Real Wage Trends, 1979 to 2019
Updated December 28, 2020
Congressional Research Service
https://crsreports.congress.gov
R45090




Real Wage Trends, 1979 to 2019

Summary
Wage earnings are the largest source of income for many workers, and wage gains are a primary
lever for raising living standards. Reports of stagnant median wages have therefore raised
concerns among some that economic growth over the last several decades has not translated into
gains for al worker groups. To shed light on recent patterns, this report estimates real (inflation-
adjusted) wage trends at the 10th, 50th (median), and 90th percentiles of the wage distributions for
the workforce as a whole and for several demographic groups, and it explores changes in
educational attainment and occupation for these groups over the 1979 to 2019 period.
Key findings of this report include the following:
Real wages rose at the top of the distribution, whereas wages rose at lower
rates or fell at the middle and bottom. Real (inflation-adjusted) wages at the
90th percentile increased over 1979 to 2019 for the workforce as a whole and
across sex, race, and Hispanic ethnicity. However, at the 90th percentile, wage
growth was much higher for White workers and lower for Black and Hispanic
workers. By contrast, middle (50th percentile) and bottom (10th percentile) wages
grew to a lesser degree (e.g., women) or declined in real terms (e.g., men).
The gender wage gap narrowed, but other gaps did not. From 1979 to 2019,
the gap between the women’s median wage and men’s median wage became
smal er. Gaps expanded between the median wages for Black and White workers
and for Hispanic and non-Hispanic workers over the same period.
Real wages fell for workers with lower levels of educational attainment and
rose for highly educated workers. Wages for workers with a high school
diploma or less education declined in real terms at the top, middle, and bottom of
the wage distribution, whereas wages rose for workers with at least a college
degree. The wage value of a college degree (relative to a high school education)
increased markedly over 1979-2000. The college wage premium has leveled
since that time, but it remains high. High-wage workers, as a group, benefited
more from the increased payoff to a college degree because they are the best
educated and had the highest gains in educational attainment over the 1979 to
2019 period.
Education and occupation patterns appear to be important to wage trends.
Worker groups studied in this report were more likely to have earned a bachelor’s
or advanced degree in 2019 than workers in 1979, with the gains in college
degree attainment being particularly large for workers in the highest wage
groups. For some low- and middle-wage worker groups, however, these
educational gains were not sufficient to raise wages. Workers’ occupational
categories appear to matter as wel and may help explain the failure of education
alone to raise wages.
The focus of this report is on wage rates and changes at selected wage percentiles, with some
attention given to the potential influence of educational attainment and the occupational
distribution of worker groups on wage patterns. Other factors are likely to contribute to wage
trends over the 1979 to 2019 period as wel , including changes in the supply and demand for
workers, labor market institutions, workplace organization and practices, and macroeconomic
trends. This report provides an overview of how these broad forces are thought to interact with
wage determination, but it does not attempt to measure their contribution to wage patterns over
the last four decades. For example, changes over time in the supply and demand for workers with
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Real Wage Trends, 1979 to 2019

different skil sets (e.g., as driven by technological change and new international trade patterns)
are likely to affect wage growth. A declining real minimum wage and decreasing unionization
rates may lead to slower wage growth for workers more reliant on these institutions to provide
wage protection, whereas changes in pay-setting practices in certain high-pay occupations, the
emergence of superstar earners (e.g., in sports and entertainment), and skil -biased technological
changes may have improved wage growth for some workers at the top of the wage distribution.
Macroeconomic factors, business cycles, and other national economic trends affect the overal
demand for workers, with consequences for aggregate wage growth, and may affect employers’
production decisions (e.g., production technology and where to produce) with implications for the
distribution of wage income. These factors are briefly discussed at the end of the report.
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Contents
Introduction ................................................................................................................... 1
Real Wage Trends ........................................................................................................... 2
Wage Trends for Low, Middle, and High Earners by Sex, Race, Ethnicity, and
Educational Attainment ................................................................................................. 7
Low-Wage Workers ................................................................................................... 8
Middle-Wage Workers................................................................................................ 9
High-Wage Workers ................................................................................................... 9
Wage Gaps ............................................................................................................... 9
Wages by Educational Attainment: The College Premium .............................................. 10
Skilled Trades ......................................................................................................... 13
Worker Characteristics by Wage Group ............................................................................ 14
Low-Wage Workers ................................................................................................. 16
Middle-Wage Workers.............................................................................................. 17
High-Wage Workers ................................................................................................. 17

Factors Affecting Wage Trends........................................................................................ 21
Market Factors ........................................................................................................ 21
Institutional Factors ................................................................................................. 23
Macroeconomic Factors............................................................................................ 24

Figures
Figure 1. Annualized Real Wage Growth by Percentile and Demographic ................................ 6
Figure 2. Wages at Selected Percentiles, by Sex, Race, and Ethnicity, in 1979 and 2019 ............. 8
Figure 3. Median Wage Ratios, 1979-2019 ........................................................................ 10
Figure 4. Median Wage by Educational Attainment ............................................................ 12
Figure 5. College Degree Wage Premium and Advanced Degree Wage Premium, Relative
to a High School Education or Less............................................................................... 13
Figure 6. Median Hourly Wages by Broad Occupation Group, May 2019 .............................. 15

Tables
Table 1. Real Wage Trends over 1979-2019, by Selected Demographic Characteristics .............. 4
Table 2. Wage Trends by Education and the Higher-Education Wage Premium ....................... 11
Table 3. Occupations with High Projected Employment Growth and High Annual
Earnings That Do Not Require a Post-Secondary Degree .................................................. 14
Table 4. Low-Wage Workers’ Educational Attainment and Occupation, by Selected
Demographics, 1979 and 2019 ..................................................................................... 18
Table 5. Middle-Wage Workers’ Educational Attainment and Occupation, by Selected
Demographics, 1979 and 2019 ..................................................................................... 19
Table 6. High-Wage Workers’ Educational Attainment and Occupation, by Selected
Demographics, 1979 and 2019 ..................................................................................... 20

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Table B-1. Worker Characteristics by Wage Tercile, 1979 and 2019 ...................................... 29

Appendixes
Appendix A. Data Used in this Report .............................................................................. 26
Appendix B. Demographic and Occupational Composition of the Wage Distribution in
1979 and 2019 ........................................................................................................... 28

Contacts
Author Information ....................................................................................................... 30


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Introduction
Wage earnings are the largest source of income for many workers, and wage gains are a primary
lever for raising living standards.1 Evidence that wage growth has stagnated among low- and
middle-wage workers has therefore been viewed with concern and has raised questions about the
patterns and magnitudes of these trends.
This report addresses such questions by examining real (inflation-adjusted) wage trends over the
1979 to 2019 period.2 Specifical y, it uses cross-sectional data collected from the Current
Population Survey (CPS), a national y representative sample of workers, to estimate real hourly
wages at the 10th, 50th (median), and 90th percentiles of the wage distribution in each year, and
then explores how those wage levels change over time.3 The sample comprises employed (full-
and part-time), nonmilitary nonfarm wage and salary earners aged 25 to 64 years. Final y, al
hourly wages were converted to 2019 dollars using the Consumer Price Index for Al Urban
Consumers, U.S. City Average (CPI-U).4 Appendix A provides details on the methodology used
in this report.
While wages are typical y the primary component of compensation—accounting for about 70%
of compensation for the average worker—non-wage compensation, such as employer-provided
health insurance, paid leave, and retirement contributions, plays a role in living standards as
wel .5 Workers may experience gains or losses in wages but overal compensation may not track
these changes exactly because of the cost of non-wage compensation. For example, a 2015 study
from the Bureau of Labor Statistics (BLS) found that while the overal median wage fel between
2007 and 2014, total compensation was statistical y unchanged, mainly due to the rising costs of
health insurance.6 In addition, due to the relative costs and provisions of benefits for workers at

