The Distribution of Earnings of Wage and Salary Workers in the United States, 1994-2003
Congressional research reportDec 2, 2004
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The Distribution of Earnings of Wage and
Salary Workers in the United States, 1994-2003
Updated December 2, 2004
Gerald Mayer
Economic Analyst
Domestic Social Policy Division
Congressional Research Service ˜ The Library of Congress
The Distribution of Earnings of Wage and
Salary Workers in the United States, 1994-2003
Summary
The distribution of individual earnings is an indicator of a nation’s economic
well-being. The trend in the distribution of earnings is related to policy debates
affecting both individual earnings and family income, including debates on education
and training, health care, the minimum wage, immigration, foreign trade, tax policy,
and other government programs and proposals.
Most studies have found that the distribution of earnings became more unequal
over much of the 1980s. From the end of the eighties and into the early nineties the
distribution of earnings was relatively unchanged. This report uses data from the
monthly Current Population Survey (CPS) to analyze trends in the distribution of
weekly earnings from 1994 to 2003. The report uses three measures of inequality:
the Gini coefficient, the share of total earnings received by each quintile (i.e., fifth)
of earners, and the ratio of earnings received by workers at the 90th 50th and 10th
percentiles. Together, these measures indicate whether the distribution of earnings
has changed and, if so, how it has changed.
This report analyzes trends in the distribution of earnings for three groups of
workers: all wage and salary workers, hourly workers, and salaried workers. Wage
and salary workers are persons who work for a private or public employer. Hourly
workers are mainly workers who are paid an hourly wage. Salaried workers are
mainly workers who receive an annual, monthly, or weekly salary. Hourly workers
and salaried workers are separate subgroups of all wage and salary workers.
The analysis in this report indicates that inequality is greater among all wage and
salary workers than among either hourly or salaried workers. The analysis indicates
that inequality declined from 1994 to the late 1990s or early 2000s. During the
economic expansion of the 1990s, workers at the bottom of the earnings distribution
experienced improvements in both earnings and hours worked.
In the early 2000s, inequality increased. The measures of inequality used in this
report do not agree on when inequality began to increase. But inequality began to
increase earlier for salaried than for hourly workers.
From 1994 to 2003, inequality declined more among hourly workers, but
increased more among salaried workers. Among women, inequality declined more
and then increased more among hourly than among salaried workers. Among men,
inequality declined more among hourly workers, but increased more among salaried
workers.
Finally, from the mid-1990s to the early 2000s, among both men and women
there was a slight decline in share of earnings received by the middle three quintiles
of salaried workers. Some analysts define the middle three quintiles of earners as the
middle class.
Contents
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Definition of Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Measures of the Distribution of Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Gini Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Earnings Ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Share of Earnings by Quintile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Changes in the Distribution of Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Reducing Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
The Distribution of Earnings from the 1960s to the 1990s . . . . . . . . . . . . . . 6
Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Major Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Gini Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
All Wage and Salary Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Hourly and Salaried Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Male and Female Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Earnings Ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
All Wage and Salary Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Hourly and Salaried Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Male and Female Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Share of Earnings by Quintile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
All Wage and Salary Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Hourly and Salary Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Male and Female Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Appendix: Data Source and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Gini Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Topcoded Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Confidence Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
List of Figures
Figure 1. Illustration of Lorenz Curves and Gini Coefficients for Two
Groups of Workers in the Same Year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Figure 2. Illustration of Lorenz Curves and Gini Coefficients for the Same
Group of Workers in Different Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
List of Tables
Table 1. Relative Size and Median Weekly Earnings of Employed Labor
Force, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Table 2. Gini Coefficients for Wage and Salary Workers, Hourly Workers,
and Salaried Workers, 1994-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Table 3. Occupation of Employed Wage and Salary Workers, Hourly Workers,
and Salaried Workers, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Table 4. Earnings Ratios for All Wage and Salary Workers, 1994-2003 . . . . . . 14
Table 5. Earnings Ratios for Hourly Workers, 1994-2003 . . . . . . . . . . . . . . . . . 15
Table 6. Earnings Ratios for Salaried Workers, 1994-2003 . . . . . . . . . . . . . . . . 17
Table 7. Share of Total Weekly Earnings of All Wage and Salary Workers
by Quintile, 1994-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Table 8. Share of Total Weekly Earnings of Hourly Workers by Quintile,
1994-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Table 9. Share of Total Weekly Earnings of Salaried Workers, by Quintile,
1994-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
The Distribution of Earnings of Wage
and Salary Workers in the
United States, 1994-2003
The distribution of individual earnings is an indicator of a nation’s economic
well-being. The distribution of earnings influences policy debates affecting both
individual earnings and family income.1
This report analyzes the trend in weekly earnings for employed wage and salary
workers in the United States from 1994 through 2003. Wage and salary workers
account for almost 90% of all workers. Most of the remaining workers are persons
who are self-employed. The report provides separate analyses for men and women
and for hourly and salaried workers.
The also report analyzes the labor market income of individual workers. The
report does not examine family or household earnings or income.
Background
The trend in the distribution of earnings is an indicator of whether the relative
supply of and demand for different skills have changed. A market economy may
result in a distribution of earnings that some policymakers find socially unacceptable,
leading to programs or proposals to reduce inequality. Thus, the trend in the
distribution of earnings can influence policy debates on many legislative issues,
including the amount and kind of spending for education and training, the minimum
wage, welfare, access to health care, immigration, foreign trade, housing, taxes, fiscal
policy, as well as other federal programs and policy proposals.
The results of a study of trends in the distribution of earnings can be affected,
however, by the definition of earnings, whose earnings are examined (e.g., all
workers, prime-age workers, full-time workers, etc.), the measure of inequality, the
time period studied, and the availability of data.
1
Earnings account for the largest share of both individual and family income. For
individuals in 2003, wages and salaries accounted for 76.9% of total pretax income for
persons 16 and over (and 84.6% for persons between 16 and 64). For families, wages and
salaries accounted for 76.8% of pretax family income. (For the latter calculation, families
include single individuals.) Calculated by the Congressional Research Service (CRS) from
the March 2004 Current Population Survey (CPS).
CRS-2
Definition of Earnings
Earnings are payments that individuals receive for their labor services.
Individuals are generally paid for a period of time worked (e.g., an hourly wage) or
the quantity of goods or services produced (e.g., a piece rate). Individuals may also
receive a salary, which is a given amount paid every week, month, year, or other time
period. Earnings may be defined as cash wages only or as total compensation. The
latter consists of cash wages plus fringe benefits (e.g., employer-paid health
insurance, paid vacations, and employer contributions to a retirement plan).
Although wages represent the largest source of money income for the average
family, many individuals and families receive cash or in-kind benefits from sources
other than work (e.g., interest, dividends, cash assistance under the Temporary
Assistance for Needy Families program (TANF), or food stamps). Also, one worker
may support more individuals than another worker with the same amount of
earnings. Some families have more wage earners than other families. Accordingly,
the results of an analysis of individual earnings will likely differ from a study that
uses a measure of total income or a different unit of analysis (e.g., the family or
household).2 Since individual earnings represent payments that individuals receive
for their contribution to output in a market economy, this report focuses on the
earnings of individual workers, where earnings consist of cash wages before taxes.
Individual earnings can be examined from three basic perspectives: the level
of real earnings, the distribution of earnings, and earnings mobility (i.e., how the
earnings of individual workers change over time). This report analyzes recent trends
in the distribution of individual earnings. The report analyzes sample data for each
year from 1994 through 2003. The data for each year provide a snapshot of earnings
for that year. Thus, the report does not examine the trend in real earnings or how the
earnings of individuals change over time.
Measures of the Distribution of Earnings
Different measures of the distribution of earnings provide different information
and can lead to different conclusions about the trend in inequality. Some measures
identify whether the distribution of earnings differs among groups or has changed
over time. These measures may not reveal where the distribution differs or has
changed. For example, the Gini coefficient may identify an increase or decrease in
earnings inequality, but it does not identify which part of the earnings distribution has
changed (e.g., whether there are relatively more earners at the upper, middle, or lower
parts of the distribution). Therefore, analyses of the distribution of earnings that use
the Gini coefficient are often supplemented with other measures of inequality, such
as the ratio between the earnings of workers at the 90th and 10th percentiles or the
share of earnings received by each quintile (i.e., fifth) of workers.
