Must-Read: Sylvia Allegretto, Arindrajit Dube, Michael Reich and Ben Zipperer: Credible Research Designs for Minimum Wage Studies: A Response to Neumark, Salas and Wascher

Must-Read: At this stage, Neumark, Salas, and Wascher’s are only hurting themselves–and hurting confidence not just in their minimum-wage work but their non-minimum wage work as well–by their refusal to acknowledge that the overwhelming weight of the evidence is that Card and Krueger were right: that increases in the minimum wage from levels currently present in the United States have at most very small disemployment effects.

Sylvia Allegretto, Arindrajit Dube, Michael Reich and Ben Zipperer: Credible Research Designs for Minimum Wage Studies: A Response to Neumark, Salas and Wascher: “We assess the Neumark, Salas and Wascher (NSW) critique of our minimum wage findings…

…Recent studies, including one by NSW, obtain small employment elasticities for restaurants, -0.06 or less in magnitude. The substantive critique in NSW thus centers primarily upon teens. Using a longer (1979-2014) sample than used by NSW and in our own previous work, we find clear evidence that teen minimum-wage employment elasticities from a two-way fixed-effects panel model are contaminated by negative pre-existing trends. Simply including state-specific linear trends produces small and statistically insignificant estimates (around -0.07); including division-period effects further reduces the estimated magnitudes toward zero. A LASSO-based selection procedure indicates these controls for time-varying heterogeneity are warranted. Including higher order state trends does not alter these findings, contrary to NSW. Consistent with bias in the fixed-effects estimates from time-varying heterogeneity, first-difference estimates are small or positive. Small, statistically insignificant, teen employment elasticities (around -0.06) obtain from border discontinuity design with contiguous counties. Contrary to NSW, such counties are more similar to each other than to other counties. Synthetic control studies also indicate small minimum wage elasticities (around -0.04). Nearby states receive significantly more weight in creating synthetic controls, providing further support for using regional controls. Finally, NSW’s preferred new matching estimates are plagued by a problematic sample that mixes treatment and control units, obtains poor matches, and shows the largest employment drops in areas with relative minimum wage declines.

Exploding wealth inequality in the United States

There is no dispute that income inequality has been on the rise in the United States for the past four decades. The share of total income earned by the top 1 percent of families was less than 10 percent in the late 1970s but now exceeds 20 percent as of the end of 2012.  A large portion of this increase is due to an upsurge in the labor incomes earned by senior company executives and successful entrepreneurs. But is the rise in U.S. economic inequality purely a matter of rising labor compensation at the top, or did wealth inequality rise as well?

Before we answer that question (hint: the answer is a definitive yes, as we will demonstrate below) we need to define what we mean by wealth. Wealth is the stock of all the assets people own, including their homes, pension saving, and bank accounts, minus all debts. Wealth can be self-made out of work and saving, but it can also be inherited. Unfortunately, there is much less data available on wealth in the United States than there is on income. Income tax data exists since 1913—the first year the country collected federal income tax—but there is no comparable tax on wealth to provide information on the distribution of assets. Currently available measures of wealth inequality rely either on surveys (the Survey of Consumer Finances of the Federal Reserve Board), on estate tax return data, or on lists of wealthy individuals, such as the Forbes 400 list of wealthiest Americans.

Download the pdf version of this brief for a complete list of sources

In our new working paper, “Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data,” we try to measure wealth in another way.  We use comprehensive data on capital income—such as dividends, interest, rents, and business profits—that is reported on individual income tax returns since 1913. We then capitalize this income so that it matches the amount of wealth recorded in the Federal Reserve’s Flow of Funds, the national balance sheets that measure aggregate wealth of U.S. families. In this way we obtain annual estimates of U.S. wealth inequality stretching back a century.

