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Accounting for Wealth in the Measurement of
Household Income
Edward N. Wolff*
New York University, NBER, and Levy Economics Institute
Ajit Zacharias
The Levy Economics Institute
For Presentation at the 2008 World Congress on National Accounts and Economic
Performance for Nations, May 12-17, 2008 [May 14, Session 3C]
*Corresponding author: Edward N. Wolff, 19 West 4th St., 6th Floor, Department of
Economics, New York University, NY 10003, U.S.A. Email: Edward.wolff@nyu.edu
Ajit Zacharias, Levy Economics Institute, Bard College, Annandale-on-Hudson, NY
12504, U.S.A. Email: zacharia@levy.org
ABSTRACT: We analyze the level and distribution of economic well-being in the United States
during the 1980s and 1990s based on the standard measure of money income and a measure in
which income from wealth is calculated as the sum of lifetime annuity from nonhome wealth
and imputed rental-equivalent for owner-occupied homes. Over the 1982-2000 period, median
well-being increases faster when these adjustments are made than when standard money income
is used. This adjustment also widens the income gap between African-Americans and whites but
increases the relative well-being of the elderly. Adding imputed rent and annuities from
household wealth to household income considerably increases measured inequality and the
share of income from wealth in inequality. However, both measures show about the same rise in
inequality over the period. We also find an increasing share of wage and salary income in our
expanded definition of income among the richest one percent over the period but do not find
that the “working rich” have largely replaced rentiers at the top of the economic ladder.
Keywords: living standards, household wealth, inequality.
JEL codes: D31, D6, H4, P16.
2
1. Introduction
Conventional measures of household economic well-being do not adequately reflect the
advantage from asset ownership or the disadvantage from liabilities. Income generated from
asset ownership is usually counted in the form of property income (the sum of dividends,
interest and rent), but this does not reflect the “stock” dimension of the advantage from asset
ownership and is, at best, a partial measure of the “flow” dimension. The disadvantage from the
burden of debt is not captured at all in standard income measures. We believe that a better
indicator of economic well-being than money income would incorporate a measure of
sustainable consumption over time. Such a measure needs to take wealth into account in a more
comprehensive manner than is done in the standard measures. The index that we propose would
use annual income (excluding property income) as its basis and then add to it a constant annuity
from non-home wealth as well as annual imputed rent to owner-occupied housing. This type of
index thus provides a measure of potential consumption of marketed commodities in the current
year (see Section 3.2 for further discussion).
The argument for including a better measure of income from wealth is a part of the
wider agenda to improve measures of household economic well-being.1 An international panel
1 There are three other major approaches to construction of well-being measures: the aggregate approach, the
indicators approach and the subjective approach. The distinguishing feature of the aggregate approach is that it
results in a summary monetary measure of well-being of the nation. Usually, the strategy is to start with standard
macroeconomic categories, such as personal consumption or the GDP, and then modify it by adding items (valued
in money) believed to enhance well-being and subtracting items believed to be detrimental to well-being. The most
well-known and regularly published index belonging to this family is the Genuine Progress Indicator, estimated by
the nonprofit organization Redefining Progress. In our view, the key problem with this approach is that what may
be considered as bad or good for well-being is largely a decision made by the researcher and this renders the index
of a substantially arbitrary character.
The indicators approach typically includes of a variety of noneconomic variables, such as health,
environment and educational attainment, in addition to variables usually considered as economic. Some
researchers prefer to combine these different indicators to form a composite index (e.g. Liu 1976), while others
report national performance with respect to these different indicators (e.g. Henderson, Lickerman et al. 2000).While
the details and implementation of the indicators approach are apparently very different from the aggregate
approach, essentially it too possesses a similar arbitrariness with respect to which indicators are to be included and
whether the changes in the indicators can be considered “good” or “bad.” A further well-known problem with
composite indexes is the choice of weights attached, either explicitly or implicitly, to the different indicators.
Another approach, developed by Osberg and Sharpe (2002), combines current per capita consumption
flows, net accumulation of stocks of productive resources, a measure of income distribution, and an index of
3
of experts addressing this task has lamented the preponderant focus on money income and the
absence of an appropriate concept of money income (The Canberra Group, 2001). Several
authors have recently proposed measures that could provide a better understanding of the level
and distribution of economic well-being (e.g., Smeeding and Weinberg, 2001; Wolff and
Zacharias, 2003). From the early 1980s, the United States Bureau of the Census has published
experimental measures of income that include, among other things, expanded definitions of
income from wealth comprising imputed return on home equity and realized capital gains.
Our aim in this paper is to analyze the level and distribution of economic well-being in
the United States during the 1980s and 1990s using the standard measures (that is, gross money
income and gross money income plus realized capital gains) and our new wealth-adjusted
measure. Admittedly, an adequate measure of economic well-being must take into account
components other than money income and wealth—such as the value of household production
(Wolff, Zacharias and Caner, 2004). We ignore those components here because we want to
concentrate here only on the effects of modifying the standard measures for wealth. The method
of reckoning income from wealth here as the sum of lifetime annuity and imputed rental-
equivalent represents one way of incorporating wealth. However, we also conduct a set of
sensitivity analyses with alternative methods to see how robust our findings are.
The remainder of the paper has the following structure. We begin by briefly
summarizing previous attempts to incorporate wealth into a measure of well-being (Section 2).
We then describe the main sources of data and concepts of wealth used in the study (Section 3).
This is followed by a discussion of how we incorporate wealth into a combined income-net
worth measure. In Section 4, we look at the effects of the incorporation of wealth into income
economic security to produce their "Index of Economic Well Being." Their index is more inclusive than ours in
valuing leisure, household economies of scale, the environment and public goods, life expectancy, etc. – indicators
that are not included in ours. However, whereas our measure can be imputed on a household basis and thus allows
us to compute inequality and median values, their index is based on aggregate data.
By using the results from a survey asking people directly about their satisfaction or happiness about
several aspects of their lives, a subjective index can be formed by appropriate statistical methods (e.g. Campbell,
Converse et al. 1976). However as pointed out by Amartya Sen, subjective perceptions of well-being among those
who need to survive in such a society are powerfully shaped by the ideological mechanisms and cultural norms of a
society (Sen 1989).
4
on the level of well-being for the total population as well as for specific sub-groups. Its effect on
inequality is discussed next (Section 5). Decomposition analysis is deployed to examine two
issues: the contribution of income from wealth to the level and changes in inequality; and, how
the incorporation of wealth alters the rankings of, and relative income differentials among
households. A critical comparison of our estimates of top income shares and those of Piketty
and Saez (2003) is also undertaken to assess whether the “working rich” or rentiers (those
relying primarily on capital income) were at the top of the economic ladder during the period
under scrutiny. A sensitivity analysis is conducted in Section 6 by replacing our definitions of
income from wealth with alternatives: imputed return on home equity and bond-coupon returns.
Concluding remarks are presented in the Section 7.
2. A review of previous literature
It is often believed that income and wealth are almost interchangeable as measures of
household well-being. That is to say, many believe that households with high income almost
always (or, indeed, necessarily) have high wealth, and low income households are low wealth
ones. However, Radner and Vaughan (1987) find that this was not the case by tabulating the
joint distribution of income and wealth by quintile on the basis of the 1979 Income Survey
Development Program (ISP) file. They find that a strong positive correlation between income
and wealth. For example, in the bottom income quintile, 40.5 percent of the households are in
the bottom net worth quintile, while only 6.5 percent are in the top net worth quintile (see Table
1). In the top income quintile, only 4.5 percent are in the bottom net worth quintile, while 44.5
percent fall in the top net worth quintile. However, the correlation is far from perfect. No net
worth quintile contains more than 44 percent of the households in the corresponding income
quintile. Moreover, in the three middle income quintiles, each net worth quintile has at least 10
percent of the households in the income quintile. Income and wealth, while positively
correlated, are distributed rather differently among households. Wealth thus represents another
dimension of well-being over and above income.
(Table 1 about here)
5
We have updated the results to 2001 on the basis of the 2001 Survey of Consumer
Finances. The pattern is quite similar to the 1979 data. If anything the correlation between
income and wealth appears a bit higher, with a somewhat higher percentage of households in the
bottom income quintile who are also in the bottom net worth quintile and, likewise, a somewhat
higher percentage of households in the top income quintile who are also in the top net worth
quintile. For 2001, we computed a correlation coefficient between income and wealth of only
0.53.
There have been several attempts to combine the income and wealth dimension into a
single index of household well-being. The most common technique is to convert the stock of
wealth into a flow and add that flow to current income. In this approach, wealth is converted
into a lifetime annuity for the expected remaining life of the household. The annuity is defined
as a stream of annual payments which are equal over time and which will fully exhaust the stock
of initial wealth. This annuity is then added to obtain an augmented measure of household
income after property income is first subtracted from current money income so that there is no
double counting of the returns from household wealth.
One of the first examples of this approach is by Weisbrod and Hansen (1968) on the
basis of the 1962 Survey of the Financial Characteristics of Consumers (SFCC). The original
data show that the share of the top two income classes ($15,000 and over in 1962 dollars) was 5
percent of total current money income in 1962, and that of the bottom income class (less than
$3,000) was 20 percent. They then used both an assumed 4 percent and a 10 percent annuity
rate on household net worth, and find that the share of the top two income classes increased
from 5 to 8 percent at a 4 percent annuity rate and to 10 percent at a 10 percent rate, while the
share of the bottom income class fell from 20 percent to 18 and then to 17 percent.
A second study, by Taussig (1973), made use of the 1967 Survey of Economic
Opportunity (SEO) database. Three calculations of the Gini coefficient were made: (i) current
(after-tax) money income; (ii) the sum of current income and a 6 percent annuity on household
wealth; and (iii) the sum of current income and a 6 percent annuity on household wealth after
6
adjustments for underreporting of assets among high income households. Results were also
computed by age group. When the adjusted annuity (iii) is added to current money income, the
measured Gini coefficient for all households rose from 0.36 to 0.39. Inequality also increased
for all age groups, though the disequalizing effect was considerably stronger for older age
groups.
A third study, by Wolfson (1979), is based on the 1970 Canadian Survey of Consumer
Finances. Wolfson employed the same general technique as Taussig, except that he used both a
4 percent and a 10 percent annuity rate and also included a separate calculation for the sum of
current money income and imputed rent on owner-occupied housing (valued at 8 percent of net
equity). He found that among all households the inclusion of a wealth annuity with money
income had no effect on the Gini coefficient, which remained in the range of 0.36 to 0.37. The
share of total income of the top 5 percent of households increased but the share of the bottom 20
percent also rose. Results also show relatively little change in measured inequality from adding
a wealth annuity for younger age groups but do show a disequalizing effect for older
households.
Wolff (1990) examined the effects of adding the return to wealth on measured poverty.
Using the 1983 Survey of Consumer Finances (SCF), he found that the inclusion of both
imputed rent to owner-occupied housing and a 3 percent bond coupon rate on non-home wealth
lowered the overall poverty rate by 4.8 percent. However, the effect was much stronger for the
elderly (an 11.5 percent reduction) than the non-elderly (only a 3.1 percent reduction).
