This has become a hot topic, prompted by, among other things, the launch in November of the Washington Center for Equitable Growth, President Obama’s inequality and mobility speech in early December, and Ezra Klein’s recent comments. My current thinking is here.
Archive for the 'Inequality' Category
Over the past several years a large group of researchers in Europe, the U.S., and a few other countries have been looking at the rise of income inequality and its social, cultural, and political impacts. A number of working papers are available from the project’s webpage. One on the United States is here.
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, here, here, here). These are said to increase stress, which in turn prompts overeating. How well does this square with the evidence?
OVER TIME IN THE UNITED STATES
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.
ACROSS THE RICH COUNTRIES
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.
ACROSS THE STATES
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.
ACROSS INDIVIDUALS IN THE U.S.
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.
WHAT SHOULD WE DO?
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.
Alan Krueger, Chair of President Obama’s Council of Economic Advisers, gave a talk a few weeks ago on inequality. Krueger described the sharp increase in income inequality in the United States since the 1970s and discussed some undesirable consequences it may have. One of those consequences is reduced intergenerational mobility (relative intergenerational income mobility, to be more precise). Krueger provided a graph showing that nations with greater income inequality tend to have a stronger correlation between the earnings of parents and their children (less mobility). This has sparked a wide-ranging discussion about the link between income inequality and mobility (Winship, Corak, Winship, Corak, Cowen, Yglesias, Wolfers, Smith, Winship, Quiggin, Cowen, Quiggin, Bernstein).
Here’s what the pattern looks like according to Miles Corak, the source of Krueger’s data (enlarged version here). The vertical axis in the chart is immobility; lower means more mobility. The horizontal axis is income inequality a few decades earlier.
Nations with lower income inequality tend to have more intergenerational mobility, and the association is quite strong. There are concerns about the data. But suppose the data are accurate, and suitable for testing this link. What does the association depicted in this chart tell us about the magnitude of inequality’s impact? How much would reducing income inequality in the United States help?
For most, the aim isn’t high intergenerational mobility per se; it’s low inequality of opportunity. Mobility serves as an indicator (not perfect, but not bad) of equality of opportunity.
Money ought to be good for children’s opportunity. Kids growing up in households with higher incomes are more likely to have good health care, low stress, learning-centered preschools, good elementary and secondary schools, extracurricular activities that promote cognitive skills and earnings-enhancing noncognitive traits, and access to a strong university. It would be surprising, therefore, if inequality of parents’ incomes did not contribute to inequality of opportunity among their children.
But how large is the effect? After all, money isn’t the only thing that matters; a good bit of our abilities and motivations when we reach adulthood stem from nonmonetary influences such as genetics, in-utero developments, our parents’ habits and behaviors, and peers. Also, there are diminishing returns to money; beyond a certain point, more parental income probably helps only a little, if at all.
Yet the graph suggests a large impact. Apart from data concerns, is there any reason to question this? I think so. First, let’s set aside the low- and middle-income countries (Argentina, Brazil, Chile, China, Pakistan, Peru, Singapore). Mobility processes in these countries may or may not be comparable to those in the rich nations. Next, notice that inhabiting the lower-left corner of the chart are the four Nordic nations: Denmark, Finland, Norway, and Sweden. These countries have been providing affordable high-quality early education to a substantial portion of children age 1 to 5 for roughly a generation. James Heckman and Gøsta Esping-Andersen, among others, have argued that early education is perhaps the single most valuable thing a society can do to equalize opportunity. These countries also feature late tracking in elementary and secondary schools and heavy subsidies to ensure college is affordable for all. These public services, rather than low income inequality, might be the chief reason the Nordic countries have such high intergenerational mobility.
What would that imply for the cross-country association between income inequality and intergenerational mobility? As the following chart shows, if we leave out the Nordic nations the association is still there, but the countries are widely dispersed around the line, suggesting weaker grounds for confidence that the association is a strong one. (For the statistically inclined, the r-squared is .47 with the Nordics included and .16 without them.)
Is it possible, then, for a country to have high income inequality but also low inequality of opportunity? John Quiggin is skeptical. He suggests the UK experience has debunked this “third way” notion. I’m not so sure. Imagine a rich nation with America’s income inequality and Nordic public services: affordable high-quality early education, K-12 schooling with late tracking and equal funding, and widespread access to good-quality universities. And perhaps also comprehensive prenatal care. Would its opportunity (mobility) structure look more like America’s or more like Sweden’s?
