Order Code RL32639
CRS Report for Congress
Received through the CRS Web
Inequality in the Distribution of Income:
Trends and International Comparisons
Updated October 7, 2005
Brian W. Cashell
Specialist in Quantitative Economics
Government and Finance Division
Congressional Research Service ˜ The Library of Congress

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Inequality in the Distribution of Income:
Trends and International Comparisons
Summary
Economic theory alone does not establish any basis for preferring a more or less
equal distribution of income. Nonetheless, a common aim of policy is promoting
equality of opportunity. An extremely unequal distribution of income may be
considered an indication of a lack of equal opportunity. Arguments for a more equal
distribution of income than that which would result from market forces are based on
a number of propositions. One is a common assumption made in economic analysis
known as diminishing marginal utility of income. This is the notion that each
additional dollar of income yields less utility, or satisfaction. If the assumption of
diminishing marginal utility of income is accepted, then, in theory, it should be
possible to increase the overall well-being (utility) of society by taking some from
those with high incomes and giving it to those with low incomes. A second,
noneconomic, justification for policies designed to make the income distribution
more equal is concern that society prevent its members from falling below some
minimum standard of living.
Existing measures of income fall well short of an ideal that would accurately
indicate how well off individuals or households are. Not all kinds of income are
counted. Taking the existing measures at face value, however, several observations
can be made. First, the distribution of income in the United States has become
increasingly unequal since the late 1960s. Second, the U.S. income distribution is
the most unequal of all major industrialized countries. Some of the greater income
equality found in other major industrialized countries may be due to the fact that
government transfers are more directly targeted at lower income households.
The distribution of earnings is more unequal than is the distribution of
household income. Of particular interest is that the gap in earnings between highly
educated or skilled workers and less skilled workers has grown substantially.
Explanations focusing on world trade and national demographics have been
suggested, but the one most widely accepted is that technological advances in recent
years have increased the demand for more highly skilled labor relative to its supply.
Policies that boost the supply of skilled workers would thus seem likely to narrow
that gap and act as an equalizing influence on the income distribution. But, the large
gap in pay between skilled and unskilled workers that has developed would itself
seem to be a substantial incentive for prospective and current workers to expand their
education and training.
This report will be updated as developments warrant.

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Contents
Evaluating Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Measuring Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Measuring Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Income Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
International Comparisons of Income Distributions . . . . . . . . . . . . . . . . . . 10
Explaining International Differences . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Explaining Recent Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Conclusions and Policy Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
List of Figures
Figure 1. Distribution of Household Income – 1967 and 2004 . . . . . . . . . . . . . . . 6
Figure 2. Household Income Gini Index, 1967 – 2004 . . . . . . . . . . . . . . . . . . . . . 7
List of Tables
Table 1. Distribution of Household Income by Quintile . . . . . . . . . . . . . . . . . . . . 5
Table 2. Estimates of Family Household Income Mobility . . . . . . . . . . . . . . . . . 9
Table 3. Summary Measures of Income Distributions for Selected Countries . . 11

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Inequality in the Distribution of Income:
Trends and International Comparisons
The U.S. economy has been growing since November 2001. Further,
productivity growth has been relatively rapid, but there has been concern that wages
have not kept pace, and that business owners are profiting at the expense of workers.1
The popular press seems to find no shortage of anecdotal evidence regarding workers
who are losing ground in spite of a growing economy. At times when the benefits
of economic growth do not seem to be shared by all, there tends, not unexpectedly,
to be an increased focus on how much disparity in living standards there is across the
population.
There are a number of legislative issues for which the shape of the income
distribution may be an important consideration. Among them are tax rates and the
minimum wage. This report examines the distribution of income in the United
States, including factors that may help explain it, how it has changed over time, and
how it compares with those of other countries.
Evaluating Distributions
Economic theory does not establish a basis for preferring any particular degree
of equality in the distribution of income. In theory, at least with respect to labor
income, what matters is that the distribution result from efficient markets where final
demand for goods and services and the relative productivity of the firms producing
those goods and services determine the demand for labor in each sector of the
economy, and the earnings of each of those jobs.
The shape of the income distribution is also a function of labor supply. The
willingness of workers to take jobs depends on the pay as well as relative preferences
for labor and leisure. The ability of workers to command a given wage is also a
direct function of their educational attainment and skill level (their “human capital”).
Changes in the age distribution can also affect the income distribution as workers
tend to earn more as they get older.
But, even in an economically “ideal” world where the income distribution was
solely attributable to the workings of efficient markets there may still be moral,
ethical, or philosophical reasons for preferring an alternative outcome.
1 For more on this topic, see CRS Report RL32563, Productivity and Wages, by Brian W.
Cashell.

