The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
Comments are moderated and will not appear until the moderator has approved them.
Please submit appropriate comments. Inappropriate comments include content that is abusive, harassing, or threatening; obscene, vulgar, or profane; an attack of a personal nature; or overtly political.
In addition, no off-topic remarks or spam is permitted.
June 30, 2014
The Implications of Flat or Declining Real Wages for Inequality
A recent Policy Note published by the Levy Economics Institute of Bard College shows that what we thought had been a decade of essentially flat real wages (since 2002) has actually been a decade of declining real wages. Replicating the second figure in that Policy Note, Chart 1 shows that holding experience (i.e., age) and education fixed at their levels in 1994, real wages per hour are at levels not seen since 1997. In other words, growth in experience and education within the workforce during the past decade has propped up wages.
The implication for inequality of this growth in education and experience was only touched on in the Policy Note that Levy published. In this post, we investigate more fully what contribution growth in educational attainment has made to the growth in wage inequality since 1994.
The Gini coefficient is a common statistic used to measure the degree of inequality in income or wages within a population. The Gini ranges between 0 and 100, with a value of zero reflecting perfect equality and a value of 100 reflecting perfect inequality. The Gini is preferred to other, simpler indices, like the 90/10 ratio, which is simply the income in the 90th percentile divided by the income in the 10th percentile, because the Gini captures information along the entire distribution rather than merely information in the tails.
Chart 2 plots the Gini coefficient calculated for the actual real hourly wage distribution in the United States in each year between 1994 and 2013 and for the counterfactual wage distribution, holding education and/or age fixed at their 1994 levels in order to assess how much changes in age and education over the same period account for growth in wage inequality. In 2013, the Gini coefficient for the actual real wage distribution is roughly 33, meaning that if two people were drawn at random from the wage distribution, the expected difference in their wages is equal to 66 percent of the average wage in the distribution. (You can read more about interpreting the Gini coefficient.) A higher Gini implies that, first, the expected wage gap between two people has increased, holding the average wage of the distribution constant; or, second, the average wage of the distribution has decreased, holding the expected wage gap constant; or, third, some combination of these two events.
The first message from Chart 2 is that—as has been documented numerous other places (here and here, for example)—inequality has been growing in the United States, which can be seen by the rising value of the Gini coefficient over time. The Gini coefficient’s 1.27-point rise means that between 1994 and 2013 the expected gap in wages between two randomly drawn workers has gotten two and a half (2 times 1.27, or 2.54) percentage points larger relative to the average wage in the distribution. Since the average real wage is higher in 2013 than in 1994, the implication is that the expected wage gap between two randomly drawn workers grew faster than the overall average wage grew. In other words, the tide rose, but not the same for all workers.
The second message from Chart 2 is that the aging of the workforce has contributed hardly anything to the growth in inequality over time: the Gini coefficient since 2009 for the wage distribution that holds age constant is essentially identical to the Gini coefficient for the actual wage distribution. However, the growth in education is another story.
In the absence of the growth in education during the same period, inequality would not have grown as much. The Gini coefficient for the actual real wage distribution in 2013 is 1.27 points higher than it was in 1994, whereas it's only 0.49 points higher for the wage distribution, holding education fixed. The implication is that growth in education has accounted for about 61 percent of the growth in inequality (as measured by the Gini coefficient) during this period.
Chart 3 shows the growth in education producing this result. The chart makes apparent the declines in the share of the workforce with less than a high school degree and the share with a high school degree, as is the increase in the shares of the workforce with college and graduate degrees.
There is little debate about whether income inequality has been rising in the United States for some time, and more dramatically recently. The degree to which education has exacerbated inequality or has the potential to reduce inequality, however, offers a more robust debate. We intend this post to add to the evidence that growing educational attainment has contributed to rising inequality. This assertion is not meant to imply that education has been the only source of the rise in inequality or that educational attainment is undesirable. The message is that growth in educational attainment is clearly associated with growing inequality, and understanding that association will be central to the understanding the overall growth in inequality in the United States.
