Archive for the 'Health' Category

Is rising obesity a product of income inequality and economic insecurity?

June 10, 2012

Three decades ago, 15% of American adults were obese. Today 35% are. Obesity has increased in many other rich nations too. Why?

Although we burn fewer calories now than in the past, reduced physical activity likely played only a minor role in obesity’s rise. Instead, the main cause was a sharp increase in the number of calories we consume. Why did that happen? The most common story focuses changes in the supply of food. Tasty, high-calorie food became cheaper and more easily accessible in larger quantities, so we began eating more of it.

In the past several years some researchers have advanced an alternative hypothesis that blames rising income inequality and/or economic insecurity (see here, herehere, here). These are said to increase stress, which in turn prompts overeating. How well does this square with the evidence?


Begin with the over-time trend in the United States. The obesity rate was flat in the 1960s and 1970s and then shot up in the 1980s. The timing fits for income inequality, which has increased significantly since the 1970s. Economic insecurity, too, has risen during this period, though I’m not sure its increase has been large enough to have produced the massive jump in obesity that occurred.

Note that for the over-time story to work, we need to assume little or no time lag. There was a large rise in obesity during the 1980s. Income inequality and economic insecurity began to rise in the late 1970s at the earliest. If it takes a long time for them to have an impact on obesity, they probably can’t have caused the 1980s obesity increase.

Given that for both hypotheses the key mechanism is stress, we might ask whether stress has increased, and whether the timing fits with the rise in income inequality, economic insecurity, and obesity. None of the research I’ve seen looks into this, but some cite a paper published a decade ago that finds evidence of a rise in anxiety in the U.S. Curiously, though, that study concluded that anxiety rose steadily from the 1950s through the early 1990s. This doesn’t match up well at all with the over-time trends in income inequality, economic insecurity, or obesity.


The chief empirical evidence cited in support of the income inequality and economic insecurity hypotheses is the pattern across affluent countries. However, there’s a problem with the country-level obesity data. In most of these nations the only available data are from interviewees’ self-reports of their height and weight, which are likely to underestimate the true obesity rate. If the degree of bias is similar in all countries, this wouldn’t pose much of a problem for an analysis of cross-country differences. But we don’t know whether the bias is similar across countries or not.

The following chart shows the data we have for the late 2000s. For a few countries — the U.S., Australia, Canada, Ireland, and Finland — there are obesity estimates both from self-reports and from actual measurements of people’s height and weight. The latter are likely to be quite accurate. In each of these five countries the obesity rate based on self-reports is lower. But in the U.S., Canada, and Ireland it’s quite a bit lower, whereas in Finland it’s somewhat lower and in Australia it’s only slightly lower.

This leads me to worry a good bit about the degree of bias for many of the countries. It’s certain that that the U.S. has the highest obesity rate and that Japan has the lowest. But apart from those two nations it’s difficult to be confident. In almost all of the other countries, the obesity rate according to the self-reported data is within a fairly narrow band, between 10% and 16%. Maybe these countries’ true obesity rates line up in the same way they appear in the chart. But maybe not. Bottom line: we should be very cautious in drawing inferences from the cross-country data.

Okay, with that caveat, what does the cross-country evidence suggest? Let’s start with the simple cross-section. (I’ll come to over-time patterns in a moment.) The next chart shows the association between obesity and income inequality as of the mid-to-late 2000s. I’ve used obesity rates based on self-reports for most of the countries and the true rate minus a few percentage points for the others. As the solid line indicates, the association is positive; nations with higher income inequality tend to have higher obesity rates.

But the positive association is driven entirely by the group of six English-speaking nations in the upper-right portion of the chart: Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States. If we exclude them, the association disappears entirely (dashed line). That’s not due to lack of variation in income inequality among the remaining nations. The Nordic countries are well over to the left with low inequality, while Portugal, Spain, Italy, and Japan are well over to the right. But there is no relationship between income inequality and obesity among these 14 non-English-speaking countries.

Maybe there’s something about the English-speaking nations, other than high income inequality, that has resulted in high obesity. Here is where the economic insecurity hypothesis comes in. In a recent paper in the journal Economics and Human Biology, Avner Offer, Rachel Pechey, and Stanley Ulijaszek write “Among affluent countries, those with market-liberal welfare regimes (which are also English-speaking) tend to have the highest prevalence of obesity. The impact of cheap, accessible high-energy food is often invoked in explanation. An alternative approach is that overeating is a response to stress, … that market-liberal countries have an environment of greater economic insecurity, and that this is the source of the stress that drives higher levels of obesity.” Offer and his colleagues conclude that economic insecurity is a good predictor of the cross-country variation in obesity, much better than income inequality.

