Inequality in the Distribution of Income: Trends and International Comparisons

Inequality in the Distribution of Income:
Trends and International Comparisons
Updated October 20, 2008
Brian W. Cashell
Specialist in Macroeconomic Policy
Government and Finance Division

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, non-
economic, 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.

Contents
Evaluating Distributions............................................1
Measuring Income.................................................2
Measuring Inequality...............................................4
International Comparisons of Income Distributions .......................8
Explaining International Differences..............................10
Explaining Recent Trends..........................................11
Conclusions and Policy Considerations................................15
List of Figures
Figure 1. Distribution of Household Income: 1967 and 2007................6
Figure 2. Household Income Gini Index, 1967 - 2007.....................7
List of Tables
Table 1. Distribution of Household Income by Quintile....................5
Table 2. Summary Measures of Income Distributions for Selected Countries..9

Inequality in the Distribution of Income:
Trends and International Comparisons
Although an economic expansion began in November 2001, and productivity
growth has been relatively rapid since then, there is concern that wages have not kept
pace with overall economic growth, and that business owners have profited at the
expense of workers.¹ There is 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. Now that the economy seems to be somewhat less vigorous, those
concerns are amplified.
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 RL33606, Faster Productivity Growth: Who
Benefits?, by Brian W. Cashell.

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. Raising the
minimum standard of living may thus serve 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 stamps³
or housing subsidies. Neither does it include capital gains.
With respect to the distribution of overall economic well-being, a limited
measure such as money income may be misleading. For example, consider the case

² See N. Gregory Mankiw, Principles of Economics (Fort Worth Texas: The Dryden Press,

1998).

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, P60-231,
August 2006, Appendix A.

where two families are in every way equal in terms of wealth and 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 it is concern about living standards that prompts 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

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 much less than the one-fifth of total income it would get
if the distribution were perfectly equal, while the top 20% accounts for more than
twice what it would get in an equal distribution. 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 2007, the share of income accounted for by the three middle
quintiles fell from 52.3% to 46.9%.⁴

⁴ There is no official definition of the “middle class.” For further discussion, see CRS
Report RS22627, Who Are the Middle Class?, by Brian W. Cashell.

Table 1. Distribution of Household Income by Quintile
Percentage Share of Total Household Income
BottomSecondThirdFourthFifthTop 5%
1967 4.0 10.8 17.3 24.2 43.6 17.2
1977 4.2 10.2 16.9 24.7 44.0 16.8
1980 4.2 10.2 16.8 24.7 44.1 16.5
1990 3.8 9.6 15.9 24.0 46.6 18.5
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
2005 3.4 8.6 14.6 23.0 50.4 22.2
2006 3.4 8.6 14.5 22.9 50.5 22.3
2007 3.4 8.7 14.8 23.4 49.7 21.2
^Source:^{U.S. Departm}^eⁿ^t^of^C^o^m^m^e^{rce, U.S. C}^eⁿ^s^u^s^Bu^reau^.
A second indicator of the relative degree of inequality in the distribution of
income is the Gini index, also known as 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 and among distributions. They can be distributions from
two different populations, or of the same population at different points in time. Thus,
it can show if one distribution is more or less equal than another, and if there is any
tendency for one distribution to become more or less equal over time.
To illustrate how the Gini index is calculated, Figure 1 shows the distribution
of household income for both 1967 and 2007. 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 cumulative share of income accounted for by those households.

Figure 1. Distribution of Household Income: 1967 and 2007

100
80e
mline of perfect equality
60nco
f i
40t o1967
cen2007
er
20p
0
02040608010
percent of households
^Source:^{U.S. Departm}^eⁿ^t^of^C^o^m^m^e^{rce, U.S. C}^eⁿ^s^u^s^Bu^reau^.
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.
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 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
2007, 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 United States since 1967.

Figure 2. Household Income Gini Index, 1967- 2007

0.48
0.47
0.46
0.45
0.44
0.43
0.42
0.41
0.40
0.39
0.38
1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007
^Source:^{U.S. Departm}^eⁿ^t^of^C^o^m^m^e^{rce, U.S. C}^eⁿ^s^u^s^Bu^reau^.
As the chart shows, since the late 1960s, the trend has been one of almost
steadily increasing household income inequality. Although the index fell slightly in
2007, it still indicates a distribution that is much more unequal than it has been for
most of the 40 years the data have been collected.
In addition to the Gini index, there are other summary measures that describe
changes in the income distribution. Among those measures are ratios of incomes at
different points in the distribution. For example, the ratio of the income of those at^th^th
the 90 percentile in the distribution to that of those in the 10 percentile gives a
estimate of the overall inequality in a distribution. Similarly, the ratio of the median^th^th
income level (50 percentile) to the 10 percentile income level and the ratio of the
90^th percentile income level to the median income can be computed. Changes in
those ratios may indicate whether a change the degree of inequality is due to changes
at the top or at the bottom of the distribution.
Economists at the Federal Reserve Bank of Richmond examined changes in⁵
those ratios to shed some light on changes in the distribution. They found that,
between 1961 and 2002, 75% of the increase in the 90-10 income ratio was
accounted for by the increase in the 90-50 income ratio. In other words, changes in
the upper half of the income distribution accounted for most of the overall increase
in inequality. The authors also examined changes in the distribution of the top 10%
of the distribution. They found that, between 1961 and 2003, the share of labor
income accruing to the top 10% rose from 27% to 37%. More than 60% of that
⁵ Kevin A. Bryan and Leonardo Martinez, “On the Evolution of Income Inequality in the
United States,” Economic Quarterly, Federal Reserve Bank of Richmond, spring 2008.

