Wage by percentile Wage ratio Year/range 10th 20th 30th 40th 50th 60th 70th 80th 90th 95th 50th/10th 95th/50th 95th/10th 2000 $9.09 $11.13 $13.25 $15.05 $17.89 $21.01 $25.18 $30.51 $40.17 $51.37 1.97 2.87 5.65 2007 $9.33 $11.24 $13.26 $15.57 $18.48 $21.68 $25.92 $32.08 $43.18 $55.75 1.98 3.02 5.97 2018 $10.15 $12.12 $14.16 $16.22 $19.14 $22.42 $27.25 $34.41 $48.34 $64.25 1.89 3.36 6.33 2019 $10.07 $12.31 $14.64 $16.71 $19.33 $22.95 $27.94 $34.98 $48.08 $67.14 1.92 3.47 6.66 Annualized percent changes Wage ratio change 2000–2019 0.5% 0.5% 0.5% 0.6% 0.4% 0.5% 0.5% 0.7% 1.0% 1.4% 0.0 0.6 1.0 2000–2007 0.4% 0.1% 0.0% 0.5% 0.5% 0.4% 0.4% 0.7% 1.0% 1.2% 0.0 0.1 0.3 2007–2019 0.6% 0.8% 0.8% 0.6% 0.4% 0.5% 0.6% 0.7% 0.9% 1.6% -0.1 0.5 0.7 2018–2019 -0.7% 1.5% 3.4% 3.0% 1.0% 2.4% 2.5% 1.7% -0.5% 4.5% 0.0 0.1 0.3

Note: The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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While the CPS remains one of the best data sets to analyze hourly wage growth across and within demographic groups, some caution should be exercised in interpreting data, because of top-coding and volatility issues. First, top-coding of weekly earnings is catching an increasing number and share of workers as inequality continues to climb, making it increasingly difficult to obtain reliable measures of 95th-percentile wages, particularly for male workers and white workers. Therefore, we make an adjustment when examining recent wage levels and trends for these workers. Second, because the CPS exhibits a fair amount of year-to-year volatility, one-year changes in wages by decile or by demographic group in the CPS—while providing new and valuable information—should be taken with a grain of salt. For a more in-depth examination of these considerations, see Gould 2019. For a full discussion of EPI’s use of the CPS-ORG data, see EPI’s Methodology for Measuring Wages and Benefits (EPI 2019).

In the full business cycle from 2000 to 2007, growth was relatively slow overall and relatively unequal; the gains at the 90th and 95th percentiles were higher than at the middle or bottom of the wage distribution. After growing at about the same rate from 2000 to 2007, wages for the bottom grew significantly faster than wages for the middle from 2007 to 2019, slightly decreasing the 50/10 wage ratio, or the ratio of wages at the middle to wages at the bottom. However, because of the large and disproportionate gains at the top, both the 95/50 ratio (the ratio of 95th-percentile wages to median wages) and the 95/10 ratio (the ratio of 95th-percentile wages to 10th-percentile wages) grew substantially from 2007 to 2019.

With the caveat that, as discussed above, we need to be careful not to assign too much meaning to one-year changes given concerns about data volatility, we note the following trends over the past year: The one-year change in the median wage from 2018 to 2019 was 1.0%, compared with 1.5% at the 20th percentile and a loss of 0.7% at the 10th percentile. The strongest growth in the overall wage distribution occurred at the 95th percentile, at 4.5%.

On the whole, the trends between 2018 and 2019 suggest a continuation of growing wage inequality, with the top in particular pulling away from the middle and bottom. The loss for low-wage workers is somewhat surprising given that the labor market continues to tighten, and tighter labor markets have historically provided disproportionate benefit to wage growth at the bottom. However, the composition of the low-wage workforce may play a role as more previously sidelined workers (re)enter the labor force and find jobs. At this point in the business cycle, these (re)entering workers are less likely to be attached to the labor force in general and wield little bargaining power to garner higher wages; this group might include, for example, workers with lower levels of educational attainment. Further, the bottom 10% of the overall U.S. workforce is increasingly found in states with a minimum wage no higher than the federal minimum of $7.25 per hour, meaning they were less likely to be affected by state-level minimum wage increases across the country (EPI 2020b).1 For more on the relationship between state-level minimum wages and wage growth for low-wage workers, see the section “Wage growth at the bottom was faster in states that increased their minimum wage in 2019” later in this report.

Figure F illustrates the trends in wages for selected deciles (and the 95th percentile), showing the cumulative percent change in real hourly wages from 2000 to 2019. The overall story of inequality is clear. The lines demonstrate that those with the highest wages have had the fastest wage growth in recent years. From 2000 to 2019, the 95th-percentile wage grew nearly four times as fast as wages at the median (30.7% vs. 8.0%). By 2019, the 95/10 ratio had grown to 6.7 from 6.0 in 2007 and 5.6 in 2000 (see Table 1). This means that on an hourly basis the 95th-percentile wage earner was paid 6.7 times what the 10th-percentile wage earner was paid ($67.14 per hour vs. $10.07 per hour). Similar trends are found in the 95/50 wage ratio, with those at the top pulling away from those in the middle. In 2019, the 95th-percentile wage earner was paid 3.5 times as much as the median worker ($67.14 vs. $19.33), compared with 3.0 times as much in 2007 and 2.9 times as much in 2000.

Figure F

High-wage earners have continued to pull away from everyone else since 2000: Cumulative percent change in real hourly wages, by wage percentile, 2000–2019

year ?10th? ?50th? ?90th ?95th
2000 0.0% 0.0% 0.0% 0.0%
2001 3.1% 1.5% 3.4% 1.7%
2002 6.3% 3.1% 4.5% 5.4%
2003 5.9% 3.1% 4.5% 4.5%
2004 4.0% 4.3% 5.6% 5.5%
2005 1.5% 2.7% 4.9% 5.7%
2006 0.9% 3.1% 6.8% 6.7%
2007 2.6% 3.3% 7.5% 8.5%
2008 3.8% 2.5% 7.4% 9.1%
2009 4.7% 4.8% 10.9% 11.6%
2010 3.8% 4.2% 11.4% 11.0%
2011 1.1% 1.6% 9.1% 10.0%
2012 -0.6% 0.3% 9.8% 11.3%
2013 0.0% 0.8% 10.7% 13.2%
2014 0.9% 0.9% 9.5% 11.6%
2015 5.7% 2.5% 14.0% 18.8%
2016 6.6% 4.4% 16.1% 20.0%
2017 11.0% 5.3% 18.6% 21.7%
2018 11.6% 7.0% 20.3% 25.1%
2019 10.8% 8% 19.7% 30.7%
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Note: The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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The gender wage gap continues to shrink but remains significant; wage inequality is higher and growing more among men than among women.

Analyzing wages at different points in the wage distribution over time can mask different outcomes for men compared with women as well as changes in the gender composition of the workforce. Table 2 replicates the analysis of wage deciles for men and women separately, with a comparison of gender wage disparities over 2000–2019. Figures G and H accompany this table, illustrating the cumulative percent change over 2000–2019 in real hourly wages of men and women at selected wage percentiles.

It is important to keep in mind that the top-coding issue in the CPS disproportionately impacts analysis of men’s wages more than analysis of women’s wages because men’s wages are higher and, at the high-end of the wage distribution, wage growth has been much faster for men than for women over the last 20 years. Because more than 5% of men’s weekly earnings were top-coded in the CPS in 2016, 2017, 2018, and 2019, growth in the 95th-percentile men’s wage is estimated using a slightly lower point in the male wage distribution. Depending on the share that is top-coded, we alternatively apply the growth rate of the 93rd or 94th percentile for each of those years to the 95th percentile in 2015.2

Table 2

Hourly wages of men and women, by wage percentile, selected years, 2000–2019 (2019$)

Wage by percentile Wage ratio
10th 20th 30th 40th 50th 60th 70th 80th 90th 95th 50th/10th 95th/50th 95th/10th
Men
2000 $9.77 $12.01 $14.78 $17.23 $20.30 $23.80 $28.43 $34.22 $45.27 $57.36 2.08 2.83 5.87
2007 $9.88 $12.31 $14.76 $17.37 $20.33 $23.94 $28.54 $35.53 $47.62 $61.73 2.06 3.04 6.25
2018 $10.34 $12.70 $15.25 $17.73 $20.47 $24.56 $29.98 $38.15 $53.03 $77.01 1.98 3.76 7.44
2019 $10.93 $13.23 $15.27 $18.01 $21.00 $25.02 $30.16 $38.55 $54.28 $78.64 1.92 3.74 7.19
Annualized percent changes Wage ratio change
2000–2019 0.6% 0.5% 0.2% 0.2% 0.2% 0.3% 0.3% 0.6% 1.0% 1.7% -0.16 0.92 1.32
2000–2007 0.2% 0.4% 0.0% 0.1% 0.0% 0.1% 0.1% 0.5% 0.7% 1.1% -0.02 0.21 0.38
2007–2019 0.8% 0.6% 0.3% 0.3% 0.3% 0.4% 0.5% 0.7% 1.1% 2.0% -0.14 0.71 0.95
2018–2019 5.7% 4.2% 0.2% 1.5% 2.6% 1.9% 0.6% 1.0% 2.4% 2.1% -0.06 -0.02 -0.25
Women
2000 $8.84 $10.41 $11.96 $13.72 $15.75 $18.30 $21.73 $26.52 $34.67 $42.85 1.78 2.72 4.85
2007 $8.89 $10.52 $12.33 $14.28 $16.55 $19.28 $23.27 $28.46 $37.73 $47.58 1.86 2.87 5.35
2018 $9.73 $11.24 $13.02 $15.17 $17.24 $20.33 $24.46 $30.56 $42.00 $54.03 1.77 3.13 5.55
2019 $9.92 $11.91 $13.47 $15.11 $17.84 $20.54 $24.96 $31.24 $42.95 $55.71 1.80 3.12 5.61
Annualized percent changes Wage ratio change
2000–2019 0.6% 0.7% 0.6% 0.5% 0.7% 0.6% 0.7% 0.9% 1.1% 1.4% 0.02 0.40 0.76
2000–2007 0.1% 0.2% 0.4% 0.6% 0.7% 0.7% 1.0% 1.0% 1.2% 1.5% 0.08 0.15 0.50
2007–2019 0.9% 1.0% 0.7% 0.5% 0.6% 0.5% 0.6% 0.8% 1.1% 1.3% -0.06 0.25 0.26
2018–2019 2.0% 6.0% 3.5% -0.3% 3.5% 1.0% 2.0% 2.2% 2.3% 3.1% 0.03 -0.01 0.06
Wage disparities (women’s wages as a share of men’s)
2000 90.4% 86.7% 80.9% 79.6% 77.6% 76.9% 76.4% 77.5% 76.6% 74.7%
2007 90.0% 85.5% 83.5% 82.3% 81.4% 80.5% 81.6% 80.1% 79.2% 77.1%
2018 94.1% 88.5% 85.4% 85.5% 84.2% 82.8% 81.6% 80.1% 79.2% 70.2%
2019 90.8% 90.0% 88.2% 83.9% 85.0% 82.1% 82.7% 81.0% 79.1% 70.8%

