Inequality remained a prominent theme in public debate during 2015, likely helped by the unexpected rise and resilience of democratic socialist Bernie Sanders' run for the Democratic presidential nomination. Although the labor market continued its slow recovery, wage growth remained fairly weak—especially for middle and low earners. The upper middle class continues to pull away from the middle, not least in terms of income and wealth.

But it has also become much clearer that inequality is a geographical issue, as much as a social and economic one. Whether the focus is on the more immediate matter of income inequality or the slower-burning issue of intergenerational mobility, there is huge variation between different places in the United States.

National income trends are important, of course. But they can often disguise deep differences by place. The income required to be ‘rich,’ at least by comparison to those around you, varies significantly between different cities, for example. A household income of $100,000 puts you on almost on the top rung (around the 95^th percentile) of the income ladder in Detroit. But to reach the same heights in San Jose, California, you’d need an income three times as great, according to calculations by my colleague Alan Berube.

There are also very large differences in the extent of income inequality in different metropolitan areas. Using the inequality measure used in another recent paper by Berube, the ratio between incomes at the 20^th percentile and the 95^th percentile, shows that while some cities have large gaps between rich and poor, others look almost Scandinavian in their egalitarian distributions. Here are the 20/95 ratios for the three most equal and unequal cities in the U.S.:

In a groundbreaking research paper in 2014, Raj Chetty and his team at the Equality of Opportunity Project at Harvard showed that rates of intergenerational income mobility also vary considerably between different cities. It was always a stretch to compare the U.S. to Denmark on this front, given the colossal differences between the countries. But such comparisons became virtually unconscionable once the variations within the U.S. become apparent.

This year, Chetty and his co-author Nathaniel Hendren went a step further and a big step closer to showing a causal impact of place on the prospects for children raised in different locations. Again relying on large administrative datasets, the two scholars were able to show the variation in earnings for the folk hailing from, say, Baltimore versus Baton Rouge.

Professor Chetty presented his new research at a Brookings event in June (which you can view here), just weeks after the eruption of protest and violence in Baltimore following the death of Freddie Gray. One striking finding was that the worst place in America to grow up, in terms of subsequent earnings, is Baltimore City. Critically, Chetty’s research design allows him to show that these differences do not reflect the characteristics of the people of Baltimore; but the characteristics of Baltimore itself. This downward effect on earnings is particularly bad for boys, as we highlighted in an earlier blog:

In related work, Chetty and his colleagues also show that children who move to a better place see an improvement in their own earnings—and that the younger they are when they move, the bigger the impact. The children of families who move as a result of the U.S. Department of Housing and Urban Development’s Moving to Opportunity program showed sizable improvements in their own outcomes, as Jonathan Rothwell highlighted in his blog, 'Sociology’s revenge: Moving to Opportunity (MTO) revisited.'

Race, place and opportunity

One of the findings from Chetty’s earlier work is that race, place, and opportunity intersect in important ways. Cities with more segregation, and those with larger black populations, tend to show weaker upward mobility patterns. In order to understand the obstacles to upward mobility, policymakers have to adopt both a place-conscious (Margery Turner) and a race-conscious perspective. This policy was the subject of another Brookings event in November, with contributions from the Deputy Prime Minister of Singapore, the Governor of Delaware, and the Mayor of Newton, Mass. (The event can still be viewed here; for my highlights see this piece.) Being poor and black is generally not the same as being poor and white. Being poor in Cleveland is not the same as being poor in Charlotte.

Many states and cities are upping their game on issues of equality and opportunity, for both bad and good reasons. The bad reason is the relative inertia of the federal government. The good reason is a growing recognition that many of the levers for improving opportunity lie in the hands of institutions and agents at the state and metro level. Colorado has adopted a life-cycle opportunity framework and is pioneering efforts to integrate health and social policy. Charlotte has a high-profile taskforce (which I advise) on improving opportunity. Cincinnati has pledged to lift 10,000 children out of poverty within five years. Louisville is leading a push on school desegregation. Kalamazoo is adding greater student supports to its existing promise of free college. Baltimore’s program to reduce infant mortality has shown remarkable success. Durham, N.C. has rolled out a universal home visiting program.

Many of these efforts are building on the emerging ideas around 'collective impact,' harnessing local resources of many kinds around a clearly-articulated, shared goal. Given the scholarship showing just how much particular places influences individual and broader outcomes, this is likely to be where much of the most important policy development will take place in coming years. In terms of equality—and especially equality of opportunity—we need to think local, and act local, too.

Amid anger and protests in Baltimore following the death of 25-year-old Freddie Gray from a spinal injury sustained after being arrested by police, much of the discussion has focused on the poverty-ridden neighborhood in which Gray grew up (Sandtown-Winchester, on the city’s west side). Conversation has centered around the economic disadvantages that Gray, his peers, and so many young adults are facing in certain neighborhoods throughout Baltimore and in other U.S. metro areas.

Metropolitan Policy Program Fellow Jennifer Vey spoke yesterday with CNN’s Maggie Lake on the poverty and economic inequality prevalent in Baltimore—particularly in impoverished neighborhoods like that of Gray’s and throughout the country.

In the interview, Vey says that, “it’s important to look at the events of the last few days in Baltimore against a backdrop of poverty, of entrenched joblessness, of social disconnectedness that’s prevalent in many Baltimore neighborhoods…but that isn’t unique to Baltimore, and I think that’s a really important point here, that we really need to put these issues in a much broader national context.

“I think what this really indicates is we’ve been operating under an economic model for quite some time that clearly isn’t working for large numbers of people in this country.”

Vey also discusses how we can work to break the cycle:

“What we’re really focused on at Brookings is trying to understand how cities and metropolitan areas can really be trying to grow the types of advanced industries that create good jobs, that create more jobs, and also focusing on how then, people can connect back to that economy. What can we do to make sure that more people are participating in that economic growth as it happens?”

She goes on to say that investment in education, workforce programs, and infrastructure are all key in incorporating everyone into a prosperous economy.

To learn more about poverty in Baltimore, read this piece by Karl Alexander.

Inequality remained a prominent theme in public debate during 2015, likely helped by the unexpected rise and resilience of democratic socialist Bernie Sanders' run for the Democratic presidential nomination. Although the labor market continued its slow recovery, wage growth remained fairly weak—especially for middle and low earners. The upper middle class continues to pull away from the middle, not least in terms of income and wealth.

But it has also become much clearer that inequality is a geographical issue, as much as a social and economic one. Whether the focus is on the more immediate matter of income inequality or the slower-burning issue of intergenerational mobility, there is huge variation between different places in the United States.

National income trends are important, of course. But they can often disguise deep differences by place. The income required to be ‘rich,’ at least by comparison to those around you, varies significantly between different cities, for example. A household income of $100,000 puts you on almost on the top rung (around the 95^th percentile) of the income ladder in Detroit. But to reach the same heights in San Jose, California, you’d need an income three times as great, according to calculations by my colleague Alan Berube.

