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.

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.

Factoring inequality into economic growth

The National Bureau of Economic Research released a working paper by Harvard University economist Nathaniel Hendren on August 4 that provides a new way of looking at the relationship between inequality and growth. His paper develops a new statistic, the inequality deflator, which allows researchers to adjust the value of an economic variable, such as average household income, for different levels of inequality. Because averages don’t tell us anything about distribution, the deflator lets us compare those averages by adjusting for the different distributions.


The accompanying graphic shows how much household income increased in the United States after adjusting for rising income inequality between 1979 and 2012. With more attention being paid to the relationship between inequality and growth, Hendren’s inequality deflator can become a powerful tool for understanding the linkages between the two.

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.

Is Piketty’s treatment of housing an excuse to ignore him?

French economist Thomas Piketty’s treatment of housing as capital in his blockbuster “Capital in the 21st Century” is not an excuse to ignore his predictions about rising economic inequality. “Capital in the 21st Century” is clear from the beginning that housing—and real estate generally—ought to be included in the definition of “capital” for the book’s purpose, which is to examine the aggregate effect of accumulated wealth that produces an annual return through no effort on the part of its owner.

A whole set of Piketty “rebuttalsattacks that treatment of housing as capital. The critics focus on that aspect of his analysis because a large proportion of the increase in the capital-to-income ratio that he emphasizes is thanks to the accumulation of housing and real estate at market prices. In some countries, the rise in the value of housing accounts for all of the increase in the capital-to-income ratio since 1970. And even beyond housing, Piketty’s approach is not consistent with standard neo-classical economic theory in several important ways—and so the critics have looked to housing as a reason to cling to their theory in the face of his countervailing facts.

But if housing were not counted as part of capital in Piketty’s analysis then the wealth distribution patterns he explores would be even more skewed. He identifies one key historical phenomenon that is unique to the 20th century—the rise of what he calls a  “patrimonial middle class,” that is, non-trivial, inheritable wealth holdings by those between the 50th and 90th percentiles of the wealth distribution in advanced economies, rather than the top decile holding nearly all the wealth as was true in the 19th century and previously.

So, even though total capital accumulated (as a percentage of national income) has already reached the level where it was in the late 19th century, inequality has not yet attained its prior height, at least not in Europe, because that capital is partly held by the middle class. And their wealth is largely in housing.

This means that removing housing from consideration would by fiat skew the wealth distribution as much as Piketty predicts it will be skewed in the future if that patrimonial middle class dwindles, which is the dire outcome his whole book warns against. In other words, the arguments put forward as part of an attempt to discount Piketty’s prediction about the rise in capital’s share of income would, if accepted, put his prediction about the evolution of wealth inequality into effect mechanically. So how should economists interpret housing wealth?

Is housing capital?

Claim:  Housing is not “productive” capital, like machines or factories or even farmland.

Response: In fact, housing is productive in the sense that it produces a good—shelter, broadly conceived—that economic agents value. Someone who owns his or her home doesn’t have to pay rent, and the owner of an investment property will earn rent exactly as does the owner of some piece of “productive” capital equipment. Second, capital functions as a store of value, and not simply as an input to production—a function that Piketty’s historical data show has been vital over the long run.

To take the argument a bit further, the value in real estate is often a matter of proximity—of “location, location, location”—and proximity to productive economic activity or to agents whom it is valuable to know is a very real economic resource. That proximity is capitalized as real estate. An identicallysized and equipped dwelling in Manhattan, Kansas costs much less than one in the more famous Manhattan because of the productivity and amenity benefits that come from living and working near many other people in New York City.

Arguably, in a world of increasing population density, location has been getting more important, and incumbent owners of real estate have been the main beneficiaries. Klaus Desmet at the University of Charles III and Esteban Rossi-Hansberg at Princeton University have a 2014 paper called “Spatial Development” that emphasizes population densification as the cause of productivity gains, location rent dynamics, and inter-sectoral employment flows in the United States. Piketty doesn’t discuss spatial trends as such, but the dynamics of housing wealth are entirely consistent with the argument in “Capital in the 21st Century.”

Excluding housing and real estate from the capital stock is convenient if the goal is to dismiss Piketty’s data and predictions, but that interpretation is not warranted by economic theory or empirical analysis.

