Myth: The black unemployment rate is at an all-time low, and that means the economy is “working great” for all black workers.

Reality: Too many black workers are still out of work—black workers are twice as likely to be unemployed as white workers.

Even with a historically low average annual black unemployment rate of 6.1% in 2019, black workers are twice as likely to be unemployed as white workers overall and are more likely to be unemployed than white workers at every education level. Only black workers with some college or more education have an unemployment rate lower than the overall unemployment rate of white workers.

Black workers are more likely to be unemployed than white workers at every education level: Unemployment rates by race and education, 2019

Education Black White, non-Hispanic
All 6.1% 3.0%
Less than high school 14.7% 8.3%?
High school 8.3% 3.9%?
Some college 4.9% 2.9%?
College 3.4% 2.2%?
Advanced 2.3% 1.7%
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Updated with Jan.–Dec. 2019 data, from?Black Workers Endure Persistent Racial Disparities in Employment Outcomes, Economic Policy Institute, 2019.

Source:?EPI analysis of U.S. Census Bureau data

Estimates are based on a 12-month average (January 2019–December 2019). “Black” includes blacks of Hispanic ethnicity. Whites are non-Hispanic.

Source:?EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau. Updated from Figure A in Jhacova Williams and Valerie Wilson, Black Workers Endure Persistent Racial Disparities in Employment Outcomes, Economic Policy Institute, August 2019.

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Myth: If black workers had better skills, they would have better employment outcomes.

Reality:?Having a college degree doesn’t guarantee a college-level job, especially for black workers.

It is true that workers with higher levels of education have better employment outcomes. But in today’s economy getting a college degree doesn’t provide the universal boost that it used to. We have a high underemployment rate—a high share of college graduates who are working in jobs that do not require a college degree. And as the chart shows, black college graduates are more likely than white college graduates to be employed in occupations that do not require a college degree.

Black college graduates are more likely than white college graduates to be underemployed when it comes to their skills: Share of workers with a college degree who are not employed in a college occupation, by race, 2019

Race/ethnicity Rate
Black 39.4%
White non-Hispanic 30.9%
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Adapted from?Black Workers Endure Persistent Racial Disparities in Employment Outcomes, Economic Policy Institute, 2019.

Source:?EPI analysis of U.S. Census Bureau data

Estimates are based on a 12-month average (July 2018–June 2019). “Black” includes blacks of Hispanic ethnicity. Whites are non-Hispanic. College graduates include those with a bachelor’s degree or more education. For how "college occupation" is defined, see the methodology in Jhacova Williams and Valerie Wilson,?Black Workers Endure Persistent Racial Disparities in Employment Outcomes, Economic Policy Institute, August 2019

Source:?EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau. Adapted from Figures B and C in Jhacova Williams and Valerie Wilson, Black Workers Endure Persistent Racial Disparities in Employment Outcomes, Economic Policy Institute, August 2019.

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Myth: The strong economy and historically low unemployment must mean historically strong wage growth among black workers, and especially among highly educated black workers.

Reality: Wages for black college graduates have actually fallen in the current recovery.

In a recovery, as the unemployment rates falls, you expect wages to grow. But in that respect this current recovery significantly lags the recovery of the late 1990s. Both recoveries have had similar declines in the unemployment rate, but wages today have not grown nearly as fast or as evenly across race and gender as they did during the late 1990s. Today, workers with bachelor’s degrees are not seeing nearly the level of wage growth that this group saw in the late 1990s. In fact, wages fell for black college graduates between 2015 and 2019, even as unemployment rates were falling significantly.

Wage growth was stronger among workers with bachelor’s degrees in the late 1990s than during the current expansion: Real average wage growth, workers with bachelor’s degrees, 1996–2000 and 2015–2019

Demographic 1996–2000 2015–2019
Men 10.9% 7.8%
Women 9.8% 3.0%
White 10.6% 6.6%
Black 11.5% -0.3%


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Adapted from Wage Growth Is Weak for a Tight Labor Market—and the Pace of Wage Growth Is Uneven Across Race and Gender,?Economic Policy Institute, 2019.

Source:?EPI analysis of U.S. Census Bureau data


In order to include data from the first half of 2019, all years refer to the 12-month period ending in June. Sample includes workers with a bachelor’s degree only.

Source: EPI?analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau. Adapted from Figure B in Elise Gould and Valerie Wilson,?Wage Growth is Weak for a Tight Labor Market—and the Pace of Wage Growth is Uneven Across Race and Gender, Economic Policy Institute, August 2019.

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Posted February 3, 2020 at 4:34 pm

Primer—The state of the union for working people

In preparation for President Trump’s State of the Union speech, the Economic Policy Institute has assembled research from the last year that examines the real state of the union for working people on wages, manufacturing and trade, taxes, labor standards, housing, and immigration.

Wages and employment

Manufacturing and trade


Read more

Posted January 31, 2020 at 4:51 pm by Josh Bivens

The signal the unemployment rate provides can change a lot over time: EPI Macroeconomics Newsletter

In 2019 the unemployment rate was below 4% for the second straight year, the first time this has happened since 1968 and 1969. Despite the current stretch of low unemployment, by many other measures the labor market does not seem particularly tight. Most obviously, wage growth has been accelerating a bit, but is still disappointing relative to what wage growth we would expect at this level of unemployment.

Productivity growth has firmed up slightly in recent years, but employers still aren’t acting like labor costs are something they’re particularly worried about containing through investments in capital equipment or better processes.

The late 1990s?is an obvious reference for highlighting how unresponsive wage and productivity growth have been to low unemployment in recent years. In these years, low unemployment coincided with notable accelerations in both wage and productivity growth. In this newsletter, we highlight some reasons why the headline unemployment rate measured in the late 1990s does not provide quite the expected apples-to-apples comparison with the unemployment rate of today. Key findings are:

  • The unemployment rate that signifies labor market tightness falls as the workforce gets older and becomes better educated. All else equal, a workforce that is growing older and more educated should steadily, over time, reduce the unemployment rate that is consistent with a given wage target. These compositional changes in the workforce have occurred and have reduced unemployment by roughly 0.3 percentage points since 2000, meaning that?an unemployment rate of 3.7% today is equivalent in its effect on wage growth to a 4.0% unemployment rate in 2000.
  • Today’s measured unemployment rate captures fewer jobless workers than it used to. Growing nonresponse in the survey used to calculate the unemployment rate has reduced the unemployment rate consistent with a given wage target over time by another 0.3 percentage points since 2000. Growing evidence shows that nonresponse to this survey is not random: rather it is jobless workers who are less likely to respond to the survey that is used to calculate unemployment. This biases the measured unemployment rate downward.
  • There may be a bigger pool of workers competing for jobs than the unemployment rate suggests. Adults not in the labor force today seem substantially more substitutable with adults officially classified as “unemployed” than was the case in the late 1990s recovery. For example, the share of newly employed workers who enter employment from out of the labor force is substantially higher in recent years than in past periods of low unemployment, and the downward pressure that adults not participating in job searches put on wages is higher now than in the late 1990s. In short, many potential workers today are not being classified as unemployed, and hence may be missed by focusing only on the unemployment rate as a measure of labor slack.

The rest of this brief highlights evidence on these three points.

A lower unemployment rate is needed to signify labor market tightness with an older and better-educated workforce

All else equal, workers with more experience and education credentials have lower rates of unemployment. The economic intuition for this is that more experienced and more educated workers have skills that are in greater demand by employers at any given level of economy-wide slack. This demand premium for more experienced workers holds in the aggregate despite the fact that age discrimination afflicts many workers, i.e., the unemployment/age gradient is clearly downward sloping.

