Demographers and actuaries make the following distinction between life expectancy and longevity: Life expectancy refers to the average number of years someone will live from a given age, whereas longevity refers to how long he or she might live if everything goes well, typically expressed as the probability of living beyond a certain age such as 85, 90 or even 100.
A growing body of evidence shows that many people are ignorant of their so-called longevity risk—the probability of living a very long time—and the complications that presents.
Drs. Hurwitz and Mitchell note that retirement calculators provide information about average life expectancy, but not longevity. They have found that about five times as many Census Bureau publications relate to life expectancy as longevity. Thus, people who have planned appropriately for their life expectancy might miss how likely they are to live longer.
People can look up their longevity risk with an online Longevity Illustrator maintained by the American Academy of Actuaries and Society of Actuaries, based off the latest mortality data from the Social Security Administration.
As seen from subtracting the total cost shown in Table 2 from the total income shown in Table 1, Social Security paid out $56.3 billion more in benefits and expenses than it collected in income.
Because Social Security has trust funds, the total costs of 2021 were still met. However, the trust funds declined in 2021 by the $56.3 billion that costs exceeded income. At the end of 2020, the trust funds totaled $2,908.3 billion, and at the end of 2021, the trust funds totaled $2,852.0 billion.
The more fundamental changes affect the measurement of future services (previously termed as “Reserves”). Many insurance accounting regimes have tried to stabilize their financial statements over the years; therefore, they calculated their reserves based on historic information—locked-in assumptions for insurance parameters as well as historic interest rates. The latter, however, are not in line with the use of market values for the asset side of the balance sheet, which is now perceived as the only fair-value representation for the different stakeholders. Therefore, the measurement of the liabilities in IFRS 17 will always be based on current assumptions.
Due to the compound effect over many projected years, the regular update of assumptions (particularly interest rate or discounting assumptions) can make long-term liabilities much more volatile.
Examine the quality of the theory behind the correlated variables. Is there good reason to believe, as validated by research, the variables would occur together? If such validation does not exist, then the relationship may be spurious. For example, is there any validation to the relationship between the number of driver deaths in railway collisions by year (the horizontal axis), and the annual imports of Norwegian crude oil by the U.S., as depicted below?36 This is an example of a spurious correlation. It is not clear what a rational explanation would be for this relationship.
Systemic Influences and Socioeconomics ❑ Checking for and removing of systemic biases is difficult. ❑ Systemic biases can creep in at every step of the modeling process: data, algorithms, and validation of results. ❑ Human involvement in designing and coding algorithms, where there is a lack of diversity among coders ❑ Biases embedded in training datasets ❑ Use of variables that proxy for membership in a protected class ❑ Statistical discrimination profiling shopping behavior, such as price optimization ❑ Technology-facilitated advertising algorithms used in ad targeting and ad delivery
Author(s): David Sandberg, Data Science and Analytics Committee, AAA
Social Security was originally funded by a tax on the wages of covered workers plus interest on accumulated taxes not yet paid out as benefits. Later, a tax on the benefits of some beneficiaries was added. • Both the tax rate and the limit on wages subject to taxation have been raised periodically to fund increases in the scope and amount of benefits. • According to the 2021 Social Security Trustees Report, accumulated assets will be depleted by 2034 and income to the system thereafter will be insufficient to pay all scheduled benefits when due. • Some or all of this shortfall can be averted by raising the tax rate on wages, increasing the limit on wages subject to taxation, broadening coverage to include all state and local government employees, increasing taxes on benefits, and/or creating new taxes dedicated to funding Social Security benefits. • This issue brief explores a wide variety of proposals for increasing system revenue that have been made over the years by members of Congress, government-appointed panels and commissions, and outside experts.
Author(s): American Academy of Actuaries Social Security Committee
From its inception, the formulas for determining benefits payable under the Social Security System have included elements of individual equity and social adequacy, so that benefits vary in proportion to differences in worker contributions, yet benefits are sufficient to meet the deemed financial needs of most workers and covered dependents. • According to the 2021 Social Security Trustees Report, accumulated assets will be depleted by 2034 and income to the system thereafter will be insufficient to pay all scheduled benefits when due. • Some or all of this shortfall can be averted by changing the primary formula for retired worker benefits, changing the formulas for determining the benefits of eligible spouses and other dependents of workers, and/or changing the formula for computing annual cost-of-living increases. • This issue brief explores a wide variety of proposals for changing the formulas for determining benefits that have been made over the years by members of Congress, government-appointed panels and commissions, and outside experts, with an eye toward how the proposed changes would affect the balance between individual equity and social adequacy.
Author(s): American Academy of Actuaries Social Security Committee
THE ACADEMY hosted a May 26 webinar, “What Is Unfair Discrimination in Insurance?” in which presenters explored the current regulatory infrastructure relating to unfair and unlawful discrimination in insurance and the challenges presented by the increased use of big data and artificial Intelligence (AI)-enabled systems.