1 According to Congressional Budget Office (CBO) analysis of incomes in 2017, wage and salary income made up at
least 62% of market income for households in the lower 95 % of the income distribution. Labor income comprised
nearly 58% of market income for households in the 96 th to 99th percentiles. At 31%, labor earnings make up a lower,
but still significant, share of household income among the top 1%. CBO defines market income as labor income,
business income, capital gains realized from the sale of assets, capital income excluding capital gains, and income
received in retirement for past services or from other sources. Conceptually, these percentages underestimate labor
income because they exclude business income, and some business owners contribute labor to their firms and are
compensated in the form of business income in lieu of wages. CBO, The Distribution of Household Incom e and
Federal Taxes, 2017
, October 2020, supplementary data, at https://www.cbo.gov/publication/56575.
2 T he analysis starts in 1979 because that is the first year for which comparable data to future years are available.
3 T he data used to create annual hourly wage distributions (1979-2019) are from the Current Population Survey (CPS)
Outgoing Rotation Groups (ORGs). Appe ndix A documents methods used to address outliers (i.e., implausibly low or
high wage reports), the Census Bureau’s practice of “top-coding” information on earnings, and other issues.
4 T he CPI-U, which is a measure of the average change over time in prices paid by consumers for a market basket of
goods and services, is commonly used to compare the real (inflation-adjusted) value of earnings or spending data at
different points in time. T he CPI-U, for example, is the most common index used to adjust state minimum wage rates.
Other indices used to adjust for inflation in wage studies include the Consumer Price Index Research Series Using
Current Methods (CPI-U-RS) and the Price Index for Personal Consumption Expenditures (PCE). As a point of
comparison, from 1979 to 2019, the average annual increases in the CPI -U, CPI-U-RS, and PCE were 3.2%, 3.0%, and
2.7%, respectively. For a detailed description of indices used to adjust wages and a comparison of the values for
different indices, see CRS Report R44667, The Federal Minim um Wage: Indexation , by David H. Bradley. T here is no
correction for regional price differences.
5 In June 2020, about 32% of the average worker’s total compensation was in the form of employer-provided benefits.
See Bureau of Labor Statistics, U.S. Department of Labor, Em ployer Costs for Em ployee Com pensation – June 2020
2020
, USDL-20-1736, Washington, DC, September 17, 2020, https://www.bls.gov/news.release/pdf/ecec.pdf.
6 Kristen Monaco and Brooks Pierce, Compensation Inequality: Evidence from the National Compensation Survey,
Bureau of Labor Statistics, U.S. Department of Labor, Monthly Labor Review, Washington, DC, July 2015,
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different points in the wage distribution, trends in wage and compensation inequality may differ
over time.7
Because the data are cross-sectional, the trends identified in this report describe patterns among
groups of workers at different percentiles in the wage distribution, but not the experience of
individual workers. That is, because the CPS does not track the wages of a fixed group of workers
over long periods of time, a finding that median wages have stagnated over the 1979 to 2019
period does not necessarily mean that a worker earning the median wage in 1979 personal y
experienced zero wage growth over this period. Individuals can and do move throughout the
wage distribution over time. Instead, wage stagnation at the median indicates that the wage level
below which half the population earns has not risen considerably between 1979 and 2019, as
might be expected if overal living standards had increased broadly (i.e., such that the entire wage
distribution shifted upwards).
In summary, analysis of the data shows that overal wages rose in real terms over the 1979 to
2019 period at the top of the wage distribution, increased more modestly at the middle of the
wage distribution, and rose to an even lesser degree at the bottom of the distribution. Within these
overal trends, there were important differences in patterns across demographic groups (e.g.,
median wages for women increased, whereas those for men declined). Differential patterns of
wage growth narrowed the gap between median hourly earnings of men and women (i.e., the
gender wage gap), but other wage gaps did not show such change over time. Real wages fel for
workers with lower levels of educational attainment (i.e., a high school degree or less) and rose
for highly educated workers, contributing to a wage gap between workers with different
educational attainment levels that grew markedly over the 1979 to 2000 period and has plateaued
since then. The rising wage premium to post-secondary education has likely contributed to
relatively high wage growth at the top of the distribution, because workers there have greater
shares of college-educated workers. Occupational composition of worker groups appears to
matter as wel and may explain the failure of education alone to raise wages for some groups. The
report closes with a brief discussion of three groups of factors—market, institutional, and
macroeconomic—that are widely thought to contribute to wage patterns.
Real Wage Trends
This section describes trends in real hourly wages over the 1979 to 2019 period at selected wage
percentiles for nonmilitary, nonfarm workers between the ages of 25 and 64; wage patterns are
disaggregated by sex, race, Hispanic ethnicity, and education. Wage trends for low-, middle-, and
high-wage groups are examined by plotting wages at the 10th, 50th, and 90th percentiles of each
demographic group’s wage distribution over the period of study.8

https://doi.org/10.21916/mlr.2015.24.
7 For example, in the 2007 to 2014 period, BLS found that wage inequality was lower than compensation inequality
due in part by more costly benefits for higher-wage workers. Kristen Monaco and Brooks Pierce, Com pensation
inequality: evidence from the National Com pensation Survey
, Bureau of Labor Statistics, U.S. Department of Labor,
Monthly Labor Review, Washington, DC, July 2015, https://doi.org/10.21916/mlr.2015.24.
8 Wage percentiles indicate the wage level below which a certain share of a population falls. For example, a 10th
percentile of $12.00 for the overall population of wage earners indicates that 10% of wage earners have wages less than
$12.00. Likewise, a 10th percentile wage of $9.75 for women indicates that 10% of female wage earners have wages
less than $9.75. T his report uses the conventional approach of studying wages at the 10 th, 50th, and 90th percentiles to
estimate wage trends for low, middle, and high-wage earners, respectively. As a check, the same analysis presented in
this report was conducted at the 20th and 80th percentiles to test that these patterns were not unique to the 10 th and 90th
percentile wage trends. T hese checks confirmed that similar patterns of wage growth held across the demographic
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Wage trends are examined separately within demographic groups because workers in these
groups are not distributed proportionately within the overal wage distribution. A sole focus on
the overal wage distribution would therefore mask important differences in wage trends between
groups. For example, because workers at the top of the distribution are disproportionately male,
White, and, non-Hispanic (see Appendix B), tracking trends only in the overal distribution
provides information mainly for those workers and may miss trends among relatively high-
earning workers in other groups. Appendix B provides detailed data on the composition of
different parts of the wage distribution in 1979 and 2019.
In addition to trends, estimated wage levels (i.e., dollars per hour) are presented at various points
in time and wages are compared and contrasted across worker groups. As is always the case,
wage estimates are influenced by the methodology used to produce them. For example, potential
outliers are addressed by excluding very high and very low wages from the sample; related
studies that do not “trim” their data in this way may achieve different wage estimates at the
various percentiles.9 The methods used in this report are summarized in Appendix A.
As noted earlier, data used to analyze wage trends are cross-sectional, meaning that a separate
national y representative sample of workers is used to describe wages in each year. For this
reason, trends in this section do not demonstrate wage patterns for a fixed set of workers.
Individual workers can and often do move throughout the wage distribution over time, such that a
worker at the 50th percentile in 1980 may be at a higher or lower percentile in subsequent years.10
Table 1 provides graphic presentations of real hourly wages across different demographic groups
from 1979 to 2019. Also presented is the cumulative percentage change in real hourly wages at
the 10th, 50th, and 90th percentiles between 1979 and 2019. It is worth noting that this measure is
calculated using wage data only in those two years, and wil therefore be very sensitive to year-to-
year changes at the endpoints.11 A negative cumulative percentage does not indicate, for example,
that wages have fal en continuously over the entire 1979 to 2019 period.

groups, with some exceptions. Cumulative wage growth at the 80 th percentile, while lower than that at the 90 th
percentile, was positive and higher than that at the median. Cumulative wage growth at the 20 th percentile tends to be
lower than that at the median and close or higher than that at the 10 th percentile, but this was not always the case. For
example, Black workers and Hispanic workers had higher cumulative wage growth rates at the 20 th percentile than at
the median.
9 Similarly, the earnings data used in this study are “top-coded” for very high earners, which means that actual earnings
are not observed above a given dollar level (called a “top-code”). T here are several ways of addressing this empirical
challenge; CRS’s methods are described in Appendix A.
10 In addition, wage trends in this study reflect patterns among employed workers. Unemployed workers and th ose not
participating in the labor market are not included in the analysis. T he large job losses that occurred during the 2007 to
2009 economic recession as well as the continued pattern of declining labor force participation rates since the late
1990s may affect wage trends, particularly at the lower end of the distribution. For example, if low-wage workers drop
out of the labor force because they are discouraged by their earnings prospects, the reduction in labor supply (and
compositional effects) may result in wages higher than they would be if such workers remained in the workforce. In
this study, it is not possible to estimate the size of such an effect.
11 For example, the cumulative percentage change between 1979 and 2019 in hourly wages for non -Hispanic Black
workers at the 10th percentile was 7.7% (Table 1). T he cumulative percentage change between 1979 and 2018 was -
0.3% for this group, between 1979 and 2017 it was 2.1%; between 1979 and 2016 it was -0.9%. T he year-to-year
difference is in each of these examples driven entirely by year -to-year changes in the 10th percentile wage level for non-
Hispanic Black workers over the 2016 to 2019 per iod.
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Real Wage Trends, 1979 to 2019

Table 1. Real Wage Trends over 1979-2019, by Selected Demographic Characteristics
Demographic
Real Wage Trends
Cumulative % Change in Real Wages

Shaded Bars = Recessions
10th percentile
50th percentile 90th percentile
Overal
6.5%
8.8%
41.3%

Men
-7.7%
-3.0%
41.9%

Women
9.6%
28.8%
70.6%

White (Non-
Hispanic)
11.8%
13.5%
46.3%

Black (Non-Hispanic)
7.7%
1.2%
28.5%

Hispanic
-0.6%
-2.2%
14.0%

Non-Hispanic
6.7%
10.1%
42.7%

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Sources: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Recession data are from the National Bureau of Economic Research, at http://www.nber.org/cycles.html.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Periods of recession are shaded in gray. Dol ar amounts are adjusted
for inflation using the Bureau of Labor Statistics Current Price Index for Al Urban Consumers (CPI-U);
https://www.bls.gov/cpi/.
Wages at the 90th percentile increased across demographic groups, ranging from rates of 14.0%
(Hispanic workers) to 70.6% (women). Overal , wages at the 90th percentile increased from an
estimated $39.14 to $55.29 (a 41.3% increase) over the 40 years between 1979 and 2019, but the
growth rate was not constant. After increasing by $5.10 ($39.14 to $44.24) over the 20 years from
1979 to 1999, wages at the 90th percentile grew by an estimated $11.05 over the 20 years from
1999 to 2019.12
Median wage trends were not uniform across demographic groups, with wages decreasing for
some groups (e.g., men and Hispanic workers) but increasing for others (e.g., women). Overal ,
median wages increased from an estimated $21.14 to $23.00 (a 8.8% increase) over the 1979 to
2019 period. Wages at the 10th percentile followed a similar pattern (i.e., declining for men and
Hispanic worker groups, but rising for others). Overal , wages at the 10th percentile increased in
real terms from an estimated $11.27 to $12.00 (a 6.5% increase).
To explore how real wage trends evolved over the 1979 to 2019 period, Figure 1 shows
annualized wage growth rates over various time periods (roughly a decade each) by wage
percentile and demographic group. Considering first wage growth at the 10th and 50th percentiles,
Figure 1 reveals that the 10th percentile wage declined in real terms during the 1980s for al
groups, and, with the exception of women, the median (50th percentile) wage declined as wel . In
the 1990s, 10th percentile and median wages increased for nearly al demographic groups. This
was followed by a general slowdown (and some modest declines) in real wage growth in 2000-
2010, after which (i.e., 2010-2019) 10th percentile and median wages grew for all demographic
groups. Annualized real wage growth at the 90th percentile was positive in al periods and for al
demographic groups except Black workers and Hispanic workers, for whom the 90th percentile
wage declined slightly during the 1980s.