Wealth inequality, it turns out, has followed a spectacular U-shape evolution over the past 100 years. From the Great Depression in the 1930s through the late 1970s there was a substantial democratization of wealth. The trend then inverted, with the share of total household wealth owned by the top 0.1 percent increasing to 22 percent in 2012 from 7 percent in the late 1970s. (See Figure 1.) The top 0.1 percent includes 160,000 families with total net assets of more than $20 million in 2012.

Figure 1


Figure 1 shows that wealth inequality has exploded in the United States over the past four decades. The share of wealth held by the top 0.1 percent of families is now almost as high as in the late 1920s, when “The Great Gatsby” defined an era that rested on the inherited fortunes of the robber barons of the Gilded Age.

In recent decades, only a tiny fraction of the population saw its wealth share grow. While the wealth share of the top 0.1 percent increased a lot in recent decades, that of the next 0.9 percent (families between the top 1 percent and the top 0.1 percent) did not. And the share of total wealth of the “merely rich”—families who fall in the top 10 percent but are not wealthy enough to be counted among the top 1 percent—actually decreased slightly over the past four decades. In other words, family fortunes of $20 million or more grew much faster than those of only a few millions.

The flip side of these trends at the top of the wealth ladder is the erosion of wealth among the middle class and the poor. There is a widespread public view across American society that a key structural change in the U.S. economy since the 1920s is the rise of middle-class wealth, in particular because of the development of pensions and the rise in home ownership rates. But our results show that while the share of wealth of the bottom 90 percent of families did gradually increase from 15 percent in the 1920s to a peak of 36 percent in the mid-1980, it then dramatically declined. By 2012, the bottom 90 percent collectively owns only 23 percent of total U.S. wealth, about as much as in 1940  (see Figure 2.)

Figure 2


The growing indebtedness of most Americans is the main reason behind the erosion of the wealth share of the bottom 90 percent of families. Many middle class families own homes and have pensions, but too many of these families also have much higher mortgages to repay and much higher consumer credit and student loans to service than before. For a time, rising indebtedness was compensated by the increase in the market value of the assets of middle-class families. The average wealth of bottom 90 percent of families jumped during the stock-market bubble of the late 1990s and the housing bubble of the early 2000s. But it then collapsed during and after the Great Recession of 2007-2009.  (See Figure 3.)

Figure 3


Since the housing and financial crises of the late 2000s there has been no recovery in the wealth of the middle class and the poor. The average wealth of the bottom 90 percent of families is equal to $80,000 in 2012—the same level as in 1986. In contrast, the average wealth for the top 1 percent more than tripled between 1980 and 2012. In 2012, the wealth of the top 1 percent increased almost back to its peak level of 2007. The Great Recession looks only like a small bump along an upward trajectory.

How can we explain the growing disparity in American wealth? The answer is that the combination of higher income inequality alongside a growing disparity in the ability to save for most Americans is fuelling the explosion in wealth inequality. For the bottom 90 percent of families, real wage gains (after factoring in inflation) were very limited over the past three decades, but for their counterparts in the top 1 percent real wages grew fast. In addition, the saving rate of middle class and lower class families collapsed over the same period while it remained substantial at the top. Today, the top 1 percent families save about 35 percent of their income, while bottom 90 percent families save about zero.

The implications of rising wealth inequality and possible remedies

If income inequality stays high and if the saving rate of the bottom 90 percent of families remains low then wealth disparity will keep increasing. Ten or twenty years from now, all the gains in wealth democratization achieved during the New Deal and the post-war decades could be lost. While the rich would be extremely rich, ordinary families would own next to nothing, with debts almost as high as their assets. Paris School of Economics professor Thomas Piketty warns that inherited wealth could become the defining line between the haves and the have-nots in the 21st century. This provocative prediction hit a nerve in the United States this year when Piketty’s book “Capital in the 21st Century” became a national best seller because it outlined a direct threat to the cherished American ideals of meritocracy and opportunity.