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Arguments for a more equal distribution of income than that which results from
market forces are based on a number of propositions.2 One is founded on a common
assumption made in economic analysis known as diminishing marginal utility of
income. This refers to the idea that each additional dollar of income yields less and
less satisfaction (in economic jargon, utility) than the first. Put another way, this
proposition presumes that one additional dollar of income means less to someone
making $100,000 than it does to someone making $20,000.
If the assumption of diminishing marginal utility of income is accepted, then in
theory it should be possible to increase the overall well-being (utility) of society by
taking money from those with high incomes and giving it to those with low incomes.
The difficulty with that proposition is that doing so may have economic costs that
offset part, if not all, of any gain in overall utility.
A second justification for policies designed to make the income distribution
more equal is concern that society prevent its members from falling below some
minimum standard of living. This may be due to pure altruism, or the sense that luck
has something to do with one’s place in the income distribution, or the belief that
when more people have a stake in society, the more tranquil it will be. Thus, raising
the minimum standard of living serves as a kind of insurance.
Finally, a common aim of policy is promoting equality of opportunity. An
extremely unequal distribution of income may be considered an indication of lack of
equal opportunity.
Beyond these considerations, economics has little to say about the desirability
of any particular income distribution, but economists have developed ways to
measure changes in the distribution, and have searched for causes of variations in the
distribution over time.
Measuring Income
As is the case with any number of economic statistics, income data have
limitations. The Census Bureau, in an annual survey, collects data from a sample
based on the concept of money income. Money income accounts for a wide range
of income sources, but it is unavoidably incomplete. Money income includes income
from earnings, interest and dividends, Social Security, and other forms of social
insurance. It does not include the value of non-money benefits such as food stamps
or housing subsidies. Neither does it include capital gains.3
If the primary interest is the distribution of overall economic well-being, then
using a limited measure such as money income may be misleading. For example,
consider the case where two families are in every way equal in terms of wealth and
2 See N. Gregory Mankiw, Principles of Economics (Fort Worth Texas: The Dryden Press,
1998), pp. 431-434.
3 For a complete explanation of what is included in money income, see U.S. Department of
Commerce, U.S. Census Bureau, Current Population Reports: Consumer Income, P60-229,
Aug. 2005, Appendix A.

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CRS-3
income, neither owns their home, but they both have substantial savings in interest
earning assets. Suppose one family takes funds that are earning interest and uses
them to buy their home. No one would argue that that family is now worse off, but
the existing measures of money income would indicate that to be the case. In fact,
the family that buys its home is earning an implicit income in the use of the house
just as they would earn rental income if they rented it to the other family in the
example. Not counting this implicit income in existing measures may have a
significant effect on the shape of the income distribution. If homeownership rates
change over time, or the share of assets invested in owner-occupied housing changes,
and those changes affect one part of the distribution more than another, then existing
data concerning changes in the shape of the distribution of income would be
misleading.
Existing measures of income also ignore the value of leisure. Thus, in the case
of two individuals whose measured incomes differ only because one of them works
longer hours, the difference in their incomes may overstate the difference in their
economic well-being because the one who is working longer hours is sacrificing
leisure time.
Another weakness in existing measures of income is that they do not account
for the implicit income yielded by homemakers or other work done at home.
Consider two different married-couple households with the same income and both
husband and wife are working. If in one of the households, the husband quits his job
to stay at home and raise children, that household will experience a drop in money
income. But the work done at home is not without value, and the measured
difference in the incomes of the two households will overstate the difference in living
standards between the two households.
The time period in which income is measured may also affect comparisons in
the economic well-being of different households. Over the course of the business
cycle, unemployment rises and falls and so do incomes. Some households may tend
to be more affected than others by these cycles, and so the stage of the business cycle
can have a significant effect on relative incomes.
Similarly, individuals’ incomes generally vary substantially over the course of
their lifetimes. New entrants to the labor force typically have lower incomes than
those who have been working for some time. After retirement, income tends to drop
off. Because of changes in income associated with this life cycle, the demographic
mix of the population can have a major influence on measures of income disparity.
If concern about living standards is what drives interest in the distribution of
income, then changes in wealth (e.g., the value of real estate, or other components of
net worth) might also be taken into account. That too would be difficult because it
would involve not just measuring actual changes, but distinguishing transitory from
permanent changes in wealth over short periods of time.
Another difficulty in comparing incomes is deciding what is the relevant
population. In the case of labor income, the distribution of income among
individuals or among workers may be of most interest. But when it comes to overall
living standards, it may be more appropriate to consider the distribution of income