How should the value of housing be calculated?

 Claim: Piketty’s use the market price of housing distorts his analysis because housing should be priced according to its discounted rental stream, which is a measure of its “fundamental value.” The market price is subject to bubble dynamics, which according to the standard economic theory of bubbles would occur when the price deviates from some notion of its fundamental value, such as if a fruit tree were to cost more than the appropriately discounted sum of all the fruit it will ever produce.

Response: This critique fails for the same reason the argument that housing isn’t capital fails—because capital, broadly conceived to include wealth, is a store of value, and thus the stream of annual rent from its users isn’t the only relevant aspect of the return to its owner. The price of housing in central metropolitan areas has been on an upward march for the past several decades, in part because economic agents foresee dynamics of the kind described by economists Desmet and Rossi-Hansberg and in part because investing in real estate abroad is an effective way for the wealthy global elite to stash their cash overseas. At times, this has given rise to the property bubbles observed in Japan before 1990 and in the United States before 2006, but the resulting firesales do not erase the long-run trend. The fluctuation in housing market prices probably occurs in part because of misperceptions or misevaluations of the timing of long-run trends, but that does not imply the trend doesn’t exist.

Is housing really as substitutable with labor as Piketty assumes?

Claim:  Including housing in the stock of capital should reduce Piketty’s assumed value for the marginal rate of substitution between capital and labor, and hence his prediction of an increasing capital share of income. Piketty’s assumption that the marginal rate of substitution between capital and labor is greater than one is important since it implies that the aggregate rental rate of capital will not decline by as much as the stock of capital increases.  In other words, workers face no threat of losing their jobs just because more houses exist since there’s “no way to substitute a house for a worker.”

Response: Piketty’s main argument that the marginal rate of substitution is greater than one—that workers are indeed threatened by capital accumulation, including housing—is based on the dual U-shaped historical evolutions of the capital-to-income ratio and capital’s share of income in the very long run. That implies that when capital is accumulated, the resulting decline in the price of capital is not large enough to offset the increase in its quantity. Hence, the total share of income going to capital is higher when there’s more capital. If the marginal rate of substitution were less than one, the capital share would move inversely with the capital-to-income ratio, and if it were equal to one, as neo-classical theorists generally assume, the capital share would not change at all with the capital-to-income ratio.

All the evidence that the marginal rate of substitution between capital and labor is less than one cited by the critics is drawn from relatively short-run studies of the tradeoff between capital and labor at the firm- or industry-level, and there are very good reasons to believe that long-run elasticities are higher. Piketty’s long-run, aggregate evidence already includes the historical value of housing in capital, so this critique doesn’t bring anything more to the table—Piketty already has an excellent empirical case for assuming the marginal rate of substitution is high: the dual U shapes cited above.

Moreover, the aggregate marginal rate of substitution incorporates a much larger range of empirical economic phenomena than simply how easy it is to substitute between two factors in the production of a single good. So interpretations that adhere narrowly to that premise—such as econometric estimates at the firm or industry level—are bound to fail. The neo-classical argument holds that the price of capital is determined by its marginal productivity, and that marginal productivity declines mechanically as the quantity of capital increases. That is the so-called Ricardian scarcity principle, named for the 19th century thinker David Ricardo.

The rate at which it declines depends on how substitutable capital and labor are. The argument that they are not very substitutable implies that additional capital is relatively useless—and hence that its price will get much lower as its quantity increases. Notably, if what is relevant about housing and its value dynamics is that it acts as a store of value, then there’s no reason to believe that diminishing marginal productivity is operative. That concept relates to the additional output produced by increasing the use of one input in production while holding all others constant, but there’s no production going on if what’s being amassed is a store of value.

In this sense, housing wealth accumulation is like hording a precious metal: how useful the metal is in the production of other goods is irrelevant to the value of the horde. Finally, there are strong empirical reasons to believe that the price of housing, and of capital in general, is not only determined by marginal productivity as in the traditional, neo-classical macroeconomic model. That is the subject of my next response.

Are housing price increases due to supply restrictions?