2
For analyses of the distribution of household income, see CRS Report RS20811, The
Distribution of Income, by Brian W. Cashell; CRS Report RL32639, Inequality in the
Distribution of Income: Trends and International Comparisons, by Brian W. Cashell; and
U.S. Department of Commerce, Bureau of the Census, Income, Poverty, and Health
Insurance Coverage in the United States: 2003, P 60-226, Aug. 2004, pp. 27-33.
CRS-3
This report uses the
following three measures of
earnings inequality: the
Gini coefficient; the ratio of
earnings at the 90th, 50th,
and 10th percentiles; and the
share of total earnings
received by each quintile of
workers.
Together, the
measures indicate whether
the distribution of earnings
has changed and, if so, how
it has changed.
Figure 1. Illustration of Lorenz Curves and
Gini Coefficients for Two Groups of
Workers in the Same Year
Gini Coefficient.
The Gini coefficient is a
measure of earnings
equality that ranges from 0
to 1. If the earnings of all
individuals are the same, the
Gini coefficient is equal to
0, representing complete
equality of earnings. If one
worker receives all the earnings, the Gini coefficient is equal to 1. Thus, a larger
coefficient indicates a greater degree of inequality. The Gini coefficient is calculated
by using the formula shown in the Appendix. Graphically, the Gini coefficient is
illustrated in Figure 1. The horizontal axis shows the percent of all earners, while
the vertical axis shows the percent of earnings received by all earners. The diagonal
line represents total earnings equality. For example, on the diagonal line, 25% of
earners receive 25% of earnings, 50% of earners receive 50% of earnings, and so on.
In Figure 1 the two dotted lines — called Lorenz curves — illustrate two
possible earnings distributions. The Gini coefficient is the ratio of (a) the area
between the diagonal line and the Lorenz curve and (b) the total area under the
diagonal line. Figure 1 illustrates the distribution of earnings for two groups of
workers in the same year. The distribution of earnings in the first group of workers
(where the Gini coefficient is .163) is more equal than the distribution of earnings in
the second group of workers (the Gini coefficient is .289). For the first group of
workers, the bottom 60% of workers receive half of all earnings; the top 40% of
earners receive the other half of earnings. In the second group of workers, the bottom
70% of earners receive half of all earnings; the top 30% of earners receive the other
half of earnings.
The Gini coefficient may not capture a change in the shape of the distribution
of earnings. To illustrate, Figure 2 shows the distribution of earnings for the same
group of workers in two different years. The figure shows that the distribution of
CRS-4
earnings has changed. But
the Gini coefficient is the
same (.276) in both years.
In year two, both lower paid
and higher paid workers
receive a larger share of
total earnings than they
received in year one. For
example, in year two the
first 20% of workers
receive 10% of earnings,
compared to 7.5% of
earnings in year one. In
year two, workers in the
middle of the distribution
receive a smaller share of
earnings than they received
in year one.3 Changes in
the shape of earnings
distributions will be
discussed again below in
the section on “Findings.”
Figure 2. Illustration of Lorenz Curves and
Gini Coefficients for the Same Group of
Workers in Different Years
Earnings Ratios. Earnings ratios are often calculated using the earnings of
workers at the 10th 50th and 90th percentiles. Earnings ratios are calculated by first
ranking all workers from the lowest to the highest paid. The earnings of workers at
the 10th percentile exceed the earnings of workers below the 10th percentile, and are
less than the earnings of workers above the 10th percentile. Likewise, the earnings
of workers at the 50th percentile — i.e., median earnings — exceed the earnings of
workers below, but are less than the earnings of workers above, the 50th percentile.
The earnings of workers at the 90th percentile exceed the earnings of workers below
the 90th percentile. In a large survey sample, many workers have the same wage.
Thus, there are generally many workers whose earnings place them at either the 10th
50th or 90th percentiles. The 90/10 earnings ratio is calculated by dividing the
earnings of workers at the 90th percentile by the earnings of workers at the 10th
percentile. Similarly, the 50/10 ratio is calculated by dividing median earnings by the
earnings of workers at the 10th percentile. If all workers earned the same wage, the
90/10, 90/50, and 50/10 ratios would all be equal to 1. The greater the earnings
ratios, the greater the degree of inequality.
3
At the lower end of the earnings distribution in Figure 2, the Lorenz curve for year two
is higher than the curve for year one. This difference means that at the lower end of the
distribution, workers in year two receive a larger share of total earnings than workers in year
one. However, the curves for years one and two intersect at the point representing 50% of
all earners. In both years, the lower 50% of earners receive 30% of all earnings. Because
(a) workers at the lower end of the distribution receive a greater share of total earnings in
year two than in year one and (b) the lower half of earners in both years receive the same
share of total earnings, then (c) workers near the segment of the distribution that includes
50% of earners must receive a smaller share of total earnings in year two than in year one.
CRS-5
Share of Earnings by Quintile. To calculate the share of earnings received
by each quintile of earners, workers are first ranked from lowest to highest paid.
Workers are then divided into five equal-size groups, or quintiles. The total earnings
received by each quintile is divided by the total earnings of all workers. If everyone’s
earnings were the same, each quintile would receive one-fifth of all earnings. The
greater the share of earnings received by the highest paid workers (i.e., the top
quintile) and the smaller the share of earnings received by the lowest paid workers
(i.e., the bottom quintile) the greater the degree of inequality. Although there is no
official definition of the middle class, some analysts and policymakers refer to the
middle or middle three quintiles of workers as middle income, or middle class,
earners.
Changes in the Distribution of Earnings
The distribution of earnings may change for several reasons. These reasons can
be grouped into three broad categories: changes in the supply of labor, changes in
the demand for labor, and institutional changes. The supply of labor can be affected
by many factors, including changes in the age distribution of the population, labor
force participation rates for different population groups, the level of educational
attainment, the amount of immigration, and the level and kind of tax revenues and
government spending (e.g., marginal tax rates and income transfer payments may
affect decisions to work and how much to work). Some of the factors that may affect
the demand for labor include technological change, changes in the composition of
foreign trade, changes in the regulation of industry, and changes in consumer tastes.
Institutional changes include factors such as the real value of the minimum wage, the
degree of unionization, and the regulation of markets that affect the supply of or
demand for labor.4
Reducing Inequality. The general reasons for changes in the distribution of
earnings suggest ways to reduce earnings inequality. For instance, governments can
adopt policies that reduce the relative supply of or increase the relative demand for
4
The literature on the causes of changes in the distribution of earnings is extensive. These
causes are reviewed in Frank Levy and Richard J. Murnane, “U.S. Earnings Levels and
Earnings Inequality: A Review of Recent Trends and Proposed Explanations,” Journal of
Economic Literature, v. 30, Sept. 1992, pp. 1354-1371; George E. Johnson, “Changes in
Earnings Inequality: The Role of Demand Shifts,” Journal of Economic Perspectives, v. 11,
spring 1997, pp. 41-54; Robert H. Topel,“Factor Proportions and Relative Wages: The
Supply-Side Determinants of Wage Inequality,” Journal of Economic Perspectives, v. 11,
spring 1997, pp. 55-74; Nicole M. Fortin and Thomas Lemieux, “Institutional Changes and
Rising Wage Inequality: Is There a Linkage?” Journal of Economic Perspectives, v. 11,
spring 1997, pp. 75-96; Yolanda K. Kodrzycki, “Labor Markets and Earnings Inequality:
A Status Report,” New England Economic Review, May/June 1996, pp. 11-24; CRS Report
98-441, Is Globalization the Force Behind Recent Poor U.S. Wage Performance? An
Analysis, by Craig K. Elwell; and Sheldon Danziger and Peter Gottschalk, America Unequal
(Cambridge, MA: Harvard University Press, 1995), pp. 127-148. For a discussion of the
effect of changes in the real value of the minimum wage on the distribution of earnings, see
David S. Lee,“Wage Inequality in the United States During the 1980s: Rising Dispersion
or Falling Minimum Wage?” The Quarterly Journal of Economics, v. 114, Aug. 1999, pp.
