State of Working America Wages 2018: Wage inequality marches on—and is even threatening data reliability

Rising wage inequality and sluggish hourly wage growth for the huge majority of workers have been defining features of the american labor grocery store for about four decades, despite firm productivity growth. The U.S. economy of the last several years has been no exception. Although the unemployment rate continued to fall and engagement in the labor movement marketplace continued to grow over the last year, most workers are experiencing centrist engage emergence and tied workers who have seen more significant gains are precisely making up ground lost during the Great Recession and slow recovery quite than getting ahead .
This composition analyzes data from the Current Population Survey ( CPS ) and details the most up-to-date hourly wage trends through 2018 across the wage distribution and education categories, highlighting crucial differences by rush and sex. By looking at real ( i, inflation-adjusted ) hourly wages by percentile, we can compare what is happening over prison term for the lowest-wage workers ( those at the 10th and 20th percentiles ) and for middle-wage workers ( those at or near the 50th percentile ) with engage trends for the highest-wage workers ( those at the 90th and 95th percentiles ) .
The data show not only rising inequality in general, but also the persistence, and in some cases worsening, of wage gaps by gender and race. What besides stands out in this last year of data is that, while wages are growing for most workers, wage growth continues to be slower than would be expected in an economy with relatively low unemployment. Given this slow wage emergence, policymakers should not presume that the economy has already achieved ( or even surpassed, as some claim ) full employment. rather, policymakers should try to keep parturiency markets a tight as possible for equally long as possible to see if engage emergence lost during the Great Recession can be clawed back, and to see if wage disparities by gender and race can be reduced .

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New in this report: Accounting for “top-coding” in the CPS

The CPS is one of the best measures of hourly pay because it allows researchers to analyze differences across the wage distribution and by demographic characteristics. however, for confidentiality reasons, the CPS “ top-codes ” hebdomadally earnings : All workers who report weekly earnings above $ 2,884.61 ( annual earnings for full-year workers above $ 150,000 ) are recorded as having weekly earnings of precisely $ 2,884.61, to preserve the anonymity of respondents. This top-code measure of $ 2,884.61 hasn ’ thyroxine changed or been updated for ostentation in 20 years and, as a result, a growing share of workers are assigned this weekly earnings respect preferably than having their actual wages reported. Because these workers ’ actual wages are masked by the top-code, it has become hard to uncover the extent of top-end wage levels and growth. other data, such as data from the Social Security Administration, illustrates that wage growth is far more saturated at the top than can be illustrated using the CPS, with growth at and within the peak 1 percentage exhibiting growth orders of magnitude faster than at the 95th percentile. In the most holocene class of data, the top-code is assigned to more than 5 percentage of weekly earnings for male workers in the CPS ; with no adaptation, this would compromise our 95th-percentile hourly wage estimates. For the purposes of this report, we use what we think is an acceptable proxy for wage growth at this percentile, as described in the “ Methodological considerations ” part of this report .

Summary of key findings

Below is a drumhead of the cardinal findings of this report. These findings are outlined in greater detail in subsequent sections of the report .
Considerations and cautions when using the Current Population Survey (CPS) to measure wages in a world of growing inequality. Regarding stagnant top-codes, month-to-month excitability, and the data sample, we find that :

  • 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, caution should be exercised when examining recent wage levels and trends for these workers.
  • Because the CPS exhibits a fair amount of year-to-year volatility, one-year changes in wages by decile in the CPS—while providing new and valuable information—should be taken with a grain of salt.
  • Caution should be exercised when making comparisons with prior-year versions of this report, as the data sample has changed; notably, the analysis here includes all workers 16 years of age and older to be both consistent with other Bureau of Labor Statistics analyses and reflective of a growing number of workers ages 65 and older in the labor market.

Wage inequality. From 2000 to 2018, wage growth was strongest for the highest-wage workers, continuing the drift in rising wage inequality over the last four decades .

  • Since 2007, the labor market peak before the Great Recession, the strongest wage growth has continued to be within the top 10 percent of the wage distribution.
  • From 2017 to 2018, relatively fast growth continued at the top (2.7 percent at the 95th percentile), but the 20th and 30th percentiles saw the strongest growth at 4.8 percent and 3.7 percent, respectively. Median wages grew 1.6 percent over the year.

Wage inequality by gender. While engage inequality has broadly been on the rise for both men and women, wage inequality is higher and growing more among men than among women .

  • Because of their relatively high wages, particularly at the top of the wage distribution, men are far more likely to be affected by the top-code in recent years, making it more difficult to accurately assess 95th-percentile wage levels and wage growth.
  • From 2017 to 2018, men at the 95th percentile saw large wage gains, while those at the middle and very bottom of their wage distribution experienced downright wage losses. Since 2000, men’s wages at the 95th percentile grew 42.0 percent, more than twice as fast as at the 90th percentile (17.1 percent), while the median man’s wage barely budged, rising only 0.8 percent over the entire 18-year period.
  • Women have experienced more equal wage growth since 2000, and their wage growth from 2017 to 2018 was relatively more broadly shared as well, with stronger growth among the bottom 30 percent than among the top 20 percent. Since 2000, wage inequality has grown less among women compared with men.

Gender wage gap. The “ gender wage col ” refers to the historically persistent remainder between what men and women are paid in the workplace. While significant gender engage gaps remained across the wage distribution, the sex engage col at the median continued to shrink over the last year, with a typical charwoman paid 84 cents on the typical man ’ s dollar in 2018 ( or, facing a 16 percentage wage gap ) .

  • The gender wage gap at the 10th percentile remains the smallest across the wage distribution and it has narrowed since 2000; it is currently at 5.9 percent.
  • As inequality among men has continued to increase, it is not surprising that the gender wage gap at the top grew significantly and that 95th-percentile women are paid 33.6 percent less in 2018 than 95th-percentile men.
  • The regression-adjusted average gender wage gap narrowed slightly from 2000 to 2018 and is currently at 22.6 percent. This measure accounts for differences in educational attainment, age, and other potentially relevant characteristics for wages, and reports the gender wage gap remaining after these statistical controls are used.

Wage growth in states with minimum wage increases. From 2017 to 2018, wages of the lowest-wage workers grew more in states that increased their minimal wage in 2018 .

  • On average, in the 29 states without minimum wage increases in 2018, the 10th-percentile wage rose 1.6 percent; in states with minimum wage increases in 2018 (including the District of Columbia), the average 10th-percentile wage rose by 2.1 percent.
  • The differential is larger when looking across recent years with many minimum wage increases: Between 2013 and 2018, when 26 states and D.C. experienced at least one minimum wage increase, the 10th-percentile wage grew much faster in those states (and in D.C.) than in states without any increase (13.0 percent vs. 8.4 percent).
  • In both comparison periods, both men and women at the 10th percentile saw greater wage growth in states with minimum wage changes versus those without.

Wage growth by race/ethnicity. At every decile, engage emergence since 2000 was faster for ashen and spanish american workers than for black workers .

  • Over the last 18 years, wage growth for white and Hispanic workers has been about four times faster than that of black workers in the 20th through the 70th percentiles of their respective wage distributions. The 60th and 70th percentiles of the black wage distribution remain below their 2000 levels.
  • Because of their higher wages, the 95th percentile white wage has to be imputed using the same method as described for male workers. Regardless of measurement, between 2017 and 2018, the strongest wage growth among white workers was at the 95th percentile, while white workers at the 10th percentile experienced downright declines. White wages grew across the wage distribution since 2000.
  • Over the entire period from 2000 to 2018, Hispanic workers experienced relatively more broadly based wage growth, with strong growth at the top as well as at the median and at the bottom. From 2017 to 2018, however, Hispanic workers’ wages faltered, with outright declines (or stagnation) for the top half of the wage distribution.

Racial/ethnic wage gaps. Wage gaps by race/ethnicity describe how much less african American and hispanic workers are paid relative to white workers. Throughout the engage distribution, black–white wage gaps were larger in 2018 than in 2000 ; conversely, hispanic workers have been lento closing the gap with white workers in the bottom 80 percentage of the wage distribution .

  • The regression-adjusted black–white wage gap (controlling for education, age, gender, and region) has become larger over the last year (EPI 2019d).
  • While the Hispanic–white wage gap has remained fairly constant over the last 18 years (12.3 percent in 2000 compared with 11.8 percent in 2018), the black–white gap was significantly larger in 2018 (16.2 percent) than it was in 2000 (10.2 percent). In 2000, the regression-adjusted Hispanic–white wage gap was larger than the regression-adjusted black–white wage gap. By 2018, the reverse was true.

Wage growth by education. From 2000 to 2018, the strongest wage growth occurred among those with progress degrees, those with college degrees, and those with less than a eminent school diploma .

  • Over the last year, the strongest wage growth occurred among those with some college and those with advanced degrees, a reversal from 2016 to 2017, when wages fell among these workers.
  • The wages of those with a high school diploma rose faster than the wages of those with a college degree over the last two years, narrowing the gap between college and high school wages. As a result, the college wage premium—the regression-adjusted log-wage difference between the wages of college-educated and high school–educated workers—fell from 50.6 percent to 48.4 percent between 2016 and 2018.
  • Between 2000 and 2018, the college wage premium rose slightly, from 47.0 percent to 48.4 percent over that whole period. The growth in the college wage premium was nowhere near fast enough to explain the total rise in wage inequality over that time.
  • For the first time in this recovery, workers with some college just reached their 2000 wage level in 2018.

Wage growth by education and gender. Since 2000, engage growth for those with a college or advance academic degree was faster for men than for women, while engage increase for those with a high school diploma or some college 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 of different levels of education are not quite as large for women as they are for men.
  • While there has been a slow narrowing of gender wage gaps since 2000 for those with high school diplomas and for those with some college, gender wage gaps were wider than in 2000 among those with less than high school, those with college degrees, and those with advanced degrees.
  • At every education level, women are paid consistently less than their male counterparts, and the average wage for a man with a college degree is higher than the average wage for a woman with an advanced degree.