I suspect it’s true that the English-speaking countries tend to have more economic insecurity than other rich nations. But I have limited faith in the insecurity measures Offer and his coauthors use in their analyses. To be economically secure is to have sufficient resources to cover one’s expenses. Economic insecurity is a product of low income, of significant income decline coupled with lack of private or public insurance and lack of assets, or of inadequate insurance to head off large unexpected expenses. At the moment I don’t think we have an especially valid and reliable measure of economic insecurity for cross-country analysis.

What do the over-time patterns in obesity tell us? They’re shown in the following chart, with data based on measured height and weight shown in solid lines and data based on self-reports shown in dashed lines. Note the similarity between the trend for the U.S. and the trends for Australia, New Zealand, and the U.K. The pace of increase since the 1970s has been about the same in all four of these countries. This suggests that there is indeed something different not just about the United States but about the English-speaking nations as a group.

And yet, the differing data sources should give us pause. The data for the other two English-speaking countries, Canada and Ireland, are from self-reports, and their over-time trends look very much like those of the non-English-speaking nations with self-report data. Maybe the apparent difference between the English-speaking countries and other rich countries (apart from Japan) in the pace of obesity’s rise is simply a function of data source. Unfortunately, we can’t be sure.

If the English-speaking countries do in fact stand apart in their obesity rates or trends, there is an alternative hypothesis that ought to be considered. Rather than being driven by income inequality or economic insecurity, it might owe to these countries’ weak regulation of food and restaurants and to their lack of a well-entrenched healthy eating culture. Large-portion restaurants, particularly fast-food ones, may have proliferated more rapidly in the English-speaking nations. Junk food may have become available in grocery and convenience stores sooner and in larger quantities. And the shift away from home cooking and limited snacking may have occurred more quickly and decisively. This strikes me as more plausible than the suggestion that Americans and their counterparts in other English-speaking nations suddenly began eating more due to heightened stress. It’s also consistent with my own anecdotal impressions, though I haven’t seen any hard data.

Offer and colleagues include a measure of the price of a McDonald’s Big Mac relative to per capita GDP. This is likely to capture only part of what I’m referring to, and in their analysis it doesn’t account for much of the cross-country variation. A better measure, though still only a partial one, might be something like the number of restaurants per capita. One study finds that within the United States this correlates strongly with the prevalence of obesity over time.


To my knowledge there are no state-level studies of the impact of economic insecurity on obesity, perhaps because we lack good state-level data on economic insecurity. Income inequality has received more attention, and some researchers have concluded that there is a positive association between inequality and obesity across the states (e.g. chapter 7 here).

Obesity data for the U.S. states are available from 1995 to 2010. The data are from self-reports, so here too we should be wary. But we can hope that the degree of bias is similar in each state.

Here is the cross-sectional pattern as of the late 2000s. The predicted positive association is there (solid line). But it is driven by eight southern states in the upper-right portion of the chart: Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Oklahoma, Tennessee, and West Virginia. (Four other deep-south states — Georgia, North Carolina, South Carolina, and Texas — are close by.) Without these states there is no correlation (dashed line). It’s not difficult to think of plausible alternative causes of these states’ high obesity rates, most notably diet.

Indeed, since the states differ in a slew of ways that might affect obesity — from food preferences to education to affluence to physical activity — our best bet is to compare changes over time. If income inequality is an important determinant of obesity, we would expect states in which income inequality has increased most rapidly to have experienced the fastest rise in obesity. Have they? As the next chart shows, the answer is no.

(Two technical details: First, do we need to allow a longer time lag for the effect to show up? Perhaps, but if so the case for inequality as a major contributor to the rise in obesity is, as I suggested earlier, much less compelling. Second, is there a positive association between the level of income inequality in the mid-1990s and subsequent changes in obesity? The answer again is no.)

Now, it’s possible that analyzing the period from the mid-1990s through the late 2000s isn’t very informative. It could be that the strong causal effects are visible only in the 1980s, when the obesity rise began, and patterns since then don’t shed much light. Unfortunately, there’s no way to know, because we don’t have 1980s obesity data for many of the states. What can be said is that given the available data, the case for income inequality as a key determinant looks weak.