There are also very large differences in the extent of income inequality in different metropolitan areas. Using the inequality measure used in another recent paper by Berube, the ratio between incomes at the 20^th percentile and the 95^th percentile, shows that while some cities have large gaps between rich and poor, others look almost Scandinavian in their egalitarian distributions. Here are the 20/95 ratios for the three most equal and unequal cities in the U.S.:

In a groundbreaking research paper in 2014, Raj Chetty and his team at the Equality of Opportunity Project at Harvard showed that rates of intergenerational income mobility also vary considerably between different cities. It was always a stretch to compare the U.S. to Denmark on this front, given the colossal differences between the countries. But such comparisons became virtually unconscionable once the variations within the U.S. become apparent.

This year, Chetty and his co-author Nathaniel Hendren went a step further and a big step closer to showing a causal impact of place on the prospects for children raised in different locations. Again relying on large administrative datasets, the two scholars were able to show the variation in earnings for the folk hailing from, say, Baltimore versus Baton Rouge.

Professor Chetty presented his new research at a Brookings event in June (which you can view here), just weeks after the eruption of protest and violence in Baltimore following the death of Freddie Gray. One striking finding was that the worst place in America to grow up, in terms of subsequent earnings, is Baltimore City. Critically, Chetty’s research design allows him to show that these differences do not reflect the characteristics of the people of Baltimore; but the characteristics of Baltimore itself. This downward effect on earnings is particularly bad for boys, as we highlighted in an earlier blog:

In related work, Chetty and his colleagues also show that children who move to a better place see an improvement in their own earnings—and that the younger they are when they move, the bigger the impact. The children of families who move as a result of the U.S. Department of Housing and Urban Development’s Moving to Opportunity program showed sizable improvements in their own outcomes, as Jonathan Rothwell highlighted in his blog, 'Sociology’s revenge: Moving to Opportunity (MTO) revisited.'

Race, place and opportunity

One of the findings from Chetty’s earlier work is that race, place, and opportunity intersect in important ways. Cities with more segregation, and those with larger black populations, tend to show weaker upward mobility patterns. In order to understand the obstacles to upward mobility, policymakers have to adopt both a place-conscious (Margery Turner) and a race-conscious perspective. This policy was the subject of another Brookings event in November, with contributions from the Deputy Prime Minister of Singapore, the Governor of Delaware, and the Mayor of Newton, Mass. (The event can still be viewed here; for my highlights see this piece.) Being poor and black is generally not the same as being poor and white. Being poor in Cleveland is not the same as being poor in Charlotte.

Many states and cities are upping their game on issues of equality and opportunity, for both bad and good reasons. The bad reason is the relative inertia of the federal government. The good reason is a growing recognition that many of the levers for improving opportunity lie in the hands of institutions and agents at the state and metro level. Colorado has adopted a life-cycle opportunity framework and is pioneering efforts to integrate health and social policy. Charlotte has a high-profile taskforce (which I advise) on improving opportunity. Cincinnati has pledged to lift 10,000 children out of poverty within five years. Louisville is leading a push on school desegregation. Kalamazoo is adding greater student supports to its existing promise of free college. Baltimore’s program to reduce infant mortality has shown remarkable success. Durham, N.C. has rolled out a universal home visiting program.

Many of these efforts are building on the emerging ideas around 'collective impact,' harnessing local resources of many kinds around a clearly-articulated, shared goal. Given the scholarship showing just how much particular places influences individual and broader outcomes, this is likely to be where much of the most important policy development will take place in coming years. In terms of equality—and especially equality of opportunity—we need to think local, and act local, too.

MEDIA RELEASE

Low-Income Boys in Higher Inequality Areas Drop Out of School More Often than Low-Income Boys in Lower Inequality Areas, Limiting Social Mobility, New Brookings Paper Finds
“Economic despair” may contribute if those at the bottom do not believe they have the ability to achieve middle class status

Greater income gaps between those at the bottom and middle of the income distribution lead low-income boys to drop out of high school more often than their counterparts in lower inequality areas, suggesting that there is an important link between income inequality and reduced rates of upward mobility, according to a new paper presented today at the Brookings Panel on Activity. The finding has implications for social policy, implying a need for interventions that focus on bolstering low-income adolescents' perceptions of what they could achieve in life.

In “Income Inequality, Social Mobility, and the Decision to Drop Out Of High School,” Brookings Nonresident Senior Fellow and University of Maryland economics professor Melissa S. Kearney and Wellesley economics professor Phillip B. Levine propose a channel through which income inequality might lead to less upward mobility—often assumed to be the case but not yet fully proven. The conventional thinking among economists is that income inequality provides incentives for individuals to invest more in order to achieve the higher income position in society, but Kearney and Levine observe that if low-income youth view middle-class life as out of reach, they might decide to invest less in their own economic future.

See an interactive map of inequality by state, plus more findings »

The authors focus on income inequality in the lower half of the income distribution, as measured by income gaps between the 10th and 50th percentiles of the income distribution rather than income gaps between the the top and bottom of the income distribution, which has been more of a focus in popular culture. They show this "lower-tail" inequality is more relevant to the lives of poor youth because the middle is a more realistic ambition. Furthermore, their research could reconcile a puzzle: social mobility does not appear to be falling, despite the rise in income inequality. But, as Kearney and Levine point out, U.S. income inequality has been rising because the top of the distribution has been pulling away from the middle, not because the bottom is falling farther behind the middle.

The authors look specifically at high school drop-out rates through a geographic lens, noting the link between highly variable rates of high school completion and income inequality across the country. One-quarter or more of those who start high school in the higher inequality states of Louisiana, Mississippi, Georgia, and the District Columbia fail to graduate in a four-year period, as compared to only around 10 percent in Vermont, Wisconsin, North Dakota, and Nebraska—lower inequality states. Their econometric analysis goes on to show that low-income youth—boys in particular—are 4.1 percentage points more likely to drop out of high school by age 20 if they live in a high-inequality location relative to those who live in a low-inequality location.

Kearney and Levine examine a number of potential explanations for this link, including differences in educational inputs, poverty rates, demographic composition, and other factors. Ultimately, the evidence suggests that there is something specific about areas with greater income gaps that lead low-income boys there to drop out of school at higher rates than low-income boys elsewhere. The authors' research suggests that adolescents make educational decisions based on their perceived returns to investing in their educational development: a greater distance to climb to get to the middle of the income distribution could lead to a sense that economic success is unlikely—what they term “economic despair.”

"Income inequality can negatively affect the perceived returns to investment in education from the perspective of an economically disadvantaged adolescent,” they write. “Perceptions beget perceptions."

Digging into reasons students themselves give for dropping out, they find that low-income students from more unequal places are more likely to give up on their educational pursuits. Surprisingly, survey evidence shows that academic performance does not have as large an impact on low-income students in high inequality states: 51 percent of dropouts in the least unequal states reported that they dropped out because they were performing poorly, as compared to only 21 percent of students who dropped out in the most unequal states.

The finding suggests that economic despair could play an important role: if a student perceives a lower benefit to remaining in school, then he or she will choose to drop out at a lower threshold of academic difficulty. They also note that while the wage premium of completing high school should reduce the dropout rate, household income inequality has an offsetting negative effect.