 Claim: There’s been a great deal of research into the dynamics of the housing market since the housing bubble burst, starting in 2006, and especially the unsurprising conclusion that local housing supply elasticity is related to price dynamics, and further, that political pressure by homeowners and the mortgage lending industry, especially on the west coast of the United States, has constrained housing supply and led to the enormous price swings. Those supply restrictions have nothing to do with the rise in the capital-to-income ratio and the reasons for it proposed by Piketty.

Response: This explanation for housing price dynamics isn’t actually distinct from Piketty’s narrative. The Economist commentator Ryan Avent wrote about this eloquently: “Over the last few decades technological changes have greatly increased the return to locating in large cities filled with skilled people. Being in such places makes workers more productive and raises the income they are able to earn. But skilled cities have not allowed housing supply to expand to meet rising demand. Housing has therefore been rationed by price, pushing less productive workers toward cities where housing supply growth is higher and housing cost growth is lower.

As a result, fewer people live in the most productive places, and quite a lot of the gain from employment in productive places is captured by landowners earning rents thanks to artificial housing scarcity. This may mean lower overall productivity, more income inequality, and more income flowing to capital rather than labour.” In other words, what we have here is collective political action to make sure the price of housing remains high just as increases in the bargaining power of capital relative to labor have contributed to the decline of the labor share. There is no room in the neoclassical model for these effects—only for the Ricardian scarcity principle and diminishing marginal productivity—but that doesn’t mean they aren’t there.

The answers are clear

Piketty’s framework, including his decision to count housing as capital, does not map directly onto the standard neoclassical economic growth model—but his approach is more consistent with empirical reality in several key ways. The critics who want to cling to their outdated theories have latched onto his interpretation of housing as a way to do so, but given their theory’s many empirical shortcomings, they are seriously misguided.

How important is the college wage premium to reducing inequality?

The college wage premium—the difference between average earnings among those with a college (but no graduate) degree and those who do not attend college—has increased substantially in recent years while the premium for those who attend “some college” without actually earning a degree has not changed at all. This fact leads many observers to conclude that a college degree is the best way for young adults to attain the skills they need to earn more and thus reverse growing inequality. (See Figure 1.)

Figure 1


This view, however, has serious problems. The idea that not enough people are graduating from college implies that the much-reported rise in income inequality is thanks to a “shortage” of highly skilled college grads able to meet the labor market’s need. That idea has been conclusively debunked. The fact that the wage premium only kicks in when a college student receives a diploma, rather than gradually appearing in the cross section of people who go to college for one, two, or three years but don’t earn a degree, casts serious doubt on the idea that it’s the skills content of college that matters. Furthermore, the fact that buying an expensive degree correlates with high income certainly doesn’t imply that causation runs from buying the expensive degree to the high income.

So where in the academic literature did this notion of the four-year college degree as the solution to labor market inequality arise? The idea that the college wage premium reflects a rise in the labor market’s demand for skills stems in large part from a 1992 paper by Harvard University economist Lawrence Katz and University of Chicago economist Kevin Murphy, who argued that since there’s been both a rise in the college wage premium and a rise in the proportion of the population with college degrees, demand in the market for skilled labor has increased against a somewhat elastic but essentially unchanged supply curve.

Along similar lines, Katz and another Harvard professor Claudia Goldin, published a paper in 2007 that tracks the college premium over the long run and posits that its dynamics are explained by the race between education and technology. They argue that “skill-biased technological change” creates a demand for college degrees that takes time to be reflected in the skill composition of the workforce.

The story about the race between education and technology leaves questions unanswered. First, it cannot explain the significant differences between the income distributions across countries, especially at the very high end. Technological change and the distribution of individuals’ skills seem to be uniform across countries, at least in the developed economies, and yet their income distributions are very different. For instance, the distributions of harmonized standardized test results for high school math students in the United States and France are basically the same, yet the top ten percent of income earners accrue 25 percent of total labor market income in France and 35 percent in the United States- and the shares are even more skewed higher up the distribution.

Second, the argument that increasing inequality is caused by a shift in the demand for scarce skilled labor is only theoretical: it’s not at all clear where that technological change comes from. Every attempt to operationalize the theory of skill-biased technological change has run up against problematic data. Since the late 1990s, most of the increase in the college wage premium (which has not grown much during the last fifteen years) is due declining absolute wages for those with less education. That is the exact time period in which the “IT revolution” is supposed to have had a wide impact on experience of the middle class in the labor market. And it has had an impact—on the industrial mix of workers, but not on their wages. If there is a race between education and technology, currently the runners are tied: both supply and demand for skills have shifted such that wages are unaffected.