Lower unemployment among more experienced and educated workers means that a given unemployment rate (say 4%) achieved in two different years can signify different things about the labor market if the composition of the workforce has changed. An unemployment rate of 4% might signal a moderate degree of slack for a highly educated and more experienced workforce, but may signal a very tight labor market for a workforce that is younger and with fewer credentials. Figure A shows the actual unemployment rate and the composition-adjusted unemployment rate for two time periods: 1997–2000 and 2016–2019. Both periods saw unemployment below 5%. In the first period, the difference between actual and composition-adjusted unemployment is trivial (essentially by construction—we fix the demographic composition of the workforce at its 1995 level, as described in the note to the figure). By the 2016–2019 period, the composition-adjusted unemployment rate is nearly 0.3 percentage points higher. In essence, after controlling for age and education, the unemployment rate today has to be roughly 0.3 percentage points lower to signify the same level of labor market slack as it did during the late 1990s recovery. We also adjusted unemployment by race, ethnicity, and gender (not shown in the figure), but this changed the composition-adjusted unemployment rates only trivially compared with the effects of age and experience.

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Posted January 31, 2020 at 10:27 am by Ben Zipperer

On EITC Awareness Day, remember that the EITC and minimum wage work together to raise incomes

Today is Earned Income Tax Credit (EITC) Awareness Day, an effort to make low-income taxpayers aware of the tax credit that provides an important boost to low- and moderate-income families. It also provides the opportunity to address a common misconception around the EITC.

Policy discussions sometimes describe EITC expansions and minimum wage increases as alternative, competing policies for helping low-income workers. But, as economist Jesse Rothstein and I explain in a new report, this framing is incorrect. The two policies are actually complementary. A minimum wage increase and EITC expansion are more effective together than either is on its own.

Federal, state, and local increases in minimum wages have raised the incomes of low-wage workers and their families. The best published scholarship estimates that a $12 an hour minimum wage in 2017—very similar in real terms to current proposals for a gradual increase to a $15 an hour federal minimum wage—would have lowered the number of individuals living in poverty by six million, with disproportionately large effects for people of color.

In contrast, the EITC is a refundable tax credit available to low-income families who have positive earned income: Eligible households receive a net tax refund that supplements their earnings. In 2018, over 22 million working families and individuals received an average credit of nearly $3,200. Like the minimum wage, a large body of research indicates that the EITC reduces poverty, and the tax credit also improves health and educational outcomes. In addition, the EITC can also raise total incomes above the low floor guaranteed by the minimum wage in many parts of the country. The current EITC refund adds 39%—or about $5,800—to the pretax earnings of a single parent with two children working full-time at the federal minimum wage.

Read more

Posted January 30, 2020 at 5:38 pm by Robert E. Scott

Wilbur Ross’s comments and Trump administration trade policies offer few answers for growing, job-destroying China trade deficit

This morning, Commerce Secretary Wilbur Ross claimed that the coronavirus outbreak in China “will help accelerate the return of jobs to North America.” This comment is not only cruel and inhumane, but it’s also a testament to just how little the Trump administration understands about America’s trade problems and how to solve them. Even the administration’s less off-the-cuff plans for rebuilding U.S. manufacturing have little chance of working. For example, as I noted previously, President Trump’s “phase one” trade deal with China is unlikely to significantly reduce the massive U.S. job losses that have resulted from growing U.S. trade deficits with China.

A new EPI analysis shows that growing trade deficits with China cost 3.7 million U.S. jobs between 2001 and 2018, including 700,000 jobs lost in the first two years of the Trump administration. Job losses occurred in all 50 states, every congressional district, and every industry. Manufacturing was hit the hardest, with 2.8 million jobs lost. Given this toll and the Trump administration’s rhetoric, you’d think they’d look for real solutions. Instead, Trump appears desperate to sign his deal, any deal, so that he can claim progress on reducing trade deficits. But he is shortsighted on trade because his arrangement with Beijing ignores at least two key problems. First, it assumes that China will suddenly obey trade rules and commitments it has never previously respected. And second, it limits Washington’s ability to respond to the currency misalignment currently hampering U.S. exporters.

Read more

Posted January 27, 2020 at 3:18 pm by Heidi Shierholz

Weakened labor movement leads to rising economic inequality

The basic facts about inequality in the United States—that for most of the last 40 years, pay has stagnated for all but the highest paid workers and inequality has risen dramatically—are widely understood. What is less well-known is the role the decline of unionization has played in those trends.?The share of workers covered by a collective bargaining agreement dropped from?27 percent to 11.6 percent between 1979 and 2019, meaning the union coverage rate is now less than half where it was 40 years ago.

Research shows that this?de-unionization accounts for a sizable share of the growth in inequality over that period—around 13–20 percent for women and 33–37 percent for men. Applying these shares to annual earnings data reveals that working people are now losing on the order of?$200 billion per year?as a result of the erosion of union coverage over the last four decades—with that money being redistributed upward, to the rich.

The good news is that restoring union coverage—and strengthening workers’ abilities to join together to improve their wages and working conditions in other ways—is therefore likely to put at least $200 billion per year into the pockets of working people. These changes could happen through organizing and policy reform. Policymakers have introduced legislation, the?Protecting the Right to Organize (PRO) Act, that would significantly reform current labor law. Building on the reforms in the PRO Act, the?Clean Slate for Worker Power Project?proposes further transformation of labor law, with innovative ideas to create balance in our economy.?Read more

Posted January 24, 2020 at 2:27 pm by Richard Rothstein

The Trump administration’s new housing rules will worsen segregation

In “The Neighborhoods We Will Not Share,” an article published online at The New York Times, I describe how the Trump administration has proposed a rule that will make it virtually impossible to challenge many policies that reinforce residential racial segregation.

This is no small matter. Segregation underlies many of our most serious social problems. Educators can’t seem to make significant progress in their efforts to close the racial gap in academic achievement that persists in large part because we enroll the most socially and economically disadvantaged children in poorly resourced schools, located in poorly resourced neighborhoods. Health disparities by race stem, in part, from so many African Americans consigned to areas where they have less access to healthy air and healthy foods, and are more subject to stressful conditions. Black men’s high and unjustifiable rates of incarceration depend significantly on their concentration in segregated neighborhoods without good employment opportunities in the formal economy or the transportation to access good jobs. And segregation prevents us from overcoming our very dangerous and frightening political polarization, highly correlated with race. How can we ever develop the common national identity essential to the preservation of our democracy if so many African Americans and whites live so far from each other that we have no ability to understand and empathize with each other’s life experiences?

In my book The Color of Law, I described how 20th century federal, state, and local policies—explicitly racial—created, reinforced, and sustained racial boundaries in every metropolitan area in the United States. These unconstitutional government activities still predict today’s segregated landscape. For example, the explicit exclusion of black working class families from single-family homes, for which white working class family purchases were subsidized, bears substantial responsibility for the black-white wealth gap—while black family incomes are about about 60% of white family incomes, the median black household wealth is less than 10%of white household wealth, an enormous disparity that was propelled by the equity appreciation of white property while African Americans were consigned to neighborhoods where no similar appreciation occurred. The wealth gap predicts much of our contemporary racial inequality.Read more

Posted January 17, 2020 at 5:09 pm by Josh Bivens

Yes, David Brooks, there really is a class war

New York Times columnist David Brooks, in an article sub-titled “No, Virginia, there is no class war,” recently trotted out an old argument about why wage growth has been so sluggish for so many U.S. workers for so long: they’re just not very good workers. Specifically, he argues that “wages are still mostly determined by skills and productivity.” Ergo, if there is growing inequality in wages, it must be driven by inequality in workers’ own productivity.

