U.S. Census highlights rising economic inequality

The U.S. Census Bureau’s latest set of annual reports on income, poverty, and health insurance coverage in the United States demonstrates that economists and policy makers alike need to come to grips with the short- and long-term affects of economic inequality on economic growth and prosperity. The two reports present data for 2013, four and half years after the official start of the economic recovery from the Great Recession that began in December 2007 and ended in June 2009. Although there are a few bright spots, most of the data reported are dismal and the implications for income inequality are disturbing.

Most tellingly, the long-term trend lines in rising income inequality are essentially unchanged over more than four decades—across several business cycles—which means we need to understand the short- and long-term factors that result in stagnant incomes for all but the most wealthy. So let’s parse the numbers.

The first report deals with income and poverty while the second report describes health insurance coverage. The first report found that real median household income (the income of the household in the middle of the income distribution) in 2013 was stagnant for the second year in a row after having fallen for the four consecutive years after 2007. At $51,900, median household income was still 8 percent, or nearly $4,500, below its level in 2007, roughly equal to what it was nearly 20 years ago in 1995, and less than what it was in 1989. The only racial or ethnic group to experience a statistically significant increase in annual income last year was members of Hispanic households, who earned 3.5 percent more in 2013 than in 2012.

The median earnings of full-time, year-round workers did not improve, though the number of such workers increased by 2.8 million, which reflects the growth in jobs in 2013 and the gradual shift from part-time to full-time work that has been ongoing since 2010. The gap between the median earnings of men and women who worked full time, year round, was slightly reduced, but the gap was not statistically different from what it was in 2012—meaning that the data are not precise enough for the Census Bureau to state unequivocally that the earnings gap had narrowed.

Moreover, the reported improvement in the female-to-male earnings ratio, from 77 cents on the dollar in 2012 to 78 cents last year, was not just a function of an increase in the earnings of women, something we could all celebrate, but also a function of the long-term continuing stagnation in the earnings of full-time, year-round, male workers. This is a worrisome phenomenon. In fact, the median earnings of full-time, year-round male workers were no higher in 2013 than they were more than 40 years ago in 1972.

One positive finding in this year’s poverty-and-income report is that the overall poverty rate declined from 15 percent in 2012 to 14.5 percent in 2013. As the report notes, this was the first decrease in the poverty rate since 2006. However, the report cannot tell us how much of the reduction in poverty was due to an improvement in the economy and in earnings versus an increase in government transfer payments to low income households or other factors.

Almost the entire decline in poverty is attributable to a reduction in the poverty of children under the age of 18 alongside a reduction in the poverty rate of Hispanics. The poverty rate for children fell from 21.8 percent to 19.9 percent, and an estimated 1.4 million fewer children lived in poverty. The poverty rate among Hispanics dropped from 25.6 percent to 23.5 percent, indicating that nearly 900,000 Hispanics (almost 600,000 of whom were children under age18) were no longer living in poverty.

Still, some 45.3 million people were living in poverty in 2013, including 14.7 million children. And, as was true in prior years, those with the highest poverty rates include women, children, people of color, and the disabled.

The Census bureau report measures income inequality in a wide variety of ways. They include:

  • Six different income ratios such as the 90th/10th ratio, which is the income of the household that is earning more than 90 percent of other households (i.e. the household at the 90th percentile) divided by the income of the household earning less than 90 percent of households (i.e. the household at the 10th percentile).
  • The Gini coefficient, which summarizes the income dispersion in a number that varies from 0 to 1 and indicates greater inequality as it approaches 1.
  • The Mean logarithmic deviation of income, which is a measure of the gap between the median and average income.
  • The Theil index, which summarizes the dispersion of income in a number that varies from 0 to 1 with higher numbers indicating more inequality.
  • The Atkinson measure, which suggests the end of the income distribution that contributed most to inequality.

None of the measures in the report indicates any reduction in income inequality in 2013 relative to 2012. By every measure, income inequality in 2013 was higher than in previous years or equally as high as has ever been reported by the Census bureau since it started collecting these data in 1967.

Here are just two cases in point. The household income at the high earning 90th percentile was 12.1 times greater than the income of the household at the low earning 10th percentile—the widest gap ever reported by the Census Bureau. Similarly, the Gini index of income inequality, one of the most commonly used measures of income inequality, was 0.476 and indistinguishable from the record high of 0.477 reported in 2012 and 2011.