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CRS-4
among households. Most households can be presumed to pool resources and enjoy
some economies of scale. In other words, because some costs of living are fixed, a
family of four may not need twice as much income as a married couple for each
family member to enjoy roughly the same living standard. That adds another
complication in comparing incomes for households of different sizes.
Measuring Inequality
For the sake of simplicity and clarity, one way the Census Bureau publishes
income distribution data is by “quintile.” The population is ordered from lowest to
highest income and then divided into five groups of equal size. The income within
each group is summed and then compared to the total income of the population. If
income were all equally divided and every household had the same income, then each
quintile would account for 20% of total income. To the extent that each quintile falls
short of, or exceeds, a 20% share, it is an indication of the degree of inequality in the
distribution.
Table 1 presents data on the share of total household money income accounted
for by each quintile, as well as for the top 5%, since 1967. The figures indicate that
the bottom fifth accounts for less than one-fifth of the income it would get if the
distribution were perfectly equal, while the top 20% accounts for more than twice as
much. The top 5% accounts for more than four times the 5% share it would get if the
distribution were perfectly equal.
There are also several trends evident at the two ends of the distribution.
Between 1967 and the late 1970s, the bottom fifth experienced a slight increase in
its income share, while the share accruing to the top 5% of households in the
distribution fell. Since 1980, however, the share accruing to the bottom fifth has
fallen, and the shares accounted for by both the top 20% and the top 5% have risen.
Between 1967 and 2004, the share of income accounted for by the three middle
quintiles fell from 52.1% to 46.6%.4
4 There is no official definition of the “middle class.”

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Table 1. Distribution of Household Income by Quintile
Percentage Share of Total Household Income
Bottom
Second
Third
Fourth
Fifth
Top 5%
1967
4.0
10.8
17.3
24.2
43.8
17.5
1977
4.4
10.3
17.0
24.8
43.6
16.1
1978
4.3
10.3
16.9
24.8
43.7
16.2
1979
4.2
10.3
16.9
24.7
44.0
16.4
1980
4.3
10.3
16.9
24.9
43.7
15.8
1981
4.2
10.2
16.8
25.0
43.8
15.6
1982
4.1
10.1
16.6
24.7
44.5
16.2
1983
4.1
10.0
16.5
24.7
44.7
16.4
1984
4.1
9.9
16.4
24.7
44.9
16.5
1985
4.0
9.7
16.3
24.6
45.3
17.0
1986
3.9
9.7
16.2
24.5
45.7
17.5
1987
3.8
9.6
16.1
24.3
46.2
18.2
1988
3.8
9.6
16.0
24.3
46.3
18.3
1989
3.8
9.5
15.8
24.0
46.8
18.9
1990
3.9
9.6
15.9
24.0
46.6
18.6
1991
3.8
9.6
15.9
24.2
46.5
18.1
1992
3.8
9.4
15.8
24.2
46.9
18.6
1993
3.6
9.0
15.1
23.5
48.9
21.0
1994
3.6
8.9
15.0
23.4
49.1
21.2
1995
3.7
9.1
15.2
23.3
48.7
21.0
1996
3.7
9.0
15.1
23.3
49.0
21.4
1997
3.6
8.9
15.0
23.2
49.4
21.7
1998
3.6
9.0
15.0
23.2
49.2
21.4
1999
3.6
8.9
14.9
23.2
49.4
21.5
2000
3.6
8.9
14.8
23.0
49.8
22.1
2001
3.5
8.7
14.6
23.0
50.1
22.4
2002
3.5
8.8
14.8
23.3
49.7
21.7
2003
3.4
8.7
14.8
23.4
49.8
21.4
2004
3.4
8.7
14.7
23.2
50.1
21.8
Source: Department of Commerce, Bureau of the Census.

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CRS-6
A second indicator of the relative degree of inequality in the distribution of
income is the Gini index, or the index of income concentration. The Gini index is
a single number which can range between zero and one. It is an index, and therefore
is not expressed in terms of any particular unit of measurement, but it does allow
comparisons between distributions. They can be distributions from two different
populations, or of the same population at different points in time. Thus, it can show
if there is any tendency for income to become more or less equally distributed.
To illustrate how the Gini index is calculated, Figure 1 shows the distribution
of household income for both 1967 and 2004. The horizontal axis represents the
cumulative share of households, beginning at the low end of the distribution and
working up to the household with the highest income. The vertical axis represents
the corresponding cumulative share of income accounted for by those households.
Figure 1. Distribution of Household Income – 1967 and 2004
100
80
Line of perfect equality
60
40
1967
20
2004 distribution
0
0
20
40
60
80
100
Percent of Households
Source: Department of Commerce, Bureau of the Census.
The curved lines in Figure 1 represent actual distributions of household income.
(These are also known as ‘Lorenz’ curves.) The straight diagonal line depicts the
distribution if it were perfectly equal, in other words if each household had the same
income (e.g., with 100 households each household would account for 1% of total
household income). The more equal the actual distribution is, the closer the line is
to the hypothetical diagonal.