Wage growth by education and race/ethnicity. From 2000 to 2018, wage increase for ashen and black workers tended to be faster for those with more education than for those with less department of education .

  • Average wages grew faster among white and Hispanic workers than among black workers for all education groups from 2000 to 2018.
  • Among black workers, only college- and advanced-degree holders had higher wages than in 2000, but their wage growth was considerably slower than wage growth for white or Hispanic workers with those same degrees.
  • From 2017 to 2018, wage growth was strongest for those with an advanced degree in all racial/ethnic groups, while wages fell most for black workers with less than a high school diploma.
  • Black–white wage gaps by education were larger in 2018 than in 2000 for all education groups, while Hispanic–white wage gaps were narrower for workers with less than high school or high school diploma levels of education. At nearly every education level, workers of color were paid consistently less than their white counterparts.

Methodological considerations

This section describes our methodology for addressing data limitations due to top-coding, adenine well as other considerations to keep in mind when using CPS data and EPI ’ randomness wage series reports to understand wage trends .

Top-coding of weekly earnings

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.

For this composition, when workers directly report an hourly engage, this engage is used. For workers who do not directly report an hourly engage, hourly wages are calculated by dividing reported weekly earnings by their common hours worked in a week. The CPS weekly earnings serial is top-coded to protect the anonymity of high-wage respondents. In drill, this means that if a respondent reports weekly earnings above a certain value— $ 2884.61 in 2018—his or her weekly earnings are simply recorded as being that respect ( i.e., $ 2884.61 ). The top-code value has remained at $ 2884.61 since 1998, even as high-end wages have continued to rise. This hebdomadally wage level translates into $ 150,000 annually for full-year workers .
Because actual wages are masked for these high earners, it has become intemperate to uncover the extent of top-end wage levels and growth. other data, such as data from the Social Security Administration, illustrates that engage increase is far more concentrate at the top than can be seen using the CPS, with emergence at and within the top 1 percentage exhibiting growth that is orders of magnitude faster than at the 95th percentile ( see Appendix Figure A for these trends ) .
historically, the Bureau of Labor Statistics ( BLS ) has sporadically made adjustments to the weekly earnings top-code when it has begun to reach increasingly larger shares of workers. unfortunately, BLS has chosen to set the top-code at nominal values without adjusting for inflation. between 1973 and 1988, the top-code ride at $ 999 ; from 1989 to 1998, it sat at $ 1,923 ; and, since 1998, it has remained at $ 2,884.61. Best practices for consistent and accurate data use propose not merely increasing the top-code but besides indexing it over meter to keep it from hitting an increasing contribution of the work force. Since the top-code was set in 1998 and has not risen since then, even to adjust for inflation, it is catching an increasing number and share of workers as engage growth at the top continues to climb. In the overall wage distribution, over the period analyzed in this report, the share of workers reporting weekly earnings at or above the top-code rose from 0.8 percentage in 2000 to 4.2 percentage in 2018. The share of men and white workers hitting the top-code is much higher than for workers overall. In 2018, the share of white workers with hebdomadally earnings hitting the top-code was 5.2 percentage. For working men, that share was 5.9 percentage in 2018 .
For weekly earnings above the top-code, EPI assigns those top-coded workers the impute mean above the top-code assuming the earnings distribution is Pareto above the 80th percentile. however, because the highest-percentile wage we examine is the 95th percentile, ampere retentive as the top-code generally affected only the top 2–3 percentage of workers, we could have confidence that our 95th-percentile estimate was largely unaffected by how binding the top-code was. now that the top-code hits over 5 percentage of the engage distribution of men and white workers, however, our 95th-percentile engage estimate has basically become the weekly earnings top-code divided by a measure of usual hours. This makes our measure of 95th-percentile wages for working men and white workers unreliable .
As mentioned previously, the top-code is peculiarly binding when examining the 95th-percentile engage for both white and male workers. Because of this, we have used the 94th percentile as a proxy in those cases, so that values below the weekly top-code imputation are driving measured engage levels and wage growth, not the imputation itself. Given these limitations, some caution should be exercised when examining late engage levels and trends for these workers .

Year-to-year volatility in the CPS

Because the CPS exhibits a fair amount of year-to-year volatility, one-year changes in wages by decile in the CPS—while providing new and valuable information—should be taken with a grain of salt; longer-term trends should be given more weight.

Every calendar month, policymakers, analysts, and journalists look to the monthly jobs report to assess the health of the tug commercialize. Along with payroll use growth and the unemployment rate, nominal wage emergence is a key indicator of the meanness of the labor marketplace, a measure of workers ’ ability to secure pay increases from their employers. As workers become scarce, employers have to pay more to attract and retain the workers they want. sol making an accurate appraisal of the state of wage emergence is essential to a complete sympathy of labor movement market dynamics and to determining how close the U.S. economy may indeed be to wide employment .
The Bureau of Labor Statistics releases two surveys every month as part of their employment Situation report : the Current Population Survey ( CPS ) and the Current Employment Statistics surveil ( CES ). The CPS collects use and demographic information from households, while the CES collects information from employers ’ payroll records. Gould ( 2018 ) compares holocene trends in both of these series. In kernel, given the larger sample size and the benchmarking of CES employment to unemployment insurance tax records, it has been well established that the CES is the better survey for assessing overall employment growth ( Gould 2003 ) .
Both serial are consistent in how to interpret overall emergence in wages. The growth rate in nominal private-sector earnings in the cerium from 2017 to 2018 was 3.0 percentage. The nominal growth in the distinctive CPS wage—that is, at the median—was 4.1 percentage. In Gould ( 2018 ), I demonstrate that calculating growth rates from the CPS in three-year averages corresponds quite well to CES annual average growth rates. The average emergence rate of the median engage in the CPS over the last three years was 3.4 percentage. therefore, it is important to not read excessively a lot into the slightly stronger engage growth exhibited over one year in the CPS. Given the Federal Reserve ’ s 2 percentage inflation target, and given 1.5 percentage long-run vogue productiveness growth, wages should be growing by at least 3.5 percentage annually and for a confirm period of prison term for workers to reap the benefits of economic increase ( EPI 2019c ). While the growth seen in the average and median engage for U.S. workers over the past year is welcome and expected given the steadily improving labor movement commercialize, holocene trends indicate that the economy has a ways to go before reaching entire use. The holocene pickup in the last quarter of 2018 in the CES is peculiarly promise ; however, trends in both surveys indicate that workers are still trying to make up for crunch lost during the Great Recession and its consequence. Given that workers have limited leverage to bid up their wages, some lax remains in the undertaking market .
The CPS remains the best serial for measuring wages and engage increase by demographic characteristics ampere well as across the engage distribution. Given the limitations of the CPS, however, I suggest taking swings in year-to-year differences with a large granulate of salt and paying more attention to longer-term trends. even so, I do report cross-cutting differences from the CPS for the most late year ; a look at the most current available data remains valuable to understanding how today ’ mho economy is serving U.S. workers across the undertaking market .

Changes in EPI’s worker sample

Caution should be exercised when making comparisons with prior-year versions of this report, as the data sample has changed.

For many years, the Economic Policy Institute has reported engage growth using the CPS. For multiple sets of analyses, such as The State of Working America ( Mishel et aluminum. 2012 ) and previous versions of the State of Working America Data Library ( EPI 2019d ), most wage analysis was limited to workers who are 18­ to 64 years previous. To be both reproducible with early Bureau of Labor Statistics analyses and reflective of a growing number of workers age 65 and older in the labor commercialize, the analysis in this study reports hourly wages for all workers 16 years of age and older. Because of this change in the datum sample, caution should be used in making comparisons with prior-year versions of this report. Appendix Figure B provides the historical emergence rates using the “ new ” series for choose percentiles spinal column to 1979 .

Wage inequality across the wage distribution

Wage growth from 2000 to 2018 continues long-run trends in rising inequality.

Since 1979, “ real ” ( inflation-adjusted ) hourly pay for the huge majority of american workers has diverged from economywide productiveness, and this deviation is at the ancestor of numerous american english economic challenges. After tracking preferably closely in the three decades following World War II, growing productiveness and typical actor compensation diverged ( shown in Appendix Figure C ). From 1979 to 2017, productiveness grew 70.3 percentage, while hourly compensation of production and nonsupervisory workers grew good 11.1 percentage. Productivity thus grew six times faster than typical actor compensation .
A natural interview that arises from this fib is just where did the “ excess ” productivity go ? A significant parcel of it went to higher corporate profits and increase income accruing to capital and commercial enterprise owners ( Bivens et aluminum. 2014 ). But a lot of it went to those at the very top of the wage distribution, as shown in Appendix Figure A. The top 1 percentage of earners saw accumulative gains in annual wages of 157.3 percentage between 1979 and 2017—far in overindulgence of economywide productivity growth and closely four times faster than average wage growth ( 40.1 percentage ). Over the lapp menstruation, exceed 0.1 percentage earnings grew 343.2 percentage, with the latest spike heel reflecting the sharp addition in administrator compensation ( Mishel and Wolfe 2018 ) .
While the CPS-ORG—the primary data set used in this report—does not allow disaggregation within the circus tent 5 percentage of the earnings distribution, it is silent instructive for measuring the growth in engage inequality over the last 40-odd years. Appendix Figure B illustrates that for all but the highest earners, hourly engage growth has been weak. If it hadn ’ t been for a time period of potent across-the-board wage growth in the late 1990s, wages for most would have fallen outright. median hourly wages rose 14.0 percentage between 1979 and 2018, compared with an addition of 4.1 percentage for the 10th-percentile proletarian ( i.e., the worker who earns more than only 10 percentage of workers ). Over the lapp period, the 95th-percentile actor saw growth of 56.1 percentage .
wage growth since the Great Recession has continued to follow this drift : slower growth for most compared with faster growth for those at the lead. Table 1 shows hourly wages by wage decile ( and at the 95th percentile ) and includes data from 2000 ( the previous commercial enterprise hertz point ), 2007 ( the most holocene business bicycle extremum ), and the two most late years of data ( 2017 and 2018 ). For a full discussion of EPI ’ s function of the CPS-ORG data, see EPI ’ s methodology for measuring wages and benefits ( EPI 2019a ). In the full occupation bicycle from 2000 to 2007, growth was relatively boring overall and relatively inadequate ; the gains at the 90th and 95th percentiles were higher than at the center or bottom of the engage distribution. After growing at practically the like rate from 2000 to 2007, wages for the bottom grew approximately twice ampere fast as wages for the center from 2007 to 2018, slightly narrowing the proportion of wages at the 50th and 10th percentiles of the wage distribution ( i, the 50/10 engage gap, or the gap between the middle and the bottom ). however, because of the big and disproportionate gains at the peak, both the 95/50 col ( the break between acme and the middle ) and the 95/10 gap ( the gap between the clear and the bed ) grew well from 2007 to 2018 .
mesa 1

