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Longevity benefits -

The calculation of this adjustment factor is much simpler than the first, consisting only of the observed average life expectancy for the entire cohort divided by the life expectancy of the individual's lifetime earnings quartile.

For example, for a man in the lowest earnings quartile in the s birth cohort, we divide For men born in the s, the first adjustment would increase the PIA of those in the lowest quartile by 13 percent and lower the PIA of those in the highest quartile by 12 percent Table 1.

By contrast, with the second adjustment, men in the lowest quartile of the s birth cohort would have a 10 percent increase in their PIA and those in the highest quartile would have a 9 percent decrease Table 2.

Conceptually, the second method adjusts benefits only for expected future differences in longevity whereas the first method also incorporates an adjustment for past changes. Thus, the second method results in a smaller departure from scheduled benefits.

Under both adjustments, the PIA would increase for those with lower lifetime earnings and decrease for those with higher lifetime earnings. However, under the second approach, the adjustments would be somewhat smaller. We also observe that the adjustment for those with lower lifetime earnings is generally greater for members of more recent cohorts than for those in the earlier cohorts.

In presenting these potential adjustments, we acknowledge that they constitute only two of many alternative conceptual approaches to adjusting PIA s to offset differential longevity. We do not argue that either adjustment is truly correct.

Rather, we demonstrate that adjusting for differential longevity with such methods would generally increase the PIA and retirement benefits for those with relatively low lifetime earnings and decrease those of individuals with higher lifetime earnings.

We anticipate that, by either method, this approach would reduce the cross-quartile gap in lifetime benefits attributable to longevity gains and would reduce poverty by compressing the distribution of benefits. We estimate the effects of these two potential adjustments on initial and lifetime benefits in dollars and on poverty rates under the official and supplemental measures.

Table 3 shows results for currently scheduled benefits without any adjustments. We see a more dramatic differential in lifetime benefits across cohorts.

Under the official measure of poverty at age 70, the rate increases with each successive cohort for men and for beneficiaries overall; the rate increases for both men and women under the supplemental measure.

Table 4 shows the projected effect of the first longevity adjustment relative to the benefits scheduled under current law shown in Table 3. The adjustment would result in virtually no net change in overall median initial monthly benefits. However, individuals in the lowest lifetime earnings quartile in any of the birth cohorts would see sizable increases in median initial monthly benefits.

We observe similar results in each cohort. A general pattern clearly emerges of benefit increases in the lower quartiles of lifetime earnings, with larger increases for later cohorts, and benefit reductions in the higher quartiles.

We observe the same pattern for lifetime benefits. As with the initial monthly benefit, the lowest quartile of the s cohort would accrue the greatest increase in lifetime benefits. The adjustment would reduce the official poverty rate overall and in each cohort.

Further, poverty would be reduced in the two lowest quartiles, with no increase in the two highest quartiles. The reduction in poverty would affect all cohorts, becoming increasingly pronounced in the later cohorts.

Some of the estimated poverty reductions are sizable. For example, in the s birth cohort, longevity adjustment 1 reduces the official poverty rate for the lowest quartile by 2.

For the supplemental poverty rate, we observe a similar pattern. Overall, the projected supplemental poverty rate would decline by 0. Likewise, the adjustment would reduce poverty for each cohort in the lowest quartiles and overall.

In the s birth cohort, for example, the adjustment would reduce the supplemental poverty rate for the lowest three quartiles, and the net effect for the entire birth cohort would be a reduction of 0.

Table 5 shows the effects of the second longevity adjustment on retirement benefits relative to currently scheduled benefits. Across cohorts, the pattern of changes in initial benefits under this adjustment is similar to that of the first adjustment. Increases in initial benefits for those in the lowest earnings quartile, which become more pronounced in each successive year cohort, are a consistent pattern.

Decreases in initial benefits for those in the highest quartile, regardless of cohort, are a similarly consistent pattern. These changes in initial benefits translate into a narrowing of the distribution of median lifetime benefits.

The patterns for poverty effects are similar to those of the first longevity adjustment. Table 6 tabulates the effect of both longevity adjustments on projected benefits and poverty rates for men and women by lifetime earnings quartile and year birth cohort.

Broad patterns emerge of benefit reductions for workers with higher lifetime earnings and increases for those with lower earnings, regardless of sex and birth cohort. Substantial decreases in the official and supplemental poverty rates for beneficiaries with lower lifetime earnings, and little or no increase for those with higher lifetime earnings, appear in all four birth cohorts and for men and women alike.