Another piece of evidence sometimes cited as supporting the economic insecurity hypothesis is the social gradient in obesity in the United States — the fact that obesity rates have tended to be highest among the poor, those who are least secure economically.

Of course, there are reasons other than stress why poor people might be more vulnerable to obesity, such as less education, less ability to afford healthy food, and less access to such food. Here too, then, it’s helpful to examine changes over time rather than just levels. One potentially useful piece of information is changes in obesity among women after the mid-1990s welfare reform. That reform placed strict time limits on receipt of cash assistance for women with low income. If there is any change in recent decades that ought to have heightened stress among low-income women, welfare reform is it. According to the economic insecurity hypothesis, obesity rates in the period after the mid-1990s should have risen more rapidly among low-income women than among other women.

But that isn’t what happened, as the next chart shows. Obesity rates actually increased a bit more rapidly among middle-income and high-income women than among those with low incomes.


About a third of American adults are obese. Obesity tends to have adverse financial and mental and physical health consequences for these individuals, and it’s estimated to cost the country about 1% of GDP in medical expenses each year. It’s a significant social and economic problem.

What’s the best strategy for reducing obesity? Some scholars have come to believe that reducing income inequality and/or economic insecurity is a key part of the cure. I’m not persuaded that the evidence supports this view. Though there’s a lot of uncertainty due to data limitations, my best guess is that inequality and insecurity have played a minor role, if any, in obesity’s rise.

Why the surge in obesity?

May 31, 2012

The Weight of the Nation is a four-part series on obesity in America by HBO Films and the Institute of Medicine, with assistance from the Centers for Disease Control (CDC) and the National Institutes of Health (NIH). It’s been showing on HBO and can be viewed online. Each of the four parts is well done and informative.

Obesity is defined as having a body mass index (BMI) of 30 or more. For a person 6 feet tall, that means a weight of more than 220 pounds. For someone 5’6″, the threshold is 185 pounds. People who are obese tend to earn less and are more likely to be depressed. They are at greater risk of diabetes, heart disease, stroke, and some types of cancer, and they tend to die younger. The CDC estimates the direct and indirect medical care costs of obesity to be $150 billion a year, about 1% of our GDP.

The chart below, which appears several times in The Weight of the Nation, shows the trend in obesity among American adults since 1960, the first year for which we have good data. The data are from the National Health and Nutrition Examination Survey (NHANES). They are collected from actual measurements of people’s height and weight, rather than from phone interviews, so they’re quite reliable. After holding constant at about 15% in the 1960s and 1970s, the adult obesity rate shot up beginning in the 1980s, reaching 35% in the mid-2000s.

What caused the surge in obesity? The standard explanation is too much eating and too little physical activity, and The Weight of the Nation sticks with this story. But it shouldn’t, because the evidence suggests one of these two hypothesized culprits has been far more important than the other.

Here is the trend in eating, measured as average calories in the food supply (adjusted for loss and spoilage) according to data from the Department of Agriculture. This chart too is from The Weight of the Nation. The timing of change matches that for obesity; the level is flat through the 1970s and then rises sharply beginning in the 1980s. An alternative series, measuring energy consumption per capita, goes back to 1950 (see figure 6, chart F here); it too shows little or no change until 1980, and then a sharp jump. The rise in food consumption correlates closely with the rise in obesity.

That isn’t true of physical activity. We’re less active now than we were half a century ago, but the timing of the decline in activity doesn’t match up with the shift in obesity.

We don’t have good historical data for a comprehensive measure of activity, such as calories expended, so we have to look instead at individual components. We can begin with the most-often-cited culprit: television. Here too The Weight of the Nation presents data, shown below, with the suggestion that TV watching is a significant cause of rising obesity. But the trend doesn’t support that inference. Time spent watching television has increased steadily since 1950. There was no sudden rise in the 1980s.

What about video games, the internet, and smartphones? The internet and smartphones arrived on the scene too late to account for the rise in obesity in the 1980s and most of the 1990s. The timing doesn’t work for video games either; they’re played mostly by the young, beginning in the 1980s, but obesity rates rose sharply in the 1980s and 1990s among adults of all ages, even among the elderly (see table 2 here).

More Americans now have sedentary jobs and drive to work. Yet as David Cutler, Edward Glaeser, and Jesse Shapiro noted in a paper published nearly a decade ago, these shifts have been going on for a long time, with no acceleration in the 1980s.