977-1023.
CRS-6
less-skilled labor. One way to reduce the relative supply of less skilled labor is
through improvements in education and training or other forms of earningsproducing human capital (e.g., improved health care) for less skilled persons. One
way to increase the relative demand for less skilled workers in the short term is
through fiscal and monetary policies that maintain full employment (e.g., increased
spending relative to revenues during an economic downturn). In the longer term, one
way to increase the relative demand for less-skilled labor is through policies that
increase the demand for goods and services produced with less-skilled labor (e.g.,
policies that increase the foreign demand for such goods and services). Governments
can also increase equality by adopting policies that directly increase the relative
before-tax wages of lower paid workers (e.g., with a higher minimum wage) or that
raise the after-tax earnings of lower wage workers (e.g., with progressive taxation —
including tax credits like the Earned Income Tax Credit (EITC)). Improvements in
the distribution of earnings may, however, involve tradeoffs with an efficient
allocation of labor; e.g., subsidies, tax preferences, or a higher minimum wage may
affect the supply of or demand for labor.
The Distribution of Earnings from the 1960s to the 1990s
Studies of the distribution of earnings in the United States use different data and
methods and often reach different conclusions. Most research indicates that earnings
inequality increased during much of the 1980s. Studies have concluded that from the
end of the eighties and into the nineties the distribution of earnings was relatively
unchanged. However, inequality may have increased during the early- to mid-1990s.
For example, one study of hourly earnings concluded that the 90/10 earnings ratio
increased by 11.9% from 1979 to 1986, fell by 2.6% from 1986 to 1992, and
increased by 2.4% from 1992 to 1995. The same study concluded that the Gini
coefficient increased by 5.7% from 1979 to 1986, by 0.7% from 1986 to 1992, and
by 4.0% from 1992 to 1995.5
The distribution of earnings in recent decades has differed among men and
women. Among men, the distribution of earnings became more unequal through
most of the 1960s, 1970s, and 1980s. Among women, the evidence indicates that,
in the 1970s, there was either a decrease in inequality or, at least, that inequality did
not increase. In the early- to mid-1980s, inequality increased among both men and
women. Among men, there was some evidence of a decline in middle income (or
“middle class”) earnings during the 1980s.6
5
Robert I. Lerman, “Reassessing Trends in U.S. Earnings Inequality,” Monthly Labor
Review, v. 120, Dec. 1997, pp. 21-22.
6
Peter Henle and Paul Ryscavage, “The Distribution of Earned Income Among Men and
Women, 1958-77,” Monthly Labor Review, v. 103, Apr. 1980, pp. 4-8; Lynn A. Karoly, The
Trend in Inequality Among Families, Individuals, and Workers in the United States: A
Twenty-Five-Year Perspective (Santa Monica, CA: RAND Corporation, 1992), pp. 31-43,
67-69; W. Norton Grubb and Robert H. Wilson, “Trends in Wage and Salary Inequality,
1967-88,” Monthly Labor Review, v. 115, June 1992, pp. 25-28; Arthur F. Jones Jr. and
Daniel H. Weinberg, The Changing Shape of the Nation’s Income Distribution: 1947-1998,
U.S. Census Bureau, P60-204, June 2000, pp. 2-3.
CRS-7
Findings
The remainder of this report analyzes trends in the distribution of weekly
earnings for wage and salary workers in the United States from 1994 to 2003.
Following an introduction, the section provides a brief summary of the major
findings. Next, the section provides a more detailed summary of the findings.
Although this report analyzes the trend in the distribution of individual weekly
earnings, it does not examine the trend in real earnings or how the earnings of
individual workers change over time (i.e., earnings mobility).
Introduction
This report analyzes trends in the distribution of earnings for three groups of
workers: all wage and salary workers, hourly workers, and salaried workers. Wage
and salary workers are persons who work for a private or public employer. Hourly
workers are mainly workers who are paid an hourly wage. Salaried workers are
mainly workers who receive an annual, monthly, or weekly salary. Salaried workers
may also include persons who are paid by commission. Hourly workers and salaried
workers are separate subgroups of all wage and salary workers. Because there are
differences in the labor market characteristics of men and women, the distribution of
earnings among men and women are analyzed separately.7
The analysis uses three measures of inequality: the Gini coefficient; the ratio
of earnings at the 90th 50th and 10th percentiles; and the share of total earnings
received by each quintile of workers.
The analysis uses data from the monthly Current Population Survey (CPS). The
CPS is a household survey conducted by the U.S. Bureau of the Census for the
Bureau of Labor Statistics (BLS) of the U.S. Department of Labor. The CPS defines
earnings as cash wages before taxes and other deductions. The monthly CPS does
not collect information on the earnings of persons who are self-employed. Persons
who work without pay on a family farm or business do not have earnings to report
from that employment. The analysis is based on employed persons who report
positive weekly earnings. A detailed explanation of the data and methodology used
in this report is provided in the Appendix.
Table 1 shows the relative sizes and median weekly earnings of all employed
wage and salary workers, hourly workers, and salaried workers. In 2003, an average
7
In general, women tend to work fewer hours per week than men, spend less time in the
labor force, and enter and leave the labor force more often than men. The distribution of
women by occupation and industry also differs from men. Among college graduates, the
distribution of women by college major differs from men. CRS Report 98-278, The Gender
Wage Gap and Pay Equity: Is Comparable Worth the Next Step?, by Linda Levine.
Women’s Bureau, U.S. Department of Labor, Earnings Differences Between Women and
Men, available at [http://permanent.access.gpo.gov/lps49666/wagegap2.htm]. Daniel E.
Hecker, “Earnings of College Graduates: Women Compared With Men,” Monthly Labor
Review, v. 121, Mar. 1998, pp. 64-66.
CRS-8
of 137.8 million persons were employed each month. Of this number, 122.2 million
were wage and salary workers, 72.9 million were hourly workers, and 49.2 million
were salaried workers. Table 1 shows that salaried workers had higher median
weekly earnings ($788) than hourly workers ($420). The median weekly earnings
of all wage and salary workers ($540) fell between the median earnings of hourly and
salaried workers.
In addition to wage and salary workers, in 2003, and additional 15.3 million
workers were self-employed, and an estimated 118,000 persons worked as unpaid
employees in a family business.8
Table 1. Relative Size and Median Weekly Earnings of
Employed Labor Force, 2003
Number
(in thousands)
Median Weekly Earnings
Employed
137,736
Not available a
Wage and salary workers b
122,150
$540
Hourly workers
72,916
$420
Salaried workers
49,234
$788
Labor Force Group
Source: Calculated by CRS from the monthly CPS.
a. Employed persons include individuals who are self-employed and persons who work on a family farm or
business without pay. The monthly CPS does not collect information on earnings for persons who are
self-employed.
b. The earnings calculations include only those wage and salary workers with positive weekly earnings.
Major Findings
The analysis in this section indicates that inequality is greater among
all wage and salary workers than among either hourly or salaried
workers. Two of three measures of inequality used in this report
indicate that inequality is greater among all wage and salary workers
than among either men or women.
! Inequality declined from 1994 to the late 1990s or early 2000s.
Inequality increased in the early 2000s.
! The measures of inequality used in this report do not agree on when
inequality began to increase. But inequality began to increase earlier
for salaried than for hourly workers.
! From 1994 to 2003, inequality declined more among hourly workers,
but increased more among salaried workers.
! Among women, inequality declined more and then increased more
among hourly than among salaried workers.
! Among men, inequality declined more among hourly workers, but
increased more among salaried workers.
! Among men, from 1994 to 1999, there was a hollowing out of the
earnings distribution: earnings at the top and bottom of the
!
8
Unpaid family members are persons who work without pay for 15 hours or more a week
on a family farm or business.
CRS-9
distribution increased more than earnings in the middle of the
distribution.