But the evidence he cites is totally unconvincing on this.

First, he notes that wages for lower-wage workers have recently grown more rapidly than for middle-wage workers. But it’s been shown again and again that this is driven in large-part by those states that have raised their minimum wages. It’s also been shown that tighter labor markets disproportionately benefit the lowest-paid workers. The argument that changes in relative bargaining power and economic leverage have been the prime mover of wage trends in recent decades is not an argument that wages can never rise, period. When policies change—like minimum wages increase and the Fed allows labor markets to tighten without slamming on the interest rate brakes—good things happen. We just need to change a lot more policies.

Second, he cites a study that looks at wage and productivity growth in high-skill and low-skill industries between 1989 and 2017. The first odd bit of this evidence is that the wage growth he reports the study claims for high and low-skill industries is essentially identical: 26 percent versus 24 percent. The second odd bit is that this means even high-skill industries only gave average annual wage increases of 0.8 percent over that time, even as aggregate productivity grew by almost twice as fast over that time (about 1.4 percent annually). Finally, and most important, using industry-level productivity growth to infer anything about the productivity of individuals working in these industries cannot be done. To put it most simply, productivity growth within an industry can occur because each input used in production gets more productive, or, there is a shift in the mix of inputs. This might sound wonky but I’ll explain a bit more in the next paragraph:Read more

Posted January 16, 2020 at 8:00 am by Jhacova Williams

This MLK Day, remember Emmett Till and voter suppression

“We can never be satisfied as long as the Negro is the victim of the unspeakable horrors of police brutality…We cannot be satisfied as long as the Negro in Mississippi cannot vote and the Negro in New York believes he has nothing for which to vote.” —Martin Luther King Jr.

Two historic events occurred in American history in different years on August 28. In 1955, Emmett Till was lynched in Mississippi—and in 1963, Martin Luther King Jr. addressed the nation from Washington, D.C., with his I Have a Dream” speech. While both events have been ingrained in many Americans’ memories, few are aware that they share a common link between brutality and voter suppression.

The prevailing belief of the circumstances surrounding 14-year-old Emmett Till’s killing is that he was accused of whistling at a white woman. Yet, the truth is he was lynched as an act of voter intimidation. After being acquitted by an all-white jury, one of Emmett Till’s killers confessed to the lynching and gave voting as the first reason he killed Emmett.

“But I just decided it was time a few people got put on notice. As long as I live and can do anything about it, [racial slur] are gonna stay in their place. [Racial slur] ain’t gonna vote where I live. If they did, they’d control the government.”—J.W. “Big Milam”

Although Emmett Till was brutally lynched 65 years ago, historical events like his killing continue to suppress the political participation of black Americans. Using data on historical lynchings and present-day voter registration of blacks in southern states, Figure A shows that blacks who live in counties that experienced more lynchings in the past are less likely to register to vote today.Read more

Posted January 13, 2020 at 11:16 pm by Robert E. Scott

China trade deal will not restore 3.7 million U.S. jobs lost since China entered the WTO in 2001

The White House has announced plans for a ceremony to sign a “phase one” trade deal with China on Wednesday, although details of the agreement have yet to be announced. As one analyst noted, this deal may not amount to more than a hill of soybeans. It is unlikely to significantly reduce massive U.S. job losses due to growing U.S. trade deficits—the difference between imports and exports—which are dominated by trade deficits in manufactured goods. As shown in a forthcoming EPI report to be released later this month, growing U.S. trade deficits with China eliminated 3.7 million U.S. jobs between 2001 and 2018 alone (see Figure A), including 2.8 million jobs in manufacturing (details will be provided in the forthcoming report).

Figure A

U.S. jobs displaced by the growing goods trade deficit with China since 2001 (in thousands of jobs)

Year ?Jobs displaced (thousands)
2001 0.0?
2002 218.1
2003 445.7
2004 852.1
2005 1,306.1
2006 1,651.5
2007 1,964.5
2008 2,030.4
2009 1,686.2
2010 2,295.0
2011 2,616.8
2012 2,764.6
2013 2,812.3
2014 2,993.2
2015 3,197.9
2016 2,965.2
2017 3,339.8
2018 3,704.7
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Source:?Authors’ analysis of U.S. Census Bureau American Community Survey data, Bureau of Labor Statistics Employment Projections program data, and U.S. International Trade Commission Interactive Tariff and Trade DataWeb database. Adapted from Rob Scott and Zane Mokhiber, Growing China Trade Deficits Cost 3.7 Million American Jobs between 2001 and 2018, Economic Policy Institute, forthcoming.

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Trade deficits and jobs losses with China continued to grow during the first two years of the Trump administration—despite the administration’s heated rhetoric and imposition of tariffs. The U.S. trade deficit with China rose from $347 billion in 2016 to $420 billion in 2018, an increase of 21.0%. U.S. jobs displaced by those China trade deficits increased from nearly 3.0 million jobs lost in 2016 to 3.7 million jobs lost in 2018, an increase of more than 700,000 jobs lost or displaced in the first two years of the Trump administration.

Although the bilateral trade deficit with China has declined in 2019 (through November), the overall U.S. trade deficit in non-oil goods, which is dominated by trade in manufactured and farm products, has continued to increase, suggesting that trade diversion has grown in importance. These are important topics for future research.

While growing exports support some American jobs, growing imports eliminate existing jobs and prevent new job creation—as imports displace goods that otherwise would have been made in the United States by domestic workers. As a result, growing trade deficits result in increasing U.S. job losses. The top half of Table 1 shows just how much the trade deficit has grown: The U.S. trade deficit with China increased from $83.0 billion in 2001 to $420 billion in 2018. While U.S. exports to China increased in this period, growing exports were overwhelmed by the massive growth of imports from China, which increased by $437 billion in this period.?Read more

Posted January 10, 2020 at 4:19 pm by Elise Gould

The labor market continues to improve in 2019 as women surpass men in payroll employment, but wage growth slows

Today’s Bureau of Labor Statistics (BLS) jobs report provides the opportunity to look at 2019 as a whole and in comparison with previous years. As the recovery has strengthened over the last several years, we’ve generally seen improvements in most measures of the labor market: employment, unemployment, and wage growth. These measures tell a consistent story—an economy on its way to full employment, but not there yet. Wage growth continues to be the lagging indicator, which is not as strong as would be expected given the health of the labor market and actually slowed through much of 2019.

Payroll employment growth in December was 145,000, bringing average job growth in 2019 to 176,000. This is a bit softer than the 223,000 average for 2018, but still more than enough to keep up with growth in the working-age population and pull in thousands of workers off the sidelines every month.

Figure A

Average monthly total nonfarm employment growth, 2006–2019

Year Average monthly total nonfarm employment growth
2006 175
2007 95
2008 -296
2009 -421
2010 86
2011 173
2012 181
2013 192
2014 251
2015 227
2016 193
2017 179
2018 223
2019 176
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Source:?Data are from the Current Employment Statistics (CES) series of the Bureau of Labor Statistics and are subject to occasional revisions. This chart was based on data accessed in January 2020.

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For the first time in nearly 10 years, women’s share of payroll employment has just surpassed that of men’s. The figure below shows payroll employment for both men and women since 2000. From 2000 to 2007, men’s share of total employment was about 1–2% higher than women’s. In the recession, employment fell markedly in male-dominated professions—notably manufacturing and construction—and women’s share of employment rose in kind. Since 2010, women’s and men’s employment have both increased, with men’s growing faster than women’s initially. In the last couple of years, women’s payroll employment has grown just a bit faster than men’s.

