service sector; share of the export market of the pharmaceutical industry and GVA per capita. Ukraine entered the cluster, whose countries have such a costs structure for R&D in high-tech sectors of the economy, which does not provide a high level of GVA per capita. Recommendations on the development of high-tech industries of Ukraine in order to increase the socio-economic development of the country are provided.

scholarly journals Assessment of the Development of the European Oecd Countries with the Application of Linear Ordering and Ensemble Clustering of Symbolic Data

2019 ◽  
Vol 19 (2) ◽  
pp. 117-133
Author(s):  
Marcin Pełka
Keyword(s):  
Cluster Analysis ◽  
Multidimensional Scaling ◽  
Human Development Index ◽  
Oecd Countries ◽  
Linear Ordering ◽  
Ensemble Clustering ◽  
Single Model ◽  
Symbolic Data ◽  
Development Analysis ◽  
Complex Indicators

Abstract The research background of the paper covers the development of a country, that can be measured in various ways. Simple indicators, like GDP and also complex indicators such as HDI (human development index), can be used to measure country development. However, usually countries are divided into groups via setting some arbitrary levels of final measure. What is more, the composite (complex) indices have some problems and errors. The main purpose of the paper is the assessment of the development of the selected European OECD countries with the application of the linear ordering and ensemble clustering of symbolic data as well as comparison of the ensemble clustering with a single model. Research methodology covers linear ordering with the application of multidimensional scaling for a visualisation of results and ensemble clustering for symbolic data. The results are compared according to adjusted Rand and silhouette indices. The obtained results show that ensemble clustering for symbolic data can be a useful tool in country development analysis and allows reaching better results than a single model. The novelty of the proposed approach is to use a cluster analysis to obtain the clusters of countries with similar variables’ values (indicators of development) and the application of multidimensional scaling for symbolic data in order to visualise linear ordering results.

scholarly journals Management of Innovations in Finance Education: Cluster Analysis for OECD Countries

2020 ◽  
pp. 235-244 ◽  
Author(s):  
Bohuslava Mihalcova ◽  
Peter Gallo ◽  
Jozef Lukac
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Financial Literacy ◽  
Financial Education ◽  
Oecd Countries ◽  
Vocational Education And Training ◽  
School Students ◽  
Personal Finances ◽  
Long Time ◽  
Market Needs

Financial literacy and financial education is a concept that helps people in orientation in both the financial markets and the area of personal finances. Financial literacy can be acknowledged through financial education. Financial education should enable individuals to develop their decision-making competencies. It includes issues such are the understanding of money, how to deal with them within the risk of their investment. The issue of financial literacy for primary and secondary school students has been monitored over a long time through several evaluations. In this case, it is very well known the PISA rating, which was lastly performed in the first half of 2018. It represents the ability of how to use skills and knowledge in managing one’s financial resources while achieving maximum prosperity. Financial education should enable individuals to develop their decision-making competencies related to money. The paper aims to perform a cluster analysis based on the data available from the PISA 2015 measurement in selected OECD countries. The analysis represents results in a cluster of countries. The study analysed of financial literacy 15-year-olds students. The study observed similar research results in both the area of financial literacy and mathematical literacy. From the selected PISA 2015 test results applied through cluster analysis, we decided that the Slovak students were placed in a cluster together with students from Spain, Chile and Lithuania. The study observed the quality of vocational education and training is criticized mainly by employers because the area of education is inadequately responsive to labour market needs. Employee stress that it is inadequately linked to practice. Vocational training is inadequate compared to general schools. The methodology we recommend should also be applied to the results of the PISA 2018 evaluation, which are not yet available to the public. We would like to address this issue in future. The cluster analysis helps to reveal competitiveness in the area of financial literacy. The results of our research within Slovak students were based on a similar level of financial literacy among Slovak pupils as in Spain, Chile and Lithuania. Keywords cluster analysis, financial literacy, PISA, pupils, OECD countries, Slovakia.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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