! From the mid-1990s to the early 2000s, among both men and women
there was a slight decline in share of earnings received by the middle
three quintiles of salaried workers. Some analysts define the middle
three quintiles of earners as middle income, or middle class,
workers.
Gini Coefficients
Table 2 shows Gini coefficients for all wage and salary earners, hourly workers,
and salaried workers, respectively. A larger Gini coefficient indicates greater
inequality.
Table 2 shows that, for the period 1994 to 2003, the distribution of earnings
among all wage and salary workers was more unequal than among either hourly or
salaried workers. Inequality among all workers was also greater than among either
men or women. In 2003, the Gini coefficient among all wage and salary workers was
.399, compared to .357 for hourly workers and .365 for salaried workers. The Gini
coefficient was .393 for men and .382 for women.
One reason why there is greater inequality among wage and salary earners than
among either hourly or salaried workers is that hourly and salaried workers are
concentrated in different occupations. Table 3 shows that, in 2003, 55.3% of
salaried workers were employed in managerial or professional occupations, compared
to 18.6% of hourly workers. By contrast, 52.3% of hourly workers were employed
in service, construction, installation, production, or transportation occupations,
compared to 20.5% of salaried workers.
(The monthly CPS does not collect information on the earnings of selfemployed persons. Table 3 shows that the distribution of self-employed persons by
occupation is more like the distribution of salaried than hourly workers: 45.5% of
self-employed workers were employed in managerial or professional occupations;
30.2% were employed in service, construction, installation, production, or
transportation occupations.)
All Wage and Salary Workers. The Gini coefficient for all wage and salary
workers indicates that, from 1994 to 2003, inequality declined from 1994 to 1999 and
then increased from 1999 to 2002. From 1994 to 1999, the Gini coefficient fell from
.397 to .394, and then increased to .399 in 2002 and 2003.
The Gini coefficient for all wage and salary workers does not indicate whether
there are differences in the distribution of earnings between men and women. Table
2 indicates that, from 1994 to the late 1990s, the decline in inequality among wage
and salary workers was mainly among women. On the other hand, the increase in
inequality in the early 2000s was among both men and women. In 1994, the Gini
coefficient for women was .382; in 2000 it was .376. The coefficient then increased
to .382 in both 2002 and 2003. Among men, the change in the Gini coefficient from
CRS-10
1994 to 1999 was not statistically significant.9 From 1999 to 2003 the coefficient for
men increased from .384 to .393.
Hourly and Salaried Workers. The Gini coefficient for all wage and salary
workers does not indicate whether there are differences between hourly and salaried
workers. Table 2 shows that, from 1994 to the late 1990s or early 2000s, inequality
declined among both hourly and salaried workers. From 1994 to 2001, the Gini
coefficient for hourly workers fell from .369 to .353. From 1994 to 1999, the Gini
coefficient for salaried workers fell from .362 to .358.
Table 2. Gini Coefficients for Wage and Salary Workers, Hourly
Workers, and Salaried Workers, 1994-2003
Year
All workers
Men
A. Wage and Salary Workers
1994
0.397
0.386
1995
0.397
0.384
1996
0.397
0.386
1997
0.398
0.387
1998
0.395
0.384
1999
0.394
0.384
2000
0.395
0.387
2001
0.396
0.387
2002
0.399
0.392
2003
0.399
0.393
B. Hourly Workers
1994
0.369
0.352
1995
0.364
0.348
1996
0.364
0.349
1997
0.363
0.351
1998
0.358
0.345
1999
0.355
0.342
2000
0.354
0.343
2001
0.353
0.343
2002
0.355
0.343
2003
0.357
0.345
C. Salaried Workers
1994
0.362
0.357
1995
0.362
0.354
1996
0.360
0.354
1997
0.360
0.355
1998
0.360
0.354
1999
0.358
0.351
2000
0.361
0.356
2001
0.362
0.357
2002
0.364
0.360
2003
0.365
0.362
Source: Calculated by CRS from the monthly CPS.
9
Women
0.382
0.381
0.381
0.382
0.381
0.377
0.376
0.379
0.382
0.382
0.360
0.354
0.353
0.348
0.347
0.343
0.340
0.341
0.348
0.351
0.338
0.339
0.336
0.339
0.340
0.336
0.338
0.340
0.341
0.341
See “Confidence Intervals” in the Appendix for a brief discussion of significance tests.
Unless stated otherwise, the comparisons discussed in this report are significant at the 5%
confidence level.
CRS-11
Table 3. Occupation of Employed Wage and Salary Workers,
Hourly Workers, and Salaried Workers, 2003
Wage and
SelfHourly Salaried
Salary
employed
Workers Workers
Workers
Workers
12.6%
5.0%
24.0%
29.0%
Management, business, and financial occupations a
Professional and related occupations b
20.8%
13.6%
31.3%
16.5%
Service occupations c
16.5%
21.8%
8.7%
12.9%
Sales and related occupations d
10.9%
10.1%
12.1%
16.9%
Office and administrative support occupations e
15.5%
18.2%
11.5%
4.2%
Construction and extraction occupations f
5.2%
7.0%
2.6%
0.6%
Installation, maintenance, and repair occupations g
3.7%
4.7%
2.3%
10.9%
Production occupations h
7.6%
10.7%
3.0%
3.2%
Transportation and material moving occupations i
6.4%
8.1%
3.9%
2.6%
Farming, fishing, and forestry occupations
0.8%
0.8%
0.6%
3.3%
Total
100.0%
100.0%
100.0%
100.0%
Occupation
Source: Calculated by CRS from the monthly CPS.
a. Management, business, and financial occupations include executives, managers, wholesale and retail
buyers, claims adjusters, budget analysts, financial advisors, tax preparers, and others.
b. Professional and related occupations include engineers, scientists, lawyers, doctors, teachers,
healthcare practitioners, social workers, and others.
c. Service occupations include dental assistants, nursing aides, firefighters, police officers, chefs,
cooks, waiters and waitresses, hairdressers, childcare workers, and others.
d. Sales and related occupations include cashiers, travel agents, salespersons, insurance agents,
financial services sales agents, real estate agents, and others.
e. Office and administrative support occupations include tellers, file clerks, hotel clerks, receptionists,
secretaries, computer operators, office clerks, and others.
f. Construction and extraction occupations include carpenters, electricians, roofers, plasterers,
painters, sheet metal workers, and others.
g. Installation, maintenance, and repair occupations include aircraft mechanics, car mechanics security
system installers, telecommunication line installers, office machine repairers, and others.
h. Production occupations include bakers, machinists, tailors, welders, machine operators, and others.
i. Transportation and material moving occupations include airline pilots, truck drivers, bus drivers,
taxi drivers, railroad conductors, service station attendants, laborers, and others.
Table 2 also shows that, based on the Gini coefficient, inequality increased
among salaried workers beginning in 1999 and among hourly workers beginning in
2001. From 1999 to 2003, the Gini coefficient for salaried workers increased from
.358 to .365. From 2001 to 2003, the Gini coefficient for hourly workers increased
from .353 to .357.
The economic expansion of the 1990s probably played a role in reducing
inequality, while the economic slowdown of 2001 likely played a role in increasing
inequality.10 Expansions are typically characterized by a decline in unemployment,
10
According to the National Bureau of Economic Research (NBER), which dates the peaks
and troughs of the business cycle, the last completed recession began in Mar. 2001 and
ended in Nov. 2001. NBER, Business Cycle Dating Committee, National Bureau of
Economic Research, NBER, July 17, 2003, available on the Internet at
(continued...)
CRS-12
an increase in the average workweek, and often by an increase in the relative earnings
of lower paid workers. Nationally, the unemployment rate fell from 6.1% in 1994
to 4.0% in 2000, before rising to 5.8% in 2003.11 From 1994 to 2000, the percentage
of wage and salary workers employed full-time (35 or more hours per week)
increased from 81.6% to 83.8%, and then declined to 82.9% in 2003. The percentage
of hourly workers employed full-time increased from 75.5% to 77.9%, and fell to
76.3% in 2003. The percentage of salaried workers employed full-time increased
from 91.6% to 92.8%, and was 92.7% in 2003.