What should be done to avoid this dystopian future? We need policies that reduce the concentration of wealth, prevent the transformation of self-made wealth into inherited fortunes, and encourage savings among the middle class. First, current preferential tax rates on capital income compared to wage income are hard to defend in light of the rise of wealth inequality and the very high savings rate of the wealthy. Second, estate taxation is the most direct tool to prevent self-made fortunes from becoming inherited wealth—the least justifiable form of inequality in the American meritocratic ideal. Progressive estate and income taxation were the key tools that reduced the concentration of wealth after the Great Depression. The same proven tools are needed again today.

There are a number of specific policy reforms needed to rebuild middle class wealth.  A combination of prudent financial regulation to rein in predatory lending, incentives to help people save—nudges have been shown to be very effective in the case of 401(k) pensions—and more generally steps to boost the wages of the bottom 90 percent of workers are needed so that ordinary families can afford to save.

One final reform also needs to be on the policymaking agenda: the collection of better data on wealth in the United States. Despite our best efforts to build wealth inequality data, we want to stress that the United States is lagging behind in terms of the quality of its wealth and saving data. It would be relatively easy for the U.S. Treasury to collect more information—in particular balances on 401(k) and bank accounts—on top of what it already collects to administer the federal income tax. This information could help enforce the collection of current taxes more effectively and would be invaluable for obtaining more precise estimates of the joint distributions of income, wealth, saving, and consumption. Such information is needed to illuminate the public debate on economic inequality. It is also required to evaluate and implement alternative forms of taxation, such as progressive wealth or consumption taxes, in order to achieve broad-based and sustainable economic growth.

Emmanuel Saez is a professor of economics and director of the Center for Equitable Growth at the University of California-Berkeley. Gabriel Zucman is an assistant professor of economics at the London School of Economics.

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.

Miscalculating the wealth of the rich reveals unintended biases

In an ambitious effort, economists Philip Armour and Richard Burkhauser of Cornell University, and Jeff Larrimore of the Joint Committee on Taxation attempt to produce an estimate of trends in inequality based on a definition of income more relevant to understanding economic wellbeing. Specifically, they try to estimate the so-called Haig-Simons metric that defines annual income as consumption plus change in net wealth. According to this definition of income, the authors claim inequality has not been rising over time, leading some strident conservative voices to latch on to it—contrary to numerous other studies and measures

It is important to understand the Haig-Simons metric (named after early 20th century economists Robert Haig and Henry Simons), the methods used by Armour, Burkhauser, and Larrimore for estimating income using this metric, and their presentation of the data regarding this income metric. By my assessment, their working paper does not constitute an informative addition to the inequality discussion. The Haig-Simons measure is interesting but convolutes wealth and income. What’s more, their methodological choices bias the results to downplay relative income growth at the top, and the statistics they report are not sufficiently detailed to assess the implications of their findings.

Is the Haig-Simons income measure useful?

The Haig-Simons income measure has the advantage of avoiding volatility based on the timing of the realization of capital gains on the sale of assets by individuals. Other annual income measures may be too volatile because some people time asset sales based on taxes or other factors, which makes annual income a noisy proxy for wellbeing. Thus, this measure could provide a more comprehensive assessment of the variation in household balance sheets by cutting out timing decisions.

Yet the Haig-Simons measure introduces substantial volatility as well based on changes in the market valuation of assets. Someone with a large stock portfolio, for example, whose portfolio fell substantially could have a negative Haig-Simons income despite being high in the income distribution. Using this measure, the billionaire founder of Facebook, Mark Zuckerberg, would have been considered one of the poorest people in the world in 2012 because his net worth fell by $4.2 billion.

The key point is that assets held in company stock or in stock market indices vary substantially in valuation over any time scale. The Haig-Simons measure attempts to factor out volatility in realized capital income but at the same time introduces potentially higher volatility in the valuation of capital holdings.