But, some will respond, you can’t get those services if income inequality is high. The rich will block the heavy taxation needed to fund them. Maybe. But income inequality has been rising in Sweden. In fact, in the late 1990s and mid 2000s the top 1%’s share of income (including capital gains) in Sweden was about the same as in the 1970s United States (see figure 7 in this paper by Atkinson, Piketty, and Saez). So far this hasn’t undermined Swedish taxation, though it’s probably too soon to draw any firm conclusions.
Suppose income inequality continues to rise in Sweden but its public services hold up. Will Sweden’s intergenerational mobility a few decades from now look like ours does today? I’d predict no. I suspect opportunity-enhancing programs can overcome a good bit of the harm done by income inequality.
That’s not to say we shouldn’t also try to reduce income inequality. I think we should. The point is that if we want to reduce inequality of opportunity, reducing income inequality isn’t the only way, and perhaps not even the best way, to do it.
That’s the title of a short article of mine in the current Pathways magazine. Pathways ought to be on the reading list of anyone interested in living standards, poverty, inequality, and mobility. And it’s free.
A few other worthwhile recent reads on these topics:
Center on Budget and Policy Priorities, A guide to statistics on historical trends in income inequality
Scott Winship, Mobility impaired
Miles Corak, The decline of the American dream
Reihan Salam, Understanding America’s income mobility problem
Mike Brewer and Liam Wren-Lewis, Why did Britain’s households get richer?
I’ve just finished Walter Isaacson’s fascinating book on Steve Jobs’ fascinating life. Among the many intriguing things about Jobs’ story is that it may shed some light on a particular interpretation of America’s economic performance over the past generation.
Between 1979 and 2007, inflation-adjusted hourly wages for Americans at the median and below were essentially flat. Household incomes in the lower half increased, but not very much. Both wages and incomes for many ordinary Americans trailed far behind growth of the economy. At the same time, the earnings and incomes of those at the top exploded (see here, here, here, here).
One story sometimes told about the 1980s, 1990s, and pre-crash 2000s links these two developments to offer an optimistic verdict on the evolution of living standards for America’s lower half. The story goes something like this: A winner-take-all economy reduces income growth for low-to-middle Americans. But it nevertheless produces a substantial rise in living standards for them. It does so by increasing financial incentives for inventiveness and hard work, which yields leaps in consumption that aren’t reflected in the price data used to measure changes in the cost of living.
To put it more precisely, the story has four parts:
1. Returns to success soared in fields such as entertainment, athletics, finance, and high tech, as well as for CEOs. These markets became “winner-take-all,” and the amounts reaped by the winners mushroomed.
2. For those with a shot at being the best in their field, this increased the financial incentive to work harder or longer or to be more creative.
3. This rise in financial incentives produced a rise in excellence — new products and services and enhanced quality.
4. These improvements haven’t been satisfactorily captured in the price index by which we assess changes in the cost of living. Watching Michael Jordan or LeBron James play basketball is a qualitatively superior experience relative to what came before in a way that isn’t reflected in the price of a ticket or of a cable TV subscription. Similarly, the Macintosh, iPod, iTunes, iPhone, and iPad are so different from and superior to anything that preceded them that what they add to living standards isn’t likely to be adequately measured.
I think there’s a good bit of truth to parts 1, 2, and 4 of this story. But I’m skeptical about part 3.
This brings me to Steve Jobs. Apple and its delightful, user-friendly, (eventually) affordable gadgets play a key role in this story. The question is: Would Jobs and his teams of engineers, designers, and others at Apple have worked as hard as they did to create these new products and bring them to market in the absence of massive winner-take-all financial incentives?
In the things-have-improved-more-than-the-income-data-make-it-seem story, the answer is no. The financial incentive is the critical spur to inventiveness and hard work.
But I don’t find anything in Isaacson’s account of Jobs that supports this view. Jobs himself seems to have been driven mainly by a passion for the products, for winning the competitive battle, and perhaps for status among peers. The satisfaction of achieving excellence and of beating one’s opponents appears to have been far more important than monetary compensation. Excellence and victory were their own reward, rather than a means to the end of financial riches. In this respect Jobs was little different from scores of inventors and entrepreneurs over the ages, or for that matter from Bill Russell, Larry Bird, and Michael Jordan.
The rise of winner-take-all compensation occurred simultaneously with surges in innovation and productivity in certain fields, but that doesn’t mean it was the cause of those surges.
Reducing poverty is widely viewed as a key objective of a good society. The U.K.’s Labour government set a formal poverty reduction target in the late 1990s, and the European Union recently did so as well. In the United States, public opinion surveys consistently find a solid majority saying government spends too little money on assistance to the poor.