Note: The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Even using the potentially slightly slower growth rate in recent years at the 93rd and 94th percentiles as a proxy, long-term trends suggest that low- and middle-wage men have fared comparatively poorly and that wage ratios between the top and the middle (the 95/50 ratio) and the top and the bottom (the 95/10 ratio) have increased more for men than for women. Men’s wages at the 95th percentile grew 37.1% from 2000 to 2019, almost twice as fast as at the 90th percentile (19.9%), while at the median, men’s wages barely rose, increasing only 3.4% over the entire 19-year period. Wage growth for lower-wage working men (at the 10th and 20th percentiles) was considerably stronger than for those at or near the middle of the wage distribution, increasing 11.9% and 10.2%, respectively (EPI 2020c).

After seeing their wages fall between 2017 and 2018 (Gould 2019), men at the middle and bottom of the wage distribution saw their wages rise in 2019: a 2.6% increase at the 50th percentile and a striking 5.7% increase at the 10th percentile, along with a 4.2% increase at the 20th percentile. Table 2 shows that our imputed 95th-percentile men’s wage grew 2.1% between 2018 and 2019, on par with its growth since 2007.

Figure G

Disproportionate wage growth since 2000 for those at the top has contributed to widening inequality among men in the workforce: Cumulative percent change in real hourly wages of men, by wage percentile, 2000–2019

Year 10th? 50th? 90th 95th
2000 0.0% 0.0% 0.0% 0.0%
2001 3.1% 0.7% 2.2% 2.2%
2002 2.8% 2.1% 4.6% 4.4%
2003 2.0% 2.4% 4.3% 5.1%
2004 2.5% 0.6% 5.4% 7.5%
2005 0.5% -1.6% 4.3% 6.0%
2006 1.1% -0.3% 5.2% 6.3%
2007 1.1% 0.1% 5.2% 7.6%
2008 -0.2% 0.0% 6.3% 8.8%
2009 0.9% 2.8% 9.7% 14.8%
2010 0.6% 0.4% 9.5% 13.8%
2011 -1.8% -2.1% 7.6% 11.0%
2012 -2.4% -1.6% 8.6% 16.5%
2013 -2.0% -2.6% 9.8% 15.1%
2014 -0.9% -3.5% 7.6% 13.1%
2015 0.5% -0.2% 14.2% 23.2%
2016 7.1% 0.9% 13.1% 29.9%
2017 6.7% 2.3% 15.2% 27.3%
2018 5.9% 0.8% 17.1% 34.2%
2019 11.9% 3.4% 19.9% 37.1%
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Notes: The xth-percentile wage is the wage at which x% of wage earners earn less and (100–x)% earn more.?The 95th-percentile men’s wage is imputed using the growth rates of the 93rd and 94th percentiles from recent years as needed, since the weekly earnings top code continues to capture a large and growing share of the men’s wage distribution, making it difficult to accurately measure top-level wages. For more information on this issue, see?Gould’s?State of Working America Wages 2018?(2019).

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Figure H

Women’s wages are more compressed than men’s wages, but inequality among women has increased since 2000: Cumulative percent change in real hourly wages of women, by wage percentile, 2000–2019

Year 10th? 50th? 90th 95th?
2000 0.0% 0.0% 0.0% 0.0%
2001 0.4% 2.1% 1.8% 2.8%
2002 2.8% 5.1% 3.1% 6.1%
2003 2.6% 6.1% 5.7% 7.5%
2004 2.1% 4.7% 6.1% 7.4%
2005 0.9% 4.1% 7.4% 8.6%
2006 0.2% 4.8% 6.9% 9.6%
2007 0.7% 5.1% 8.8% 11.0%
2008 1.1% 6.1% 9.5% 11.3%
2009 4.0% 8.6% 11.4% 13.4%
2010 5.0% 8.1% 14.0% 15.6%
2011 2.4% 7.3% 11.4% 14.7%
2012 0.4% 5.5% 11.2% 14.8%
2013 0.0% 4.7% 11.9% 16.2%
2014 -0.7% 3.4% 12.4% 17.9%
2015 3.7% 5.8% 16.8% 20.9%
2016 7.5% 7.9% 18.5% 24.0%
2017 7.4% 9.3% 20.0% 23.9%
2018 10.2% 9.5% 21.1% 26.1%
2019 12.3% 13.3% 23.9% 30.0%
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Note:?The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Women also experienced a growth in wage inequality from 2000 to 2019, with the 95th percentile continuing to pull away from the middle and bottom of the wage distribution. Wages at the 90th and 95th percentiles grew about twice as fast as for middle- and low-wage earners over the 19-year period. However, wage inequality among women in 2019 was not as high as it was among men: A 95th-percentile woman was paid 5.6 times as much as a 10th-percentile woman, while the 95/10 ratio for men was 7.2. While inequality has grown modestly among women, the growth in women’s wages is more broadly shared across the wage distribution than the growth in men’s wages. Across wage deciles, women’s wages grew between 10.1% (at the 40th percentile) and 30.0% (at the 95th percentile), while men’s wage growth spanned 3.3% (at the 30th percentile) to 37.1% (at the 95th percentile) between 2000 and 2019.

Median wages for women grew 3.5% between 2018 and 2019 compared with 2.0% at the bottom (10th percentile) and 3.1% at the top (95th percentile). (Again, we do not recommend drawing conclusions about economic trends based on a single year of data; long-term trends give a more reliable picture of what’s going on in the economy.) The largest growth over the year was found at the 20th percentile, where wages grew 6.0%. It is intriguing that faster wage growth for men was at the 10th percentile (with a wage of $10.93 in 2019) while the fastest wage growth for women was at the 20th percentile (with a wage of $11.91 in 2019). A discussion of the role of not only tight labor markets, but also state-level minimum wages for faster wage growth at the bottom of the wage distribution, follows this section.

The “gender wage gap” refers to historically persistent differences between what men and women are paid in the workplace. While significant gender wage gaps remain across the wage distribution, the gender wage gap at the median continued to shrink, with the typical woman earning 85 cents for every dollar a man earned in 2019 (that is, women faced a 15% wage gap). Unfortunately, the narrowing of the gender wage gap at the median between 2000 and 2019 was due in part to particularly slow wage growth in the median men’s wage, which rose only 3.4% over the 19-year period (or 0.2% annually), rather than tremendously fast growth for women (which rose 0.7% on an annual basis—well below economywide productivity growth). If we can stem the tide of rising inequality and claw back the disproportionate gains going to those at the top of the overall wage distribution—which led to wage growth that was about 10 times as fast at the top as at the middle for men (see Figure G)—it would be economically feasible to see both men’s and women’s median wages rise while simultaneously closing the gender wage gap (Davis and Gould 2015). The gender wage gap at the bottom of the wage distribution, while considerably narrower than at the top, has reversed recent years’ gains, and is now back to near where it was in 2000, with women’s 10th-percentile wage 9.2% less than men’s. The largest gender wage gap occurs among the highest-paid workers, with higher-earning women facing a 29.2% pay penalty.

The regression-adjusted average gender wage gap (controlling for education, age, race, and region) showed a small narrowing between 2000 and 2019, from 23.9% to 22.6% (Appendix Table 1), while much greater progress was made between 1979 and 2000; the regression-adjusted gender wage gap was 37.7% in 1979 (EPI 2020c).

Wage growth at the bottom was faster in states that increased their minimum wage in 2019.

In 2019, the minimum wage was increased in 16 states and the District of Columbia through legislation or referendum and in eight states because the minimum wage is indexed to inflation in those states. One state, New Jersey, is double-counted in this tally, as it had both a legislated and an indexed increase in 2019. Most of the minimum wage increases occurred at the start of the year, though some occurred later in the year. For this analysis, we rely on average changes in the minimum wage from 2018 to 2019; therefore, we also include any minimum wage changes that happened during the second half of 2018 without an actual change in 2019, which would imply an increase in the average minimum wage workers faced in 2019 versus 2018; this occurred only in Maryland, where the minimum wage increased from $9.25 to $10.10 in July 2018. Connecticut has the latest minimum wage change in the two-year period, occurring in October 2019, and is still counted among the minimum wage changers for this analysis.

Figure I shows in green the states with minimum wage increases that occurred through legislation or referendum in 2019; states in blue had automatic increases resulting from indexing the minimum wage to inflation. Workers in states that increased their minimum wage between 2018 and 2019 account for about 55% of the U.S. workforce. Comparing the average minimum wage in each state across 2018 with the average across 2019, the amounts of the nominal minimum wage increases, legislated or indexed, ranged from $0.05 (0.5%) in Alaska to $1.00 (9.1%?10.0%) in California, Massachusetts, and Maine.

