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

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 2000-2017.

Description: This report contains historical U.S. population crude mortality rates by gender and single year of age for calendar years 2000-2015.

Description: View the U.S. Population Crude Mortality Rates 2000-2016.

Description: This report covers historical mortality from 1999-2018 by race as categorized by population data from the Centers for Disease Control and Prevention’s (CDC) WONDER database. Mortality for all deaths and six specific causes of death are considered over this period. Additionally, in 2020, the COVID-19 pandemic has influenced mortality as a whole and this report shows the breakdown of COVID-19 deaths by race.

Description: This paper proposes a new mortality table as a minimum reserve standard for individual annuities - the 1971 Individual Annuity Mortality Table, '1971 IAM Table,' which is given herein. The construction of the new table is described and it is compared with current valuation standards.

Description: The SOA has collaborated with LIMRA, Reinsurance Group of America (RGA), and TAI on an ongoing effort to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience. The data collected for this study included 2.8 million deaths from life insurance policies covering the period January 1, 2015 to September 30, 2020.

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

Description: A comparison of recently used mortality assumptions among state-based and large-city public pension plans to the Pub-2010 Mortality Tables.

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.