The choice between staying in school and dropping out may reflect actual or perceived differences from the benefits of graduating. For instance, the authors note their past research showing that youth from low-income households who grow up in high lower-tail inequality states face lifetime incomes that are over 30 percent lower than similar children in lower inequality states. They also highlight other research showing that the overwhelming majority of 9th graders aspire to go to college, but by 11th grade, low-SES students are substantially less likely to expect they will enroll in college, even among those students with high test scores.

"There are important policy implications for what types of programs are needed to improve the economic trajectory of children from low-SES backgrounds," they write. "Successful interventions would focus on giving low income youth reasons to believe they have the opportunity to succeed. Such interventions could focus on expanded opportunities that would improve the actual return to staying in school, but they could also focus on improving perceptions by giving low-income students a reason to believe they can be the "college-going type." For example, interventions might take the form of mentoring programs that connect youth with successful adult mentors and school and community programs that focus on establishing high expectations and providing pathways to graduation. They could also take the form of early-childhood parenting programs that work with parents to create more nurturing home environments to build self-esteem and engender positive behaviors."

Read the full paper from Kearney and Levine here »

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• How “economic despair” affects high school graduation rates for America’s poorest students

Income and wealth inequality in the U.S. is a stark reality.  Research from a variety of fields demonstrates that children born into poor families tend to end up less educated, less healthy, more prone to contact with the police, and less likely to accumulate wealth over a lifetime.  In contrast, children born into well-off families tend to exhibit better outcomes on all of these dimensions.

How should social scientists and policymakers understand and address intergenerational mobility in the U.S.? This question is difficult to answer—and highly politicized.  To start with, there are several possible mechanisms driving high intergenerational persistence of economic outcomes.  These are often characterized as factors related either to “nurture” or “nature.” 

The “nurture” hypothesis asserts that poor parents lack critical resources such as wealth or information.  Such parents may therefore find it difficult to make the education and time investments that would promote better economic outcomes for their children.  If this is true, then children born into poor families never reach their full potential because of a lack of household resources. 

A second possible mechanism is often referred to as the “nature” hypothesis.  Economically successful parents might be more likely to have successful children.  Such an account hinges on the idea that there are heritable biological traits or abilities that more successful parents “pass on” to their children.

To complicate the matter further, the mechanisms of nature and nurture almost certainly operate at the same time.  Moreover, it is likely that abilities and investments interact in complicated ways. For example, a particular investment might do more to improve the outcomes of a lower-ability child than a higher-ability child, or vice versa.  Understanding this process, and how it affects intergenerational mobility, is notoriously difficult.  However, greater clarity is precisely what is needed to guide effective policy. 

If a lack of investment is the dominant mechanism explaining intergenerational persistence in economic outcomes, then we as a society may be wasting human potential.  Policies correcting under-investments in human capital could therefore be justified as economically efficient. In contrast, if the intergenerational transmission of ability plays a role, then investments in poor children’s human capital may not be enough.  To clarify, it is critical to state that the distinction we make here between “high-ability” and “low-ability” individuals should not be interpreted as a claim that some people are naturally or biologically superior to others.  We use “ability” as shorthand to describe those traits that are rewarded in the existing labor market.  Even if these abilities are linked to heritable biological factors, this does not mean that their impact on life outcomes is immutable or fixed.  Modifying environments could substantially affect genetic disparities. The case of vision and eyeglasses offer one classic example.  There may well be biological factors that explain variation in eyesight “ability,” but these biological differences will matter more or less for life outcomes depending on the availability of glasses and other medical interventions.  In short, it is very possible that the consequences of biological differences can be moderated by appropriate changes in the environment.     

Until now, researchers have typically used variables such as cognitive test scores to measure ability endowments related to human capital.  Yet, these traditional measures are subject to the critique that they are the products of earlier investments in human capital. This makes it difficult to distinguish between the “nature” and “nurture” hypotheses using such data.  Two individuals with similar ability endowments but different levels of household resources are likely to exhibit different cognitive test scores, for example. 

Using genetic information to measure ability endowments can help us better understand the intergenerational transmission of human capital.  As a measure, genetic information has a clear advantage over cognitive test scores because it is fixed at conception. Advances in measuring differences in DNA across individuals, together with very recent advances in behavioral genetics research, now make it possible to link genetic differences across people to behavioral traits.  These new discoveries have even extended to educational attainment, which was once thought to be too complicated and removed from direct biological processes for genetic analysis.

In a recent research paper, we use genetic information to better understand the nature of intergenerational mobility.  We follow the cutting edge in behavioral genetics research, which guides us in computing a type of genetic “score” for any individual.  We compute this so-called “polygenic score” for each person in a sample of over 8,000 individuals from the Health and Retirement Study (HRS). The score, which appears to be related to cognition, personality, and facility with learning, has some predictive power for educational attainment. In particular, it explains between 3.2 percent and 6.6 percent of the variation across individuals (depending on the specification). Thus, knowing the exact value of an individual’s score will tell you very little about that person (over 90 percent of the variation is explained by other factors).  However, the average relationship in the population between the score and human capital outcomes can offer some important lessons.  

Using the polygenic score, we believe we can gain new insights into how ability endowments interact with an individual’s environment to generate economic outcomes.  There is a long-standing debate in the economics literature about how ability and investments interact.  One idea is that both ability and investments are needed for success, i.e., that they complement one another. Though our findings show evidence of this type of interaction, the story that emerges from our analysis is somewhat more nuanced.  We show that ability and the environment (measured by parents’ socioeconomic status or SES) complement one another for generating higher degrees, such as college completion, but substitute for one another in generating lower levels of educational attainment such as a high school degree.  In other words, our findings suggest that ability or being born into a well-off family are enough to get an individual through high school.  For college, however, ability and a well-off family are important predictors of success.

"In other words, our findings suggest that ability or being born into a well-off family are enough to get an individual through high school. For college, however, ability and a well-off family are important predictors of success."

Another set of results concerns the wages of high-ability individuals.  We show that individuals who completed college earned substantial returns on their ability starting in the early 2000s.  Individuals without a college degree did not. The post-2000 rise in returns may be driven in part by “skill-biased technological change.”   As new technologies are adopted in the workplace, the people who benefit most are those with the skills required to adapt to and master new ways of working.  It is not difficult to imagine that people with genetic variants associated with higher education may have found it easier to adapt to computers and other new technologies.  However, we also find that a higher polygenic score was not helpful for individuals who did not complete college, likely because the lack of a college degree shut them out of careers that would have allowed them to creatively use new technologies.  This is a troubling finding given the role of childhood SES in predicting college completion.  It means that poor children with high abilities are less likely to attend college and, subsequently, are less likely to benefit from their ability.  Again, these findings suggest wasted human potential.

Using genetic data to compare individuals from different socioeconomic backgrounds, we also find that children from lower SES backgrounds systematically acquire less education when compared to similarly capable individuals from high SES backgrounds.  Among other things, this suggests that access to education may be an important obstacle, even for the highest ability children.  Our analysis offers some suggestive evidence regarding which environments are especially harmful. For example, acute negative events like physical abuse in childhood can lead to a dramatic loss of economic potential—reducing financial wealth in late adulthood for the highest ability individuals by over 50 percent.