12 Put another way, annualized wage growth was 0.6% over 1979-1999 and 1.1% over 1999-2019.
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Real Wage Trends, 1979 to 2019

Figure 1. Annualized Real Wage Growth by Percentile and Demographic

Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979 -2019.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Dol ar amounts are adjusted for inflation using the Bureau of Labor
Statistics Current Price Index for Al Urban Consumers (CPI-U); https://www.bls.gov/cpi/.
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Wage Trends for Low, Middle, and High Earners by
Sex, Race, Ethnicity, and Educational Attainment
Aggregate trends and overal averages can mask important dynamics within groups. For example,
although women as a group saw sizable wage gains across the 10th, 50th, and 90th percentiles from
1979 to 2019, the trends and growth rates varied considerably between Black and White women
and between Hispanic and non-Hispanic women.13 Similar variation occurred within other
demographic groups. Further, comparing rates of change can be misleading because worker
groups start (in 1979) at different base wages.14 For example, women’s wage growth over 1979-
2019 at the median was 28.8%, compared to a 3.0% wage loss experienced by men at the median.
However, the median wage for women in 2019 was still lower than the male median wage in the
same year.
This section explores these patterns by disaggregating the major trends in real hourly wages by
sex, race, and Hispanic ethnicity; these are presented in Figure 2, below. The discussion is
organized by earner group—low wage (10th percentile), median wage (50th percentile), and high
wage (90th percentile). It bears repeating that the data used to analyze wage trends are cross-
sectional, and as such do not capture individuals’ movements between earner groups (e.g., an
individual worker may move from a lower to higher earnings group over time, or vice versa).
Women experienced rising wage levels at the 10th, 50th, and 90th percentiles in nearly al
demographic groups—the exception is Hispanic women at the 10th percentile. Among male
workers, the 10th percentile wage fel for al demographic groups except Black men between 1979
and 2019, and the median wage fel for Black men and Hispanic men but increased modestly for
White men. Wages at the 90th percentile rose for al male groups.15

13 T he race/ethnicity categories in this report —White, Black, and Hispanic—are mutually exclusive. T hat is, a “White”
or “Black” worker is non-Hispanic.
14 For example, a $5 increase translates into 50% growth if wages were $10 in 1979 and into 25% growth if wages were
$20 in 1979.
15 In interpreting trends in wages for different groups, it is important to note that changes for one wage distribution
(e.g., women overall) do not represent averages of more detailed demographic groups within this overall distribut ion.
For example, the wage distribution for women overall is separate from groups within “women” overall – White
women, Black women, and Hispanic women, which each represent a distinct distribution. T hus, when interpreting the
results, trends for groups for larger demographic are not the weighted average of the subgroups within that larger
demographic.
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Real Wage Trends, 1979 to 2019

Figure 2. Wages at Selected Percentiles, by Sex, Race, and Ethnicity, in 1979 and 2019
Wages in 2019 dol ars

Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Notes: White and Black worker groups refer to non-Hispanic White and non-Hispanic Black workers,
respectively. Dol ar amounts are adjusted for inflation using the CPI-U.
Low-Wage Workers
Wages at the 10th percentile fel in real terms over 1979-2019 for Hispanic women and Hispanic
men and White men, and increased to varying degrees for other groups.16 In 1979, wages at the
10th percentile ranged from $10.22 for Black and Hispanic women to $14.68 for White men,
whereas in 2019 wages in the 10th percentile ranged from $10.00 for Hispanic women to $14.38
for White men.
Men’s wages at the 10th percentile fel by 7.7% ($14.09 to $13.00) from 1979 to 2019. Within the
group of low-wage male earners, however, White men experienced the largest percentage decline
from 1979 to 2019, a drop of 2.0% ($14.68 to $14.38), and a 1.8% decline for Hispanic men
($11.45 to $11.25); Black men’s wages increased by 3% ($11.10 to $11.43).17

16 T his pattern of wage growth for low-wage workers differs from patterns between 1979 and 2018, over which period
the 10th percentile wage declined to some degree for all groups. Recent wage growth in the lower portion of the wage
distribution may be driven in part by recent state-level minimum wage increases. See CRS Report R43792, State
Minim um Wages: An Overview
, by David H. Bradley and Abigail R. Overbay.
17 As noted earlier (see footnote 11), when analysis compares only two data points (in this case 1979 and 2019),
findings are sensitive to year-to-year changes in at the endpoints. For example, when the 1979 to 2017 period is
considered, the wages of Hispanic men at the 10 th percentile had the largest percentage decline (by 8.9%), followed by
White men (7.6% decline), and Black men (6.0% decline).
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Real Wage Trends, 1979 to 2019

Women’s wages at the 10th percentile rose by 9.6% between 1979 and 2019, from $10.25 to
$11.24. When looked at by race and ethnicity, it appears that the overal improvement in wages
among low-wage women was driven mainly by the gains (13.5%) in hourly earnings for White
women ($10.57 to $12.00) and, to some extent, by the 1.3% gains for Black women ($10.22 to
$10.35). For low-wage Hispanic women, 10th percentile wages fel by 2.2% from $10.22 to
$10.00.
Middle-Wage Workers
Wage trends at the median (50th percentile) diverged sharply between men and women from 1979
to 2019. Overal , median wages for men fel by 3.0% but rose by 28.8% for women. In 1979,
median wages ranged from $13.74 for Hispanic women to $26.42 for White men, whereas in
2019 median wages ranged from $15.87 for Hispanic women to $27.78 for White men.
While median wages for White men rose by 5.1%, from $26.42 to $27.78, over the 1979 to 2019
period, median wages for Black and Hispanic men fel . Median wages for Black men fel by
7.6%, from $20.82 to $19.23, and for Hispanic men by 8.8%, from $19.73 to $18.00.
Median wages for White women had the largest increase at 35.0% ($16.73 to $22.60), whereas
median wages for Black women increased by 23.9% ($14.69 to $18.20) and for Hispanic women
by 15.5% ($13.74 to $15.87).
High-Wage Workers
At the 90th percentile, wages grew across al groups, but the magnitude and levels varied by sex
and race. Overal , wages for men at the 90th percentile rose by 41.9% and for women by 70.6%.
In 1979, wages at the 90th percentile ranged from $25.01 for Hispanic women to $44.03 for White
men, whereas in 2019 wages at the 90th percentile ranged from $33.63 for Hispanic women to
$68.83 for White men.
Wages for White men at the 90th percentile rose by 56.3% from 1979 to 2019, from $44.03 to
$68.83. Although wages at the 90th percentile for Black and Hispanic men also rose over this
period, they did not increase by as much. The 90th percentile wage for Black men increased by
22.1% (from $35.23 to $43.00) and for Hispanic men by 11.4% ($34.52 to $38.46).
White women at the 90th percentile experienced the largest percentage increase in wages of any
group examined in this study, with wages increasing by 70.6%, from $28.62 to $48.82. Among
Black women, the 90th percentile wage increased by 51.1%, from $27.04 to $40.87, and for
Hispanic women the increase was 34.4%, from $25.01 to $33.63.
Wage Gaps
Differential wage growth over 1979 to 2019 affected wage inequality within and between
demographic groups. The superior wage growth at the 90th percentile, alongside weaker growth or
declining wages at the bottom half of the distribution, translated into growing wage inequality
within al demographic groups, but groups varied by the degree of increased inequality. For
example, the 10th percentile wage for men was 32.0% of the 90th percentile male wage in 1979; in
2019 this ratio fel to 20.8% (i.e., the 10th percentile wage moved further away from the 90th
percentile wage over time). Among White men, the ratio fel from 33.3% to 20.9% between 1979
and 2019. The ratio declined from 31.5% to 26.6% for Black men and from 33.2% to 29.3% for
Hispanic men.
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Real Wage Trends, 1979 to 2019

As measured at the median, strong wage growth among female workers and wage loss among
men led to a narrowing of the gender wage gap. Women’s median wage as a share of men’s
median wages), increased from 62.8% to 83.5%.18 Other median wage differentials (Figure 3) did
not show similar narrowing, however. The wage gap between Black and White workers grew, as
did the gap between median-wage Hispanic workers and median-wage non-Hispanic workers.
Figure 3. Median Wage Ratios, 1979-2019

Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Periods of recession are shaded in gray. Dol ar amounts are adjusted
for inflation using the CPI-U. Al graphics use the same scale: 0%-100% on vertical axis, and years 1979-2019 on
the horizontal axis.
Wages by Educational Attainment: The College Premium
The rise in real hourly wages for workers with higher levels of educational attainment stands out
among wage trends over the 1979 to 2019 period.19 Specifical y,
 Among workers with a bachelor’s or advanced degree, wages at the 10th, 50th,
and 90th percentiles rose in real terms between 1979 and 2019, with increases of
6.9%, 15.2%, and 42.1%, respectively (Table 2), suggesting rising demand for
college-educated workers (that is not offset by rising supply of such workers),
improved bargaining conditions for them, or both.
 Over the same period, wages declined markedly at the 10th, 50th, and 90th
percentiles for workers with a high school diploma (or equivalent) or less
education, suggesting increasingly few labor market opportunities for less-
educated workers, a decrease in wage bargaining power, or both. The median
wage for high-school-educated workers fel by 11.1%, whereas the wage at the
10th and 90th percentiles fel by 5.4% and 8.3%, respectively (Table 2).