The standard poverty measure in comparisons of rich nations is a “relative” one. The poverty line for each country is set at a percentage, usually 60% or 50%, of that country’s median household income.
Which countries have been most successful in reducing relative poverty in recent decades? And how have they done it? Here’s what the picture looks like for twenty affluent nontiny longstanding democracies. The data are from three sources: the Luxembourg Income Study (LIS), considered the most reliable for comparative purposes; the European Union’s Statistics on Income and Living Conditions (EU SILC), which covers recent years for EU countries; and the OECD.
As it turns out, there is hardly any success to explain. In almost every one of these nations the relative poverty rate was no lower in 2007, the peak year of the pre-crash business cycle, than at the end of the 1970s. The only clear exception is Ireland. Denmark also reduced poverty according to the LIS data, but the OECD data suggest little or no change. Portugal is another possibility. A few countries succeeded in reducing relative poverty during certain portions of this period, such as the U.K. in the early 2000s.
What accounts for this near-universal failure?
A relative measure of poverty is essentially a measure of inequality in the lower half of the income distribution. A nation’s relative poverty rate is determined largely by three things: wage inequality among individuals in the bottom half of the distribution, employment inequality among households in the bottom half, and the generosity of the public safety net. The wage distribution has become more unequal in many countries, though by no means all. This owes to a host of developments, including globalization, deregulation of product and labor markets, manufacturing decline, weakening of collective bargaining, and increased immigration of people with language barriers and/or limited job skills. The trend in employment likewise has tended to be inegalitarian, depending on the magnitude and character of the rise in single-adult households, the movement of women into jobs, and government efforts to promote employment. Government transfers have increased in a number of countries, but often only enough to offset the rise in market inequality. And in a few nations transfers have stagnated or decreased. (More discussion here, here, here, here, and here.)
I prefer a focus on absolute incomes and living standards rather than on relative poverty, and that approach yields a very different conclusion about progress in recent decades. Still, the widespread failure of rich countries to make any headway in reducing relative poverty rates is striking.
Income inequality in America has soared over the past generation. But some see little cause for concern. One reason is that our inequality statistics — Gini coefficient, share of income going to the top 1%, and so on — are calculated based on households’ income in a single year. This misses the fact that people move up and down over time. Our incomes in any given year may be more dispersed now than several decades ago, but if many of us are switching places from year to year, why the fuss?
Two claims need to be distinguished here. One says there’s enough movement up and down in the income distribution over time (in technical lingo, relative intragenerational income mobility) that we needn’t worry about single-year inequality at all. It doesn’t matter whether inequality is high or low; it doesn’t matter whether it’s rising or falling. Single-year income inequality is simply irrelevant, on this view, because there is a lot of mobility. Since “a lot” and “enough” are in the eye of the beholder, evidence can’t confirm or refute this claim.
A second claim says that the rise in income inequality has been offset by a rise in mobility. Here we can look to the data for a verdict. Has income mobility increased?
For the bulk of the population — everyone but the richest — we have multiple sources of mobility data. One is the Panel Study of Income Dynamics (PSID); another is earnings records from the Social Security Administration. Both indicate that there has been no increase in income mobility in recent decades (see also here).
What about at the top? A good bit of the past generation’s rise in inequality consists of growing separation between the rich, especially the top 1%, and the rest of America. But has this been accompanied by increased churn among those at the top? In 2007 the Treasury Department released a study based on analysis of tax records. It included data on movement out of the top 1% over two nine-year periods: 1987-1996 and 1996-2005.
Single-year income inequality rose sharply during these two periods. The share of income going to the top 1% of households jumped from 11% in 1987 to 14% in 1996 to 18% in 2005. The Treasury study found that mobility, by contrast, was essentially unchanged.
The large increase in income inequality has not been offset by a rise in mobility at the top.
A “winner-take-all” market is one in which the top stars get paid much more than anyone else. It’s an apt description of the American economy in recent decades. Top financiers, CEOs, entertainers, and athletes now routinely earn more than ten million dollars a year, and the share of all income (after taxes) going to the top 1% of households jumped from 8% in 1979 to 17% in 2007.
What impact does the rise in the share taken by those at the top have on the incomes of those in the middle? On one view it’s bad: if the additional millions going to the “winners” had instead been spread among those in the middle, the latter would have been better off. Others suggest the impact is good: winner-take-all markets help make the pie bigger than it otherwise would have been, and a larger pie means a larger slice for the middle class in absolute terms, even if that slice has shrunk relative to the slice of those at the top.*
Pay in major league baseball is a good test case. Since the 1970s professional baseball has had the two defining characteristics of a winner-take-all market: owners’ and/or consumers’ judgment that top stars are much more valuable than the next best, and stars’ ability to exit if offered better pay elsewhere. Salaries for baseball’s top players have skyrocketed. Also helpful: unlike in pro football and basketball, baseball teams’ total pay is not limited by a salary cap.