We can turn again to a sector approach as one explanation for why women’s employment has now just surpassed men’s in December. Men make up 77% of employment in construction and manufacturing combined. Coincidentally, women make up 77% of employment in education and health services. Between 2018 and 2019, construction and manufacturing together increased by 356,000, but education and health services employment increased much more—by 603,000. Furthermore, manufacturing employment has faltered late in the year, helping women’s employment eke ahead of men’s in December.

It is important to note that in absolute terms the shares of men’s and women’s employment haven’t changed that dramatically. But, it holds true that women’s payroll employment is now 50.04% of the total, the first time it has been a majority since the depths of the (construction and manufacturing-led) Great Recession.

Figure B

Women’s share of payroll employment ekes ahead of men’s in December 2019: Payroll employment, men and women, 2000 to 2019

Date Payroll employment, women Payroll employment, men
Jan-2000 62861 68159
Feb-2000 62936 68200
Mar-2000 63087 68522
Apr-2000 63294 68606
May-2000 63499 68619
Jun-2000 63457 68622
Jul-2000 63444 68803
Aug-2000 63521 68719
Sep-2000 63635 68729
Oct-2000 63624 68741
Nov-2000 63755 68815
Dec-2000 63791 68931
Jan-2001 63863 68849
Feb-2001 63922 68882
Mar-2001 63894 68867
Apr-2001 63964 68511
May-2001 63995 68431
Jun-2001 63951 68361
Jul-2001 64022 68165
Aug-2001 63979 68064
Sep-2001 63958 67833
Oct-2001 63790 67678
Nov-2001 63676 67482
Dec-2001 63619 67378
Jan-2002 63645 67223
Feb-2002 63622 67130
Mar-2002 63627 67105
Apr-2002 63593 67043
May-2002 63569 67078
Jun-2002 63582 67113
Jul-2002 63572 67032
Aug-2002 63621 66982
Sep-2002 63573 66951
Oct-2002 63600 67043
Nov-2002 63630 67002
Dec-2002 63574 66914
Jan-2003 63592 67004
Feb-2003 63604 66857
Mar-2003 63489 66757
Apr-2003 63501 66693
May-2003 63472 66738
Jun-2003 63429 66780
Jul-2003 63421 66786
Aug-2003 63308 66859
Sep-2003 63460 66819
Oct-2003 63523 66950
Nov-2003 63551 66939
Dec-2003 63604 67001
Jan-2004 63645 67142
Feb-2004 63666 67178
Mar-2004 63773 67383
Apr-2004 63873 67553
May-2004 63996 67714
Jun-2004 64036 67771
Jul-2004 64037 67827
Aug-2004 64007 67948
Sep-2004 64135 67977
Oct-2004 64287 68179
Nov-2004 64327 68194
Dec-2004 64397 68247
Jan-2005 64512 68279
Feb-2005 64611 68439
Mar-2005 64662 68510
Apr-2005 64823 68713
May-2005 64895 68811
Jun-2005 65025 68932
Jul-2005 65121 69193
Aug-2005 65171 69346
Sep-2005 65276 69307
Oct-2005 65214 69459
Nov-2005 65321 69691
Dec-2005 65327 69841
Jan-2006 65394 70052
Feb-2006 65466 70287
Mar-2006 65552 70511
Apr-2006 65587 70634
May-2006 65546 70715
Jun-2006 65577 70765
Jul-2006 65811 70727
Aug-2006 65938 70775
Sep-2006 66064 70796
Oct-2006 66180 70690
Nov-2006 66317 70765
Dec-2006 66468 70800
Jan-2007 66585 70908
Feb-2007 66733 70840
Mar-2007 66835 70975
Apr-2007 66916 70944
May-2007 67058 70954
Jun-2007 67135 70953
Jul-2007 67174 70881
Aug-2007 67273 70759
Sep-2007 67352 70762
Oct-2007 67417 70773
Nov-2007 67484 70815
Dec-2007 67623 70786
Jan-2008 67630 70792
Feb-2008 67662 70678
Mar-2008 67703 70589
Apr-2008 67683 70373
May-2008 67671 70201
Jun-2008 67627 70079
Jul-2008 67628 69880
Aug-2008 67480 69749
Sep-2008 67349 69420
Oct-2008 67166 69122
Nov-2008 67018 68543
Dec-2008 66805 68052
Jan-2009 66554 67520
Feb-2009 66317 67015
Mar-2009 66068 66461
Apr-2009 65822 66013
May-2009 65704 65787
Jun-2009 65550 65476
Jul-2009 65423 65262
Aug-2009 65348 65153
Sep-2009 65239 65020
Oct-2009 65173 64888
Nov-2009 65128 64945
Dec-2009 65063 64741
Jan-2010 65082 64725
Feb-2010 65006 64709
Mar-2010 65072 64823
Apr-2010 65076 65056
May-2010 65296 65370
Jun-2010 65168 65362
Jul-2010 65080 65362
Aug-2010 65026 65411
Sep-2010 64956 65417
Oct-2010 65047 65595
Nov-2010 65085 65680
Dec-2010 65106 65733
Jan-2011 65115 65744
Feb-2011 65157 65915
Mar-2011 65237 66067
Apr-2011 65391 66234
May-2011 65364 66356
Jun-2011 65443 66512
Jul-2011 65466 66550
Aug-2011 65484 66654
Sep-2011 65558 66816
Oct-2011 65654 66924
Nov-2011 65712 66998
Dec-2011 65777 67137
Jan-2012 65952 67317
Feb-2012 66061 67470
Mar-2012 66147 67622
Apr-2012 66187 67665
May-2012 66289 67662
Jun-2012 66316 67707
Jul-2012 66402 67774
Aug-2012 66463 67883
Sep-2012 66543 67992
Oct-2012 66617 68076
Nov-2012 66704 68147
Dec-2012 66791 68297
Jan-2013 66876 68407
Feb-2013 66956 68606
Mar-2013 67067 68631
Apr-2013 67189 68701
May-2013 67265 68849
Jun-2013 67332 68963
Jul-2013 67449 68951
Aug-2013 67593 69049
Sep-2013 67688 69143
Oct-2013 67767 69289
Nov-2013 67920 69403
Dec-2013 67967 69423
Jan-2014 67977 69590
Feb-2014 68054 69681
Mar-2014 68156 69829
Apr-2014 68309 70003
May-2014 68395 70138
Jun-2014 68491 70366
Jul-2014 68567 70517
Aug-2014 68657 70615
Sep-2014 68833 70750
Oct-2014 68964 70877
Nov-2014 69097 71030
Dec-2014 69246 71150
Jan-2015 69327 71282
Feb-2015 69478 71379
Mar-2015 69538 71396
Apr-2015 69660 71574
May-2015 69831 71722
Jun-2015 69930 71793
Jul-2015 70052 71964
Aug-2015 70108 72030
Sep-2015 70202 72069
Oct-2015 70378 72232
Nov-2015 70503 72342
Dec-2015 70646 72479
Jan-2016 70762 72453
Feb-2016 70950 72497
Mar-2016 71100 72581
Apr-2016 71213 72679
May-2016 71296 72611
Jun-2016 71454 72735
Jul-2016 71672 72853
Aug-2016 71783 72877
Sep-2016 71931 72999
Oct-2016 71968 73090
Nov-2016 72017 73211
Dec-2016 72133 73310
Jan-2017 72208 73487
Feb-2017 72285 73551
Mar-2017 72327 73636
Apr-2017 72399 73777
May-2017 72453 73851
Jun-2017 72546 73987
Jul-2017 72683 74054
Aug-2017 72761 74163
Sep-2017 72795 74147
Oct-2017 72885 74317
Nov-2017 73012 74410
Dec-2017 73098 74498
Jan-2018 73234 74533
Feb-2018 73421 74676
Mar-2018 73520 74759
Apr-2018 73646 74829
May-2018 73837 74908
Jun-2018 73999 75008
Jul-2018 74091 75094
Aug-2018 74229 75238
Sep-2018 74329 75246
Oct-2018 74480 75372
Nov-2018 74605 75443
Dec-2018 74724 75551
Jan-2019 74890 75697
Feb-2019 74994 75649
Mar-2019 75119 75677
Apr-2019 75233 75779
May-2019 75329 75745
Jun-2019 75381 75871
Jul-2019 75549 75869
Aug-2019 75668 75969
Sep-2019 75832 75998
Oct-2019 75958 76024
Nov-2019 76107 76131
Dec-2019 76246 76137
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Source: EPI analysis of Bureau of Labor Statistics' Current Employment Statistics public data series

