No doubt you recognize this symbol. It’s the iconic Nike swoosh, one of the most familiar brand identifiers in the universe.

Through brilliant marketing, it has come to represent so much more than a shoe company. The swoosh stands for greatness, triumph, transcendence: the desire to win and the will to make it happen.

But to demographers, the swoosh also looks a lot like something else: the mortality curve.

Mortality curves are just what you would expect. They depict the odds of a person dying at any given age. Demographers use this information to calculate useful summary statistics of population health, like life expectancy.

One of the most interesting features of the mortality curve is its swoosh shape. During infancy, a person’s odds of dying are among the highest faced in a lifetime. Make it to early childhood and the chances of demise drop quickly, typically reaching a minimum in the early teen years. From there, mortality risk rises steadily into old age.

That’s nice—but what does it have to do with public policy?

Determining Swoosh Shape

The swoosh pattern is remarkably consistent. It holds both across different populations at any point in time, as well as within societies over time. But not all swooshes are created equal.

The ideal population is one whose swoosh is lower and flatter. This means the odds of dying at any age are lower, increasing less rapidly with age.

Like many things in life, societal wealth dictates outcome: in this case, swoosh shape. As countries get richer, their swooshes fall and flatten out.

Imagine a hollowed-out swoosh. The upper outline corresponds to mortality in a poor country, while the lower bound reflects the mortality of a richer country. At any age, a person’s chances of dying are less in the richer country.

Just as we can’t all afford the latest Nike shoes, not everyone gets to have the best swoosh.

At the international level, this much is obvious. You don’t need to be a development economist to appreciate that your likelihood of survival is better in Canada than in Uganda. You don’t need to know what a health gradient is to feel its effects. (For the record, it’s just a fancy way of saying income and health are positively correlated.)

However, there are also swoosh gaps within the U.S.

Annie Lowery called considerable attention to this issue in the New York Times. Although data should be interpreted cautiously—mortality rates can be quite volatile in places with small populations—it certainly seems Americans living in more prosperous places live longer.

The Mortality Swoosh

The graph below is a very simple illustration of the mortality gap types that exist in the U.S. I calculated the (unweighted) mortality rate by age group for the 10 richest and 10 poorest states (as measured by per capita GDP), using data from the CDC.

As you can see, the curve for poor states lies above the curve for rich states at all ages. (The picture is for females, but the same would be true of males, albeit males are more likely to die at any age than females—yet another health gap.)*

What do these mortality gaps mean in real terms?

Well, one way of summarizing the difference is to compare life expectancies between these two groups of states. Life expectancy at birth in the 10 richest states was 79.6 years in 2010. In the 10 poorest states, it was 77.3 years—a difference of 2.3 years, according to data compiled by the Kaiser Family Foundation.

That’s a pretty astounding difference. You get nearly two-and-a-half extra years just based on where you live! If this strikes you as unfair, good—it is.

Sharing the Swoosh

Before you pack up and move to Connecticut or Massachusetts, it’s important to consider several caveats. Lots of factors influence health. It might not be that wealth improves health, but instead that both are impacted by some other factor. Education, for instance, might improve both paychecks and health habits. It’s also possible that causality may run in reverse: healthier people earn more money.

The same goes for working more overtime or seeking a higher-paying job: hold your horses. At the household level, the relationship between health and wealth is even less clear. Most of the health gains associated with higher incomes are at the community level, and come from reductions in infant and child mortality through such things as better sanitation and health care.

For individuals, more wealth could mean less health. Working more hours may lead to more stress and less exercise; if having more money means you eat more or drive more, you may well reduce your chances at longevity.

Finally, much of the data on mortality, including that which I’ve reported here, comes with considerable uncertainty: measurement errors can produce misleading signals.

Nevertheless, including health as a measure of inequality is an important task for policy. Our discussions of inequality, however passionate they may be, tend to dwell on money. The important lesson of the mortality swoosh is that wealth and health tend to go together. Those less fortunate in one dimension are often also trailing in the other. Disadvantage begets disadvantage.

The takeaway is that we must view inequality more holistically. Policy needs to consider comprehensive well-being. To be sure, crafting such multidimensional policies are more challenging than simply focusing on dollars. But sometimes, going the extra mile makes all the difference. In other words, we need to just do it.


*The mortality rate—the number of deaths per 1,000 in the population—is plotted on a log scale, so that each step on the vertical axis represents the same proportional change in mortality; for example, moving from 1 to 4 is quadrupling, as is moving from 4 to 16. Economists frequently make use of such log transformations to make patterns clearer when data is clustered or skewed.  You can interpret the roughly straight lines from age 25 onwards as saying that the odds of death increase proportionately for each decade of adulthood—which is a lot different than saying your absolute chances of dying increases the same amount from 25 to 35 as they do from 75 to 85.