In addition, the basic federal minimum wage was raised from $4.25 to $4.75 an
hour in October 1996 and to $5.15 an hour in September 1997.12 This increase may
have had an impact on the weekly earnings of lower wage workers.
Male and Female Workers. The Gini coefficient for wage and salary
workers indicates that, from 1994 to the end of the decade, inequality fell among
women, but not among men. In the early 2000s, inequality increased among both
men and women. From 1994 to 2000, the Gini coefficient among women fell from
.382 to .376, and then increased to .382 in both 2002 and 2003. Among men, the
decline in the coefficient from 1994 to 1999 was not statistically significant. From
1999 to 2003, the coefficient increased from .384 to .393.
The decline in the Gini coefficient for women in the 1990s was mainly among
hourly, and not salaried, women. From 1994 to 2000, the coefficient for hourly
women fell from .360 to .340. The change in the coefficient from 1994 to 1999
among salaried women was not statistically significant.
The Gini coefficient for wage and salary men suggests that there was not a
significant change in inequality from 1994 to 1999. But the Gini coefficient also
indicates that, during this period, inequality declined among male hourly workers as
well as among male salaried workers. From 1994 to 1999, the Gini coefficient
among hourly men fell from .352 to .342; the coefficient for salaried men fell from
.357 to .351. The apparent inconsistency between the Gini coefficient for all wage
and salary men and for hourly and salaried men examined separately will be
discussed below in the analysis of earnings ratios.
Finally, Table 2 indicates that, for both men and women, inequality increased
from the late 1990s or early 2000s among both hourly and salaried workers. From
1999 to 2003, the Gini coefficient for hourly men rose from .342 to .345; for salaried
men the coefficient increased from .351 to .362.13 From 2000 to 2003, the coefficient
10
(...continued)
[http://www.nber.org].
11
U.S. Department of Labor, Bureau of Labor Statistics, available on the Internet at
[http://data.bls.gov/].
12
CRS Report 98-960, The Federal Minimum Wage and Average Hourly Earnings of
Manufacturing Production Workers, by William G. Whittaker.
13
The increase in the Gini coefficient for men from 1999 to 2003 was significant at the 10%
(continued...)
CRS-13
for hourly women increased from .340 to .351. For salaried women, the coefficient
increased from .336 in 1999 to .341 in both 2002 and 2003.
Earnings Ratios
The Gini coefficient measures the overall degree of inequality, but may not
capture changes in the shape of the earnings distribution. Thus, analyses of the
distribution of earnings using the Gini coefficient are often supplemented with other
measures of earnings inequality. This section examines ratios in the earnings of
workers at the 90th, 50th, and 10th percentiles. The next section examines the share
of earnings received by each quintile of workers.
Like the above analysis of earnings using the Gini coefficient, Tables 4 through
6 show that the distribution of weekly earnings among all wage and salary workers
was more unequal than among either hourly or salaried workers.
All Wage and Salary Workers. The earnings ratios for all wage and salary
workers in Table 4 indicate that inequality declined from 1994 to the early 2000s,
and then increased. From 1994 to 2000, the 90/10 ratio for all wage and salary
workers fell from 7.48 to 6.80, and rose to 7.00 in 2003.14 The same pattern held for
both men and women. Among men, from 1994 to 2001, the 90/10 ratio fell from
6.33 to 6.01, and increased to 6.41 in 2003. For women, the 90/10 ratio fell from
7.50 in 1994 to 6.60 in 2001, and rose to 7.05 in 2003. Thus, unlike the Gini
coefficient, the 90/10 ratio suggests that, from 1994 to the end of the decade,
inequality declined among both men and women (and not just among women).
The reason for the decline in the 90/10 ratios from the mid-1990s to the early
2000s was that weekly earnings at the 10th percentile increased more than weekly
earnings at the 90th percentile. From 1994 to 2000, earnings among all wage and
salary workers at the 10th percentile increased by 38.9% (from $126.00 to $175.00),
while earnings at the 90th percentile increased by 26.3% (from $942.30 to
$1,190.40).15 Among men, from 1994 to 2001, earnings at the 10th percentile
increased by 41.2% (from $170.00 to $240.00) and by 34.0% (from $1,076.00 to
$1,442.30) at the 90th percentile. Among women, during the same time period,
13
(...continued)
confidence level.
14
The increase in the 90/10 ratio from 2000 to 2003 for all wage and salary workers was
significant at the 10% confidence level.
15
Weekly earnings at the 10th percentile can increase relative to earnings at higher
percentiles because of a relative increase in hourly wages, a relative increase in the average
workweek, or both. For example, from 1994 to 2000, the average number of hours usually
worked per week among workers in the lowest decile of wage and salary workers increased
more than the average workweek among workers in the highest decile. Among workers in
the lowest decile, average hours usually worked increased by 1.1 hours (from 19.2 to 20.3
hours), compared to an increase of 0.4 hours (from 43.5 to 43.9 hours) among workers in
the top decile. Similarly, from 1994 to 2000, average hourly earnings among workers in the
first decile increased by 31.5% (from $4.38 to $5.76), compared to an increase of 24.1%
(from $18.73 to $23.25) among workers in the top decile.
CRS-14
earnings at the 10th percentile increased by 50.0% (from $100.00 to $150.00) and by
32.0% (from $750.00 to $990.38) at the 90th percentile.
By contrast, the reason for the increase in inequality at the beginning of the
current decade is that earnings at the 90th percentile increased more than earnings at
the 10th percentile. From 2000 to 2003, earnings among all wage and salary workers,
increased by 8.6% at the 10th percentile (from $175.00 to $190.00) and by 11.8%
(from $1,190.40 to $1,330.00) at the 90th percentile. Among men, from 2001 to
2003, earnings did not increase at the 10th percentile, but increased by 6.6% (from
$1,442.30 to $1,538.00) at the 90th percentile. Among women, during the same
period, earnings at the 10th percentile did not increase, but increased by 6.7% (from
$990.38 to $1,057.00) at the 90th percentile.
Table 4. Earnings Ratios for All Wage and Salary Workers,
1994-2003
Year
90/10 Ratio
90/50 Ratio
50/10 Ratio
All Workers
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
7.48
7.37
7.41
7.10
7.03
7.10
6.80
6.83
6.96
7.00
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
6.33
6.40
6.41
6.14
6.25
6.14
6.02
6.01
6.25
6.41
2.36
2.40
2.41
2.34
2.35
2.40
2.38
2.43
2.45
2.46
3.17
3.07
3.08
3.03
2.99
2.95
2.86
2.81
2.84
2.84
2.24
2.25
2.31
2.27
2.27
2.30
2.31
2.36
2.42
2.40
2.82
2.85
2.78
2.70
2.75
2.67
2.61
2.54
2.58
2.67
2.32
2.35
2.33
2.31
2.30
2.27
2.36
2.29
2.29
2.29
3.23
3.12
3.12
2.98
3.02
3.04
2.83
2.88
2.97
3.07
Men
Women
1994
7.50
1995
7.33
1996
7.26
1997
6.88
1998
6.92
1999
6.92
2000
6.67
2001
6.60
2002
6.79
2003
7.05
Source: Calculated by CRS from the monthly CPS.
CRS-15
Table 4 also shows that the 50/10 ratio of earnings for all wage and salary
workers declined from 1994 to 2001 (from 3.17 to 2.81). The same pattern held for
men (from 1994 to 2001) and women (from 1994 to 2000).
Hourly and Salaried Workers. Tables 5 and 6 indicate that, from 1994 to
the end of the decade, inequality declined among hourly workers but that among
salaried workers inequality was unchanged. From 1994 to 2001, the 90/10 ratio of
earnings for hourly workers declined from 6.97 to 5.93. The 50/10 and 90/50 ratios
also declined (from 1994 to 2002 and from 1996 to 2000, respectively). Among
salaried workers, on the other hand, the changes in the earnings ratios were not
statistically significant.