18 T he gender wage gap is 100% minus the ratio of women’s to men’s median wages. So, the gap decreased from
37.2% (=100%-62.8%) in 1979 to 16.5% (=100%-83.5%) in 2019.
19 T he shares of workers in each category of educational attainment have shifted a great deal since 1979. In 1979, for
example, about 31% of the population age 25 and older had at least some college education, whereas th e other 69% had
a high school degree (or equivalent) or less education. By 2019, these percentages were almost reversed—62% with at
least some college and 38% with a high school diploma or less education. See U.S. Census Bureau, CPS Historical
Tim e Series Tables
, “ T able A-1. Years of School Completed by People 25 Years and Over, by Age and Sex: Selected
Years 1940 to 2019,” Washington, DC, 2020, https://www2.census.gov/programs-surveys/demo/tables/educational-
attainment/time-series/cps-historical-time-series/taba-1.xlsx.
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Real Wage Trends, 1979 to 2019

 The higher-education wage premium—the percent difference between the median
wage for bachelor’s or advanced degree holders and the median wage for
workers with a high school education or less—grew considerably from 1979 to
2000, from about 49.8% to 93.6%.20 The premium has remained high since that
time, but the growth in the gap has slowed; the premium was 94.2% in 2019.
Table 2. Wage Trends by Education and the Higher-Education Wage Premium
Cumulative % Change in
Real Wage Levels over 1979-
Education Group
Real Wage Trends
2019
50th
90th

Shaded Bars = Recessions
10th
percentile
percentile percentile
Col ege Degree Holders
6.9%
15.2%
42.1%

High School Diploma or Less
-5.4%
-11.1%
-8.3%
Education

Sources: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Recession data (in gray) are from the National Bureau of Economic Research, at http://www.nber.org/cycles.html.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Periods of recession are shaded in gray. Dol ar amounts are adjusted
for inflation using the CPI-U.
Figure 4 shows real median wages for workers at five different levels of educational attainment
from 1979 to 2019—less than a high school degree, high school degree or equivalent, some
college (including associate degrees and non-degree-holders with some college education),
bachelor’s degree, or advanced degree. The data show fal ing real median wages for workers with
less than a bachelor’s degree over the 1979 to 2019 period and rising wages for workers with at
least a bachelor’s degree. One commonality across al education groups is that most of the
changes, increasing or decreasing real wages, occurred in the 1980s and 1990s, with slower
changes occurring since about 2000 across groups. Specifical y, Figure 4 shows the following:
 Workers with less than a high school degree saw a fal in median wages from
$17.19 in 1979 to $12.99 in 2000 (a 24.4% decline); between 2000 and 2019,
wages increased by 13.5% to $14.75.
 The median wage for workers with a high school degree also fel , from $19.87 in
1979 to $17.11 in 2000; the median wage for this group increased modestly
(0.2%) over 2000 to 2019, when the median wage was $17.14.
 For workers with some college education, the median wage fel from $22.86 in
1979 to $20.79 in 2000 (a 9.1% decline) and $20.00 in 2019 (a 3.8% decline over

20 T he premium describes the difference between college-educated workers’ median wage and high school (or less)
educated workers’ median wage, as a percentage high school (or less) educated workers’ median wage.
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Real Wage Trends, 1979 to 2019

the 2000 to 2019 period). Thus, nearly three-quarters of the total decrease
occurred in the 1980s and 1990s.
 Although the median wage for workers with a bachelor’s degree rose by 9.2%,
from $26.42 to $28.85, over the 1979 to 2019 period, a considerable share of
these gains (88%) occurred between 1979 and 2000.
 For workers with education above a bachelor’s degree, median wages increased
by more than $8.00, or 27.5%, from 1979 to 2019. Median wages for this group
increased in the 2000 to 2019 period, albeit at a slower pace than in the 1979 to
2000 period.
Figure 4. Median Wage by Educational Attainment
Wages in 2019 dol ars

Sources: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Recession data (in gray) are from the National Bureau of Economic Research, at http://www.nber.org/cycles.html.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Periods of recession are shaded in gray. Dol ar amounts are adjusted
for inflation using the CPI-U.
Figure 5 shows the higher-education premium, which is the percentage difference between the
median wages received by workers with a bachelor’s degree and those with an advanced degree
(shown separately), and the median wage received by workers with a high school degree or less.21
Although the wage premium for workers with higher education rose in the 1979 to 2000 period,

21 T he rising higher-education premium suggests that labor market conditions and wage-setting institutions evolved in a
way that was relatively more beneficial for workers holding at least a bachelor’s degree (e.g., demand for skilled
workers increased relative to demand for high -school-educated workers); a body of research supports this view.
Nonetheless, others have pointed out that the differential between college degree holders and high-school-educated
workers may be overstated because highly educated workers—more so than less-educated workers—tend to
concentrate in cities with very high costs of living. See, for example, Enrico Moretti, “Real Wage Inequality,”
Am erican Econom ic Journal: Applied Econom ics, vol. 5, no. 1 (2013), pp. 65-103.
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Real Wage Trends, 1979 to 2019

the premium has been approximately flat since 2000 for workers with a bachelor’s degree. For
workers with advanced degrees, the wage premium continued to rise after 2000 but at a much
slower rate than in the 1979 to 2000 period.
Figure 5. College Degree Wage Premium and Advanced Degree Wage Premium,
Relative to a High School Education or Less

Sources: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Recession data (in gray) are from the National Bureau of Economic Research, at http://www.nber.org/cycles.html.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage. Periods of recession are shaded in gray. Dol ar amounts are adjusted
for inflation using the CPI-U.
Skilled Trades
The previous section highlighted the strong wage growth experienced by workers with at least a
bachelor’s degree (relative to workers with a high school degree or less education) over the 1979
to 2000 period, and the high and sustained wage premium for these workers thereafter (see
Figure 5). Such trends suggest elevated relative demand for skil ed workers, whereas labor
market conditions for less-skil ed workers have become less favorable. Formal education is a
common measure of worker skill, but it is not the only one. Workers can gain skil s and expertise
through nondegree postsecondary programs (e.g., certifications), apprenticeships, and on-the-job
training (formal y and informal y acquired). Recent Bureau of Labor Statistics (BLS) data and
projections point to strong and continuing demand for workers in this “middle-skil ” range (i.e.,
education and/or training beyond high school but less than a college degree) in some occupations.
For example, the occupations in Table 3 typical y do not require a post-secondary degree for
entry positions had median annual earnings in 2019 that were greater than the overal median of
$39,810 and were projected by BLS to grow by at least 50,000 jobs and with average or better
employment growth between 2019 and 2029.

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Table 3. Occupations with High Projected Employment Growth and High Annual
Earnings That Do Not Require a Post-Secondary Degree
Typical
Median
Typical Education
On-the-Job
Earnings
Employment
Occupation
Needed for Entry
Training
(2019)
(2019)
Exercise trainers and group fitness
High school diploma
Short-term on-
$40,390
373,700
instructors
or equivalent
the-job training
Licensed practical and licensed
Postsecondary
None
$47,480
721,700
vocational nurses
nondegree award
Computer user support specialists
Some col ege, no
None
$52,270
687,200
degree
Industrial machinery mechanics
High school diploma
Long-term on-
$53,590
399,400
or equivalent
the-job training
Sales representatives of services,
High school diploma
Moderate-term
$56,130
1,070,500
except advertising, insurance,
or equivalent
on-the-job
financial services, and travel
training
Electricians
High school diploma
Apprenticeship
$56,180
739,200
or equivalent
Sources: Bureau of Labor Statistics Occupational Employment Projections, at https://www.bls.gov/emp/
ep_data_occupational_data.htm; and Occupational Employment Statistics, at http://www.bls.gov/oes/.
Note: Median annual earnings across al occupations stood at $39,810 in 2019.
Worker Characteristics by Wage Group
Table 1
shows a general pattern of strong wage growth at the top of the wage distribution over
the 1979 to 2019 period, with slower growth or fal ing wages at the median and bottom of the
distribution. Although these patterns hold in general across demographic groups, there is
considerable variation in the magnitudes and patterns of change across sex, race, and Hispanic
ethnicity. For example, whereas both men and women experienced significant wage growth at the
90th percentile of their respective distributions, wage growth among female workers was nearly
30 percentage points higher than it was among men. And, although median wages for non-
Hispanic workers rose over 1979 to 2019, median wages fel for Hispanic workers.
To better understand these cross-group differences, this section compares and contrasts workers’
educational attainment and occupational distribution in 1979 and 2019.22 Because greater
educational attainment general y has a positive relationship with wages (Figure 4), worker
groups that have seen educational gains over 1979 to 2019 are more likely to have experienced
wage gains than those that did not (or did to a lesser degree).23 Shifts in occupation may affect
wage trends as wel . Occupations require different mixes of skil s and work experience, and
where the workers meeting these requirements are scarcer, wages tend to be higher. The range of