“Between 1910 and 1970, the share of people employed in jobs that are highly active like farm workers and laborers fell from 68 to 49 percent. Since then, the change has been more modest. Between 1980 and 1990, the share of the population in highly active jobs declined by a mere 3 percentage points, from 45 to 42 percent. Occupation changes are not a major cause of the recent increase in obesity.

“Changes in transportation to work are another possible source of reduced energy expenditure — driving a car instead of walking or using public transportation. Over the longer time period, cars have replaced walking and public transportation as a means of commuting. But this change had largely run its course by 1980. In 1980, 84 percent of people drove to work, 6 percent walked, and 6 percent used public transportation. In 2000, 87 percent drove to work, 3 percent walked, and 5 percent used public transportation. Changes of this minor magnitude are much too small to explain the trend in obesity.”

Another reason to doubt the importance of declining physical activity is that the elderly probably have become more active over time, rather than less, and yet we observe a rise in obesity among the elderly too, similar in timing and magnitude to that of younger adults (again see table 2 here).

In short, the evidence suggests that reduced physical activity has not been a key cause of the surge in obesity in America (more here, here, here, here, here).

This doesn’t mean physical activity plays no role in determining which persons become obese. And it doesn’t mean an increase in activity won’t help reduce obesity’s prevalence. But it does suggest that a strategy focused on increasing activity — and The Weight of the Nation leans in this direction — may not get us as far as we’d like. To make serious progress in reducing obesity, we need to significantly reduce the number of calories many of us consume.

America’s inefficient health-care system: another look

July 10, 2011

America’s health-care system differs from its counterparts in other affluent nations in a number of ways: greater fragmentation among payers and price-setters, stronger incentives for overuse of advanced diagnostic and treatment technology, higher administrative costs, less access to care for some. We might therefore expect it to perform less efficiently — to achieve poorer health outcomes for a given amount of expenditure (see here, here, here).

The following chart is sometimes viewed as evidence in favor of this hypothesis. The chart plots life expectancy at birth by per capita health expenditures as of 2007. Twenty affluent nations are included. Among these countries the U.S. spends by far the most money on health care and yet has the lowest life expectancy.

The inference is problematic, however, because America differs from the other countries in a number of ways that may affect health outcomes. It has a higher murder rate. It has more obesity. The U.S. population is more spatially dispersed than those of most other countries, so rural residents may live farther away from medical providers. Given these and other differences, how confident can we be that health spending is less effective in the U.S. than elsewhere?

Here’s a better way to compare. This chart shows trends in life expectancy by trends in health spending from 1970 to 2008.

The United States still stands out, and in a big way. Our gain in life expectancy per additional health spending is much smaller than in other countries, particularly after the early 1980s when we reached expenditures of about $2,500 per person (in 2005 dollars) and life expectancy of around 74-75 years.

The advantage of analyzing country differences in change is that it takes constant nation-specific factors out of play. It’s not a foolproof analytical strategy, but it reduces the likelihood of mistakenly inferring causation from correlation.

What we need to be wary of is life expectancy depressors that may have increased more or decreased less in the U.S. than in the other countries. Are there any? Not smoking: our rate of decline is in the middle of the pack. Not homicide: it’s decreased more here than elsewhere. Probably not spatial dispersion: Americans began moving back into cities in recent decades. One possibility, though, is obesity. Not only is it more prevalent here; it’s also increased more.

This kind of analysis is by no means conclusive. Life expectancy and total spending are highly aggregated indicators; it’s important to also examine more fine-grained measures of health-care effort and outcomes (see here, here, here).  But to the extent we treat the aggregate patterns as informative, a comparison of changes over time, rather than of levels, is likely to be our most valuable guide.

Update: Second chart now corrected, thanks to commenter Roger Chittum.

A brief snapshot of hardship in America

December 12, 2010

Here, compiled by the Center on Budget and Policy Priorities.

Inequality as a social cancer

January 18, 2010

Income inequality makes a lot of things we care about worse, according to a new book, The Spirit Level: Why Greater Equality Makes Societies Stronger, by Richard Wilkinson and Kate Pickett. Looking across 20 or so rich nations and across the 50 American states, Wilkinson and Pickett find that countries and states with greater income inequality tend to have lower life expectancy, higher infant mortality, more mental illness, more obesity, higher rates of teen births, more murder, less trust, and less upward mobility.