In sum, the Weisbrod and Hansen and Taussig studies found that the distribution of
income becomes more unequal once the returns to wealth are included as part of total income,
though the Wolfson study found no effect. However, the disequalizing effects were not great in
the first two studies: a 3 to 5 percentage point increase in the share of the top two income classes
in the Weisbrod and Hansen study and an increase in the overall Gini coefficient of 0.03 (about
10 percent) in the Taussig study. There are two reasons for these small effects. First, though
household income and wealth are positively correlated, they are not perfectly correlated, so that
7
there are households with low income but high wealth and also with high income but low
wealth. Second, the annuity payments are small relative to current money income, typically on
the order of 10 percent or so. As a result, their inclusion in augmented income does not alter the
overall distribution of income very much. Moreover, annuities are much smaller for younger
households than older ones, both because younger ones have lower wealth and because they
have a longer remaining life expectancy. As a result, wealth annuities generally have a more
disequalizing effect for older households than younger ones, as reported in the Taussig and
Wolfson studies.2
3. Data and concepts
3.1. Household wealth
Our basic data source is the Federal Reserve Board’s Surveys of Consumer Finances
(SCF) for 1983, 1989, 1995, and 2001. The SCF is the premier survey on household wealth in
the United States, conducted every three years. Completed interviews in the SCF amount to
4262, 3143, 4299 and 4449 households, respectively for 1983,1989, 1995, and 2001. Each
survey consists of a core representative sample combined with a high-income supplement.
For our purposes here, we divide net worth into two components. The first is the gross
value of owner-occupied housing and its corresponding liability, mortgage debt on the principal
residence. The remainder, “nonhome wealth” equals the sum of (1) other real estate owned by
the household and net equity in unincorporated businesses; (2) cash and demand deposits, time
and savings deposits, certificates of deposit, money market accounts and the cash surrender
value of life insurance plans; (3) government bonds, corporate bonds, foreign bonds, and other
financial securities, corporate stock and mutual funds, equity in trust funds; and (4) the cash
surrender value of defined-contribution pension plans, including IRAs, Keogh, and 401(k)
plans; less other (nonhome) debt such as auto and credit card loans.
2 See Moon (1977), Lerman and Mikesell (1988), Citro and Michael (1995), and Caner and Wolff (2004) for
related discussion and analyses.
8
We exclude two types of assets that are sometimes included in broader definitions of
wealth. The first is consumer durables such as automobiles. Though cars have a resale value and
can be converted to cash, it can only be done so by compromising current consumption. Indeed,
cars are rarely sold except as a trade-in on new cars or in a financial emergency. The second is
the value of future retirement income from Social Security and defined-benefit (DB) private
pension plans. Since both are a source of future income, it would be desirable to include them in
our accounting framework. However, because of data limitations, it is not possible to do so for
1983, the first year in our period of analysis.3
Table 2 shows mean values for different assets and liabilities over the four years in 2001
dollars. While mean net worth climbed by 82 percent between 1983 and 2001, the median
increased by only 36 percent, a result indicative of rising inequality over this period. The mean
value of houses, real estate and business equity, and liquid assets grew between 35 and 55
percent, less than the overall percentage increase of total assets. The biggest gains were
recorded for financial assets (including stocks) of 162 percent and pension assets of 660 percent.
The mean value of liabilities expanded by 66 percent, an increase less than that of total assets.
Mortgage debt grew by 117 percent while other debt actually contracted by 2.7 percent. This
trend is likely to stem from the facts that mortgage interest rates are lower than those on
consumer debt and that mortgage interest is tax-deductible while other interest is not.
Thre other interesting trends are of note. First, the percentage of households with zero or
negative net worth was fairly high in 2001 (17.6 percent) and increased over the period by 2.1
percentage points. Second, the percentage with net worth of under $1,000 (in 2001 dollars) was
also high in 2001 (21.4 percent) and also increased over the period, by 1.0 percentage points.
Third, median net worth grew by 36 percent over the 1983-2001 period. This was respectable
but considerably less than the 82 percent gain in mean net worth. Both trends indicate widening
wealth inequality over the period.
(Table 2 about here)
3 In particular, the 1983 data contain too many missing values to allow an imputation of both Social Security and
DB pension wealth.
9
3.2. The imputation of annuities and rent on owner-occupied housing
The index that we propose here uses annual income (excluding property income) as its
basis and then adds to it a constant annuity from non-home wealth as well as annual imputed
rent to owner-occupied housing. In contrast to the standard Haig-Simons definition of income,
(i.e. the net increase in the purchasing power to consume or actual consumption plus change in
net worth), our measure includes all regular cash income, in-kind income, capital gains (both
realized and unrealized), and imputed rent (as well as imputed home production). Our wealth-
adjusted metric differs from the Haig-Simons definition in that it seeks to approximate a
measure of sustainable consumption over time. In this sense it is similar to the Haig-Simons
notion of a regular flow of income. It differs primarily by using an annuity flow from
accumulated wealth instead of the annual return on net worth.
This type of index thus provides a measure of potential consumption of marketed
commodities in the current year.4 We are not arguing that it is optimal in any sense for
households to consume the same amount every year, since the marginal utility of consumption
will, in general, vary over the life cycle. Moreover, it is also likely that a family has a bequest
motive so that it is not necessarily the case that the family will consume down its entire wealth
over the lifetime. What our metric (and those of the studies reviewed in the previous section)
attempt to do is to approximate potential and sustainable consumption over a given period of
time, in much the same spirit as the Haig-Simons notion of “regular” income.
The most common technique of combining income and wealth into a single measure of
household well-being is to convert the stock of wealth into a flow and add that flow to current
income. The income flow generated by wealth can be computed either as a lifetime annuity or a
bond coupon (that is, a fixed interest rate on the value of the asset). We incorporate household
net worth by adding to the amount of money income left after deducting property income (the
4 Since we exclude leisure here, our measure reflects only marketed commodities.
10
sum of dividends, interest and rent), the imputed rental cost of owner-occupied housing and the
lifetime annuity value of non-home net worth.5
Our approach differs from the standard approach in two significant ways. First, we
distinguish between home and non-home wealth. Housing is a universal need and owning a
house frees the owner from the obligation of paying rent, leaving that much more resources for
spending on other needs. Hence, benefits from owner-occupied housing are reckoned in terms of
the replacement cost of the services derived from it, i.e. a rental equivalent.6
We impute rent for owner-occupied housing by distributing the total amount of imputed
rent in the GDP to homeowners in the ADS, based on the values of their house.7 Formally,
imputed rent can be expressed as ()*
ii
I
RhHIR
=
, where i
I
Rand i
h are the imputed rental cost
and the value of house, respectively, of household i, while
I
R and Hare the weighted sums of
the same over households.8 On average, imputed rent was 5.6 percent and 5.4 percent
(respectively) of the total value of houses in 1982 and in 2000.9
Another difference in our approach compared to the earlier ones cited above is that we
use actual historical rates of return in computing lifetime annuities. Moreover, we take into
account the differences in the portfolio composition of non-home wealth by computing the
5 In our sensitivity analysis conducted in Section 6 below, we also show alternative estimates based on return on
home equity and the bond coupon approach.
6 This is consistent with the approach adopted in most national income accounts.
7 The NIPA procedure is to assign each unit of owner-occupied housing a rental equivalent on the basis of actual
market rents paid on a tenant-occupied unit of similar value. (See NIPA table 7.12, line 209 for the estimated
imputed rent.)
8 An alternative would be to use a “foregone returns” approach. It posits that by tying up their financial resources in
acquiring a home, the owners are foregoing the returns that they could have earned by investing the same in
financial assets. In our sensitivity analysis conducted in Section 6 below, we shall show alternative estimates based
on this approach as well.
9 It should be noted that we treat housing differently from financial wealth by imputing a rental equivalent value
(and implicitly assuming either a bequest motive or the illiquidity of home equity). We believe that this is
reasonable though we should recognize that an asset like housing, which is often held until death because of the
stream of services it provides is therefore imputed the same annual value as people age – even as the utility value of
excess rooms in the empty nest shrinks. We donot believe that there is necessarily an inconsistency here, since the
service flows from a house remain constant over time (though the utility from the home may decline over time).
11
lifetime annuity as the weighted average of annuity flows generated by individual non-home
wealth components with portfolio shares of these six components as weights. The lifetime
annuity amount calculated is such that (i) it is the same for all remaining years of the younger
spouse’s life;10 and (ii) it brings wealth down to zero at the end of the expected lifetime.
Formally, the annuity value of non-home wealth can be written as the product of (1x6) and
(6x1) vectors: (, , , )*
iijiii j
Afrracesexage W
⎡⎤⎡⎤
=⎣⎦⎣⎦
. Each element i
f
of the first vector gives the
annuity flow that household iwould receive each year if it held $1 in wealth component
j
. This
amount is a function of the total real rate of return on the non-home wealth component, j
r, and
of the race, sex and age of the spouse with the longer remaining life expectancy. Multiplying
this factor, i
f
, by the total amount of money held in the th
j
component, j
W, gives us the total
annuity generated by this component.
The total real rate of return, j
r, of each non-home wealth component
j
, is the average of
annual rates over a relatively long period of time, varying from 14 to 40 years, depending on the
asset (see Table 3). The rationale for employing this method, instead of using the rate of return
in an arbitrarily chosen year, is that the annuity value estimated this way is a better indicator of
the resources available to the household on a sustainable basis over its lifetime. The total rates
of return data we use are inclusive of both the capital gains and the income generated by the
assets. In order to avoid double counting, we net out from the total income measure any
property income already included in money income.
The average rates of return by asset type were estimated from the data on asset holdings
published by the Federal Reserve in the Flow of Funds Accounts for the United States and
10 Information on remaining lifetimes are taken from the life-tables published by the U.S. National Center for
Health Statistics for various years. Remaining lifetimes are reported by sex and three racial groups (white, nonwhite
and black) for all the years included in this study except 2001, for which separate estimates are available only for
whites and blacks. We estimated the remaining lifetimes for the nonwhite group by assuming that the proportion
between black and nonwhite lifetime at each age was the same in 2001 and 1996. The latter year was the last year
for which separate estimates are available for nonwhites and blacks.
12
financial market information included in the 2005 Economic Report of the President.11 The
results are shown in Table 3. In this breakdown, pension assets had the highest real rate of return
at 4.6 percent per year, though the period covered is only from 1986 to 2000. The rate of return
for this asset is calculated over a comparatively shorter period, reflecting its relatively recent
appearance in the Flow of Funds data. Financial assets had the second highest rate of return, at
3.8 percent per year, followed by real estate and business equity at 2.4 percent per year. Liquid
assets had the lowest real rate of return – only 1.0 percent per year over the 1960-2000 period.
(Table 3 about here)
4. Trends in the level of well-being
4.1. Overall trends
Table 4 shows trends in mean and median income using three different definitions over
the years 1982 to 2000.12 Line 1 shows the results using the U.S. Census Bureau’s standard
definition of money income. It is first of note that mean money income climbed by 35 percent
between 1982 and 2000 while the median inched up by only 9 percent, suggesting a steep rise in
inequality. Line 2 shows trends in SCF income, which is the sum of money income and realized
capital gains. Its mean value gained 42 percent over the period, roughly 7 percentage points
more than money income, indicating a strong growth in realized capital gains over these years.
In contrast, the median value of SCF income increased by only 9 percent.
Line 3 shows results for our wealth-adjusted measure, WI, including imputed rent on
owner-occupied housing and the annuitized value of non-home wealth. Its mean value shows an
even more robust growth than that of SCF income, 49 percent over the period. The median rose
by nearly 18 percent, almost double the increase in median money income or median SCF
income. Further analysis show that the main factor behind the sharp gains in wealth-adjusted
11 The Flow of Funds data are available at: http://www.federalreserve.gov/releases/z1/Current/ and the 2005
Economic Report of the President is available at: http://www.gpoaccess.gov/eop/. Details on the data taken from
the Flow of Funds, including series identifiers are available from the authors upon request.