Of course, one must be very cautious when interpreting any genetic association.  In particular, it is important to think carefully about correlation versus causality.  The same parents that pass along genetic material predicting educational attainment may also be more likely to have the resources to invest in their children.  Still, since we base our comparisons on individuals from different socioeconomic backgrounds, but with similar polygenic scores, we offer evidence that economic disparities are not solely due to nature.

In summary, recent advances in behavioral genetics have identified specific genetic variants that predict educational attainment.  The fact that such genes exist confirms previous work (largely using data on twins) showing that “nature” matters for economic outcomes.  Our research demonstrates that “nurture” matters, too.  Perhaps more importantly, our research demonstrates that the roles of “nature” and “nurture” are intertwined and that understanding the role of “nurture” (in the form of human capital investments over the life-cycle) is key to understanding how “nature” (in the form of ability endowments) operates.  In particular, we show that similarly apt individuals with different childhood SES see very different returns to their ability.  This means that policies helping children born into disadvantaged circumstances may be justified not solely for ethical reasons rooted in social justice, but perhaps also as an economically efficient way to mitigate wasted human potential.

Finally, we believe that continued progress in understanding the mechanisms underlying how “nature” affects economic outcomes will eventually lead to policies that help people who are born with different abilities.  For example, our findings suggest that some individuals had more difficulty than others in adapting to new workplace technologies, such as computers. With a fuller understanding of this process, policymakers may be able to devise better training programs or improved school curricula that help individuals of all levels of ability to better respond to a changing technological environment.  In other words we believe that our research shows that learning more about the specifics of “nature” may help us to better “nurture” all individuals in society to help them to reach their full potential.      

Editor’s note: The authors contributed equally to this posting and to the research upon which the posting is based. They are listed alphabetically by last name.

On Thursday, July 7, the International Monetary Fund (IMF) revised its economic outlook for South Africa. Despite “considerable economic and social progress” since 1994, the IMF report cited high income inequality, among other factors, in its projection of slow growth and increased unemployment in the medium term. Earlier this year, in the Brookings Africa Growth Initiative’s Foresight Africa 2016, we explored this pressing problem—high income inequality—across the continent. The initial takeaway was that sub-Saharan Africa has greater in-country income inequality than other developing countries around the world. However, after separating seven outlier economies—Angola, the Central African Republic, Botswana, Zambia, Namibia, Comoros, and South Africa—we noted that income inequality, measured by the Gini coefficient, in the rest of the region actually mirrors the rest of the developing world, which currently stands at 0.39. All seven outlier economies have Gini coefficients above 0.55, a level reached by only four other countries worldwide: Suriname, Haiti, Colombia, and Honduras. 

It is important to explore precisely why this disparity exists. Notably, sub-Saharan Africa is not only an outlier in income inequality, but also in the relationship between economic growth and poverty reduction. Generally, in the developing world, every 1 percent of growth reduces poverty 4 percent. In sub-Saharan Africa, however, every 1 percent of growth only reduces poverty by 3 percent. In Foresight Africa 2016, Brookings Nonresident Senior Fellow Haroon Bhorat suggests that this disparity may be because of the commodity booms that have sustained growth periods in African economies, which bring extraordinary returns to capital but limited job growth. Alternatively, these commodity booms may have accompanied a fall in manufacturing output; growth is thus concentrated in the low-productivity services sector. In any case, this graph forces us to consider exactly what type of structural transformation is necessary for continued economic growth and acknowledge that inequality in sub-Saharan Africa might require different solutions in different countries.

For a more in-depth discussion on this issue, see Foresight Africa 2016 and Bhorat’s discussion of African inequality in relation to the Sustainable Development Goals.

Omid Abrishamchian contributed to this post.

INTRODUCTION

In recent years, there has been a resurgence of interest in the issue of inequality. Part of this resurgence can be traced to new evidence of persistent and widening wealth gaps. Average incomes may be converging globally as a result of high growth in emerging markets, stronger growth in many poor countries, and slow growth in rich countries. However, the evidence also shows that within countries a parallel process of income divergence, marginalization and rising inequality is also taking place. Put differently, the rising tide of global prosperity is not lifting all boats.

Much of the international debate on inequality focuses on the distribution of income across and within countries. Other dimensions of inequality have received less attention. This is unfortunate. Amartya Sen has described development as “a process of expanding the real freedoms that people enjoy” by building human capabilities or their capacity to lead the kind of life they value. Income is a means to that end but it is a limited indicator of well-being. Moreover, a person’s income reflects not just personal choice but also their opportunities for improving health, literacy, political participation and other areas. Education is one of the most basic building blocks for the “real freedoms” that Sen describes. People denied the chance to develop their potential through education face diminished prospects and more limited opportunities in areas ranging from health and nutrition, to employment, and participation in political processes. In other words, disparities in education are powerfully connected to wider disparities, including international and intra-country income inequalities. This is why education has been identified as one of the most critical factors in breaking down the disadvantages and social inequalities that are limiting progress toward the United Nations’ Millennium Development Goals (MDGs)—development targets adopted by the international community for 2015.

Understanding patterns of educational inequality is critical at many levels. Ethical considerations are of paramount importance. Most people would accept that children’s educational achievements should not be dictated by the wealth of their parents, their gender, their race or their ethnicity. Disparities in educational opportunities are not just inequalities in a technical sense, they are also fundamental in equities—they are unjust and unfair. In an influential paper, John Roemer differentiated between inequalities that reflect factors such as luck, effort and reasonable reward, and those attributable to circumstances that limit opportunity (Roemer 1988).1 While the dividing line may often be blurred, that distinction has an intuitive appeal. Most people have a high level of aversion to the restrictions on what people—especially children—are able to achieve as a result of disparities and inherited disadvantages that limit access to education, nutrition or health care (Wagstaff, 2002). There is a wide body of opinion across political science, philosophy and economics that equal opportunity—as distinct from equality of outcomes—is a benchmark of egalitarian social justice. The theories of distributive justice associated with thinkers such as Amartya Sen, John Rawls, Ronald Dworkin and John Roemer argue, admittedly from very different perspectives, that public policy should aim at equalizing opportunity to counteract disadvantages associated with exogenous circumstances over which individuals or social groups have no control. Given the role of education as a potential leveler of opportunity, it is a national focal point for redistributive social justice.

Considerations of economic efficiency reinforce the ethical case for equalizing educational opportunities. Education is a powerful driver of productivity, economic growth, and innovation. Econometric modeling for both rich and poor countries suggests that an increase in learning achievement (as measured by test score data) of one standard deviation is associated on average with an increase in the long-run growth rate of around 2 percent per capita annually (Hanushek and Wößmann, 2010; Hanushek, 2009; Hanushek and Wößmann, 2008). Such evidence points to the critical role of education and learning in developing a skilled workforce. Countries in which large sections of the population are denied a quality education because of factors linked to potential wealth, gender, ethnicity, language and other markers for disadvantage are not just limiting a fundamental human right. They are also wasting a productive resource and undermining or weakening the human capital of the economy.