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CRS-7
The Gini index is a ratio of the area between the diagonal and the line
representing the actual distribution, and the total area under the diagonal. The closer
the actual distribution is to the hypothetical equal distribution, the smaller the area
will be between the two lines. If the actual distribution and the diagonal coincide,
then the Gini is zero, indicating a perfectly equal distribution of income. In that case,
each household would have identical incomes.
At the other extreme, consider a distribution where all of the income accrued to
a single household. In that case, the actual curve would lie on the edge of the graph
and the Gini index would be one. An increasing Gini index number indicates an
increasingly unequal distribution. When comparing any two distributions, the one
described by a higher Gini index number is the more unequal. Between 1967 and
2004, the actual distribution moved further away from the hypothetical equal
distribution and the Gini index rose indicating that the distribution of household
income has become more unequal. Figure 2 plots the Gini index for the distribution
of household income in the U.S. since 1967.
Figure 2. Household Income Gini Index, 1967 – 2004
0.48
0.46
0.44
0.42
0.4
0.38
1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
Source: Department of Commerce, Bureau of the Census.
As the chart shows, since the late 1960s the trend has been one of almost
steadily increasing household income inequality. The distribution is now more
unequal than it has been at any time in the nearly 40 years the data have been
collected.

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CRS-8
Income Mobility
The measures discussed above present a snapshot of the income distribution at
different points in time and show that the distribution has become more unequal. But
that description is incomplete. Those households at the bottom of the distribution in
one year may not be the same as those in another. Some households, to be sure,
remain in the bottom, middle, or top of the distribution for long periods of time.
Others, for a variety of reasons, may move up or down the distribution. In some
cases it may be due to a run of good or bad luck. Some households may be more
vulnerable to the ups and downs of the business cycle. Over longer periods of time
the tendency of income to rise over the course of individual life cycles may explain
a household’s change in income relative to the rest of the population.
The likelihood that individual households may rise out of the bottom of the
distribution, or that they may fall from the top, may temper perceptions of the degree
of inequality at any given point in time. There are those who argue that the amount
of satisfaction yielded by a given level of income is dependent on how it compares
with the rest of the population. If that is true, it may also be true that someone at the
bottom of a given distribution who does not expect to remain there will be more
satisfied with their situation than someone with the same income who does.
Table 2 presents estimates indicating how much individual family households
move either up or down in the overall distribution.5 For three separate time periods,
individual families in a sample are tracked in order to tell if they moved up or down
in the distribution between the beginning and end of the interval. Each row in the
table shows where families that began the interval in that quintile ended up at the end
of the interval. For example, the first row shows how many families that began 1969
in the bottom quintile of the distribution stayed in the bottom quintile or moved up
in the distribution by 1979.
5 These figures are from Katharine Bradbury and Jane Katz, “Women’s Labor Market
Involvement and Family Income Mobility When Marriages End,” New England Economic
Review, Fourth Quarter 2002, pp. 41-65.

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Table 2. Estimates of Family Household Income Mobility
Quintile in 1979
Quintile in 1969
bottom
second
third
fourth
fifth
bottom
49.2
24.5
13.8
9.1
3.3
second
23.2
27.8
25.2
16.2
7.7
third
10.2
23.4
24.8
23.0
18.7
fourth
9.9
15.0
24.1
27.4
23.7
fifth
5.0
9.0
13.2
23.7
49.1
Quintile in 1989
Quintile in 1979
bottom
second
third
fourth
fifth
bottom
50.4
24.1
15.0
7.4
3.2
second
21.3
31.5
23.8
15.8
7.6
third
12.1
23.3
25.0
24.6
15.0
fourth
6.8
16.1
24.3
27.6
25.3
fifth
4.2
5.4
13.4
26.1
50.9
Quintile in 1998
Quintile in 1989
bottom
second
third
fourth
fifth
bottom
53.3 23.6
12.4
6.4
4.3
second
25.7
36.3
22.6
11.0
4.3
third
10.9 20.7
28.3
27.5
12.6
fourth
6.5
12.9
23.7
31.1
25.8
fifth
3.0
5.7
14.9
23.2
53.2
Source: Bradbury and Katz (see text).
It is clear from the data in Table 2 that there is a significant amount of mobility
in the distribution, keeping in mind that these are 10-year intervals. In each case,
about half of families beginning in either the top or bottom quintile were still in the
same quintile at the end of the interval. For the other quintiles, the proportion of
families not moving up or down in the distribution ranged from about one-quarter to
one-third of the families in each quintile. In the case of families starting out in either
the second or third quintile, the percentage of families that moved up in the
distribution exceeded the percentage of families that fell in the distribution for all of
the intervals shown.

