This chart is based on results from one of the data visualization tools Magali Barbieri created for the SOA. (Credit: Magali Barbieri)
Magali Barbieri, a demographer with the University of California-Berkeley, says the gap between the life expectancy of the U.S. residents in the top socioeconomic status category and U.S. residents in the lowest category grew sharply between 1999 and 2018.
In 1999, for example, a baby girl born into a family in the top 10% of the population in terms of socioeconomic status could expect to live to age 81.3 years, or 3.4 years longer than a baby girl born into the bottom decile.
In 2018, a baby girl born into the top decile could expect to live 5.9 years longer than a baby girl born in the bottom decile.
For boys, the life expectancy gap at birth increased from 5.5 years in 1999 to 7.3 years in 2018.
Barbieri found similar gap widening occurring for U.S. residents for other age groups. For annuity issuers, for example, one key age group is people turning 65. Some people retire at age 65 and then shift part or all of their retirement savings into annuities that offer lifetime income benefits.
For men, the life expectancy gap at age 65 increased to 3.1 years in 2018, from 1.8 years in 1999.
For women, the gap increased to 1.7 years, from 1.3 years.
Resources
- Barbieri study resources are available here.
- An article about the lifespan of teachers is available here.
Barbieri prepared the report for several subgroups at the Society of Actuaries (SOA), which posted the report and a collection of related resources, including a set of data graphing tools, on its website.
The SOA says in a disclaimer note that the views expressed in the report are those of the author, and that many factors go into overall mortality and mortality improvement trends.
The Methods
In the executive summary, Barbieri says that she created county-level socioeconomic status scores using data on factors such as education, occupation, employment, and housing price and quality from the U.S. Census Bureau's American Community Survey, in addition to income. She then created socioeconomic category life expectancy figures by using the socioeconomic status scores and county population figures.