International development commitments provide another rationale for equalizing educational opportunities. This is for two reasons. First, the commitments envisage education for all and achievement of universal primary education by 2015. Second, there is mounting evidence that inequality is acting as a brake on progress toward the 2015 goals. Since around 2005, the rate of decline in the out-of-school population has slowed dramatically. Based on current trends, there may be more children out of school in 2015 than there were in 2009. Caution has to be exercised in interpreting short-run trends, especially given the weakness of data. However, the past three editions of the UNESCO Education for All Global Monitoring Report (GMR) have highlighted the role of inequality in contributing to the slowdown with governments struggling to reach populations that face deeply entrenched disadvantages (UNESCO, 2008, 2010, 2011). Therefore, picking up the pace toward the 2015 goals requires a strengthened focus on equity and strategies that target the most marginalized groups and regions of the world (Sumner and Tiwari, 2010; UN-DESA, 2009; UNESCO, 2010). It should be added that disparities in education relate not just to access, but also to learning achievement levels.

Accelerated progress in education would generate wider benefits for the MDGs. Most of the world’s poorest countries are off-track for the 2015 MDG target of halving income poverty and a long way from reaching the targets on child survival, maternal health and nutrition. Changing this picture will require policy interventions at many levels. However, there is overwhelming evidence showing that education—especially of young girls and women—can act as a potent catalyst for change. On one estimate, if all of sub-Saharan Africa’s mothers attained at least some secondary education, there would be 1.8 million fewer child deaths in the region each year. Thus while education may lack the “quick fix” appeal of vaccinations, it can powerfully reinforce health policy interventions.

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The Horatio Alger ideal of upward mobility has a strong grip on the American imagination (Reeves 2014). But recent years have seen growing concern about the distance between the rhetoric of opportunity and the reality of intergenerational mobility trends and patterns.

The related issues of equal opportunity, intergenerational mobility, and inequality have all risen up the agenda, for both scholars and policymakers. A growing literature suggests that the United States has fairly low rates of relative income mobility, by comparison to other countries, but also wide variation within the country. President Barack Obama has described the lack of upward mobility, along with income inequality, as “the defining challenge of our time.” Speaker Paul Ryan believes that “the engines of upward mobility have stalled.”

But political debates about equality of opportunity and social and economic mobility often provide as much heat as light. Vitally important questions of definition and motivation are often left unanswered. To what extent can “equality of opportunity” be read across from patterns of intergenerational mobility, which measure only outcomes? Is the main concern with absolute mobility (how people fare compared to their parents)—or with relative mobility (how people fare with regard to their peers)? Should the metric for mobility be earnings, income, education, well-being, or some other yardstick? Is the primary concern with upward mobility from the bottom, or with mobility across the spectrum?

In this paper, we discuss the normative and definitional questions that guide the selection of measures intended to capture “equality of opportunity”; briefly summarize the state of knowledge on intergenerational mobility in the United States; describe a new microsimulation model designed to examine the process of mobility—the Social Genome Model (SGM); and how it can be used to frame and measure the process, as well as some preliminary estimates of the simulated impact of policy interventions across different life stages on rates of mobility.

The three steps being taken in mobility research can be described as the what, the why, and the how. First, it is important to establish what the existing patterns and trends in mobility are. Second, to understand why they exist—in other words, to uncover and describe the “transmission mechanisms” between the outcomes of one generation and the next. Third, to consider how to weaken those mechanisms—or, put differently, how to break the cycles of advantage and disadvantage.

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Forty years after its initial publication, Equality and Efficiency: The Big Tradeoff remains an influential work from one of the most important macroeconomists over the last century, Arthur M. Okun (1928-1980). Okun’s theory on market economies reminds readers of an engaging dual theme: the market needs a place, and the market needs to be kept in its place. Articulated in a way that remains relevant even during today’s discussions on broadening gaps in income inequality, Okun emphasized that institutions in a capitalist democracy prod us to get ahead of our neighbors economically after telling us to stay in line socially.

On May 4, The Brookings Institution Press re-released Okun’s classic work with a new foreword from Former Treasury Secretary Lawrence H. Summers, in addition to “Further Thoughts on Equality and Efficiency,” a paper published by Okun in 1977. The event included opening remarks from Brookings Senior Fellow George Perry, with a keynote address from Larry Summers. Following these remarks, David Wessel moderated a panel discussion with former Chair of the Council of Economic Advisers Greg Mankiw, Economic Studies’ Melissa Kearney and Justin Wolfers, and Washington Center for Equitable Growth's Heather Boushey regarding the history and impact of Okun’s work.

Download a copy of Lawrence Summers' opening remarks.

Ted Gayer, Vice President and Director of Economic Studies and Joseph Pechman Senior Fellow, reads Lawrence Summers's opening remarks.

David Wessel (right), Director of the Hutchins Center on Fiscal and Monetary Policy, moderates a panel discussion with N. Gregory Mankiw, Melissa Kearney, and Heather Boushey.

Janet Yellen, Chair of the Board of Governors of the Federal Reserve System, listens to the discussion from the audience. To Yellen's right is former Congressional Budget Office director, Doug Elmendorf.

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• 40 years later- The relevance of Okun’s "Equality and Efficiency: The Big Tradeoff"

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• 050415 Summers Okun Speech

Editor’s note: The following remarks were prepared and delivered by Gary Burtless at a roundtable sponsored by the American Tax Policy Institute on January 7, 2016. Video of Burtless’ remarks are also available on the Institute’s website. Download the related slides at the right. 

To eliminate suspense from the start: Nothing I am about to say undermines the popular narrative about recent inequality trends.  For the past 35 years, U.S. inequality has increased.  Inequality has increased noticeably, no matter what income definition you care to use.  A couple of things you read in the newspaper are untrue under some income definitions. For example, under a comprehensive income definition it is false to claim that all the income gains of the past 2 or 3 decades have gone to the top 1 percent, or the top 5 percent, or the top 10 percent of income recipients.  Middle- and low-income Americans have managed to achieve income gains, too, as we shall see.

Tax policy certainly affects overall inequality, but I shall leave it for Scott, David, and Tracy to take that up. Let me turn to my main job, which is to distinguish between different reasonable income measures.

The crucial thing to know is that contradictory statements can be made about some income trends because of differences in the definition of income.  In general, the most pessimistic statements about trends rely on an income definition that is restrictive in some way.  The definition may exclude important income items, items, for example, that tend to equalize or boost family incomes.  The definition may leave out adjustments to income … adjustments that tend to boost the rate of income gain for low- or middle-income recipients, but not for top-income recipients.

The narrowest income definition commonly used to evaluate income trends is Definition #1 in my slide, “pretax private, cash income.”  Columnists and news reporters are unknowingly using this income definition when they make pronouncements about the income share of the “top 1 percent.”  The data about income under this definition are almost always based on IRS income tax returns, supplemented with a bit of information from the Commerce Department’s National Income and Product Account (NIPA) data file.

The single most common income definition used to assess income trends and inequality is the Census Bureau’s “money income” definition, Definition #2 on the slide.  It is just the same as the first definition I mentioned, except this income concept also includes government cash transfer payments – Social Security, unemployment insurance, cash public assistance, Veterans’ benefits, etc.