The following plot of life expectancy by income inequality shows a pattern that appears again and again in The Spirit Level.

“The problems in rich countries,” Wilkinson and Pickett conclude, “are not caused by the society not being rich enough (or even by being too rich) but by the scale of material differences between people within each society being too big. What matters is where we stand in relation to others in our own society” (p. 25).

The book has received a good bit of attention. It’s been reviewed in a number of major newspapers and been the focus of events at progressive think tanks in London and Washington, DC. It’s easy to see why. Many progressives worry about inequality. Here is a book, referencing hundreds of social scientific studies and making extensive use of quantitative data, which says, in effect, that many of our social problems can be significantly eased by reducing income inequality.

Is it correct? I was initially skeptical, and after reading the book I remain so.

What’s the causal link?

It wouldn’t be surprising to find that inequality in the income distribution contributes to inequality in health, education, and so on. And there’s plenty of evidence that it does. Wilkinson and Pickett make a different claim: income inequality worsens the average level of health, education, safety, trust, and other good things. How does it do that?

Wilkinson and Pickett say high inequality increases status competition, which in turn increases stress and anxiety, which leads to social dysfunction.

“Greater inequality seems to heighten people’s social evaluation anxieties by increasing the importance of social status…. If inequalities are bigger, so that some people seem to count for almost everything and others for practically nothing, where each one of us is placed becomes more important. Greater inequality is likely to be accompanied by increased status competition and increased status anxiety.” (pp. 43-44)

Here’s how they see stress as the link between income inequality and a key health outcome, lower average life expectancy:

“One of the most important recent developments in our understanding of the factors exerting a major influence on health in rich countries has been the recognition of the importance of psychological stress…. The most powerful sources of stress affecting health seem to fall into three intensely social categories: low social status, lack of friends, and stress in early life…. Much the most plausible interpretation of why these keep cropping up as markers for stress in modern societies is that they all affect — or reflect — the extent to which we do or do not feel at ease and confident with each other. Insecurities which can come from a stressful early life have some similarities with the insecurities which can come from low social status, and each can exacerbate the effects of the other.” (p. 39)

“So how do the stresses of adverse experiences in early life, of low social status, and lack of social support make us unwell? … The psyche affects the neural system and in turn the immune system — when we’re stressed or depressed or feeling hostile, we are far more likely to develop a host of bodily ills, including heart disease, infections and more rapid ageing. Stress disrupts our body’s balance, interferes with what biologists call ‘homeostasis’ — the state we’re in when everything is running smoothly and all our physiological processes are normal.”  (p. 85)

Here’s the hypothesized link with obesity:

“People with a long history of stress seem to respond to food in different ways from people who are not stressed. Their bodies respond by depositing fat particularly round the middle, in the abdomen, rather than lower down on hips and thighs…. The body’s stress reaction causes another problem. Not only does it make us put on weight in the worst places, it can also increase our food intake and change our food choices, a pattern known as stress-eating or eating for comfort.” (p. 95)

And educational achievement:

“New developments in neurology provide biological explanations for how our learning is affected by our feelings. We learn best in stimulating environments when we feel sure we can succeed. When we feel happy or confident our brains benefit from the release of dopamine, the reward chemical, which also helps with memory, attention, and problem solving. We also benefit from serotonin which improves mood, and from adrenaline which helps us to perform at our best. When we feel threatened, helpless and stressed, our bodies are flooded by the hormone cortisol which inhibits our thinking and memory. So inequalities of the kind we have been describing in this chapter, in society and in our schools, have a direct and demonstrable effect on our brains, on our learning and educational achievement.” (p. 115)

Other mechanisms are discussed at various points in the book, including oppositional culture, perceived expectations of inferiority, and humiliation. But stress is the key.

An important question here, which Wilkinson and Pickett don’t address, concerns the tightness of the link between the degree of income inequality in a society and the degree of status competition. The United States has the most unequal income distribution among rich countries, but I’m not certain this results in it having more status competition than other countries. Some European nations with less income inequality have a long history of class divisions. American culture is relatively informal, and Americans tend to be optimistic about the possibility of upward mobility. As a result, perceptions of status divisions may be less pronounced in the U.S. than in some other nations. The same is true for the American states. The states with the highest income inequality include Alabama, Arkansas, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Wyoming. Is status competition greatest in these states? I’m not sure.

How strong is the effect?

