Simple labels and quick dichotomies are usually the easiest way to
describe any given state of affairs, but genuine understanding suffers
if reality is subjected to substantial simplification. When that reality
is as complex as the economic status of U.S. households, generalizations
are bound to lead to distortion and confusion. Media reports and even
academic work too often reflect such misrepresentation.
In a study of economic inequality published in the Summer 2002 Quarterly
Review, four economists go well beyond such superficial treatment,
and their analysis reveals a subtle and multilayered interplay of
income, earnings, wealth and demographic status among U.S. citizens
in the 1990s. They show a nation of wide financial diversity and mobility,
substantial growth in various measures of economic well-being and
yet a surprising lack of change in the overall picture of economic
inequality during the decade
The analysis, "Updated Facts
on the U.S. Distributions of Earnings, Income, and Wealth",
by Santiago Budría Rodríguez, Javier Díaz-Giménez,
Vincenzo Quadrini and José-Víctor Ríos-Rull,
is, as its title implies, an update of a 1997
report, (also published in the Quarterly Review). Both
analyses rely on the Survey of Consumer Finances (SCF), a national
survey of about 4,000 households sponsored by the Federal Reserve
to obtain a comprehensive financial profile of U.S. citizens.
Tools of the trade
There are many ways to measure economic status. The authors use three:
Earningswages and salaries, plus a large portion of business
and farm income; earnings, therefore, are the portion of a household's
income that comes from its labor.
Incomerevenue from all sources before taxes but after transfers
(such as welfare and Social Security); defined thus, income includes
earnings but exceeds it by revenue from capital (rent or dividends,
for example) as well as government payments.
Wealththe net worth of households (assets minus debts);
wealth, therefore, is the stock held by a household of all its unspent
income (in other words, its accumulated savings).
There are also many ways of quantifying inequality; the study looks primarily
at percentage distributions (often by grouping into quintiles) and Gini
indices. A quintile is a fifth, of course, and the study categorizes various
economic status measures into five groups: the top and bottom fifths and
those in between, with the first quintile being the lowest or having the
A Gini index summarizes inequality in a single number, with 1 as complete
inequalityone household holding all and the others having nothingand
0 as total equalityall households owning exactly the same amount.
The Gini index has the obvious advantage of simplicity, like an average
number. But just like an average number, it can also obscure details that
a percentage distribution more fully describes.
The economists use these tools to describe the United States as of the
1998 SCF and the changes that have taken place since the 1992 SCF.
The data show clearly that U.S. earnings, income and wealth were
all very unequally distributed in 1998. Wealth was by far the most
concentrated measure of economic status, with a Gini index of .803,
and income was the least unequally distributed, with a Gini of .553.
In more intuitive terms, the wealthiest 1 percent of households held
1,335 times the wealth of the bottom 40 percent, while the top 1 percent
of households in terms of income received 73 times the income of the
bottom 40 percent. Earnings distribution fell in between the other
two measures, with a Gini index of .611 and a top 1/bottom 40 ratio
The same three measures viewed by quintiles: The fifth wealth quintile
had an average net worth of $1.2 million; the first quintile's average
wealth was actually negative, debts exceeding assets by $4,100. For
income, a fifth-quintile household made $159,100 a year, on average,
while a first-quintile family brought in $6,400. As for earnings,
a top 20 percent household earned $127,500 annually, while a bottom
fifth household actually lost $300.
Still one more way to look at economic concentration: Households in
the top 1 percent of the 1998 wealth distribution owned 34.7 percent
of total wealth. The top 1 percent of income recipients took in 17.5
percent of total income. And the top 1 percent of earners received
15.3 percent of total earnings reported by the survey sample. Comparison
of these last two figures points out a curious quirk: While earnings
are more unequally distributed than income for the sample as a whole,
this is reversed for the richest in both respects, where inequality
is greater for income than for earnings. (See chart.)
The Lorenz Curves for the U.S. Distributions
of Earnings, Income and Wealth
Parsing rich and poor
This wide variation in economic inequality according to what is
being measured implies that the terms "rich" and "poor"
are themselves ambiguous. Income-poor is by no means the same as wealth-poor.
Indeed, the economists point out that while the rich tend to be rich
by all three measures, such is not the case for the poor: "The
earnings-poor," they write, "are surprisingly wealthy. ...
The income-poor own significant amounts of wealth."
For example, almost 23 percent of the households in the SCF sample
have zero earningsno wages or salaryand 0.24 percent have
negative earnings. Yet the average wealth of the bottom 1 percent
of the earnings distribution is about three times the average wealth
of the total sample. Two explanations: Many earnings-poor households
are retirees who have built up estates, and many households with negative
earnings are headed by business owners under financial stress.
At the same time, many of the wealth-poor do fairly well in terms
of earnings and income. About 2.5 percent of households have zero
wealth and 7.4 percent have negative wealth (a fact that helps to
explain the high concentration of the wealth distribution). But the
average earnings of the bottom 1 percent in the wealth distribution
put them in the fourth quintile (the top 60 percent to 80 percent)
of the earnings distribution. The explanation here is demographic:
Over 60 percent of the lowest wealth quintile are single and a disproportionate
number of them are young. So the young and/or single bring in good
money but haven't built up much wealth.
Demographics of inequality
As this last example demonstrates, exploring income, earnings and
wealth inequality without looking at age and marital status, or other
variables like employment and education, is like driving across country
without a map. Demographics help dramatically in understanding the
lay of the land.
