Population ageing, labour productivity and economic welfare in the European Union

Abstract

This paper examines the impact of the ageing dimensions and other economic and social variables on labour productivity and poverty risk within the European Union (EU). Taking into account the sizable dissimilarities among the EU Member States, our research is configured on four specific panels, according to the UNECE/European Commission mapping of EU countries, based on the Active Ageing Index data and methodology for 2018. We have compiled a complex dataset with official data to measure the ageing features, labour market dimensions and poverty, along with other economic and social representative variables, during 1995-2017. The methodological endeavour is critical and analytical, grounded on an extensive quantitative research. Two multifactorial macro-econometric models are applied in order to evaluate the direct implications of the ageing dimensions and other utter credentials on labour productivity, respectively on poverty levels. Structural equation modelling further entails an integrative examination of the total, indirect and bidirectional connections between the ageing phenomenon, several socio-economic indicators and the labour market performance, with a final impact on poverty. Results show different yet extremely significant labour market and poverty impacts for the ageing representative groups of EU countries, which require specific policy interventions and tailored strategies.

Workforce Participation, Ageing, and Economic Welfare: New Empirical Evidence on Complex Patterns across the European Union

“…In order to restrain the unfavorable impact over labor productivity, authors [11] (p. 18) highlighted "the crucial role played by labor market reforms such as increases in active labor market policies on training or increase in the availability of medical inputs" and also older employees' training and innovation. Cristea et al [5] deepen the analysis of the influence of employees aged 55-64 years on labor productivity on the four groups of EU countries, according to the AAI scores. Authors [5] showed that, within countries with the lowest AAI results, the ageing workforce (55-64 years) induce a downsized labor productivity, while for the other countries, with higher AAI scores, the effects are favorable.…”

“…Cristea et al [5] deepen the analysis of the influence of employees aged 55-64 years on labor productivity on the four groups of EU countries, according to the AAI scores. Authors [5] showed that, within countries with the lowest AAI results, the ageing workforce (55-64 years) induce a downsized labor productivity, while for the other countries, with higher AAI scores, the effects are favorable.…”

“…In all countries all over the world, the demographic studies entail that a series of societal challenges have emerged, where elderly cohorts are increasing at a steady pace and are expected to continue growing over the following decades [1]. us, the ageing population, which means the increasing of life expectancy and decreasing of birth rates, has become a major issue, affecting the labor market equilibrium and, also, the welfare of older people, mainly, due to a weak viability and sustainability of the pension funds [2], health deterioration, or lower social involvement [3][4][5][6].…”

“…As regards Europe, which becomes the oldest continent in the world, the European Union (EU) has registered the highest old-age dependency ratio (65+/ (15-64)), representing 31% in 2019, compared to 13.95% at the worldwide level [9], being projected to increase at 57% in 2100 [10]. As much, against the background of a declining active labor force (15-64 years), the implications of demographic ageing on economic and social well-being are more and more visible within all countries, but with different intensities [5,[11][12][13][14][15]. is requires an accurate and specific analysis by distinct groups of countries with similar characteristics.…”

A review of model-based scenario analysis of poverty for informing sustainability

“…The ratio of people living below the poverty line has been calculated by income distribution (Cuaresma et al, 2018), household income (Lázár et al, 2020), household consumption (Ahmed et al, 2018), and growth of gross domestic product (GDP) (Ashimov et al, 2019;Ndhleve et al, 2017). Some models estimated poverty rates based on labor productivities and education levels (Cristea et al, 2020) or ecological factors such as topography, rainfall, and desertification (Zhou et al, 2020). The poverty population indicator is similar to the poverty rate, which is defined as the number of people living below a given poverty line.…”