Wilkinson and Pickett are convinced that the effect of income inequality on social well-being is real, and perhaps it is. But if so, how strong is the effect? Social scientists frequently discover statistically significant effects that turn out to be trivially small in magnitude.

Look again at the chart above, which shows life expectancy by income inequality across affluent nations. If you follow the regression (“best-fit”) line, you’ll see it suggests that going from very high income inequality to very low income inequality will increase life expectancy by approximately two years (from about 77.5 to 79.5). The same is true across the 50 U.S. states. Is that a large impact?

One way to think about this is to consider how much life expectancy has changed in these countries over time. Let’s compare 1980 to 2006. I got data for these two years from the OECD for 21 of the 23 countries included in Wilkinson and Pickett’s graph. In 1980 the average life expectancy in these countries was 71 years. By 2006 it had jumped to 78 years. This increase is not simply a function of the poorer countries making huge leaps. In the three richest countries — Norway, the United States, and Switzerland — life expectancy rose by five or six years. The smallest rise, in the Netherlands, was four years.

If Wilkinson and Pickett’s estimate of the impact of income inequality is correct, reducing inequality in the United States to Sweden’s level would improve life expectancy by two years. Yet in the past generation life expectancy in the U.S. increased by more than twice that amount. By this gauge, inequality’s effect isn’t an especially large one.

Are the correlations true?

The point-in-time associations in Wilkinson and Pickett’s graphs are their key piece of evidence. Are they accurate? In studies such as this, there almost always is reason to worry about data and measurement choices. I’ll mention just one here. Wilkinson and Pickett measure income inequality for countries using data from the United Nations’ Human Development Report. It’s not a bad choice, but a more reliable source when comparing across nations is the Luxembourg Income Study (LIS). The LIS has data for fewer countries, but if an association is genuine it ought to hold for a subset of the countries examined by Wilkinson and Pickett.

The following chart plots life expectancy by income inequality as of 2005, using LIS data for inequality. There is no association.

Actually, this isn’t so much because of the difference in data source; it’s mainly a function of the particular countries that drop out when switching to the LIS data. The association in Wilkinson and Pickett’s chart rests heavily on the position of Japan, Singapore, and Portugal, none of which are in the LIS database. A small number of countries, often the United States and Japan, exert a good bit of influence on the patterns in a number (though not all) of Wilkinson and Pickett’s scatterplots. This is worrisome.

Are cross-sectional point-in-time associations the appropriate empirical test?

Patterns of association across countries or states at a single point in time may be very useful evidence. Or they might not. With this kind of evidence, we worry about other ways in which countries differ from one another that could be the true drivers of the observed association.

To supplement cross-sectional snapshots, we can, where data availability permits, look at what happens over time. Wilkinson and Pickett presumably would think this a good idea. In the book’s final chapter they note that the level of income inequality has changed in a number of these countries over the last few decades. And in the conclusion to an article that summarizes the book, they say “Standards of health and social well-being in rich societies may now depend more on reducing income differences than on economic growth without redistribution.” If income inequality is reduced, they’re suggesting, life expectancy and other social outcomes should improve; if inequality rises, outcomes are likely to worsen.

Yet The Spirit Level includes virtually no analysis or discussion of over-time developments. There is one over-time chart in the chapter on trust, a brief discussion in the chapter on crime, and a few references to other studies in a summary chapter. But as best I can tell, that’s all.

This is an important omission, because researchers who have examined over-time relationships between income inequality and average levels of health have tended to find no support for the hypothesized link (Jennifer Mellor and Jeffrey Milyo; Jason Beckfield; Andrew Leigh, Christopher Jencks, and Tim Smeeding). Here’s one way to see this. The following chart plots life expectancy on the vertical axis and income inequality on the horizontal. Each country is shown at two points in time, around 1980 and around 2005. For each country, a line connects the two data points. In most of the countries income inequality has increased and yet so has life expectancy. That’s not what Wilkinson and Pickett’s argument and findings would lead us to expect. Moreover, in the two countries where inequality was already low and then decreased, the Netherlands and Denmark, life expectancy rose the least.

I haven’t looked carefully at over-time data for the other outcomes Wilkinson and Pickett examine. But a few trends in the United States seem problematic for their argument. Average educational achievement has improved over the past generation even while income inequality soared. Violent crime began increasing in the mid-1960s, well before the rise in inequality, and it has dropped considerably since the early 1990s. Trends such as these don’t necessarily mean inequality has had no effect, but at the very least they call into question its magnitude.