It should be noted, too, that the income data reported by the Census Bureau understate the degree of income inequality. The reason: research shows that the data, derived from a survey of people, tends to overstate the incomes of low earners and understate the incomes of high earners. Thus, the true distribution of income is more uneven than indicated by the reported data.

The bottom line is that after nearly five years of economic recovery and growth in national income most Americans have not experienced an increase in their earnings while the earnings of those at the top have largely returned to their pre-recession level. The wages of men in particular have stagnated while women, children, and people of color have suffered in disproportionate numbers from the ravages of poverty. By every measure, income inequality is at a record high or on par with the record highs reported by Census in 2012 and 2011.

The second report, which deals with health insurance coverage, provides additional data that confirm the high degree of inequality revealed in the first report. Unfortunately, because of a redesign in the questions asked of respondents, it is not possible to compare results from this year’s report to prior years.  Thus, the second report does not provide a perspective on whether or not inequality in health insurance coverage is growing. A different Census Bureau study, the American Community Survey, provides annual estimates of health insurance coverage that have closely followed trends in the Current Population Survey Annual Social and Economic Supplement, also commonly known as the March CPS. The American Community Survey data suggest that there have been recent improvements in health insurance: the percent of the population without health insurance fell from 15.5 percent in 2010 to 14.5 percent in 2013.


The most recent Census Bureau survey found that nearly 42 million residents, or 13.4 percent of the population, did not have health insurance coverage for the entire 2013 calendar year. The lower a household’s income, the more likely they were to lack health insurance. For example, 24.9 percent of households living in poverty had no health insurance during the year while only 5.3 percent of households earning more than $150,000 lacked insurance in 2013. Those most eligible for government provided health insurance typically had the highest insurance coverage. For instance, only 1.6 percent of those over age 65 and 7.6 percent of those under age 19 lacked insurance compared to 18.4 percent of the rest of the population. Race and ethnicity also influence coverage as nearly a quarter of all Hispanics and 1 in 6 blacks lacked health insurance coverage compared to just 1 in 10 non-Hispanic whites.


The quality of health care that people get tends to be a function of both insurance coverage and the quality of health insurance. While these data provide information about coverage, they tell us nothing about the quality of health insurance. But, from other sources we know that lower income households, blacks, and Hispanics tend to have poorer quality insurance even when they are covered which further exacerbates inequality in health care services.

A central concern of the Washington Center on Equitable Growth is that these high and persistent levels of income inequality and other forms of inequality, such as in health care, may have detrimental effects on long-term economic growth and the well-being of most Americans. Though the Census Bureau data provide a useful snapshot at a particular moment in time of the levels of income, poverty, health insurance, and income inequality, they do not tell us what is causing these levels or their economic implications.

To promote rapid and widely shared growth may require attention to both short and long-run demand and supply factors. For instance, we may need to better understand the role that demand plays in promoting business sales, creating jobs, and boosting wages. Likewise, we may need to better comprehend the productivity or long-term supply side effects of investments in the health, education, and training of people. In the coming years, Equitable Growth will analyze the data ourselves and provide annual grants to other academics across an array of social sciences in an attempt to provide answers.

Marriage promotion isn’t the only solution to America’s mobility problem

We at the Washington Center for Equitable Growth and much of the economics blogosphere have given substantial attention to the recent work on mobility in the United States by Harvard economists Raj Chetty and Nathanial Hendren and University of California—Berkeley economists Emmanuel Saez and Patrick Kline. One of the study’s key findings is that there is strong, statistically significant relationship between the share of single mothers in an area and the gap in mobility between children from high- and low-income families (Chetty and his co-authors refer to this as a measure of relative mobility).

Brad Wilcox, University of Virginia sociologist and Director of the National Marriage Project, employs this finding to promote pro-marriage policies. His research on the issue is intriguing (though he bases his recommendations on regressions that suffer from multicollinearity, because all of the independent variables are highly correlated with each other, and thus his analysis is statistically questionable). His analysis may leave policymakers with the wrong message. When policymakers focus on marriage as the most important path to higher economic mobility, it allows them to ignore the pro-family policies that can help improve mobility. After looking at the data more deeply, I think they are drawing the wrong conclusions and should look at ways to support families of all types instead of pushing a specific family model.