A slightly more expansive definition (#3) also adds food stamp (or SNAP) benefits plus other government benefits that are straightforward to evaluate. Items of this kind include the implicit rent subsidy low-income families receive in publicly-subsidized housing, school lunch subsides, and means-tested home heating subsidies.

Now we come to subtractions from income. These typically reflect families’ tax obligations.  The Census Bureau makes estimates of state and federal income tax liabilities as well as payroll taxes owed by workers (though not by their employers).  Since income and payroll taxes subtract from the income available to pay for other stuff families want to buy, it seems logical to also subtract them from countable income. This is done under income Definition #4.  Some tax obligations – notably the Earned Income Credit (EIC) – are in fact subtractions from taxes owed, which would not be a problem in the case of families that still owe positive taxes to the government.  However, the EIC is refundable to taxpayers, meaning that some families have negative tax liabilities:  The government owes them money.  In this case, if you do not take taxes into account you understate low-income families’ incomes, even as you’re overstating the net incomes available to middle- and high-income families.

Now let’s get a bit more complicated.  Forget what I said about taxes, because our next income definition (#5) also ignores them.  It is an even-more-comprehensive definition of gross or pretax income.  In addition to all those cash and near-cash items I mentioned in Definition #3, Definition #5 includes imputed income items, such as: 

• The value of your employer’s premium contribution to your employee health plan;
• The value of the government’s subsidy to your public health plan – Medicare, Medicaid, state CHIP plans, etc.
• Realized taxable gains from the sale of assets; and
• Corporate income that is earned by companies in which you own a share even though it is not income that is paid directly to you.

This is the most comprehensive income definition of which I am aware that refers to gross or pre-tax income.

Finally we have Definition #6, which subtracts your direct and indirect tax payments.  The only agency that uses this income definition is principally interested in the Federal budget, so the subtractions are limited to Federal income and payroll taxes, Federal corporate income taxes, and excise taxes.

Before we go into why you should care about any of these definitions, let me mention a somewhat less important issue, namely, how we define the income-sharing group over which we estimate inequality.  The most common assessment unit for income included under Definition #1 (“Pre-tax private cash income”) is the Federal income tax filing unit.  Sometimes this unit has one person; sometimes 2 (a married couple); and sometimes more than 2, including dependents.

The Census Bureau (and, consequently, most users of Census-published statistics) mainly uses “households” as reference units, without any adjustment for variations in the size of different households.  The Bureau’s median income estimate, for example, is estimated using the annual “money income” of households, some of which contain 1 person, some contain 2, some contain 3, and so on.

Many economists and sociologists find this unsatisfactory because they think a $20,000 annual income goes a lot farther if it is supporting just one person rather than 12.  Therefore, a number of organizations—notably, the Luxembourg Income Study (LIS), the Organisation of Economic Cooperation and Development (OECD), and the Congressional Budget Office (CBO)—assume household income is equally shared within each household, but that household “needs” increase with the square root of the number of people in the household.  That is, a household containing 9 members is assumed to require 1½ times as much income to enjoy the same standard of living as a family containing 4 members.  After an adjustment is made to account for the impact of household size, these organizations then calculate inequality among persons rather than among households.

How are these alternative income definitions estimated?  Who uses them?  What do the estimates show?  I’ll only consider a two or three basic cases.

First, pretax, private, cash income. By far the most famous users of this definition are Professors Thomas Piketty and Emmanuel Saez.  Their most celebrated product is an annual estimate of the share of total U.S. income (under this restricted definition) that is received by the top 1 percent of tax filing units.

Here is their most famous chart, showing the income share of the top 1 percent going back to 1913. (I use the Piketty-Saez estimates that exclude realized capital gains in the calculation of taxpayers’ incomes.) The notable feature of the chart is the huge rise in the top income share between 1970—when it was 8 percent of all pretax private cash income—and last year—when the comparable share was 18 percent.  

I have circled one part of the line—between 1986 and 1988—to show you how sensitive their income definition is to changes in the income tax code.  In 1986 Congress passed the Tax Reform Act of 1986 (TRA86). By 1988 the reform was fully implemented.  Wealthy taxpayers noticed that TRA86 sharply reduced the payoff to holding corporate earnings inside a separately taxed corporate entity. Rich business owners or shareholders could increase their after-tax income by arranging things so their business income was taxed only once, at the individual level.  The result was that a lot of income, once earned by and held within corporations, was now passed through to the tax returns of rich individual taxpayers. These taxpayers appeared to enjoy a sudden surge in their taxable incomes between 1986 and 1988.  No one seriously believes rich people failed to get the benefits of this income before 1987.  Before 1987 the same income simply showed up on corporate rather than on individual income tax returns.

A final point:  The chart displayed in SLIDE #6 is the source of the widely believed claim that U.S. inequality is nowadays about the same as it was at the end of the Roaring 1920s, before the Great Depression.  That is close to being true – under this income definition.

Census “money income”: This income definition is very similar to the one just discussed, except that it includes cash government transfer payments.  The producer of the series is the Census Bureau, and its most famous uses are to measure trends in real median household income and the official U.S. poverty rate. Furthermore, the Census Bureau uses the income definition to compile estimates of the Gini coefficient of household income inequality and the income shares received by each one-fifth of households, ranked from lowest to highest income, and received by the top 5 percent of households.

Here is a famous graph based on the Bureau’s “median household income” series.  I have normalized the historical series using the 1999 real median income level (1999 and 2000 were the peak income years according to Census data).  Since 1999 and 2000, median income has fallen about 10 percent.  If we accept this estimate without qualification, it certainly represents bad news for living standards of the nation’s middle class. The conclusion is contradicted by other government income statistics that use a broader, more inclusive income definition, however.

And here is the Bureau’s most widely cited distributional statistic (after its “official poverty rate” estimate).  Since 1979, the Gini coefficient has increased 17 percent under this income definition. (It is worth noting, however, that the portion of the increase that occurred between 1992 and 1993 is mainly the result of methodological changes in the way the Census Bureau ascertained incomes in its 1994 income survey.)

When you hear U.S. inequality compared with that in other rich countries, the numbers are most likely based on calculations of the LIS or OECD.  Their income definition is basically “Cash and Near-cash Public and Private income minus Income and Payroll taxes owed by households.”  Under this income definition, the U.S. looks relatively very unequal and America appears to have an exceptionally high poverty rate.  U.S. inequality has been rising under this income definition, as indeed has also been the case in most other rich countries. The increase in the United States has been above average, however, helping us to retain our leadership position, both in income inequality and in relative poverty.

We turn last to the most expansive income definition:  CBO’s measure of net after-tax income.  I will use CBO’s tabulations using this income definition to shed light on some of the inequality and living standard trends implied by the narrower income definitions discussed above.

Let’s consider some potential limitations of a couple of those definitions.  The limitations do not necessarily make them flawed or uninteresting.  They do mean the narrower income measures cannot tell us some of the things that users claim they tell us.

An obvious shortcoming of the “cash pretax private income” definition is that it excludes virtually everything the government does to equalize Americans’ incomes.  Believe it or not, the Federal tax system is mildly progressive.  It claims a bigger percentage of the (declared) incomes of the rich than it does of middle-income families’ and especially the poor.  Any pretax income measure will miss that redistribution.