22 Many other factors are likely to influence wage patterns and contribute to cross-group variations in wage growth, but
are not addressed here. For example, changes in employment policies that affect bargaining power (e.g., no -hire rules)
and changes within occupation (e.g., in terms of worker requirements and the task content of certain jobs, such as
nursing) are not explored here.
23 For example, given that college degree holders, on average, earn higher wages than non -degree holders, a group that
increased its share of college-educated workers over that time period might be expected to see greater wage gains than
a group that did not —given the significant rise in the college premium between 1979 and 2019.
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occupational wages is il ustrated in Figure 6, which shows median hourly wages spanning $11.65
(food preparation and serving workers) to $50.80 (managers) in May 2019; across al occupations
the median hourly wage was $19.14. As such, wages might grow faster for a demographic group
that was more successful at shifting workers from low-paying to higher-paying occupations.24
Figure 6. Median Hourly Wages by Broad Occupation Group, May 2019

Source: Bureau of Labor Statistics, Occupational Employment Statistics, at http://www.bls.gov/oes/.
The next three tables show data on education levels and broad occupation group of low-wage
workers in 1979 and 2019 (Table 4), middle-wage workers in 1979 and 2019 (Table 5), and high-
wage workers in 1979 and 2019 (Table 6). For the purposes of this portion of analysis, low-wage
workers are those with wages at the 5th to 15th percentiles, middle-wage workers are those with
wages at the 45th to 55th percentiles, and high-wage workers are those with wages at the 85th to
95th percentiles. The earnings groups are expanded by +/- five percentage points (in contrast to
earlier analysis of workers at the 10th, 50th, and 90th percentiles) because this section describes the
educational attainment and occupational composition of worker groups, and including more
workers in each group al ows for more precise estimate of education and occupational
percentages. Overal , the analysis shows the following:
 Workers were more likely to have completed a bachelor’s or advanced degree in
2019 than workers in 1979, with the gains in educational attainment being
particularly large for workers in the highest wage group. The higher education
level of low- and middle-wage workers in 2019, compared to 1979, is noteworthy

24 Shifts in educational attainment and occupation are likely to be strongly correlated because some higher-paying
occupations require a college degree.
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in light of slightly rising or declining (depending on the specific demographic
group) real wages over the 1979 to 2019 period; in general, wages tend to rise
with education.
 Across al demographic and wage groups, workers lost employment shares in
production work. Low-wage workers were general y concentrated in service jobs
in 2019, whereas high-wage workers, to varying degrees, moved into managerial,
executive, professional, and technical jobs. Occupational shifts for middle-wage
workers differed across demographic groups.
The tables and discussion in this section describe worker characteristics by earnings group (low,
middle, and high) in 1979 and 2019. As noted elsewhere, the data used in this report are cross-
sectional and do not follow a fixed group of individuals over time. This means that the
educational and occupational changes discussed below do not capture a set of individuals’
education and job outcomes between 1979 and 2019, but the compositional change of workers in
the three earner groups in these two years. For example, a rise in the share of college-degree
holders in the middle-wage group does not necessarily reflect the share of middle-wage workers
in 1979 that went on to complete a college degree.
Low-Wage Workers
Across demographic groups, low-wage workers increased their educational attainment between
1979 and 2019: the shares of workers who ended their schooling at or before high school
graduation declined, and the shares of workers who completed some postsecondary education
increased. Women in particular experienced strong gains in educational attainment, in absolute
and relative terms. Over the 1979 to 2019 period, the shares of low-wage women with a
bachelor’s degree or higher rose from 4% to 17%, slightly exceeding the share of low-wage men
with a bachelor’s degree or higher in 2019. Concurrently, women’s 10th percentile wages grew in
real terms by 9.6% over the same period (see Table 1). But educational gains do not translate into
wage growth for al groups. The share of low-wage male and Hispanic workers with increased
education also rose from 1979 to 2019—albeit less than the gains compared to low-wage
women—but these groups’ wages at the 10th percentile fel in real terms, suggesting that other
factors counterbalanced the upward pressure on wages typical y generated by greater educational
attainment.
The prominence of service occupations in 1979 and 2019 (28% and 33% of low-wage workers,
respectively) and sharp decline in production jobs between 1979 and 2019 are noteworthy
features of low-wage workers’ occupational distribution.25 Service occupations command a range
of wages, but many pay less at the median than production jobs (see Figure 6). Al demographic
groups have a lower percentage of workers in production occupations in 2019 compared to 1979.
Notably, workers that experienced declining wages over the 1979 to 2019 period were those that
mostly experienced an increased share of employment in service occupations (e.g., male and
Hispanic workers). This suggests that occupational shifts may help explain wage trends for low-
wage workers.

25 Service occupations include food preparation and service jobs, building maintenance, protective services, personal
services (e.g., child care, hairdressers), and health care support jobs (e.g., home health aides, orderlies, dental
assistants).
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Real Wage Trends, 1979 to 2019

Middle-Wage Workers
Among middle-wage workers, al demographic groups made considerable gains in educational
attainment over the 1979 to 2019 period. For example, shares of workers with a high school
diploma or less schooling declined by 26 percentage points among men and 47 percentage points
among women, and shares of college degree holders increased.
In addition to educational gains, women’s strong (28.8%) median wage growth over 1979 to 2019
may be related to marked occupational shifts over that period. In particular, middle-wage women
moved from clerical and production jobs to higher-paying executive and managerial jobs, and to
professional and technical occupations. Likewise, wage loss among Hispanic workers (who
experienced a 2.2% decline at the median) occurred alongside gains in educational attainment and
a 16 percentage point decline in production employment that was offset by gains in other
occupation groups, particularly service jobs.
High-Wage Workers
Although wage patterns varied across demographic groups for low-wage and middle-wage
workers, wages grew in real terms at the 90th percentile for al groups over the 1979-2019 period.
Education gains and heightened concentration of employment in executive and professional
occupations appear to help explain strong wage growth. The strong performance of high-wage
workers (i.e., at the 90th percentile of wages) suggests that labor market demand for skil ed
workers increased over the 1979 to 2019 period, or that this group otherwise improved its
bargaining position over compensation.26 High-wage workers increased their educational
attainment dramatical y between 1979 and 2019, and—with the exception of Hispanic workers—
were predominantly college degree holders in 2019. This finding for Hispanic workers should be
put in the context of noteworthy compositional changes for this group. In particular, Pew
Research Center reports that Hispanics are an increasingly diverse population, which may affect
cross-time comparisons (i.e., differences in Hispanic worker characteristics in 2019 and 1979
may be greater than those for other worker groups).27 Over the same period, high-wage workers
became concentrated in executive, administrative, and managerial jobs and professional,
technical, and related jobs, such that by 2019 these occupations represented more than 50% of
employment in each group (more than 80% of employment when Hispanic workers are excluded
from analysis).

26 Another interpretation is that the bargaining position of cert ain highly paid workers (e.g., CEOs) improved. A
broader discussion of factors influencing wage patterns at the top of the earnings distribution is in CRS Report R44705,
The U.S. Incom e Distribution: Trends and Issues, by Sarah A. Donovan, Marc Labonte, and Joseph Dalaker .
27 Antonio Flores, How the U.S. Hispanic population is changing, Pew Research Center, September 18, 2017,
http://www.pewresearch.org/fact -tank/2017/09/18/how-the-u-s-hispanic-population-is-changing/.
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Table 4. Low-Wage Workers’ Educational Attainment and Occupation, by Selected Demographics, 1979 and 2019
Black (Non-
White (Non-
Non-

Overall
Male
Female
Hispanic)
Hispanic)
Hispanic
Hispanic

1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019














Education
High School Diploma or Less
80%
54%
73%
57%
85%
53%
91%
58%
77%
44%
92%
74%
79%
47%
Some Col ege
13%
29%
14%
27%
11%
30%
7%
30%
14%
35%
7%
19%
14%
32%
Bachelor’s Degree and Higher
7%
17%
12%
16%
4%
17%
2%
12%
9%
22%
1%
8%
8%
20%














Occupation
Executive, Administrative, and Managerial
4%
5%
8%
5%
2%
4%
1%
3%
6%
6%
1%
3%
4%
6%
Professional, Technical, and Related
7%
10%
8%
7%
6%
12%
4%
7%
9%
15%
3%
5%
7%
12%
Sales
13%
13%
6%
10%
19%
16%
5%
11%
13%
13%
10%
11%
13%
14%
Administrative Support, Including Clerical
20%
16%
7%
11%
15%
17%
6%
11%
27%
21%
7%
9%
22%
18%
Service
28%
33%
19%
27%
36%
39%
51%
44%
21%
25%
32%
39%
28%
31%
Construction and Extraction
2%
4%
8%
9%
NA
NA
4%
2%
1%
3%
4%
9%
2%
2%
Instal ation, Maintenance, and Repair
1%
2%
7%
4%
NA
NA
1%
2%
1%
2%
2%
3%
1%
1%
Production
18%
10%
20%
12%
19%
8%
19%
10%
17%
9%
32%
10%
16%
9%
Transportation and Material Moving
6%
9%
16%
14%
3%
5%
10%
10%
6%
7%
8%
11%
6%
8%
Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Notes: “Low-wage workers” refers to workers at the 5th-15th percentiles of their respective wage distribution. “NA” indicates an estimated percentage of less than 1%.

