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The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development. [ABSTRACT FROM AUTHOR]

Objective: The world population is aging rapidly and the well-being of older people is of great interest. Therefore, this study investigates the determinants of life satisfaction among the oldest-old (i.e. individuals aged 80 or over) in China. Materials and methods: We use the 2011/2012 Chinese Longitudinal Healthy Longevity Survey data (n = 6530) for this paper. Logistic regression is used to analyse the effects of socio-demographic, economic, health, instrumental activities of daily living, family and community factors on life satisfaction and depression among the oldest-old in China. Results: Our analysis confirms the significance of many factors affecting life satisfaction among the oldest-old in China. Factors that are correlated with life satisfaction include respondent’s sex, education, place of residence, self-rated health status, cognitive ability (using mini mental state examination), regular physical examination, perceived relative economic status, access to social security provisions, commercialized insurances, living arrangements, and number of social services available in the community (p<0.05 for all these variables). Although life satisfaction is negatively associated with instrumental activities of daily living (β = -0.068, 95%CI = -.093—.043), and depression (β = -0.463, 95%CI = -.644—.282), the overall effect of self-rated health status is positive (p<0.001). This confirms the primacy of health as the determinant of well-being among the oldest-old. Conclusions: Majority of the oldest-old in China rated their life satisfaction as good or very good. Our findings show that health and economic status are by far the most significant predictors of life satisfaction. Our finding on the primacy of health and relative income as determinants of well-being among the oldest-old, and the greater influence of self-rated health status over objective health measures is consistent with the findings of many past studies. Our results suggest that efforts should be directed at enhancing family support as well as health and social service provisions in the community to improve life satisfaction of older people. [ABSTRACT FROM AUTHOR]