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CRS-10
International Comparisons of Income Distributions
In addition to tracking changes in inequality over time, international
comparisons of the distributions of income may also put current measures in
perspective. But, just as available measures of income in the United States differ
from the ideal, so do measures of income in different countries differ from each
other. Fortunately, there is a source for data that allows international comparisons
of income inequality.
The Luxembourg Income Study (LIS) project has assembled survey data from
a large number of different countries’ statistical programs on social and economic
indicators.6 Among those statistics available are data on household income.
Although there are differences in individual definitions of income across countries,
these data can be adjusted so that they reflect a more consistent measure of income.
In making these adjustments to get to a common measure of income, however,
the definition of income had to be limited. In limiting what is counted as income, the
actual measures that allow cross-country comparisons are in some cases more
removed from the ideal measure than they might otherwise be.
The LIS uses a measure of after-tax household money income as its standard.7
This necessarily excludes the imputed values for owner-occupied housing and unpaid
homework. It does include some “near cash” subsidies such as food stamps, housing
subsidies, and certain scholarships. Income is also measured net of income and
payroll taxes but not of sales, value added, and other indirect taxes. That may make
the distributions seem more equal in those countries that rely heavily on income
taxes, which tend to be progressive.
The standard of living afforded to a household by a given amount of income is
also affected by the size of the household. An adjustment is made to these household
income data to reflect the fact that the income must be divided among the members
of the household. The LIS study assumes that there are some economies of scale and
that each additional member of a household requires a slightly smaller amount of
income to maintain the overall standard of living of the household as a whole.
The LIS reports data allowing direct comparisons of income distributions of 29
different industrialized countries. Table 3 presents summary data on the income
distribution from those countries. The countries are listed in order from the one with
the lowest Gini index number (most equal distribution) to the one with the highest
(most unequal distribution).
6 Information about the Luxembourg Income Study can be found at their website. See
[http://www.lisproject.org].
7 Peter Gottschalk and Timothy M. Smeeding, Empirical Evidence on Income Inequality
in Industrialized Countries, Luxembourg Income Study Working Paper No. 154, revised
Feb. 1999.

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Table 3. Summary Measures of Income Distributions
for Selected Countries
Country
Year
Gini Index
P /P
P /P
90
10
90
50
Slovak Republic
1996
0.241
2.88
1.62
Finland
2000
0.247
2.90
1.64
Netherlands
1999
0.248
2.98
1.67
Slovenia
1999
0.249
3.15
1.67
Norway
2000
0.251
2.80
1.59
Sweden
2000
0.252
2.96
1.68
Denmark
1997
0.257
3.15
1.62
Czech Republic
1996
0.259
3.01
1.79
Luxembourg
2000
0.260
3.24
2.15
Austria
2000
0.260
3.17
1.73
Germany
2000
0.264
3.39
1.82
Belgium x
2000
0.277
3.31
1.74
Romania
1997
0.277
3.38
1.80
Switzerland 2000
0.280
3.34
1.82
France
1994
0.288
3.54
1.91
Poland
1999
0.293
3.59
1.88
Hungary
1999
0.295
3.57
1.94
Taiwan
2000
0.296
3.81
1.96
Canada
2000
0.302
3.95
1.88
Australia
1994
0.311
4.33
1.95
Ireland
2000
0.323
4.56
1.89
Italy
2000
0.333
4.48
1.99
Spain
2000
0.340
4.78
2.09
United Kingdom
1999
0.345
4.58
2.15
Israel
2001
0.346
5.01
2.16
Estonia
2000
0.361
5.08
2.34
United States
2000
0.368
5.45
2.10
Russia
2000
0.434
8.37
2.76
Mexico
2002
0.471
9.36
3.09
Source: Luxembourg Income Study.