Hourly wages of all workers, by wage percentile, 2000–2018 (2018 dollars)

Wage by percentile Wage ratio
10th 20th 30th 40th 50th 60th 70th 80th 90th 95th 50th/10th 95th/50th 95th/10th
2000 $8.93 $10.93 $13.01 $14.78 $17.57 $20.64 $24.73 $29.97 $39.46 $50.46 1.97 2.87 5.65
2007 $9.17 $11.04 $13.03 $15.29 $18.15 $21.29 $25.46 $31.50 $42.41 $54.76 1.98 3.02 5.97
2017 $9.92 $11.36 $13.40 $15.56 $18.49 $22.00 $26.58 $33.64 $46.78 $61.42 1.87 3.32 6.19
2018 $9.97 $11.91 $13.91 $15.94 $18.80 $22.02 $26.76 $33.79 $47.48 $63.10 1.89 3.36 6.33
Annualized percent changes Wage ratio change
2000–2018 0.6% 0.5% 0.4% 0.4% 0.4% 0.4% 0.4% 0.7% 1.0% 1.2% -0.1 0.5 0.7
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
2007–2018 0.8% 0.7% 0.6% 0.4% 0.3% 0.3% 0.5% 0.6% 1.0% 1.3% -0.1 0.3 0.4
2017–2018 0.5% 4.8% 3.7% 2.4% 1.6% 0.1% 0.7% 0.5% 1.5% 2.7% 0.0 0.0 0.1

Notes: Sample based on all workers ages 16 and older. The xth-percentile engage is the engage at which x % of engage earners earn less and ( 100-x ) % earn more .
Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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With the caveat that, as discussed above, we need to be careful to not assign excessively much intend to annual changes given concerns about data volatility, we note the following trends over the past year : The annual change in the median wage from 2017 to 2018 was 1.6 percentage, compared with 2.7 percentage at the 95th percentile and 0.5 percentage at the 10th percentile. The strongest growth in the overall wage distribution occurred at the 20th and 30th percentiles, at 4.8 percentage and 3.7 percentage, respectively .
Figure A illustrates the trends in wages for choice deciles ( and the 95th percentile ), showing the accumulative percentage change in real hourly wages from 2000 to 2018. The overall narrative of inequality is clear. The lines demonstrate that those with the highest wages have had the fastest wage growth in holocene years. From 2000 to 2018, the 95th-percentile engage grew over three times faster than wages at the median. By 2018, the 95/10 ratio had grown to 6.3 from 6.0 in 2007 and 5.6 in 2000 ( see Table 1 ). This means that on an hourly footing the 95th-percentile wage earner was paid 6.3 times what the 10th-percentile wage earner was paid. exchangeable trends are found in the 95/50 engage ratio, with those at the top pulling away from those at the middle. In 2018, the 95th-percentile wage earner was paid 3.4 times more than the median worker compared with 3.0 times more in 2007 and 2.9 times more in 2000 .
trope A

High-wage earners have continued to pull away from everyone else since 2000 : accumulative percentage change in real hourly wages, by engage percentile, 2000–2018

year  10th   30th   50th   70th  90th  95th
2000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
2001 3.1% 1.1% 1.5% 1.1% 3.4% 1.7%
2002 6.3% 3.2% 3.1% 2.5% 4.5% 5.4%
2003 5.9% 4.3% 3.1% 4.3% 4.5% 4.5%
2004 4.0% 2.1% 4.3% 3.5% 5.6% 5.5%
2005 1.5% -0.6% 2.7% 2.1% 4.9% 5.7%
2006 0.9% -2.2% 3.1% 1.3% 6.8% 6.7%
2007 2.6% 0.1% 3.3% 3.0% 7.5% 8.5%
2008 3.8% -0.1% 2.5% 3.5% 7.4% 9.1%
2009 4.7% 1.8% 4.8% 5.8% 10.9% 11.6%
2010 3.8% 0.4% 4.2% 5.2% 11.4% 11.0%
2011 1.1% -1.5% 1.6% 3.7% 9.1% 10.0%
2012 -0.6% -2.3% 0.3% 2.4% 9.8% 11.3%
2013 0.0% -1.3% 0.8% 4.2% 10.7% 13.2%
2014 0.9% -2.3% 0.9% 3.0% 9.5% 11.6%
2015 5.7% -1.5% 2.5% 6.0% 14.0% 18.8%
2016 6.6% 1.2% 4.4% 5.9% 16.1% 20.0%
2017 11.0% 3.0% 5.3% 7.5% 18.6% 21.7%
2018 11.6% 6.9%  7.0% 8.2% 20.3% 25.1% 

chart Data Download data The data below can be saved or copied directly into Excel . The data underlying the calculate .

Note: Sample based on all workers ages 16 and older .
Source: EPI psychoanalysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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Wages by gender

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. Table 2 replicates the analysis of wage deciles for men and women individually, with a comparison of sex engage disparities over 2000–2018. Figures B and C play along this table, illustrating the accumulative percentage exchange over 2000–2018 in real hourly wages of men and women at choose engage percentiles .
table 2

Hourly wages of men and women, by wage percentile, 2000–2018 (2018 dollars)

Wage by percentile Wage ratio
10th 20th 30th 40th 50th 60th 70th 80th 90th 95th 50th/10th 95th/50th 95th/10th
Men
2000 $9.60 $11.79 $14.52 $16.93 $19.94 $23.38 $27.93 $33.61 $44.46 $56.34 2.08 2.83 5.87
2007 $9.70 $12.09 $14.50 $17.06 $19.97 $23.51 $28.03 $34.90 $46.77 $60.63 2.06 3.04 6.25
2017 $10.23 $12.27 $14.76 $17.26 $20.41 $24.46 $29.41 $36.91 $51.24 $72.80 1.99 3.57 7.11
2018 $10.16 $12.48 $14.97 $17.42 $20.10 $24.12 $29.44 $37.47 $52.08 $79.98 1.98 3.98 7.87
Annualized percent changes Wage ratio change
2000–2018 0.3% 0.3% 0.2% 0.1% 0.0% 0.2% 0.2% 0.6% 0.9% 2.0% -0.10 1.15 2.00
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
2007–2018 0.4% 0.2% 0.3% 0.1% 0.0% 0.2% 0.3% 0.6% 1.0% 2.5% -0.08 0.94 1.62
2017–2018 -0.7% 1.7% 1.4% 0.9% -1.5% -1.4% 0.1% 1.5% 1.7% 9.9% -0.02 0.41 0.76
Women
2000 $8.68 $10.23 $11.75 $13.48 $15.47 $17.97 $21.35 $26.05 $34.05 $42.09 1.78 2.72 4.85
2007 $8.74 $10.34 $12.11 $14.03 $16.26 $18.93 $22.86 $27.95 $37.06 $46.73 1.86 2.87 5.35
2017 $9.32 $10.80 $12.40 $14.74 $16.90 $19.86 $23.95 $29.65 $40.88 $52.16 1.81 3.09 5.59
2018 $9.56 $11.04 $12.79 $14.90 $16.93 $19.96 $24.02 $30.02 $41.25 $53.07 1.77 3.13 5.55
Annualized percent changes Wage ratio change
2000–2018 0.5% 0.4% 0.5% 0.6% 0.5% 0.6% 0.7% 0.8% 1.1% 1.3% -0.01 0.41 0.70
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
2007–2018 0.8% 0.6% 0.5% 0.5% 0.4% 0.5% 0.5% 0.7% 1.0% 1.2% -0.09 0.26 0.20
2017–2018 2.5% 2.2% 3.2% 1.1% 0.2% 0.5% 0.3% 1.2% 0.9% 1.8% -0.04 0.05 -0.04
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%
2017 91.1% 88.0% 84.0% 85.4% 82.8% 81.2% 81.4% 80.3% 79.8% 71.6%
2018 94.1% 88.5% 85.4% 85.5% 84.2% 82.8% 81.6% 80.1% 79.2% 66.4%

Notes: Sample based on all workers ages 16 and older. The xth-percentile engage is the wage at which x % of wage earners earn less and ( 100-x ) % earn more .
Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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It is important to keep in take care that the top-coding return in the CPS disproportionately impacts analysis of men ’ s wages more than analysis of women ’ s wages because men ’ mho wages are higher and their high-end wage growth grew much faster over the last 20 years. Because more than 5 percentage of men ’ s weekly earnings were top-coded in the CPS in 2016, 2017, and 2018, the 95th percentile is estimated using the emergence rate of the 94th percentile for each of those years to the 95th percentile in 2015. If the 95th percentile had been reported using EPI ’ s top-coding operation, the emergence rate between 2017 and 2018 would have been a whopping 19.7 percentage .
even using the potentially slenderly slower growth pace in late years at the 94th percentile as a proxy, long-run trends suggest that low- and middle-wage men have fared relatively ailing and that engage gaps between the top and the middle ( the 95/50 ratio ) and the lead and the bottom ( the 95/10 ratio ) have increased more for men than for women. Men ’ second wages at the 95th percentile grew 42.0 percentage from 2000 to 2018, more than doubly vitamin a fast as at the 90th percentile ( 17.1 percentage ), while at the median, men ’ second wages scantily budged, rising lone 0.8 percentage over the integral 18-year period. Wage emergence for lower-wage working men ( at the 10th and 20th percentiles ) was well stronger than for those at or near the middle of the wage distribution .
From 2017 to 2018, men saw their wages fall at the middle and bed of the wage distribution : a 1.5 percentage flatten at the fiftieth percentile and a 1.4 percentage sink at 60th percentile, along with a 0.7 percentage decay at the 10th percentile. mesa 2 shows that the 95th-percentile men ’ south wage grew 9.9 percentage, continuing to pull away from wages across the rest of the men ’ south engage distribution .
figure B