My issue brief, “A Regional Look at Single Moms and Mobility,” indicates that the Pacific states of California, Hawaii, Oregon, and Washington stand out for having relatively high rates of single mothers while also having relatively high mobility. (See map) These states tend to have more family-friendly laws like paid sick days so that parents can take care of sick children and they have relatively generous parental leave so that new parents can spend more time with their newborn children. This analysis is far from definitive, but it does imply that these kinds of pro-family policies can improve mobility in the absence of a high rate of two-parent households.

States with Family Friendly Policies have Better Economic Mobility

A regional look at single moms and upward mobility

One of the big takeaways from the recent work by Harvard economists Raj Chetty and Nathanial Hendren and University of California—Berkeley economists Emmanuel Saez and Patrick Kline is that differences in family structure are strongly associated with differences in economic mobility.[1] Some scholars and policymakers have used these findings as an opportunity to encourage marriage promotion policies, but a deeper look at the data raises some important questions.[2]

For example, why is it that the West Coast states of California, Oregon, and Washington stand out for having relatively high rates of single mothers while also having relatively high rates of economic mobility? Conversely, why are other parts of America characterized by low rates of single mothers but very low rates of economic mobility?

Understanding economic mobility can yield insights into whether and how economic inequality and economic growth are linked. And scholars and policymakers across academic disciplines and political divides agree that understanding how single mothers fare in our economy is critical to future economic growth, powered by their contributions today and the future contributions of their children.

Read a PDF of the full document
Read a PDF of the appendix

This issue brief explores where single mothers are more likely to be moving up the economic ladder, relying upon the latest research on mobility in the United States by Chetty and his colleagues.[3] One of their study’s key findings is that there is a strong, statistically significant relationship between a higher gap in mobility between children from high- and low-income families, which Chetty and his co-authors refer to as relative mobility, and a higher share of families headed by single mothers.

This insight enables us to look at this “mobility gap” in an area, compare the share of single-mother households, and then look at the difference between the share of single mothers and the mobility gap.[4] This difference tells us whether the mobility gap is higher, lower, or about what would be expected given the share of single mothers in an area. In the pages that follow, this issue brief will present these data analyses in more detail. But the upshot of this analysis is that states with more family friendly laws, [5] such as paid sick days so that parents can take care of sick children and relatively generous parental leave policies so that new parents can spend more time with their newborn children, are more likely to have a relatively high rates of economic mobility despite high rates of single mothers—among them California, Oregon, and Washington.

Conversely, parts of America, particularly parts of the Rust Belt, are significantly less mobile than one would expect given the relatively low share of households headed by single mothers. This analysis is far from definitive, but it does suggest that pro-family policies can improve mobility in the absence of a high rate of two-parent households.

Our first map in this issue brief tells the tale, at least for those born in the United States when the tail end of Generation X—1965-1980—gave way to the Millennials born between 1981 and 2000. (See Figure 1.)

Figure 1

States with Family Friendly Policies have Better Economic Mobility

Parsing the data on mobility and single mothers

These differences in the mobility of single moms that we mapped on Figure 1 can be further refined down to metropolitan regions of the country. Figure 2 below shows the relationship between the share of households led by single mothers and the gap in mobility between the children of high- and low-income families. Each dot represents a commuting zone (areas within which people are more likely to live and work). The arrow represents the “best-fit-line” on these points and indicates that places with a higher share of single mothers also tend to have a higher gap in mobility—indicating lower relative mobility. Because of the strength of this relationship, many commentators have identified marriage promotion as the silver bullet policy to improve economic mobility.

Figure 2

Single Mothers Struggle with Upward Mobility

While Figure 2 provides a pretty convincing picture that a higher rate of single motherhood is associated with a higher gap in mobility between children from high- and low-income families, there may be something missing. High- and low-income children born in large West Coast cities such as Seattle and San Diego have a much smaller gap in mobility than high- and low-income children born in cities such as Cincinnati or Indianapolis, despite having a similar share of single mothers. So before making sweeping policy proclamations, we should dig a little deeper to understand this relationship.

Figure 3 is a map of the mobility gap between the children of high- and low-income families. The mobility gap is the difference in incomes as adults between people born into the lowest-earning and highest-earning households. A lower ‘mobility gap’ implies greater mobility, and vice versa.[6] The lightly colored areas in the map are those that have a low mobility gap, while the dark green areas have a much higher mobility gap. The South and much of the Rust Belt have particularly low economic mobility, while the West Coast and many parts of the Great Plains states are particularly economically mobile.

Figure 3

How Hard is it to Climb the Ladder?

