Abstract
Data quality issues at advanced old age, such as incompleteness of registration of vital events and age misreporting, compromise estimates of the death rates and remaining life expectancy at those ages. Following up on Horiuchi and Coale (Population Studies 36: 317-326, 1982), Mitra (Population Studies 38: 313-319, 1984, Population Studies 39: 511–512, 1985), and Coale (Population Studies 39: 507–509, 1985), we examine the conventional approaches to constructing life tables from data deficient at advanced ages and the two adjustment methods by the mentioned authors. Contrary to earlier reports by Horiuchi, Coale, and Mitra, we show that the two methods are consistent and useful in drastically reducing the estimation errors in life expectancy as compared to the conventional approaches, i.e., the classical open age interval model and extrapolation of the death rates. Our results suggest complementing the classical estimates of life expectancy by adjustments using Horiuchi-Coale, Mitra, or other appropriate methods and avoiding the extrapolation method as a tool for estimating the life expectancy.
Highlights
The life table model, which describes the current mortality profile in terms of a hypothetical survival of individuals in a synthetic cohort, is an important tool in studying mortality and its many implications such as insurance policies, social policies, and population projections (Chiang 1978; Preston et al 2001)
Our results show that the violation of the stationary population assumption of the classical life table method has strong consequences for the accuracy of life expectancy estimates
For a currently low-mortality population, closing the life table at age 65 would produce an Human Mortality Database (HMD)-average upward bias of more than 3 years and even larger root-mean squared error (RMSE) in the life expectancy at birth calculated by the classical method
Summary
The life table model, which describes the current mortality profile in terms of a hypothetical survival of individuals in a synthetic cohort, is an important tool in studying mortality and its many implications such as insurance policies, social policies, and population projections (Chiang 1978; Preston et al 2001). Contrary to earlier reports by Horiuchi, Coale, and Mitra, we show that the two methods are consistent and useful in drastically reducing the estimation errors in life expectancy as compared to the conventional approaches, i.e., the classical open age interval model and extrapolation of the death rates.
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