By Julie L. Hotchkiss, a research economist and senior policy adviser at the Atlanta Fed, and
Fernando Rios-Avila, a research scholar at the Levy Economics Institute of Bard College
August 21, 2008
The “What’s Fair” contest
At Café Hayek, George Mason’s Russell Roberts opens up a brand new “Inequality Chart Contest.” The chart in question is based on work by Thomas Piketty (professor, Paris School of Economics) and Emmanuel Saez (professor, University of California Berkeley), the essence of which is that the rich have gotten richer and everyone else not so much. (You can find a link to the Piketty-Saez paper, as well as updated data and executive summaries, on Emmanuel Saez’ homepage. Russell links to more information from the Center on Budget and Policy Priorities.)
Here’s the picture…
… and the contest is to construct “ONE sentence explaining ONE thing that is wrong with concluding that these numbers are evidence that the U.S. economy has become more tilted toward the rich at the expense of the poor.”
In the spirit of prompting reflection on issues of inequality and fairness, I invite you to think about the following three pictures, generated from Internal Revenue Service (IRS) tax data through 2006:
Let’s focus on the 1 percent of income-earners (by IRS defined Adjusted Gross Income, or AGI). If you look at the average federal tax rate paid by this group—that is, taxes paid divided by AGI—it did fall substantially over the period from 2000–2006. The average tax rates for other income groups fell as well, but not as dramatically.
If you instead prefer to look at taxes paid, the share the top 1 percent forked over to the federal government rose from 37.4 percent in 2000 to 39.9 percent in 2006. The share paid by the next highest 4 percent rose only slightly over this period, and the share paid by all other groups actually fell or stayed roughly the same.
On the other hand, concentrating on the share of taxes paid relative to the share of income earned by each group would lead you to the conclusion that not much had changed between the year 2000 and 2006.
So here’s the contest: Explain in one sentence which one of those pictures tells us whether the federal income-tax system has become more or less “fair.”
February 13, 2007
Poverty In The Suburbs
Once prized as a leafy haven from the social ills of urban life, the suburbs are now grappling with a new outbreak of an old problem: poverty. Currently, 38 million Americans live below the poverty line, which the federal government defines as an annual income of $20,000 or less for a family of four. But for the first time in history, more of America's poor are living in the suburbs than the cities—1.2 million more, according to a 2005 survey. "The suburbs have reached a tipping point," says Brookings Institution analyst Alan Berube, who compiled the data.
Cleveland, as it turns out, is chosen as the poster city for suburban poverty:
Six years ago, Brian Lavelle moved out of the city of Cleveland to the nearby suburb of Lakewood for what he thought would be a better life... Then, three years ago, the steel mill closed and Lavelle found that the life he dreamed of was just that, a dream. The suburbs, he quickly learned, are a tough place to live if you're poor.
... five years ago, a Hunger Network food pantry in Bedford Heights, a struggling suburb of Cleveland, served 50 families a month. Now more than 700 families depend on it for food.
Howard and Jane Pettry, of Middleburg Heights, Ohio [another Cleveland suburb], see themselves as working-class—just facing hard times. In December, Jane was laid off from her job at a local supermarket, and a week later Howard had a heart attack and missed a month of work from his job at a grain mill. Now Jane's collecting unemployment and they're staring at the poverty line as they struggle to pay the mortgage and the bills.
Is it really the case that the suburbs are increasingly poverty-ridden? I'm suspicious. Not too long ago, my colleagues Mark Schweitzer and Brian Rudick examined the case of Cleveland specifically:
Cleveland is the poorest big city in the United States, according to the Census Bureau, with nearly a third of the city’s residents living in poverty. The city’s poverty rate also rose since it was last measured. These numbers have received a lot of attention since they were released, but unfortunately, they are easily misinterpreted.
The numbers tell us that Cleveland has many poor people. But the numbers don’t tell us that Clevelanders have become worse off, that the region’s economy has deteriorated, or even that there are more poor people in the city than before.
A closer look at the American Community Survey results suggests that Cleveland’s poverty rate reflects the fact that the region’s poor are concentrated in the central city, while the wealthier live in the suburbs. The rate may have risen because people are moving out of the city, and those that leave are disproportionately better-off than those that stay behind. Neither of these facts on its own says anything about the economic health of the region, but they do say something about the relative desirability of living in the city versus its outskirts.