More seriously, it excludes all government transfer payments.  You may think the rich get a bigger percentage of their income from government handouts compared with middle class and poorer households.  That is simply wrong.  The rich get a lot less.  And the percentage of total personal income that Americans derive from government transfer payments has gone way up over the years.  In the Roaring 1920s, Americans received almost nothing in the form of government transfers. Less than 1 percent of Americans’ incomes were received as transfer payments.  By 1970—near the low point of inequality according to the Piketty-Saez measure—8.3 percent of Americans’ personal income was derived from government transfers.  Last year, the share was 17 percent. None of the increase in government transfers is reflected in Piketty and Saez’s estimates of the trend in inequality.  Inequality is nowadays lower than it was in the late 1920s, mainly because the government does more redistribution through taxes and transfers.

Both the Piketty-Saez and the Census “money income” statistics are affected by the exclusion of government- and employer-provided health benefits from the income definition. This slide contains numbers, starting in 1960, that show the share of total U.S. personal consumption consisting of personal health care consumption.  I have divided the total into two parts. The first is the share that is paid for out of our own cash incomes (the blue part at the bottom).  This includes our out-of-pocket spending for doctors’ charges, hospital fees, pharmaceutical purchases, and other provider charges as well as our out-of-pocket spending on health insurance premiums. The second is the share of our personal health consumption that is paid out of government subsidies to Medicare, Medicaid, CHIP, etc., or out of employer subsidies to employee health plans (the red part). 

As everyone knows, the share of total consumption that consists of health consumption has gone way up.  What few people recognize is that the share that is directly paid by consumers—through payments to doctors, hospitals, and household health insurance premium payments—has remained unchanged.  All of the increase in the health consumption share since 1960 has been financed through government and employer subsidies to health insurance plans. None of those government or employer contributions is counted as “income” under the Piketty-Saez and Census “money income” definitions.  You would have to be quite a cynic to claim the subsidies have brought households no living standard improvements since 1960, yet that is how they are counted under the Piketty-Saez and Census “money income” definitions.

Final slide: How much has inequality gone up under income definitions that count all income sources and subtract the Federal income, payroll, corporation, and excise taxes we pay?  CBO gives us the numbers, though unfortunately its numbers end in 2011.

Here are CBO’s estimates of real income gains between 1979 and 2011.  These numbers show that real net incomes increased in every income category, from the very bottom to the very top.  They also show that real incomes per person have increased much faster at the top—over on the right—than in the middle or at the bottom—over on the left.  Still, contrary to a common complaint that all the income gains in recent years have been received by folks at the top, the CBO numbers suggest net income gains have been nontrivial among the poor and middle class as well as among top income recipients.

Suppose we look at trends in the more recent past, say, between 2000 and 2011.  That lower panel in this slide presents a very different picture from the one implied by the Census Bureau’s “money income” statistics.  Unlike the “money income numbers” [SLIDE #9], these show that inequality has declined since 2000.  Unlike the “money income numbers” [SLIDE #8], these show that incomes of middle-income families have improved since 2000.  There are a variety of explanations for the marked contrast between the Census Bureau and CBO numbers.  But a big one is the differing income definitions the two conclusions are based on.  The more inclusive measure of income shows faster real income gains among middle-income and poorer households, and it suggests a somewhat different trend in inequality.

Alert voters everywhere realize the economy is neither as strong as claimed by the party in power nor the disaster described by the opposition. The election season will bring many passionate but dubious claims about economic trends. People running for office know that voters rank the economy near the top of their concerns. Of course, perceptions of the economy differ from one voter to the next. A few of us are soaring, more are treading water, and too many are struggling just to stay afloat.

Since reaching a low point in 2009, total U.S. output—as measured by real GDP—has climbed 15 percent, or about 2.1 percent a year. The recovery has been long-lived and steady, a tribute to the stewardship of the Administration and Federal Reserve. The economic rebound has also been disappointingly slow in view of the depth of the recession. GOP office seekers will mention this fact a number of times before November.

Compared with the worst months of the Great Recession, the unemployment rate has dropped by half. It now stands at a respectable 4.9 percent, almost 3 points lower than the rate when President Obama took office and far below the rate in fall 2009 when it reached 10 percent. Payroll employment has increased for 77 consecutive months. Since hitting a low in January 2010, the number of workers on employer payrolls has surged 14.6 million, or about 190,000 a month. While the job gains are encouraging, they have not been fast enough to bring the employment-to-population ratio back to its pre-recession level. June’s job numbers showed that slightly less than 80 percent of adults between 25 and 54 were employed. That’s almost 2 percentage points below the employment-to-population rate on the eve of the Great Recession.

One of the most disappointing numbers from the recovery has been the growth rate of wages. In the first 5 years of the recovery, hourly wages edged up just 2 percent a year. After factoring in the effect of consumer price inflation, this translates into a gain of exactly 0 percent. The pace of wage gain has recently improved. Workers saw their real hourly pay climb 1.7 percent a year in the two years ending in June.

The economic bottom line for most of us is the rate of improvement in our family income after accounting for changes in consumer prices. No matter how household income is measured, income gains have been slower since 2007 than they were in earlier decades. The main reason is that incomes produced in the market—in the form of wages, self-employment income, interest, dividends, rental income, and realized capital gains—fell sharply in the Great Recession and have recovered very slowly since then. That a steep recession would cause a big drop in income is hardly a surprise. Employment, company profits, interest rates, and rents plunged in 2008 and 2009, pushing down the incomes Americans earn in the market. The bigger surprise has been the slow recovery of market income once the recession was behind us.

Some critics of the recovery argue that the income gains in the recovery have been highly skewed, with a disproportionate share obtained by Americans at the top of the income ladder. Economist Emmanuel Saez tabulates U.S. income tax statistics to track market income gains at the top of the distribution. His latest estimates show that between 2009 and 2015 income recipients in the top 1 percent enjoyed real income gains of 24 percent. Among Americans in the bottom nine-tenths of the income distribution, average market incomes climbed only 4 percent.

However, Saez’s estimates also show that top income recipients experienced much bigger income losses in the Great Recession. Between 2007 and 2009 they saw their inflation-adjusted incomes drop 36 percent (see Chart 1). In comparison, the average market income of Americans in the bottom nine-tenths of the distribution fell just 12 percent. These numbers mean that top income recipients have not yet recovered the income losses they suffered in the Great Recession. In 2015 their average market income was still 13 percent below its pre-recession level. For families in the bottom nine-tenths of the distribution, market income was “only” 8 percent below its level in 2007.

Only about half of households rely solely on market income to support themselves. The other half receives income from government transfers. What is more, this fraction tends to increase in bad times. Many retirees rely mainly on Social Security to pay their bills; they depend on Medicare or Medicaid to pay for health care. Low-income Americans often have little income from the market, and they may rely heavily on public assistance, food stamps, or government-provided health insurance. When joblessness soars the percentage of families receiving government benefits rises, largely because of increases in the number of workers who collect unemployment insurance.