Interestingly, Wilkinson and Pickett report that anxiety, the mechanism through which they believe income inequality causes social dysfunction, has been increasing steadily in the United States and other rich nations over the past half-century. But as they note, that isn’t due to rising income inequality: “That possibility can be discounted because the rises in anxiety and depression seem to start well before the increases in inequality which in many countries took place during the last quarter of the twentieth century. (It is possible, however, that the trends between the 1970s and 1990s may have been aggravated by increased inequality.)” (p. 35). This leaves us with an important unanswered question: Why would income inequality be a key determinant of stress across countries at a point in time, as Wilkinson and Pickett posit, but not within countries over time?

In sum, longitudinal developments offer further grounds for skepticism about the effect of income inequality on average levels of health, education, safety, and other social goods.

What to do

Improving social outcomes is certainly a worthwhile aim. What’s the best way to do it? According to Wilkinson and Pickett,

“Attempts to deal with health and social problems through the provision of specialized services have proved expensive and, at best, only partially effective…. The evidence presented in this book suggests that greater equality can address a wide range of problems across whole societies.”

I wish it were that simple. I share Wilkinson and Pickett’s conviction that it would be good for America and some other affluent nations to reduce income inequality, but this book hasn’t convinced me that doing so would help us to make much headway in improving health, safety, education, and trust. To achieve those gains, my sense is that our best course of action is greater commitment to specialized programs and services, coupled with poverty reduction.

Then again, I’m not certain that Wilkinson and Pickett are wrong. I’ve focused here mostly on the effect of inequality on life expectancy, because that is the social outcome for which the hypothesized causal link (stress) seems most plausible and because it has received the most attention in prior research. I’m skeptical that income inequality has much of an impact on average life expectancy. But perhaps life expectancy will turn out to be the exception to the rule.

Should progressives oppose the health-care reform bill?

January 6, 2010

Why would a progressive oppose the health-care reform bill that’s now on the table? Three main reasons have been offered.

One is that the bill will require (most) people to have health insurance. This means some low-income Americans, those who don’t get health insurance from their employer or from the government (Medicaid or Medicare), will have to buy insurance from a private insurer. They’ll receive a subsidy to help offset the cost, but for most the subsidy will be only partial; a new insurance policy may cost a family as much as 8% of its income. These people, the argument goes, will therefore be worse off.

I don’t see the logic in this. Unless you’re a libertarian, I’m not sure why you’d believe forcing people to spend money on something that’s in their self-interest — and calculations show that it clearly is in the interest of those who need health-care services — makes them worse off. Think of the Social Security and Medicare tax. It amounts to forced savings of nearly 8% of earnings — perhaps twice that, since the portion employers contribute arguably comes out of pay. But there is a benefit that outweighs the cost: guaranteed income and health care during retirement years, plus the accompanying peace of mind.

A second argument against the health-care reform bill is that health insurance companies and pharmaceutical firms will benefit. But opposing the bill on the grounds that it will benefit the already-powerful amounts to prioritizing equality over the well-being of America’s poor and lower middle-class (and others too, since the reform will sharply limit insurers’ ability to refuse or restrict insurance to people with preexisting conditions or greater likelihood of illness).

Here I think progressives ought to turn to John Rawls, the most influential moral philosopher of the past century. Rawls’s full view of justice is complex, and I won’t attempt to explicate it here. (There’s a nice summary in chapter 6 of Michael Sandel’s new book Justice.) The key point is that we ought to care more about the absolute well-being of the poorest than about the gap between the rich and the poor or between the powerful and the powerless. Rawls didn’t feel inequality is irrelevant, but he argued that it is secondary. This, he suggested, is what we all would believe if we thought about it carefully enough. I think he’s right.

The third reason for opposing the bill is a belief that it can be replaced by a better one in the not-too-distant future. Unfortunately, as many commentators have pointed out (Hacker, Klein, Krugman, Skocpol, Starr), experience suggests that is very unlikely.

Coverage expansion and cost control in health-care reform

November 14, 2009

“People say you can’t do coverage without cost control. I think it’s the opposite. You can’t do cost control before coverage. We would do a huge amount for the cause of cost control just by covering people…. Once you get coverage off the table, the conversation gets more focused on cost control.”

That’s health economist Jon Gruber’s bottom line on health care reform. It’s my view too, and it’s the premise underlying the House and Senate bills. I hope it turns out to be right.


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