Figure I

The minimum wage increased in 23 states and the District of Columbia in 2019: States with minimum wage increases in 2019, by type of increase

State Abbreviation Category
Alaska AK Indexed
Alabama AL No change
Arkansas AR Legislated or ballot measure
Arizona AZ Legislated or ballot measure
California CA Legislated or ballot measure
Colorado CO Legislated or ballot measure
Connecticut CT Legislated or ballot measure
District of Columbia DC Legislated or ballot measure
Delaware DE Legislated or ballot measure
Florida FL Indexed
Georgia GA No change
Hawaii HI No change
Iowa IA No change
Idaho ID No change
Illinois IL No change
Indiana IN No change
Kansas KS No change
Kentucky KY No change
Louisiana LA No change
Massachusetts MA Legislated or ballot measure
Maryland MD Legislated or ballot measure
Maine ME Legislated or ballot measure
Michigan MI Legislated or ballot measure
Minnesota MN Indexed
Missouri MO Legislated or ballot measure
Mississippi MS No change
Montana MT Indexed
North Carolina NC No change
North Dakota ND No change
Nebraska NE No change
New Hampshire NH No change
New Jersey NJ Legislated or ballot measure
New Mexico NM No change
Nevada NV No change
New York NY Legislated or ballot measure
Ohio OH Indexed
Oklahoma OK No change
Oregon OR Legislated or ballot measure
Pennsylvania PA No change
Rhode Island RI Legislated or ballot measure
South Carolina SC No change
South Dakota SD Indexed
Tennessee TN No change
Texas TX No change
Utah UT No change
Virginia VA No change
Vermont VT Indexed
Washington WA Legislated or ballot measure
Wisconsin WI No change
West Virginia WV No change
Wyoming WY No change
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Notes: Minimum wage increases passed through either legislation or ballot measure took effect on January 1, 2019, in Arkansas, Arizona, California, Colorado, Delaware,?Maine,?Massachusetts, Michigan, Missouri, New York, Rhode Island, and Washington. Alaska, Florida, Minnesota, Montana, New Jersey, Ohio, South Dakota, and Vermont increased their minimum wages in 2019 because of indexing to inflation. New Jersey, Oregon, and Washington, D.C., legislated minimum wage increases that took effect on July 1, 2019. Note that Connecticut legislated a minimum wage increase that took effect on October 1, 2019. This sample considers all changes after January 2018 and before December 2019; therefore, Maryland is included even though the legislated minimum wage increase for Maryland took effect on July 1, 2018. Note that after indexing to inflation on January 1, 2019, New Jersey legislated a minimum wage increase on July 1, 2019; therefore, New Jersey appears twice in these lists.

Source: EPI analysis of state minimum wage laws. See EPI’s minimum wage tracker?for the most current state-level minimum wage information.

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When we compare 10th-percentile wage growth among states that are grouped by whether they had any minimum wage increase or not, the comparison yields highly suggestive results. As shown in Figure J, when looking at 10th-percentile wages, growth in states without minimum wage increases was much slower (0.9%) than in states with any kind of minimum wage increase (4.1%).3 This result holds true for both men and women at the 10th percentile. The 10th-percentile men’s wage grew 3.6% in states with minimum wage increases, compared with 0.7% growth in states without any minimum wage increases, while women’s 10th-percentile wages grew 2.8% in states with minimum wage increases and 1.4% in states without.4

Figure J

Wage growth at the bottom was strongest in states with minimum wage increases in 2019: 10th-percentile wage growth, by presence of 2019 state minimum wage increase and by gender, 2018–2019

States with minimum wage increases States without minimum wage increases
Overall 4.1% 0.9%
Men 3.6% 0.7%
Women 2.8% 1.4%
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Notes: Minimum wage increases passed through either legislation or ballot measure took effect on January 1, 2019, in Arkansas, Arizona, California, Colorado, Delaware,?Maine,?Massachusetts, Michigan, Missouri, New York, Rhode Island, and Washington. Alaska, Florida, Minnesota, Montana, New Jersey, Ohio, South Dakota, and Vermont increased their minimum wages in 2019 because of indexing to inflation. New Jersey, Oregon, and Washington, D.C., legislated minimum wage increases that took effect on July 1, 2019. Note that Connecticut legislated a minimum wage increase that took effect on October 1, 2019. This sample considers all changes after January 2018 and before December 2019; therefore, Maryland is included even though the legislated minimum wage increase for Maryland took effect on July 1, 2018. Note that after indexing to inflation on January 1, 2019, New Jersey legislated a minimum wage increase on July 1, 2019; therefore, New Jersey appears twice in these lists.

Sources:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org, and EPI analysis of state minimum wage laws. See EPI’s minimum wage tracker?for the most current state-level minimum wage information.

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It is not surprising that these differences are smaller than what has been seen in earlier years because as the economy gets closer to full employment,5 we would expect tighter labor markets to boost the 10th-percentile wage across all states regardless of changes in the minimum wage.6 Furthermore, 2019 changes in state minimum wages came on the heels of other recent changes to minimum wages in many of the same states in recent years. In fact, when we compare states that have had any minimum wage change since 2013—26 states plus D.C.—with states that did not have a minimum wage change during that time, the pattern is even more pronounced.

As shown in Figure K, wage growth at the 10th percentile in states with at least one minimum wage increase from 2013 to 2019 was almost 90% faster than in states without any minimum wage increases (17.6% vs. 9.3%). As expected, given women’s lower wages in general, this result is even stronger for women (16.1% vs. 7.6%), though men also experienced much faster 10th-percentile wage growth in states with minimum wage increases than in those without (16.0% vs. 9.3%).

Figure K

Wage growth at the bottom was strongest in states with minimum wage increases between 2013 and 2019: 10th-percentile wage growth from 2013 to 2019, by presence of state minimum wage increase between 2013 and 2019 and by gender

States with minimum wage increases between 2013 and 2019 States with no minimum wage increases between 2013 and 2019
Overall 17.6% 9.3%
Men 16.0% 9.3%
Women 16.1% 7.6%
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Note:?Alaska,?Arizona, Arkansas, California, Colorado, Connecticut, Delaware, the District of Columbia, Florida, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, New Jersey, New York, Ohio, Oregon, Rhode Island, South Dakota, Vermont, and Washington increased their minimum wages at some point between 2013 and 2019.

Sources:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org, and EPI analysis of state minimum wage laws. See EPI’s minimum wage tracker?for the most current state-level minimum wage information.

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From 2000 to 2019, within-group wage inequality grew for white, black, and Hispanic workers.

Table 3 examines wage deciles (and the 95th-percentile wage) for white non-Hispanic, black non-Hispanic, and Hispanic workers from 2000 to 2019. From 2000 to 2019, the strongest growth among white, black, and Hispanic workers occurred at the top of the wage distribution, a sign that wage inequality is growing within each of these groups as well as among workers overall. At every decile, wage growth since 2000 has been faster for white and Hispanic workers than for black workers. After suffering declines in the aftermath of the Great Recession, 2019 is the first time wages at all deciles of the black wage distribution have exceeded their 2000 and 2007 levels.

Table 3

Hourly wages by race/ethnicity and wage percentile, selected years, 2000–2019 (2019$)

Wage by percentile
10th 20th 30th 40th 50th 60th 70th 80th 90th 95th
White
2000 $9.52 $11.83 $14.11 $16.43 $19.28 $22.46 $26.88 $32.68 $42.75 $53.73
2007 $9.75 $12.23 $14.53 $17.19 $19.91 $23.70 $28.05 $34.47 $45.98 $59.25
2018 $10.26 $12.70 $15.27 $17.98 $20.94 $25.02 $30.06 $37.37 $50.95 $71.61
2019 $10.56 $13.05 $15.45 $18.12 $21.32 $25.07 $30.31 $38.39 $52.04 $73.38
Annualized percent changes
2000–2019 0.5% 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.9% 1.0% 1.7%
2000–2007 0.3% 0.5% 0.4% 0.7% 0.5% 0.8% 0.6% 0.8% 1.0% 1.4%
2007–2019 0.7% 0.5% 0.5% 0.4% 0.6% 0.5% 0.6% 0.9% 1.0% 1.8%
2018–2019 2.9% 2.8% 1.2% 0.8% 1.8% 0.2% 0.8% 2.7% 2.1% 2.5%
Black
2000 $8.94 $10.46 $11.96 $13.57 $15.28 $17.82 $20.87 $25.15 $31.95 $38.68
2007 $8.90 $10.57 $12.26 $13.70 $15.46 $17.98 $21.01 $25.09 $34.04 $42.50
2018 $9.32 $10.62 $12.21 $13.90 $15.35 $17.72 $20.80 $26.39 $36.02 $48.83
2019 $9.61 $11.10 $12.93 $14.88 $16.12 $18.56 $21.76 $26.92 $36.03 $47.94
Annualized percent changes
2000–2019 0.4% 0.3% 0.4% 0.5% 0.3% 0.2% 0.2% 0.4% 0.6% 1.1%
2000–2007 -0.1% 0.1% 0.4% 0.1% 0.2% 0.1% 0.1% 0.0% 0.9% 1.4%
2007–2019 0.6% 0.4% 0.4% 0.7% 0.3% 0.3% 0.3% 0.6% 0.5% 1.0%
2018–2019 3.1% 4.5% 5.9% 7.0% 5.0% 4.7% 4.6% 2.0% 0.0% -1.8%
Hispanic
2000 $8.59 $9.60 $10.61 $11.94 $13.44 $15.03 $17.90 $21.85 $28.82 $37.07
2007 $8.80 $9.90 $11.15 $12.41 $14.32 $16.20 $18.76 $23.58 $31.01 $40.72
2018 $9.97 $11.15 $12.25 $13.76 $15.31 $17.40 $20.34 $25.07 $34.26 $45.04
2019 $9.94 $11.84 $12.90 $14.43 $15.89 $17.96 $20.47 $25.06 $34.93 $46.33
Annualized percent changes
2000–2019 0.8% 1.1% 1.0% 1.0% 0.9% 0.9% 0.7% 0.7% 1.0% 1.2%
2000–2007 0.3% 0.5% 0.7% 0.6% 0.9% 1.1% 0.7% 1.1% 1.1% 1.4%
2007–2019 1.0% 1.5% 1.2% 1.3% 0.9% 0.9% 0.7% 0.5% 1.0% 1.1%
2018–2019 -0.3% 6.2% 5.3% 4.9% 3.8% 3.2% 0.7% 0.0% 1.9% 2.9%
Wage disparities
Black as a share of white
2000 93.8% 88.4% 84.8% 82.6% 79.2% 79.3% 77.6% 76.9% 74.7% 72.0%
2007 91.3% 86.4% 84.4% 79.7% 77.7% 75.8% 74.9% 72.8% 74.0% 71.7%
2018 90.8% 83.7% 80.0% 77.3% 73.3% 70.8% 69.2% 70.6% 70.7% 68.2%
2019 91.0% 85.0% 83.7% 82.1% 75.6% 74.0% 71.8% 70.1% 69.2% 65.3%
Hispanic as a share of white
2000 90.2% 81.1% 75.2% 72.7% 69.7% 66.9% 66.6% 66.9% 67.4% 69.0%
2007 90.2% 81.0% 76.8% 72.2% 71.9% 68.4% 66.9% 68.4% 67.5% 68.7%
2018 97.2% 87.8% 80.2% 76.5% 73.1% 69.5% 67.6% 67.1% 67.2% 62.9%
2019 94.1% 90.7% 83.5% 79.6% 74.6% 71.6% 67.5% 65.3% 67.1% 63.1%

Notes: The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.?Race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race). The 95th-percentile white wage is imputed using the growth rates of the 94th percentile from recent years, as needed, since the weekly earnings top code continues to capture a large and growing share of the white wage distribution, making it difficult to accurately measure top-level wages. For more information on this issue, see?Gould’s?State of Working America Wages 2018?(2019).

