Speaker: Martin Genz
There exists a variety of literature on the question how the age distribution of deaths changes over time as life expectancy increases. However, corresponding terms like extension, compression, or rectangularization are sometimes defined only vaguely, and statistics used to detect certain scenarios can be misleading. The matter is further complicated since often mixed scenarios prevail and the considered age range can have an impact on observed mortality patterns.
In this paper, we establish a unique classification framework for realized mortality scenarios building on four statistics which describe changes of the deaths curve over time. The statistics determine whether elements of extension or contraction, compression or decompression, left or right shifting mortality, and what we call concentration or diffusion are present. We can identify not only pure, but also mixed scenarios where two or more statistics change at the same time. Furthermore, the framework can not only test the presence of a particular scenario, but also assign a unique scenario to any observed mortality evolution. Moreover, it can detect different mortality scenarios for different age ranges in the same population.
In addition to this intellectual concept, we present a methodology for the implementation of our classification framework. We suggest a three step approach to get from raw time series for each statistic to a classification of trends and identification of trend changes. Finally, we apply the framework to mortality data for US females.