Analysis by age is a case in point. Inequality in earnings and income
tends to be higher among older households than among young households,
but wealth inequality decreases steadily from a very extreme level
at younger ages until age 40, at which point it remains quite steady,
at a Gini index between .700 and .800.
Looking at employment status, the study confirms what one might expect:
Workers, who make up almost 60 percent of the sample, are significantly
wealth-poorer than the sample average. Retirees, almost 20 percent
of the sample, tend to be earnings- and income-poor, but wealth-rich.
The self-employed, 11 percent of the total, are very well-off by all
three economic measures, with income over 2 times the average and
wealth about 3.3 times the average. And nonworkers are poor by all
measures. Marital status also has the impact one expects: Married
people are better off in terms of income, earnings and wealth than
households headed by single people.
In terms of education, those with more of it tend to be better off
than those with less, as anticipated. But somewhat surprisingly, inequality
as measured by the Gini index tends to be the same among those with
no high school, those with a high school education and those with
a college degree. In other words, all of the education groupings show
very similar levels of inequality: Income inequality among the college-educated,
for example, is as high as it is among those without high school diplomas.
(This is less true for earnings distribution among no-high school
The American dream
The ability to pull oneself up by one's bootstraps is American legend,
but households move in the other direction as well. Aging, the success
or failure of business ventures, good luck and bad health all play
a part in economic mobility, which, the authors note, "makes
inequality an essentially dynamic phenomenon."
To measure mobility, the economists rely on the Panel Study of Income
Dynamics, funded by the National Science Foundation, since the PSID
(unlike the SCF) follows the same set of households over time. By
comparing the earnings, income and wealth status of the same households
in the early and mid-1990s, they build a picture of changes in mobility
They find, for example, that households in the bottom earnings quintile
are by far the least mobile: 90 percent of the families in that quintile
in 1989 remained there in 1994. But only 34 percent of households
in the second-to-bottom earnings quintile in 1989 were still there
five years later: Some moved up and others down.
Income mobility is far greater than earnings mobility, according to
the data, and wealth mobility is somewhat lower than that of income.
(Age and retirement clearly play a big role in inhibiting earnings
mobility. Mobility is considerably higher among households whose heads
were between 35 and 45 years old in 1989 than among all households.)
Still, even for incomethe economic measure with greatest mobilitythe
chance of moving from the poorest to richest quintile in just five
years was low: Just 2 percent of households made that bootstrap leap.
Changes during the 1990s
A comparison of Gini indices between the 1992 and 1998 SCF data
shows that there were just small changes in overall inequality during
the decade. The Gini index of income inequality, for example, decreased
from .574 to .553a minor decline. Other measures of inequality
confirm the stability of that overall picture.
But a number of details did change. The correlation between earnings,
income and wealth, for example, changed significantly from one period
to the next. Earnings and income were not so tightly linked in the
last period as in the first. By contrast, the correlation between
income and wealthmarginal in 1992was substantial in 1998.
This change, suggest the economists, may be connected to the emergence
of the "new economy," since the correlation between wealth
and business income was much higher at the end of the decade than
at the beginning.
The relative economic conditions of the earnings- and wealth-poor
showed little change over the decade. Their shares of total wealth
and earnings remained largely the same. Among the income-poor, however,
relative conditions declined: The share of households with zero or
negative income doubled during the decade and their share of total
SCF sample wealth in 1998 was half what it was in 1992.
The rich fared better. The earnings-rich, income-rich and wealth-rich
households all became relatively wealth-richer. The share of total
wealth owned by the top earnings quintile, for example, was 49 percent
in 1992 and 55 percent later in the decade. The top 1 percent of earners
increased their wealth share from 15.7 percent to 18.3 percent.
These changes were even larger for the income-rich. And the top 1
percent of wealth holders increased their share of the wealth pie
from 31.4 percent to 34.7 percent. In other respects the wealth-rich
remained little changed: At the end of the decade as at the beginning,
the wealthiest obtained most of their income from businesses and capital;
they were still, on average, married and over 45 years old.
The demographics of economic inequality changed to some degree during
the 1990s. For instance, although the share of the total sample represented
by workers increased by 4.6 percentage points, their relative income,
earnings and wealth all decreased somewhat. The relative income of
retirees declined, as well; in 1992, the income of the average retiree
was 78 percent of the total sample, but by 1998, it had dropped to
Changes were also seen in status of different education groups. One
surprising example: The relative average earnings of households headed
by college-educated individuals was 5.8 times greater than that of
no-high school households in 1992, but that edge decreased to 4.7
times larger by 1998. But wealth moved in the opposite direction during
that time span; college households became wealth-richer relative to
no-high school households.
The economic conditions of singles with dependents improved significantly
during the decade, relative both to singles without dependents and
to married households. In 1992, the average earnings of singles with
dependents were 88 percent of singles without dependents; by 1998,
singles with dependents had turned the tables, with earnings 106 percent
that of singles without. But single females remained poor compared
to their male counterparts throughout the decade.
As these changes imply, there were also some differences in economic
mobility during the decade. Earnings mobility decreased slightly,
but income and wealth mobility both increased. The most striking mobility
difference, according to the economists, was a significant jump in
mobility of households in the bottom wealth quintiles.
The overwhelming impression left by the economists' analysis is
that of complexity. The data provide a robust demonstration of the
fact that inequality cannot be summed up in a simple word or phrase:
Different measures of economic welfare, various gauges of inequality,
diverse sources of economic well-being and dissimilar types of people
all interact over time in ways that defy easy description. And a true
understanding of this dynamic phenomenon demands both careful analysis
and close attention. As the authors conclude, "Inequality is
a complex and multidimensional subject."