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Quality of Life Indicators in Selected European Countries: Hierarchical Cluster Analysis Approach


  • Žmuk Berislav

    () (Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia)


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.

Suggested Citation

  • Žmuk Berislav, 2015. "Quality of Life Indicators in Selected European Countries: Hierarchical Cluster Analysis Approach," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 1(1-2), pages 42-54, December.
  • Handle: RePEc:vrs:crebss:v:1:y:2015:i:1-2:p:42-54:n:4

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    References listed on IDEAS

    1. Joseph G. Hirschberg & Esfandiar Maasoumi & Daniel J. Slottje, 2001. "Clusters of attributes and well-being in the USA," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 445-460.
    2. Ed Diener & Eunkook Suh, 1997. "Measuring Quality Of Life: Economic, Social, And Subjective Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 40(1), pages 189-216, January.
    3. Hirschberg, Joseph G. & Maasoumi, Esfandiar & Slottje, Daniel J., 1991. "Cluster analysis for measuring welfare and quality of life across countries," Journal of Econometrics, Elsevier, vol. 50(1-2), pages 131-150, October.
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    More about this item


    quality of life indicators; Ward’s method; outlier detection; European countries; analysis of variance (ANOVA);

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D60 - Microeconomics - - Welfare Economics - - - General
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General


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