Disproportionate wage growth since 2000 for those at the top has contributed to widening inequality among men : accumulative percentage exchange in real hourly wages of men, by wage percentile, 2000–2018

year 10th  30th  50th  70th  90th 95th
2000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
2001 3.1% -1.5% 0.7% 1.2% 2.2% 2.2%
2002 2.8% -2.3% 2.1% 0.6% 4.6% 4.4%
2003 2.0% -0.7% 2.4% 1.8% 4.3% 5.1%
2004 2.5% -0.1% 0.6% 0.8% 5.4% 7.5%
2005 0.5% -2.3% -1.6% 0.0% 4.3% 6.0%
2006 1.1% -1.5% -0.3% -1.0% 5.2% 6.3%
2007 1.1% -0.1% 0.1% 0.4% 5.2% 7.6%
2008 -0.2% -2.5% 0.0% 0.8% 6.3% 8.8%
2009 0.9% 0.1% 2.8% 4.7% 9.7% 14.8%
2010 0.6% -2.6% 0.4% 3.0% 9.5% 13.8%
2011 -1.8% -5.9% -2.1% 0.1% 7.6% 11.0%
2012 -2.4% -5.7% -1.6% -1.2% 8.6% 16.5%
2013 -2.0% -6.7% -2.6% -0.3% 9.8% 15.1%
2014 -0.9% -5.2% -3.5% -1.2% 7.6% 13.1%
2015 0.5% -4.4% -0.2% 2.2% 14.2% 23.2%
2016 7.1% 0.2% 0.9% 4.1% 13.1% 29.0%
2017 6.7% 1.7% 2.3% 5.3% 15.2% 29.2%
2018 5.9%  3.1%  0.8%  5.4% 17.1%  42.0% 

chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the calculate .

Notes: Sample based on all workers ages 16 and older .
Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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digit C

Women ’ randomness wages are more compress than men ’ second wages, but inequality among women has increased since 2000 : accumulative percentage change in real number hourly wages, by engage percentile, 2000–2018

Year 10th  30th  50th  70th  90th 95th 
2000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
2001 0.4% 2.5% 2.1% 1.0% 1.8% 2.8%
2002 2.8% 5.5% 5.1% 4.3% 3.1% 6.1%
2003 2.6% 4.9% 6.1% 5.9% 5.7% 7.5%
2004 2.1% 3.3% 4.7% 5.6% 6.1% 7.4%
2005 0.9% 3.2% 4.1% 5.0% 7.4% 8.6%
2006 0.2% 4.9% 4.8% 5.4% 6.9% 9.6%
2007 0.7% 3.1% 5.1% 7.1% 8.8% 11.0%
2008 1.1% 0.6% 6.1% 5.9% 9.5% 11.3%
2009 4.0% 3.1% 8.6% 9.5% 11.4% 13.4%
2010 5.0% 2.3% 8.1% 8.5% 14.0% 15.6%
2011 2.4% 0.8% 7.3% 6.7% 11.4% 14.7%
2012 0.4% -1.4% 5.5% 6.1% 11.2% 14.8%
2013 0.0% 0.6% 4.7% 7.4% 11.9% 16.2%
2014 -0.7% -0.5% 3.4% 8.0% 12.4% 17.9%
2015 3.7% 2.0% 5.8% 9.4% 16.8% 20.9%
2016 7.5% 6.2% 7.9% 12.2% 18.5% 24.0%
2017 7.4% 5.5% 9.3% 12.2% 20.0% 23.9%
2018 10.2%  8.9% 9.5%  12.5%  21.1%  26.1% 

chart Data Download data The data below can be saved or copied directly into Excel . The data underlying the trope .

Notes: Sample based on all workers ages 16 and older. The xth-percentile wage is the engage at which x % of wage earners earn less and ( 100-x ) % earn more .
Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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Women besides experienced a increase in wage inequality from 2000 to 2018, with the 95th percentile continuing to pull away from the middle and bottom of the engage distribution. Wages at the 90th and 95th percentiles grew about twice equally debauched as for middle- and low-wage earners over the 18-year menstruation. however, engage inequality among women in 2018 was not equally high as it was among men : A 95th-percentile woman was paid 5.6 times more than a 10th-percentile woman, while the 95/10 ratio among men was 7.9. While inequality has grown modestly among women, the growth in women ’ second wages is more broadly shared across the wage distribution than men ’ second, with stronger increase among the bottom 30 percentage than among the circus tent 20 percentage from 2017 to 2018. In accession, women at all deciles registered higher wages in 2018 than in 2007 or 2000 .
The “ sex engage opening ” refers to historically persistent differences between what men and women are paid in the workplace. While significant sex engage gaps remain across the engage distribution, the gender wage opening at the median continued to shrink, with the distinctive woman earning 84 cents for every dollar a man earned in 2018 ( that is, they faced a 16 percentage wage opening ). unfortunately, the little constrict of the sex engage gap at the median between 2017 and 2018 was due to losses in the median man ’ randomness wage rather than any increase in the median womanhood ’ south engage. 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, it would be economically feasible to see both men ’ randomness and women ’ sulfur wages rise while simultaneously closing the gender wage col ( EPI 2018a ). The sex wage gap at the bottom of the engage distribution continued to narrow between 2017 and 2018, with the col at the 10th percentile falling from 8.9 percentage to 5.9 percentage. not surprisingly, as the 95th-percentile engage for men rose sharply between 2017 and 2018, the gender engage break at the top grew significantly, with higher-earning women facing a 33.6 percentage pay penalty .
The regression-adjusted average gender wage gap ( controlling for education, old age, raceway, and area ) showed a small constrict between 2000 and 2018, from 23.9 percentage to 22.6 percentage, while much greater progress was made between 1979 and 2018 ; the regression-adjusted sex engage opening was 37.7 percentage in 1979 .

Wage growth and the minimum wage

Wage growth at the bottom was faster in states that increased their minimum wage in 2018.

In 2018, the minimum wage was increased in 13 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. Most of these increases occurred at the beginning of the year, though some occurred later in the year. Figure D shows in green the states with minimum engage increases that occurred through legislation or referendum in 2018 ; states in blue had automatic increases resulting from indexing the minimum wage to ostentation. Workers in states that increased their minimum engage in 2018 report for about 50 percentage of the U.S. work force. Comparing the average minimum engage in 2017 with the average in 2018, the amounts of the nominative minimal wage increases, legislated or index, ranged from $ 0.04 ( or 0.4 percentage ) in Alaska to $ 1.00 ( or 11.1 percentage ) in Maine .
design D

The minimal wage increased in 21 states and the District of Columbia in 2018 : States with minimal engage increases in 2018, by character of increase

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    State Abbreviation Category
    Alaska AK Indexed
    Alabama AL No change
    Arkansas AR No change
    Arizona AZ Legislated or ballot measure
    California CA Legislated or ballot measure
    Colorado CO Legislated or ballot measure
    Connecticut CT No change
    District of Columbia DC Legislated or ballot measure
    Delaware DE Legislated or ballot measure
    Florida FL Indexed
    Georgia GA No change
    Hawaii HI Legislated or ballot measure
    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 No change
    Maryland MD Legislated or ballot measure
    Maine ME Legislated or ballot measure
    Michigan MI Legislated or ballot measure
    Minnesota MN Indexed
    Missouri MO Indexed
    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 Indexed
    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 Legislated or ballot measure
    Washington WA Legislated or ballot measure
    Wisconsin WI No change
    West Virginia WV No change
    Wyoming WY No change

    chart Data Download data The data below can be saved or copied directly into Excel . The data underlying the human body .

    Notes: Minimum wage increases passed through either legislation or ballot measuring stick took impression on January 1, 2018, in Arizona, California, Colorado, Hawaii, Maine, Michigan, New York, Vermont, Rhode Island, and Washington. Alaska, Florida, Minnesota, Missouri, Montana, New Jersey, Ohio, and South Dakota increased their minimal wages in 2018 because of indexing to inflation. Maryland, Oregon, and Washington, D.C., legislated minimal wage increases that took effect on July 1, 2018. Delaware legislated a minimum engage increase that took impression on October 1, 2018 .
    Source: EPI psychoanalysis of submit minimal engage laws. See EPI ’ s minimum wage tracker ( EPI 2019b ) for the most stream state-level minimum engage data .
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    When we compare 10th-percentile wage growth among states that are grouped by whether they had a minimum engage addition or not, the comparison yields highly suggestive results. As shown in Figure E, when looking at 10th-percentile wages, growth in states without minimum engage increases was slower ( 1.6 percentage ) than in states with any kind of minimum wage increase ( 2.1 percentage ). This result holds true for both men and women at the 10th percentile. The 10th-percentile men ’ randomness wage grew 1.8 percentage in states with minimum wage increases, compared with 0.3 percentage growth in states without any minimal wage increases, while women ’ randomness 10th-percentile wage grew 1.7 percentage in states with minimum engage increases and 1.0 percentage in states without .
    human body east

    wage growth at the bottom was strongest in states with minimal engage increases in 2018 : 10th-percentile wage growth, by presence of 2018 express minimum wage increase and by gender, 2017–2018

    States with minimum wage changes States without minimum wage changes
    Overall 2.1% 1.6%
    Men 1.8% 0.3%
    Women 1.7% 1.0%

    chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the calculate .