Government benefits, which are not counted in Saez’s calculations, replace part of the market income losses families experience in a weak economy. As a result, the net income losses of most families are much smaller than their market income losses. The Congressional Budget Office (CBO) recently published statistics on market income and before-tax and after-tax income that shed light on the size and distribution of household income losses in the Great Recession and ensuing recovery. The tabulations show that, except for households at the top of the distribution, net income losses were far smaller than the losses indicated in Saez’s income tax data.

For example, among households in the middle fifth of the before-tax income distribution, average market income fell more than 10 percent in the Great Recession (see Chart 2). If we include government transfers in the income definition, average income fell 4.4 percent. If we account for the federal taxes families pay, average net income fell just 1 percent. In contrast, among households in the top 1 percent of the distribution, average market income fell 36 percent, average income including government transfers fell 36 percent, and average income net of federal taxes fell 37 percent. Government transfers provided little if any protection to top-income households.

The CBO income statistics end in 2013, so they do not tell us how net income gains have been distributed in the last couple of years. Nonetheless, based on Saez’s income tax tabulations it is very unlikely top income recipients have recovered the net income losses they experienced in the Great Recession. All the available statistics show household income gains since 2007 have been negligible or small, and this is true across the income distribution.

It is popular to say slow income gains in the middle and at the bottom of the distribution are due to outsize income gains among families at the top. While this story is at least partly true for the three decades ending in 2007, it does not fit the facts for the years since 2007. CBO’s latest net income tabulations show that inequality was almost 5 percent lower in 2013 than it was in 2007. The Great Recession hurt the incomes of Americans up and down the income distribution, but the biggest proportional income losses were at the very top. To be sure, income gains in the recovery after 2009 have been concentrated among top income recipients. Even so, their income losses over the recession and recovery have been proportionately bigger than the losses suffered by middle- and low-income families.

Editor's note: This piece originally appeared in Real Clear Markets.

Two recently posted papers by Brookings colleagues purport to show that “even a large increase in the top marginal rate would barely reduce inequality.”[1]  This conclusion, based on one commonly used measure of inequality, is an incomplete and misleading answer to the question posed: would a stand-alone increase in the top income tax bracket materially reduce inequality?  More importantly, it is the wrong question to pose, as a stand-alone increase in the top bracket rate would be bad tax policy that would exacerbate tax avoidance incentives.  Sensible tax policy would package that change with at least one other tax modification, and such a package would have an even more striking effect on income inequality.  In brief:

• A stand-alone increase in the top tax bracket would be bad tax policy, but it would meaningfully increase the degree to which the tax system reduces economic inequality.  It would have this effect even though it would fall on just ½ of 1 percent of all taxpayers and barely half of their income.
• Tax policy significantly reduces inequality.  But transfer payments and other spending reduce it far more.  In combination, taxes and public spending materially offset the inequality generated by market income.
• The revenue from a well-crafted increase in taxes on upper-income Americans, dedicated to a prudent expansions of public spending, would go far to counter the powerful forces that have made income inequality more extreme in the United States than in any other major developed economy.

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Today marks Equal Pay Day. How are we doing? We have come a long way since I wrote my doctoral dissertation on the pay gap back in the late 1960s. From earning 59 percent of what men made in 1974 to earning 79 percent in 2015 (among year-round, full-time workers), women have broken a lot of barriers. 

There is no reason why the remaining gap can’t be closed. The gap could easily move in favor of women. After all, they are now better educated than men. They earn 60 percent of all bachelor’s degrees and the majority of graduate degrees. Adjusting for educational attainment, the current earnings gap widens, with the biggest relative gaps at the highest levels of education:

If we want to encourage people to get more education, we can't discriminate against the best educated just because they are women.

One source of the current gap is the fact that women still take more time off from work to care for their families. These family responsibilities may also affect the kinds of work they choose. Harvard professor Claudia Goldin notes that they are more likely to work in occupations where it is easier to combine work and family life. These divided work-family loyalties are holding women back more than pay discrimination per se. This should change when men are more willing to share equally on the home front, as Richard Reeves and I have argued elsewhere.  

But there is much to be done while waiting for this more egalitarian world to arrive. Paid family leave and more support for early child care and education would go a long way toward relieving families, and women in particular, of the dual burden they now face. In the process, the pay gap should shrink or even move in favor of women. 

The Economic Policy Institute (EPI) has just released a very informative report on these issues. They call for an aggressive expansion of both early childhood education and child care subsidies for low and moderate income families. Specifically, they propose to cap child care expenses at 10 percent of income, which would provide an average subsidy of $3,272 to working families with children and much more than this to lower-income families. 

The EPI authors argue that child care subsidies would provide needed in-kind benefits to lower income families (check!), boost women’s labor force participation in a way that would benefit the overall economy (check!), and reduce the gender pay gap (check!). In short, childcare subsidies are a win-win-win.

For present purposes I want to focus on the likely effects on the pay gap. In the mid-1990s, the U.S. had the highest rate of female labor force participation compared to Germany, Canada, and Japan. Now we have the lowest. One reason is because other advanced countries have expanded paid leave and child care support for employed mothers while the U.S. has not:

If we want to eliminate the pay gap and perhaps even reverse it, the primary focus must be on women’s continuing difficulties in balancing work and family life. We should certainly attend to any remaining instances of pay discrimination in the workplace, as called for in the Paycheck Fairness Act. But the biggest source of the problem is not employer discrimination; it is women’s continued double burden.

The Horatio Alger ideal of upward mobility has a strong grip on the American imagination (Reeves 2014). But recent years have seen growing concern about the distance between the rhetoric of opportunity and the reality of intergenerational mobility trends and patterns.

The related issues of equal opportunity, intergenerational mobility, and inequality have all risen up the agenda, for both scholars and policymakers. A growing literature suggests that the United States has fairly low rates of relative income mobility, by comparison to other countries, but also wide variation within the country. President Barack Obama has described the lack of upward mobility, along with income inequality, as “the defining challenge of our time.” Speaker Paul Ryan believes that “the engines of upward mobility have stalled.”

But political debates about equality of opportunity and social and economic mobility often provide as much heat as light. Vitally important questions of definition and motivation are often left unanswered. To what extent can “equality of opportunity” be read across from patterns of intergenerational mobility, which measure only outcomes? Is the main concern with absolute mobility (how people fare compared to their parents)—or with relative mobility (how people fare with regard to their peers)? Should the metric for mobility be earnings, income, education, well-being, or some other yardstick? Is the primary concern with upward mobility from the bottom, or with mobility across the spectrum?

In this paper, we discuss the normative and definitional questions that guide the selection of measures intended to capture “equality of opportunity”; briefly summarize the state of knowledge on intergenerational mobility in the United States; describe a new microsimulation model designed to examine the process of mobility—the Social Genome Model (SGM); and how it can be used to frame and measure the process, as well as some preliminary estimates of the simulated impact of policy interventions across different life stages on rates of mobility.

The three steps being taken in mobility research can be described as the what, the why, and the how. First, it is important to establish what the existing patterns and trends in mobility are. Second, to understand why they exist—in other words, to uncover and describe the “transmission mechanisms” between the outcomes of one generation and the next. Third, to consider how to weaken those mechanisms—or, put differently, how to break the cycles of advantage and disadvantage.

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