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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White workers. We estimate that the strongest wage growth among white workers from 2000 to 2019 was at the 95th percentile. To estimate wage growth for the past year only, from 2018 to 2019, we impute the growth rate for the 95th percentile using the 94th-percentile growth rate from 2018 to 2019. We do this to account for the fact that 5.5% of white workers had weekly earnings at or above the top code. Using our imputation method, we find that wage growth for white workers was much faster over the last year among the highest and lowest wage earners, with a notable 2.9% wage increase at the 10th percentile. In addition to top-coding issues, smaller sample sizes within demographic groups mean wage changes tend to be volatile from year to year, so these changes should be taken with a grain of salt. Since 2000, however, wages have grown three times as fast for white workers at the 95th percentile as for white workers at the middle or bottom of the wage distribution.

Hispanic workers. Over the entire period from 2000 to 2019, Hispanic workers experienced more broadly based wage growth, with wages increasing across their wage distribution: There was strong growth at the top (25.0%) as well as at the median (18.2%) and the bottom (15.7%). Over the last year (2018 to 2019), Hispanic workers’ wage growth was strongest among moderate-wage workers—in the 20th to 40th percentiles—while the 10th percentile lost ground.

Black workers. Between 2018 and 2019, the vast majority of black workers had stronger wage growth than in any other year since 2000; however, black wages at the top have not seen improvement since 2018, while 95th-percentile wages for Hispanic and white workers have risen 2.9% and 2.5%, respectively, since 2018. (Again, when looking at all of these numbers, we need to keep in mind that the CPS data is subject to a certain amount of volatility from year to year; for data on black wages, that volatility is likely to be even more pronounced because of the smaller data sample represented by the black population.) Before 2019, what was particularly striking about black wages was slow wage growth since 2000, nearly across the board. In 2019, the tide turned and all deciles have finally exceeded their 2000 and 2007 levels. Even so, white and Hispanic workers had much faster growth across the board since 2000 than black workers, while black workers have just been making up for lost ground as opposed to actually getting ahead.

From 2000 to 2019, the overall black–white wage gap grew, while the overall Hispanic–white wage gap narrowed slightly.

The bottom section of Table 3 displays wage gaps by race/ethnicity. Wage gaps by race/ethnicity track how much less African American and Hispanic workers are paid relative to white workers; here, black and Hispanic wages are shown as a share of white wages at each decile of their respective wage distributions. Compared with white workers, black workers have been losing ground since 2000, with larger black–white wage gaps across the entire distribution.7 In 2000, black wages at the median were 79.2% of white wages. By 2019, they were only 75.6% of white wages, representing an increase in the wage gap from 20.8% to 24.4%. Conversely, Hispanic workers have been slowly closing the gap with white workers at the bottom 70% of the wage distribution. In 2000, median Hispanic wages were 69.7% of white wages and, by 2019, they were 74.6% of white wages, representing a narrowing of the gap from 30.3% to 25.4%. The 95th-percentile Hispanic–white wage gap still remains significantly wider than its 2000 level.

The regression-adjusted black–white and Hispanic–white wage gaps (controlling for education, age, gender, and region) both narrowed over the last year (Appendix Table 1). While the regression-adjusted Hispanic–white wage gap narrowed a bit, from 12.3% in 2000 to 10.8% in 2019, the regression-adjusted black–white gap was much larger in 2019 (14.9%) than it was in 2000 (10.2%). In 2000, the Hispanic–white wage gap was larger than the black–white wage gap. In 2019, the reverse was true.

Appendix Table 1 also shows the black–white and Hispanic–white wage gaps for men and women separately. It’s worth noting that these wage gaps are much wider for men than for women, reflecting, in part, the sizeable gender wage penalty experience by white women. Further, between 2000 and 2019, the regression-adjusted black–white wage gap widened significantly for both men (+4.4 percentage points) and women (+4.8 percentage points), while the regression-adjusted Hispanic–white wage gap narrowed for men (?2.3 percentage points) and remained about the same for women (-0.1 percentage points).

Wage growth has generally been faster among the more educated, particularly among men, since 2000.

Table 4 presents the most recent data on average hourly wages by education for all workers and by gender, and Figure L displays the cumulative percent change in real average hourly wages by education. (The discussion throughout identifies each group as mutually exclusive such that those identified as having a college degree have no more than a bachelor’s degree.)

Table 4

Average hourly wages by gender and education, selected years, 2000–2019 (2019$)

Wage by education
Less than high school High school Some college College Advanced degree
All
2000 $12.82 $18.18 $20.69 $31.84 $40.18
2007 $13.27 $18.39 $20.83 $32.53 $41.34
2018 $13.93 $18.79 $20.70 $33.96 $44.59
2019 $14.08 $18.91 $20.97 $34.63 $45.07
Annualized percent changes
2000–2019 0.5% 0.2% 0.1% 0.4% 0.6%
2000–2007 0.5% 0.2% 0.1% 0.3% 0.4%
2007–2019 0.5% 0.2% 0.1% 0.5% 0.7%
2018–2019 1.1% 0.6% 1.3% 2.0% 1.1%
Men
2000 $14.09 $20.52 $23.40 $36.19 $44.98
2007 $14.50 $20.44 $23.30 $37.18 $46.84
2018 $15.47 $20.72 $23.25 $39.29 $52.18
2019 $15.33 $20.96 $23.58 $39.99 $52.38
Annualized percent changes
2000–2019 0.4% 0.1% 0.0% 0.5% 0.8%
2000–2007 0.4% -0.1% -0.1% 0.4% 0.6%
2007–2019 0.5% 0.2% 0.1% 0.6% 0.9%
2018–2019 -0.9% 1.1% 1.4% 1.8% 0.4%
Women
2000 $10.94 $15.34 $17.82 $26.77 $33.78
2007 $11.32 $15.67 $18.24 $27.43 $34.91
2018 $11.67 $15.71 $17.88 $28.33 $36.68
2019 $12.20 $16.15 $18.44 $29.55 $38.64
Annualized percent changes
2000–2019 0.6% 0.3% 0.2% 0.5% 0.7%
2000–2007 0.5% 0.3% 0.3% 0.3% 0.5%
2007–2019 0.6% 0.3% 0.1% 0.6% 0.8%
2018–2019 4.5% 2.8% 3.2% 4.3% 5.3%
Wage disparities (women’s wages as a share of men’s)
2000 77.7% 76.1% 77.5% 75.3% 76.5%
2007 78.1% 78.0% 79.7% 75.1% 75.9%
2018 75.5% 77.9% 78.6% 73.4% 72.3%
2019 79.6% 77.1% 78.2% 73.9% 73.8%

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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The U.S. workforce is split roughly into thirds by educational attainment (EPI 2020c). One-third (34.1%) of U.S. workers have a high school diploma or did not complete high school. A little less than one-third (27.7%) of workers have some college, meaning they may have an associate degree or have completed part of a two- or four-year college degree. The remaining 38.1% have a bachelor’s or advanced degree. It is important to keep in mind when analyzing the labor market or discussing economic policy that 61.9% of the workforce do not have a four-year college degree. If the economy is going to deliver decent wages for most U.S. workers, it needs to deliver for the six in 10 workers who do not have a four-year college degree.

Figure L

For workers with some college education, wages have finally surpassed their 2000 level: Cumulative percent change in real average hourly wages, by education, 2000–2019

year Less than high school? ?High school? Some college? ?College? ?Advanced degree?
2000 0.0% 0.0% 0.0% 0.0% 0.0%
2001 1.5% 1.4% 1.6% 1.7% 0.5%
2002 3.5% 2.8% 2.4% 2.2% 3.0%
2003 4.1% 3.4% 2.1% 1.9% 1.7%
2004 3.1% 2.5% 1.8% 0.9% 3.3%
2005 2.0% 1.3% 0.3% 1.3% 2.4%
2006 1.6% 1.8% 0.1% 1.5% 2.7%
2007 3.5% 1.2% 0.7% 2.2% 2.9%
2008 2.8% 0.6% -0.7% 1.6% 3.4%
2009 5.0% 3.0% 0.6% 2.4% 6.8%
2010 2.6% 1.3% -0.5% 2.7% 6.1%
2011 2.0% -0.7% -3.3% 0.0% 3.4%
2012 0.9% -1.4% -4.7% 0.9% 5.4%
2013 -0.3% -2.3% -5.0% 1.6% 5.5%
2014 -0.1% -2.2% -5.1% 0.1% 2.8%
2015 3.9% 0.0% -2.2% 4.2% 5.3%
2016 5.3% 0.9% -1.4% 6.3% 7.9%
2017 8.5% 2.2% -1.6% 6.1% 7.4%
2018 8.7% 3.4% 0.1% 6.7% 11.0%
2019 9.8%?pointoptions showlabel labelcolor=”#4383a3″ labely=+6] 4.0%? 1.4%? 8.8%? 12.2%?
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From 2000 to 2019, the strongest wage growth occurred among those with advanced degrees (12.2%), those with college degrees (8.8%), and those with less than a high school diploma (9.8%). Given that those with less than a high school diploma are often the lowest-wage workers in general, it is likely that some of their recent gains can be attributed to state-level increases in the minimum wage. Also, these workers represent a small and shrinking share of the overall workforce, only 8.0% of workers in 2019 (EPI 2020c). The average wage for workers with some college has finally exceeded its 2007 level before the Great Recession began and is now 1.4% higher than it was in 2000, with wages for this group rising just shy of 0.1% per year over the last 19 years.

