How Coronavirus Pandemic and Other Natural Events Affect Life Insurance Mortality Rates

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The story is told that a group of intellects convened a meeting in which they would discuss the greatest inventions of mankind. After several participants suggested inventions like the wheel, nuclear power, printing press, automobile, computers, my uncle George (fictitious) suggested to this august group that the most important invention was the thermos jug. Taken back just a little, the group inquired of him why he would suggest that invention.

“Well, it keeps things cold and it keeps things hot.” 


“How do it know?” was his reply.

Of course, with a little bit of research the answer to Uncle George’s consternation is easy to explain.

So it is with determining a person’s chance of dying in any given year. How do we know? Here’s some thoughts


In the earliest history of the life insurance concept, there was no special way to determine who would or wouldn’t die. It was only an accepted fact that someone would. With that observation, it is recorded that members of the Roman military had an informal way of compensating a fallen comrade’s family if one were to die in battle. Since the soldiers were given rights to spoils of war, each soldier would take a portion of his plunder and place it on the shield of his deceased comrade. This was taken to the family so they would have some way of taking care of themselves. This would work all right when the army was victorious but not so well when they were defeated. 

In order to solve this issue, a payment plan was established in which each soldier would have from his wages a portion set aside which would accumulate for his retirement and for compensation to the family in the event of an untimely death. 

Also, in ancient Rome an early form of life insurance was created under “burial clubs” where the cost of the member’s funeral and survivors assistance was covered by dues the members paid. Since they had no way of knowing what the cost was going to be, these types of life insurance were spotty at best. It was also not distributed until the end of the year when the accounting was done and allocations were distributed.

The Enlightenment Era or Age of Reason during the 17th through 19th centuries with their intellectual and scholarly movements brought about many societal changes. One of those scholarly movements had to do with the theory of probabilities and statistical analysis. What was the chance that a two sided coin would come up either heads or tails? What was the probability that a certain color of calf would be born to a white or black cow? What was the chance a certain asteroid would hit the earth? 

Edmund Halley (circa 1652-1742) was one of those intellectual and scholarly participants. He was a renowned astronomer most noted for his plotting the course of a comet which had been observed since 240 BC on a similar celestial orbit. In 1705, by using Isaac Newton’s theories of gravitation and planetary motion, he correctly predicted the comet would appear again in the year of 1758. Even though he died in 1742, when the comet appeared as predicted in 1758, the Royal Society named it after him posthumously. 

Computations for Life Tables

Even though his first love was astronomy, Halley made forays into financial economics, demography, and actuarial sciences. He derived formulae for approximating the annual percentage rate of interest implicit in financial transactions and annuities. He also developed the first life table based on sound demographic data; and he discussed several applications of his life table, including calculations of life contingencies. 

Halley based his life table on a project he was involved with in recording births, deaths, and the ages of people when they died for the city of Breslau, Germany, a town of 34,000. The life table showed the number of people surviving to any age from a cohort born the same year. This was a particularly good source because in Breslau the birth year and death year of each person was recorded, where other cities did not require the specific death year to be part of the public record. 

The intent of this information is not to belabor the details of Halley’s computations but to recognize his contribution to this field of knowledge. A detailed explanation of computations can be found at A mathematician will find it very interesting and informative.

One can imagine how laborious it must have been to hand record each individual for these life tables. Though we still use life tables similar to the ones he developed and we still make calculations of life contingencies as he did, we do so with high speed computers.


Here is one observation Halley makes at a point in time where he seems to have tired of the laborious calculations involved in his formulas. “How unjustly we repine at the shortness of our lives” and “think ourselves wronged if we attain not old age.” After observing that only about half of Breslau’s 1,238 newly born children survive 17 years, Halley added that we should not fret about “untimely death” but rather “submit to that dissolution which is the necessary condition of our perishable materials.” He concluded this train of thought by observing the “blessing” we have received if we have lived more than the median years of life at birth. Halley’s second, and last, comment dealt with human fertility. He calculated approximately 15,000 persons between ages 16 and 45 and estimated that at least 7,000 were “women capable to bear children.” He reckoned that 1238 births relative to 7,000 fertile women were “but little more than a sixth part.” If all women in this age group were married, Hally thought “four of six should bring a child every year.” Celibacy was to be discouraged and large families encouraged because “the strength and glory of a king” was in direct proportion to the “magnitude of his subjects.” Halley concluded with a carrot and stick policy prescription: the stick part was that celibacy should be discouraged through “extraordinary taxing and military service,” and the carrot was that large families should be encouraged through society finding employment for poor people and through laws such as the “jus trium liberorum among the Romans (having three or more children brought political privileges and extra rations of corn).” 


