Two interesting news were reported this week. Forbes Magazine reported that the net worth of the wealthiest 400 Americans increased by 13 percent compared to last year. This is hardly surprising, since the magnitude of the top fortunes have been growing rapidly over the last 30 years.
The second news, reported by New York Times’ Sabrina Tavernise, is “Life Spans Shrink for Least-Educated Whites in the U.S.” The real shocker is the magnitude of the shrinkage: between 1990 and 2008 the life expectancy of white women without a high school diploma declined by five years. At the same time white men who dropped out of high school lost three years of life. And we are not talking about a tiny group – Americans without a high school diploma constitute 12 percent of the population.
Are these two developments related? As far as I have seen most media reported them separately without making a connection. Tavernise, for example, discusses several possible explanations, but never comes to a conclusion. As she reports, “researchers offered theories for the drop in life expectancy, but cautioned that none could fully explain it.”
There are a multitude of possible proximate mechanisms explaining this drop in life expectancy (and Tavernise discusses many of them: more risky behaviors, overdoses from prescription drugs, smoking, less access to health care). But I would argue that the ultimate mechanism has to do with the growth of inequality in America over the last 30 years. So the two news stories are actually related. Incidentally, Tavernise never mentions the word ‘inequality.’
Inequality can take many forms. One is ‘categorical inequality,’ such as the discrimination on the basis of race or gender. This kind of inequality has been declining in the U.S. (although there is still much room for improvement). But at the same time different kinds of inequality, ‘quantitative’ rather than categorical, have been increasing.
One example is inequality in health, which could be measured by a variety of proxies, such as life expectancy. Life expectancy of the least educated white men is currently 67.5 years, while for men with a college degree or better it is 80.4 years. That is a gap of nearly 13 years. For white women the gap is just over 10 years. This is a really huge difference.
Another kind of ‘quantitative’ inequality is that of income or wealth. These two kinds of economic inequality are related. Clearly, one can acquire a great wealth only by enjoying large incomes (typically, over a number of years). Large wealth is also a source of income, when it is invested (and large fortunes always are). Because wealth and income are connected, trends in wealth and income inequality tend to move in parallel. In my research into the dynamics of economic inequality I tend to focus on wealth, because it is easier to quantify. For example, it is hard to trace income inequality in America for the period before the federal government began collecting income taxes (in 1913), but there are reasonably good data for the magnitude of top fortunes (so we can backproject the Forbes-type data to the nineteenth and even eighteenth century).
In particular, we know that during the nineteenth century the largest fortunes grew much faster than the median household wealth, culminating in a period known as the Gilded Age. We are currently living through what many commentators have called the ‘Second Gilded Age.’
But the two Gilded Ages were separated by a period when economic inequality was decreasing – roughly between 1910 and 1980. This period is sometimes called “the Great Compression.” So the dynamics of inequality in the US between 1800 and today were cyclic – up, then down, and now again up.
Interestingly, inequality in health was also cyclic, and its up- and down-trends closely trace the dynamics of economic inequality. Take a look at this graph from my forthcoming book on structural-demographic analysis of American history:
Long-term dynamics of life expectancies and average heights (stature) of native-born Americans
There are two measures of health here, life expectancy at age 10 and the average height. Around 1800 Americans were one of the healthiest nations on Earth. They certainly were the tallest (according to John Komlos and other researchers in the new discipline of anthropometrics). But during the nineteenth century, as economic inequality started to grow, common Americans began losing ground. By the end of the century they lost about 8 years of life expectancy and 4 cm (almost 2 inches) of average height.
Why do I treat this trend as one of increasing health inequality? Because, at least for stature, we have data for both average Americans and those who were better off. At the same time that the height of an average army recruit was declining, the heights of West Point cadets (who came from more privileged backgrounds) were actually growing. Because there are many more common Americans than the elites, the trajectory of the average height reflected the declining standards of life of the majority of people rather than the good health of the small proportion, the elites.
A 12 year old boy working in a sweatshop, from How the Other Half Lives by Jacob Riis.