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Turning to the household survey, the labor market continues to not only absorb population growth, but also chip away at the slack remaining in the labor market—namely workers who continue to be sidelined and who I expect will enter or re-enter the labor market as opportunities for jobs and better pay expand.?As the unemployment rate has continued to fall between 2018 and 2019, labor force participation has increased as people re-enter the labor market and find jobs. Since December 2018, the unemployment rate dropped 0.4 percentage points (3.9% to 3.5%) while the employment-to-population ratio, or the share of the population with a job, rose 0.4 percentage points (60.6% to 61.0%). This means the unemployment rate over the last year fell for the right reasons—not because workers gave up looking, but because more would-be workers actually found jobs.

Read more

Posted January 9, 2020 at 10:28 am by Elise Gould

What to watch on jobs day: An assessment of the 2019 labor market

The last Bureau of Labor Statistics (BLS) jobs report of 2019 comes out on Friday, giving us a chance to step back and look at how working people fared over the entire year. The report also marks the 12th anniversary of the?official start of the Great Recession. My expectation is that the December data will confirm that the economy has nearly recovered its immediate pre-Great Recession health—the last year before the Great Recession hit. Wage growth, which slowed over the last year, is a notable exception.

However, as I have often noted, 2007 should not be considered a benchmark for a fully healthy economy for America’s workers. Almost all labor market measures were notably weaker in 2007 than they were at the previous business cycle peak in 2000. There was very little reason to think that the U.S. economy in 2007 was at full employment. If one looks at the stronger business cycle peak of 2000 as a more appropriate benchmark, the economy in 2019 looks even further from full employment. Many working people are still not seeing the recovery reflected in their paychecks—and the economy will not be at genuine full employment until employers are consistently offering workers meaningfully higher wages.

In this blog post—and Friday when the December numbers come out—I’m going to look at average payroll employment growth over the last several years. Because there is always a bit of volatility in the monthly data—especially in the household series that has a smaller sample size—taking a year-long approach allows us to smooth out the bumps and take stock of the key measures: payroll employment growth, the unemployment rate, the employment-to-population ratio, and nominal wage growth.

The figure below shows average nonfarm employment growth for 2007–2018 and for the first 11 months of 2019. With an average of 180,000 new jobs being added each month, job growth in 2019 is a bit softer than 2018 and more in line with what we saw in 2017. This pickup in 2018 can be attributed to the?shift in federal policy from austerity to stimulus?in the form of both tax cuts and an increase in government spending. In particular, Congress boosted spending by almost $150 billion, contributing significantly to economic growth in 2018. But, in 2019, spending held steady at $150 billion, meaning there was no additional government spending to continue stimulating demand, and we saw a mild softening of employment growth.

Figure A

Average monthly total nonfarm employment growth, 2006–2019

Year Average monthly total nonfarm employment growth
2006 175
2007 95
2008 -296
2009 -421
2010 86
2011 173
2012 181
2013 192
2014 251
2015 227
2016 193
2017 179
2018 223
2019 180
ChartData Download data

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Note: Because full 2019 monthly employment data are not yet available, the chart compares average monthly job growth between January and November for 2019.

Source:?Data are from the Current Employment Statistics (CES) series of the Bureau of Labor Statistics and are subject to occasional revisions. This chart was based on data accessed in January 2020.

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At the current pace of growth, however, the labor market continues to not only absorb population growth, but also chip away at the slack remaining in the labor market—namely workers who continue to be sidelined and who I expect will enter or re-enter the labor market as opportunities for jobs and better pay expand. As it turns out (and what we’ve long argued), workers who left or never entered the labor force during the Great Recession and its aftermath were not necessarily permanently sidelined, but have systematically been returning to the labor market as job opportunities have strengthened. Over the last few years, the newly employed have been coming both from the ranks of the unemployed as well as from outside the labor force, those who were not actively seeking work the month prior to finding a job. In fact, as the figure below illustrates, the share of newly employed workers who did not look for work the previous month is at a historic high. About three-fourths of newly employed workers are coming from outside the labor force.

Figure B

Share of newly employed workers who said that they were not actively searching for work in the previous month