Because initial benefits are higher for men than for women, the dollar value of the benefit adjustment and the reductions in poverty are in most instances greater for men than for women. These patterns appear under either longevity adjustment.

Studies have shown that differential increases in life expectancy across lifetime earnings levels alter the progressivity of lifetime Social Security retirement benefits Waldron , ; Goldman and Orszag Workers with relatively low life expectancies at age 65 also tend to have lower lifetime earnings, lower benefit amounts, and higher poverty rates.

Thus, adjusting the benefit formula to offset changes in lifetime benefits driven by differential life expectancy could address unintended trends in system progressivity and old-age poverty. This article explores two particular examples of one conceptual approach to adjusting benefits for differential life expectancy.

Both adjustments aim to allow any given beneficiary to receive about the same relative advantage from increasing societal life expectancies. The first adjustment would increase or reduce an individual's benefits by a factor that would match that of a beneficiary with the cohort-average life expectancy relative to that of an earlier birth cohort.

The second adjustment allows each individual in a given cohort to collect longevity-adjusted benefits by equalizing average life expectancy within the cohort. Both adjustments increase benefits for individuals in the lowest quartiles of the lifetime earnings distribution and decrease benefits for those in the highest quartiles.

Thus, the distribution of benefits is compressed. The analysis shows that these adjustments would affect currently scheduled benefits as anticipated, and the effect would expand for successive cohorts because the longevity gap by socioeconomic status is projected to widen. Poverty rates based on both the official and supplemental measures would decline for those at the bottom of the lifetime earnings distribution.

In the higher earnings quartiles, poverty rates would be unaffected under the official poverty measure and would increase incrementally under the supplemental measure. Using either measure, overall poverty would decline. This research extends prior work studying benefit adjustments for differential gains in longevity.

Those analyses considered benefit adjustments for differential mortality as one approach among a range of policies that might be employed in response to poverty among older women Couch and others or in conjunction with other measures intended to address increasing life expectancy, such as raising the full retirement age Reznik and others Here, we project the effect of adjustments relative to currently scheduled benefits.

All of these analyses show that adjusting benefits to account for differential mortality reduces poverty primarily by increasing benefits for those with the shortest life expectancies.

Although the microsimulation methods used in the analysis are sophisticated and incorporate many factors, they rely primarily on historical patterns of individual earnings and mortality.

Recent events such as the COVID pandemic have clearly altered patterns of employment Couch, Fairlie, and Xu and mortality. Although this analysis does not reflect these recent changes, we expect that the general effect of the types of adjustments analyzed here would nonetheless be similar if we were able to account for them.

Even so, the results of this study should be qualified as not reflecting the effects of COVID Once the patterns wrought by the pandemic have become clearer, reconsidering the effect of this type of benefit adjustment would be appropriate.

Finally, this analysis does not consider the Disability Insurance and Supplemental Security Income programs. For individuals who have disabilities that would qualify them for these programs, one might anticipate higher mortality than that of the general population, and that these individuals would have relatively low lifetime earnings.

Thus, adjustments to the calculation of benefits, similar to those considered here, might also address differential longevity for disabled individuals. Future studies might analyze the effect of benefit adjustments based on differential changes in mortality to examine the potential implications for disability-program enrollment.

In , SSA 's Office of Research, Evaluation, and Statistics updated MINT 8 to the intermediate assumptions of Board of Trustees ; this article uses that updated version.

Further, records with missing benefit values are not included in the results. html aime. One potential implication of doing so is that lifetime access to economic resources could be understated for women with high-earning spouses.

However, we exclude the ever-disabled population in the calculation of life expectancy, whereas Goldman and Orszag included that group.

This difference may affect the comparisons because the life expectancy of the ever-disabled population is lower, on average, than that of the general population. However, testing the validity of that supposition was beyond the scope of this analysis. First, we compute the percentage difference between scheduled and adjusted benefits for each individual in the sample.

Then, we determine the median among those individual percentage differences. Arias, Elizabeth. Hyattsville, MD : National Center for Health Statistics. The Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds.

Washington, DC : Government Publishing Office. Bosworth, Barry, Gary Burtless, and Kan Zhang. Later Retirement, Inequality in Old Age, and the Growing Gap in Longevity between Rich and Poor.

Washington, DC : Brookings Institution. Bound, John, Arline T. Geronimus, Javier M. Rodriguez, and Timothy A. Burtless, Gary. Congressional Budget Office. Social Security Policy Options, Washington, DC : CBO.