12 The income reported in the survey is for the previous year (for example, the 2001 survey has information on
income received during 2000) though the wealth data are as of the year of the survey. However, for consistency, we
refer throughout this paper to the income year rather than the survey year.
13
income is the steep rise in annuitized wealth, whose mean soared by 93 percent over these years.
Mean imputed rent, on the other hand, grew by an anemic 13 percent.
(Table 4 about here)
We have also put in an additional row into Table 4 which shows the calculation of W1 if
rates of return are the same for all households (see line 6). This new calculation makes clear the
impact of (a) considering the flow equivalent value of wealth and (b) considering the impact of
differential asset returns on the flow equivalent value of wealth. As is apparent, trends in both
mean and median WI are virtually unaffected by this procedure. It is clear that the variance of
wealth levels across households is much more important than the variation of rates of return.
Another concern is that in comparing the different years in Table 4, one is comparing
populations with a different demographic structure. For example, as the Baby Boomers age, our
methodology implies that the same asset (e.g. a bond) owned at different ages will provide a
different annuity equivalent, even if its current market value remains unchanged (because
$100,000 buys a smaller annuity for a 45 year old than for a 65 year old). An important issue is
how much the changing age profile of the US matters for time trends in WI. We have therefore
included both the median and mean age over the 1982-2000 period in Table 4. There is only a
very modest increase in average age over the period so that it is unlikely that the changing age
structure has much impact on changes in WI over the period.
4.2. Racial/ethnic differences
The racial income gap was wider in 2000 and grew even more steeply between 1982
and 2000 when realized capital gains are included in income and the gap became still wider and
grew even more when imputed rent and annuitized wealth (though mainly the latter) are added
to money income. These results reflect the fact that the wealth gap between African-Americans
and whites is considerably larger than the income gap. In 2001, for example, the ratio of mean
14
net worth between blacks and non-Hispanic whites was only 0.14, compared to a ratio of 0.57 in
money income.13
In 1982, the ratio of median MI between African-Americans and non-Hispanic whites
was 0.56 and the corresponding ratio of mean income was 0.57 (see Table 5 and Figure 1). By
2000, the ratio of medians actually edged upward a bit to 0.57 while that of means slipped to
0.50. The ratios of both median and mean SCF income in 1982 were slightly lower than those of
money income. The ratio of median SCF income remained unchanged in 2000 while the ratio of
mean SCF income plummeted from 0.55 to 0.46, much lower than that of mean money income.
Likewise, the ratio of median WI in 1982 was somewhat lower that than of SCF income, while
the ratio of mean WI was a full 5 percentage points lower. In this case, the ratio of median WI
fell from 0.53 in 1982 to 0.49 in 2000, while that of mean WI fell even more steeply, from 0.50
to 0.41.
(Table 5 and Figure 1 about here)
The pattern of results is very similar for Hispanics. In particular, there was a more
precipitous drop in WI than standard MI, with the ratio of median MI between Hispanics and
whites falling by 8 percentage points and that of mean MI by 10 percentage points, while the
corresponding ratios for WI declined by 11 and 13 percentage points, respectively. Moreover,
by 2000 the ratio of medians was much lower for WI, 0.50, than for MI, 0.59, as was the ratio of
means, 0.43 versus 0.54.
The pattern is also similar for the fourth category, Asians and other races (“Asians” for
short). In 1982 there was virtual parity in MI between Asians and whites. However, by 2001 the
ratio slipped to 0.80 for median MI and 0.85 for mean MI. This drop is likely the result of a
large Asian immigration and a big expansion of the Asian population in the intervening years.
13 A considerable literature has developed which discusses the reasons behind the large size of the racial wealth gap
in comparison to the income gap (see, for example, Blau and Graham, 1990; Oliver and Shapiro; 1995; Menchik,
and Jianakoplos, 1997; and Wolff and Gittleman, 2004). One of the key factors explaining the large racial wealth
gap is differences in inheritances between the two racial groups. Using the Panel Study of Income Dynamics, Wolff
and Gittleman (2004) reported that the actual ratio of mean wealth between African-Americans and whites changed
from 0.25 to 0.28 between 1984 to 1994. But if African Americans had inherited the same amount as whites during
this1984-94 period, this ratio would have been 4 percentage points higher in 1994 -- that is, 0.32.
15
The ratio of WI in 1982 was slightly below parity, a ratio of 0.95 for the median and 0.89 for the
mean. However, by 2000 these ratios had plummeted to 0.73 and 0.78, respectively.
4.3. Age differences
Table 6 shows the same set of results by age of householder (also see Figure 2). The
effect of using wealth-adjusted income instead of money income is to increase the relative well-
being of older groups relative to younger ones. There are two reasons. First, the wealth-income
ratios are higher for older households. Second, mortality rates are higher for older individuals
than younger ones, which result in larger annuity flows per dollar of wealth. Moreover, because
of the tilt in age-wealth profiles in favor of older household over the years 1982 to 2000, wealth-
adjusted income grows faster relative to money income for older groups than for younger ones.
The results are quite dramatic. For age group 65-74, the ratio of median WI to the
overall median grew more than the corresponding ratio of MI (10 versus 5 percentage points)
over the 1982-2000 period, as did the ratio of mean WI to the overall mean (3 versus -10
percentage points). By 2000, the ratio of median WI for this age group to the overall median
was 0.87, compared to 0.71 for median MI, while the ratio of mean WI for the age group to the
overall mean was actually over one (1.10) compared to the corresponding ratio of 0.78 for MI.
Results are similar for age group 75 and over. By 2000 the mean WI of this group reached 90
percent of the overall, compared to 50 percent for MI.
For age groups 45 to 54 and 55 to 64, the WI figures relative to the overall are quite
similar to those for MI. On the other hand, the two youngest age groups show a deterioration in
their relative level of well-being when WI is used as the index of well-being instead of MI. For
the under 35 age group, the ratio of mean WI to the overall was 0.54 in 2000, compared to a
ratio of 0.67 on the basis of MI, while for age group 35 to 44 the corresponding ratios are 0.97
and 1.15. Measured growth in well-being between 1982 and 2000 also appears slower for these
two age groups when WI is used as the metric instead of MI.
(Table 6 and Figure 2 about here)
16
Indeed, in absolute terms, there was virtually no change in median MI or in median WI
for the under 35 age group, and only a modest increase for age group 35 to 44. Moreover, if one
compares the median income of the same baby boomer pseudo-cohort (35 to 44 year olds in
1982 compared with 55 to 64 year olds in 2000) there is almost a 9 percent decline in median
WI and only a 3 percent increase in median WI. On the other hand, the average MI of that same
pseudo-cohort increases by a third and mean WI more than doubles -- another indication of the
huge increase in inequality for the baby-boomers.
4.4. Household type
Table 7 shows median and mean income according to alternative definitions of income
for five household types (also see Figure 3). We first look at married couples with children.14
These households tend to fall in the 25 to 55 age range, so that their wealth-income ratios also
tend to be below average. Moreover, since these households are relatively young, their life
expectancies are longer than average, so that their annuity to wealth ratios are lower than
average. On the other hand, this group has an above average homeownership rate, so that the
value of imputed rent should be above average. In 1982, the median money income of the group
was 37 percent above average and their mean income was 23 percent above average. There was
a marked improvement in both median and mean MI for these households between 1982 and
2000 to 58 and 41 percent above average, respectively. The wealth adjusted median income of
this group was 35 percent above average in 1982, about the same as their relative MI, while
their wealth-adjusted mean income was 13 percent above average, about 10 percentage points
less than their relative money income. Over the period, their relative median and mean WI grew
less than their relative median and mean MI, reaching only 49 and 24 percent above average,
respectively. The main reason for the slower growth in WI is the relative decline in imputed rent
for this group of households.
(Table 7 and Figure 3 about here)
14 This category refers to families with children under the age of 18 living at home. The income of adult children
living at home is included in household income.
17
Single female-headed families with children constitute a group characterized by a very
low wealth to income ratio and a low homeownership rate. In 1982 they were well below
average in terms of MI and even further below average (3 to 4 percentage points) in terms of
WI. Their relative median MI declined slightly to 53 percent of the overall median in 2000, and
their relative mean MI dropped sharply to 38 percent. However, their median WI fell more
steeply, to 46 percent of the overall in 2000, and their mean WI collapsed even more, to only 32
percent of the overall mean.
Married couples without children are older than average and therefore have high wealth-
income ratios, high annuity to wealth ratios, and a large homeownership rate. In 1982, their
median and mean MI was, respectively, 28 and 33 percent above average, similar levels to
married couples with children. However, between 1982 and 2000, there was very little change in
their relative position (unlike married couples with children). The wealth-adjusted median and
mean income of this group was, respectively, 36 and 46 percent above average in 1982, greater
than their relative MI. However, here too, there was very little change in their relative median
and mean WI over the period. By 2000, their relative wealth-adjusted median income level was
identical to that of married couples with children, though their relative mean WI was 18
percentage points above because of their greater wealth holdings.
The relative money income of single-female headed households without children was
very similar to that of single-female headed families with children in both 1982 and 2000.
However, the relative WI of the former was from three to ten percentage points greater than the
latter, a reflection of their higher non-home wealth holdings and their higher homeownership
rate. The relative income position of single-male headed households without children lies in
between that of single-female headed households and married couples. Both their median and
mean MI in 1982 was 75 percent of the overall mean. Their median MI remained about the
same in 2000 though their mean MI slipped to 63 percent of the overall mean. Their relative WI
was about the same as their relative money income in 1982 but both their median and mean WI
fell between 1982 and 2000.
18
5. Inequality of well-being
5.1. Overall trends
We next turn to trends in inequality using the three income measures. Table 8 shows
time trends in Gini coefficients for the three income measures, as well as for net worth. On the
basis of the SCF data and the Census concept of money income, the Gini coefficient climbed by
a considerable amount, 0.093, between 1982 and 2000. The SCF definition of income leads to
higher measured inequality in each year because of the concentration of capital gains in the
upper income classes. In 2000 the difference in Gini coefficients between the two income
concepts was 0.025. Inequality on the basis of SCF income shows an even sharper increase than
money income, a gain of 0.111 over the period. The likely reason is the bull market of 2000 and
the large realized capital gains in corporate stocks of that year.
(Table 8 about here)
WI shows the highest level of inequality among all the income measures in all the years.
Its level is considerably higher than that of money income (by 0.040 in 2000) but it shows about
the same change over the 1982-2000 period as the Gini coefficient for money income. The last
line shows the Gini coefficient for net worth. As expected, its value is much higher than that of
any of the three income concepts. However, interestingly, inequality in net worth shows a much
more modest rise over the 1982-2000 period than any of the three income concepts.15 Another
15 Wolff (2003) argues that the reason for this apparent discrepancy is the failure to include Defined Benefit (DB)
pension wealth in the conventional definition of household wealth. In particular, the period 1989 to 2001 was
characterized by a dramatic transformation of the pension system, with Defined Contribution plans substituted for
DB plans. As a result, if DB pension wealth were included in the standard wealth definition, wealth inequality for
households aged 40 and over would show a large increase over the 1983 to 2001 period, commensurate with that of
income inequality.