The potential harm in misattributing rising income inequality to a race between education and technology — a race that technology is winning — is that it could lead to perverse policy prescriptions. Trying to get more enrolled college students to undertake the cost of finishing their degrees might lead to yet further tuition hikes, especially if that route receives government subsidies, without significantly improving their outcomes in the labor market or reducing inequality overall.


Heritage Weighs into the Inequality Discussion with Some Problematic Data Analysis

It is great that Heritage Foundation pundit Stephen Moore and The University of Ohio economic historian Richard Vedder are talking about economic inequality in the opinion pages of The Wall Street Journal, but they seem to have missed the mark. They correctly note that the states (and the District of Columbia) with the highest economic inequality, at least as measured by the Gini coefficient of income inequality, tend to also be “blue” states (those that tend to elect Democrats). They go on to argue Democratic policies are failing to reduce inequality.

This piece and its underlying data analysis have three fundamental flaws:

  • The Gini coefficient they are referencing is of income and does not factor in the effect of taxes or transfers. Thus, the measure they are using explicitly misses the impact of the policies that they claim are ineffective.
  • They are suffering from one of the cardinal sins of data analysis: omitted variable bias. More populous areas also tend to have higher inequality, at least in part because higher density allows for higher incomes. Furthermore, cities and urban areas also tend to elect more progressive leaders for a variety of reasons. Thus population density is the omitted variable. They fundamentally misunderstand (or at the very least ignore) the relationship between inequality and population density.
  • Finally, they are factually incorrect to say the 1980s and 1990s are emblematic of the very laudable notion that  “a rising tide lifts all boats.” As can be seen in the figure below, median hourly compensation has been essentially flat since 1970 despite the fact that per capita economic growth more than doubled over the same period.



It is certainly possible that they made these errors because they are neophytes to the inequality discussion, but it is important to correct them now so that these spurious claims do not propagate. Now that pundits from the Heritage Foundation are dipping their toes into the inequality discussion, I hope that they can bring some new and interesting policy ideas instead of misinformation and boilerplate rhetoric to the discussion.

Morning Must-Read: Robert Reich: How to Shrink Inequality

Bob Reich: Robert Reich (How to Shrink Inequality): “Some inequality of income and wealth is inevitable…

…if not necessary. If an economy is to function well, people need incentives to work hard and innovate. The pertinent question is… at what point do these inequalities… pose a serious threat to our economy… equal opportunity and our democracy. We are near or have already reached that tipping point…. But a return to the Gilded Age is not inevitable. It is incumbent on us to dedicate ourselves to reversing this diabolical trend…. 1) Make work pay…. 2) Unionize low-wage workers…. 3) Invest in education…. 4) Invest in infrastructure…. 5) Pay for these investments with higher taxes on the wealthy…. 6) Make the payroll tax progressive…. 7) Raise the estate tax and eliminate the “stepped-up basis” for determining capital gains at death…. 8) Constrain Wall Street…. 9) Give all Americans a share in future economic gains…. 10) Get big money out of politics…. We need a movement for shared prosperity—a movement on a scale similar to the Progressive movement at the turn of the last century…. Time and again, when the situation demands it, America has saved capitalism from its own excesses. We put ideology aside and do what’s necessary. No other nation is as fundamentally pragmatic…. But we must organize and mobilize…

Afternoon Must-Read: Corey Robin: Clarence Thomas’s Counterrevolution

Corey Robin: Clarence Thomas’s Counterrevolution: “What I think Thomas took away… are two ideas.

First, not only is racism a perdurable element of the American experience… but it is also a protean and often-hidden element of that experience… so profoundly inscribed in the white soul that you’ll never be able to remove it. You see this belief in quiet, throwaway lines in his opinions that you can easily miss if you’re reading too fast. In 1992, in one of his early cases, Georgia v. McCollum, Thomas stated:

Conscious and unconscious prejudice persists in our society. Common sense and common experience confirms this understanding.

The point was so obvious and self-evident to Thomas it didn’t need elaboration or explanation.

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