To understand the relative merits of the Haig-Simons metric in assessing comprehensive income it is important to discern the relative magnitudes of these volatilities across the entire distribution of income. One way to think about it is that people can choose to sell a stock or other asset whenever they want, which will introduce volatility in their income because of the asset sale. But the value of assets someone holds will vary substantially, too, because stock prices change every day whether or not shares are sold. Thus, measures of income that include capital gains can be variable because of the sale of stock while measures that look at net wealth will also be variable because the stock prices are also volatile. Because it exacerbates the volatility in the valuation of wealth, the Haig-Simmons measure’s primary advantage—that it reduces volatility from the sale of assets—may be swamped.

Additionally, by ignoring the liquidity of assets, the Haig-Simons measure obfuscates changes in wealth and economic wellbeing. Inflation in housing prices during the 2000s led the net wealth of many to increase, which would show up as a rising Haig-Simons income. With hindsight we know that much of this valuation was a bubble and that there was a minimal real improvement in economic wellbeing.

It is unclear why a single reductionist measure is needed for both wealth and income when one measure for wealth and a separate measure for income could be used. Looking at several measures can provide a textured understanding beyond a single number. If asset volatility from timing decisions were determined to be a primary source of noise in short-term inequality trends, then a multi-year average could be applied to other measures of income. This would have the advantage of reducing the volatility from timing decisions while avoiding obviously absurd assessments of income.

Are their methods for determining the Haig-Simons measure sufficient?

The authors attempt to determine a comprehensive measure of both consumption and the change in net worth using the Haig-Simons metric. To compute this measure of income, they make several adjustments to the data to include near cash benefits such as estimating the value of health insurance provided to individuals either from the government of from employers. For changes in net worth, a single national housing index was used to account for changes housing wealth across the country and only the Dow Jones Industrial Average was used for all types of stock income. There are also limitations on details of high-income households in the survey data that they use. Each of these methodological choices will artificially bias their estimates toward a lower valuation of income growth at the top of the distribution.

So let’s examine each of the components they use in turn.

Their method for assessing the value of health insurance is not useful in comparing time trends because it ignores changes in the structure of insurance products over time, variation in the quality of the insurance products provided, and the substantial growth of health care costs beyond inflation over most of they period of their study. Before the enactment and initial implementation of the Affordable Care Act of 2010, there was a trend in health insurance for higher deductible products (these have a lower actuarial value), which would mean that comparisons across time in the value of health insurance would overstate their value in later years relative to early years.

Thus, the relative value of the health insurance product used in their analysis is inflated and will lead to artificially inflated income growth for people whose health insurance constitute a larger share of their “comprehensive” income than others. Because health insurance will probably constitute a smaller share of the consumption of high-income people, the authors’ methodological decisions on accounting for health insurance will artificially imply a reduction in income growth at the top of the income spectrum relative to other segments.

Similarly, the authors’ methodological choices for valuation of housing and stocks are likely to artificially reduce their estimates of the net worth of those at the top. For each of these factors, the authors used a single growth rate for all incomes. By using a single product and not testing the impact of heterogeneous rates—meaning variations in the rates of return—the authors artificially reduce the variability. This reduction in variation will particularly attenuate growth in asset values on the high side and therefore result in artificially low incomes among the top of the distribution.

To their credit, the authors’ acknowledge this shortcoming:

“…when imputing yearly accrued capital gains we assume that all investments receive the ordinary rate of return. Hence we will not capture extranormal returns received by some individuals on their investments.”

Yet after acknowledging this flaw they did not attempt to vary rates of return on the high end of the income distribution or otherwise account for this bias. In fact, a recent paper by Fabian T. Pfeffer, Sheldon Danziger, and Robert F. Schoeni from the University of Michigan find the wealth of the richest households grew faster than the median household, and much faster than households below the median.

