DIFFERENCES AND SIMILARITIES IN PATTERNS OF AGEING SOCIETY IN THE EUROPEAN UNION
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Volume Title
Publisher
Technická Univerzita v Liberci
Technical university of Liberec, Czech Republic
Technical university of Liberec, Czech Republic
Abstract
Population ageing is a demographic issue that emphasises the need to be interested in the lives of the most vulnerable population group: the elderly population. The paper investigates the ageing process and their relations among the European Union member countries from 2009 to 2019. These countries are assessed and dispersed to the appropriate clusters according to several indicators related to the areas that affect the lives of the elderly population: namely, the health status, the labour market conditions, and financial security. The focus is on the age group 55 years and over as it is a disadvantaged age group in the job application process regarding ageing society. It is a significant aspect of public finance system. The European Union Statistics on Income and Living Conditions, the Labour Force Survey, and the European System of Integrated Social Protection Statistics data are involved. The quantitative approaches are applied in the cluster analysis and followed by the panel data linear regression analysis. The dendrograms visualise the three clusters representing the mutual relations and the ageing patterns among the explored countries. The heat maps are created to prove the potential relations among the observed countries. The panel regression model demonstrates that the three variables – part-time employment, the income inequality, and the material and social deprivation – are statistically significant in all the regression models for the whole area and the three clusters. The analytical outcome could be applied as a valuable resource to government and national representatives. It can help identify the objectionable determinants for a custom policy and implement appropriate measures to improve the situation of the elderly population.
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Subject(s)
ageing, life expectancy, cluster analysis, regression analysis, European Union
Citation
ISSN
1212-3609