Men in the top 1 percent of the income distribution live 15 years longer than men in the bottom 1 percent.
A very poor American male has about the life expectancy of the average resident of Sudan or Pakistan.
A low-income resident of San Francisco lives so much longer than his or her counterpart in Detroit that it's equivalent to San Francisco literally curing cancer.
And these yawning disparities are getting worse, not better. Between 2001 and 2014, rich women gained 2.92 years of life while poor women only gained 0.04 years.
All these statistics come from a massive new project on life expectancy and inequality that was just published in the Journal of the American Medical Association. But they're not why the paper was so widely covered. As vivid as these comparisons are, they're not telling us anything we didn't already know.
After all, we already knew that life expectancy was split by income in America.
We already knew there were neighborhoods in Baltimore with higher infant mortality than the West Bank or Venezuela, and communities in the city with lower total life expectancy than North Korea.
We already knew that recent gains in life expectancy have been unevenly shared, and that retirees with above-average incomes are living six years longer than they were in the '70s but retirees with below-average incomes are living only 1.3 years longer.
Instead, what this paper told us is that the ways income inequality is affecting life expectancy aren't what we thought they were and thus are likely immune to most of the policy solutions we've been considering. It's not about being able to buy health insurance or medical care. It's not about residential segregation or the environmental toxicity of poor areas. It's not even about money, or at least not primarily about money. The numbers didn't change much when unemployment or social cohesion or religiousness was taken into account.
So what did work? Living a big, rich city, preferably one in California. As for why that works, well, that's where things get interesting, and maybe even just a tiny bit hopeful.
Many of the ways we thought inequality hurt health are wrong
Virtually all debates about inequality are bedeviled by confusion about cause and effect.
One problem that gets caught up in the inequality argument is stagnant median wages and persistent poverty. But is inequality a cause of stagnant wages and persistent poverty or a byproduct of them? And if it's the latter, then shouldn't we be focused on stagnant median wages and persistent poverty rather than the broader question of inequality?
Another version of the inequality argument takes disparities in political power as the central problem. But is inequality leading to disparities in political power, or is it a byproduct of them? And if it's the latter, then isn't campaign finance reform an easier and more direct target?
The same problems afflict the discussion about the way economic inequality maps onto health inequality. Is inequality of resources the driving problem? Or is inequality merely correlating to or reflecting the real problems?
This research, led by Harvard's Raj Chetty, was built to answer that question. And it did answer it, in a way. Its answer is that it's damn complicated. The structure of this paper mostly consists of Chetty and his co-authors systematically disproving the obvious pathways by which economic inequality would lead to health inequality.
An obvious theory is that the problem is the poor can't afford health insurance or good medical care. Maybe that explains these disparities?
No, "life expectancy for low-income individuals was not significantly correlated with measures of the quantity and quality of medical care provided, such as the fraction insured and measures of preventive care." Even joining Medicare at age 65 didn't much change life expectancy. (This paper, in that respect, joins a deeper well of literature questioning whether health insurance is really as valuable as we think.)
Is it that low incomes lead to dangerous environments — perhaps the poor breathe in more pollution and have access to less fresh food?
Chetty and his co-authors test this in an ingenious way. They reason that if the problem is that concentrations of poverty lead to environmental risks, then the poor will do better in areas where they're not segregated from the rich. The truth turns out to be the opposite: Low-income life expectancy is higher and health inequality is lower in areas with more income segregation, not less.
Is it that visible income inequality afflicts the poor with a toxic form of social stress, as some previous studies have suggested?
No, nearby income inequality appears to cut life spans more for the rich than it does for the poor, interestingly. Nor are related theses about inequality leading to social isolation borne out: Measures of social cohesion and religiousness had no effect on low-income life expectancy.
Perhaps it's simpler than all that. Maybe the problem is driven by unemployment and other labor market dysfunction?
The authors dash this theory, too. "Neither unemployment nor long-term population and labor force change was significantly associated with life expectancy for individuals in the lowest income quartile."