The mobility gap is an obviously useful measure of the chances of the children of families achieving the American Dream, but to complete our analysis presented in Figure 1, we need to calculate the share of single mothers in our nation. Figure 4 does that, with lightly colored places indicating an area in which a higher share of households are headed by single mothers than average for the country, while dark green indicates a lower share. The South and much of the West have higher shares of single mothers that the rest of the country, while the Great Plains states in particular have the lowest share of households led by single mothers.

Figure 4

Concentrations of Single Mothers

This brings us back to the first map in this issue brief. It looks at the difference between the actual mobility gap and what one would expect the mobility gap to be based only on knowing the share of single mothers. The brown indicates those places where you would expect mobility to be better given the relatively low share of households led by single mothers, while the green indicates those places where the mobility is better than you would expect given the high share of single mothers. The geographic distribution of the colors is telling. Table 1 details the states with the more long-standing family friendly policies.

Table 1

States with Longstanding Family-Friendly Policies

The West Coast states of California, Oregon, and Washington stand out for having relatively high rates of single mothers while also having relatively high mobility. Several New England states, among them Rhode Island, Maine, and Vermont, also have higher levels of mobility than would be expected given the share of households headed by single mothers. As seen in Table 1, these states tend to historically have had more family-friendly laws,[7] such as parental leave so that new parents can spend more time with their newborn children. This analysis is far from definitive, but it does imply that these kinds of pro-family policies can improve mobility in the absence of a high rate of two-parent households.

To look a little deeper into the relationship between mobility and marriage, we also did an international comparison akin to the famous Great Gatsby Curve.[8] Figure 5 has the intergenerational earnings elasticity (a measure of the variability in earnings for one generation that is associated with the variability in earnings from the previous generation.) from University of Ottawa economist Miles Corak[9] plotted against the share of children that live primarily with one parent using data from the OECD.[10]

At the international level, we find the opposite relationship between single parenthood and mobility that we see from data in the United States, as seen in Figure 2. While it appears that higher shares of children primarily living in single-parent households is weakly associated with lower intergenerational earnings elasticity, the United States is an outlier, and by excluding it from the calculations, the cross-country association is quite strong. This is almost certainly not an indication that single-parent households are better for mobility, but instead an indication that what matter are other differences between countries, such as public policy. Those countries that have a larger share of single mothers, other than the United States, also tend to do a much better job providing support services to families of all types.[11] (See Figure 5.)

Figure 5

The United States Compares Poorly in Economic Mobility

By comparing the maps of mobility with family structure and also seeing the international relationship, we see that those places with stronger support for families of all types appear to be less impacted by any adverse effect on mobility from having a high share of single mothers. This suggests that low mobility stemming from a high proportion of single-parent households may be largely a function of policy choices rather than an inherent characteristic associated with the prevalence of different family structures.


While none of this analysis constitutes definitive economic analysis, it does highlight areas for researchers to focus their efforts and also for policymakers to consider. Marriage promotion may be one possible policy solution to the low mobility in the United States, though evidence would be needed to show what it is about marriage that leads to higher economic mobility. But we may want to try what already appears to be working in some states and in other developed nations too.

The notion that low mobility is driven by family structure alone allows policymakers to ignore their role in the problem and implies solutions that ignore more fundamental economic and work-related problems. Thus, it is important for researchers to do more than just a cursory analysis of these data and to understand the mechanisms of mobility before making strong policy recommendations.


[1] Raj Chetty et al., Where Is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States, Working Paper (Cambridge, MA: National Bureau of Economic Research, January 2014), http://www.nber.org/papers/w19843.

[2] W. Bradford Wilcox, If You Really Care About Ending Poverty, Stop Talking About Inequality, The Atlantic, January 8th, 2014,  http://www.theatlantic.com/business/archive/2014/01/if-you-really-care-about-ending-poverty-stop-talking-about-inequality/282906/

[3] Chetty et al, 2014.

[4] We used the residual from a regression of single mothers on the relative mobility as the difference.

[5] Waldfogel, Jane. 1999. “Family Leave Coverage in the 1990s.” Monthly Labor Review 10 (October): 13–21.

[6] Raj Chetty et al., Where Is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States, Working Paper (Cambridge, MA: National Bureau of Economic Research, January 2014), pp.2-3, http://www.nber.org/papers/w19843.

[7] National Conference of State Legislatures, State family and Medical Leave Laws, December, 31st, 2013, http://www.ncsl.org/research/labor-and-employment/state-family-and-medical-leave-laws.aspx

[8] Alan Krueger, “The Rise and Consequences of Inequality” (Center for American Progress, January 12, 2012). http://www.whitehouse.gov/sites/default/files/krueger_cap_speech_final_remarks.pdf.