Another issue with this approach has to do with their choices of survey data sets. This work relies heavily on surveys such as the Current Population Survey, the Medical Expenditure Panel Survey, and the Survey of Consumer Finances. Economist Philip Vermeulen of the European Central Bank finds that survey data on wealth tend to have a hard time assessing wealth at the top because of systematically biased underreporting among the wealthy skews the estimates lower. Thus, a fundamental limitation of this work is its ability to measure trends at the top, which are central to the claims made about the study’s findings.

Are the data reported sufficient to assess trends in the Haig-Simons measure?

The three authors report average growth estimates by quintile for several different income measures. This is good because it allows for a partial assessment about which components of the income construction are driving the results. But more information is needed. The authors’ choices about which rates of return to apply strongly influence the results; providing results from sensitivity tests (checks to see how much the results rely on various assumptions) with alternate measures would be useful. Without information on these sensitivity tests, there is no way to assess the effect of the biases discussed above.

Furthermore, they do not provide error estimates or statistics on their matching approaches across data sets. Statistics regarding the matching approach (and sensitivity tests of the matching) are particularly important to assess the implications on quality of the methodological choices. These omissions are disconcerting because it makes it impossible to assess the quality of the results and therefore their utility to the current discussion.


The authors attempted an ambitious analysis of incomes, which should be commended, but their execution is insufficient to support the broad proclamations made by many pundits about declines in inequality. Given the study’s clear methodological biases and weaknesses, the claims of the paper’s first author in a report from the Manhattan Institute dramatically overstate the implications. The Haig-Simons income measure may be useful, but there are better ways of getting the same, if not more, information using other measures of wealth and income without conflating the two.

The authors’ methods should work well enough for estimates of the Haig-Simons income for measuring the assets of low- and middle-income people for whom capital gains are a relatively unimportant source of income, but there are fundamental biases that will result in artificially low growth rates among their high-income counterparts. Finally, their reported results are insufficient to assess whether the trends are entirely driven from their methodological choices and also if the results are discernible from noise. I look forward to further research by the three authors and others that more effectively measures the assets of the wealthy—a key to understanding the links between economic inequality and growth.

The prison boom and black-white economic inequality

Over the past 40 years, the observed earnings gap between African American men and their white counterparts closed slowly but steadily. The average black employed worker earned about a quarter less than the average white employed worker with similar experience in 2010 compared to about a third less in 1970. Such enduring earnings inequality is nothing to celebrate, but at least the trend line is encouraging.

Or is it?

Those reported earnings gains among black men fail to take account of different trends in incarceration and employment, which not only skews labor market statistics but also masks the debilitating economic consequences of the mass incarceration of African American men over the past several decades. When properly accounted, there is little reason to believe that the labor market prospects for black men relative to white men have improved over the past 40 years.

Let’s start with the “prison boom,” or more precisely, the trend in incarceration rates, which have more than doubled over the past 30 years. Today, more than 2.3 million people are locked up in local jails, state prisons, or federal prisons. Although this prison boom affected all racial and ethnic groups, it has had a disproportionate effect on African American men. In the 2010 Census, almost one in ten African American men ages 20 to 34 were institutionalized, while the corresponding rate for white men was only about one in fifty.

Further, on any given day in 2010, about one third of African American men who were high school drop-outs between the ages 20 and 34 lived in jails, prisons, mental health institutions, or nursing homes, and there is good reason to believe that the fraction in prison or jail exceeded the employment rate for this group. Of course, this is just at any given point in time. The fraction incarcerated at some point in life is even higher—about two-thirds by age 34, according to a recent book by sociologist Becky Pettit from the University of Washington.

While these statistics are not new to criminologists, they imply that a growing share of the U.S. population is missing from the government’s main source of information about the labor market: the Current Population Survey. The CPS only covers the non-institutionalized population, but the federal government uses it to calculate important measures of labor market outcomes such as wages, labor force participation, and unemployment rates as well as official poverty statistics, including the Census Bureau’s new Supplemental Poverty Measure.

