The COVID-19 pandemic has affected mortality in different ways. In the first half of 2020, many people died because of the illness. However, the number of deaths due to, for example, car accidents decreased as a result of the lockdown.
There continues to be a lot of uncertainty about the duration of the pandemic. How long it lasts depends on how the virus evolves, whether a working vaccine is invented, and whether the vaccine works in the long term. Because of these uncertainties, there are questions about how future mortality projections and future cash flow projections will emerge.
Global events like climate change, immigration, and the opioid epidemic have the potential to affect mortality rates, that is, the rate at which people die. Mortality rates drive many factors that affect our everyday living, like pensions, healthcare, and taxes, as Milliman principal and consulting actuary Dale Hagstrom discusses in the latest episode of our Critical Point podcast.
To listen to the entire podcast, click here. Also, to hear past Critical Point episodes, click here.
National mortality tables are crucial inputs to the quantification of mortality and longevity risks. In the absence of data specific to insured or pension populations, national mortality tables are based on general population data. Recent work by Milliman demonstrates problems with the reliability of these reference tables, including false cohort effects, and offers methodological improvements for their construction. In this paper, Milliman consultants Laurent Devineau, Alexandre Boumezoued, and Dale Hagstrom present both historical perspectives and new solutions to this problem.
Insurance companies can generate value when pricing, setting deterministic margins, determining economic capital, and determining its optimal mix of business by performing stochastic modeling with volatile mortality assumptions. In this case study, Milliman consultants Dan Theodore and Stuart Silverman explore relevant questions related to margins using a simple combination of life insurance and payout annuity products by applying stochastic projections of future mortality rates. It also compares percentile values from the stochastic projections to results using deterministic projections and margins. In addition, the study demonstrates the relative diversification benefit of the longevity exposure from the annuity product along with the mortality exposure of the life insurance product.
In my professional work I have had occasion to study the 1918 influenza pandemic that was so deadly for young adults. It appeared in waves, primarily during 1918 with smaller waves in 1919 and 1920. The Civil War raged for four years. Hacker estimates that nearly 23 percent of white males born in Southern slave states who were in the age cohort 20 to 24 in 1860 died as an “excess death” between the 1860 and 1870 censuses. Using a broad definition of “military age,” Hacker estimates that more than 13 percent of white males born in Southern slave states who were ages 10 to 44 in 1860 died as an excess death between the 1860 and 1870 censuses.
By comparison, 1.4 percent of males and 1.1 percent of females in the age 25 to 34 cohort died as an excess flu and pneumonia death during 1918–1920 (in which we assume the 1917 levels of flu and pneumonia deaths would have been the normal level for those three years if not for the new strain of influenza). Smaller proportions died from the flu in other age cohorts.
While his approach is wide-ranging, Hacker has built enough granularity into his model to produce valid conclusions. He compares actual male survival rates (defined by the 1860 and 1870 censuses for key cohorts) with the expected male survival rates based on the experience of the decades immediately adjacent to the 1860-1870 decade. Instead of using enumerated deaths to attempt a single best estimate, Hacker uses the census enumerations of the living to develop probable ranges of excess war deaths. He finds that the most probable number of war deaths—750,000—is more than 20 percent higher than previous estimates and that a realistic range varies from 650,000 at a lower bound (5 percent above the traditional estimate) to as many as 850,000.
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