Table 5. Earnings Ratios for Hourly Workers, 1994-2003
Year
90/10 Ratio
90/50 Ratio
All Workers
1994
6.97
2.25
1995
6.84
2.27
1996
6.78
2.29
1997
6.41
2.26
1998
6.40
2.22
1999
6.20
2.15
2000
6.08
2.13
2001
5.93
2.20
2002
6.00
2.23
2003
6.23
2.23
Men
1994
5.88
2.08
1995
5.89
2.06
1996
5.89
2.10
1997
5.87
2.20
1998
5.67
2.14
1999
5.53
2.14
2000
5.38
2.11
2001
5.26
2.08
2002
5.21
2.08
2003
5.43
2.13
Women
1994
6.41
2.18
1995
6.31
2.15
1996
6.15
2.14
1997
5.97
2.09
1998
5.87
2.13
1999
5.86
2.08
2000
5.77
2.06
2001
5.76
2.06
2002
6.03
2.11
2003
6.14
2.12
Source: Calculated by CRS from the monthly CPS.
50/10 Ratio
3.10
3.01
2.97
2.83
2.88
2.88
2.85
2.70
2.69
2.80
2.83
2.86
2.81
2.67
2.65
2.59
2.56
2.53
2.50
2.55
2.94
2.93
2.87
2.85
2.75
2.82
2.80
2.80
2.86
2.89
The earnings ratios for hourly and salaried workers indicate that inequality has
increased in recent years. From 2001 to 2003, the 90/10 ratio for hourly workers
CRS-16
increased from 5.93 to 6.23. From 2000 to 2003, the 90/10 ratio for salaried workers
rose from 5.33 to 5.77.
Male and Female Workers. Finally, Tables 5 and 6 indicate that from 1994
to the late 1990s and early 2000s, among men inequality declined among both hourly
and salaried workers. Among women, inequality declined among hourly, but not
salaried, workers. From the early 2000s to 2003, the data in Tables 5 and 6 suggest
that inequality increased mainly among hourly women.
From 1994 to 2002, the 90/10 ratio for hourly men fell from 5.88 to 5.21. The
50/10 ratio fell from 2.83 to 2.50. Among salaried men, the decline in the 90/10 ratio
from 1994 to 1999 was not statistically significant, but the 50/10 ratio fell from 2.59
to 2.33.
Among women, from 1994 to 2001, the 90/10 ratio for hourly workers fell from
6.41 to 5.76; the 90/50 ratio fell from 2.18 in 1994 to 2.06 in 2000. On the other
hand, the changes in the earnings ratios for salaried women were not statistically
significant.
Table 5 shows that, from 2001 to 2003, the 90/10 ratio increased among hourly
women, indicating that inequality increased. The changes in the earnings ratios for
hourly men and for salaried men and women do not suggest a clear trend in
inequality.
Finally, the Gini coefficient for all men indicates that, from 1994 to 1999, the
distribution of earnings did not change. On the other hand, the analysis of earnings
ratios indicates that inequality among men declined. But Tables 4 through 6 also
indicate that, from 1994 to 1999, the 90/50 ratios for all, hourly, and salaried men
increased.16 The reason for the increase in the 90/50 ratios among men is that
earnings in the middle of the distribution did not rise as much as earnings at either
the top or bottom of the distribution. For example, for salaried men, earnings at the
10th percentile increased by 33.3% (from $259.61 to $346.15). Earnings at the 90th
percentile increased by 24.9% ($673.03 to $807.00). But median earnings rose by
19.9% (from $1,384.61 to $1,730.00). This is the scenario illustrated in Figure 2.
The Gini coefficient did not identify a change in the shape of the earnings
distribution: Among men, there was a slight hollowing out of the middle of the
earnings distribution.17
16
For hourly men, the 90/50 ratio was significant from 1995 to 1999 at the 10% confidence
level.
17
An analysis of annual family income for the years 1994 through 2000 found a similar
hollowing out in the distribution of income. From 1994 to 2000, inflation-adjusted median
income increased by 14.4% for families in the first quintile, by 9.6% in the middle quintile,
and by 19.3% in the top decile. The study defined income as total cash income before taxes.
Families were defined as the “primary economic unit” in a household. The study defined
a family as an individual or couple, along with others in the household who are “financially
interdependent” with the main individual or couple. Ana M. Aizcorbe, Arthur B.
Kennickell, and Kevin B. Moore, “Recent Changes in U.S. Family Finances: Evidence from
the 1998 and 2001 Survey of Consumer Finances,” Federal Reserve Bulletin, v. 89, Jan.
2003, pp. 2-5, 30.
CRS-17
Table 6. Earnings Ratios for Salaried Workers, 1994-2003
Year
90/10 Ratio
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
5.58
5.46
5.66
5.38
5.77
5.42
5.33
5.45
5.69
5.77
90/50 Ratio
50/10 Ratio
All workers
2.08
2.10
2.13
2.10
2.14
2.14
2.11
2.13
2.22
2.20
2.68
2.60
2.67
2.56
2.69
2.53
2.53
2.56
2.56
2.63
2.06
2.08
2.11
2.05
2.10
2.14
2.14
2.22
2.13
2.08
2.59
2.50
2.47
2.50
2.56
2.33
2.44
2.47
2.45
2.53
2.00
2.00
2.00
1.97
2.00
2.02
2.03
2.01
2.00
2.01
2.78
2.78
2.67
2.74
2.88
2.61
2.67
2.61
2.69
2.77
Men
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
5.33
5.21
5.21
5.13
5.38
5.00
5.22
5.49
5.21
5.27
Women
1994
5.56
1995
5.56
1996
5.35
1997
5.38
1998
5.77
1999
5.26
2000
5.42
2001
5.25
2002
5.38
2003
5.55
Source: Calculated by CRS from the monthly CPS.
Share of Earnings by Quintile
Tables 7 through 9 show the share of total weekly earnings received by quintile
for all wage and salary workers, hourly workers, and salaried workers, respectively.
Like the analysis of the Gini coefficient, the tables show that the distribution of
earnings among all wage and salary workers is more unequal than among either
hourly or salaried workers, and more unequal among all workers than among men or
women.
All Wage and Salary Workers. Table 7 shows that inequality declined
from 1994 to the end of decade. In the early 2000s, the data indicate a small increase
in inequality.
CRS-18
From 1994 to 2000, the share of earnings received by the lowest quintile of
wage and salary earners increased from 5.0% to 5.3%. The increase in the share of
earnings received by the highest quintile was not statistically significant.
The pattern for male and female wage and salary workers was similar to the
pattern for all wage and salary earners: from 1994 to 2000, the share of earnings
received by the lowest quintiles of male and female workers increased (from 5.4%
to 5.6% for men and from 5.1% to 5.5% for women).18 Changes in the share of
earnings received by the highest quintile of male (from 1994 to 1998) and female
(from 1994 to 2000) earners were not statistically significant.
In the early 2000s, the changes in the share of earnings received by quintile
suggest a slight increase in inequality. From 2000 to 2003, the share of earnings
received by the lowest quintile of wage and salary workers fell from 5.3% to 5.2%.19
From 2000 to 2002, the share of earnings received by the highest quintile increased
from 45.0% to 45.4%.
Hourly and Salary Workers. Tables 8 and 9 suggest that, from 1994 to the
end of the decade, inequality declined among hourly, but not salaried, workers. The
tables also suggest that, in the early part of the current decade, inequality increased
among both hourly and salaried workers.
Among hourly workers, the share of earnings received by the lowest quintile of
workers increased from 5.4% in 1994 to 6.0% in 2000. From 1994 to 2001, the share
of earnings received by the highest quintile of hourly earners fell from 42.4% to
41.6%. Among salaried workers, from 1994 to 1999, the changes in the shares of
earnings received by the bottom and top quintiles were not statistically significant.
From 2000 to 2003, the share of earnings received by the lowest quintile of
hourly earners fell from 6.0% to 5.8%, suggesting an increase in inequality.20 Among
salaried workers, from 1999 to 2002, the share of earnings received by the highest
quintile increased from 42.2% to 42.9%, which also suggests an increase in
inequality. Among salaried workers, the share of earnings received by the middle
three quintiles of workers declined from 1996 to 2002 (from 52.0% to 51.1%). In the
early 2000s, the increased share of earnings received by the highest quintile was
partially offset by the decline in the share of earnings received by the middle three
quintiles.