It's worth noting here that these results aren't uncontroversial, and there's other research — sometimes experimental research — that comes to very different conclusions. As Nobel Prize–winning economist Angus Deaton writes in an accompanying commentary, "All of these measures can (and in some cases should) be challenged as inadequate representations of what they purport to measure, and are unlikely to persuade many that health care is unimportant or that other measures of environment, such as environmental pollution, are not harmful to health and longevity."
But when clear relationships that you expected to see are hard to find, and you get into the game of this statistical control versus that statistical control, the lesson is often that whatever you're measuring is probably not that as powerful a driver of outcomes as you thought. That, at least, is my takeaway from this section of the report: not that it's impossible some of these factors do influence health, but that they don't explain all that much of health inequality.
The answer for cutting health inequality is in California ... somewhere
The paper, thankfully, does more than tell us what doesn't work for reducing health inequality. It also tells us what does appear to work.
"The strongest pattern in the data was that low-income individuals tend to live longest (and have more healthful behaviors) in cities with highly educated populations, high incomes, and high levels of government expenditures, such as New York, New York, and San Francisco, California," the authors write.
Let's be a bit more geographically specific. Of the 10 metro areas where poor men live the longest, six of them — San Francisco, Los Angeles, Santa Rosa, San Diego, San Jose, and Santa Barbara — are in California. The remainder are Miami, New York, Newark, and Boston. These are big, rich, diverse, dense blue cities. They also have a lot of internal income inequality.
The authors have a few hypotheses for why living in these cities might be beneficial. Perhaps these cities pass more aggressive public health policies — California, for instance, has been a national leader on smoking bans, and New York led the way on cutting trans fats. Perhaps there's more funding for public services in these cities, though it's hard to say which public services would be leading to these gains in low-income life expectancy.
Perhaps there's a behavioral component, where people in poorer areas pick up healthier behaviors from people in richer areas, though if that's the case it's not clear why life expectancy is better for the poor when they live in more economically segregated areas.
Harvard's David Cutler, a co-author on the study, guesses it's some mix of these. "It's some combination of formal public policies and the effect that comes when you're around fewer people who have behaviors like smoking, and therefore you smoke less," he told my colleague Julia Belluz.
One theory the researchers mention in passing is that these areas have high numbers of immigrants, and perhaps that makes a difference. That fits some of the data — it would help explain the beneficial effects of economic segregation, for instance, as that observation might be picking up on immigrant-heavy areas with high levels of social support. But it seems to conflict with other observations, like the fact that social capital and religiousness have so little effect.
Sadly, the study isn't able to prove anything even approaching causality for any of these explanations. It also can't tell us if there's potentially some unobservable — and thus uncontrollable — difference between the kinds of poor people who move to big cities in rich, blue states and the kinds of poor people who don't. And if there's any clear message embedded in the findings, it's to be careful making sweeping pronouncements based on suggestive, but far from proven, observational data.
So long as we're adding caveats, Deaton, in his commentary, also raises an interesting alternative interpretation of that data. He writes:
It is possible that much of the correlation with income is attributable to a combination of (1) reverse causality from health to income throughout the life course; (2) the effects of parental incomes on child health and child education, which in turn have profound effects on those children’s health and income in adulthood; and (3) the direct causal effects of education and cognitive function on health and on income, working through better behaviors, greater thought for the future, and a better ability to deal with the health care system.
Still, there are some conclusions that seem pretty firm in this study. Whatever is leading to the massive inequality in life expectancy is more complex than mere income inequality. Healthy behaviors matter more than access to medical care, and should probably get more attention from policymakers. Something good is happening in dense, blue cities, and that speaks to the need to study and export their successes but also the need to change housing and occupational licensing laws in places like San Francisco and Los Angeles, so more people can afford to live there and benefit from whatever it is that they've figured out.
A broader political lesson of the study is that whatever policies are driving the differences here, they're happening at the state and local level, not just (or even mainly) at the federal level.