Presenters were Daniel Schwarcz, an award-winning professor and scholar; former Illinois Director of Insurance Nat Shapo; and Brian Mullen, chairperson of the task force currently revising ASOP No. 12, Risk Classification ( for All Practice Areas). General Counsel and Director of Professionalism Brian Jackson moderated.
Mullen opened by providing background on ASOP No. 12. Schwarcz discussed prohibitions on “unfair discrimination”—which occurs when an insurer considers factors unrelated to actuarial risk—in rates and underwriting. He noted that machine learning AI tends to produce the same results as intentional proxy discrimination. As a result, insurance becomes less available and less affordable to individuals because of their race, sex, genetics, health, or income. He also discussed a proposed definition of proxy discrimination, practical tests for proxy discrimination, and the benefits of such a definition.
How did we get here? As long predicted, demographics explain a good deal: In a decade, the entirety of the boomer generation — some 70 million Americans born between 1946 and 1964 — will have hit retirement age. As a result, the number of people receiving Social Security benefits come 2034 will be more than double the beneficiaries in 1985.
But what wasn’t known as accurately was how much longer those boomers would live. “From 1940 to 2019, life expectancies at age 65 have increased by about 6.5 years,” says Amy Kemp, chair of the Social Security Committee of the American Academy of Actuaries.
The impact: Many workers will be receiving benefits for a longer period of time. And those with higher incomes, which are generally those who receive higher benefit amounts, tend to live longer on average.
The American Academy of Actuaries presents this summary of select significant regulatory and legislative developments in 2021 at the state, federal, and international levels of interest to the U.S. actuarial profession as a service to its members.
The Academy focused on key policy debates in 2021 regarding pensions and retirement, health, life, and property and casualty insurance, and risk management and financial reporting.
Responding to the COVID-19 pandemic, addressing ever-changing cyber risk concerns, and analyzing the implications and actuarial impacts of data science modeling continued to be a focus in 2021.
Practice councils monitored and responded to numerous legislative developments at the state, federal, and international level. The Academy also increased its focus on the varied impacts of climate risk and public policy initiatives related to racial equity and unfair discrimination in 2021.
The Academy continues to track the progress of legislative and regulatory developments on actuarially relevant issues that have carried over into the 2022 calendar year.
The American Academy of Actuaries has released a new public policy paper and issue brief cautioning that clarification may be needed regarding estimated life expectancy showing significant decreases in light of the COVID-19 pandemic.
“Reports of considerable decreases in life expectancy driven by COVID-19 may certainly garner attention, but they can potentially be misleading when based on a technical measure that assumes heightened pandemic mortality will persist indefinitely,” said Academy Senior Pension Fellow Linda K. Stone. “Service to the public is core to the American Academy of Actuaries’ mission, and we would be remiss not to share the actuarial profession’s expertise to help the public interpret such reports.”
The Academy’s new Essential Elements paper,Clarifying Misunderstanding of Life Expectancy and COVID-19, which is based on a December 2021 issue brief developed by the Academy’s Pension Committee, Interpreting Pandemic-Related Decreases in Life Expectancy, cites the potential of confusion arising from recent Centers for Disease Control and Prevention (CDC) estimates of significant life expectancy decreases primarily due to COVID-19. The CDC used a measurement known as “period life expectancy” to estimate life expectancy changes in 2020, publishing in July 2021 a preliminary estimate of a 1.5-year year-over-year decrease, and in December 2021 a final estimate of a 1.8-year decrease. However, the CDC’s methodology and the estimated decreases assume that the heightened mortality of the COVID-19 pandemic during the 2020 year will persist indefinitely—an unlikely scenario.
Period life expectancy measures demonstrate fluctuations that reflect events that influenced mortality in this particular period.14 For example, the Spanish flu pandemic of 1918 resulted in a dramatic decrease in period life expectancy, which was more than offset by an increase in period life expectancy the next year. A male baby born in 1917 had a period life expectancy of 52.2 years, while a male baby born in 1918 had a period life expectancy of only 45.3 years—a reduction of almost 7 years.15 The following year, a male newborn had a period life expectancy of 54.2, an increase of almost 9 years over the period life expectancy calculated in 1918 for a newborn male. These changes are much larger than those seen thus far with COVID-19, demonstrating the relative severity of that earlier pandemic relative to the current one.
It is instructive to review the impact of calculating life expectancies on a cohort basis, rather than a period basis, for these three cohorts of male newborns in the late 1910s. Using mortality rates published by the SSA for years after 1917, for a cohort of 1917 male newborns, the average life span was 59.4; for the 1918 cohort, average life span was 60.0; and for 1919, it was 61.5. Even these differences are heavily influenced by the fact that the 1917 and 1918 cohorts had to survive the high rates of death during 1918, while the 1919 cohort did not.
If both period and cohort life expectancy are measured as of 1920 for each of these groups (the 3-year-old children who were born in 1917, 2-year-old children who were born in 1918 and 1-year-old children who were born in 1919), differences are observed in these measures as they narrow substantially because the high rates of mortality during 1918 have no effect on those who survived to 1920. This is summarized in the table below.