Male and Female Workers. Tables 8 and 9 suggest that from the mid1990s to the end of the decade, inequality declined among both hourly and salaried
men. Among women, inequality declined mainly among hourly workers. The data
in Tables 8 and 9 suggest that, in the early 2000s, inequality increased among hourly
women and salaried men.
18
The increased share of earnings received by the lowest quintile of men was significant at
the 10% confidence level.
19
The reduced share of earnings received by the lowest quintile of wage and salary workers
was significant at the 10% confidence level.
20
The decrease in the share of earnings received by the lowest quintile of earners was
significant at the 10% confidence level.
CRS-19
From 1994 to 2000, the share of earnings received by hourly men in the lowest
quintile increased from 5.9% to 6.3%. From 1994 to 1999, the share of earnings
received by salaried men in the lowest quintile increased from 6.1% to 6.5%. From
1999 to 2003, on the other hand, the share of earnings received by the lowest quintile
of salaried men fell from 6.5% to 6.1%.21
From 1994 to 2000, the share of earnings received by hourly women in the
lowest quintile increased from 5.6% to 6.1%, while the share of earnings received by
women in the highest quintile fell from 41.7% to 40.4%. In the early 2000s,
however, this trend reversed: the share of earnings received by the lowest quintile fell
(from 6.1% to 5.8% between 2000 and 2003) and the share of earnings received by
the highest quintile increased (from 40.4% to 41.3% between 2001 and 2003).22
Finally, from the mid-1990s to the early 2000s, among both men and women
there was a slight decline in share of earnings received by the middle three quintiles
of salaried workers. From 1996 to 2002 the share of earnings received by the middle
three quintiles of male salaried workers fell from 51.9% to 51.1%. From 1996 to
2003, the share of earnings received by the middle three quintiles of female salaried
workers fell from 54.2% to 53.2%.23 Some analysts define the middle class as the
middle three quintiles of earners.24
21
The changes in the share of earnings received by salaried men in the lowest quintile
from 1994 to 1999 and from 1999 to 2003 were significant at the 10% confidence level.
22
The changes in the share of earnings received by hourly women in the lowest and
highest quintiles were significant at the 10% confidence level.
23
The reduction in the share of earnings received by the three middle quintiles of salaried
earners was significant at the 10% confidence level for both men and women.
24
An analysis of household income concluded that the percentage of households earnings
between $35,000 and $49,000 (in inflation-adjusted, or constant, dollars) fell from 22.3%
in 1967 to 15.0% n 2003. Griff Witte, “As Income Gap Widens, Uncertainty Spreads,”
Washington Post, Sept. 20, 2004, p. A1.
CRS-20
Table 7. Share of Total Weekly Earnings of All Wage and Salary Workers by Quintile, 1994-2003
1994
1995
1996
1997
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.0
10.7
16.1
23.5
44.8
5.0
10.7
16.1
23.5
44.7
5.0
10.7
16.1
23.5
44.8
5.1
10.7
15.9
23.3
45.0
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.4
11.0
16.3
23.4
44.0
5.4
11.1
16.4
23.3
43.9
5.4
11.0
16.3
23.3
44.0
5.4
10.9
16.2
23.2
44.2
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.1
11.1
16.6
23.9
43.3
5.2
11.1
16.5
23.9
43.3
5.2
11.1
16.5
23.9
43.4
5.3
11.2
16.4
23.6
43.6
1998
1999
All workers
5.2
5.3
10.7
10.8
16.0
15.9
23.2
23.2
44.9
44.9
Men
5.6
5.6
11.0
11.0
16.2
16.2
23.2
23.1
44.0
44.2
Women
5.3
5.4
11.2
11.3
16.4
16.5
23.6
23.5
43.5
43.3
Source: Calculated by CRS from the monthly CPS. Details may not add to totals because of rounding.
2000
2001
2002
2003
5.3
10.8
15.9
23.0
45.0
5.3
10.8
15.8
23.0
45.1
5.2
10.7
15.8
23.0
45.4
5.2
10.7
15.8
23.0
45.3
5.6
11.0
16.1
22.9
44.5
5.6
10.9
16.0
22.9
44.6
5.6
10.8
15.8
22.8
45.0
5.5
10.8
15.9
22.9
45.0
5.5
11.3
16.4
23.4
43.3
5.4
11.3
16.4
23.4
43.5
5.3
11.2
16.4
23.5
43.7
5.3
11.2
16.4
23.5
43.6
CRS-21
Table 8. Share of Total Weekly Earnings of Hourly Workers by Quintile, 1994-2003
1994
1995
1996
1997
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.4
11.5
16.7
23.9
42.4
5.5
11.7
16.8
23.9
42.1
5.6
11.7
16.8
23.8
42.1
5.7
11.8
16.7
23.6
42.2
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.9
11.9
17.1
24.1
41.0
5.9
12.0
17.3
24.2
40.7
5.9
12.0
17.2
24.2
40.7
6.0
12.0
17.1
23.9
41.1
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.6
11.8
17.1
23.8
41.7
5.7
12.0
17.3
23.8
41.3
5.7
12.1
17.3
23.7
41.2
5.9
12.3
17.3
23.7
40.9
1998
1999
All Workers
5.8
5.9
11.9
12.1
16.8
16.9
23.6
23.5
41.9
41.6
Men
6.2
6.3
12.2
12.3
17.1
17.1
23.9
23.8
40.7
40.5
Women
5.9
6.0
12.3
12.5
17.3
17.4
23.6
23.5
40.9
40.6
Source: Calculated by CRS from the monthly CPS. Details may not add to totals because of rounding.
2000
2001
2002
2003
6.0
12.1
16.9
23.5
41.6
5.9
12.1
16.9
23.4
41.6
5.9
12.1
16.9
23.4
41.7
5.8
12.1
16.9
23.5
41.8
6.3
12.3
17.0
23.7
40.6
6.3
12.3
17.0
23.8
40.6
6.4
12.3
17.0
23.6
40.8
6.3
12.3
17.0
23.6
40.9
6.1
12.5
17.4
23.5
40.4
6.1
12.5
17.5
23.5
40.4
5.9
12.3
17.4
23.4
41.0
5.8
12.2
17.3
23.6
41.1
CRS-22
Table 9. Share of Total Weekly Earnings of Salaried Workers, by Quintile, 1994-2003
1994
1995
1996
1997
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
5.9
12.0
16.8
23.1
42.3
5.8
12.0
16.8
23.1
42.3
5.9
12.0
16.8
23.1
42.1
6.0
12.0
16.7
22.9
42.4
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
6.1
12.1
16.8
22.9
42.1
6.2
12.1
16.8
22.9
42.0
6.2
12.1
16.8
23.0
41.9
6.3
12.1
16.8
22.7
42.2
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
6.0
12.8
17.7
23.6
40.0
5.9
12.7
17.8
23.7
39.9
6.0
12.8
17.7
23.7
39.8
6.0
12.8
17.5
23.4
40.3
1998
1999
All Workers
6.0
6.1
12.1
12.1
16.7
16.7
22.9
22.9
42.4
42.2
Men
6.4
6.5
12.1
12.2
16.7
16.7
22.9
22.7
42.0
41.9
Women
6.0
6.1
12.8
12.8
17.5
17.6
23.3
23.5
40.4
40.0
Source: Calculated by CRS from the monthly CPS. Details may not add to totals because of rounding.
2000
2001
2002
2003
6.1
12.0
16.6
22.8
42.5
6.1
12.0
16.5
22.8
42.7
6.0
11.9
16.5
22.7
42.9
6.0
11.9
16.5
22.8
42.8
6.4
12.0
16.6
22.7
42.2
6.4
11.9
16.6
22.9
42.2
6.3
11.9
16.5
22.7
42.6
6.1
11.8
16.5
22.9
42.6
6.2
12.7
17.5
23.4
40.2
6.2
12.8
17.4
23.1
40.6
6.1
12.8
17.4
23.0
40.7
6.1
12.7
17.4
23.1
40.6
CRS-23
Appendix: Data Source and Methodology
The analysis in this report uses data from the monthly Current Population
Survey (CPS). The CPS is a household survey conducted by the U.S. Bureau of the
Census for the Bureau of Labor Statistics (BLS) of the U.S. Department of Labor.
