Steve Goss, Chief Actuary U.S. Social Security Administration The Importance of Consistent Data on Deaths and Exposure over Time, by Age, Sex, and Cause of Death Steve Goss, Chief Actuary U.S. Social Security Administration Longevity 12 Conference, Plenary Session III September 30, 2016

Issues for Social Security, Medicare and for all Insurance/Benefit Plans The aging issue, not just mortality Age distribution = fertility, mortality, immigration National population vs sub-population Plan participants: active vs. in benefit Data: need to be appropriate and consistent Death data vs. exposure data By gender, income, race, cause of death—as needed Understanding the limitations Projections only as good as the data used

Paying for “Retirement” at a Macro Level Aging (change in age distribution) mainly due to drop in birth rates 3

Mortality Data Needed: for Whom? For population “at risk” National data for universal social insurance NCHS state-reported deaths; total, by cause Census estimated population Medicare/SSA data for ages 65 and over Sub-populations Retirees by career-average earnings level “Disabled” beneficiaries by age and duration: www.ssa.gov/oact/NOTES/pdf_studies/study123.pdf

Appropriate Data: by Age Critical Age-gradient in reduction is clear

Consistent Deaths and Exposure: Inconsistency can distort measured rates U.S. NCHS/Census national data All we have under age 65 Completeness and age-reporting concerns Used for Social Security as well as HMDB NCHS deaths by cause very useful Over age 65 Medicare/SSA data are better Consistent deaths and exposure More accurate age based on past reporting Current reporting can vary over time

Death Rates 65+ : NCHS/Census have been declining faster than Medicare/SSA rates

Mortality Decline Varies Over Time Antibiotics/economy 1936-54; health spending 1968-82

Reductions Even for “Best Nations” Have Also Varied over Time 9

Mortality Rate of Decline by Sex, Age, & cause of Death: from 1979 to 2013

Mortality Varies by Career Earnings Level Trustees Report projections have reflected this for decades 11

Limitations Better data = better projections = better planning Most critical factors Understanding the conditions of the past Anticipating what is likely to be different for the future Closing the table; is there an omega? Projections for the future from past data Interpretation and judgment

For More Information… http://www.ssa.gov/oact/ Documentation of Trustees Report data & assumptions https://www.ssa.gov/oact/TR/2016/2016_Long-Range_Demographic_Assumptions.pdf Historical and projected mortality rates https://www.ssa.gov/oact/HistEst/DeathHome.html Annual Trustees Reports https://www.ssa.gov/oact/TR/index.html 13