increase in share was attributable gains in the top 1% of the distribution. Moreover,
the authors found that more than 60% of the gains in the income share of the top 1%
was due to an increased share going to the top 0.1%.
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, measures of income in different countries also differ from each other.
Fortunately, there is a source for comparable 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.
The LIS reports data allowing direct comparisons of income distributions of a
large number of different industrialized countries. Table 2 presents summary data
on the income distribution from some of them. The countries are listed in order from

⁶ Information about the Luxembourg Income Study can be found at their website. See
[ h t t p : / / www.l i s pr oj e c t .or g] .
⁷ Peter Gottschalk and Timothy M. Smeeding, Empirical Evidence on Income Inequality in
Industrialized Countries, Luxembourg Income Study Working Paper No. 154, revised
February 1999.

the one with the lowest Gini index number (most equal distribution) to the one with
the highest (most unequal distribution).
The first column of data shows the estimated Gini coefficient for each country.
The next column (P⁹⁰/P¹⁰) shows the ratio of the incomes of those at the 90^th
percentile of the distribution to the incomes of those at the 10^th percentile. For
example, for the United States in 2004, those near the top of the distribution had
more than five times the income as those near the bottom. The last column (P⁹⁰/P⁵⁰)
indicates the ratios of income at the 90^th percentile of each distribution to the median
incomes (the 50^th percentile) of that distribution. For the United States, those at the
90^th 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.
Table 2. Summary Measures of Income Distributions
for Selected Countries
CountryYearGini IndexP⁹⁰/P¹⁰P⁹⁰/P⁵⁰
Denmark 2004 0.228 2.78 1.56
Netherlands 1999 0.231 2.78 1.63
Sweden 2005 0.237 2.82 1.63
Norway 2000 0.251 2.80 1.59
Germany 2000 0.275 3.37 1.80
France 2000 0.278 3.45 1.88
Belgium 20000.2793.301.74
Australia 2003 0.312 4.24 1.98
Canada 2000 0.315 4.19 1.93
It aly 2000 0.333 4.47 1.99
United Kingdom20040.3454.462.14
United States20040.3725.682.13
Russia 2000 0.434 8.37 2.76
Mexico 2004 0.458 8.48 2.98
^Source:^Luxe^m^b^o^u^r^g^Iⁿ^c^o^m^e^St^ud^y^.
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 10^th 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.
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.

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.
⁹ Timothy M. Smeeding, Public Policy and Economic Inequality: The United States in
Comparative Perspective, Luxembourg Income Study Working Paper No. 367, February

2004.

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.
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.

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.
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 toward greater inequality. The
first has to do with trade liberalization (“globalization”), and the second has to do
with technological progress.
With regard to 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

¹² See Gottschalk and Smeeding, “Cross-National Comparisons of Earnings and Income
Inequality,” Journal of Economic Literature, vol. XXXV (June 1997).
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.

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 a high school education. For women,
the college premium in 1979 was 41%. In 2007, the advantage of a college education
was significantly higher, with college-educated men and women earning 92% and
78% more, respectively, than those with 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 31% in 2005,
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
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

¹⁴ See Gary Burtless, “International Trade and the Rise in Earnings Inequality,” Journal of
Economic Literature, vol. XXXIII (June 1995).
¹⁵ Peter Gottschalk, “Inequality, Income Growth, and Mobility: The Basic Facts,” Journal
of Economic Perspectives, vol. 11, no. 2, spring 1997.
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,
(continued...)