As the missing data problem has become more severe, these measures have become more distorted, in particular with respect to trends in racial inequality. In a recent NBER working paper, economist Derek Neal and I argue that since 1970, the economic progress of African American men relative to white men has been quite anemic. We reach this conclusion by properly accounting for the growth of the prison population over this period, and hence the misleading picture derived from average labor market earnings for employed workers.

In our paper, we treat the median weekly wages of men in their prime working years as a proxy for their overall labor market prospects. Among the employed, the ratio of median weekly wages for African Americans relative to whites increased steadily from around 65 percent in 1970 to well over 75 percent in 2010, the most recent census year. Yet this statistic substantially overstates the recent relative progress of African Americans for two reasons. First, employment rates for working age men have declined much more among blacks than among whites, and growing numbers among the non-employed are incarcerated. Second, earnings prospects are now and have always been worse for those who are not currently employed.

Thus, we estimate what we call median potential wages for blacks and whites, making adjustments for changes in the numbers of non-employed and institutionalized persons over time. We find that the labor market prospects of black men relative to white men have not improved over the past 40 years. There have been slight ups and downs (with some noteworthy progress in the 1990s), but in 2010, the ratios of median potential wages among African American men to the median potential wages of their white peers were roughly at 1970 levels, across groups with different levels of experience.

Black-white economic convergence, then, has come to a halt after substantial progress throughout most of the past century, as documented in a seminal 1989 study by James Smith of the Rand Corporation and Finis Welch, then an economics professor at the University of California-Los Angeles. While it is difficult to quantify the exact contribution of mass incarceration to the lack of black relative progress in recent decades, some studies do find suggestive evidence that incarceration harms employment and earnings opportunities long after prisoners serve their time.

Our results concerning stalled relative progress for African American men are particularly noteworthy because we are also able to demonstrate that the prison boom was primarily the result of policy choices. At first glance, one might suspect that rising incarceration rates reflect increased criminal activity as a consequence of deteriorating legal labor market opportunities for people with little formal education. But the boom in crime is long over. Criminal activity and arrests for all non-drug-related offenses peaked in the early to mid-1990s and have been on the decline ever since. Drug-related arrests increased well into the late 2000s, but due to short average sentences, drug offenses on their own contributed relatively little to the overall boom in incarceration.

Instead, the main driver of the prison boom has been a move toward more punitive corrections policies across all offense categories, not just drug crimes. Such policies include so-called Truth-in-Sentencing laws, “Three Strikes” policies, and mandatory minimum sentences. As a result, arrested alleged offenders in each violent crime category are now at least twice as likely to spend more than five years in prison then they were in the mid-1980s. The pattern is perhaps even more striking for non-violent offenses: conditional on arrest, the probability of any given sentence length has increased—often by a factor of two or more.

Overall, an alleged offender in the 2000s can expect to spend about twice as long in prison as in the 1980s, conditional on the severity of the crime. Of course, not all of this shift necessarily reflects a change in policy. In particular, technological advances such as the use of DNA evidence may have increased the probability that an alleged offender is found guilty. But these new investigative methods have been adopted by other developed countries—and none of them have experienced changes in distributions of time-served among offenders that are even remotely similar to those we have seen in the United States. Therefore, it is hard to avoid the conclusion that sentencing and parole release policies have played the leading role. We estimate that the overall shift toward more punitive corrections policies probably accounts for between 70 and 85 percent of the growth in incarceration rates since 1985.

There is now substantial evidence that the boom in incarceration had an adverse effect on the relative economic progress of African American men, and that this prison boom was primarily a policy choice and not a result of deteriorating labor market conditions. Supporters of tougher corrections policies may argue that these policies have contributed to the decline in criminal activity over the past two decades. But even with our study, the costs of that crime reduction have not been fully counted and may not have been fully realized yet.

Some recent studies provide evidence that more punitive treatment of first offenders increases recidivism rates and prolongs criminal careers, and recent trends in the demographic characteristics of prisoners are consistent with this claim. Crime in our country was once almost exclusively a young man’s game, but arrest rates and prison admission rates for men ages 40 to 49 have risen disproportionately in recent years. In addition, we have not yet seen how policies that promote mass incarceration within particular communities will impact future generations from those communities.