The monthly CPS is the main source of labor force data for the nation, including
estimates of the monthly unemployment rate. The CPS collects a wide range of
demographic, social, and labor market information. Each month, approximately
50,000 households are contacted to be interviewed, either in person or by phone. The
CPS collects labor force data for civilians 15 and over. The monthly CPS sample is
representative of the civilian noninstitutional population; it does not include persons
on active military duty.25,26
Each month one-fourth of the CPS sample is asked questions about current
hourly or weekly earnings. Hourly earnings are reported for persons who are paid an
hourly wage or who report their earnings on an hourly basis. Weekly earnings are
reported for wage and salary workers. The CPS generally defines wage and salary
workers as persons who work for a private or public employer and self-employed
persons who work in an incorporated business. The monthly CPS does not collect
information on earnings for persons who are self-employed. Therefore, in this report,
wage and salary earners exclude self-employed persons. Weekly earnings consist of
usual earnings before taxes and other deductions, and include tips, overtime pay, and
commissions usually received (at a person’s main job). In most cases, earnings
reported for a period other than a week are converted by BLS into a weekly amount.
The monthly CPS sample is representative of the civilian noninstitutional
population; it does not include persons on active duty in the Armed forces or persons
in institutions such as nursing homes or correctional facilities. The survey collects
information on persons who are temporarily absent from a surveyed household and
who have no other usual address. These persons include individuals who are on
vacation, away on business, and college students. The survey includes civilian
noninstitutional persons living in group quarters. (Group quarters are living quarters
where residents share common facilities. Examples may include group homes,
fraternities, or sororities.)27
The analysis in the report is based on persons ages 15 and over who are
employed and who report positive weekly or hourly earnings. The analysis includes
25
U.S. Bureau of the Census, Measuring 50 Years of Economic Change, Current Population
Reports, P60-203, Sept. 1998, p. D-1.
26
U.S. Department of Labor, Bureau of Labor Statistics, Basic Monthly Survey, available
at [http://www.bls.census.gov/cps/bglosary.htm].
27
U.S. Department of Labor, Bureau of Labor Statistics, Employment and Earnings, v. 48.
Jan. 2001, pp. 232, 236, 241. U.S. Department of Labor, Bureau of Labor Statistics,
Current Population Survey: Design and Methodology, Technical Paper 63, U.S.
Department of Labor, Mar. 2000, pp. 1-1, 3-7–3-9, 5-4, 6-5.
CRS-24
both full-time and part-time workers.28 The monthly data for each year from 1994
to 2003 were combined to calculate annual monthly averages.
In this report, average hourly earnings for wage and salary workers were
calculated by dividing usual weekly earnings by usual hours worked per week. The
calculations of average hourly earnings for both wage and salary workers and hourly
workers are weighted averages (i.e., weighted by the CPS person weight).
Monthly CPS data for years before 1994 are not strictly comparable to data for
the years 1994 and later. In January 1994 a number of changes were made in the
monthly CPS. The major change was a redesigned questionnaire. The new
questionnaire was intended to improve the quality of labor market information. The
redesigned questionnaire modified the definitions of several labor force concepts
expanded the number of questions to collect information on additional topics. In
1994, the Census Bureau also adopted a new computer-assisted interviewing
process.29 In part, for these reasons, the analysis in this report begins with data from
January 1994.30
In January 2003, the CPS introduced population controls based on the 2000
Census. Sample weights for January 2000 through December 2002 were revised to
reflect the higher population estimates from the 2000 census and the higher rate of
population growth since the census. This report uses the revised sample weights for
2000-2002. The revised weights increase the size of the labor force but have less of
an effect on percentage calculations. In January 2003, the CPS also introduced a new
occupational classification system.31 This classification system is used in Table 3.
Gini Coefficient
The Gini coefficient, named after Italian statistician Corrado Gini, ranges from
0.0, representing perfect equality, to 1.0, representing perfect inequality. The Gini
coefficient is calculated using the following formula:
28
If the analysis of weekly earnings in this report included self-employed persons, the
results would likely have shown greater inequality. On the other hand, if the analysis of
weekly earnings excluded part-time workers, the results would likely have shown greater
equality.
29
U.S. Department of Labor, Bureau of Labor Statistics, “Revisions in the Current
Population Survey Effective January 1994,” Employment and Earnings, v. 41, Feb. 1994,
pp. 13-16.
30
The redesigned 1994 survey may have raised observed wage inequality. David Card and
John E. DiNardo, “Technology and U.S. Wage Inequality: A Brief Look,” Economic
Review, v. 87, Federal Reserve Bank of Atlanta, Third Quarter 2002, p. 51.
31
Mary Bowler, Randy E. Ilg, Stephen Miller, Ed Robison, and Anne Polivka, “Revisions
to the Current Population Survey Effective in January 2003,” Employment and Earnings,
Feb. 2003, v. 51, pp. 4-5, 7, 18.
CRS-25
where fi is the proportion of earners in interval i and pi is the proportion of total
earnings received by earners in interval i and all lower intervals.32
Topcoded Earnings
In the CPS, usual weekly earnings are topcoded. That is, if a person’s weekly
earnings exceed a certain amount, the individual’s recorded earnings are cut off at a
given level. Weekly earnings are topcoded to protect the confidentiality of survey
respondents. From 1994 through 1997, usual weekly earnings were topcoded at
$1,923. From 1998 through 2003, usual weekly earnings were topcoded at
$2,884.61. In this report, estimates of average earnings were imputed for topcoded
weekly earnings using the Pareto distribution (named after the Italian economist
Vilfredo Pareto).33 Separate estimates were made for men and women for each year
from 1994 to 2003. Earnings were imputed for persons with earnings at the topcoded
amount whose earnings were topcoded — i.e., the usual earnings of some persons are
equal to the topcoded amount, so their earnings are not topcoded. To illustrate, the
following estimates of average weekly earnings were imputed for 2003: $4,496.42
for men and $4,117.93 for women.
Confidence Levels
Estimates based on survey responses from a sample of households have two
kinds of error: nonsampling error and sampling error. Examples of nonsampling
error include information that is misreported and errors made in processing collected
information. Sampling error occurs because a sample, and not the entire population,
of households is surveyed. The difference between an estimate based on a sample
of households and the actual population value is known as sampling error. When
using sample data, researchers typically construct confidence intervals around
population estimates. Confidence intervals provide information about the accuracy
of estimated values. With a 95% confidence interval and repeated samples from a
population, 95% of intervals will generally include the actual value of a population
characteristic.
Confidence intervals also provide a way to test hypotheses. For example,
suppose that the average weekly earnings for a group of workers, based on survey
data, is $500 and that the 95% confidence interval for that estimate is $475 to $525
(i.e., $500 ± $25). If a researcher’s hypothesis is that the average earnings for this
group of workers is $460, the hypothesis would be rejected at the 95% confidence
level (i.e., the 95% confidence interval does not include $460). In a similar way,
32
U.S. Bureau of the Census, Studies in the Distribution of Income, Series P60-183, 1992,
p. 60.
The Pareto distribution is given by: N = AY - ", where Y represents the level of earnings
and N is the proportion of persons with earnings equal to or greater than Y. A and " are
coefficients that can be estimated using ordinary least squares. (Martin Bronfenbrenner,
Income Distribution Theory, Chicago, Aldine-Atherton, 1971, p. 44.) In this report, A and
" were estimated using a segment of the earnings distribution preceding, but not including,
the topcoded amount. The segment of the earnings distribution used to estimate A and "
was that segment that best predicted the number of persons whose earnings were topcoded.
33
CRS-26
confidence intervals are used to test for differences between groups and for changes
over time.
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