[9] Miles Corak, Inequality from Generation to Generation: The United States in Comparison, 2012, http://milescorak.files.wordpress.com/2012/01/inequality-from-generation-to-generation-the-united-states-in-comparison-v3.pdf.

[10] OECD, Living arrangements of children, OECD Family Database, January 7, 2010, http://www.oecd.org/social/family/41919559.pdf.

[11] Wikiprogress, Hours Worked, http://www.wikiprogress.org/index.php/Hours_Worked

Evening Must-Read: Paul Krugman (1992): The Rich, the Right, and the Facts

Paul Krugman: Inequality 1992: “I happened to notice Greg Mankiw…

…citing some bogus claims that the one percent is an ever-changing group, not a persistent elite, and I thought ‘Wait–didn’t we deal with that one long ago?’ And that brought to mind the piece I wrote for the American Prospect 22 years ago, ‘The rich, the right, and the facts.’ (It doesn’t say this on the Prospect site, but it was indeed published in 1992). See the section on income mobility.

The truth is that inequality denial is largely a crusade of cockroaches–the same bad arguments just keep coming back. Oh, and I do think that my old piece looks surprisingly contemporary. In particular, I was focused on the one percent even then. http://prospect.org/article/rich-right-and-facts-deconstructing-income-distribution-debate

Lunchtime Must-Watch: Thomas Piketty: Capital in the Twenty-First Century

Capital in the Twenty-First Century: “What are the grand dynamics that drive…

…the accumulation and distribution of capital? Questions about the long-term evolution of inequality, the concentration of wealth, and the prospects for economic growth lie at the heart of political economy. But satisfactory answers have been hard to find for lack of adequate data and clear guiding theories. In Capital in the Twenty-First Century, economist Thomas Piketty analyzes a unique collection of data from twenty countries, ranging as far back as the eighteenth century, to uncover key economic and social patterns. His findings will transform debate and set the agenda for the next generation of thought about wealth and inequality…


Diane Coyle: Capital and Destiny: “It is with some trepidation that I offer my review of Thomas Piketty’s Capital in the 21st Century….

Piketty’s construction of a long-run multi-country World Top Incomes Database for income and wealth, along with Emmanuel Saez and Anthony Atkinson, is a magnificent achievement…. Piketty shows that the income share of (marketed financial) capital (at market values) declined substantially in the second half of the 20th century but is now climbing again. His argument is that this increase is a near-inexorable trend. The mid-20th century decline was essentially the result of Depression and war, or in other words, the massive destruction of assets and social dislocation; and the capital share stayed low for some decades because economic growth was unusually high, which–he argues–will no longer be the case. Specifically, population growth has slowed or turned negative, and Piketty is clearly gloomy about the prospect of productivity growth.

It’s clear that many readers have taken this argument as a given without concerning themselves about how it adds up. It is based on two equations… the share of capital in national income (α) is defined as the rate of return on capital (r) times the ratio of the capital stock to income (β)… an accounting identity … [and] a ‘steady state’ condition: when the economy settles down in a stable way in the very long run, at its long-term potential growth rate, the ratio of capital stock to income equals the savings rate (s) divided by the growth rate (g)….

Piketty notes….

The inequality r > g is a contingent historical proposition, which is true in some periods and political contexts and not in others…

The exception was the latter part of the 20th century…. I am sceptical about the economy ever reaching the balanced growth state…. I’m also doubtful that the saving rate would not adjust…. I also wish Piketty had spent more time discussing the rate of return…. James Galbraith’s point… is marketable capital consisting mainly of financial assets the right definition to plug into a balanced growth model?…

The sense of inevitability or otherwise does matter. Piketty’s policy proposal is a global wealth tax. He’s acknowledged how unrealistic this is, but says it’s important to change the intellectual climate. True, but how about also debating the rigged markets in finance and the corporate legal framework that have contributed so significantly to the growth in very high incomes, which are quickly turned into new wealth? What about income and inheritance taxes? And rather than treating savings, the return on capital and the growth rate as givens, isn’t it worth thinking about what determines them, and what actually determines causality in the book’s simple algebra. I’m glad Capital in the 21st Century has succeeded…. It’s just a bit of a shame it does so in such a deterministic–and therefore disempowering–way.