During the time of the Roman Empire, an average citizen might expect to live 28 years. By the turn of the 20th Century, an American could anticipate celebrating his or her 48th birthday. In the early 21st Century, a Japanese woman reaching 80 would not be unusual. Welcome to the world of ‘life expectancy’ — a statistical measurement of the years a newborn child can expect to live, barring accidents and unnatural events.
Life expectancy is based on any number of factors, from incidence of disease 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 supplies, criminal activity and lack of quality medical care. All of those factors led to an average lifespan of under 30 years. The child from modern New York City benefits from disease prevention programs, clean food and water, advanced medications and economic stability. This means a lifespan of 77 years or more would not be unreasonable.
Statistics about life length are used for many reasons. Sociologists and other scientists may want to know if a particular race or population is living longer or losing ground in comparison to other groups. After a mass inoculation program for polio, for instance, the administrators would anticipate a longer life expectancy for those who were treated. Others may want to know if African-American males have a different lifespan than white American males. Such research may lead to a shift in resources to address the underlying causes.
Other professions also have an interest in life expectancy, for reasons you may not expect. Insurance companies spend countless hours collecting data on the general population, including relative lifespan. All of this data results in a table called an actuarial chart. This actuarial chart determines how many years a potential insurance client could 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 his or her beneficiary would collect the pay-out. A poor candidate for life insurance would be a heavy smoker in his mid-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 his or her maximum life expectancy, he or she usually lives 10 additional years.
Banks and other financial institutions also have an interest in life expectancy data. Loan officers may consider an 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 pension plans are also based on lifespan data. A certain percentage of retirees are not expected to reach their 75th birthday, reducing the pension obligations of their former companies.
This type of data can occasionally backfire, however. In France, for example, it is a common practice to pay a form of rent to elderly apartment dwellers for the right to assume ownership after they pass away. Since most of these residents have reached their maximum life expectancy, the young ‘renters’ rarely have to make payments for more than a few years. Many years ago, an elderly French woman in her seventies agreed to such a rental subsidy arrangement. The young renter assumed he would acquire her apartment within 10 years or so. The woman lived to be 122 years old, so the man paid nearly 50 years of rent before assuming the apartment.