    Notes: Arizona, California, Colorado, Hawaii, Maine, Michigan, New York, Vermont, Rhode Island, and Washington legislated minimal engage increases that took impression on January 1, 2018. Alaska, Florida, Minnesota, Missouri, Montana, New Jersey, Ohio, and South Dakota increased their minimum wages in 2018 because of indexing to inflation. Maryland, Oregon, and the District of Columbia legislated minimum wage increases that took effect on July 1, 2018. Delaware legislated minimum wage increases that took effect on October 1, 2018. Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    It is not surprise that these differences are smaller than what has been seen in earlier years because as the economy gets closer to wide employment, we would expect the 10th-percentile wage to increase across all states careless of changes in the minimal engage ( Gould 2017 ). furthermore, 2018 changes in state minimum wages came on the heels of other recent changes to minimum wages in many of the lapp states over the former pair of years. In fact, when we compare states that have had any minimal engage change since 2013 with states that did not have a minimum wage change during that meter, the pattern—as shown in Figure F —is even more marked. Wage increase at the 10th percentile in states with at least one minimal engage increase from 2013 to 2018 was more than 50 percentage faster than in states without any minimum wage increases ( 13.0 percentage vs. 8.4 percentage ). As expected, given women ’ s lower wages in general, this result is even stronger for women ( 13.0 percentage vs. 6.0 percentage ), though men besides experienced much faster 10th-percentile engage growth in states with minimum engage increases than in those without ( 12.0 percentage vs. 8.6 percentage ) .
    figure F

    engage growth at the bottom was strongest in states with minimal engage increases between 2013 and 2018 : 10th-percentile engage emergence from 2013 to 2018, by presence of state minimal wage increase between 2013 and 2018 and by sex

    States with minimum wage increases between 2013 and 2018 States with no minimum wage increases between 2013 and 2018
    Overall 13.0% 8.4%
    Men 12.0% 8.6%
    Women 13.0% 6.0%

    chart Data Download data The data below can be saved or copied immediately into Excel . The data underlying the design .

    Notes: Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, the District of Columbia, Florida, Hawaii, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, Nebraska, New Jersey, New York, Ohio, Oregon, Rhode Island, South Dakota, Vermont, Washington, and West Virginia increased their minimum wages at some point between 2013 and 2018. Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    Wages by race/ethnicity

    From 2000 to 2018, 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 spanish american workers from 2000 to 2018. From 2000 to 2018, the strongest emergence among white, black, and spanish american workers occurred at the peak of the wage distribution, a sign that wage inequality is growing within each of these racial/ethnic groups american samoa well as among workers overall. At every decile, wage emergence since 2000 has been quicker for white and hispanic workers than for black workers. After suffering declines in the aftermath of the Great Recession, the 60th and 70th percentiles of the black engage distribution remain below their 2000 levels. In fact, over the concluding 18 years, engage growth for white and hispanic workers has been about four times faster than engage growth for black workers in the 20th through the seventieth percentiles of their respective wage distributions. This growing differential gear is largely attributable to the fact that there has been fiddling to no wage growth for blacken workers ; it does not reflect some fantastic growth for white and hispanic workers .
    table 3

    Hourly wages by race/ethnicity and wage percentile, 2000–2018 (2018 dollars)

    Wage by percentile
    10th 20th 30th 40th 50th 60th 70th 80th 90th 95th
    White
    2000 $9.35 $11.62 $13.86 $16.14 $18.93 $22.06 $26.40 $32.10 $41.99 $52.78
    2007 $9.58 $12.01 $14.27 $16.89 $19.55 $23.28 $27.55 $33.86 $45.16 $58.19
    2017 $10.20 $12.30 $14.92 $17.41 $20.49 $24.51 $29.38 $36.32 $49.24 $67.53
    2018 $10.08 $12.47 $15.00 $17.66 $20.57 $24.58 $29.53 $36.70 $50.05 $72.05
    Annualized percent changes
    2000–2018 0.4% 0.4% 0.4% 0.5% 0.5% 0.6% 0.6% 0.7% 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%
    2007–2018 0.5% 0.3% 0.5% 0.4% 0.5% 0.5% 0.6% 0.7% 0.9% 2.0%
    2017–2018 -1.1% 1.4% 0.5% 1.4% 0.4% 0.3% 0.5% 1.1% 1.6% 6.7%
    Black
    2000 $8.78 $10.28 $11.75 $13.33 $15.00 $17.50 $20.49 $24.70 $31.38 $37.99
    2007 $8.74 $10.38 $12.05 $13.45 $15.19 $17.66 $20.63 $24.64 $33.43 $41.74
    2017 $9.12 $10.27 $11.68 $13.26 $15.31 $17.41 $20.48 $25.60 $34.61 $44.53
    2018 $9.15 $10.44 $12.00 $13.66 $15.08 $17.41 $20.43 $25.92 $35.38 $47.96
    Annualized percent changes
    2000–2018 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.3% 0.7% 1.3%
    2000–2007 -0.1% 0.1% 0.4% 0.1% 0.2% 0.1% 0.1% 0.0% 0.9% 1.4%
    2007–2018 0.4% 0.0% 0.0% 0.1% -0.1% -0.1% -0.1% 0.5% 0.5% 1.3%
    2017–2018 0.4% 1.6% 2.7% 3.0% -1.5% 0.0% -0.2% 1.2% 2.2% 7.7%
    Hispanic
    2000 $8.44 $9.43 $10.42 $11.73 $13.20 $14.76 $17.58 $21.46 $28.31 $36.41
    2007 $8.64 $9.73 $10.95 $12.19 $14.06 $15.91 $18.42 $23.16 $30.46 $39.99
    2017 $9.37 $10.37 $11.77 $13.20 $15.27 $17.12 $20.21 $24.62 $33.77 $44.24
    2018 $9.80 $10.95 $12.03 $13.52 $15.04 $17.09 $19.97 $24.63 $33.65 $44.23
    Annualized percent changes
    2000–2018 0.8% 0.8% 0.8% 0.8% 0.7% 0.8% 0.7% 0.8% 1.0% 1.1%
    2000–2007 0.3% 0.5% 0.7% 0.6% 0.9% 1.1% 0.7% 1.1% 1.1% 1.4%
    2007–2018 1.1% 1.1% 0.9% 0.9% 0.6% 0.6% 0.7% 0.6% 0.9% 0.9%
    2017–2018 4.5% 5.6% 2.2% 2.4% -1.5% -0.2% -1.2% 0.0% -0.3% 0.0%
    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%
    2017 89.4% 83.5% 78.3% 76.1% 74.7% 71.1% 69.7% 70.5% 70.3% 65.9%
    2018 90.8% 83.7% 80.0% 77.3% 73.3% 70.8% 69.2% 70.6% 70.7% 66.6%
    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%
    2017 91.9% 84.3% 78.9% 75.8% 74.5% 69.9% 68.8% 67.8% 68.6% 65.5%
    2018 97.2% 87.8% 80.2% 76.5% 73.1% 69.5% 67.6% 67.1% 67.2% 61.4%

    Notes: Sample based on all workers ages 16 and older. The xth-percentile wage is the wage at which x % of engage earners earn less and ( 100-x ) % earn more. Race/ethnicity categories are mutually single ( i.e., white non-Hispanic, black non-Hispanic, and Hispanic any subspecies ) .
    Source:  EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    From 2017 to 2018, the strongest wage growth among white workers was at the 95th percentile. Because 5.2 percentage of white workers had weekly earnings at or above the top-code, the growth rate for the 95th percentile is imputed using the 94th-percentile growth from 2017 to 2018. Unlike for men, the difference here is imperceptible when rounding—6.7 percentage growth using the 95th percentile as estimated with the Pareto distribution and besides a estimated using growth from the 94th percentile, which was not directly affected by the top-code procedure. Either way, engage increase for white workers was much faster over the stopping point year among high-wage workers than among middle- or low-wage workers. It ’ randomness important to not read excessively a lot into annual comparisons given data volatility, but wages for whiten workers at the bottom ( the 10th percentile ) of the engage distribution actually fell from 2017 to 2018. Since 2000, however, wages grew by at least 7.3 percentage for white workers at all wages deciles, including 8.6 percentage at the median .
    Over the integral period from 2000 to 2018, spanish american workers experienced more broadly based engage growth, with wages increasing across their wage distribution : There was strong growth at the clear ( 21.5 percentage ) ampere well as at the medial ( 13.9 percentage ) and the bottom ( 16.1 percentage ). Over the last year ( 2017 to 2018 ), however, spanish american workers ’ wages faltered, with outright declines ( or stagnation ) in the top half of the engage distribution .
    Black workers ’ wages fell ( or stagnated ) near the middle of the wage distribution ( at the 50th, 60th, and 70th percentiles ) between 2017 and 2018, though the losses were not as great or angstrom far-flung as for spanish american workers. ( Again, when looking at all of these numbers, we need to keep in take care that the CPS datum is subject to a certain amount of volatility from year to year ; for data on black wages, that excitability is probable to be even more marked because of the smaller data sample represented by the total darkness population. ) What is particularly strike about blacken wages is the slow engage increase since 2000 closely across the board. At the median, total darkness workers ’ wages rose alone 0.5 percentage over the stallion menstruation. The lone luminary diversions from the near-zero emergence rates at most levels were increases for black workers at the 10th percentile ( 4.2 percentage ), the 80th percentile ( 4.9 percentage ), the 90th percentile ( 12.7 percentage ), and the 95th percentile ( 26.2 percentage ). But the growth rates at the 10th and 80th percentiles are hush extremely slow, representing annualized percentage changes of 0.2 and 0.3 percentage, respectively. For perspective, over the lapp 18-year period, the slowest engage growth among hispanic workers between 2000 and 2018 was 13.6 percentage ( 0.7 percentage annualized ) at the 70th percentile .
    The bottom section of table 3 displays wage gaps by race/ethnicity, Wage gaps by race/ethnicity track how much less african American and spanish american workers are paid relative to white workers ; here, total darkness and spanish american wages are shown as a parcel of ashen wages at each decile of their respective engage distributions. Compared with white workers, black workers have been losing land since 2000, with larger black–white engage gaps across the stallion distribution. In 2000, black wages at the median were 79.2 percentage of white wages. By 2018, they were entirely 73.3 percentage of white wages, representing an increase in the engage break from 20.8 percentage to 26.7 percentage. conversely, hispanic workers have been lento closing the gap with white workers at the bottom 80 percentage of the engage distribution. In 2000, median Hispanic wages were 69.7 percentage of white wages and, by 2018, they were 73.1 percentage, representing a pin down of the break from 30.3 percentage to 26.9 percentage. The 95th-percentile Hispanic–white engage gap hush remains importantly wider than its 2000 degree .
    The regression-adjusted black–white engage gap ( controlling for education, age, sex, and region ) has become larger over the stopping point year ( EPI 2019d ). While the Hispanic–white wage gap has remained reasonably ceaseless over the last 18 years ( 12.3 percentage in 2000 compared with 11.8 percentage in 2018 ), the black–white gap was significantly larger in 2018 ( 16.2 percentage ) than it was in 2000 ( 10.2 percentage ). In 2000, the Hispanic–white engage col was larger than the black–white wage opening. In 2018, the change by reversal was true. Further, between 2000 and 2018 the regression-adjusted black–white wage gap widened importantly for both men ( +5.8 percentage points ) and women ( +5.9 percentage points ), while the Hispanic–white wage col narrowed for men ( −1.8 percentage points ) and grew for women ( +1.6 percentage points ) .