Over the last year, average wages of those with a college degree and those with some college rose the fastest, 2.0% and 1.3% respectively. After narrowing between 2016 and 2018, the gap between wages of those with a college degree and those with a high school diploma widened (EPI 2020c). However, this unadjusted college/high school wage gap remains narrower than in 2016. Similarly, the college wage premium—the regression-adjusted log-wage difference between the wages of college-educated and high school–educated workers—rose slightly from 48.4% to 49.5% between 2018 and 2019, but remains lower than in 2016 (50.6%) (EPI 2020c).

If the economy is going to deliver decent wages for most U.S. workers, it needs to deliver for the six in 10 workers who do not have a four-year college degree.

Over the entire period from 2000 to 2019, wage growth among those with a college degree rose faster than among those with a high school diploma (8.8% vs. 4.0%). Because of the faster gains for those with more credentials, the regression-adjusted college wage premium grew from 47.0% to 49.5% between 2000 and 2019. For a more thorough discussion of the college wage premium and wage inequality, see the section “Slow wage growth cannot be explained away by education shortages” later in this report.

Figures M and N display the cumulative percent change in real hourly wages by education for men and women, respectively. Since 2000, wage growth for those with a college or advanced degree was faster for men than for women, while wage growth for those with some college, a high school diploma, or less than high school was faster for women than for men. In general, the women’s wage distribution by educational attainment is more compressed; that is, the wage differences between workers at different levels of education are not quite as large for women as they are for men.

For both men and women, the largest gains since 2000 were among those with an advanced degree as well as those with a college degree or less than high school. Wages of both men and women with some college have grown the slowest among all levels of educational attainment. For the first time in this recovery, wages of men with some college have finally reached their 2000 levels.

Figure M

Wages grew more quickly for men with college or advanced degrees, and wages for men with some college finally reached their 2000 level: Cumulative percent change in real average hourly wages of men, by education, 2000–2019

year Less than high school? High school? Some college? College? Advanced degree?
2000 0.0% 0.0% 0.0% 0.0% 0.0%
2001 0.6% 0.8% 1.3% 1.7% 0.1%
2002 3.5% 1.8% 1.2% 2.3% 3.3%
2003 4.1% 1.7% 1.0% 1.9% 2.0%
2004 3.3% 1.1% 0.9% 0.8% 4.7%
2005 2.0% -0.6% -1.1% 1.3% 3.5%
2006 1.1% 0.3% -1.5% 1.3% 3.9%
2007 2.9% -0.4% -0.4% 2.8% 4.1%
2008 2.7% -0.8% -1.7% 2.2% 5.0%
2009 4.8% 1.1% -0.1% 3.9% 9.4%
2010 1.2% -0.9% -2.0% 3.1% 8.5%
2011 0.2% -2.9% -5.4% 0.0% 5.2%
2012 0.1% -3.6% -6.5% 2.0% 9.3%
2013 -1.7% -5.1% -6.4% 2.2% 9.4%
2014 -1.1% -4.6% -6.8% -0.7% 6.1%
2015 3.1% -2.4% -2.9% 5.0% 9.4%
2016 5.1% -2.0% -3.0% 8.6% 12.1%
2017 9.3% -0.1% -3.3% 7.5% 10.3%
2018 9.8% 1.0% -0.6% 8.6% 16.0%
2019 8.8%? 2.1% 0.8%? 10.5%? 16.4%
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Figure N

Average wages were higher in 2019 than in 2000 for women at all levels of educational attainment: Cumulative percent change in real average hourly wages of women, by education, 2000–2019

year Less than high school? High school? Some college? College? Advanced degree?
2000 0.0% 0.0% 0.0% 0.0% 0.0%
2001 3.0% 2.3% 2.0% 1.7% 1.5%
2002 3.2% 4.0% 4.0% 2.6% 3.0%
2003 3.6% 5.5% 4.0% 2.6% 2.6%
2004 1.9% 4.0% 3.3% 1.5% 2.4%
2005 0.9% 3.0% 2.4% 2.0% 2.6%
2006 1.5% 2.5% 2.2% 2.9% 2.8%
2007 3.4% 2.2% 2.3% 2.5% 3.3%
2008 2.2% 1.2% 1.1% 2.1% 3.9%
2009 4.9% 4.6% 2.2% 2.1% 6.2%
2010 4.4% 3.1% 1.9% 3.7% 6.0%
2011 4.4% 0.8% -0.6% 1.4% 4.6%
2012 1.6% -0.3% -2.4% 0.8% 3.7%
2013 0.8% -0.2% -3.1% 1.9% 4.2%
2014 0.6% -0.9% -3.0% 2.5% 2.5%
2015 4.5% 1.0% -1.1% 4.3% 4.6%
2016 4.6% 2.5% 0.6% 4.7% 7.1%
2017 6.9% 2.4% 0.3% 5.8% 8.6%
2018 6.7% 3.4% 0.7% 5.9% 9.7%
2019 11.5% 3.4% 1.6% 8.4% 12.3%
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While there has been a slow narrowing of gender wage gaps for those with less than high school, a high school diploma, and those with some college since 2000, gender wage gaps are wider among those with college or advanced degrees. As Figure O illustrates, women are paid consistently less than their male counterparts at every education level.

Figure O

On average, men are paid more than women at every education level: Average hourly wages by gender and education, 2019

Men Women
Less than high school $15.33 $12.20?
High school $20.96 $16.15
Some college $23.58 $18.44
College $39.99 $29.55
Advanced degree $52.38 $38.64
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Educational attainment has grown faster for women than for men between 2000 and 2019, and now women are nearly 6 percentage points more likely than men to have a college or advanced degree (EPI 2020c). Unfortunately, increasing educational attainment has not insulated women from large gender wage gaps: The average wage for a man with a college degree was higher in 2019 than the average wage for a woman with an advanced degree (by 3.5%).

From 2000 to 2019, wage growth for white and black workers was faster for those with a college or advanced degree than for those with lower levels of educational attainment.

Table 5 presents the most recent data on average hourly wages by education for white non-Hispanic, black non-Hispanic, and Hispanic workers. From 2000 to 2019, average wages grew faster among white and Hispanic workers than among black workers for all education groups (which is not surprising given that the same was true at all deciles of the wage distribution). Black workers with some college had lower wages in 2019 than in 2000.

Table 5

Average hourly wages by race/ethnicity and education, selected years, 2000–2019 (2019$)

Wage by education
Less than high school High school Some college College Advanced degree
White
2000 $13.04 $18.93 $21.36 $32.73 $40.69
2007 $13.42 $19.31 $21.58 $33.48 $42.03
2018 $14.02 $20.11 $21.98 $35.38 $45.26
2019 $13.88 $20.04 $22.26 $35.90 $45.29
Annualized percent changes
2000–2019 0.3% 0.3% 0.2% 0.5% 0.6%
2000–2007 0.4% 0.3% 0.1% 0.3% 0.5%
2007–2019 0.3% 0.3% 0.3% 0.6% 0.6%
2018–2019 -1.0% -0.3% 1.3% 1.5% 0.1%
Black
2000 $12.32 $16.04 $18.34 $27.09 $35.61
2007 $12.44 $15.95 $18.49 $27.04 $35.00
2018 $11.63 $15.85 $17.46 $27.96 $36.89
2019 $12.40 $16.37 $17.86 $27.81 $37.33
Annualized percent changes
2000–2019 0.0% 0.1% -0.1% 0.1% 0.2%
2000–2007 0.1% -0.1% 0.1% 0.0% -0.2%
2007–2019 0.0% 0.2% -0.3% 0.2% 0.5%
2018–2019 6.6% 3.3% 2.3% -0.5% 1.2%
Hispanic
2000 $12.71 $16.17 $18.85 $27.11 $36.19
2007 $13.37 $16.78 $19.16 $28.88 $39.10
2018 $14.36 $17.59 $19.00 $29.00 $39.17
2019 $14.60 $17.88 $19.23 $30.35 $40.80
Annualized percent changes
2000–2019 0.7% 0.5% 0.1% 0.6% 0.6%
2000–2007 0.7% 0.5% 0.2% 0.9% 1.1%
2007–2019 0.7% 0.5% 0.0% 0.4% 0.4%
2018–2019 1.6% 1.7% 1.2% 4.6% 4.2%
Wage disparities
Black as a share of white
2000 94.5% 84.7% 85.9% 82.8% 87.5%
2007 92.7% 82.6% 85.7% 80.8% 83.3%
2018 82.9% 78.8% 79.5% 79.0% 81.5%
2019 89.3% 81.7% 80.2% 77.5% 82.4%
Hispanic as a share of white
2000 97.5% 85.4% 88.3% 82.8% 89.0%
2007 99.6% 86.9% 88.8% 86.2% 93.0%
2018 102.4% 87.5% 86.4% 82.0% 86.5%
2019 105.1% 89.2% 86.4% 84.5% 90.1%

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Over the last year, Hispanic workers were the only group that had positive wage growth across all levels of educational attainment. (Again, we must keep in mind that year-to-year changes are subject to volatility, particularly for smaller population cuts.) Between 2018 and 2019, Hispanic workers with the highest levels of educational attainment experienced the strongest wage growth. Black workers with less than high school experienced the strongest growth, while black workers with a college degree experienced wage losses. White workers with a college degree saw faster wage growth than any other education group, while those with a high school diploma or less than high school experienced losses.

Black–white wage gaps by education were larger in 2019 than in 2000 for all education groups, while Hispanic–white wage gaps were narrower for workers at any level of educational attainment except those with some college. At nearly every education level, black and Hispanic workers were paid less than their white counterparts in 2019, while Hispanic workers were consistently paid more than black workers (Figure P).

Figure P

On average, white workers are paid more than black and Hispanic workers at nearly every education level: Average hourly wages, by race/ethnicity and education, 2019

White Hispanic Black
Less than high school $13.88 $14.60 $12.40
High school $20.04 $17.88 $16.37
Some college $22.26 $19.23 $17.86
College $35.90 $30.35 $27.81
Advanced degree $45.29 $40.80 $37.33
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Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Some convenient but misguided explanations of slow wage growth

Slow wage growth cannot be explained away by education shortages.

Some argue that wage inequality is a simple consequence of growing employer demand for—and a limited supply of—college-educated workers. This demand is often thought to be driven by advances in technology and corresponding technology-driven increases in required credentials. According to this explanation, because there is a shortage of college-educated workers, the wage gap between those with and without college degrees is widening as employers are forced to pay higher wages in the competition for college-degreed workers while those without college degrees are increasingly falling behind.