During most of the twentieth century (before 1980), as economic inequality declined, both health indices grew explosively. Americans regained their status as the tallest nation. Then came the turning point of 1980. The graph above does not really show what happened after 1980, because it is based on data published during the 1990s. But studies examining health indices in recent decades suggest that trends are not particularly encouraging. For example, John Komlos and co-author Marieluise Baur recently published an article with the telling title: “From the Tallest to (One of) the Fattest: The Enigmatic Fate of the American Population in the 20th Century.” In the abstract, they write that today
the Dutch, Swedes, and Norwegians are the tallest, and the Danes, British and Germans – even the East-Germans – are also taller, towering over the Americans by as much as 3-7 cm. Americans also live shorter. The hypothesis is worth considering that this adverse development is related to the greater social inequality, an inferior health-care system, and fewer social safety nets in the United States than in Western and Northern Europe, in spite of higher per capita income.
And this brings me back to the report on the declining life expectancy for high-school dropouts. Historical data show that rampant economic inequality results in declining standards of life for the least advantaged segments of the population. This is what happened during the nineteenth century in America, and, apparently, this is what is happening today.
How much does factoring in immigration modify that data? If immigrants were, for instance, less healthy than natives, and there was more immigration in the second half of the 19th cenutry.
The data is for native-born Americans. There was a lot of discussion and arguments and analyses back and forth, but the consensus is that the patterns are real. One of the key players in this field is Robert Fogel. See his book:
Fogel, Robert William. 2004. The escape from hunger and premature death, 1700-2000. Cambridge: Cambridge University Press.
Immigration played a role, but an indirect one. Massive immigration resulted in an oversupply of labor, which depressed wages and living standards for native-born Americans.
Careful here, Peter.
The data shows that lifespans increased substantially for all Americans on average. It also increased substantially for the average black, average hispanic and average white. And it increased substantially for the average man and average woman. Somehow this news gets spun as a bad thing because a small subset of high school drop out whites actually has an average decline, which is also significant.
As someone who managed large teams of statisticians for most of my career, but is a non statistician myself, may I share the obvious question that needs to be asked?
Has the sub segment in question changed significantly over this time?
A cursory review shows that percentage of the American population without a high school education has dropped by almost half during this time. From 22% to 12% according to the Census Bureau. In other words, a huge segment of this subgroup exited the group and became part of another group. In other words, it is entirely possible, and in light of the larger trend quite possible, that there was no decline at all. What may have happened is that the better educated, less obese, less likely to smoke less likely to have children out of wedlock and so forth group transitioned on to the high school degree group, leaving the hard core underprivileged with systemically lower lifespan behind.
Again, my take, like yours, is a hypothesis, which can be empirically verified by digging into the data. If true or partially true, it would reveal that the actual plight of the poor did not get worse, it got better (in terms of not just lifespan but education too) and that the widespread concern over the wealthy getting wealthier is perhaps influenced by the introduction of the zero sum bias on economic thinking (by assuming that the wealthy got wealthy at the expense of others rather than by benefiting others — which again can be true even if inequality increases within a nation as explained by supply and demand internationally)
This is a good point, and I agree that it is possible that the declining life expectancy of high school dropouts is due to a healthier half moving out of this group. My guess is that the drop in life expectancy is too large for this effect to be due to what is essentially a sampling artifact. However, you are right in that this possibility needs to be investigated with data,
What we really need is to look at the whole distribution of life expectancies in 1990 and 2008, and determine whether the overall variance has increased (almost certainly the case). More interesting is whether there was an absolute decrease for the lowest 10 percent.
I am getting ready for a long trip, so I don’t have time to chase down an answer to this question, but given the political importance it is likely that someone else will. If you or anybody else sees such an analysis (or another way to resolve this issue), please post a link in comments.
Just a follow on. In an interesting paper in Science this week,* Ludwig and colleagues capitalised on a randomised housing mobility experiment (“Moving To Opportunity”) to show that moving from a high to low poverty area has long term consequences for subjective well being, though without affecting economic self sufficiency. This finding suggests another dimension of interest: poverty differentials across neighbourhoods, and more broadly, the mechanisms by which neighbourhoods affect people. **
*Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults
Jens Ludwig, Greg J. Duncan, Lisa A. Gennetian, Lawrence F. Katz, Ronald C. Kessler, Jeffrey R. Kling, Lisa Sanbonmatsu.
Science 21 September 2012:
Vol. 337 no. 6101 pp. 1505-1510
DOI: 10.1126/science.1224648
http://www.sciencemag.org/content/337/6101/1505
**For more about such mechanisms, see:
D. Wilson, D. O’Brien, and A. Sesma. (2009). Human prosociality from an evolutionary perspective: variation and correlations at a city-wide scale. Evolution and Human Behavior, 30(3):190–200.