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The first column of data shows the estimated Gini coefficient for each country.
The next column (P /P ) shows the ratio of the incomes of those at the 90th
90
10
percentile of the distribution to the incomes of those at the 10th percentile. For
example, for the United States in 2000, those near the top of the distribution had
more than five times the income as those near the bottom. The last column (P /P )
90
50
indicates the ratios of income at the 90th percentile of each distribution to the median
incomes (the 50th percentile) of that distribution. For the United States, those at the
90th percentile in the distribution had more than twice the U.S. median income. Note
that the data are from different years so that, to some extent, differences between
countries may be attributable to the effects of the business cycle.
A study by Atkinson, Rainwater and Smeeding using LIS data from the late
1970s, found that most of these countries had experienced an increase in the degree
of inequality in their income distributions, with the largest increases in the United
States and the United Kingdom. That at least suggests the possibility of some
common factor influencing the distributions of these economies.8 However,
Smeeding also points out that over the last 25 years, the United States started with
the most unequal distribution among the rich nations of the world and over that
period experienced the largest increase in inequality of those rich nations.9
Although the United States appears to have a relatively unequal distribution, the
median income in the United States is higher than in other countries. Smeeding and
Rainwater analyzed income at different points in the distributions and made
adjustments for differences in the purchasing power of the different currencies.10
They found that people in the upper half of the distribution in the United States
enjoyed living standards far above their counterparts in other industrialized countries.
However, those near the bottom, at the 10th percentile in the U.S. distribution, were
not as well off as those at the same point in the distribution in the other countries
examined.
Explaining International Differences. What explains these international
differences in income distributions? The reasons fall into three categories. First,
many other countries devote a much larger share of their national output to income
transfers, which have an equalizing effect on the distribution. Second, these data are
based on income after taxes, and tax rates in these countries vary with respect to
progressivity, and thus have different effects on the equality of the distribution of
after tax income. Third, equality in the distribution of earnings, which account for
about 70% of household income in the studies using LIS data, varies substantially as
well.
8 Anthony B. Atkinson, Lee Rainwater, and Timothy M. Smeeding, Income Distribution
in OECD Countries: Evidence From the Luxembourg Income Study, Organisation for
Economic Co-operation and Development, 1995, 164 pp.
9 Timothy M. Smeeding, Public Policy and Economic Inequality: The United States in
Comparative Perspective, Luxembourg Income Study Working Paper No. 367, Feb. 2004,
30 pp.
10 Timothy M. Smeeding and Lee Rainwater, Comparing Living Standards Across Nations:
Real Incomes at the Top, the Bottom and the Middle, Luxembourg Income Study Working
Paper No. 266, May 2001, 41 pp.

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CRS-13
For those countries for which data are available, there is a strong correlation
between income shares at the lower end of the distribution and the share of GDP
accounted for by transfer payments.11 The evidence suggests, however, that for both
the United States and Britain, given the amount of money that is transferred, those
at the low end of the distribution do not benefit as much as they do in other countries.
Although taking into account taxes and transfers may affect cross-country
comparisons of income inequality, for year-round full time workers the United States
also exhibits relatively greater inequality in the distribution of earnings.

One explanation for the greater equality in earnings distributions abroad is that
wage-setting tends to be more centralized in many other countries than it is in the
United States. There are two reasons for this. First, in the private sector, union
membership may be higher, and in some cases there is a considerable share of the
labor force that is affected by union agreements whether they are union members or
not. Second, in a number of these countries, the public sector also accounts for a
greater share of employment than in the United States, and that serves as an
equalizing force.12
A study published by the Chicago Federal Reserve Bank examined income
inequality in five countries: the United States, Canada, Germany, Sweden, and
Finland.13 The study found that, after taxes and transfer payments, the U.S. income
distribution was the most unequal of the five, followed by Canada, Germany,
Finland, and Sweden. Of those countries, Germany did relatively little to redistribute
income, either through taxes or transfers, but it had the most equal distribution of
labor income. Sweden and Finland reduced income inequality through a combination
of relatively high tax rates and high levels of transfer payments. Canada and the
United States reduced income inequality with similar combinations of progressive
income taxes and transfer payments.
To the extent that greater equality in the income distribution is a result of
deliberate policy and not the result of market forces, that equality may not have been
achieved without some cost. Assuming that these costs are appreciated, they may
reflect varying degrees of willingness in different countries to tolerate inequality in
the distribution of income.
Explaining Recent Trends
Most industrialized countries have, and certainly the United States has,
experienced an increase in the degree of inequality in the distribution of income.
11 Timothy M. Smeeding, “U.S. Income Inequality in a Cross-National Perspective: Why
Are We So Different?,” Looking Ahead, National Policy Association, vol. XIX, no. 2-3,
pp. 41-50.
12 See Gottschalk and Smeedin, “Cross-National Comparisons of Earnings and Income
Inequality,” Journal of Economic Literature, vol. XXXV (June 1997), pp. 633-687.
13 Mariacristina De Nardi, Liqian Ren, and Chao Wei, “Income Inequality and
Redistribution in Five Countries,” Federal Reserve Bank of Chicago Economic Perspectives,
Second Quarter 2000, vol. XXIV, issue 2, pp. 2-20.