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 an increase in
inequality in the distribution of earnings.
Some trends in the labor market do not easily fit in the skill-biased technological
change hypothesis. Card and DiNardo examined the data to see how well events
agreed with the implications of skill-biased change.¹⁸ They found a number of
contradictions. For example, the biased change hypothesis would seem to predict
that groups that typically have lower skills would have experienced relatively slower
wage growth. Card and DiNardo argue that although women tend to be less skilled
on average than men, and that nonwhite workers tend to be less skilled than white
workers, those two groups did not experience slower wage growth and the wage gaps
between groups did not widen as the theory might have predicted. The authors also
found that the wages of college graduates with degrees in the humanities grew more
rapidly than the wages of graduates with degrees in engineering or science.
A study published by the Federal Reserve Bank of Minneapolis examined the
changing distribution of earnings since 1961.¹⁹ Eckstein and Nagypál found that, for
men, there was a substantial increase in the inequality of the earnings distribution
beginning in the mid-1970s. Between 1973 and 1995, the real earnings of men in the
bottom 25% of the earnings distribution fell. Over that same period, the real earnings
of men in the top 25% of the distribution made significant gains. They also found
that beginning in 1995, the year productivity growth picked up, the real earnings of
men in the bottom 25% of the distribution began to rise along with earnings all across
the distribution. However, the earnings of men at the top of the distribution grew
more rapidly. While the distribution continued to grow more unequal after 1995, the
rate of increase in that inequality slowed. Women’s earnings exhibited similar
although less pronounced changes in inequality.
A study by the Kansas City Federal Reserve Bank examined the connection
between productivity growth and income growth.²⁰ The authors found that between

¹⁶ (...continued)
vol. XIX, no. 2-3. See also U.S. Department of Commerce, U.S. Census Bureau, Money
Income in the United States: 1998, P60-206, September 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.
¹⁸ David Card and John E. DiNardo, “Skill-Biased Technological Change and Rising Wage
Inequality: Some Problems and Puzzles,” Journal of Labor Economics, vol. 20 no.4 2002.
¹⁹ Zvi Eckstein and Éva Nagypál, “The Evolution of U.S. Earnings Inequality: 1961-2002,”
Federal Reserve Bank of Minneapolis Quarterly Review, December 2004.
²⁰ Jonathan L. Willis and Julie Wroblewski, “What Happened to the Gains From Strong
(continued...)

1974 and 1995 only the top quintile (the 20% with the highest incomes) in the
income distribution experienced income growth equal to the growth rate of
productivity. Between 1995 and 2005, the authors found, even the top quintile in the
distribution experienced income growth below the rate of productivity increase.
They point out that limitations of the survey on which those data are based make it
difficult to identify trends at the upper end of the income distribution.
Dew-Becker and Gordon examined the relationship between labor income and
productivity and concluded that the benefits of productivity growth have been
unequally distributed.²¹ Dew-Becker and Gordon looked at Internal Revenue Service
(IRS) income data from 1966 to 2001. They concluded that over that entire period,
only the top 10% of the distribution experienced income gains equal to or greater
than the overall rate of productivity growth. Further, they found that the top 1% of
the distribution accounted for 21.6% of the income gains for that period and for
21.3% of the gains between 1997 and 2001, after productivity growth had
accelerated. Finally, they found that the top 0.1% of the distribution received as
much of the real rise in earnings as the bottom 50% between 1997 and 2001.²² The
authors suggest that some of this is may be due to the expansion of opportunities
available to “economic superstars” such as sports stars and other top celebrities
because of technologies such as cable television and the Internet.
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 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 2005, that proportion had risen to 66%. The share of
married mothers who worked full time rose from 16% to 47% over the same period.²³
In those families with working wives, their contribution to family income has
been growing.²⁴ Traditionally, wives’ earnings tend not to be highly correlated with

²⁰ (...continued)
Productivity Growth?,” Federal Reserve Bank of Kansas City, Economic Review, First
Quarter 2007.
²¹ Ian Dew-Becker and Robert J. Gordon, Where Did the Productivity Growth Go? Inflation
Dynamics and the Distribution of Income, National Bureau of Economic Research Working
Paper 11842, December 2005.
²² Aside from the direct effects of productivity on earnings Dew-Becker and Gordon found
that the acceleration in productivity resulted in a 1.2% slower rate of inflation between 1995
and 2005. That meant an increase in the purchasing power of all workers.
²³ Howard V. Hayghe and Suzanne M. Bianchi, “Married Mother’s Work Patterns: The Job-
Family Compromise,” Monthly Labor Review, June 1994.
²⁴ Howard V. Hayghe, “Working Wives’ Contribution to Family Incomes,” Monthly Labor
(continued...)

their husbands’ earnings, part of which is due to the negative correlation between
husbands earnings and wives’ labor force participation. Thus, the distribution of
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.²⁵
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.²⁶
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 1996.²⁷
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

²⁴ (...continued)
Review, August 1993.
²⁵ Peter Gottschalk and Timothy Smeeding, “Cross-National Comparisons of Earnings and
Income Inequality,” Journal of Economic Literature, vol. XXXV (June 1997).
²⁶ 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.
²⁷ Gary Burtless, “Has Widening Inequality Promoted or Retarded US Growth?” Canadian
Public Policy - Analyse de Politiques, vol. xxix, supplement/numéro spécial 2003.

counted, and thus the distribution of a household’s assets may affect one’s apparent
position in the income distribution.
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 might seem 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. That is one reason some have advocated
doing more to improve the education of the very young.
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.