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Mortality improvement (generational mortality)

A mortality table with an improvement scale, aka a generational mortality table, has several inputs required to calculate adjusted mortality rates:

1) image/ebx_-72570037.gif = the base year for projecting mortality rates, e.g., 2000

2) image/ebx_-677057368.gif = mortality base rate for a person aged x in the base year y

and

3) improvement scale for the mortality rate. This factor is a function of age and, optionally, year of improvement:

Either image/ebx_-402665804.gif = annual rate of mortality improvement for age x in year of improvement z, or

image/ebx_1411631843.gif = cumulative factor of mortality improvement for age x in year of improvement z

If mortality improvement is based on age alone, all rates are assumed to be annual (and there are no inputs for year of improvement). Details about the methodology used for projecting mortality improvement are provided in separate sections of this article, below, for scales that vary by age and scales that vary by both age and year of improvement.

Mortality projection may be fully generational, i.e., projection extends indefinitely into the future, or only to a fixed end year, which produces a static table. Note the following, with respect to calculating the projected (adjusted) mortality rates:

 

Age by Year Improvement

If the mortality improvement scale varies by both age and year of improvement, an adjusted mortality rate in year z is calculated as the product of the mortality base rate and an annual rate f or cumulative factor F of mortality improvement (described below):

image/ebx_2017541364.gif = mortality rate for a life aged x in year z

The improvement factors f or F may be input directly. If annual improvement rates f are entered, ProVal will calculate the cumulative factors F from them, as follows for the standard case z > y:

image/ebx_-1000425350.gif

Note that the index z represents the year of the resulting adjusted mortality rate. That is, the mortality improvement from year 2000 to year 2001 is given by the year of improvement 2001 and not 2000. More information regarding this convention is available in the Society of Actuaries RPEC Response to Comments on Mortality Improvement Scale BB Exposure Draft, Section 7.8.

For the special case where z < y (e.g., for entry age normal liability methods), the adjustment is instead performed as follows:

image/ebx_2129722301.gif

Note that if the cumulative improvement factors F are input directly, the factors in year y are expected to be 1. If they are not, the rates for each age will be normalized through division by the factors in year y:

image/ebx_477130447.gif

For example, consider the case with these mortality base rates and improvement scale rates:

Age (x) qx2000 fx,2001 fx,2002 fx,2003
65 0.012737 0.0261 0.0242 0.0230
66 0.014409 0.0275 0.0269 0.0255
67 0.016075 0.0274 0.0281 0.0278

 

With a base year of y = 2000, the adjusted mortality rates in years 2001-2003 would be calculated as follows:

Age (x) qx qx,2001 qx,2002 qx,2003
65 0.012737 0.012737 (1 - 0.0261) = 0.012405 0.012737 (1 - 0.0261)
(1- 0.0242) = 0.012104
0.012737 (1 - 0.0261)(1 - 0.0242)
(1- 0.0230) = 0.011826
66 0.014409 0.014409 (1 - 0.0275) = 0.014013 0.014409 (1 - 0.0275)
(1 - 0.0269) = 0.013636
0.014409 (1- 0.0275)(1 - 0.0269)
(1 - 0.0255) = 0.013288
67 0.016075 0.016075 (1- 0.0274) = 0.015635 0.016075 (1- 0.0274)
(1- 0.0281) = 0.015195
0.016075 (1- 0.0274)(1 - 0.0281)
(1- 0.0278) = 0.014773

 

 

Age-based Improvement

In the simplified case that the improvement scale does not depend on year of improvement , i.e., the rates f are the same for all years, the relation above further simplifies (for any z) to:

image/ebx_-1615109579.gif

Therefore,

image/ebx_-2090236264.gif

Thus an improvement scale without the dimension for year of improvement produces a simpler adjustment to mortality rates.

Note that this example assumes different base rates and a different scale from those in the example above:

Age (x) qx2000 fx qx,2001 qx,2002 qx,2003
65 0.015629 0.014 0.015629 (1 - 0.014) = 0.015410 0.015629 (1 - 0.014)2 = 0.015194 0.015629 (1 - 0.014)3 = 0.014982
66 0.017462 0.013 0.017462 (1 - 0.013) = 0.017235 0.017462 (1 - 0.013)2 = 0.017011 0.017462 (1 - 0.013)3 = 0.016790
67 0.019391 0.013 0.019391 (1 - 0.013) = 0.019139 0.019391 (1 - 0.013)2 = 0.018890 0.019391 (1 - 0.013)3 = 0.018645