The consequences of an ageing workforce

Unprecedented growth in the elderly population shares is experienced in China and India, and this study focuses on the implications for overall cognitive functioning development in these countries. Age-related decline in certain cognitive abilities can imply a decrease in overall cognition levels. Although the cognitive ability levels among the 50 plus are lower in India, China ages faster than India – leading to the average cognition levels to fall faster in China and reducing the differences between the two countries. Improvements in education levels, however, could potentially have an opposing effect. We estimate that projected educational increases could lead to a stabilization of our human capital measure for China and a slight increase for India, in spite of aging.

The promotion of education later in life has become a key issue in policy discourses, both with regard to strengthening economic competitiveness and to reducing social inequalities in a context of aging societies. However, country differences in participation patterns are less understood yet. The majority of available analysis are single country studies and focus only on individual factors influencing enrolment, while country-specific factors are barely taken into account. We contribute to this research area by offering a more comprehensive overview about different determinants of education later in life. Firstly, we distinguish between four different adult education activities and can therefore offer a more detailed distinction of education later in life. Secondly, we examine up to 26 European countries, and thus include more cases than most papers have done before, with the most recent data available on this topic, the European Adult Education Survey from 2007. Thirdly, we apply multilevel analysis to test for each of the four adult education activities separately theoretically important country-specific determinants that are welfare, labour market and educational characteristics as well as economic indicators.

Analyses of poverty are rarely disaggregated by age. This paper’s novelty lies in examining poverty rate among older persons, considering individual and household variables. It further examines variables associated with poverty, specifically framing the analyses within the context of economic disparity in the province. Three districts are selected and analyzed separately derived from the 2002 Indonesian National Socio-economic Survey. A formula is used to estimate the rate and severity of poverty. Logistic regression model is used. Our findings suggest that elderly poverty rate varies among the districts with the lowest (5%) occurring in the highly urbanized district of Surabaya, while the rate is much higher in others (22.2% in Pacitan, and 22.8% in Malang). Age, sex, marital status, education, living arrangement, and social participation are significant. Household variables (such as the size of household, asset ownership, and whether the household is the target of poverty alleviation policy) are also significant. In urban area, those living with children are more likely to be poorer than those without children. Yet, in less urbanized areas, those with children are less likely to be poorer than those without children.