Though we cannot make a full set of estimates on DB pension wealth here, especially because of missing values in
the 1983 data, we can show some estimates of the effects of including an annuity flow from DB pension wealth on
WI for 1989 and 2000. We use the same rate of return on DB pension wealth as on DC pension wealth (4.56
percent per annum). DB pension wealth amounted to 18.3 percent of net worth in 1989 but only 10.8 percent in
2001. Including the annuity flow from DB in WI would cause the percentage increase of mean WI over the 1988-
2000 period to fall slightly, from 24.9 to 23.6, but would cause a larger reduction in the percentage growth in
median WI, from 8.9 to 5.3 percent. On the other hand, the inclusion of the annuity flow from DB pension wealth
has virtually no effect on the change in the Gini coefficient for WI over the period. The likely reason is that the
19
telling result is that for all three income measures, as well as for net worth, the big increase in
inequality occurred between 1982 and 1988, followed by a more modest rise over the 1990s.16
The share of income from wealth in overall inequality depends crucially on how that
income is measured. We separate the total income in each income definition into two sources,
income from wealth and income from all other sources (“primary income”), and decompose
inequality by income source using the method developed by Yitzhaki and Lerman (1985).17 As
shown in Table 9, the share of income from wealth in inequality is the smallest for money
income in which standard property income (sum of interest, dividends and rents) is used as the
measure of income from wealth. The SCF definition includes realized capital gains too, which
enhances the income from wealth share in inequality by 3.7 percentage points in 1982 (from
15.3 to 19 percent) and 10.3 percentage points in 2000 (from 9.9 to 20.2 percent). As noted
above, the stock market was very bullish in 2000 and this factor accounts for the larger share of
income from wealth in inequality when realized capital gains are included. However, the share
of income of from wealth in inequality is far higher for WI than for the other two measures.
Compared with SCF income, our measure shows that the share of income from wealth in
inequality was 16.8 percentage points higher at 35.8 percent in 1982 and 17.6 percentage points
higher at 37.8 percent in 2000. Closer examination shows that annuitized nonhome wealth is the
driving force behind the larger share of income from wealth in inequality. In 2000 the share of
annuities alone in wealth-adjusted income was 24.5 percent, almost double the share of income
annuity flow from DB amounted to only 3.3 percent of WI in 1988 and 2.2 percent in 2000. See Wolff (2003, 2007)
for details on the procedures used to estimate DB pension wealth.
16 Another interesting comparison is between the SCF money income series and the CPS money income series. The
former show much higher levels of inequality (a difference of 0.087 in the Gini coefficients). The increase in the
Gini coefficient from 1982 to 2000 is about double for the SCF data than the CPS data. The differences in results
between the SCF and CPS data are likely due to the absence of top-coding and the oversampling of the rich in the
SCF.
17 This is the so-called “natural decomposition.” In this type of decomposition, the share of an income component
in inequality is the product of its concentration coefficient and its share in total income divided by the Gini
coefficient of total income.
20
from wealth in SCF income (13.5 percent) and more than three times the share in money income
(7.2 percent).
(Table 9 about here)
Although the change in the Gini coefficient between 1982 and 2000 is similar for MI and
WI, there is a striking asymmetry between the two measures with respect to the contribution
made by income from wealth to the increase in inequality (see Figure 4). Income from wealth
actually had an inequality-reducing effect on money income since its contribution to the
increase of 0.093 in the Gini was –0.016 points, indicating that the increase in inequality was
due solely to the increasing inequality of primary income. In contrast, income from wealth and
primary income contributed roughly the same amount to the increase of 0.096 in the Gini of WI.
As noted above, SCF income showed the greatest increase of inequality between 1982 and 2000
among the three income measures at 0.111. However, income from wealth accounted for only
about a quarter this increase (0.028), with the remainder coming from primary income.
(Figure 4 about here)
The Gini coefficient can be calculated by means of a covariance-based formula that
renders transparent the roles played in the determination of inequality by income gaps between
households and ranks of households in the overall distribution (Lerman and Yitzhaki, 1995). Let
y
represent income, F the cumulative distribution of income,
μ
the mean income, and
sy
μ
=. Then the Gini can be calculated as: 2cov( , ).GsF
=
If we let a and b represent two
income measures (e.g. money income and wealth-adjusted income) the difference between their
Gini coefficients can be decomposed as: 2cov( , ) 2cov( , )
ba baa bba
GG ssF sFF
−
=−+ −. The
first term in the decomposition is the change in measured inequality due to the change in
relative incomes and the second term is the change due to the change in the cumulative
distribution. In any actual sample, the cumulative distribution is approximated by ranks. Hence
the decomposition can be described as splitting the difference in the Gini between the income
measures into contributions from gap-changing and reranking components. The results from the
decomposition are shown in Table 10.
(Table 10 about here)
21
In 1982, a substantial portion of the increase in the Gini coefficient (45 percent) that we
observe when we move from MI to WI is accounted for by reranking. The role of reranking
increases dramatically in 2000, with this component accounting for the overwhelming portion
(83 percent) of the difference in inequality between the two definitions. Our definition of
income from wealth thus alters not merely the picture regarding how much households are apart
from one another in terms of well-being. The position of individual households in the hierarchy
of well-being is also significantly changed (such as the now elevated position of the elderly in
terms of WI). The bigger role of reranking in 2000 as compared to 1982 is due mainly to the
sharp increase in the share of annuities that was noted above.
Reranking also plays a role in accounting for the higher Gini coefficient of SCF income
relative to money income. However, its role is much more limited than that observed for the
shift from money income to wealth-adjusted income. The bulk of the increase in the Gini—70
percent in 1982 and 78 percent in 2000—is accounted for by changes in the income gaps
between individual households. Understandably, such gaps were higher in 2000 because much
of realized capital gains typically accrues to recipients of property income.
Further information on the relationship between rankings according to money income
and wealth-adjusted income can be obtained by examining the joint distribution of households
among the quintiles of the two distributions (see Table 11). If there were no reranking across
quintiles, then each element of the diagonal of the matrix would equal 20 percent and the off-
diagonal terms would all be zero. Generally, the majority of households in a given quintile of
MI are to be found in the same quintile of WI. In 2000, for example, in the bottom MI quintile,
87.5 percent (17.5/20.0) of the households are in the bottom WI quintile, while none are in the
top WI quintile. In the top MI quintile, there are no households that belong to the bottom WI
quintile, while 84 percent (16.8/20) fall in the top WI quintile. However, the correlation is less
strong in the three middle quintiles. For example, 33 percent (1 – 13.4/20) of the households in
the third MI quintile are not in the third quintile of WI in 2000. It is also interesting that the
diagonal terms of the matrix are consistently higher in 1982 than in 2000, indicative of a
weakening correlation between the two income measures within any given quintile. The very
22
high degree of reranking in 2000 indicated by the results of our decomposition analysis suggests
that considerable reranking must also be taking place within quintiles.
(Table 11 about here)
5.2. Income shares and income composition
Table 12 shows the actual income shares by percentile group in the four years.
According to all three income measures, there was a huge increase in the share of the top 10
percent over the 1982-2000 period. The share increased by 9.5 percentage points for MI; by
12.2 percentage points for SCF income; and by10.0 percentage points for WI. Most of the
increase of the top decile accrued to the top one percent of the overall distribution.
(Table 12 about here)
The major difference in the distribution of MI and WI is in the share of the top decile. In
1982 the income share of the top 10 percent as ranked by WI was 4.7 percentage points greater
than that of MI and by 2000 the gap had increased to 5.1 percentage points. In 1982 there was
almost no difference in the income shares of P90-95 between the two income concepts and a
slight difference in the shares of P95-99. The main difference between the two concepts was in
the share of the top one percent (P99-100)—a difference of 4.2 percentage points. In 2000, in
contrast, while there was again a very small difference in the shares of P90-95 in the two
measures, the difference in the shares of P95-99 had advanced to 2.2 percentage points and that
of the top one percent to 2.7 percentage points. Interestingly, while there was very little
difference in the income share of the top 10 percent between money income and SCF income in
1982, by 2000 the difference had mushroomed to 3.4 percentage points, mainly because of a
widening gap in the income share of the top percentile. The likely reason again is the surge in
realized capital gains in 2000 emanating from the stock market boom.
Table 13 provides more details on the differences in the distribution of MI and WI in
1982 and 2000. There are several findings of note. First, mean imputed income from wealth and
its components (imputed rent and annuities) generally increases with income decile—indicative
of the positive overall correlation between wealth and income—and, in particular, soars between
23
the ninth and top decile. However, the rate of increase between the ninth and top decile is much
higher in annuities than in imputed rent, showing the greater concentration of this type of wealth
(primarily financial assets) among households in the top decile. From the ninth to the top decile,
annuities increased more than eight-fold in 1982 and almost six-fold in 2000. Second, the value
of income from wealth as a percent of money income displays a U-shape. indicating that the
correlation between income and wealth is far from perfect, as shown, in particular, by the high
percentages for the lowest three deciles. This reflects the relatively low money incomes but high
wealth holdings of the elderly.
(Table 13 about here)
Third, the value of annuities is the main component of income from wealth, dominating
imputed rent in all income deciles. On average, imputed rent is 28 percent of annuities in 1982
and only 17 percent in 2000. Fourth, compared to property income, which we replace, annuities
are remarkably higher in all income deciles. Finally, comparing 1982 and 2000, we find a
modest increase in imputed rent as a share of money income for the lowest three deciles and
generally a slight decline for the upper deciles. The pattern is different for annuities, which
about doubles as a share of money income for all deciles except the top decile, where it remains
about constant.
It is also informative to look at the changes in the entire distributions of money income,
SCF income, and wealth-adjusted income over time (see Figure 5). Clearly, the rate of increase
is the highest for WI at all percentiles and not only at the median. Furthermore, the percentage
increase at the 95th percentile of the WI distribution is striking (63 percent). However, the
relative differences in percentage increases between MI and WI are fairly uniform across
percentiles – again reflecting the fact that the increase in the Gini coefficient between 1982 and
2000 is roughly the same for the two income concepts. It is also of note that percentage
increases over the period by percentile are quite similar for SCF income as for MI.
(Figure 5 about here)
Table 14 shows a breakdown of income sources in 1982 and 2000. On the basis of the
money income concept, earned income (the sum of wages and salaries and self-employment
24
income) constituted 83.8 percent of total personal income in 2000, while income from wealth
(in this case, property income) made up only 7.2 percent. Indeed, for the top percentile, 85.9
percent of total income was earned income and only 13.0 percent was in the form of property
income. However, when the full value of wealth is properly accounted for as in the WI measure,
then income from wealth appears far more important. Among all households in 2000, income
from wealth now constitutes 28.6 percent of all income (compared to 7.2 percent in the money
income measure) and earned income falls from 83.8 percent to 64.5 percent. For the top
percentile, income from wealth now makes up 45.7 percent of total income (up from 13.0
percent), while earned income drops from 85.9 to 53.2 percent.
(Table 14 about here)
It is also of interest that between 1982 and 2000 property income fell from 10.3 to 7.2
percent of total money income of all households. Also, on the basis of money income, earned
income rose from 76.9 to 83.8 percent of total income. For the top percentile, property income
plummeted from 29.5 to 13.0 percent of money income, while earned income climbed from 66.0
to 85.9 percent. These results seem to give the impression that the rich have switched from
being a “rentier” class to being the “working rich.” However, on the basis of the WI measure,
though income from wealth still fell in relative terms among the top one percent, in this case it
was from 61.1 to 45.7 percent of wealth-adjusted income, while earned income rose from 35.8
to 53.2 percent. Though the trends are similar for the two income measures, it is clear that in
2000 on the basis of WI income from wealth still constitutes a substantial share of the total
income of the very rich.