    Wages by education level

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

    Table 4 presents the most late data on average hourly wages by education for all workers and by sex, and Figure G displays the accumulative percentage change in real average hourly wages by department of 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. Those identified as having “ some college ” may have an associate degree or have completed function of a two- or four-year college degree. )
    From 2000 to 2018, the strongest engage growth occurred among those with progress degrees ( 11.0 percentage ), those with college degrees ( 6.7 percentage ), and those with less than a high gear school diploma ( 8.7 percentage ). Given that those with less than a high school diploma are often the lowest-wage workers in general, it is likely that some of these gains can be attributed to state-level increases in the minimal wage. These workers represent a humble and shrinking share of the overall work force, entirely about 8 percentage of workers in 2018 ( EPI 2019d ). The average engage for workers with some college has last returned to its 2000 horizontal surface, but still remains just below its 2007 level .
    Over the last class, median wages of those with some college and those with advanced degrees rose the fastest, a reversal from 2016 to 2017, when wages fell among these workers ( EPI 2019d ). between 2017 and 2018, wages of those with a high school diploma rose faster than wages of those with a college degree, continuing to narrow the opening between college and high school wages since 2016. As a solution, the college wage premium—the regression-adjusted log-wage difference between the wages of college-educated and eminent school–educated workers—fell slenderly from 50.6 percentage to 48.4 percentage between 2016 and 2018 .
    board 4

    Average hourly wages by gender and education, 2000–2018 (2018 dollars)

    Less than high school High school Some college College Advanced degree
    All
    2000 $12.59 $17.85 $20.32 $31.27 $39.47
    2007 $13.03 $18.06 $20.46 $31.95 $40.60
    2017 $13.66 $18.25 $20.01 $33.17 $42.39
    2018 $13.68 $18.45 $20.34 $33.36 $43.80
    Annualized percent changes
    2000–2018 0.5% 0.2% 0.0% 0.4% 0.6%
    2000–2007 0.5% 0.2% 0.1% 0.3% 0.4%
    2007–2018 0.4% 0.2% -0.1% 0.4% 0.7%
    2017–2018 0.1% 1.1% 1.6% 0.6% 3.3%
    Men
    2000 $13.84 $20.15 $22.98 $35.54 $44.18
    2007 $14.24 $20.08 $22.88 $36.52 $46.00
    2017 $15.13 $20.14 $22.23 $38.19 $48.72
    2018 $15.19 $20.35 $22.84 $38.60 $51.26
    Annualized percent changes
    2000–2018 0.5% 0.1% 0.0% 0.5% 0.8%
    2000–2007 0.4% -0.1% -0.1% 0.4% 0.6%
    2007–2018 0.6% 0.1% 0.0% 0.5% 1.0%
    2017–2018 0.4% 1.0% 2.7% 1.1% 5.2%
    Women
    2000 $10.75 $15.34 $17.82 $26.77 $33.78
    2007 $11.12 $15.67 $18.24 $27.43 $34.91
    2017 $11.49 $15.71 $17.88 $28.33 $36.68
    2018 $11.47 $15.86 $17.94 $28.35 $37.07
    Annualized percent changes
    2000–2018 0.4% 0.2% 0.0% 0.3% 0.5%
    2000–2007 0.5% 0.3% 0.3% 0.3% 0.5%
    2007–2018 0.3% 0.1% -0.1% 0.3% 0.5%
    2017–2018 -0.2% 1.0% 0.4% 0.1% 1.0%
    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%
    2017 75.9% 78.0% 80.4% 74.2% 75.3%
    2018 75.5% 77.9% 78.6% 73.4% 72.3%

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    figure G

    For workers with some college education, wages have just reached their 2000 degree :

    accumulative percentage change in real average hourly wages, by education, 2000–2018

    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%

    chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the figure .

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    however, engage growth among those with college degree rose faster over the entire period from 2000 to 2018 than among those with a high school diploma ( 6.7 percentage vs. 3.4 percentage ). Because of the disproportionate gains for those with more credentials, the regression-adjusted college engage premium grew from 47.0 percentage to 48.4 percentage from 2000 to 2018 .
    A prevailing floor explains wage inequality as a elementary consequence of growing employer necessitate for skills and education—often thought to be driven by advances in engineering. According to this explanation, because there is a dearth of skilled or college-educated workers, the wage gap between workers with and without college degrees is widening. however, despite its great popularity and intuitive attract, this story about recent engage trends being driven more and more by a slipstream between education and engineering does not fit the facts well, specially since the mid-1990s ( Mishel, Shierholz, and Schmitt 2013 ). When we compare the relative changes in the 95/50 engage gap and the college premium from 2000 to 2018, it is well-defined that gains in the college engage premium have not been large adequate to drive the continue steady emergence of the 95/50 engage gap ( see Gould 2018 for extra analysis on these differences since 1979 ) .
    The more outstanding floor is not one of a growing differential gear of wages between college and high school graduates, but increasingly one of growing engage inequality overall and within respective education groups. Among college graduates, there has been a significant perpetrate away at the very top of the wage distribution. The bottom 60 percentage of those with a college degree silent have lower wages than they did in 2000 or 2007. The 50th-percentile wage among those with bachelor ’ randomness degrees was 2.4 percentage lower in 2018 than it was in 2000, while the 90th-percentile engage of those with knight bachelor ’ second degrees was 9.8 percentage higher. ( The 95th engage percentile for college graduates is fraught with the same top-coding emergence as for white and male workers—but to an flush greater extent—making those comparisons less dependable. )
    Figures H and I display the accumulative percentage transfer in real hourly wages by education for men and women, respectively. Since 2000, wage emergence for those with a college or gain academic degree was faster for men than for women, while wage growth for those with a high school diploma or some college was faster for women than for men. In general, the women ’ south wage distribution by educational attainment is more compress ; that is, the engage differences between workers of 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 a college or advance degree vitamin a well as those with less than a high school diploma. Wages of men with some college remained lower than their 2000 levels. Among women, all groups have exceeded their 2000 engage levels .
    design H

    The average engage for men with some college is inactive below its 2000 flush : accumulative percentage change in real average hourly wages of men, by education, 2000–2018

    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

    chart Data Download data The data below can be saved or copied directly into Excel . The data underlying the figure .

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    human body I

    average wages were higher in 2018 than in 2000 for women at all levels of educational skill : accumulative percentage change in real number median hourly wages of women, by education, 2000–2018

    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% 

    graph Data Download data The data below can be saved or copied directly into Excel . The datum underlying the figure .

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    While there has been a slow specialize of sex wage gaps for those with a high school diploma and those with some college since 2000, sex wage gaps are wide among those with less than high school and among those with college or advance degrees. As Figure J illustrates, women are paid systematically less than their male counterparts at every education tied .
    educational attainment has grown faster for women than men between 2000 and 2018, and now women are more likely than men to have a college or advance degree ( EPI 2019d ). unfortunately, increasing educational attainment has not insulated women from big sex engage gaps : The average wage for a serviceman with a college degree was higher in 2018 than the average wage for a woman with an advanced academic degree ( by 4.1 percentage ) .
    figure J

    On average, men are paid more than women at every education level : average hourly wages by sex and education, 2018

    Men Women
    Less than high school $15.19 $11.47
    High school 20.35 15.86
    Some college 22.84 17.94
    College 38.60 28.35 
    Advanced degree 51.26 37.07

    chart Data Download data The data below can be saved or copied directly into Excel . The data underlying the calculate .