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CRS-14
More specifically, most countries have experienced an increase in the inequality of
earnings. Those studies that focus more narrowly on wages have come to similar
conclusions.
Two explanations are often cited for the trend towards greater inequality. The
first has to do with trade liberalization, and the second has to do with technological
progress.
With regard to the first of these causes, trade liberalization, the argument is that
because of reduced trade restrictions and an increasing volume of trade, less skilled
U.S. workers have become more vulnerable to direct competition from lower paid
workers in other countries. Thus, the production of goods that require less-skilled
workers has shifted overseas and the domestic demand for less-skilled workers has
declined here. The reduced demand for less-skilled workers in manufacturing here
has placed downward pressure on their wages, and therefore tended to increase the
earnings gap between skilled and unskilled workers.
Whether that is the case is not completely settled, but the hypothesis has not
been accepted by most economists. Although theoretically sound, the argument has
not yet been supported by compelling empirical evidence. For one thing, the wage
gap between skilled and unskilled workers has grown both in those industries that
tend to compete in world markets as well as those that are generally less affected by
international trade. Another reason economists have not found the trade argument
convincing is that the number of jobs affected by the increase in trade is not sufficient
to explain the magnitude of the earnings gap economy wide.14
The argument that changes in technology have affected the distribution of
earnings has been more persuasive among many economists. The most often cited
evidence for such an effect is the rapid growth in the wage premium paid to more
highly skilled or educated workers that began in 1979. In 1979, men with bachelor’s
degrees earned 50% more than did those with just a high school education. For
women, the college premium in 1979 was 41%. In 2000, the advantage of a college
education was significantly higher, with college-educated men and women earning
97% and 82% more, respectively, than those with just a high school education.
Further, that increased advantage coincided with a significant increase in the
proportion of the labor force that was college educated, from 16.4% of adults in 1979
to 27.4% in 2000, according to the Census Bureau. Even though the supply of more
highly educated workers was rising, it was apparently not rising fast enough to keep
up with increasing demand.15
One theory behind these numbers is that technological changes over the past 20
years have not affected all jobs equally. The argument is that the kinds of
14 See CRS Report 98-441, Is Globalization the Force Behind Recent Poor U.S. Wage
Performance?: An Analysis, by Craig K. Elwell. Also, Gary Burtless, “International Trade
and the Rise in Earnings Inequality,” Journal of Economic Literature, vol. XXXIII (June
1995), pp. 800-816.
15 Peter Gottschalk, “Inequality, Income Growth, and Mobility: The Basic Facts,” Journal
of Economic Perspectives, vol. 11, no. 2, spring 1997, pp. 21-40.

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CRS-15
technological advances that have occurred since the late 1970s have been biased in
favor of those jobs that require higher levels of training and education.
Not all advances require more educated workers to exploit them. Retail clerks
may no longer need to be as proficient at math and some assembly line jobs may have
become simpler and more repetitive. But the evidence shows that demand for more
highly educated workers has increased substantially. Further, those who use
computers in their work have experienced relatively larger wage gains than have
other occupations. The wage gap between less and more highly educated workers
has also been found to be correlated with rising outlays on research and
development.16
The case for “biased” technological change explaining increased income
inequality has gained wide acceptance among economists. Autor, Levy, and
Murnane published a study suggesting that technological progress affected the
equality of the earnings distribution in two ways.17 First, information technology (IT)
served as a substitute for low-skill workers reducing demand for their labor. Second,
IT served as a complement to educated and relatively high-skilled workers increasing
demand for their services. Both factors appear to have contributed to the increase in
inequality.
Although the effects of trade liberalization and technological growth are the two
most often discussed factors which might explain the increase in inequality, there are
other factors at work in the changing shape of the household income distribution.
Shifting demographic factors have also played a major role. One of the most
important shifts has been the large rise in the labor force participation of women.
Specifically, there has been a substantial increase in the number of households with
working wives. In 1970, just over half of all married mothers had some work
experience during the year. In 2000, that proportion had risen to over 70%. The
share of married mothers who worked full time rose from 16% to 42% over the same
period.18
In those families with working wives, their contribution to family income has
been growing.19 Traditionally, wives’ earnings tend not to be highly correlated with
their husbands’ earnings, part of which is due to the negative correlation between
husbands earnings and wives’ labor force participation. Thus, the distribution of
16 Gary Burtless, “Technological Change and International Trade: How Well Do They
Explain the Rise in U.S. Income Inequality?” Looking Ahead, National Policy Association,
vol. XIX, no. 2-3, pp 20-27. See also U.S. Department of Commerce, Bureau of the Census,
Money Income in the United States: 1998, P60-206, issued Sept. 1999.
17 David Autor, Frank Levy, and Richard Murnane, “The Skill Content of Recent
Technological Change: An Empirical Exploration,” NBER Working Paper 8337, June 2001,
62 pp.
18 Howard V. Hayghe and Suzanne M. Bianchi, “Married Mother’s Work Patterns: The Job-
Family Compromise,” Monthly Labor Review, June 1994, pp. 24-30.
19 Howard V. Hayghe, “Working Wives’ Contribution to Family Incomes,” Monthly Labor
Review, Aug. 1993, pp. 39-43.