CRS-18


Table 5. Middle-Wage Workers’ Educational Attainment and Occupation, by Selected Demographics, 1979 and 2019
Black (Non-
White (Non-
Non-

Overall
Male
Female
Hispanic)
Hispanic)
Hispanic
Hispanic

1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019














Education
High School Diploma or Less
60%
26%
60%
34%
68%
21%
70%
30%
55%
23%
79%
59%
59%
23%
Some Col ege
19%
29%
21%
31%
20%
32%
19%
38%
20%
30%
14%
28%
20%
30%
Bachelor’s Degree and Higher
21%
45%
20%
36%
13%
47%
11%
32%
25%
48%
7%
13%
21%
48%
Occupation














Executive, Administrative, and Managerial
11%
18%
13%
18%
7%
17%
4%
12%
13%
21%
5%
9%
11%
19%
Professional, Technical, and Related
20%
29%
15%
21%
15%
32%
14%
17%
24%
32%
8%
7%
21%
32%
Sales
5%
7%
5%
8%
5%
6%
3%
7%
5%
7%
4%
6%
5%
7%
Administrative Support, Including Clerical
20%
14%
8%
6%
45%
26%
22%
23%
19%
11%
15%
19%
19%
13%
Service
7%
8%
6%
8%
10%
10%
19%
17%
6%
6%
13%
19%
6%
7%
Construction and Extraction
5%
5%
6%
11%
NA
NA
5%
3%
4%
6%
10%
14%
5%
5%
Instal ation, Maintenance, and Repair
5%
5%
9%
10%
NA
NA
2%
2%
5%
5%
4%
1%
6%
5%
Production
19%
7%
26%
10%
15%
5%
20%
9%
17%
7%
29%
13%
19%
7%
Transportation and Material Moving
8%
6%
11%
9%
2%
2%
11%
11%
6%
5%
12%
12%
8%
6%
Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Notes: “Middle-wage workers” refers to workers at the 45th-55th percentiles of their respective wage distribution. “NA” indicates an estimated percentage of less than
1%.

CRS-19


Table 6. High-Wage Workers’ Educational Attainment and Occupation, by Selected Demographics, 1979 and 2019
Black (Non-
White (Non-
Non-

Overall
Male
Female
Hispanic)
Hispanic)
Hispanic
Hispanic

1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019
1979
2019














Education
High School Diploma or Less
40%
6%
35%
7%
39%
3%
52%
7%
40%
6%
60%
23%
39%
5%
Some Col ege
20%
12%
19%
12%
22%
11%
22%
17%
20%
12%
22%
30%
20%
11%
Bachelor’s Degree and Higher
40%
82%
46%
81%
38%
86%
26%
76%
40%
82%
18%
47%
41%
84%
Occupation














Executive, Administrative, and Managerial
23%
34%
27%
35%
13%
34%
10%
32%
24%
36%
12%
20%
23%
35%
Professional, Technical, and Related
28%
47%
28%
45%
40%
52%
20%
43%
27%
44%
14%
36%
28%
47%
Sales
5%
6%
7%
7%
6%
5%
2%
4%
6%
7%
3%
6%
5%
6%
Administrative Support, Including Clerical
7%
4%
5%
3%
29%
6%
14%
5%
7%
3%
12%
9%
7%
3%
Service
2%
3%
2%
3%
3%
2%
6%
7%
2%
3%
6%
7%
2%
3%
Construction and Extraction
12%
2%
12%
3%
NA
NA
7%
2%
12%
2%
14%
10%
12%
2%
Instal ation, Maintenance, and Repair
6%
1%
4%
NA
NA
NA
7%
1%
5%
NA
8%
5%
5%
NA
Production
12%
1%
11%
2%
7%
NA
20%
3%
12%
1%
22%
3%
12%
1%
Transportation and Material Moving
6%
1%
4%
1%
2%
NA
14%
3%
5%
1%
7%
3%
6%
1%
Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979-2019.
Notes: “High-wage workers” refers to workers at the 85th-95th percentiles of their respective wage distribution. “NA” indicates an estimated percentage of less than
1%.
CRS-20

Real Wage Trends, 1979 to 2019

Factors Affecting Wage Trends
This section briefly describes some of the major factors believed to affect wage trends. A full
discussion of these factors, and the empirical evidence associated with different causal factors, is
beyond the scope of this report. Rather, several of the primary mechanisms that are thought to
contribute to wage growth or stagnation are outlined. In many cases, individual wages are likely
determined by the interaction of several forces, such as workers’ skil s and their value to
employers, job match quality, and relative bargaining power. Broadly speaking, these factors can
be grouped into two categories: market factors (affecting the supply of and demand for workers)
and institutional factors (affecting rules governing compensation). Over time, changes in these
factors for various groups (e.g., in education and training investment, employers’ demand for
workers with certain skil s, and institutions that govern wage bargaining), along with
macroeconomic growth, play a role in shaping the wage gains or losses for those groups.
Market Factors
Workers come to labor markets—often local labor markets—with varying levels of human
capital—collections of skil s and experience, abilities, and other job-relevant attributes –where
they match with employers seeking to hire certain types of workers. Some jobs require
specialized skil s and training (e.g., medical practitioners, skil ed crafts like carpentry), whereas
others can be performed by most workers of any skil level. For example, most workers could
operate a cash register or perform simple building maintenance tasks with cursory on-the-job
training. Employers are general y wil ing to pay more to skil ed workers for two reasons. First,
skil ed workers come to the job with the required human capital to be productive and thus are
wel -positioned to help generate higher revenues for the firm. Second, because skil ed workers
are relatively scarce, employers offer higher wages to attract them away from other firms. To the
extent that workers’ skil sets become more valuable to employers over time or more scarce,
wages should rise, and vice versa.
Technological change, international trade, immigration and other factors affecting labor supply
changes, along with the quality of job matches are among the key market factors thought to
contribute to recent wage trends. These forces briefly described here; a more detailed discussion
is in CRS Report R44705, The U.S. Income Distribution: Trends and Issues, by Sarah A.
Donovan, Marc Labonte, and Joseph Dalaker.
Technological change can affect wage patterns by changing employers’ demand for certain groups
of workers.28 Where new technology raises workers’ productivity (often for high-skil ed
workers)—and their value to employers—demand wil rise, and put upward pressure on wages.
At the same time, technological progress has reduced demand where workers’ effort can be
replaced by automation or information technology.29 Technological improvements can further
affect employers’ demand for certain workers by increasing the feasibility of offshoring (i.e.,

28 For an overview, see Daron Acemoglu and David H. Autor, “Skills, T asks and T echnologies: Implications for
Employment and Earnings,” in Handbook of Labor Economics, eds. Orley Ashenfelter and David Card, vol. 4B
(Elsevier, 2011), pp. 1043-1171.
29 For example, the availability of affordable desktop computers, word processing software, voicemail, and email
eliminated many tasks traditionally performed by certain clerical staff (e.g., typists, secretaries), and increased
automation in manufacturing plants reduced the demand for certain production workers.
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moving production outside the United States) certain production tasks and services that do not
need to be performed in proximity to the consumer (e.g., book-keeping, cal -center activities).
Recent global trading patterns have altered what goods and services the United States produces,
and thereby the demand for labor to carry out that production. For example, the long-term decline
in U.S. manufacturing employment, which lasted through the end of the Great Recession, has led
a number of researchers to investigate the extent to which the decline is caused by increased
import penetration in manufacturing, which can easily be traded. Recent studies focus on the
impacts of China’s establishment (starting in 2000) as a global supplier of manufactured goods.30
Increased international competition—and particularly from China—is among factors that
contributed to factory closings and production shifts that displaced large numbers of U.S.
workers. It had additional employment consequences for firms that provided inputs and support
services to the manufacturing sector (e.g., suppliers of raw materials, delivery services,
warehousing), and affected economic conditions in surrounding communities.
Changes to labor supply over time wil also influence wages, at least in the short term. Public
attention often centers on the supply effect of immigration, but other economic changes can shift
the supply of labor as wel . For example, social and economic change dramatical y increased
women’s labor supply in the latter half of the last century. In addition, other policy mechanisms,
such as changes in income tax rates or changes affecting the payoff to labor (e.g., the Earned
Income Tax Credit) can influence the labor supply of targeted groups of workers. The labor
market effects of immigration comprise a large and complex area of economic research.31
Economic theory produces a range of possible outcomes that depend on the characteristics of
incoming immigrant workers and how they compare to a country’s existing pool of labor, the
degree to which new immigrants and existing workers compete for jobs in the same labor
markets, how employers respond to the new labor supply, macroeconomic considerations, and
other factors. That said, a large influx of a particular worker group (e.g., low skil ed workers)
translates into an increase in labor supply, and could lower wage offers in the short run.
The quality of a job match (i.e., the suitability of a particular worker to a particular job) matters to
wages as wel . Job search is costly for both workers and employers, and sometimes workers
accept less-than-optimal jobs (or employers make job offers to suboptimal candidates) to
minimize search costs. Factors affecting job match quality include workers’ information about job
openings (e.g., the existence of vacancies, job attributes and how they align with worker
preferences), employers’ ability to locate jobseekers and accurately assess worker qualifications,