Despite its intuitive appeal, this story about recent wage trends being driven more and more by a higher demand for college-educated workers does?not?fit the facts well, especially since the mid-1990s (Schmitt, Shierholz, and Mishel 2013). The evidence suggests that the demand for college graduates has grown far less in the period since the mid-1990s than it did before then. This is difficult to square with contentions that automation or changes in the types of skills employers require have been more rapid in the 2000s than in earlier decades. Rather,?automation has been slower?in the recent period than in earlier decades, as seen in the pace of productivity, capital, information equipment, and software investment—and in the speed of changes in occupational employment patterns (Mishel and Bivens 2017).

Further, our research shows that the increase in the pay gap between high earners and most workers has been far larger than what can be explained by rising returns to education. The typical U.S. worker’s experience and education have increased significantly over the last 40 years, as seen in Figure Q, while median wage growth has been persistently slow and uneven over that time (recall Figure E). Figure Q plots the growing share of workers who have at least a college degree; it also plots the average age of workers in the middle fifth of the wage distribution from 1979 to 2019. Age is a proxy for experience, which, along with education, should imply higher productivity. While age is not a perfect proxy for experience, the increase in average age by about 5.5 years implies an increase in the experience—and the likely productivity—of the typical worker. And the near doubling of educational attainment should—given most interpretations of the relationship between education and productivity—lead to much faster wage growth than the typical worker has actually experienced.

Figure Q

Middle-wage workers have more experience and education than they did four decades ago: Average age and share of workers with a college degree or more in the middle fifth of the wage distribution, 1979–2019

Year % with college or more Average age (years)
1979 16.10% 36.27
1980 16.91% 36.04
1981 16.19% 36.18
1982 17.92% 36.62
1983 18.21% 36.61
1984 18.02% 36.47
1985 17.39% 36.45
1986 17.83% 36.47
1987 17.78% 36.67
1988 17.32% 36.52
1989 17.18% 36.92
1990 17.91% 36.90
1991 17.82% 37.17
1992 17.29% 37.34
1993 18.01% 37.76
1994 17.96% 37.91
1995 19.10% 38.04
1996 18.91% 38.22
1997 19.68% 38.53
1998 20.19% 38.50
1999 19.54% 38.32
2000 19.82% 38.92
2001 20.15% 39.14
2002 21.56% 39.80
2003 21.71% 39.99
2004 21.70% 40.16
2005 21.70% 40.11
2006 23.45% 40.47
2007 23.86% 40.65
2008 24.73% 40.78
2009 25.76% 41.55
2010 26.52% 41.88
2011 26.53% 41.90
2012 26.72% 42.12
2013 28.61% 42.25
2014 29.81% 41.97
2015 30.00% 42.07
2016 29.31% 42.02
2017 31.02% 41.87
2018 31.32% 41.83
2019 32.04% 41.91
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Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau

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Further, the growing inequality of note is that between the top (or very top) and everyone else. The pulling away of the very top cannot be explained by differences in educational attainment, but rather is attributable to the escalation of?executive and financial-sector pay, among other factors (Mishel and Wolfe 2019).

This becomes more clear when we juxtapose the college wage premium with the 95/50 wage ratio. Figure R compares the change in the college wage premium over 1979–2000 and 2000–2019 with the change in the log 95/50 wage ratio. The college wage premium is the percent by which average hourly wages of four-year college graduates exceed those of otherwise equivalent high school graduates, controlling for gender, race and ethnicity, age, and geographic division. The 95/50 wage ratio is a representation of the level of inequality within the hourly wage distribution, comparing how much the 95th-percentile worker is paid relative to the 50th-percentile worker. Both are measured in log changes and shown as annual changes.

Figure R

The college wage premium cannot explain growing wage inequality since 2000: Average annual percentage-point changes in wage gaps, 1979–2000 and 2000–2019

Log 95/50 ratio College wage premium
1979–2000 0.76 1.00
2000–2019 1.00 0.13
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Notes:?The college wage premium is the percent by which hourly wages of four-year college graduates exceed those of otherwise equivalent high school graduates.?This regression-based gap is based on average wages and controls for gender, race and ethnicity, education, age, and geographic division; the log of the hourly wage is the dependent variable. The 95/50?wage ratio is a representation of the level of inequality within the hourly wage distribution. It is logged for comparability with the college wage premium.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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The regression-adjusted college wage premium grew rather quickly between 1979 and 2000 and then rose at a?much slower?rate in the 2000s, about an eighth as much. It had already?slowed considerably by the mid-1990s (Bivens et al. 2014). In contrast, the 95/50 wage ratio grew somewhat faster in the more recent period. When we?compare the relative size of the changes in each from?2000 to?2019, it is clear that the very modest gains in the college wage premium in recent years have not been large enough to plausibly drive the continued steady growth of?the 95/50 wage ratio. In fact, the log 95/50 wage ratio grew more than seven times as fast as the college premium over this period.

There are plenty of good reasons to provide widespread access to college education, but expanding college enrollment and graduation is not an answer to escalating wage inequality.

Between 1979 and 2000, the log 95/50 wage ratio and the regression-adjusted college wage premium grew at roughly the same pace. The idea that increased employer demand for education is a prime driver of inequality?appeared?to be a more plausible story then. But it is clear?that in the latter period, from?2000 to?2019, gains in the college wage premium have been very modest and far less than the continued steady growth of?the 95/50 wage ratio. Therefore, it is highly?implausible?that the growth of unmet employer needs for college graduates has driven wage inequality over the last 19 years. Given this, the correspondence in the earlier period also shouldn’t be over-interpreted as differences in education levels driving the 95/50 wage ratio.

The more salient story between 2000 and 2019 is not one of a growing differential of wages between college and high school graduates, but one of growing wage inequality between the top (and the tippy top) and the vast majority of workers. Wage inequality is driven by changes within education groups (among workers with the same education) and not between education groups. From 2000 to 2019, the overall 95th-percentile wage?grew nearly four times as fast as wages at the median (30.7% vs. 8.0%). Among college graduates only, there has also been a significant pulling away at the very top of the wage distribution, with many college-degreed workers being left behind.

Figure S displays the change in college wages from 2000 to 2019 for the average wage as well as at selected deciles of the college wage distribution. As shown previously in Figure L, average wages for college graduates grew 8.8% between 2000 and 2019. Here, it’s clear that this average masks important differences at different points on the college wage distribution. The highest percentile we show here is the 90th, because the 95th wage percentile for college graduates is fraught with top-coding issues to a greater degree than for white and male workers, making it even more difficult to obtain reliable measures of high-end wages and wage growth (as discussed in more detail in Gould 2019). Even so, the 90th-percentile wage grew nearly twice as fast as the average (15.1% vs. 8.8%) while the 50th-percentile (median) wage was actually lower in 2019 than in 2000 (-0.4%): Half of all college-degreed workers have not experienced any wage growth at all since 2000.

Figure S

Wages of the bottom 50% of college graduates are lower today than they were in 2000: Cumulative percent change in real hourly wages of workers with a college degree for the average, median, and 90th-percentile wages, 2000–2019

Year 50th 90th Average
2000 0.0% 0.0% 0.0%
2001 2.3% 3.6% 1.7%
2002 1.5% 2.4% 2.2%
2003 1.3% 3.3% 1.9%
2004 0.1% 2.8% 0.9%
2005 -1.4% 5.5% 1.3%
2006 -1.0% 4.5% 1.5%
2007 -0.3% 6.4% 2.2%
2008 -1.1% 5.9% 1.6%
2009 -0.6% 6.6% 2.4%
2010 -1.3% 5.3% 2.7%
2011 -3.1% 2.0% 0.0%
2012 -4.5% 3.9% 0.9%
2013 -2.9% 4.7% 1.6%
2014 -4.5% 3.9% 0.1%
2015 -3.1% 8.2% 4.2%
2016 -1.7% 14.5% 6.3%
2017 -3.2% 12.2% 6.1%
2018 -2.4% 9.8% 6.7%
2019 -0.4% 15.1% 8.8%
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Note:?Education groups are mutually exclusive, so “college” here refers to those with only a four-year college degree.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Between 2000 and 2019, the median high school wage grew slowly (1.7%), while the 95th-percentile high school wage grew much faster (7.5%) (EPI 2020a). The (raw) gap between median college wages and median high school wages is no wider in 2019 than in 2000. In fact, the gap actually narrowed over this period. Increases in inequality over the last 19 years clearly cannot be explained away by claims that employers face a growing shortage of college graduates and that, correspondingly, wage inequality is some unfortunate side effect of the positive gains from technological change that we neither can nor would want to alter. There are plenty of good reasons to provide widespread access to college education, but expanding college enrollment and graduation is not an answer to escalating wage inequality.

Slow wage growth cannot be explained away by including benefits or looking at total compensation.

Some have argued that to best measure pay, one should use total compensation and not simply wages. This argument is based on the theory that benefits—health benefits, in particular—have crowded out wage growth in recent years. But this argument is not borne out in the data.

Recall Figure A, which shows the divergence between productivity and pay over the last 40 years. The pay measure used in that figure includes benefits. Figure T separates out wages and measured compensation in that iconic figure, starting in 1979. The line labeled “hourly compensation,” which represents wages plus benefits, rose only slightly faster than wage growth on its own (14.9% vs. 14.0%) and therefore doesn’t do a whole lot to explain the gap between the potential for wage growth and actual wage growth. The other lines on the chart demonstrate that most of the divergence between productivity and pay over the last 40 years is due to growing inequality—both inequality in how wage income is distributed among workers and how a growing share of income accrues to (already richer) owners of capital rather than to workers.