—Armin Rick is Assistant Professor of Economics at Cornell University’s Johnson School of Management. His collaborator on this project is Professor Derek Neal of the University of Chicago Economics Department. Their paper, “The Prison Boom and the Lack of Black Progress after Smith and Welch,” was recently released by the National Bureau of Economic Research.

Who are today’s supermanagers and why are they so wealthy?

What explains the changes in top-earning occupations over the past four decades? Perhaps the most intriguing argument about the current state of income inequality in the English speaking economies that Thomas Piketty makes in his bestseller “Capital in the 21st Century” is this—“the vast majority (60 to 70 percent, depending on what definitions one chooses) of the top 0.1 percent of the income hierarchy in 2000-2010 consists of top managers.” He goes on to argue on page 302 of his book that the rise in labor income “primarily reflects the advent of ‘supermanagers,’ that is, top executives of large firms who have managed to obtain extremely high, historically unprecedented compensation packages for their labor.”


This really begs the question as to how and why these supermanagers came into existence. Nobel Laureate Robert M. Solow points out in The New Republic that this is primarily an American outcome. And Henry Engler at Thomson Reuters Accelelus’ Compliance Complete recently published an excellent piece on Piketty’s supermanagers in the United States and the United Kingdom. Both writers agreed with Piketty that these supermanagers were being vastly overly compensated given their questionable contributions to productivity.

I hope to shed a little more light on this issue by examining the change in professions comprising the top 0.1 percent of tax filers between 1979 and 2005. The purpose: to examine whether the changing composition of this super elite reflects changes in our economy that may explain the link between rising economic inequality and anemic economic growth over this period.

To do so, I used data from the April 2012 white paper “Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data,” by economists Jon Bakija of Williams College, Adam Cole of the Office of Tax Analysis at the U.S. Department of the Treasury, and Bradley Heim of Indiana University. They used tax data on the top 0.1% of filers to identify the top earning professions. The infographic below tells the tale, charting the change in occupations at the tippy top of the income ladder in 1979 and 2005.

The biggest change in the distribution of top earners is in the types of executives, managers, and supervisors at non-financial firms. In 1979, most of these people worked for large, publicly traded firms but by 2005 more were working in closely held firms. There is not enough information to provide a clearer picture as to who exactly these people are, but chances are they are employed by firms that are owned by private equity firms—the growth in the private equity industry over this period of time was substantial—and because financial professionals saw large gains, too. The share of people in the top 0.1 percent working in finance also increased substantially, to 18 percent in 2005 from 11 percent in 1979.

These findings are consistent with Piketty’s analysis in his new book. But there are alternative explanations. One is presented in George Mason economist Tyler Cowen’s latest book, “Average is Over.” He claims a skill biased-technological change is responsible for the shift in top occupations over roughly the same period. He argues that technology allows top performers to capture more of the market and thus earn substantially more than average performers. He and many other people hypothesize that this is a driver of increased economic inequality.

But if technology were a primary driver of inequality, then one would expect that skilled trades would have larger incomes and would have become a larger share in the top 0.1 percent. While there are slightly more technical types and entertainers among top earners (as can be seen in the data presented in our interactive) the biggest gains in both percentage terms and magnitude were among privately held business professionals.

Thus, the so called “average is over” argument—that that the top performers in each field will capture a bigger share of the pie—may be a driver of inequality, but it does not appear to explain the bulk of the changes in occupations at the top of the income ladder. Instead, the supermanagers appear to be capturing greater share of the wealth as is argued by Piketty and others. More detailed data would be required to assess who these people are and how workplace dynamics changed from 1979 to 2005 that would explain the change in income. The Washington Center for Equitable Growth will be examining this data in more detail in forthcoming publications.