5.3. Income sources of the rich and a comparison with Piketty and Saez
We next compare our results with those of Piketty and Saez (2003, 2001). Their data
source is the Internal Revenue Service Statistics of Income database and their income concept is
Adjusted Gross Income (AGI) less realized capital gains.18 The most striking difference is in the
18 Piketty and Saez also exclude some other small items in AGI such as taxable Social Security income. The
reference distribution is the distribution of income among taxpayers (tax units). However, the number of tax units
25
level of inequality indicated by the three measures. The share of the top 10 percent computed by
Piketty and Saez (“PS’ in Figure 6) for 2000 is 43.9 percent, very close to the 42.9 percent
figure on the basis of money income.19 Since the two income concepts are quite close, this result
is reassuring. However, not surprisingly, the share of the top 10 percent in WI is quite a bit
higher – 48.1 percent. A similar pattern is found for the share of the top one percent in 2000 –
16.9 percent from PS, 17.4 percent on the basis of MI, and 20.1 percent using WI.
(Figure 6 about here)
On the other hand, the PS results show a very similar time trend of the income shares of
the top percentiles as do both the money income and wealth-adjusted income series. According
to the PS figures the share of the top decile jumped 10.7 percentage points between 1982 and
2000, while the money income data shows a 9.5 percentage point rise and the WI figures a 10.0
percentage point increase. All three sources indicate almost no change in the income share of
the P90-P95 income group. WI shows a 3.7 percentage point rise in the share of the P95-P99,
while the PS figures indicate a 2.2 percentage point increase. In contrast, PS find a 8.6
percentage point rise in the share of the top percentile in comparison to a 6.0 percentage point
increase in their share of WI.
A key argument made by PS is that the surge in top income shares since the early 1970s
is due to the relatively sharp increase of top wages as reflected in the growing share of labor
income, at the expense of capital income, in the total income of the rich. (Piketty and Saez 2003:
17,37). We also find a sharp decline in the share of income from wealth in the total income of
the top decile on the basis of MI, but no such decline occurs on the basis of WI between 1982
and 2000 (see Table 15). Even more striking is the difference in the levels of alternative
estimates. For the richest 10 percent, the share of income from wealth in total income was 42
in each quantile is defined relative to the total number of potential tax units (had everyone been required to file a
tax return) and the share of each quantile is defined relative to the NIPA aggregate of personal income, after
adjustments required for comparability with the AGI concept excluding realized capital gains.
19 It should be noted that the PS data is for the year 1999 while our data is for 2000. As for 1982, both estimates
pertain to income during that year. It is quite unlikely that the general pattern of results that we report here will be
significantly affected by the fact that the endpoints are apart by one year.
26
percent in 2000 on the basis of WI as compared to only 12 percent for money income and a still
smaller 8 percent for PS. Within the top decile of WI, there is a notable diminution in the
relative importance of income from wealth for the richest 1 percent: the share of income from
wealth in total income declined from 61 percent in 1982 to 46 percent in 2000 for this group.
This result is an accord with that of PS. This drop may be a reflection of the enhanced salaries
of corporate executives, particularly CEOs. However, the 46 percent share in 2000 is still a far
higher level than the corresponding estimates, 12-13 percent, based on MI or PS, and does not
support the conclusion that the so-called “working rich” have largely displaced the “coupon-
clipping rentiers” at the top of the economic ladder. Indeed, the two groups now appear to co-
habitate the top end of the income distribution.
(Table 15 about here)
6. Sensitivity analysis
The last part of our research is to subject our estimates to sensitivity analysis. Two
alternative assumptions can be used to impute income values for the home and non-home
components of wealth. In the benchmark case, corresponding to WI, we estimate the imputed
rental cost by distributing the total amount of imputed rent on nonfarm, owner-occupied housing
in the GDP to homeowners, based on the gross value of housing. In our sensitivity analysis, we
assign homeowners the annual benefit of converting their home equity into an annuity, as
calculated in the same manner as the Census Bureau uses in Annual Demographic Survey
(ADS) of the Current Population Survey (see DeNavas-Walt et al. 2003). In this case, the
variation in income from home wealth is due to the value of home equity, which depends, in
turn, on house values and the remaining mortgage principal. Following the Census Bureau, we
use the rate of return on high-grade municipal bonds for each year in the calculations.20
In the benchmark case, income from nonhome wealth is estimated by the constant
lifetime annuity flow generated by nonhome wealth using average total real rates of return. In
the sensitivity analysis, we use instead a constant coupon rate of 3 percent for each asset to
20 The values are: 9.47 percent in 1983, 7.24 percent in 1989, 5.95 percent in 1995, and 5.19 percent in 2001.
27
generate income from wealth. The use of a fixed rate of return has two effects. First, it washes
out differences in individual household overall rates of return caused by differences in
household portfolios. Second, it also eliminates differences in annuity values deriving from
differences in conditional life expectancy. In particular, individuals with a shorter conditional
life expectancy will, ceteris paribus, have a higher ratio of annuity flow to nonhome wealth than
those with a longer conditional life expectancy.
Table 16 shows trends in mean and median wealth using the alternative measures. While
the mean value of imputed rent to owner-occupied housing rose by 13 percent over the 1982-
2000 period, the mean value of the return on home equity actually declined by 26 percent, a
reflection of the drop in the rate of return on municipal bonds. The mean value of both the
annutized value of nonhome wealth and bond coupon income from nonhome wealth increased
about the same rate over the period, both about doubling in size. However, mean annuity
income was over twice as great as bond income from nonhome wealth in each of the four years.
(Table 16 about here)
As a result, wealth-adjusted income WI grew faster than WI*, the alternative income
measure based on the return on home equity and bond coupon income from nonhome wealth.
Between 1982 and 2000, mean WI climbed by 49 percent, compared to a 38 percent increase in
WI*, and median WI gained 18 percent, compared to an 11 percent increase in median WI*.
Table 17 portrays inequality levels for the alternative definitions. It is at once apparent
that using the return on home equity and bond coupon income resulted in a lower level of
measured inequality compared to our preferred measure. Our analysis (not reported here)
showed that the main factor was the substitution of bond coupon income for annuity income
from nonhome wealth. In turn, the higher contribution of annuity income to inequality was a
reflection of the much higher level of annuity income than bond coupon income. Annuity
income from nonhome wealth was, on average, more than twice as great as bond coupon income
in both 1982 and 2000. However, both WI and WI* show almost identical increases in the Gini
coefficient over the 1982-2000 period.
(Table 17 about here)
28
The substitution of bond coupon income for annuity income from nonhome wealth may
have a large impact on measured racial differences in well-being. The reason is that the higher
mortality rates of African-Americans relative to whites imply a higher value of annuity
payments relative to wealth for the former in the calculation of WI. The use of the bond coupon
technique wipes out the effects of racial differences on differential mortality rates.
However, we found instead that the ratio of bond coupon income between blacks and
whites was higher than the ratio of annuity income (results not shown ). These results are due to
the fact that the annuity rate of return is higher for whites than blacks. This, in turn, reflects the
fact that white households have a different average portfolio composition than black households
and, in particular, hold a higher percentage of their assets in the form of stocks than do black
households.21 Though the mortality effect would lead to a higher ratio of annuity income than
bond income between blacks and whites, the use of a uniform rate of return dominates the
differential mortality effect and results in a higher ratio of bond than annuity income between
the two races.
(Table 18 about here)
The black-white ratio of the return on home equity was about the same as the racial ratio
of imputed rent in 1982 but much higher in 2000. A possible reason is that by 2000 white
households had a larger ratio of mortgage debt to (gross) house value than black households (the
return on home equity is based on the net value of owner-occupied housing whereas imputed
rent is based on the gross value). All told, the ratio of median WI* between African-American
and white households was three percentage points higher than the corresponding ratio of median
WI in both 1982 and 2000 and the ratio of mean WI* was five percentage points higher in the
two years. However, the ratio of both median and mean WI* between black and white
households shows about the same decline as median and mean WI between 1982 and 2000 (4
percentage points for the ratio of median values and 9 percentage points for the ratio of mean
values).
21 Wolff (2006) reports that in 2001 while white households held 25.4 percent of their total assets in the form of
stocks, the corresponding figure for black households was only 14.9 percent.
29
The elimination of the mortality differential effect by age group had a pronounced effect
on the measurement of relative well-being by age (see Table 19). The higher (conditional)
mortality rates of the elderly lead to much higher annuity values relative to their wealth holdings
in comparison to younger households. Using a bond coupon rate approach is roughly equivalent
to standardizing mortality rates across all age groups.
(Table 19 about here)
The ratio of mean bond income for the age group to the overall mean was much higher
for the younger age groups (under age 55) than the corresponding ratio of mean annuity income,
whereas the reverse was true for the older age groups (ages 65 and over). For age group 55 to
64, the ratio of bond income for that age group to the overall mean was 0.10 points higher in
1982 and 0.16 point higher in 2000 than the corresponding ratio of mean annuity income. The
effect is particularly strong for the two older age groups. For the 65 to 74 age group, the bond
income ratio was 1.77 compared to an annuity income ratio of 2.20 in 2000, while for the 75 and
over age group, the former was 2.22 in 2000 and the latter was only 1.12.22 On the other hand,
differences between the ratio of the mean return on home equity by age group to the overall
mean and the corresponding ratio of mean imputed rent on owner-occupied housing were very
slight (results not shown).
All told, the use of the bond coupon (and return on home equity) method leads to an
increase in the measured relative well-being of younger households and a corresponding
reduction of that of older households. The ratio of mean wealth-adjusted income by age group to
the overall mean in 2000 rose from 0.54 (for WI) to 0.62 (for WI*) for age group 34 and under;
from 0.97 to 1.09 for age group 35-44; and from 1.28 to 1.37 for age group 45-54. It fell from
1.41 to 1.37 for age group 55-64, from 1.10 to 0.88 for age group 65-64, and from 0.90 to 0.56
for the oldest age group. The elderly (65 and over) no longer appeared to be better off than the
average household according to the WI* measure. Similar results hold for the medians.
22 Differences in portfolio composition are less marked by age group than by race. Wolff (2006) calculates that in
2001 age group 65-74 held 25.0 percent of its total assets in the form of stocks and age group 75 and over held 29.3
percent in comparison to an overall figure of 24.5 percent.
30
However, changes over time by age group were very similar for WI* and WI. Both measures
showed a deterioration in the relative well-being of age groups under 35 and 35-44; almost no
change for age group 45-54; a substantial increase in mean well-being but no change in the
median level of well-being for age group 55-64; and small increases in mean well-being and
substantial gains in median well-being for age groups 65-74 and 75 and over.
7. Conclusion
There are three factors that determine the distributional effects from adding an annuity
flow from nonhome household wealth. The first is the variation of wealth to income ratios both
across the income distribution and among different demographic groups. The second is the joint
distribution of income and wealth. The third consists of differences in portfolio composition
among households and rates of return by asset type and the consequent variation in overall rates
of return across households.
While mean money income using the U.S. Census Bureau’s standard definition of
money income climbed by 32 percent between 1982 and 2000, our wealth-adjusted measure WI,
including imputed rent on owner-occupied housing and the annuitized value of non-home
wealth, surged by 44 percent over the period. Median money income grew by only 6 percent
over this period, while median WI rose by 15 percent. The main factor behind the sharp gains in
wealth-adjusted income is the steep rise in annuitized wealth, which soared by 87 percent over
these years. Imputed rent, on the other hand, grew by only10 percent.