Inequality in workers’ hourly pay can be measured by examining the divergence between average pay and median pay. This divergence has unambiguously risen and constitutes the single largest factor accounting for the overall gap between median hourly pay and economywide productivity growth. The loss in labor’s share of income represents the overall shift in how much of the income in the economy is received by workers in wages and benefits. As labor’s share falls, this means that a growing share of productivity gains are going to owners of capital. Both growing compensation inequality and changes in labor’s share of income represent growing income inequality over this period, and their combined influence explains the large majority of the overall gap between pay and productivity.8

Figure T

Growing inequality dominates the story of slow wage growth: Growth of productivity, real average compensation (consumer and producer), real median compensation, and real median hourly wage, 1979–2018

Year Real median hourly wage Real median hourly compensation Real consumer average hourly compensation Real producer average hourly compensation Net productivity
1979 0.0% 0.0% 0.0% 0.0% 0.0%
1980 -0.7% -0.4% -0.8% 1.3% -0.6%
1981 -1.7% -1.0% -0.5% 1.6% 1.1%
1982 -2.1% -1.0% 1.2% 3.1% 0.1%
1983 -1.9% -0.5% 1.5% 3.3% 3.1%
1984 -1.4% -0.1% 2.1% 4.2% 5.8%
1985 0.1% 1.6% 3.8% 5.9% 7.5%
1986 0.8% 2.4% 7.5% 9.4% 9.7%
1987 1.9% 3.0% 8.1% 10.9% 9.9%
1988 0.5% 1.7% 10.0% 12.8% 11.5%
1989 0.3% 1.8% 9.1% 12.2% 12.5%
1990 0.2% 1.7% 10.4% 14.4% 13.9%
1991 -0.7% 1.3% 11.6% 15.7% 14.6%
1992 0.4% 3.0% 15.4% 19.6% 18.9%
1993 1.9% 4.7% 14.8% 18.9% 19.4%
1994 0.8% 3.2% 14.0% 17.9% 20.4%
1995 -0.7% 0.8% 14.0% 18.4% 20.9%
1996 -2.2% -1.4% 14.8% 19.9% 23.1%
1997 -0.2% 0.0% 16.7% 22.0% 25.3%
1998 3.3% 3.3% 21.1% 26.7% 27.9%
1999 5.9% 5.8% 24.0% 30.4% 31.4%
2000 6.5% 6.4% 27.5% 35.5% 34.3%
2001 8.1% 8.6% 29.4% 38.1% 36.3%
2002 9.8% 11.1% 30.9% 39.4% 40.1%
2003 9.9% 11.8% 33.6% 42.4% 44.7%
2004 11.1% 13.2% 36.1% 45.0% 48.6%
2005 9.5% 11.7% 36.3% 45.9% 51.5%
2006 9.9% 11.5% 37.1% 47.1% 52.6%
2007 10.1% 11.3% 39.0% 49.2% 53.7%
2008 9.3% 10.6% 38.0% 50.8% 54.4%
2009 11.6% 13.6% 40.9% 51.6% 58.0%
2010 11.0% 13.0% 41.4% 52.5% 62.5%
2011 8.2% 10.1% 39.8% 52.2% 62.5%
2012 6.9% 8.3% 40.2% 52.7% 63.2%
2013 7.4% 9.3% 40.4% 52.4% 63.8%
2014 7.5% 9.0% 41.7% 53.7% 64.8%
2015 9.2% 10.5% 45.3% 56.2% 66.1%
2016 11.2% 12.2% 45.4% 56.4% 66.4%
2017 12.1% 13.1% 46.7% 58.2% 68.1%
2018 14.0%? 14.9% 47.4% 59.0% 69.6%?
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Notes: Data are for all workers. Net productivity is the growth of output of goods and services minus depreciation, per hour worked. “Compensation” refers to total compensation, including wages and benefits.

Source: Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org; unpublished Total Economy Productivity data from the Bureau of Labor Statistics (BLS) Labor Productivity and Costs program; and Bureau of Economic Analysis National Income and Product Accounts. For more detailed information, see the appendix of Bivens and Mishel,?Understanding the Historic Divergence Between Productivity and a Typical Worker’s Pay?(2015).

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Further, many forms of compensation are not found equally across the wage distribution. Therefore average benefits—like average wages—tend to overstate typical worker compensation or wage growth. This is certainly true with regard to employer-sponsored health insurance (ESI). Figure U shows the incidence of ESI since 1979.9 Not only has the incidence of ESI obtained through one’s own job fallen precipitously across the board, but the share of workers with ESI obtained through their own job is far less at the bottom of the wage distribution than at the top. In fact, workers in the top fifth are three times as likely to have ESI as workers in the bottom fifth. And only 59% of middle-wage workers have health insurance on the job. Health insurance costs certainly can’t be blamed for crowding out wage growth for the millions of workers who don’t have health insurance coverage at work.

Figure U

High-wage workers are more likely to have employer-sponsored health insurance than low- and middle-wage workers, and all groups have experienced significant declines in coverage since 1979: Employer-sponsored health insurance coverage rates, by wage fifth, 1979 and 2017

Date 1979 2017
Bottom fifth 37.90% 24.7%
Second fifth 60.50% 45.8%
Middle fifth 74.70% 59.0%
Fourth fifth 83.50% 67.3%
Top fifth 89.50% 74.2%
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Notes:?Health insurance coverage data are for private-sector wage and salary workers ages 18–64 who worked at least 20 hours per week and 26 weeks per year. Coverage is defined as workers who received health insurance from their own job for which their employer paid at least some of the premium.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

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Furthermore, research has shown that workers in firms with more low-wage workers have health insurance plans with cheaper premiums overall, but these workers actually contribute more dollars to their premiums because they are required to pay a higher share of the total cost of coverage when compared with workers in firms with fewer lower-wage workers (Claxton, Rae, et al. 2018). Not only are coverage rates lower in firms with more low-wage workers (33%) versus those in firms with fewer low-wage workers (64%), but, over the last several years, more and more workers are in plans with deductibles and those deductibles have increased (Claxton, Rae, et al. 2018). Because workers have seen slow wage growth—wage growth that is slower than health care cost growth—their ability to pay for premiums as well as out-of-pocket costs has been hampered (Claxton, Levitt, et al. 2018). And many health plan enrollees cannot rely on other resources to pay for increases in cost-sharing payments (Rae, Claxton, and Levitt 2017).

While health insurance costs are certainly squeezing workers, there is little evidence that changes in employer-sponsored health insurance premiums (or any other benefit cost) can explain more than a small portion of trends in workers’ wages.

Slow and unequal wage growth is not a statistical quirk that can be explained away by changing the price deflator.

In Figure E, we demonstrate that median wage growth was slow and uneven between 1979 and 2019. But on average over that time period median wages grew faster than zero percent per year, raising the question of just what benchmark we should use to define “slow” or “fast” wage growth. In Figure A, we show that wage growth for typical workers grew far slower than its potential—defined as economywide productivity growth—and, in Figures B and C, we show that much of that potential for wage growth went to the top or the very top of the wage distribution.

However, some analysts take issue with the argument that wage growth has been slow for most workers (see CEA 2018 for one example). In particular, they posit that wage growth is often measured using the wrong price deflator.

The price deflator is used to measure wages in constant dollars so that growth in wages can be assessed against growth in inflation or changes in the ability of wages to meet economic needs or standard of living. Two commonly used deflators are the CPI (Consumer Price Index) and the PCE (personal consumption expenditures) price index. Our findings of low-wage growth are based on using the CPI. The Census Bureau also uses the CPI for measuring real changes in incomes and earnings as it relates to changes in individuals’ and families’ standard of living. However, detractors argue that the CPI “overstates price increases and understates real wage growth” relative to the PCE price index (CEA 2018, 9).

We explore this question by comparing wage growth using the two deflators. Following the example shown in Bernstein 2018, we look first at the cumulative change in the real median hourly wage over the last 40 years (Figure V). The lighter blue line in Figure V plots wage growth based on the CPI, while the darker line calculates real wages using the PCE deflator. The fits and starts of typical wage growth are evident in both lines. By the mid-1990s, wages hadn’t grown past their 1979 wage levels using either deflator. Wages grew faster in the late 1990s as well as in the last five years (2014–2019), with notable flatness again between those periods of faster growth. While it is true that, over the entire period, real wage growth is notably faster using the PCE, typical wage growth only accumulates to 28.8%, or just under 0.7% annually—still slow relative to economywide productivity growth.

Figure V

Wage growth is slow regardless of which deflator is used: Cumulative change in real median hourly wages of all workers, by deflator, 1979–2019

Year Median using CPI-U-RS Median using PCE
1979 0.0% 0.0%
1980 -0.6% -0.3%
1981 -1.7% -0.9%
1982 -2.1% -0.8%
1983 -1.9% -0.6%
1984 -1.4% 0.3%
1985 0.1% 1.7%
1986 0.8% 2.0%
1987 1.9% 3.5%
1988 0.5% 1.8%
1989 0.3% 1.6%
1990 0.2% 2.0%
1991 -0.6% 1.4%
1992 0.4% 2.3%
1993 1.9% 3.9%
1994 0.8% 2.7%
1995 -0.6% 1.6%
1996 -2.3% 0.5%
1997 -0.3% 2.9%
1998 3.3% 7.2%
1999 5.9% 10.6%
2000 6.5% 12.2%
2001 8.2% 15.0%
2002 9.8% 17.1%
2003 9.9% 17.5%
2004 11.1% 19.1%
2005 9.5% 17.9%
2006 9.9% 19.0%
2007 10.1% 19.5%
2008 9.3% 19.6%
2009 11.7% 21.9%
2010 11.0% 21.1%
2011 8.2% 18.8%
2012 6.9% 17.6%
2013 7.4% 18.3%
2014 7.5% 18.6%
2015 9.3% 20.6%
2016 11.2% 23.0%
2017 12.2% 24.6%
2018 14.0% 27.1%
2019 15.1% (0.4%) 28.8% (0.7%)
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Notes: Shaded areas denote recessions. The annualized percent change since 1979 is in parentheses.?The deflators used are the Consumer Price Index for all Urban Consumers, research series using current methods (CPI-U-RS), and the personal consumption expenditures (PCE) price index.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org, and Bureau of Economic Analysis National Income and Product Accounts (Table 2.3.4)

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Another way to look at the question of slow wage growth for typical workers is to compare growth at different points of the wage distribution, to find out whether changing the deflator tells a different story about inequality. Figure W shows wage growth adjusted using the PCE deflator; Figure C from our analysis shows wage growth adjusted using the CPI deflator. While growth for all groups is somewhat faster using the PCE, it does not at all change the fact that growth is much faster at the top than at the middle and the bottom of the wage distribution. Between 1979 and 2019, growth at the 95th percentile using the PCE was almost three times as fast as growth at the median and over five times as fast as growth at the 10th percentile. The choice of deflator simply does not change the overall story of unequal and uneven wage growth over the last 40 years.

