People tend to live longer in some parts of the world than in others, the result of a cultural distribution of lifestyle choices such as smoking and becoming overweight, environmental exposure to, say, particulate air pollution and infectious disease, and access to medical technology. One can use the worldwide statistics of life expectancy to produce a “longevity-risk-adjusted global age” to compare with chronological age: longevity-risk-adjusted global age is higher than chronological age in countries with a higher late-life mortality rate and shorter life expectancy. What happens at the population level says very little about individual life expectancy, of course, as that is a matter of one’s own personal lifestyle choices, exposures, and access to medical technology, none of which necessarily have to bear any relation to the local median. This is nonetheless an interesting way to present the existing data on human life expectancy.

The boxer Muhammad Ali is quoted as saying that: Age is whatever you think it is. You are as old as you think you are. Similarly, author Mark Twain joked that: Age is just a state of mind. I say it is more about the state of your body. Clearly, there is a wide divergence of opinions about the proper definition of true age, all of which are quite distinct from the number of times you circled the sun. In fact, recent work indicates that economic behavior is highly correlated with how old people feel versus their chronological age. The pertinent question here is whether actuarial science can contribute yet another age metric, one that is consistent with heterogeneous mortality and known mathematical theories of aging. This paper argues that the answer is yes.

This paper develops a computational framework for inverting Gompertz-Makeham mortality hazard rates, consistent with compensation laws of mortality for heterogeneous populations, to define a longevity-risk-adjusted global (L-RaG) age. To illustrate its salience and possible applications, the paper calibrates and presents L-RaG values using country data from the Human Mortality Database (HMD). Under this approach, the data indicate that for a male at chronological age 55, the gap in L-RaG ages between high-mortality (e.g. Russia) and low-mortality countries (e.g. Sweden), can be as high as 20 years: a 55-year-old Swedish male has an L-RaG age of 48, whereas a 55-year-old Russian male is closer in L-RaG age to 67. Stated differently, using the language of risk-adjusted benchmarks, your true age depends on where you live.