Joseph, thanks for this. In addition, Wilkinson and Pickett have argued that less equal societies decrease quality of life for everyone – although, as several responses to my previous blog pointed out, this is a very contentious conclusion. But it makes sense that high inequality should generate suboptimal results via its negative effect on cooperation.
But wouldn’t a poor family that moves to a rich neighborhood subjectively experience higher inequality? I ask this question just to try to flesh out my understanding of the Wilkinson and Pickett model, although maybe there’s no relation between that paper and their work?
Fred R, that’s an interesting question. Since the 1950s it is has been known that people who live in close proximity can assimilate each others values.* Wilkinson and Pickett study is interesting because it shows that similar effects arise for subjective well-being/happiness from “income segregation.” Living in economically disadvantaged neighbourhoods makes you more likely to feel unhappy whereas moving out of them tends to reverse the effect, even if you do not get richer.
The study is close to a natural experiment. “Move to Opportunity” (MTO) was a lottery in the 1990s which gave some people, but not others, the chance to leave very poor neighbourhoods. Wilkinson and Pickett tracked down over 2000 people who participated in the programme (ten to fifteen years after the move) and compared them those who did not (the controls were matched for 21 baseline characteristics). The most commonly cited reasons that people applied for the MTO lottery was “to get away from gangs and drugs.”
The key interest of the Wilkinson and Pickett study is that the effects of living in poorer neighbourhoods was more predictive lower subjective well-being over the long run than economic self-sufficiency, or racial segregation. In fact, the authors found that a one standard deviation reduction in neighbourhood poverty was associated with gains to social well-being that are equal to two-thirds of the average income of MTO control group families in the long-term survey (=$20,000). That’s a big effect. So it is not just income differences that are important to long term well being, but “income-segretion” — which the authors say has been increasing since 1970.
It’s always tricky generalising to policies from single studies. For example the effects don’t turn up in this way if you look at so-called objective physical or psychological measures of health. You only get the big effects for subjective well-being i.e. happiness. But hey, happiness is important!
I take the following lesson: to improve people’s sense of well-being over the the long run we need to think about the distress caused by living in extremely poor neighbourhoods. Income segregation is one of a large number of factors that can affect happiness.
**In a classic study, Festinger, Back, & Schachter (1950) found that residential proximity predicted the formation of friendships among military families who were arbitrarily assigned to neighbourhoods, which in turn lead to a closer alignment of values. The authors called such localised assimilation “the propinquity effect.”
Thanks Mr. Bulbulia, some very interesting stuff for me to chew on.
Exactly Peter — good point about the feedback on cooperation. I should clarify that the main lesson of this study is not that poor people who move into richer neighbourhood become just as happy as their richer neighbours. They don’t. The main point is that densely poor neighbourhoods are extremely miserable places. Just moving out can make you happier even if you don’t get richer. So when we think about the problems that poverty brings to a society we should be especially mindful to focus on what poverty does when it becomes highly concentrated, in neighbourhoods.
How much data exists on height differentials (between, say, the 10th and 90th percentiles) for ancient and medieval societies? I’m wondering if it could be a metric for inequality that one could actually compare across time periods, where (for example) income and health outcomes are likely to be unavailable or not comparable.
Could you compare height spread between hunter/gatherer, ancient Roman, medieval Arab, colonial American, and modern Scandinavian societies, and what would it say about the differences in inequality between societies?
I don’t know if height distributions are available for all of these societies, just asking if they are and could be used this way.
Such data indeed exist and can be used to assess inequality. I am traveling right now and so don’t have ready access to my files, but I remember a study by John Komlos on eighteenth century Austrian heights, wherehe found a huge differential (I think, 10 cm) between the heights of the nobility and peasants. Given the nature of premodern societies the best comparison would be with some kind of a median individual and a member of the elite.
Wealth does not always translate into health. Ten generations of Roman aristocracy died out almost certainly from lead poisoning from lead plumbing and lead stoppers on wine flasks. If modern poor people are less healthy, it is probably primarily because they can only afford junk food, for the most part. The junk food is cheaper on average, but has had important nutrients removed and poisons such as aspartame, fluoride (to water) and chemical colorings, etc. added. Also there is a small difference in age of death by virtue of the fact that poor people are the most likely ones to be killed in battle or work in dusty mines. But rich is usually better. So my recommendation is grab as much as you can.