Adding imputed rent and annuities from household wealth to household income also
increases measured inequality. However, both measures show about the same rise in inequality
over the period. The Gini coefficient for money income climbed by a considerable amount,
0.093 between 1982 and 2000. The Gini coefficient for wealth-adjusted income WI is
considerably higher than that of money income (a difference of 0.040 in 2000) but shows about
the same change over the 1982-2000 period, 0.096, as the Gini coefficient for money income.
Our results here are much stronger with regard to inequality than those of Weisbrod and
Hansen (1968), Taussig (1973) or Wolfson (1979). All three studies find that the distribution of
31
income becomes more unequal once the returns to wealth are included as part of total income.
However, the disequalizing effects in these studies are not great. The main reason is that in their
work annuity payments are small relative to current money income, typically on the order of 10
percent. In contrast, in our work, we find that among all households in 2000, annuity income
from wealth constituted 29 percent of all income.
We also found that the share of income from wealth in overall inequality is much higher
for our wealth-adjusted measure than for money income—nearly four times as much in 2000
(10 vs. 38 percent). The share of the wealth component in the growth in inequality between
1982 and 2000 was also larger for WI than even SCF income that is inclusive of realized capital
gains. About a quarter of the increase in inequality of SCF income could be accounted for by the
wealth component, compared to a third for WI. These results are primarily due to the magnitude
of the annuitized value of nonhome wealth in WI.
We do find like Piketty and Saez (2003) that the share of income of the richest one
percent that emanates from labor earnings rose substantially over the period from 1982 to 2000.
However, our results do not indicate that the working rich has fully displaced rentiers at the top
of the economic ladder. On the basis of the money income concept, it is true that for the top
percentile, earned income (the sum of wages and salaries and self-employment income)
constituted the vast majority (86 percent) of total personal income in 2000, while income from
wealth (in this case, property income) made up only 13 percent. However, when the full value of
wealth is accounted for in WI, then income from wealth appears far more important. For the top
percentile, income from wealth now makes up 46 percent of total income (instead of 13
percent), while earned income is only 53 percent (instead of 86 percent). In 2000, the top one
percent relied about equally on earned income and income from wealth.
The addition of an annuity flow and imputed rent also widens the income gap between
African-Americans and whites but increases the relative well-being of the elderly. In 2000, the
ratio of median MI between African-Americans and non-Hispanic whites was 0.57 and the ratio
of means was 0.50. In contrast, the ratio of median WI was 0.049 and that of mean WI was
0.041. The racial gap also increases more between 1982 and 2000 when imputed rent and
32
annuitized wealth (though mainly the latter) are added to money income. These results reflect
the very large wealth gap between African-Americans and whites and differences in portfolio
composition, with whites have a higher share of assets in stocks (mortality differences would go
the other way, increasing the racial ratio).
The effect of using WI instead of MI is to increase the relative well-being of older
groups relative to younger ones. There are two reasons. First, the wealth-income ratios are
higher for older households. Second, mortality rates are higher for older individuals than
younger ones, which result in larger annuity flows per dollar of wealth. The results are quite
striking. The ratio of mean MI to the overall mean in 1982 was 0.88 for age group 65 to 74
while the corresponding ratio for WI was 1.07. The ratio of mean MI to overall for this age
group actually fell over the period 1982-2000 while the corresponding ratio for WI rose by three
percentage points. Results are similar for age group 75 and over. By 2000 the mean WI of this
group reached 90 percent of the overall, compared to 50 percent for MI.
It should be noted that our results might be significantly affected by the end point, 2001.
Though the stock market peaked in mid-2000, it was still strong in 2001 and did not drop
substantially until 2002. It was not until the end of 2006 that the stock market regained its
earlier peak, though housing prices have risen substantially over the period from 2001 to 2006.
Most studies of disparities in well-being among population subgroups and overall
inequality employ money income as the metric of well-being. Since earnings are the
overwhelming proportion of money income, academic and policy discussions center on
differences in earnings capacity among those in the labor force and tax-transfer policies to
alleviate the income shortfalls of those outside the labor force. Economic inequality often tends
to be reduced to earnings inequality. By employing a combined income-net worth measure, we
have attempted to demonstrate the importance of wealth inequalities in shaping overall
economic inequality and disparities among subgroups. While further research is indeed required
on several of the issues raised here, it appears certain that policies that ignore questions of asset
ownership will only have partial success in redressing the relatively high level of economic
inequality in the United States.
33
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36
Table 1. The Joint Distribution of Households Among Net Worth
and Income Quintiles, 1979 and 2001
(Percentage of households in the income and net worth quintile)
1. 1979a
Income Net Worth
Quintile
Quintile 1 2 3 4 5 All
1 8.1 4.8 3.5 2.2 1.3 20
2 5.5 4.5 3.5 3.5 2.9 20
3 3.6 4.9 5.0 3.5 3.1 20
4 2.0 4.5 4.6 5.1 3.8 20
5 0.9 1.2 3.4 5.6 8.9 20
All 20 20 20 20 20 100
2. 2001b
Income Net Worth
Quintile
Quintile 1 2 3 4 5 All
1 8.4 5.1 3.9 2.0 0.5 20
2 5.1 5.1 3.8 3.7 1.7 20
3 3.9 5.5 4.4 3.7 3.0 20
4 2.0 3.3 5.3 5.1 4.3 20
5 0.5 1.0 2.6 5.4 10.5 20
All 20 20 20 20 20
a. The source is Radner and Vaughan (1987), Table 5.6. The underlying data are from
the 1979 Income Survey Development Program (ISDP) file.
b. Authors' computations from the 2001 SCF. Income quintiles are by money income (MI).
37
Table 2. Mean value of net worth and its components (in thousands of 2001 dollars)
% Change
1983 1989 1995 2001 1983-2001
Assets 239.7 292.6 272.1 431.8 80.1
Houses1 79.2 96.9 86.5 122.6 54.8
Other real estate and business1 89.4 103.7 83.2 121.1 35.4
Liquid assets 26.7 32.8 28.4 38.2 42.9
Financial assets 37.6 42.2 48.0 98.5 162.2
Pension assets 6.7 17.2 25.9 51.3 660.4
Liabilities 31.9 35.3 41.3 53.0 66.0
Mortgage debt 18.3 24.3 30.2 39.8 117.1
Other debt 13.6 11.1 11.2 13.3 -2.7
Net worth 207.8 257.3 230.7 378.7 82.3
M
emo: Percent of households with:
net worth less than or equal to zero 15.5 17.9 18.5 17.6 --
N
et worth less than$1,000 [2001$] 20.4 23.0 22.1 21.4 --
M
emo: Median Net worth 54.5 57.8 53.3 74.0 35.9
Notes: 1. Houses refer to primary residences only. Other real estate consists of secondary
residences, land, and rental property. Businesses refer to net equity in unincorporated businesses
(both farm and non-farm).
38
Table 3. Long-term average rates of return (in percent)
Nominal Real Period
Real estate and business 6.97 2.391960-2000
Liquid assets 5.94 0.971965-2000
Financial assets 8.44 3.801960-2000
Pension assets 7.89 4.561986-2000
Mortgage debt 0.00 -4.281960-2000
Other debt 0.00 -4.281960-2000
I
nflation rate (CP
I
-U) 4.47
Notes: Real rate of return = (1+Nominal rate)/(1+Inflation rate)-1
Real estate and business: Holding gains (taken from the Flow of Funds table R.100) divided
by equity in noncorporate business (taken from the Flow of Funds table B.100).
Liquid assets: The weighted average of the rates of return on checking deposits and cash,
time and saving deposits, and life insurance reserves. The weights are the proportion of these assets
in their combined total (calculated from the Flow of Funds table B.100). The assumptions regarding
the rates of return are: zero for checking deposits, the rate of return on a 1-month CD (taken from
the table “H.15 Selected Interest Rates” published by the Federal Reserve and available at:
http://www.federalreserve.gov/releases/h15/data.htm) for time and saving deposits, and, one plus
the inflation rate for life insurance reserves.
Financial assets: The weighted average of the rates of return on open market paper,
Treasury securities, municipal securities, corporate and foreign bonds, corporate equities and
mutual fund shares. The weights are the proportion of these assets in total financial assets held by
the household sector (calculated from the Flow of Funds table B.100). The assumption regarding
the rate of return on open market paper is that it equals the rate of return on 1-month Finance paper
(taken from the table “H.15 Selected Interest Rates” published by the Federal Reserve and available
at: http://www.federalreserve.gov/releases/h15/data.htm). The data for the rates of return on other
assets are taken from the Economic Report of the President 2005, Table B.73. The assumptions
regarding Treasury securities, municipal securities, corporate and foreign bonds, and corporate
equities are, respectively, average of Treasury security yields, high-grade municipal bond yield,
average of corporate bond yields, and annual percent change in the S&P 500 index. Mutual fund
shares are assumed to earn a rate of return equal to the weighted average of the rates of return on
open market paper, Treasury securities, municipal securities, corporate and foreign bonds and
corporate equities. The weights are the proportions of these assets in the total financial assets of
mutual funds (calculated from the Flow of Funds table L.123).
Pension assets: Net acquisition of financial assets (taken from the Flow of Funds table
F.119c) divided by total financial assets of private defined-contribution plans (taken from the Flow
of Funds table L.119c).
Inflation rate: Calculated from the CPI-U published by Bureau of Labor Statistics (Series
Id: CUUR0000SA0).
39
Table 4. Household income by alternative definitions (in 2001 dollars)
1982 1988 1994 2000 % Change,
1982-2000
All Households Median Mean Median Mean Median Mean Median Mean Median Mean
1. Money income (MI) 35,717 48,079 36,228 56,278 34,655 54,412 39,081 65,087 9.4 35.4
2. SCF Income 36,016 49,195 37,426 59,582 34,763 55,847 39,081 69,827 8.5 41.9
3. Wealth-adjusted income (WI) 38,642 56,942 41,397 67,526 39,242 66,397 45,578 84,572 17.9 48.5
Memo items:
4. Income from home wealth 1,581 3,062 1,229 3,481 1,527 3,367 987 3,447 -37.6 12.6
5. Income from nonhome wealth 386 10,753 473 13,545 578 13,123 1,105 20,701 186.1 92.5
6. WIa 38,915 57,427 41,699 67,991 39,348 66,631 45,932 84,601 18.0 47.3
7. Age 45 47 45 48 46 49 47 49
Notes:
1. Money income is SCF income minus realized capital gains, net of losses
2. SCF income is the sum of its components
3. WI is MI minus property income plus income from wealth;
4. Imputed rental cost minus the annuitized value of mortgage debt
5. Annuitized value of nonhome wealth minus the annuitized value of other debt.
6. WI recomputed using the same average rate of return on wealth for all households
40
41
Table 5. Household income by alternative definitions and race/ethnic groups (in 2001 dollars)
1982 1982 Ratio to Whites 2000 2000 Ratio to Whites
Median Mean Median Mean Median Mean Median Mean
Non-Hispanic whites
1. Money income (MI) 38,540 51,658 1.00 1.00 43,586 72,806 1.00 1.00
2. SCF Income 38,764 53,011 1.00 1.00 44,738 78,871 1.00 1.00
3. Wealth-adjusted income (WI) 42,243 62,013 1.00 1.00 52,591 97,108 1.00 1.00
Memo items:
4. Income from home wealth 2,047 3,441 1.00 1.00 1,710 4,115 1.00 1.00
5. Income from nonhome wealth 761 12,764 1.00 1.00 2,209 25,811 1.00 1.00
African Americans
1. Money income (MI) 21,474 29,231 0.56 0.57 24,683 36,321 0.57 0.50
2. SCF Income 21,474 29,244 0.55 0.55 24,683 36,525 0.55 0.46
3. Wealth-adjusted income (WI) 22,324 31,093 0.53 0.50 25,714 39,356 0.49 0.41
Memo items:
4. Income from home wealth 0 1,164 0.00 0.34 0 740 0.00 0.18
5. Income from nonhome wealth 0 1,439 0.00 0.11 33 2,807 0.02 0.11
Hispanics
1. Money income (MI) 25,693 32,912 0.67 0.64 25,711 39,494 0.59 0.54
2. SCF Income 25,693 32,912 0.66 0.62 25,711 39,935 0.57 0.51
3. Wealth-adjusted income (WI) 25,719 34,523 0.61 0.56 26,365 41,709 0.50 0.43
Memo items:
4. Income from home wealth 0 1,440 0.00 0.42 0 1,120 0.00 0.27
5. Income from nonhome wealth 0 576 0.00 0.05 1 3,056 0.00 0.12
Asians and other races
1. Money income (MI) 38,356 51,619 1.00 1.00 34,967 61,544 0.80 0.85
2. SCF Income 38,356 51,702 0.99 0.98 35,111 63,534 0.78 0.81
3. Wealth-adjusted income (WI) 40,156 55,303 0.95 0.89 38,508 75,514 0.73 0.78
Memo items:
4. Income from home wealth 0 2,400 0.00 0.70 0 4,487 0.00 1.09
5. Income from nonhome wealth 19 3,688 0.03 0.29 463 15,005 0.21 0.58
42
Table 6. Household income by alternative definitions and age of household head (2001 dollars)
1982 1982 Ratio to Overall 2000 2000 Ratio to Overall
Median Mean Median Mean Median Mean Median Mean
Under 35
1. Money income (MI) 32,166 37,646 0.90 0.78 32,931 43,680 0.84 0.67
2. SCF Income 32,423 37,934 0.90 0.77 32,931 44,440 0.84 0.64
3. Wealth-adjusted income (WI) 33,173 39,072 0.86 0.69 33,608 45,729 0.75 0.54
Memo items:
4. Income from home wealth 0 1,009 0.00 0.33 0 846 0.00 0.25
5. Income from nonhome wealth 5 1,173 0.01 0.11 0 2,010 0.00 0.10
35 to 44
1. Money income (MI) 49,551 58,885 1.39 1.22 51,423 74,533 1.32 1.15
2. SCF Income 49,845 60,080 1.38 1.22 51,423 77,230 1.32 1.11
3. Wealth-adjusted income (WI) 51,617 63,246 1.34 1.11 55,055 82,043 1.22 0.97
Memo items:
4. Income from home wealth 2,063 3,049 1.30 1.00 741 2,684 0.75 0.78
5. Income from nonhome wealth 396 3,739 1.03 0.35 854 7,760 0.77 0.37
45 to 54
1. Money income (MI) 47,514 60,612 1.33 1.26 55,537 89,871 1.42 1.38
2. SCF Income 47,716 61,706 1.32 1.25 56,154 100,257 1.44 1.44
3. Wealth-adjusted income (WI) 52,146 71,562 1.35 1.26 61,576 107,966 1.37 1.28
Memo items:
4. Income from home wealth 3,147 4,455 1.99 1.45 1,517 3,970 1.54 1.15
5. Income from nonhome wealth 738 11,107 1.91 1.03 2,207 19,274 2.00 0.93
55 to 64
1. Money income (MI) 39,979 57,467 1.12 1.20 45,252 84,620 1.16 1.30
2. SCF Income 40,025 59,103 1.11 1.20 45,252 92,273 1.16 1.32
3. Wealth-adjusted income (WI) 44,908 70,610 1.16 1.24 53,211 118,918 1.18 1.41
Memo items:
4. Income from home wealth 3,256 4,511 2.06 1.47 2,834 5,234 2.87 1.52
5. Income from nonhome wealth 2,197 17,063 5.69 1.59 3,729 36,751 3.38 1.78
65 to 74
1. Money income (MI) 23,487 42,410 0.66 0.88 27,563 50,580 0.71 0.78
2. SCF Income 23,851 44,527 0.66 0.91 27,768 55,410 0.71 0.79
3. Wealth-adjusted income (WI) 28,923 60,980 0.75 1.07 38,959 92,959 0.87 1.10
Memo items:
4. Income from home wealth 3,023 4,662 1.91 1.52 3,413 5,436 3.46 1.58
5. Income from nonhome wealth 3,184 27,019 8.25 2.51 5,336 45,638 4.83 2.20
75 and over
1. Money income (MI) 13,764 26,298 0.39 0.55 18,615 32,550 0.48 0.50
2. SCF Income 14,073 27,996 0.39 0.57 18,615 35,379 0.48 0.51
3. Wealth-adjusted income (WI) 17,726 49,178 0.46 0.86 30,337 76,134 0.67 0.90
Memo items:
4. Income from home wealth 1,861 3,115 1.18 1.02 3,603 5,410 3.65 1.57
5. Income from nonhome wealth 2,125 29,096 5.50 2.71 5,396 46,009 4.88 2.22
44
Table 7. Household income by alternative definitions and parental and marital status (in 2001
dollars)
1982 1982 Ratio to
Overall 2000 2000 Ratio to
Overall
Median Mean Median Mean Median Mean Median Mean
Married couples with children
1. Money income (MI) 49,001 58,960 1.37 1.23 61,707 91,613 1.58 1.41
2. SCF Income 49,001 59,746 1.36 1.21 61,800 95,109 1.58 1.36
3. Wealth-adjusted income (WI) 51,977 64,594 1.35 1.13 66,957 105,220 1.49 1.24
Memo items:
4. Income from home wealth 2,098 3,278 1.33 1.07 1,204 3,458 1.22 1.00
5. Income from nonhome wealth 243 4,842 0.63 0.45 1,004 14,466 0.91 0.70
Single-female headed with children
1. Money income (MI) 19,270 23,302 0.54 0.48 20,569 24,767 0.53 0.38
2. SCF Income 19,380 23,627 0.54 0.48 20,569 25,315 0.53 0.36
3. Wealth-adjusted income (WI) 19,767 24,862 0.51 0.44 20,629 26,883 0.46 0.32
Memo items:
4. Income from home wealth 0 1,315 0.00 0.43 0 897 0.00 0.26
5. Income from nonhome wealth 0 872 0.00 0.08 0 1,760 0.00 0.09
Married couples without children
1. Money income (MI) 45,881 63,743 1.28 1.33 51,731 83,117 1.32 1.28
2. SCF Income 45,991 66,038 1.28 1.34 52,863 89,368 1.35 1.28
3. Wealth-adjusted income (WI) 52,547 82,956 1.36 1.46 67,020 120,417 1.49 1.42
Memo items:
4. Income from home wealth 3,072 4,523 1.94 1.48 2,803 5,273 2.84 1.53
5. Income from nonhome wealth 2,225 24,843 5.76 2.31 4,494 39,518 4.07 1.91
Single-female headed without children
1. Money income (MI) 18,352 23,934 0.51 0.50 20,055 27,300 0.51 0.42
2. SCF Income 18,433 24,258 0.51 0.49 20,055 28,300 0.51 0.41
3. Wealth-adjusted income (WI) 20,838 27,459 0.54 0.48 24,302 35,413 0.54 0.42
Memo items:
4. Income from home wealth 279 2,221 0.18 0.73 367 2,397 0.37 0.70
5. Income from nonhome wealth 259 5,382 0.67 0.50 464 8,608 0.42 0.42
Single-male headed without children
1. Money income (MI) 26,631 35,982 0.75 0.75 28,797 41,310 0.74 0.63
2. SCF Income 26,794 36,729 0.74 0.75 28,797 53,272 0.74 0.76
3. Wealth-adjusted income (WI) 28,417 41,546 0.74 0.73 31,619 55,717 0.70 0.66
Memo items:
4. Income from home wealth 0 1,455 0.00 0.48 0 1,964 0.00 0.57
5. Income from nonhome wealth 171 7,274 0.44 0.68 617 15,761 0.56 0.76
45
Table 8. Economic Inequality by Income Measure (Gini coefficients)
Change
Income Definition 1982 1988 1994 2000 1982-2000
Money income 0.456 0.533 0.545 0.549 0.093
SCF Income 0.464 0.553 0.552 0.574 0.111
Wealth-adjusted income 0.493 0.556 0.562 0.589 0.096
Memo items:
N
et worth 0.798 0.814 0.823 0.827 0.029
CPS Money Incomea 0.412 0.426 0.456 0.462 0.050
a. Source: http://www.census.gov/hhes/www/income/histinc/h04.html
Table 9. Decomposition of Inequality by Income Source and Definition, 1982 and 2000
1982 2000
Concentration
coefficient Income
share Share in
inequality Concentration
coefficient Income
share Share in
inequality
A. Money income (MI)
Primary income 0.430 0.897 0.847 0.533 0.928 0.901
Income from wealth 0.679 0.103 0.153 0.755 0.072 0.099
B. SCF income
Primary income 0.428 0.877 0.810 0.530 0.865 0.798
Income from wealth 0.716 0.123 0.190 0.860 0.135 0.202
C. Wealth-adjusted income (WI)
Primary income 0.418 0.757 0.642 0.513 0.714 0.622
Income from wealth 0.727 0.243 0.358 0.779 0.286 0.378
Imputed rent 0.447 0.054 0.049 0.506 0.041 0.035
Annuities 0.806 0.189 0.309 0.825 0.245 0.343
46
Table 10. Changing Ranks and Income Gaps, 1982 and 2000
1982 2000
A. Gini coefficients
Money income (MI) 0.456 0.549
Wealth-adjusted income (WI) 0.493 0.589
SCF Income (SI) 0.464 0.574
B. Difference between the
coefficients
a. G(WI) - G(MI) 0.037 0.040
Reranking 0.017 0.033
(Percent of total difference) 45% 83%
Changing gaps 0.020 0.007
(Percent of total difference) 55% 17%
b. G(SI) - G(MI) 0.008 0.025
Reranking 0.002 0.006
(Percent of total difference) 30% 22%
Changing gaps 0.006 0.020
(Percent of total difference) 70% 78%
Memo items:
Concentration coefficient for WI
with respect to MI 0.476 0.556
Concentration coefficient for SI
with respect to MI 0.461 0.569
Table 11. The joint distribution of households among quintiles of wealth-adjusted income (WI)
and money income (MI), 1982 and 2000
1982
WI quintile
MI quintile 1 2 3 4 5 All
1 17.9 1.7 0.2 0.1 0.1 20.0
2 2.0 15.3 2.0 0.4 0.2 20.0
3 0.0 2.9 14.8 1.8 0.4 20.0
4 0.0 0.0 3.0 15.7 1.4 20.0
5 0.0 0.0 0.0 2.0 18.0 20.0
All 20.0 20.0 20.0 20.0 20.0
2000
WI quintile
MI quintile 1 2 3 4 5 All
1 17.5 2.1 0.3 0.1 0.0 20.0
2 2.5 13.8 2.2 0.7 0.3 20.0
3 0.0 4.1 13.4 2.3 0.7 20.0
4 0.0 0.0 4.1 13.6 2.2 20.0
5 0.0 0.0 0.0 3.2 16.8 20.0
All 20.0 20.0 20.0 20.0 20.0
47
Table 12. Income shares of households in aggregate income, by selected percentiles and
income measure (in percent)
1982 1988
Money income SCF Income Wealth-adjusted
income Money income SCF Income Wealth-adjusted
income
P0-25 5.7 5.6 5.3 4.5 4.2 3.9
P25-50 14.0 13.8 13.0 11.1 11.4 11.0
P50-90 46.9 46.6 43.6 43.9 41.4 41.2
P90-100 33.4 34.1 38.1 40.5 42.9 43.9
P90-95 10.7 10.3 10.3 11.1 11.0 10.9
P95-99 12.9 13.1 13.7 15.2 15.1 16.2
P99-100 9.9 10.7 14.1 14.1 16.8 16.7
1994 2000
Money income SCF Income Wealth-adjusted
income Money income SCF Income
Wealth-adjusted
income
P0-25 3.6 3.8 3.8 4.2 3.9 3.7
P25-50 11.8 11.2 11.1 11.4 10.4 9.9
P50-90 42.7 42.3 40.4 41.4 39.4 38.3
P90-100 41.8 42.7 44.7 42.9 46.3 48.1
P90-95 10.7 10.5 10.6 10.2 10.1 10.5
P95-99 15.0 15.5 16.0 15.3 15.3 17.5
P99-100 16.1 16.7 18.1 17.4 20.9 20.1
48
Table 13. Distribution of imputed income from wealth by money income decile, 1982 and 2000 (all dollar amounts are in
2001 dollars)
1982
Lowest Second Third Fourth Fifth Sixth Seventh Eighth Ninth Top All
Income from Wealth 2,423 2,844 4,627 5,240 5,064 7,184 6,877 10,305 13,284 80,073 13,815
38.3 23.0 25.0 20.9 15.9 18.1 14.2 17.1 17.2 49.9 28.