Here are the two contending hypotheses:
1. Winner-take-all is bad for middle-pay players. Stars’ big paychecks come largely at the expense of their teams’ mid-level players.
2. Winner-take-all is good for middle-pay players. Teams that pay big money for top stars enjoy greater revenue growth via higher game attendance, richer TV deals, better jersey and hat sales, and so on. The stars collect a growing share of these teams’ total payroll, but this is more than offset by the degree to which they help boost the payroll. As a result, salaries for the middle players on these teams increase more than on other teams.
Baseball-almanac.com has data on the salaries of all major league players since the mid-1980s. I’ll examine change from 1989 to 2007, as both are business-cycle-peak years. (I exclude the four teams created after 1989. The Cincinnati Reds also are left out, due to missing 1989 salary data.)
Does paying big money for top stars enlarge the pie? On the horizontal axis of the following chart is change in the share of each team’s total pay that goes to its top three players. Consistent with what we would expect in a winner-take-all market, for most teams that share rose. For example, in 1989 the best-paid trio of players on the San Francisco Giants got 22% of the team’s total pay. In 2007 the Giants’ top three got 40% of the total pay, an increase of 18 percentage points. On the chart’s vertical axis is 1989-to-2007 change in each team’s total pay, in millions of inflation-adjusted dollars. The hypothesized positive association isn’t there. Teams that increased the portion of their pie going to their top three players haven’t gotten a faster-growing pie in return.
That points us toward hypothesis 1, which says a rising share of a team’s pay going to its top stars is bad for those in the middle. As the next chart shows, that’s indeed how things have played out. The chart plots the change in pay for each team’s middle five players from 1989 to 2007 by the change in the top three players’ share of the team’s total pay. Middle-player salaries have tended to grow less rapidly on teams in which the top three’s share has risen more.
The following set of charts elaborates a bit. It shows changes in top players’ pay and changes in middle players’ pay for four teams. The first two teams, the San Francisco Giants and Toronto Blue Jays, are on the right side of the second chart above. Pay for their top three players exploded. It rose for their middle players too, but much more modestly. The next two teams, the Baltimore Orioles and Milwaukee Brewers, are on the left side of the second chart above. Pay for their top three players rose sharply, but less than for their counterparts on the Giants and Blue Jays. Their middle players, by contrast, did better.
But that’s not the full story. To the two hypotheses listed above we should add a third:
3. Winner-take-all is bad for middle-pay players, but its harm is outweighed by other developments.
The “other” development that has mattered most is the growth in team payrolls. Total pay for the median team soared from $23 million in 1989 to $89 million in 2007. This has been the key determinant of salary growth for middle-pay major league players. On average, the pay of the middle five players rose by $300,000 less on a team with a ten-percentage-point increase in the top three players’ pay share than on a comparable team with no change in the top three’s share.** But salaries for the middle five players nevertheless increased on almost all teams, in many instances handsomely so. Across all teams, the average increase for the middle five between 1989 and 2007 was $1 million, nearly a 200% rise. Even among the six teams on which the top three players’ share of pay rose the most — those to the right in the second chart: Houston, Pittsburgh, San Francisco, Toronto, Oakland, and the L.A. Angels — the average increase for the middle five players was $540,000.
What accounts for the sharp jump in team payrolls? One element is enhanced revenues due to expanded demand for tickets, TV rights, and team paraphernalia. Another is a shift in the balance of power away from owners in favor of players. These developments have enabled some teams — the New York Yankees are the paramount example, as you can see in the second chart above — to concentrate a growing share of pay on their top three ballplayers and simultaneously provide a large rise in pay for their “middle-class” players.
Implications for the broader economy probably are limited. One, though, is that even if winner-take-all hurts middle-class incomes, if we had very rapid economic growth it might not matter much. Alas, figuring out how to get that isn’t so easy. A good substitute might be moderately strong growth coupled with strong unions (as in the 1950s and 1960s) or low unemployment (as in the late 1990s). But I’m not too optimistic about that either.
** This is based on a regression of change in middle-five players’ pay on change in top-three players’ share of team pay, change in total team pay, and 1989 level of middle-five players’ pay.
At The Monkey Cage over the next few days. It includes comments on the book by Paul Pierson and Jacob Hacker, by Will Wilkinson, and by me, with responses from Bartels.