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    From 2000 to 2018, engage growth for white and black workers tended to be faster for those with more education than for those with less ( Table 5 ). average wages grew faster among white and spanish american workers than among black workers for all education groups ( which is not surprising given that the lapp was true at all deciles of the wage distribution ). Black workers with less than a college degree had lower wages in 2018 than in 2000. consistent with our findings on the relationship between department of education and earnings for all workers ( see Table 4 ), wage growth was strongest for those with an advanced degree for all groups over the last year, while wages fell for black workers with less than a senior high school educate diploma .
    table 5

    Average hourly wages by race/ethnicity and education, 2000–2018 (2018 dollars)

    Less than high school High school Some college College Advanced degree
    White
    2000 $12.81 $18.60 $20.98 $32.15 $39.97
    2007 $13.18 $18.96 $21.19 $32.89 $41.28
    2017 $13.69 $19.49 $21.15 $34.39 $42.94
    2018 $13.77 $19.75 $21.58 $34.75 $44.46
    Annualized percent changes
    2000–2018 0.4% 0.3% 0.2% 0.4% 0.6%
    2000–2007 0.4% 0.3% 0.1% 0.3% 0.5%
    2007–2018 0.4% 0.4% 0.2% 0.5% 0.7%
    2017–2018 0.6% 1.3% 2.0% 1.1% 3.5%
    Black
    2000 $12.10 $15.75 $18.02 $26.60 $34.98
    2007 $12.22 $15.67 $18.16 $26.56 $34.38
    2017 $11.96 $15.39 $16.99 $27.02 $34.91
    2018 $11.42 $15.57 $17.15 $27.46 $36.23
    Annualized percent changes
    2000–2018 -0.3% -0.1% -0.3% 0.2% 0.2%
    2000–2007 0.1% -0.1% 0.1% 0.0% -0.2%
    2007–2018 -0.6% -0.1% -0.5% 0.3% 0.5%
    2017–2018 -4.5% 1.1% 0.9% 1.6% 3.8%
    Hispanic
    2000 $12.48 $15.88 $18.52 $26.62 $35.55
    2007 $13.13 $16.48 $18.82 $28.36 $38.40
    2017 $14.02 $17.02 $18.53 $28.38 $37.69
    2018 $14.11 $17.28 $18.66 $28.49 $38.47
    Annualized percent changes
    2000–2018 0.7% 0.5% 0.0% 0.4% 0.4%
    2000–2007 0.7% 0.5% 0.2% 0.9% 1.1%
    2007–2018 0.7% 0.4% -0.1% 0.0% 0.0%
    2017–2018 0.6% 1.5% 0.7% 0.4% 2.1%
    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%
    2017 87.3% 79.0% 80.3% 78.6% 81.3%
    2018 82.9% 78.8% 79.5% 79.0% 81.5%
    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%
    2017 102.4% 87.3% 87.6% 82.5% 87.8%
    2018 102.4% 87.5% 86.4% 82.0% 86.5%

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    Black–white wage gaps by department of education were larger in 2018 than in 2000 for all education groups, while Hispanic–white engage gaps were narrower for workers with less than high educate and for workers with high educate diploma. At closely every education tied, black and hispanic workers were systematically paid less than their white counterparts in 2018, while spanish american workers were systematically paid more than black workers ( Figure K ) .
    design K

    On average, white workers are paid more than black and spanish american workers at closely every education charge : average hourly wages, by race/ethnicity and education, 2018

    White Hispanic Black
    Less than high school $13.77 $14.11 $11.42
    High school $19.75 $17.28 $15.57
    Some college $21.58 $18.66 $17.15
    College $34.75 $28.49 $27.46
    Advanced degree $44.46 $38.47 $36.23

    chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the visualize .

    Note: Sample based on all workers ages 16 and older .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau
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    Conclusion

    From 2017 to 2018, actual hourly wages increased for many workers across the wage distribution, though not for all genders and races or ethnicities nor for workers at all levels of educational attainment. In general, the years since 2000 have been associated with a continue pulling apart of the engage distribution, with disproportionate gains at the top. Wages for those with extra schooling stay higher than wages for workers with less education, though modest increases in the college wage premium can not explain the more extreme pulling aside of the exceed earners. One drift pushing back on growing wage inequality between the top and bottom of the engage distribution is stronger growth at the 10th percentile over the last five years, particularly in states that have increased their minimum wage .
    Rising wages over the end few years have happened during a menstruation of falling unemployment, with unemployment rates dropping near to ( or even below ) pre–Great Recession lows. This is no coincidence. If the unemployment rate is allowed to continue to fall, finally low unemployment should boost low- and middle-wage workers ’ leverage adequate to see steady and boastfully engage gains. however, there is no sign that we ’ ve reached the limits of how much we can sustainably boost wage increase with lower unemployment—wage emergence remains weaker than we should expect in a amply goodly economy. This means that confident proclamations that we ’ ve achieved full employment should not be made and that the Federal Reserve should hold off on any far interest rate increases and allow the economy to continue to grow .
    Full employment is one way that workers gain adequate bargaining world power to increase their wages ; employers have to pay more to attract and retain the workers they need when idle workers are barely. The “ lever ” for higher wages that comes from full use is most important for workers at the bottom of the wage distribution : For a given fall in the unemployment rate, wage growth rises more for low-wage workers, and in the absence of stronger labor standards, it is frequently only in the tight of labor markets that low-wage workers see stronger wage increase ( Bivens and Zipperer 2018 ) .
    beyond seeking to keep department of labor markets tight, policymakers could take early steps to foster strong broad-based wage growth, such as raising the federal minimal engage, expanding eligibility for overtime pay, addressing gender and racial pay disparities, and protecting and strengthening workers ’ rights to bargain jointly for higher wages and benefits. For more policies that will raise wages, see EPI ’ s Policy Agenda ( EPI 2018b ) .

    About the author

    Elise Gould, senior economist, joined EPI in 2003. Her research areas include wages, poverty, economic mobility, and health care. She is a co-author of The State of Working America, 12th edition. In the past, she has authored a chapter on health in The State of Working America 2008/09 ; co-authored a book on health insurance coverage in retirement ; published in venues such as The Chronicle of Higher Education, Challenge Magazine, and Tax Notes ; and written for academician 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. She holds a master ’ s degree in public affairs from the University of Texas at Austin and a Ph.D. in economics from the University of Wisconsin at Madison .

    Acknowledgments

    The writer thanks Melat Kassa, Zane Mokhiber, and Julia Wolfe for their meticulous program and research aid, and Josh Bivens, John Schmitt, Heidi Shierholz, and Ben Zipperer for their valuable and persistent contributions on methodological issues .

    Appendix figures

    Appendix Figure A

    Cumulative percent change in real annual wages, by wage group, 1979–2017

    Year Bottom 90% 90th–95th 95th–99th Top 1% Average
    1979 0.0% 0.0% 0.0% 0.0% 0.0%
    1980 -2.2% -1.3% -0.2% 3.4% -1.4%
    1981 -2.6% -1.1% -0.1% 3.1% -1.7%
    1982 -3.9% -0.9% 2.2% 9.5% -1.9%
    1983 -3.7% 0.7% 3.6% 13.6% -1.1%
    1984 -1.8% 2.5% 6.0% 20.7% 1.2%
    1985 -1.0% 4.0% 8.1% 23.0% 2.4%
    1986 1.1% 6.4% 12.5% 32.6% 5.3%
    1987 2.1% 7.4% 15.0% 53.5% 7.9%
    1988 2.2% 8.2% 18.4% 68.7% 9.7%
    1989 1.8% 8.1% 18.2% 63.3% 9.0%
    1990 1.1% 7.1% 16.5% 64.8% 8.3%
    1991 0.0% 6.9% 15.5% 53.6% 6.5%
    1992 1.5% 9.0% 19.2% 74.3% 9.8%
    1993 0.9% 9.2% 20.6% 67.9% 9.1%
    1994 2.0% 11.2% 21.0% 63.4% 9.8%
    1995 2.8% 12.2% 24.1% 70.2% 11.3%
    1996 4.1% 13.6% 27.0% 79.0% 13.3%
    1997 7.0% 16.9% 32.3% 100.6% 17.9%
    1998 11.0% 21.3% 38.2% 113.1% 22.8%
    1999 13.2% 25.0% 42.9% 129.7% 26.5%
    2000 15.3% 26.8% 48.0% 144.8% 29.9%
    2001 15.7% 29.0% 46.4% 130.4% 29.3%
    2002 15.6% 29.0% 43.2% 109.3% 27.2%
    2003 15.7% 30.3% 44.9% 113.9% 27.9%
    2004 15.6% 30.8% 47.1% 127.2% 29.2%
    2005 15.0% 30.8% 48.6% 135.3% 29.5%
    2006 15.7% 32.5% 52.1% 143.4% 31.2%
    2007 16.7% 34.1% 55.4% 156.2% 33.4%
    2008 16.0% 34.2% 53.8% 137.5% 31.4%
    2009 16.0% 35.3% 53.5% 116.2% 29.9%
    2010 15.2% 35.7% 55.7% 130.8% 30.8%
    2011 14.5% 36.2% 56.9% 134.0% 30.7%
    2012 14.6% 36.3% 58.3% 148.3% 32.0%
    2013 15.1% 37.1% 59.4% 137.5% 31.7%
    2014 16.6% 38.7% 62.3% 149.0% 34.2%
    2015 20.5% 43.1% 67.9% 156.2% 38.6%
    2016 21.0% 43.5% 68.1% 148.1% 38.4%
    2017 22.2% 44.2% 69.3% 157.3% 40.1%

    chart Data Download data The data below can be saved or copied immediately into Excel . The data underlying the trope .