7
Income from home wealth 990 1,299 1,706 2,049 2,089 2,412 2,735 3,458 4,790 9,058 3,062
15.
7
10.5 9.2 8.2 6.
6
6.1 5.
6
5.8 6.2 5.
6
6.4
Income from nonhome wealth 1,433 1,545 2,921 3,191 2,975 4,772 4,142 6,847 8,494 71,014 10,753
22.
7
12.5 15.8 12.
7
9.4 12.1 8.5 11.4 11.0 44.3 22.4
Memo item:
Mean money income 6,321 12,362 18,512 25,079 31,821 39,589 48,510 60,095 77,305 160,462 48,079
Property Incomea 447 471 1,478 1,802 1,998 3,146 3,200 4,178 6,550 37,306 6,069
7.1 3.8 8.0 7.2 6.3 7.9 6.
6
7.0 8.5 23.2 12.
6
2000
Lowest Second Third Fourth Fifth Sixth Seventh Eighth Ninth Top All
Income from Wealth 2,932 4,511 7,456 8,304 11,342 13,882 15,349 18,401 24,983 133,617 24,149
52.2 35.3 38.2 31.2 32.9 31.4 27.3 25.5 25.8 47.
6
37.1
Income from home wealth 1,065 1,542 2,434 2,103 2,368 2,458 2,838 3,698 4,122 11,769 3,447
19.0 12.1 12.5 7.9 6.9 5.
6
5.1 5.1 4.3 4.2 5.3
Income from nonhome wealth 1,867 2,969 5,022 6,201 8,974 11,425 12,511 14,703 20,861 121,848 20,701
33.3 23.2 25.
7
23.3 26.1 25.9 22.3 20.4 21.
6
43.4 31.8
Memo item:
Mean money income 5,614 12,780 19,510 26,603 34,423 44,185 56,137 72,051 96,737 280,660 65,087
Property Incomea 677 300 1,039 1,516 1,889 3,318 2,606 3,885 9,495 69,001 9,403
12.1 2.3 5.3 5.
7
5.5 7.5 4.
6
5.4 9.8 24.
6
14.4
a. Property income is the sum of dividends, interest and rent.
Shaded numbers in italics show the item as a percentage of mean money income.
49
Table 14. Composition of income by income definition and selected percentiles, 1982 and 2000 (Percentage shares)
Money income (MI) Wealth-adjusted income (WI)
All P40-P60 P90-95 P95-99 P99-100 All P40-P60 P90-95 P95-99 P99-100
A. 1982
Wages and Salaries 63.6 72.0 62.8 55.6 30.1 53.7 69.0 59.8 35.1 15.6
Self-employment Income 13.3 6.1 18.6 22.8 35.9 11.2 4.5 11.7 24.3 20.2
Income from Wealth 10.3 6.2 12.7 15.0 29.5 24.3 12.2 21.6 36.6 61.1
Other Income 12.8 15.7 5.8 6.5 4.5 10.8 14.3 6.8 4.0 3.1
Total Income 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
B. 2000
Wages and Salaries 74.1 78.7 82.8 66.3 59.2 57.1 74.4 61.6 41.0 35.2
Self-employment Income 9.7 1.5 6.1 17.8 26.7 7.4 1.5 6.0 11.3 18.0
Income from Wealth 7.2 3.9 6.9 13.6 13.0 28.6 12.3 28.6 44.6 45.7
Other Income 9.0 15.9 4.2 2.3 1.1 7.0 11.7 3.8 3.1 1.0
Total Income 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Table 15. Share of Income from Wealth in Total Income, 1982 and 2000 (in percent)
P90-100 P90-95 P95-99 P99-100
1982 2000 1982 2000 1982 2000 1982 2000
PS 168841372912
MI 191213 715143013
WI 42 42 22 29 36 45 61 46
50
Table 16.Wealth-adjusted household income with alternative definitions of income from wealth (in 2001 dollars)
1982 1988 1994 2000 % Change, 1982-01
All Households Median Mean Median Mean Median Mean Median Mean Median Mean
1. Wealth-adjusted income (WI) 38,642 56,942 41,397 67,526 39,242 66,397 45,578 84,572 17.9 48.5
2. WI* = WI - A - B + C + D 38,767 53,626 39,984 61,817 37,325 58,734 43,170 73,974 11.4 37.9
Memo items:
A. Imputed rent on owner-occupied housing 1,581 3,062 1,229 3,481 1,527 3,367 987 3,447 -37.6 12.6
B. Annuity income from nonhome wealth 386 10,753 473 13,545 578 13,123 1,105 20,701 186.1 92.5
C. Return on home equity 3,128 5,952 2,168 5,508 1,422 3,447 1,655 4,422 -47.1 -25.7
D. Bond coupon income from nonhome wealth 288 4,548 359 5,808 359 5,380 685 9,129 138.1 100.7
Table 17. Economic Inequality with alternative definitions of income from wealth (Gini coefficients)
Change
Income Definition 1982 1988 1994 2000 1982-2000
1. Wealth-adjusted income (WI) 0.493 0.556 0.562 0.589 0.096
2. WI* = WI - A - B + C + D 0.457 0.526 0.533 0.555 0.098
Table 18. Household income by alternative definitions of income from wealth and race (in 2001 dollars)
1982 1982 Ratio to
Whites 2000 2000 Ratio to
Whites
Median Mean Median Mean Median Mean Median Mean
Non-Hispanic whites
1. Wealth-adjusted income (WI) 42,243 62,013 1.00 1.00 51,681 91,043 1.00 1.00
2. WI* = WI - A - B + C + D 42,406 57,848 1.00 1.00 49,402 83,697 1.00 1.00
African Americans
1. Wealth-adjusted income (WI) 22,324 31,093 0.53 0.50 25,624 39,151 0.50 0.43
2. WI* = WI - A - B + C + D 23,731 31,532 0.56 0.55 25,668 38,371 0.52 0.46
51
Table 19. Household income by alternative definitions of income from wealth and age of household head (in 2001
dollars)
1982 1982 Ratio to Overall 2000 2000 Ratio to Overall
Median Mean Median Mean Median Mean Median Mean
Under 35
1. Wealth-adjusted income (WI) 33,173 39,072 0.86 0.69 33,608 45,729 0.74 0.54
2. WI* = WI - A - B + C + D 33,675 39,799 0.87 0.74 33,708 45,588 0.78 0.62
35 to 44
1. Wealth-adjusted income (WI) 51,617 63,246 1.34 1.11 55,055 82,043 1.21 0.97
2. WI* = . WI - A - B + C + D 54,003 65,151 1.39 1.21 54,874 80,497 1.27 1.09
45 to 54
1. Wealth-adjusted income (WI) 52,146 71,562 1.35 1.26 61,576 107,966 1.35 1.28
2. WI* = . WI - A - B + C + D 53,740 71,343 1.39 1.33 59,354 101,459 1.37 1.37
55 to 64
1. Wealth-adjusted income (WI) 44,908 70,610 1.16 1.24 53,211 118,918 1.17 1.41
4. WI* = . WI - A - B + C + D 46,208 65,765 1.19 1.23 50,934 101,546 1.18 1.37
65 to 74
1. Wealth-adjusted income (WI) 28,923 60,980 0.75 1.07 38,959 92,959 0.85 1.10
2. WI* = . WI - A - B + C + D 29,057 47,451 0.75 0.88 34,581 65,142 0.80 0.88
75 and over
1. Wealth-adjusted income (WI) 17,726 49,178 0.46 0.86 30,337 76,134 0.67 0.90
2. WI* = . WI - A - B + C + D 17,672 28,324 0.46 0.53 25,576 41,516 0.59 0.56
Notes to Tables 16 to 19:
1. Money income minus property income (sum of dividends, interest and rent) plus income from home and nonhome
wealth
A. Imputed rent on owner-occupied housing less the annutized value of mortgage debt
B. Annutized value of nonhome wealth less the annutized value of other debt
C. Return on home equity
D. Bond coupon income from nonhome wealth (3% real rate of return)
52
Figure 1. Ratio of Mean Income to the Mean Income of Non-Hispanic Whites by
Race/Ethnicity and Income Definition, 2000
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
African Americans Hispanics Asians and other races
Race/Ethnicity
Ratio
Money Income
SCF Income
Wealth-adjusted Income
Figure 2. The Ratio of Mean Income to the Overall Mean by Age and Income Definition, 2000
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Under 35 35 to 44 45 to 54 55 to 64 65 to 74 75 and over
Age of Householder
Ratio
Money Income
SCF Income
Wealth-adjusted Income
53
Figure 3. The Ratio of Mean Income to the Overall Mean by Parental and Marital Status and
Income Definition, 2000
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Married couples
with children Single-female
headed with
children
Married couples
without children Single-female
headed without
children
Single-male
headed without
children
Parental and Marital Status
Ratio
Money Income
SCF Income
Wealth-adjusted Income
Figure 4. Contribution to the Total Change in the Gini Coefficient (in percentage points), 1982-
2000
9.3
10.9
-1.6
11.1
8.3
2.8
9.6
5.0 4.7
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Total change Primary
income Income f rom
wealth Total change Primary
income Income f rom
wealth Total change Primary
income Income f rom
wealth
Money income SCF income Wealth-adjusted income
Percent
54
Figure 5. Percent change in money income, SCF income and Wealth-adjusted income, 1982-2000
-10.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
5 101520253035404550556065707580859095
Percentile
Percentage Change, 1983-2001
Money Incom e
SCF Income
Wealth-Adjusted Income
55
Figure 6. Top income shares, 1982-2000
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
PS MI WI PS MI WI PS MI WI PS MI WI
P90-100 P90-95 P95-99 P99-100
1982
1988
1994
2000
57