The Long-Term Budget Outlook. Couch, Kenneth A. Fairlie, and Huanan Xu. Reznik, Christopher R. Tamborini, and Howard M.

Fox, Liana. The Supplemental Poverty Measure: Current Population Report No. Washington, DC : Census Bureau. Fox, Liana, Christopher Wimer, Irwin Garfinkel, Neeraj Kaushal, and Jane Waldfogel. Goda, Gopi Shah, John B. Shoven, and Sita Nataraj Slavov. Wise — Chicago, IL : University of Chicago Press.

Goldman, Dana P. Government Accountability Office. Retirement Security: Shorter Life Expectancy Reduces Projected Lifetime Benefits for Lower Earners.

GAO Washington, DC : GAO. Haveman, Robert, Rebecca Blank, Robert Moffitt, Timothy Smeeding, and Geoffrey Wallace. Masters, Ryan K. Hummer, and Daniel A. Adult Mortality: A Cohort Perspective. Montez, Jennifer Karas, Robert A. Hummer, Mark D. Hayward, Hyeyoung Woo, and Richard G. Adult Mortality by Race, Gender, and Age Group.

National Academies of Sciences, Engineering, and Medicine. The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses. Washington, DC : The National Academies Press.

National Center for Health Statistics. Olsen, Anya. Olshansky, S. Jay, Toni Antonucci, Lisa Berkman, Robert H. Binstock, Axel Boersch-Supan, John T.

Cacioppo, Bruce A. Carnes, Laura L. Carstensen, Linda P. Fried, Dana P. Goldman, James Jackson, Martin Kohli, John Rother, Yuhui Zheng, and John Rowe.

Panis, Constantijn, and Lee A. Near Term Model Development. Final Report, SSA Contract No. Santa Monica, CA : RAND. Pijoan-Mas, Josep, and José-Víctor Ríos-Rull. Poterba, James M.

Reznik, Gayle L. Couch, Christopher R. Sandell, Steven H. Smith, Karen E. Modeling Income in the Near Term 8 and Primer. Washington, DC : Urban Institute. Favreault, Barbara Butrica, and Philip Issa.

Modeling Income in the Near Term Version 6. Social Security Advisory Board. Social Security: Why Action Should Be Taken Soon. Each participant in the study completed a validated optimism test and provided demographic and health information.

When scientists analyzed the data, they found that the most optimistic women lived, on average, 5. The most optimistic women were also more likely to achieve exceptional longevity, defined as living over 90 years. These trends were consistent across all racial and ethnic groups.

Scientists also tested the hypothesis that optimistic women live longer because they have healthier lifestyles. Previous studies showed that optimistic people are likelier to engage in behaviors that promote health and longer lifespan.

Given this, the authors used statistical methods to determine whether lifestyle factors could explain the link between optimism and lifespan. Specifically, the study collected information on exercise, diet, body mass index, smoking history, and alcohol consumption.

These results suggest that the link between optimism and lifespan may be partly due to healthier behaviors, but that other pathways and factors are also likely to be involved. Another NIA-funded study, published in The Journals of Gerontology , explored the idea that reductions in stressful experiences could be one of the factors that explain the link between optimism and better health.

Prior studies from other research groups established that stress exposure is linked to worse health and a shorter lifespan.

In this study, a group of scientists from the VA Boston Healthcare System, Boston University, Harvard T. Chan School of Public Health, Rush Medical College, and Northwestern University analyzed the relationship between optimism, stress, and emotional well-being in older men.

Researchers found that more optimistic men experienced fewer negative emotions. These results suggest that optimism may cause older adults to avoid, direct their attention away from, or change how they think about stressful situations.

The authors note that their study is limited in that participants were all male, primarily White, and had a higher socioeconomic status than the general population. To determine whether the results apply to everyone, the study should be repeated in more diverse populations.

Results from these two studies provide important insights into how optimism may improve health and longevity. Findings from the first study show that optimism is linked to a longer lifespan across racial and ethnic groups.

Although differences in healthy behaviors can explain a modest portion of this link, that is only part of the story. The second study suggests that optimism may benefit health and well-being because it is linked to reduced exposure to stress.

Because optimism is a modifiable characteristic that can be changed with interventions like writing exercises and therapy, improving optimism may be an effective strategy to improve health and extend lifespan across racial and ethnic groups.

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