Description: The Society of Actuaries (SOA) is pleased to present historical U.S. population mortality rates by gender and single year of age for calendar years 2000-2020. To develop these rates, the SOA relied upon data furnished by the Centers for Disease Control and Prevention, Centers for Medicare and Medicaid Services, the U.S. Census Bureau, and the Social Security Administration. A document summarizing key observations and the process used to develop the rates, along with a table of the rates themselves, can be found below.

Description: This study quantifies the range of intercompany variability in mortality. Mean actual-to-expected (A/E) ratios based on amount were calculated in each quintile for every combination of six key variables.

Description: A comparison of recently used mortality assumptions among state-based and large-city public pension plans.

Description: After a sustained period of mortality improvement, U.S. population mortality improvement has stagnated in recent years. The presenters will discuss mortality improvement trends in the U.S. population and the causes of death contributing to these trends. U.S. population cause of death trends will be analyzed and compared to trends in the insured population. Mortality rates for major causes of death will be compared between the U.S. population and the insured population. In addition, assumption setting for both older age and later duration mortality as well as mortality improvement will be covered.

Description: This report discusses drivers of U.S. mortality improvement for the short and long term.

Description: U.S. Population Mortality Observations – Updated with 2021 Experience

Description: In this report, we consider high-level results from provisional mortality data released from the U.S. Centers for Disease Control and Prevention (CDC) for 2021, including looking at changes by sex and age group.

Description: This report provides an educational background on the process of building predictive models, as well as a detailed presentation of the model results. Predictive models provide a method to capture variation by multiple variables and understand the relationship between these variables. This allows for a deeper understanding of key variables than is possible under a traditional approach.

Description: This research will provide detailed insights into the risk factors that drive U.S. public-sector pension plan mortality experience. The public-sector pension plan mortality experience collected for this research will be aggregated, de-identified, and made available for further analysis. In addition, mortality tables based on this public-sector pension plan experience will be developed for use in the valuation and risk management of public-sector pension plans.

Description: Highlights the key takeaways from the 2021 published SOA PLT study on lapse and mortality experience of US term plans in the post level term period. This article explains the new insights in the latest study including a comparison of PLT experience between Jump to ART and Graded structures. The important variables driving PLT experience and the relationship between lapse and mortality experience are also discussed.