The elderly dependency rate in the U.S. keeps rising

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

The U.S. population is getting older — that’s a fact. How does the country’s aging population impact its economy? One of the most popular ways to represent the changes that come with an increase in life expectancy is the elderly dependency ratio.

As suggested by its name, the elderly dependency ratio is the ratio of the elderly population (65 years and older) to the working-age population (aged between 15 and 64).

The U.S. has long experienced a surprisingly low elderly dependency rate — it has hovered around 19 percent since the late 80s. Immigration was a major contributing factor. Another major contributor was baby boomers — this large demographic group didn’t start to leave the working age range until 2011.

Once the boomers started reaching the age of 65, the elderly dependency ratio started to grow rapidly. According to the U.S. Census Bureau’s population estimates, it reached an all-time high of 23.8 percent in 2017. The World Bank’s estimates are slightly more conservative (their estimate for 2017 is 23.5 percent), but the trend is still readily apparent.

However, this is not all that surprising; in fact, the elderly dependency ratio in the U.S. is only catching up with that of other developed countries.

The video below shows how different the dynamic was for the G-7 countries since 1960. Note that we use the World Bank estimates for all countries, including the U.S.

Our next map highlights the demographic differences between the states.

It should be noted that we are using different data here (the U.S. Census Bureau estimates), but as we said earlier, the difference in estimates is rather small.


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About Andrey Kamenov

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

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