date Share of newly employed workers who said that they were not actively searching for work in the previous month
Apr-1990 61.9%
May-1990 62.6%
Jun-1990 62.0%
Jul-1990 62.0%
Aug-1990 61.6%
Sep-1990 62.3%
Oct-1990 61.0%
Nov-1990 61.2%
Dec-1990 60.4%
Jan-1991 59.9%
Feb-1991 59.0%
Mar-1991 58.5%
Apr-1991 57.7%
May-1991 57.6%
Jun-1991 57.2%
Jul-1991 58.0%
Aug-1991 57.8%
Sep-1991 57.7%
Oct-1991 57.3%
Nov-1991 56.9%
Dec-1991 57.0%
Jan-1992 56.8%
Feb-1992 57.1%
Mar-1992 57.1%
Apr-1992 57.2%
May-1992 57.3%
Jun-1992 56.6%
Jul-1992 56.4%
Aug-1992 56.1%
Sep-1992 55.9%
Oct-1992 55.7%
Nov-1992 55.8%
Dec-1992 56.1%
Jan-1993 56.6%
Feb-1993 57.7%
Mar-1993 58.3%
Apr-1993 58.4%
May-1993 58.2%
Jun-1993 58.1%
Jul-1993 57.5%
Aug-1993 57.5%
Sep-1993 58.0%
Oct-1993 58.9%
Nov-1993 58.5%
Dec-1993 58.3%
Jan-1994 58.8%
Feb-1994 59.2%
Mar-1994 59.1%
Apr-1994 58.7%
May-1994 58.3%
Jun-1994 58.5%
Jul-1994 58.6%
Aug-1994 59.0%
Sep-1994 59.1%
Oct-1994 59.8%
Nov-1994 60.1%
Dec-1994 60.3%
Jan-1995 60.4%
Feb-1995 59.5%
Mar-1995 59.7%
Apr-1995 59.7%
May-1995 59.2%
Jun-1995 59.5%
Jul-1995 59.5%
Aug-1995 60.0%
Sep-1995 60.2%
Oct-1995 59.9%
Nov-1995 60.6%
Dec-1995 59.9%
Jan-1996 59.8%
Feb-1996 60.3%
Mar-1996 60.7%
Apr-1996 61.0%
May-1996 60.7%
Jun-1996 60.8%
Jul-1996 61.5%
Aug-1996 60.8%
Sep-1996 60.9%
Oct-1996 60.2%
Nov-1996 60.6%
Dec-1996 59.6%
Jan-1997 59.1%
Feb-1997 58.9%
Mar-1997 60.3%
Apr-1997 61.4%
May-1997 61.8%
Jun-1997 61.1%
Jul-1997 60.4%
Aug-1997 61.3%
Sep-1997 61.9%
Oct-1997 62.5%
Nov-1997 62.7%
Dec-1997 62.8%
Jan-1998 63.3%
Feb-1998 62.7%
Mar-1998 62.9%
Apr-1998 62.4%
May-1998 63.5%
Jun-1998 63.2%
Jul-1998 64.2%
Aug-1998 64.0%
Sep-1998 65.2%
Oct-1998 65.1%
Nov-1998 65.1%
Dec-1998 64.9%
Jan-1999 65.6%
Feb-1999 65.5%
Mar-1999 64.2%
Apr-1999 65.3%
May-1999 66.1%
Jun-1999 67.4%
Jul-1999 66.4%
Aug-1999 65.7%
Sep-1999 65.3%
Oct-1999 65.5%
Nov-1999 65.3%
Dec-1999 65.1%
Jan-2000 64.4%
Feb-2000 65.4%
Mar-2000 65.7%
Apr-2000 65.9%
May-2000 65.6%
Jun-2000 65.9%
Jul-2000 65.4%
Aug-2000 65.5%
Sep-2000 65.6%
Oct-2000 66.5%
Nov-2000 67.4%
Dec-2000 68.1%
Jan-2001 69.0%
Feb-2001 68.6%
Mar-2001 67.9%
Apr-2001 66.9%
May-2001 65.8%
Jun-2001 65.3%
Jul-2001 65.7%
Aug-2001 66.2%
Sep-2001 66.6%
Oct-2001 65.5%
Nov-2001 64.4%
Dec-2001 62.9%
Jan-2002 62.6%
Feb-2002 62.3%
Mar-2002 61.7%
Apr-2002 61.9%
May-2002 62.8%
Jun-2002 64.4%
Jul-2002 64.5%
Aug-2002 64.0%
Sep-2002 63.1%
Oct-2002 63.1%
Nov-2002 63.7%
Dec-2002 64.1%
Jan-2003 64.2%
Feb-2003 64.2%
Mar-2003 64.5%
Apr-2003 64.3%
May-2003 63.7%
Jun-2003 63.5%
Jul-2003 63.1%
Aug-2003 63.2%
Sep-2003 63.4%
Oct-2003 64.3%
Nov-2003 64.7%
Dec-2003 63.7%
Jan-2004 63.6%
Feb-2004 63.4%
Mar-2004 64.9%
Apr-2004 64.3%
May-2004 64.3%
Jun-2004 63.7%
Jul-2004 64.2%
Aug-2004 64.5%
Sep-2004 64.1%
Oct-2004 64.2%
Nov-2004 64.0%
Dec-2004 64.4%
Jan-2005 64.6%
Feb-2005 64.8%
Mar-2005 64.9%
Apr-2005 65.1%
May-2005 65.8%
Jun-2005 66.1%
Jul-2005 66.6%
Aug-2005 65.9%
Sep-2005 66.5%
Oct-2005 66.2%
Nov-2005 66.0%
Dec-2005 65.9%
Jan-2006 65.8%
Feb-2006 67.3%
Mar-2006 67.3%
Apr-2006 67.6%
May-2006 67.3%
Jun-2006 67.2%
Jul-2006 66.7%
Aug-2006 66.4%
Sep-2006 65.9%
Oct-2006 66.9%
Nov-2006 67.6%
Dec-2006 68.3%
Jan-2007 68.0%
Feb-2007 67.0%
Mar-2007 66.5%
Apr-2007 65.8%
May-2007 66.2%
Jun-2007 67.6%
Jul-2007 67.7%
Aug-2007 67.6%
Sep-2007 66.9%
Oct-2007 67.0%
Nov-2007 67.5%
Dec-2007 66.6%
Jan-2008 66.4%
Feb-2008 65.4%
Mar-2008 65.6%
Apr-2008 64.7%
May-2008 65.1%
Jun-2008 64.9%
Jul-2008 65.3%
Aug-2008 64.2%
Sep-2008 62.9%
Oct-2008 62.1%
Nov-2008 61.9%
Dec-2008 62.3%
Jan-2009 62.2%
Feb-2009 61.6%
Mar-2009 60.8%
Apr-2009 59.8%
May-2009 59.6%
Jun-2009 58.3%
Jul-2009 57.5%
Aug-2009 57.0%
Sep-2009 56.8%
Oct-2009 57.6%
Nov-2009 56.7%
Dec-2009 57.7%
Jan-2010 57.9%
Feb-2010 58.8%
Mar-2010 58.7%
Apr-2010 57.4%
May-2010 56.4%
Jun-2010 56.6%
Jul-2010 57.2%
Aug-2010 58.4%
Sep-2010 58.8%
Oct-2010 58.9%
Nov-2010 58.9%
Dec-2010 58.4%
Jan-2011 59.1%
Feb-2011 59.5%
Mar-2011 60.1%
Apr-2011 60.4%
May-2011 60.2%
Jun-2011 59.7%
Jul-2011 59.8%
Aug-2011 59.6%
Sep-2011 60.6%
Oct-2011 59.8%
Nov-2011 59.8%
Dec-2011 59.1%
Jan-2012 59.2%
Feb-2012 59.1%
Mar-2012 59.4%
Apr-2012 60.3%
May-2012 60.9%
Jun-2012 61.6%
Jul-2012 61.8%
Aug-2012 62.3%
Sep-2012 62.3%
Oct-2012 62.0%
Nov-2012 61.8%
Dec-2012 62.5%
Jan-2013 62.2%
Feb-2013 61.5%
Mar-2013 61.6%
Apr-2013 63.1%
May-2013 63.5%
Jun-2013 63.2%
Jul-2013 62.3%
Aug-2013 62.9%
Sep-2013 63.5%
Oct-2013 64.3%
Nov-2013 64.1%
Dec-2013 63.6%
Jan-2014 63.9%
Feb-2014 63.6%
Mar-2014 63.9%
Apr-2014 62.8%
May-2014 64.2%
Jun-2014 64.5%
Jul-2014 66.0%
Aug-2014 65.5%
Sep-2014 65.3%
Oct-2014 64.9%
Nov-2014 65.3%
Dec-2014 65.8%
Jan-2015 67.2%
Feb-2015 67.8%
Mar-2015 68.4%
Apr-2015 68.0%
May-2015 68.5%
Jun-2015 68.2%
Jul-2015 69.0%
Aug-2015 68.6%
Sep-2015 68.8%
Oct-2015 68.6%
Nov-2015 68.6%
Dec-2015 69.0%
Jan-2016 68.6%
Feb-2016 69.8%
Mar-2016 70.5%
Apr-2016 70.7%
May-2016 69.7%
Jun-2016 69.1%
Jul-2016 68.9%
Aug-2016 69.4%
Sep-2016 69.2%
Oct-2016 68.2%
Nov-2016 67.7%
Dec-2016 68.8%
Jan-2017 69.4%
Feb-2017 69.1%
Mar-2017 68.8%
Apr-2017 69.4%
May-2017 70.2%
Jun-2017 70.7%
Jul-2017 70.2%
Aug-2017 70.7%
Sep-2017 70.4%
Oct-2017 70.7%
Nov-2017 70.8%
Dec-2017 70.7%
Jan-2018 71.2%
Feb-2018 71.0%
Mar-2018 70.6%
Apr-2018 70.7%
May-2018 71.2%
Jun-2018 72.6%
Jul-2018 73.1%
Aug-2018 73.0%
Sep-2018 72.9%
Oct-2018 73.0%
Nov-2018 73.3%
Dec-2018 73.1%
Jan-2019 72.5%
Feb-2019 72.4%
Mar-2019 71.6%
Apr-2019 71.7%
May-2019 72.7%
Jun-2019 73.6%
Jul-2019 74.2%
Aug-2019 73.9%
Sep-2019 73.7%
Oct-2019 74.6%
Nov-2019 74.6%
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Note: Because of volatility in these data, the line reflects three month moving averages

Source: Bureau of Labor Statistics, Labor Force Flows: Unemployed to Employed (16 Years and Over) [LNS17100000], and Not in Labor Force to Employed (16 years and over) [LNS17200000], retrieved from FRED (Federal Reserve Bank of St. Louis).

