Suchergebnisse

International audience ; Life expectancy at birth (e0) forecasts are used to estimate future pension costs. Previous French official forecasts have often overestimated e0. Recent forecasts published by demographers provide higher e0 estimates than official forecasts for France, Sweden, Japan and the USA, and do not consider that e0 could decrease, as in previous years because of flus, heatwaves, or other outbreaks. Too optimistic forecasts make that governments may overestimate future pension needs.

International audience ; Life expectancy at birth (e0) forecasts are used to estimate future pension costs. Previous French official forecasts have often overestimated e0. Recent forecasts published by demographers provide higher e0 estimates than official forecasts for France, Sweden, Japan and the USA, and do not consider that e0 could decrease, as in previous years because of flus, heatwaves, or other outbreaks. Too optimistic forecasts make that governments may overestimate future pension needs.

International audience ; Life expectancy at birth (e0) forecasts are used to estimate future pension costs. Previous French official forecasts have often overestimated e0. Recent forecasts published by demographers provide higher e0 estimates than official forecasts for France, Sweden, Japan and the USA, and do not consider that e0 could decrease, as in previous years because of flus, heatwaves, or other outbreaks. Too optimistic forecasts make that governments may overestimate future pension needs.

International audience ; Life expectancy at birth (e0) forecasts are used to estimate future pension costs. Previous French official forecasts have often overestimated e0. Recent forecasts published by demographers provide higher e0 estimates than official forecasts for France, Sweden, Japan and the USA, and do not consider that e0 could decrease, as in previous years because of flus, heatwaves, or other outbreaks. Too optimistic forecasts make that governments may overestimate future pension needs.

This paper studies the trends in life expectancy in Malawi since independence and offers possible explanations regarding inter-temporal variations. Descriptive analysis reveals that the life expectancy in Malawi has trailed below the Sub Saharan African average. From the 1960s through to the early 1980s life expectancy improved driven mainly by rising incomes and the absence of HIV/AIDS. In the mid 1980s life expectancy declined tremendously and never improved due to the spread of HIV/AIDS, the economic slump that followed the World Bank's Structural Adjustment programmes (SAP) and widespread corruption and poor governance in the era of democracy. At the turn of the new millennium, Malawians were no healthier than their ancestors at the dawn of independence though this improved after 2004. If Malawi is to meet its health Millennium Development Goals by 2015, good governance, improved agricultural performance and an increase in health expenditure should be at the heart of its development policies.

The subject of the research is growth factors of the average life expectancy. The goal of the research is to identify the most substantial factors that can impact the level and dynamics of life expectancy, and by influencing which it may be possible to increase the average life expectancy. The research methods used are observation, data collection, analysis, and synthesis, comparison, grouping, correlation and regression analysis, econometric modelling, systematic and complex approach. The research results. Within this research, the authors have investigated more than 40 variables across 16 countries based on the data of open sources, databases, and publications of official statistical institutions of specific countries for the period from 1969 to 2017; and from 1992 to 2017 for a number of recently formed countries. For the purposes of further analysis, the authors have chosen four countries: Belarus, Ukraine, Great Britain, and China. For those countries, the corresponding regression models between the level of life expectancy and a number of factors in two variations depending on the modelling interval have been developed, and the impact degree assessment has been made. The domain of applicability of the research results is the corresponding governmental policy targeted at increasing the average life expectancy, being one of the ways to save and augment human capital which plays a fairly important role in forming the national wealth of a country. Conclusions. Based on the results of the conducted econometric modelling, various models across a number of countries and several time periods have been developed and analysed. One of them is to assess the potential impact of the development of ecologically clean production on the level of average life expectancy. With the necessity of generalized reasoning, the authors have made an aggregate impact assessment of the most substantial factors on the endogenous variable in consideration, based on the complex interpretation of the results across the four chosen countries. Thus, the most prevalent variables in the descending order of influence are: final consumption expenses per capita, gross domestic product per capita, arable land per capita, and the current unemployment level. However, it is worth mentioning that in order to achieve the desirable level and dynamics of the average life expectancy, complex influence on the whole array of factors in consideration is necessary, not just on specific ones.

Background Many studies show large differences in life expectancy across the range of education, intelligence, and socio-economic status. As educational attainment, intelligence, and socio-economic status are highly interrelated, appropriate methods are required to disentangle their separate effects. The aim of this paper is to present a novel method to estimate gains in life expectancy specifically associated with increased education. Our analysis is based on a structural model in which education level, IQ at age 18 and mortality all depend on (latent) intelligence. The model allows for (selective) educational choices based on observed factors and on an unobserved factor capturing intelligence. Our estimates are based on information from health examinations of military conscripts born in 1944–1947 in The Netherlands and their vital status through age 66 (n = 39,798). Results Our empirical results show that men with higher education have lower mortality. Using structural models to account for education choice, the estimated gain in life expectancy for men moving up one educational level ranges from 0.3 to 2 years. The estimated gain in months alive over the observational period ranges from -1.2 to 5.7 months. The selection effect is positive and amounts to a gain of one to two months. Decomposition of the selection effect shows that the gain from selection on (latent) intelligence is larger than the gain from selection on observed factors and amounts to 1.0 to 1.7 additional months alive. Conclusion Our findings confirm the strong selection into education based on socio-economic status and intelligence. They also show significant higher life expectancy among individuals with higher education after the selectivity of education choice has been taken into account. Based on these estimates, it is plausible therefore that increases in education could lead to increases in life expectancy.

In this paper changes in the quality of health adjusted life expectancy of the British population between 1991 and 1998 are analyzed. It is found that at all given age levels life expectancy increased during this period. Life expectancy at birth increased by 1 year for women and by 1.5 years for men. It is further found that the prevalence of health problems and handicaps has increased during the 1990s. For all age categories distinguished the self-assessment of the quality of health also declined, on average. We finally find that quality adjusted life expectancy declined between 1991 and 1998 rather than increased.

BackgroundMany studies show large differences in life expectancy across the range of education, intelligence, and socio-economic status. As educational attainment, intelligence, and socio-economic status are highly interrelated, appropriate methods are required to disentangle their separate effects. The aim of this paper is to present a novel method to estimate gains in life expectancy specifically associated with increased education. Our analysis is based on a structural model in which education level, IQ at age 18 and mortality all depend on (latent) intelligence. The model allows for (selective) educational choices based on observed factors and on an unobserved factor capturing intelligence. Our estimates are based on information from health examinations of military conscripts born in 1944–1947 in The Netherlands and their vital status through age 66 (n = 39,798).ResultsOur empirical results show that men with higher education have lower mortality. Using structural models to account for education choice, the estimated gain in life expectancy for men moving up one educational level ranges from 0.3 to 2 years. The estimated gain in months alive over the observational period ranges from -1.2 to 5.7 months. The selection effect is positive and amounts to a gain of one to two months. Decomposition of the selection effect shows that the gain from selection on (latent) intelligence is larger than the gain from selection on observed factors and amounts to 1.0 to 1.7 additional months alive.ConclusionOur findings confirm the strong selection into education based on socio-economic status and intelligence. They also show significant higher life expectancy among individuals with higher education after the selectivity of education choice has been taken into account. Based on these estimates, it is plausible therefore that increases in education could lead to increases in life expectancy.