It is obvious the larger numbers in a sample make statistical calculations more accurate; but due to physical constraints of handwritten tables and computations, too large of samples are so encumbersome one cannot on a timely basis draw conclusions which can assist in establishing rates necessary to meet the life insurance market place. Even though Halley developed life tables as early as 1693, actuaries were not very useful in setting life insurance rates until the 1750 era when mathematical and statistical tools were sophisticated enough to help develop modern life products.

James Dobson, a mathematician and actuary, attempted to establish a new company aimed at correctly offsetting the risks of long term life but for the unexplainable reason of his advanced age could not get a charter from the government. It therefore had to wait until 1762 for Edward Rowe Mores, a disciple of Dobson, to establish the Society for Equitable Assurances on Lives and Survivorship. It was the first mutual insurer and it pioneered age based premiums based on the mortality rate, laying “the framework for scientific insurance practice and development and the basis of modern life assurance upon which all life assurance schemes were subsequently based.” He was the first life insurance company creator who referred to his chief officer as an actuary, and that was William Morgan who served from 1775 to 1830.

This was a business title only until 1809 when Jacob Shoemaker of Philadelphia, an actuary by profession, was hired by the Pennsylvania Company on Lives and Granting Annuities to establish their rates. Massachusetts Hospital Life in Boston (1823) and New York Life and Trust Company (1830) followed suit.

Keeping up with the times

Beginning in 1941, insurance companies began using a standardized mortality table based on data provided by insurance companies and created by actuarial organizations called the Commissioners Standard Ordinary (CSO) mortality table. This table was updated in 2001 and again in 2017. It is reported that more than double the number of companies providing data in 2001 gave input for the 2017 CSO. This data gave a much more refined look at mortality since the data used included more information regarding smoking habits, substandard policies, rapidly changing medical advancements, technology, lifestyle trends, and updated reserving requirements. This table is expected to be replaced with a new CSO in January of 2020.

Complications and Challenges for Actuaries

An old adage used by life insurance salesmen in an attempt to get someone to purchase insurance is “we can tell you how many are going to die this year–we just can’t tell you which one.” This insurance has to be in place before it is needed. If not, it is like ordering up a parachute when the plane is coming down. Too Late!!

Some of the greater challenges to actuaries now include economic and political events rivelling in intensity, confronted in early 1930’s severe inflation, intensity of consumerist crusades, negative developments as the prevalent preoccupation with short term profits in business enterprises (showing guaranteed cash values at an early time in the life of a policy), demands upon actuaries to assume major unaccustomed responsibilities, and societal shifts in lifestyles. 

Examples of these societal lifestyle shifts include, under the feminist movement, women be treated equally to men when it comes to rates. It is a proven fact women have a higher morbidity rate than men while having a lower mortality rate (they go to the doctor more often but have a longer life expectancy. )This has given rise to a unisex rate which does not reflect actuarially a woman’s true computation, but that is what the movement wanted. (Not all State Insurance Commissioners have required life insurance companies to use this skewed rating system. They still recognize male and female rate calculations.)

Insurance companies no longer in their underwriting questions are allowed to ask questions regarding communicable diseases associated with lifestyle, i.e. HIV or other sexually transmitted ailments.

Just as the 1918 Flu Pandemic had a major influence on health insurance rates, the 2017 Coronavirus Pandemic will cause a temporary spike in health insurance premiums. This will be reflected in the very next go around in rate adjustments due to several factors. 

  1. Coverages which were not in the contractual covered benefits being mandated by the government to now be paid don’t just get absorbed and forgotten. 
  2. Fear and uncertainty regarding future pandemics will become a larger factor in establishing rates.
  3. Government intervention will definitely have a negative effect on premiums being charged. The concept of health insurance for all comes with a price tag even though there are some who feel the government has such deep pockets we cannot possibly tap them out.

With the data available to life insurance companies, this pandemic will show as a blip on a radar screen unless it goes well beyond its influence now. It will be informative to us all to watch the high priests of the actuarial world incorporate the societal impact of this pandemic and with other factors see how the next CSO table will be influenced.  Some may say we have reached the end of the world as we know it, but others of us say, “keep planting your gardens and fruit trees.” Halley’s comet will come around again and again and again!

(Much credit to James E. Ciecka, Professor, Dept of Economics, DePaul University for the influence he had on this article. Much of the information comes from his article “Edmond Halley’s Life Table and Its Uses,” printed in Journal of Legal Economics 15(1);pp.65-74, 2008; and to Boyce F. Lowery; CLU, ChFC article, “How will the new Insurance Table Affect You” Though the author used them for resources, he is totally responsible for the compilation of information included in this article.

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