Figure W

Using a different deflator doesn’t change the fact that most of the growth is at the top: Cumulative change in real hourly wages of all workers, by wage percentile, adjusted using PCE deflator, 1979–2019

Year 10th 50th 95th
1979 0.0% 0.0% 0.0%
1980 -6.4% -0.3% -1.4%
1981 -7.3% -0.9% -0.7%
1982 -10.7% -0.8% 1.8%
1983 -13.6% -0.6% 4.9%
1984 -15.0% 0.3% 5.7%
1985 -16.1% 1.7% 7.8%
1986 -16.2% 2.0% 9.9%
1987 -16.0% 3.5% 13.4%
1988 -15.4% 1.8% 15.8%
1989 -16.0% 1.6% 9.1%
1990 -15.0% 2.0% 11.1%
1991 -13.3% 1.4% 12.9%
1992 -12.7% 2.3% 10.9%
1993 -11.5% 3.9% 8.7%
1994 -12.2% 2.7% 14.6%
1995 -12.8% 1.6% 15.3%
1996 -13.4% 0.5% 16.6%
1997 -10.4% 2.9% 18.4%
1998 -4.4% 7.2% 22.8%
1999 -2.0% 10.6% 26.9%
2000 -1.7% 12.2% 31.5%
2001 2.3% 15.0% 35.0%
2002 5.7% 17.1% 40.2%
2003 5.6% 17.5% 39.5%
2004 3.9% 19.1% 41.1%
2005 1.9% 17.9% 42.2%
2006 1.8% 19.0% 44.2%
2007 3.9% 19.5% 47.1%
2008 5.9% 19.6% 49.1%
2009 6.6% 21.9% 52.1%
2010 5.6% 21.1% 51.2%
2011 3.5% 18.8% 50.7%
2012 2.0% 17.6% 52.9%
2013 2.7% 18.3% 55.7%
2014 3.9% 18.6% 53.8%
2015 8.8% 20.6% 63.7%
2016 10.0% 23.0% 65.8%
2017 15.0% 24.6% 68.8%
2018 16.0% 27.1% 74.0%
2019 15.6% 28.8% 82.6%
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Notes: Shaded areas denote recessions.?The xth-percentile wage is the wage at which x% of wage earners earn less and (100?x)% earn more.?Wages are adjusted using the personal consumption expenditures (PCE) price index.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org, and Bureau of Economic Analysis National Income and Product Accounts (Table 2.3.4)

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Conclusions

Wage growth over the last 40 years has been slow, uneven, and unequal. These phenomena are the result of a series of policies that have reduced the leverage of most workers to achieve faster wage growth. Such policies include tolerating (or even encouraging) excessive unemployment; failing to routinely raise the federal minimum wage to protect workers’ purchasing power; writing the rules of globalization to let employers use them as a tool for wage suppression; the enforced withering of labor standards like the overtime threshold governing how many workers are entitled to higher pay for longer hours; and sharp cuts in marginal tax rates, deregulation, and loose corporate governance oversight, which led to explosions in executive and financial-sector pay (Bivens and Zipperer 2018; Bivens 2013; Cooper, Gould, and Zipperer 2019; McNicholas, Sanders, and Shierholz 2017; Mishel and Wolfe 2019).

Declining union membership has also played a major role in slow and unequal wage growth. This erosion was not driven by workers’ declining interest in unions but rather by concerted employer opposition along with state and federal policy that has made it near impossible for workers to form unions in the face of unwilling employers (Rosenfeld, Denice, and Laird 2016; McNicholas et al. 2019).

To stem inequality and see healthy wage growth for the vast majority of workers, we need to use all the tools in our toolbox to reverse these policy trends.

Macroeconomic policy matters. Rising wages over the last few years have happened during a period of falling unemployment, with unemployment rates dropping to historical lows. This is no coincidence. If the unemployment rate is allowed to continue to fall, eventually low unemployment should boost low- and middle-wage workers’ leverage enough to see steady and large wage gains. Full employment is one way that workers gain enough bargaining power to increase their wages; employers have to pay more to attract and retain the workers they need when workers are scarce. The lever for higher wages that comes from full employment is most important for workers at the bottom of the wage distribution, as well as for workers that have historically faced discrimination in the labor market. For a given fall in the unemployment rate, wage growth rises more for these workers, and in the absence of stronger labor standards, it is often only in the tightest of labor markets that these workers see stronger wage growth (Bivens and Zipperer 2018; Wilson 2015).

However, there is no sign that we’ve reached the limits of how much we can sustainably boost wage growth with lower unemployment—wage growth remains weaker than we should expect in a fully healthy economy. This means that confident proclamations that we’ve achieved full employment should not be made and that the Federal Reserve should continue to allow the economy to grow (see Bivens 2020).

Labor policy matters. Beyond seeking to keep labor markets tight, policymakers could take other steps to foster strong broad-based wage growth, such as raising the federal minimum wage; expanding eligibility for overtime pay; addressing gender, racial, and ethnic pay disparities; and protecting and strengthening workers’ rights to bargain collectively for higher wages and benefits. Analysis of the relationship between 10th-percentile wage growth and state-level minimum wages suggests that policy matters. For more policies that will raise wages, see EPI’s First Day Fairness Agenda (McNicholas, Sanders, and Shierholz 2018).

Acknowledgments

I would like to acknowledge Bernard and Anne Spitzer Charitable Trust, the Ford Foundation, and Open Society Foundation for their generous support of this research. I also want to thank Maria Cancian for inviting me to present in an APPAM 2019 Super Session, forcing me to think harder about some of these issues, and Katherine Swartz, who attended the session and offered up useful insights on health insurance costs. Last, I wish to thank my coworkers at EPI who have helped to get this paper across the finish line. I am particularly grateful for Melat Kassa’s programming expertise, Jori Kandra’s command of WordPress, and?Krista Faries’s commitment to clarity.

About the author

Elise Gould joined the Economic Policy Institute in 2003.?Her research areas include wages, poverty, inequality, economic mobility, and health care.?She is a co-author of?The State of Working America, 12th?Edition. Gould authored a chapter on health in?The State of Working America 2008/09;?co-authored a book on health insurance coverage in retirement; has published in venues such as?The Chronicle of Higher Education,?Challenge Magazine, and?Tax Notes; and has written for academic journals including?Health Economics,?Health Affairs,?Journal of Aging and Social Policy,?Risk Management & Insurance Review,?Environmental Health Perspectives, and?International Journal of Health Services.?Gould has been quoted by a variety of news sources, including Bloomberg, NPR,?The Washington Post,?The?New York Times, and?The?Wall Street Journal, and her opinions have appeared on the op-ed pages of?USA Today?and?The?Detroit News. She has testified before the U.S. House Committee on Ways and Means, Maryland Senate Finance and House Economic Matters committees, the New York City Council, and the District of Columbia Council. Gould received her?Ph.D. in Economics from the University of Wisconsin at Madison.

Appendix

Appendix Table 1

Regression-adjusted gender and racial/ethnic wage gaps, 2000 and 2019

Wage gaps by gender and race
2000 2019 Change
Gender wage gap 23.9% 22.6% -1.3 ppt.
Black–white wage gap
Overall 10.2% 14.9% 4.8 ppt.
Men 17.8% 22.2% 4.4 ppt.
Women 3.4% 8.2% 4.8 ppt.
Hispanic–white wage gap
Overall 12.3% 10.8% -1.5 ppt.
Men 15.6% 13.3% -2.3 ppt.
Women 8.0% 7.9% -0.1 ppt.

Note:?The regression-adjusted wage gaps control for education, age, race/ethnicity, gender, and region.

Source:?Author’s analysis of EPI Current Population Survey Extracts, Version 1.0 (2020), https://microdata.epi.org

Copy the code below to embed this chart on your website.

Endnotes

1. While the share of the overall workforce living in states that rely on the federal minimum wage has been stable over time (37.6% since 2014), the share of the low-wage workforce that resides in those states has increased from a low in this recovery of 41.0% in 2012 to 46.3% in 2019.

2. We decide on the appropriate percentile to use in the imputation of growth rates for the 95th percentile using data on the share of weekly earnings for the group that is top-coded as well as the share in neighboring wage bins that receive the top code. Since top-coding of weekly earnings does not map exactly onto top hourly wages, we settled on a percentile difference when the top-coded share was below 10% in that hourly wage bin. For more on our imputation procedure, see Gould 2019 and the “Methodology: Wage variables” web page at EPI 2020a.

3. Including Connecticut as a changer in this analysis—even though the increase did not occur until October 2019—only serves to mute the effect. Notably, the 10th-percentile wage in Connecticut actually fell between 2018 and 2019; including Connecticut among the state-changers reduces the gap in the 10th-percentile change between those states with and without minimum wage changes in those two years; including Connecticut among the non-changers yields a growth in the 10th percentile in minimum-wage-changing states of 4.2% versus 0.9% among those without a change.

4. It is important to note that there appears to be no relationship between changes in the median wage and changes in the minimum wage. Between 2018 and 2019, the median wage in states with minimum wage changes increased 0.7% while it increased 2.1% in non-changing states. Median wage growth was faster in non-changing states for men (2.1% vs. 1.1%) and women (3.6% vs. 2.6%). These differences are much smaller and they also operate in the opposite direction from the differences at the 10th percentile. This belies any claims that strong wage growth at the 10th percentile is simply due to strong overall wage growth in those states and that 10th-percentile wages in those states would have risen with or without the minimum wage increases.

5. Full employment is defined as “the level of employment at which additional demand in the economy will not create more employment. All workers who seek a job have one, they are working for as many hours as they want to or can, and they are receiving a wage that is broadly consistent with their productivity” (Bernstein and Baker 2013).

6. See, for example, Gould 2017.

7. As always, it’s important to remember the historical and social contexts for differences in black and white labor market experiences and labor market outcomes (Razza 2019). Workers’ ability to claim higher wages rests on a host of social, political, and institutional factors outside of their control. Furthermore, occupational segregation plays a significant role in these gaps, for both black men (Hamilton, Austin, and Darity 2011) and black women (Banks 2019). Trends in black–white wage gaps found here are supported by other important research (Wilson and Rodgers 2016).

8. See Bivens and Mishel 2015 for a more thorough description of the decomposition of these factors.

9. Health insurance coverage data are for private-sector wage and salary workers ages 18–64 who worked at least 20 hours per week and 26 weeks per year. This sample is chosen to focus on those with regular employment. “Coverage” is defined as receiving health insurance from one’s own job for which one’s employer paid for at least some of the premium.

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