30 T hese include Daron Acemoglu, David Autor, and David Dorn, Gordan H. Hanson, and Brendan Price, “Import
Competition and the Great US Employment Sag of the 2000s,” Journal of Labor Economics, vol. 34, no. 1 (Part 2
2016), pp. S141-S198; and Justin R. Pierce and Peter K. Schott, “ T he Surprisingly Swift Decline of U.S.
Manufacturing Employment,” American Economic Review, vol. 106, no. 7 (July 2016), pp. 1632-1662; and David H.
Autor, David Dorn, and Gordon H. Hanson, The China Shock: Learning from Labor Ma rket Adjustm ent to Large
Changes in Trade
, National Bureau of Economic Research, 21906, January 2016, http://www.nber.org/papers/w21906.
T he results of these studies should be considered with a few caveat s in mind. For one, these studies focus on gross
employment changes in the manufacturing sector; they do not account for potential employment gains in other sectors
(e.g., U.S. export sectors and related sectors like transportation and warehousing). Also t he proliferation of complex
international supply chains increasingly blurs line between foreign and domestic outputs and complicates empirical
analyses such as these. Finally, these studies do not account for the potential positive impact lower -priced imports can
have on the real incomes of a broad range of consumers in the economy.
31 A detailed discussion of what economic theory predicts about the labor market impacts of immigration for the United
States, and a review of the empirical literature is in Nat ional Academies of Sciences, Engineering, and Medicine, The
Econom ic and Fiscal Consequences of Im m igration
, ed. Francine D. Blau and Christopher Mackie (Washington, DC:
T he National Academies Press, 2016); see also CRS Report R42988, U.S. Im m igration Policy: Chart Book of Key
Trends
, by William A. Kandel.
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and geographic mobility. Better job matches increase workers’ value, and to the extent that
workers can bargain effectively for a portion of that improvement, wages rise.
Institutional Factors
Labor market institutions are the set of formal and informal rules that govern compensation, and
include the minimum wage, the strength and structure of labor unions, and employment practices
that affect workers’ ability to bargain over compensation. Changes to institutions over time can
therefore affect wage trends as wel .
Minimum wages may affect wage growth through two primary channels. First, and most directly,
minimum wages set a floor for low-wage workers. Second, to the extent that employers maintain
wage differentials between the lowest-wage workers and those higher in the wage distribution,
minimum wage increases may affect both minimum wage workers and those with earnings above
those levels. Minimum wage earners may see declines in real wages to the extent that the
minimum wage is not increased, or increases do not keep pace with inflation. The federal
minimum wage, for example, was not increased from 1981 through 1989, thus fal ing in real
value for nearly a decade. Recent evidence suggests that the decline in the real value of the
federal minimum wage in the 1980s played a moderate role in increasing the wage gap between
low and middle earners.32
Changes in unionization, employment policies, and workplace organization can affect workers’
relative bargaining power and influence wage growth. For example, the evidence of a “union
wage premium” suggests that, other factors being equal, union members have higher wages
compared to nonunion members. Empirical evidence indicates that the private-sector union wage
premium is in the 10%-20% range.33 However, over time these gains apply to a shrinking pool of
workers, as the union membership rate declined from 20.1% in 1983 to 10.3% in 2019, with
much of that decline in the private sector. As such, empirical work in this area has suggested that
the decline in unionization contributed to stagnating wages and rising inequality, particularly in
the 1980s.34 These effects are particularly meaningful for middle-wage workers and for men,
because traditional y male “blue collar” jobs, such as manufacturing and construction, had higher
unionization rates.
The use of employment policies to restrict firms’ competition for workers may affect wages by
limiting workers’ relative bargaining power. Many workers achieve wage gains by changing jobs.
The gains associated with job mobility (i.e., movement between jobs) are therefore restricted,
plausibly, where franchise agreements include provisions that prohibit employers from hiring
workers from other firms affiliated with the same franchisor (i.e., no-poach or no-hire provisions)
or where employment contracts include provisions restricting workers from accepting job offers
from firms in the same industry (i.e., noncompete clauses). A recent study of no-poach provisions
in franchise contracts found that 58% contained some restriction on franchisees’ ability to recruit
and hire workers from other firms within the franchise system.35

32 David H. Autor, Alan Manning, and Christopher L. Smith, “T he Contribution of the Minimum Wage to US Wage
Inequality over T hree Decades: A Reassessment,” American Economic Journal: Applied Economics, vol. 8, no. 1
(January 2016), pp. 58-99.
33 See, for example, Fernando Rios-Avila and Barry T . Hirsch, “Unions, Wage Gaps, and Wage Dispersion: New
Evidence from the Americas,” Industrial Relations, vol. 53, no. 1 (January 2014), pp. 1-27.
34 David Card, “T he Effect of Unions on Wage Inequality in the U.S. Labor Market,” Industrial and Labor Relations
Review
, vol. 54, no. 2 (January 2001), pp. 296 -315.
35 Alan B. Krueger and Orley Ashenfelter, Theory and Evidence on Employer Collusion in the Franchise Sector,
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In addition, a movement toward greater use of contractors and subcontractors in some industries
has, by some accounts, reduced the bargaining power of certain worker groups (e.g., lower-paid
workers in service occupations) and put downward pressure on their wages.36 For example, many
companies that traditional y employed their own janitorial staff now obtain cleaning and
maintenance services through a separate vendor. Although such restructuring can be beneficial in
terms of efficiency gains, this workplace movement also disassociates workers from the general
pay schedule of the industry and from large firms more specifical y. Such workplace models (e.g.,
service contractors not part of the core business for which they are providing services) operate in
highly competitive markets, which puts pressure on employers to keep operating costs (including
labor costs) low, and poses greater chal enges for union organizing.
At the same time, changes in pay-setting practices in certain high-pay occupations, the emergence
of superstar earners (e.g., in sports and entertainment), and other factors may have improved
wage growth for some workers at the top of the wage distribution.37
Macroeconomic Factors
In general, aggregate employment increases with economic growth. This occurs because as
innovations bring new and better products to market, consumer demand for goods and services
rises, and al things equal, so does employment.38 Macroeconomic forces can also affect
employment through changes on the production side (i.e., by changing the costs of producing
goods and services). In the long run, labor productivity (i.e., output produced per hour of labor)
and wages tend to move together, as lower production costs cause firms to expand production and
increase their demand for labor. The degree to which greater demand for workers translates into
growth in aggregate earnings (i.e., the sum of al workers’ earnings across the workforce) and the
distribution of those earnings among workers depends on variety of factors, including market and
institutional factors discussed above, and overarching macroeconomic forces. A growing gap
between labor productivity and compensation39 and the related decline in labor’s share of gross
domestic income (GDI) from 57.2% of GDI in 1979 to 53.4% of GDI in 2019,40 suggests a shift

Princeton University, Industrial Relations Section, Work ing Paper #614, Princeton, NJ, September 1, 2017, p. 7,
http://dataspace.princeton.edu/jspui/bitstream/88435/dsp014f16c547g/3/614.pdf.
36 David Weil, The Fissured Workplace (Cambridge, MA: Harvard University Press, 2014).
37 For example, studies have questioned whether the close relationship at some corporations between chief executive
officers (CEOs) and their boards (which set their pay) creates “principal-agent” problems that have allowed CEOs
undue influence over setting their own pay. T hese arguments are evaluated in CRS Report RL33935, The Econom ics of
Corporate Executive Pay
, by Gary Shorter and Marc Labonte.
38 Private sector consumption is an important component of gross domestic product (GDP). U.S. Bureau of Economic
Analysis data indicate that personal consumption expenditures have made up at least 60% of GDP since 1979, and its
share of GDP increased between 1979 and 2019. T he share has varied around 68% since 2009. U.S. Bureau of
Economic Analysis, Shares of Gross Dom estic Product: Personal Consum ption Expenditures, retrieved from Federal
Reserve Economic Database, Series DPCERE1A156NBEA, Federal Reserve Bank of St. Louis;
https://fred.stlouisfed.org/.
39 B. Ravikumar and Lin Shao, Labor Compensation and Labor Productivity: Recent Recoveries and the Long -Term
Trend
, Federal Reserve Bank of St. Louis, Economic Synopses, No. 16, August 12, 2016,
https://research.stlouisfed.org/publications/economic-synopses/2016/08/12/labor-compensation-and-labor-productivity-
recent -recoveries-and-the-long-term-trend/.
40 GDI measures overall economic activity by the incomes generated from producing gross domestic product (GDP),
which is a measure of final expenditures.
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in these forces such that national income growth translates into lower growth in aggregate
earnings than in the past.41
Similarly in times of economic recession, private sector demand for goods and services declines,
putting strain on the labor market. Employment levels fal and high unemployment rates (together
with declining revenues) put downward pressure on overal wage growth. Countervailing that
pressure is a tendency of employers to retain their most productive workers, which affects both
the composition of the workforce (i.e., who remains after layoffs) and creates an incentive for
workers to increase effort and productivity to avoid a layoff.42 Macroeconomists also observe that
middle-skil workers experience relatively higher job loss during recession, which may further
contribute to differential wage growth because displaced workers tend to reenter the labor market
at lower wage levels and may increase competition for other jobs held by middle- and lower-
skil ed workers. Although difficult to observe in aggregate wage statistics, research based on
microeconomic data indicates wages tend to fal during recessions and rise during recoveries (i.e.,
wages are procyclical), although the wage response appears to vary from recession to recession.43


41 T here are many views on what drives the decline in labor’s share of income. T he results of a BLS analysis suggests
that technological change is an important driver; notably BLS finds that the decline in labor’s share of income is
pronounced in information-technology industries (e.g., software publishers and wireless telecommunications carriers);
others have emphasized the role of increased global integration, including trade in final and intermediate goods, and
declines in the labor’s bargaining power over compensation. Michael Brill, Corey Holman, Chris Morris, Ronjoy
Raichoudhary, and Noah Yosif, Understanding the labor productivity and com pensation gap , Bureau of Labor
Statistics, Beyond the Numbers: Productivity, vol. 6, no. 6, June 2017, https://www.bls.gov/opub/btn/volume-6/
understanding-the-labor-productivity-and-compensation-gap.htm. Data on labor’s share of gross domestic income in
1979 and 2017 are from Federal Reserve Economic Database, Shares of gross dom estic incom e: Com pensation of
em ployees, paid, Percent, Annual, Not Seasonally Adjusted
, Federal Reserve Bank of St. Louis, Series
A4002E1A156NBEA, http://fred.stlouisfed.org. Compensation data do not include labor income paid to small business
owners.
42 Edward P. Lazear, Kathryn L. Shaw, and Christopher Stanton, “Making Do With Less: Working Harder during
Recessions,” Journal of Labor Economics, vol. 34, no. S1 (January 2016), pp. 333-360.
43 Michael W. L. Elsby, Donggyun Shin, Gary Solon, “ Wage Adjustment in the Great Recession and Other Downturns:
Evidence from the United States and Great Britain ,” Journal of Labor Econom ics, vol. 34, no. S1 (January 2016), pp.
246-291.
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Appendix A. Data Used in this Report
The data used to create annual hourly wage distributions over the 1979-2019 period are from the
Current Population Survey (CPS) Outgoing Rotation Groups (ORGs). The CPS is a large-scale
household survey conducted monthly by the Census Bureau. CPS participants are interviewed for
four consecutive months, then leave the survey for eight months, when they reenter the survey for
a final four months. The ORGs are made up of respondents completing their fourth month in the
survey (i.e., before they go out on an eight-month hiatus) and those completing their eighth and
final interview. Unlike other groups, the ORGs are asked about their usual earnings and hours
worked, making them a particularly useful sample for hourly wage studies.
This report’s sample comprises individuals 25 to 64 years old who were employed in nonfarm,
nonmilitary wage and salary jobs during the survey week and reported enough information to
compute an hourly wage. Excluded from the sample are self-employed workers, Armed Forces
members, workers in agricultural occupations, and workers whose wages were imputed by the
Census Bureau. As others have done, CRS excluded Census-imputed wages due to the finding by
Hirsch and Schumacher (2002) that a large portion of them were imputed with error.44
CRS estimates hourly wages by dividing workers’ reported usual weekly earnings by their usual
weekly hours of work. For workers who report they are paid by the hour, their reported hourly
rate of pay were used. Wages represent earnings before deductions. For workers who are not paid
by the hour (non-hourly workers), wages include tips, overtime pay, and commissions.
Unfortunately, this information on overtime, tips, and commissions is not collected for hourly
workers before 1994 and is therefore not included here in hourly wage estimates for them.45
Wages are weighted by the product of a worker’s CPS weight and their weekly hours (i.e., wages
are hours-weighted).
CPS earnings data are “top-coded”—that is, any reported earnings above a given top-code value
are replaced with the top-code value—to reduce the likelihood that any particular survey
respondent can be identified in the data. In 1979, the first year of data, weekly earnings are top-
coded at $999 per week. The top-code changes twice over the 1979-2019 period: it was raised to
$1,923 per week in 1989 and to $2,884.61 per week in 1998. Although necessary to maintain the
anonymity of survey respondents, top-coding is problematic to studies that attempt to characterize
the wage distribution on a year-by-year basis, because the wage distribution is not observable
above the top-code value, and the top-code value changes over time. Researchers have addressed
top-coded values using a variety of methods. CRS follows the Center for Economic and Policy
Research’s method by modeling earnings as having a log-normal distribution and replacing top-
coded values with gender-specific estimates of the mean value of weekly earnings above the top-
code value.46

44 Barry Hirsch and Edward Schumacher, “Match Bias in Wage Gap Estimates Due to Earnings Imputation,” Journal
of Labor Econom ics
, vol. 22, no. 3 (2002), pp. 689-722.
45 It is possible to estimate overtime, tips, and commission for hourly workers after 1994. However, doing so would
create an inconsistent series and interfere with the attempt to describe trends over the full 1979-2019 period. T o the
extent that the compensation structure (i.e., the relative contribution of base wages plus o vertime, tips, and
commissions) has changed over time for hourly workers, the reported wages for hourly workers could understate or
overstate wage trends.
46 As a sensitivity check, wage trends are also estimated using methods applied by Autor, Manning, an d Smith (2016),
and did not find notably different trends. David H. Autor, Alan Manning, and Christopher L. Smith, “ T he Contribution
of the Minimum Wage to US Wage Inequality over T hree Decades: A Reassessment, ” Am erican Econom ic Journal:
Applied Econom ics
, vol. 8, no. 1 (January 2016), pp. 58-99. Data and statistical codes used in this paper are at
http://economics.mit.edu/faculty/dautor/data/ams_aej_15.
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Following standard practice, wage outliers (i.e., implausibly low or high wage reports) were
addressed by excluding wages that are less than $0.50 in 1989 dollars and greater than $150 in
1989 dollars. Hourly wages were converted to 2019 dollars using the Consumer Price Index for
Al Urban Consumers, U.S. City Average (CPI-U). The CPI-U, which is a measure of the average
change over time in prices paid by consumers for a market basket of goods and services, is
commonly used to compare the real (inflation-adjusted) value of earnings or spending data at
different points in time. The CPI-U, for example, is the most common index used to adjust state
minimum wage rates.
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Appendix B. Demographic and Occupational
Composition of the Wage Distribution in
1979 and 2019
This report has looked at wage trends by demographic group and earner category, and worker
characteristics within those groups. For example, the median wage for women in a given year is
defined with respect to the distribution of women’s wages (not the overal wage distribution).
Table B-1 explores the interaction between demographic groups and earnings from a different
perspective. It describes the composition of the workforce overal and within the bottom, middle,
and top third of the overal wage distribution.
Overal , the workforce was more diverse in 2019 than it was in 1979 (i.e., the share of White
workers and non-Hispanic workers decreased), and the sex composition more balanced. In 2019,
workers were older and better educated (i.e., a higher share of workers with at least a bachelor’s
degree). The share of workers in production jobs fel sharply between 1979 and 2019 (with losses
in other job categories as wel , such as administrative support and clerical work), with gains in
employment share in many categories—the largest gains being in professional, technical, and
related occupations.
These compositional changes did not al occur, however, to the same degree in each third of the
overal wage distribution. For example, Black workers remained overrepresented in the bottom
third of the distribution; the share of Black workers in the top third of wage earners rose by 1
percentage point between 1979 and 2019. Similarly, although female workers and Hispanic
workers gained shares in the upper wage tercile (i.e., top third), they remained underrepresented
among top earners in 2019.
In terms of shifting occupational composition, from 1979 to 2019
 in the bottom third of the wage distribution, the share of workers in production
work declined by 8 percentage points and in administrative support and clerical
jobs by 6 percentage points. Over the same period, workers in the bottom third
became more concentrated in service-sector employment (24% to 28%).
 in the middle wage tercile, the share of workers in production work declined by
11 percentage points and in administrative support work by 5 percentage points.
On the other hand, workers in this tercile increased their share of employment by
9 percentage points in professional, technical, and related jobs, and by 6
percentage points in executive, administrative, and managerial occupations.
 in the top third of the wage distribution, the share of workers in executive,
administrative, and managerial occupations and professional, technical, and
related jobs increased from 44% in 1979 to 75% in 2019.
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Table B-1. Worker Characteristics by Wage Tercile, 1979 and 2019

Overall
Bottom Third
Middle Third
Top Third

1979
2019
1979
2019
1979
2019
1979
2019







Race

Whitea
87%
78%
83%
75%
88%
81%
92%
80%
Black
10%
11%
14%
15%
10%
11%
6%
7%
Other
2%
10%
3%
10%
2%
9%
2%
13%

Hispanic Ethnicity







Non-Hispanic
95%
85%
93%
77%
95%
87%
97%
94%
Hispanic
5%
15%
7%
23%
5%
13%
3%
6%

Sex







Male
56%
52%
30%
44%
59%
53%
83%
60%
Female
44%
48%
70%
56%
41%
47%
17%
40%
Age








25-34 years
40%
30%
40%
37%
45%
31%
34%
21%
35-44 years
25%
27%
24%
23%
24%
27%
29%
30%
45-54 years
21%
24%
21%
21%
19%
23%
23%
28%
55-64 years
14%
19%
16%
18%
13%
19%
13%
21%

Education







High School Diploma or Less
61%
30%
77%
49%
60%
27%
45%
9%
Some Col ege
18%
26%
14%
31%
20%
30%
20%
17%
Bachelor’s Degree and Higher
21%
44%
9%
20%
20%
43%
35%
73%

Occupation







Executive, Administrative, and Managerial
12%
18%
5%
6%
11%
17%
20%
32%
Professional, Technical, and Related
17%
27%
9%
12%
19%
28%
24%
43%
Sales
7%
8%
9%
10%
5%
7%
5%
7%
Administrative Support, Including Clerical
18%
13%
23%
17%
20%
15%
10%
5%
Service
12%
14%
24%
28%
7%
8%
3%
4%
Construction and Extraction
5%
5%
2%
5%
5%
6%
8%
3%
Instal ation, Maintenance, and Repair
5%
3%
2%
3%
6%
5%
7%
2%
Production
18%
7%
18%
10%
19%
8%
15%
2%
Transportation and Material Moving
7%
6%
7%
9%
8%
6%
7%
2%
Source: CRS estimates using Current Population Survey Outgoing Rotation Group data for 1979 and 2019.
Notes: Sample comprises nonfarm wage and salary workers who are 25-64 years old and provide sufficient
information to compute an hourly wage.
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a. Race is described irrespective of Hispanic ethnicity. The share of the overal population of workers that was
White and non-Hispanic in 1979 was 80% and Black non-Hispanic was 10%; these shares were 63% and 10%
in 2019.

Author Information

Sarah A. Donovan
David H. Bradley
Specialist in Labor Policy
Specialist in Labor Economics



Acknowledgments
Research support for this report was provided by Paul Romero.

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Congressional Research Service
R45090 · VERSION 13 · UPDATED
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