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CRS-16
husbands’ earnings has been less equal than is the distribution of total household
earnings. However, wives’ earnings have in recent years become more highly
correlated with their husbands’ earnings; consequently, that has been a factor in the
rising inequality in the distribution of household income.20
Changes in the distribution of wealth may have also had an effect on the income
distribution over time. The distribution of household wealth is more unequal than
is the distribution of either earnings or total income.21
An analysis by Burtless attempted to identify the proximate causes of the
increase in inequality. By controlling for both changes in earnings inequality and
changes in the composition of households, he was able to estimate how much of the
change in inequality each variable accounted for between 1979 and 1996.22
Of the total increase in U.S. personal income inequality Burtless found that 28%
was accounted for by increased inequality in men’s earnings and 5% by increased
inequality in women’s earnings. The changing composition of households also
played a role. Between 1979 and 1996 there was an increase in the correlation
between husband and wife earnings, and that contributed an estimated 13% to the
overall increase in inequality. This is the result of a surge in women’s employment
and the general tendency of married couples to have similar educational backgrounds
and earnings potential. The increase in correlation resulted in a rise in the share of
income accruing to households in the upper income classes.
At the same time there was a decline in the percentage of husband-wife
households. Income is more unequally distributed among single adult households
than it is among married-couple households. Burtless estimated that this shift
accounted for 21% of the increase in inequality between 1979 and 1996. Thus,
changes in the composition of households accounted for a third of the overall
increase in inequality.
Conclusions and Policy Considerations
Existing measures of income fall well short of the theoretical ideal that would
indicate how well off individuals or households are. Not all kinds of income are
counted, and thus the distribution of a household’s assets may affect one’s apparent
position in the income distribution.
20 Peter Gottschalk and Timothy Smeeding, “Cross-National Comparisons of Earnings and
Income Inequality,” Journal of Economic Literature, vol. XXXV (June 1997), pp. 633-687.
21 Interestingly, there is a fairly low correlation between wealth and income, which may
largely be due to the fall in income that typically follows retirement. For more information
on the distribution of household wealth, see CRS Report RL30327, The Distribution of
Household Wealth in the United States, by Brian W. Cashell.
22 Gary Burtless, “Has Widening Inequality Promoted or Retarded US Growth?” Canadian
Public Policy - Analyse de Politiques, vol. xxix, supplement/numéro spécial 2003, pp. S185-
S201.

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CRS-17
Taking the existing measures at face value, however, several observations can
be made. First, the distribution of income in the United States has become
increasingly unequal since the late 1960s. Second, the U.S. income distribution is
more unequally distributed than is the case for a large selection of other industrialized
countries.
The distribution of earnings is more unequal than is the distribution of
household income. Of particular interest is that the gap in earnings between highly
educated or skilled workers and less skilled workers has grown substantially. Several
explanations have been offered, but the one most widely accepted is that
technological advances in recent years have increased the demand for more highly
skilled labor relative to its supply. Policies that boosted the supply of skilled workers
would thus seem likely to narrow that gap and act as an equalizing influence on the
income distribution.
Given the large gap in pay between skilled and unskilled workers that has
developed, it would seem that there is little need for additional incentives for
prospective and current workers to continue their education and training. Any
additional incentive would likely pale in comparison to a lifetime of higher earnings,
assuming workers understand these relationships.

In many cases there may be costs associated with policies designed to reduce
income inequality. Changes in tax rates, subsidies for the unemployed, or other
transfers for low-income households may also have undesirable effects in the labor
market and discourage some from taking jobs.
Some of the greater income equality found in other countries, however, may be
due to the fact that existing transfers are more directly targeted at lower income
households. Redirecting transfer payments from middle to lower class households
could increase equality in the distribution without an increase in the amount of
income being redistributed.

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