Political debates over the past two decades have placed the issues of income and wealth inequality front and center. While these debates tend to focus on the causes and consequences of inequality, they often presuppose that inequality has taken off dramatically in recent years. But has inequality, in fact, increased?
As it turns out, answering this question is more difficult than it may appear. Questions such as: “inequality of what?,” “over what period?,” “measured how?” may seem like trivia designed to distract from the broader issue, but they are in fact all choices that an analyst seeking to study this question must address. In this post, we show that economic inequality in the US is sensitive to such measurement issues and, depending on the approach chosen, one can marshal evidence to show that inequality has increased, stayed the same, or even decreased over time. These results speak to both the danger of using single measures to characterize entire distributions, as well as to the precise dynamics of changing inequality in the US.
To approach this issue, we use publicly available data from Kuhn, Schularick, and Steins (2020) “Income and Wealth Inequality in America, 1949-2016”. This paper draws from the Survey of Consumer Finances to construct a panel of income and wealth dynamics over the past 70 years. We construct time series of inequality over different variables, different time periods, and using different measures, and show that these choices have meaningful effects.
A first issue in measuring inequality involves the choice of variable we which to measure. Frequently, “wealth”, “income”, and “consumption” are used interchangeably under a broad notion of economic inequality, but this type of language can mask large and important differences, both statistically and economically. To see this, consider the Gini coefficient taken over four different measures of economic wellbeing from the data: total income, capital income, total assets, and net wealth.
Fig. 1: Gini Coefficients by measures of wealth.
While all measures seem to have increased since 1970, there is substantial heterogeneity both in level and in changes. First, in levels, we notice that capital income reflects a far higher degree of inequality that any other measure. This is perhaps not so surprising, given that capital income reflects interest, dividends, rents, and capital gains in markets in which many Americans never participate. Also, unlike variables like labor income, capital income can take on negative values (e.g. from negative capital gains), which even further skews the distribution.
But even measures like net wealth and total income have vastly different degrees of inequality; at times, the distribution of net wealth was almost twice as unequal as total income. And while inequality in total assets, capital income, and net wealth, have increased somewhat gradually, inequality of total income has risen by more than 30% over the past 50 years. There are even periods, such as during the 1970s, when inequality in total income and net wealth moved in opposite directions. Questions like “to what extent is America unequal” and “to what extent has this inequality increased” thus depend a lot on what type of inequality we are talking about.
The figure above highlights the importance of being precise about the notion of inequality one wishes to measure. Net wealth — defined as a household’s assets less its debt — speaks to accumulated assets, such as stocks, bonds, and the equity share of housing. Total income, on the other hand, speaks to the flow of income a household earns each year. While income furnishes households with the ability to accumulate net wealth, they clearly do not move in lockstep, with differences attributed to things like equity and house prices. This is a key argument of Kuhn, Schularick, and Steins (2020) that we will revisit in the conclusion.
A second issue involves the choice of sample period. The point here is more about presentation of data than about statistical analysis itself, but given the importance of data visualization to economic arguments, we illustrate how sensitive statements about the direction of inequality — let alone the magnitude — can be to the sample period analyzed.
For all four economic measures above, we consider the absolute change in the Gini coefficient over different sample time periods. We compare it to the unconditional change over 1970-2016: