The U.S. Wealth Distribution: Off the Charts

Abstract

Although extreme and rising levels of U.S. wealth inequality have generated much public and scientific interest, building intuition on the shape and scale of today’s wealth distribution remains difficult. Prior research tends to conceptualize and measure wealth inequality in one of two ways: As the concentration of assets among the superwealthy (i.e., wealth concentration among the top 1 percent or even top 0.1 percent) or as a population-wide phenomenon of distributional inequality (i.e., wealth inequality among the remaining 99 percent). Of course, both perspectives are valid and important; they simply focus on different slices of the overall wealth distribution, sometimes because of limitations of the data that are being used. Extreme concentration of wealth at the very top and very high levels of inequality within the remainder of the distribution thus coexist. Yet jointly visualizing both aspects and relating them to each other is challenging. This contribution addresses this challenge by providing an intuitive and interactive visualization of the distribution of U.S. wealth in 2019 that spans the full population, from households in net debt to multibillionaires.

Description

Figure 1a shows estimated household net worth in 2019 U.S. dollars on the basis of the latest available 2019 wave of the Survey of Consumer Finances (SCF), which is widely held to be the gold-standard survey to capture U.S. households’ assets and liabilities (Keister and Moller 2000; Killewald, Pfeffer, and Schachner 2017). The figure plots the threshold value of each percentile of the wealth distribution.¹ In the interactive version (available at https://asherdvirdjerassi.github.io/wealth_thresholds_viz/figure1.html), the exact estimated values are revealed upon hovering over each bar. For instance, highlighting the 75th percentile of the wealth distribution reveals that three quarters of all U.S. households in 2019 had a net worth of $404,100 or less. The most obvious visual impression of Figure 1 is the strong right skew of the wealth distribution. In fact, in this initial display, the bottom half of the distribution all but disappears, reflective of the empirical reality that the wealth held by the majority of U.S. households pales in comparison with the wealth of, say, the wealthiest 10 percent.

Figure 1.

Wealth thresholds: (a) percentiles 1 through 99 and (b) adding the Forbes 400 threshold.

Note: Figure 1a displays the threshold value of each percentile of the wealth distribution as estimated from the 2019 Survey of Consumer Finances.

Figure 1b adds the threshold value of the 2019 Forbes 400 list (i.e., the level of wealth required to be part of the Forbes list of the 400 wealthiest individuals in 2019), using a rescaled y-axis. An interactive version of this figure is available at https://asherdvirdjerassi.github.io/wealth_thresholds_viz/figure1.html.

However, the interactive figure also allows the reader to zoom in on select parts of the wealth distribution by highlighting the percentile range of interest. For instance, when selecting the bottom half of the distribution (by highlighting the left half of the x-axis), the chart is automatically rescaled to reveal large wealth gaps even within that group of households, ranging from the median net worth of $121,800 to a net debt of −$91,590 at the first percentile. Such zoomed-in display also more clearly reveals that 1 in 10 U.S. households are in net debt; that is, they owe more than they own.

Zooming out again (by selecting “Reset Zoom”) and turning to the other end of the distribution, we note that the estimated net worth level required to be in the top 1 percent is more than $11.1 million. Although the SCF goes to great lengths to oversample and interview households that are expected to hold very high wealth (see the Supplemental Material for further details), it purposefully excludes the wealthiest households from its sampling frame because of concerns about identifiability. Prior research that draws on the SCF but also holds an interest in the very top of the wealth distribution has therefore jointly analyzed these data with wealth estimates from the Forbes 400 list (e.g., Saez and Zucman 2020; Wolff 2017). The Forbes 400 is a list of the wealthiest 400 individuals in the United States, whose net worth is estimated annually by Forbes magazine. The wealth level required to be part of the Forbes 400 in 2019 can be added to the figure by clicking on the subtitle “Include Forbes 400 wealth threshold” (to produce Figure 1b). The graph is again rescaled to add the $2.1 billion floor of the Forbes 400 list and now emphasizes the radical concentration of wealth at the very top as the remainder of the wealth distribution practically disappears from view.

In conclusion, our interactive visualization can serve as a tool to explore and gain intuition of the drastic inequality in wealth across the full population while at the same time allowing the display of a level of wealth at the top that could safely be described as “off the charts.” It is difficult to have both aspects of wealth inequality simultaneously in view and perhaps even in mind. Yet it is important to jointly display and conceptually relate the fact that 1 in 10 U.S. households (about 13.4 million households) have less than no wealth, whereas a few hundred families hold wealth that towers over even the wealth at the 99th percentile. After all, these two aspects of wealth inequality may not be unrelated to each other.

Biographies

Fabian T. Pfeffer is an associate professor in the Department of Sociology, a research associate professor at the Institute for Social Research, and director of the Stone Center for Inequality Dynamics at the University of Michigan. He also leads the Wealth and Mobility Study, which creates a new public data infrastructure for the study of wealth inequality and mobility. His own research investigates social inequality and its maintenance across time and generations, and recent publications document cross-national variation in wealth inequality (with Nora Waitkus in the American Sociological Review), trace the buffer function of wealth for avoiding material hardship (with Richard Rodems in the Journal of European Social Policy), and propose a new method that can help detect, reduce, and even remove unobserved confounding (with Felix Elwert in Sociological Methods and Research).

Asher Dvir-Djerassi is a PhD student in the Department of Sociology and the Ford School of Public Policy at the University of Michigan and a graduate student fellow at the Stone Center for Inequality Dynamics. His research is partially funded by the National Institutes of Health. At the Center for Inequality Dynamics, he is a core member of the Wealth and Mobility Study, a multiyear study using Internal Revenue Service administrative tax data to estimate population-level wealth dynamics. These data serve as the foundation for his dissertation project, which concerns wealth stratification in the United States. His research is situated within fiscal sociology, social demography, simulation methods, and comparative historical institutionalism. Asher holds an MS in data science from the City University of New York and a BA in economics from Hampshire College and conducted a year of graduate coursework in economics at Sciences Po–Paris.