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Life expectancy has increased dramatically over time due to factors such as disease prevention, clean food and water, advanced medicine, and economic stability. Sociologists, insurance companies, and financial institutions use lifespan statistics for research and decision-making purposes. However, relying on life expectancy data can sometimes backfire, as seen in the case of a French woman who lived to be 122 and caused her young tenant to pay nearly 50 years’ rent.
During the time of the Roman Empire, an average citizen could expect to live 28 years. In the early 20th century, an American could anticipate celebrating his 48th birthday. In the early 21st century, a Japanese woman reaching her 80s would not have been unusual. Welcome to the world of “life expectancy”, a statistical measurement of the years a newborn can expect to live, barring accidents and unnatural events.
Life expectancy is based on any number of factors, from disease incidence to personal lifestyle choices to environmental conditions. Genetically speaking, a child born in ancient Rome is no different than a child born in New York City in 2005. But the Roman child faced more communicable diseases, unsanitary food and water, criminal activity, and lack of quality medical care. All these factors have led to an average life span of less than 30 years. The child of modern New York City benefits from disease prevention programs, clean food and water, advanced medicines, and economic stability. This means that a life span of 77 years or more would not be unreasonable.
Lifespan statistics are used for many reasons. Sociologists and other scientists may want to know whether a particular race or population is living longer or losing ground to other groups. After a mass polio vaccination program, for example, administrators would predict a longer life expectancy for those treated. Others may want to know if African American males have different life spans than White American males. Such research can lead to a shift of resources to address the underlying causes.
Other professions are also interested in life expectancy, for reasons you might not expect. Insurance companies spend countless hours collecting data on the general population, including their lifespan. All of this data translates into a table called an actuarial chart. This actuarial chart determines how many years a prospective insurance customer can be expected to live. The ideal candidate for a life insurance policy, for example, would have many more years to live and pay premiums, before its beneficiary collects payment. A poor life insurance candidate would be a chain smoker in his 60s with a history of heart disease. The actuarial chart would reveal that he has already exceeded his life expectancy. The good news is that once a person reaches their maximum life expectancy, they usually live 60 years longer.
Banks and other financial institutions are also interested in life expectancy data. Loan officers may consider the applicant’s age as part of the approval process. Lenders need to know if a borrower will most likely be alive to make the final payment. Some financial benefits such as retirement plans are also based on lifespan data. A certain percentage of retirees are expected not to reach the age of 75, reducing the pension obligations of their former companies.
However, this type of data can occasionally backfire. In France, for example, it is common practice to pay a form of rent to elderly apartment residents for the right to assume ownership after their death. Since most of these residents have reached their maximum life expectancy, young “renters” rarely have to make payments for more than a few years. Many years ago, an elderly French woman in her 70s agreed to a similar rent subsidy deal. The young tenant thought she would buy her apartment within about 10 years. The woman lived to be 122, so the man paid nearly 50 years’ rent before taking over the apartment.
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