The definition of the Cleveland MSA has recently changed to exclude Ashtabula County, but under either the old or new definition, poverty is far lower outside the city than inside it.
... Looking at the poverty rate of the MSA [metropolitan statistical area] reveals that Cleveland (officially the Cleveland-Lorain-Elyria MSA) is not too different from other U.S. cities. The poverty rate of the Cleveland MSA is just a little above the average for all metropolitan areas in the United States. And like other cities in the country, Cleveland’s suburbs have far lower poverty than the city itself.
...the 2005 rise in metro area poverty figures seen in this figure is almost entirely accounted for by the increase in the number of poor people in the city of Cleveland, rather than in the suburbs. In the 2004-2005 shift that boosted the poverty rate for the MSA, for example, the number of poor people in the MSA grew by 43,540, but 36,991 of that increase occurred in the city.
It is true that the number of poor people in the suburbs is larger than the number of poor people in the city -- according to Schweitzer and Rudick "about two-thirds of the metro area’s poor live outside the city of Cleveland" -- but this is simply a function of the fact that the population of the MSA is heavily concentrated outside of the city. In terms of poverty trends, there just isn't that much happening in the suburbs:
If one wants to use Cleveland to tell a story about poverty -- especially concentrated poverty -- the action is still in the central city.
November 22, 2006
Inequality: Not Just Made In The USA
China’s poor grew poorer at a time when the country was growing substantially wealthier, an analysis by World Bank economists has found.
The real income of the poorest 10 per cent of China’s 1.3bn people fell by 2.4 per cent in the two years to 2003, the analysis showed, a period when the economy was growing by nearly 10 per cent a year. Over the same period, the income of China’s richest 10 per cent rose by more than 16 per cent...
China, which had relatively even income distribution in 1980 when it embarked on market reforms, is now “less equal” than the US and Russia, using the Gini co-efficient, a standard measure of income disparities.
The Wall Street Journal has more (on page A4 of today's print edition):
The reason for the income decline at the bottom isn't clear. The World Bank hasn't completed its analysis and its conclusions haven't been published. Even so, the data call into question an economic model that economists have held up as an example for other developing nations.
"This finding is very important. If true, it sheds doubt on the argument that a rising tide lifts all boats," said Bert Hofman, the World Bank's chief economist in China...
Many observers place part of the blame on the way China dismantled its social-welfare system as it phased out state control of the economy -- without building up much to replace it. Health care has become a point of particular concern, as costs shoot up without any widespread system of medical insurance to cover them.
Here is an important piece of information:
The World Bank's Mr. Hofman says the bank's analysis shows the majority of China's poorest 10% appear to be only temporarily poor, thrown down by some setback like sudden illness, the loss of a job or the confiscation of land. That suggests that a basic social safety net, like medical insurance or unemployment benefits, could help move them back out of poverty. Only about 20% to 30% of the poorest appear to be long-term poor, and even they have some savings.
... the survey compares snapshots of the lowest tier of Chinese society at two different points, rather than tracking the same of group of households over time. So, it doesn't necessarily mean that the people who were in the poorest 10% of society in 2001 were all 2.5% worse off in 2003.
Temporary bouts of economic hardship are clearly a much different thing than persistent poverty traps. And if, in fact, poverty is predominantly transitory, we should perhaps be more circumspect about declaring that a rising tide fails to raise all boats. Rising inequality -- here and elsewhere -- may be very well be a problem. But policymakers would be well advised to understand what problem it is, before the surgery begins.
- Business Cycles
- Business Inflation Expectations
- Capital and Investment
- Capital Markets
- Data Releases
- Economic conditions
- Economic Growth and Development
- Exchange Rates and the Dollar
- Fed Funds Futures
- Federal Debt and Deficits
- Federal Reserve and Monetary Policy
- Financial System
- Fiscal Policy
- Health Care
- Inflation Expectations
- Interest Rates
- Labor Markets
- Latin AmericaSouth America
- Monetary Policy
- Money Markets
- Real Estate
- Saving Capital and Investment
- Small Business
- Social Security
- This That and the Other
- Trade Deficit
- Wage Growth