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Further evidence of a steadily improving economy is the unemployment rate, which—after falling steadily for eight years from its peak in the fourth quarter of 2009—continued to fall through 2019 to a low of 3.5% in November, an average of 3.7% for the first 11 months of the year. It is now far below its Great Recession peak (10.0%), and significantly below its pre-Great Recession low of 4.4% in the spring of 2007. But despite today’s low water mark, there is still room for improvement. And evidence suggests that the unemployment rate may be overstating the strength of the labor market. The previous figure supports this claim, given that a record high share of newly employed workers are coming from outside the labor force and are not counted in the official measure of unemployment in the previous month, despite clearly being ready and willing to work.Read more

Posted January 8, 2020 at 4:32 pm by Jhacova Williams

College athletes and Ph.D. students both work for the university, but only one earns a salary

Beginning in January 2021, new rules will go into effect that will allow NCAA student-athletes to profit from the use of their names, images, and likeness. While the details of these new rules will require much deliberation among each NCAA division, one thing will not be considered—salaries for college athletes from the universities.

Why won’t college athletes be paid a salary?

Several reasons are floating around. One reason is the NCAA does not consider college athletes employees of the universities. Another reason is that these players are given a lot of perks. In a recent?Los Angeles Times article, Dan Radakovich, athletic director at Clemson University, argued against paying college athletes since they have access to “world-class facilities, world-class coaching, and incredible academic support.”

But there already exists a group of students who are employees of the university, have access to world-class facilities, teaching, and academic support, and no one calls them selfish when they receive their salaries. Who are these students? Ph.D. students.

Wait, are you saying Ph.D. students receive a salary?

Yes, because they work for the university. A large percentage of Ph.D. students are funded via fellowships or assistantships. Funding, which covers tuition and provides a stipend, varies across institutions and doctoral programs due to what can be viewed as “educational hierarchy.” Assistantships require that Ph.D. students’ work anywhere from 20 to 40 hours per week that include duties such as grading, managing labs, or lecturing. Additionally, doctoral students are awarded (or sometimes apply for) money that allows them to attend international or out-of-state conferences to present their research and network with others in their field.

In short, Ph.D. students sign a contract with an institution, agree to work a certain number of hours per week, maintain a certain GPA, and conduct research. In exchange, the university covers their tuition and pays them a salary. What do college football players do? Sign a contract (you may have seen signing day on ESPN), maintain a certain GPA, and kick butt on Saturday, which requires countless hours of practice! Additionally, their success can help recruit up to tens of thousands of students and generate millions of dollars for the institution.

Read more

Posted December 18, 2019 at 1:37 pm by David Cooper

Nearly 7 million workers will start the new year with higher wages

Note: This post was updated to clarify that Delaware’s minimum wage increase took effect on October 1, 2019.

At the start of the new year, minimum wages will have gone up in 22 states, lifting pay for 6.8 million workers across the country.i In total, workers affected by the increases will earn an extra $8.2 billion over the course of 2020 as a result of the changes. The increases range from a $0.10 inflation adjustment in Florida to $1.50 per hour raises in New Mexico and Washington. Affected workers who work year-round will see their annual pay go up between $150 and $1,700, on average, depending on the size of the minimum wage increase in their state.

The map and table below describe the increases in each state. Note that these estimates do not account for changes in local minimum wages separate from state law.ii There are 22 cities and counties with higher minimum wages taking effect on January 1, all of which can be found in EPI’s Minimum Wage Tracker. The estimates also do not include any “indirectly affected workers” making just above the new minimum wage who may receive raises as employers adjust their overall pay scales.

Figure A

State minimum wage increases will raise pay for nearly 7 million workers on January 1: States with minimum wage increases effective January 1, 2020, by type of increase

State Share of workforce directly benefiting Type of increase New minimum wage as of Jan. 1, 2020 Amount of increase Total workers directly benefiting Total increase in annual wages Average increase in annual earnings of year-round workers
Alabama -1 0.00%
Connecticut 0 0.00%
Georgia -1 0.00%
Hawaii 0 0.00%
Idaho -1 0.00%
Indiana -1 0.00%
Iowa -1 0.00%
Kansas -1 0.00%
Kentucky -1 0.00%
Louisiana -1 0.00%
Mississippi -1 0.00%
Nebraska 0 0.00%
Nevada 0 0.00%
New Hampshire -1 0.00%
North Carolina -1 0.00%
North Dakota -1 0.00%
Oklahoma -1 0.00%
Oregon 0 0.00%
Pennsylvania -1 0.00%
Rhode Island 0 0.00%
South Carolina -1 0.00%
Tennessee -1 0.00%
Texas -1 0.00%
Utah -1 0.00%
Virginia -1 0.00%
Washington D.C. 0 0.00%
West Virginia 0 0.00%
Wisconsin -1 0.00%
Wyoming -1 0.00%
Ohio 1 1.60% Inflation adjustment ?$?????????????????? 8.70 ?$?????? 0.15 ?????????????????????? 84,000 ?$??????? 12,303,000.00 ?$??????????????????????????????????????? 150.00
South Dakota 1 1.70% Inflation adjustment ?$?????????????????? 9.30 ?$?????? 0.20 ???????????????????????? 7,300 ?$?????????? 1,560,000.00 ?$??????????????????????????????????????? 220.00
Florida 1 1.90% Inflation adjustment ?$?????????????????? 8.56 ?$?????? 0.10 ???????????????????? 160,700 ?$??????? 23,766,000.00 ?$??????????????????????????????????????? 150.00
Montana 1 1.90% Inflation adjustment ?$?????????????????? 8.65 ?$?????? 0.15 ???????????????????????? 8,900 ?$?????????? 1,588,000.00 ?$??????????????????????????????????????? 180.00
Minnesota 1 2.40% Inflation adjustment ?$???????????????? 10.00 ?$?????? 0.14 ?????????????????????? 68,100 ?$??????? 11,030,000.00 ?$??????????????????????????????????????? 162.00
New Mexico 2 2.70% Legislation ?$?????????????????? 9.00 ?$?????? 1.50 ?????????????????????? 22,900 ?$??????? 20,736,000.00 ?$??????????????????????????????????????? 900.00
Alaska 1 3.00% Inflation adjustment ?$???????????????? 10.19 ?$?????? 0.30 ?????????????????????? 10,500 ?$?????????? 5,348,000.00 ?$??????????????????????????????????????? 510.00
Illinois 2 3.30% Legislation ?$?????????????????? 9.25 ?$?????? 1.00 ???????????????????? 192,900 ?$????? 173,533,000.00 ?$??????????????????????????????????????? 900.00
Michigan 2 3.40% Legislation ?$?????????????????? 9.65 ?$?????? 0.20 ???????????????????? 147,000 ?$??????? 32,907,000.00 ?$??????????????????????????????????????? 220.00
Delaware 2 4.00% Legislation ?$?????????????????? 9.25 ?$?????? 0.50 ?????????????????????? 17,200 ?$??????? 10,811,000.00 ?$??????????????????????????????????????? 630.00
New York 2 4.00% Legislation ?$???????????????? 11.80 ?$?????? 0.70 ???????????????????? 411,700 ?$????? 399,246,000.00 ?$??????????????????????????????????????? 970.00
Vermont 1 5.20% Inflation adjustment ?$???????????????? 10.96 ?$?????? 0.19 ?????????????????????? 16,200 ?$?????????? 3,932,000.00 ?$??????????????????????????????????????? 240.00
Missouri 3 5.40% Ballot measure ?$?????????????????? 9.45 ?$?????? 0.85 ???????????????????? 153,600 ?$????? 123,505,000.00 ?$??????????????????????????????????????? 800.00
Maryland 2 7.60% Legislation ?$???????????????? 11.00 ?$?????? 0.90 ???????????????????? 204,300 ?$????? 216,530,000.00 ?$??????????????????????????????????? 1,060.00
Arkansas 3 11.00% Ballot measure ?$???????????????? 10.00 ?$?????? 0.75 ???????????????????? 119,300 ?$????? 113,142,000.00 ?$??????????????????????????????????????? 950.00
Washington 3 11.60% Ballot measure ?$???????????????? 13.50 ?$?????? 1.50 ???????????????????? 386,000 ?$????? 655,972,000.00 ?$??????????????????????????????????? 1,700.00
New Jersey 2 11.70% Legislation ?$???????????????? 11.00 ?$?????? 1.00 ???????????????????? 460,400 ?$????? 480,308,000.00 ?$??????????????????????????????????? 1,040.00
Massachusetts 2 12.00% Legislation ?$???????????????? 12.75 ?$?????? 0.75 ???????????????????? 420,600 ?$????? 409,981,000.00 ?$??????????????????????????????????????? 970.00
Colorado 3 12.10% Ballot measure ?$???????????????? 12.00 ?$?????? 0.90 ???????????????????? 318,400 ?$????? 382,354,000.00 ?$??????????????????????????????????? 1,200.00
California 2 16.90% Legislation ?$???????????????? 13.00 ?$?????? 1.00 ???????????????? 2,950,200 ?$? 4,376,241,000.00 ?$??????????????????????????????????? 1,480.00
Maine 3 16.90% Ballot measure ?$???????????????? 12.00 ?$?????? 1.00 ???????????????????? 102,900 ?$????? 130,250,000.00 ?$??????????????????????????????????? 1,270.00
Arizona 3 17.70% Ballot measure ?$???????????????? 12.00 ?$?????? 1.00 ???????????????????? 511,900 ?$????? 653,915,000.00 ?$??????????????????????????????????? 1,300.00