    Source: EPI analysis of Kopczuk, Saez, and Song ( 2010, Table A3 ) and Social Security Administration engage statistics
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    Appendix Figure B

    Cumulative change in real hourly wages of all workers, by wage percentile, 1979–2018

    10th percentile 30th percentile 50th percentile 70th percentile 95th percentile
    1979 0.0% 0.0% 0.0% 0.0% 0.0%
    1980 -6.7% -3.5% -0.6% -3.1% -1.8%
    1981 -8.1% -3.7% -1.7% -2.4% -1.5%
    1982 -11.8% -3.0% -2.1% -1.4% 0.5%
    1983 -14.7% -4.5% -1.9% -0.7% 3.5%
    1984 -16.4% -6.3% -1.4% 0.1% 3.9%
    1985 -17.5% -7.3% 0.1% -1.7% 6.1%
    1986 -17.2% -4.1% 0.8% 0.8% 8.6%
    1987 -17.3% -3.5% 1.9% 1.6% 11.6%
    1988 -16.5% -2.3% 0.5% 1.0% 14.3%
    1989 -17.1% -4.1% 0.3% 2.0% 7.7%
    1990 -16.5% -4.8% 0.2% 0.0% 9.1%
    1991 -15.1% -4.4% -0.6% -0.8% 10.6%
    1992 -14.4% -3.9% 0.4% -0.5% 8.8%
    1993 -13.1% -3.9% 1.9% 0.3% 6.6%
    1994 -13.8% -5.0% 0.8% 1.1% 12.4%
    1995 -14.7% -5.9% -0.6% 0.7% 12.7%
    1996 -15.7% -4.5% -2.3% 1.2% 13.4%
    1997 -13.2% -2.7% -0.3% 2.3% 14.8%
    1998 -7.8% -0.1% 3.3% 4.7% 18.3%
    1999 -6.1% 2.5% 5.9% 6.5% 21.5%
    2000 -6.7% 4.8% 6.5% 8.2% 24.9%
    2001 -3.8% 6.0% 8.2% 9.3% 27.0%
    2002 -0.8% 8.1% 9.8% 10.9% 31.6%
    2003 -1.2% 9.3% 9.9% 12.9% 30.5%
    2004 -3.0% 7.0% 11.1% 12.0% 31.7%
    2005 -5.3% 4.2% 9.5% 10.5% 32.0%
    2006 -5.9% 2.5% 9.9% 9.6% 33.2%
    2007 -4.3% 5.0% 10.1% 11.4% 35.5%
    2008 -3.2% 4.7% 9.3% 12.0% 36.3%
    2009 -2.4% 6.7% 11.7% 14.5% 39.4%
    2010 -3.2% 5.3% 11.0% 13.8% 38.6%
    2011 -5.7% 3.3% 8.2% 12.2% 37.3%
    2012 -7.3% 2.4% 6.9% 10.8% 39.0%
    2013 -6.7% 3.5% 7.4% 12.7% 41.4%
    2014 -5.9% 2.5% 7.5% 11.4% 39.4%
    2015 -1.4% 3.3% 9.3% 14.7% 48.3%
    2016 -0.6% 6.0% 11.2% 14.6% 49.8%
    2017 3.5% 8.0% 12.2% 16.3% 52.0%
    2018 4.1% 12.0%  14.0%  17.1%  56.1%

    chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the name .

    Notes: Shaded areas denote recessions. The xth-percentile wage is the wage at which x % of engage earners earn less and ( 100−x ) % earn more .
    Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata
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    Appendix Figure C

    Productivity growth and hourly compensation growth, 1948–2017

    Year Hourly compensation Productivity
    1948  0.0% 0.0%
    1949 6.2% 0.7%
    1950 10.5% 8.8%
    1951 11.7% 11.1%
    1952 15.0% 14.7%
    1953 20.8% 18.2%
    1954 23.5% 20.5%
    1955 28.7% 25.7%
    1956 33.9% 27.2%
    1957 37.1% 30.1%
    1958 38.1% 32.5%
    1959 42.5% 37.5%
    1960 45.4% 40.3%
    1961 47.8% 44.5%
    1962 52.3% 49.7%
    1963 54.9% 55.3%
    1964 58.3% 59.8%
    1965 62.3% 64.6%
    1966 64.7% 68.6%
    1967 66.7% 71.1%
    1968 70.5% 76.3%
    1969 74.4% 77.4%
    1970 76.3% 79.2%
    1971 81.7% 85.2%
    1972 90.8% 90.7%
    1973 90.9% 95.7%
    1974 86.6% 92.5%
    1975 86.5% 96.0%
    1976 89.3% 100.7%
    1977 92.8% 103.5%
    1978 95.6% 105.0%
    1979 93.2% 103.6%
    1980 88.3% 102.4%
    1981 87.6% 107.6%
    1982 87.9% 106.9%
    1983 88.5% 109.8%
    1984 87.0% 116.7%
    1985 86.4% 119.8%
    1986 87.4% 123.0%
    1987 84.7% 126.4%
    1988 84.0%   131.3%
    1989 83.7% 130.0%
    1990 82.4% 132.2%
    1991 82.0% 134.0%
    1992 83.2% 142.0%
    1993 83.4% 141.5%
    1994 83.9% 144.4%
    1995 82.7% 147.5%
    1996 82.9% 153.8%
    1997 84.9% 159.8%
    1998 89.3% 167.5%
    1999 92.0% 173.8%
    2000 92.9% 181.7%  
    2001 95.6% 186.5%
    2002 99.5% 193.1%
    2003 101.6% 200.7%
    2004 100.5% 209.0%
    2005 99.7% 215.3%
    2006 99.9% 221.1%
    2007 101.4% 217.1%
    2008 101.4% 213.5%
    2009 109.3% 219.5%
    2010 111.0% 232.2%
    2011 108.5% 235.2%
    2012 106.5% 241.2%
    2013 108.4% 239.9%
    2014 109.1% 245.2%
    2015 112.4% 246.7%
    2016 114.4% 244.0%
    2017 114.7%  246.6% 

    chart Data Download data The data below can be saved or copied directly into Excel . The datum underlying the trope .

    Notes: Data are for recompense ( wages and benefits ) of production/nonsupervisory workers in the private sector and net productivity of the entire economy. “ net productivity ” is the growth of output of goods and services less depreciation per hour worked .
    Source: EPI analysis of Bureau of Labor Statistics and Bureau of Economic Analysis data. Updated from figure A in Bivens et alabama. 2014 .
    EPI analysis of unpublished Total Economy Productivity data from Bureau of Labor Statistics ( BLS ) Labor Productivity and Costs program, engage data from the BLS Current Employment Statistics, BLS Employment Cost Trends, BLS Consumer Price Index, and Bureau of Economic Analysis National Income and Product Accounts. Updated from figure A in Raising America ’ sulfur Pay : Why It ’ randomness Our central economic policy Challenge, by Josh Bivens, Elise Gould, Lawrence Mishel, and Heidi Shierholz, Economic Policy Institute, 2014 .
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    Endnotes

    For more information about the CPS and CES employment measures, see BLS 2019 .
    See EPI 2019e for an synergistic calculator illustrating this discrepancy .
    Regression-adjusted figures are not shown in the tables in this report but are available in the State of Working America Data Library ( EPI 2019d ) .

    References

    Bivens, Josh, Elise Gould, Lawrence Mishel, and Heidi Shierholz. 2014. Raising America ’ mho Pay : Why It ’ s Our central economic policy Challenge. Economic Policy Institute Briefing Paper no. 378, June 2014 .
    Bivens, Josh, and Ben Zipperer. 2018. The Importance of Locking in Full Employment for the Long Haul. Economic Policy Institute, August 2018 .
    Bureau of Labor Statistics ( BLS ). 2019. “ Comparing Employment from the BLS Household and Payroll Surveys ” ( world wide web foliate ). last update February 1, 2019 .
    economic Policy Institute ( EPI ). 2016. The Agenda to Raise America ’ randomness Pay. last update December 6, 2016 .
    economic Policy Institute ( EPI ). 2018a. Gender Pay Gap Calculator. last update March 1, 2018 .
    economic Policy Institute ( EPI ). 2018b. policy Agenda. December 2018 .
    economic Policy Institute ( EPI ). 2019a. methodology for Measuring Wages and Benefits. last update February 2019 .
    economic Policy Institute ( EPI ). 2019b. minimal wage Tracker. last update January 8, 2019 .
    economic Policy Institute ( EPI ). 2019c. nominal wage Tracker. last update February 1, 2019 .
    economic Policy Institute ( EPI ). 2019d. state of Working America Data Library. last update February 2019 .
    economic Policy Institute ( EPI ). 2019e. engage Calculator. next update extroverted February 2019 .
    Gould, Elise. 2003. Measuring employment Since the recovery : A Comparison of the Household and Payroll Surveys. Economic Policy Institute Briefing Paper no. 148, December 2003 .
    Gould, Elise. 2017. The State of American Wages 2016 : Lower Unemployment Finally Helps Working People Make Up Some Lost land on Wages. economic Policy Institute, March 2017 .
    Gould, Elise. 2018. The State of American Wages 2017 : Wages Have last Recovered from the Blow of the Great Recession but Are still Growing Too Slowly and Unequally. Economic Policy Institute, March 2018 .
    Kopczuk, Wojciech, Emmanuel Saez, and Jae Song. 2010. “ Earnings Inequality and Mobility in the United States : tell from Social Security Data Since 1937. ” quarterly Journal of Economics 125, no. 1 : 91–128 .
    Mishel, Lawrence, Josh Bivens, Elise Gould, and Heidi Shierholz. 2012. The State of Working America, 12th edition. Ithaca, N.Y. : Cornell Univ. Press .
    Mishel, Lawrence, Heidi Shierholz, and John Schmitt. 2013. Don ’ thyroxine Blame the Robots : Assessing the Job Polarization Explanation of Growing Wage Inequality. Economic Policy Institute, Center for Economic and Policy Research Working Paper, November 2013 .
    Mishel, Lawrence, and Julia Wolfe. 2018. “ lead 1.0 percentage Reaches Highest Wages Ever—Up 157 Percent Since 1979. ” Working Economics ( Economic Policy Institute web log ), October 18, 2018.

    Social Security Administration. assorted years. Wage Statistics [ database ] .
    U.S. Census Bureau, Current Population Survey basic monthly microdata ( U.S. Census Bureau CPS basic ). versatile years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. Accessed January 2019 at hypertext transfer protocol : //thedataweb.rm.census.gov/ftp/cps_ftp.html .
    U.S. Census Bureau, Current Population Survey Outgoing Rotation Group microdata ( U.S. Census Bureau CPS-ORG ). assorted years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. Accessed January 2019 at hypertext transfer protocol : //thedataweb.rm.census.gov/ftp/cps_ftp.html .

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