Notes:?*The New York minimum wage changes take effect on December 31, 2019. Delaware's minimum wage increase took effect on October 1. “Legislation” indicates that the new rate was established by the legislature. “Ballot measure” indicates the new rate was set by a ballot initiative passed by voters. “Inflation adjustment” indicates that the new rate was established by a formula, reflecting the change in prices over the preceding year.

Directly affected workers will see their wages rise because the new minimum wage rate exceeds their current hourly pay. This does not include additional workers who may receive a wage increase through “spillover” effects, as employers adjust overall pay scales.

Estimates for New York reflect changes in the minimum wage applicable to upstate New York ($11.80) and Nassua, Suffolk, and Westchester counties ($13.00). New York City's minimum wage reached $15 at the end of 2018 and there are no further increases scheduled.

Population growth between the data period and January 2020 estimated using state-specific projections for growth in the total population or the population ages 15—69, where available. Nominal wage growth between the data period (2017) and January 2020 estimated using the 3-year average of nominal wage growth of the bottom 20 percent of wage earners in each state from 2015 to 2018.??A full methodology is available in Minimum Wage Simulation Model Technical Methodology.


Source: Economic Policy Institute Minimum Wage Simulation Model using data from the Census Bureau, Bureau of Labor Statistics, and Congressional Budget Office.?See Minimum Wage Simulation Model technical methodology [].

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In seven states, the changes are the result of automatic annual inflation adjustments. Alaska, Florida, Minnesota, Montana, Ohio, South Dakota, and Vermont all have provisions in their state minimum wage laws that require the wage be adjusted annually to reflect changes in prices over the preceding year. Doing so ensures that the minimum wage never declines in purchasing power, and workers paid the minimum wage can afford the same amount of goods and services year after year. 10 other states and the District of Columbia have enacted similar automatic adjustment provisions in their minimum wage laws that will begin after their minimum wages reach a higher statutory level in the coming years.

The increases in nine states—California, Delaware, Illinois, Maryland, Massachusetts, Michigan, New Jersey, New Mexico, and New York—are the result of legislation passed by state lawmakers to raise their state’s wage floors. Lawmakers in six of these states—California, Illinois, Maryland, Massachusetts, New Jersey, and New York—enacted legislation that will eventually bring their state minimum wages to $15 an hour. For 2020, minimum wages in these states will range between $11.00 and $13.00.

In six states—Arizona, Arkansas, Colorado, Maine, Missouri, and Washington—the January 1 raises result from ballot measures passed by the state’s voters. In the last several election cycles, voters have increasingly passed higher minimum wages, often in the face of inaction by their state legislatures. In fact, voters in Missouri passed a higher state minimum wage at the ballot box after state lawmakers nullified city minimum wage ordinances that had been enacted by local governments in Kansas City and St. Louis.Read more

Posted December 18, 2019 at 11:13 am by Lawrence Mishel and Melat Kassa

Top 1.0% of earners see wages up 157.8% since 1979

Newly available?wage data for 2018 show that annual wages for the top 1.0% were nearly flat (up 0.2%) while wages for the bottom 90% rose an above-average 1.4%. Still, the top 1.0% has done far better in the 2009–18 recovery (their wages rose 19.2%) than did those in the bottom 90%, whose wages rose only 6.8%. Over the last four decades since 1979, the top 1.0% saw their wages grow by 157.8% and those in the top 0.1% had wages grow more than twice as fast, up 340.7%. In contrast those in the bottom 90% had annual wages grow by 23.9% from 1979 to 2018. This disparity in wage growth reflects a sharp long-term rise in the share of total wages earned by those in the top 1.0% and 0.1%.

These are the results of EPI’s?updated series on wages?by earning group, which is developed from published Social Security Administration data and updates the wage series from 1947–2004 originally published by Kopczuk, Saez and Song (2010). These data, unlike the usual source of our other wage analyses (the Current Population Survey) allow us to estimate wage trends for the top 1.0% and top 0.1% of earners, as well as those for the bottom 90% and other categories among the top 10% of earners. These data are not top-coded, meaning the underlying earnings reported are actual earnings and not “capped” or “top-coded” for confidentiality.

Figure A

Cumulative percent change in real annual wages, by wage group, 1979–2018

Year Bottom 90% 90th–95th 95th–99th Top 1%
1979 0.0% 0.0% 0.0% 0.0%
1980 -2.2% -1.3% -0.2% 3.4%
1981 -2.6% -1.1% -0.1% 3.1%
1982 -3.9% -0.9% 2.2% 9.5%
1983 -3.7% 0.7% 3.6% 13.6%
1984 -1.8% 2.5% 6.0% 20.7%
1985 -1.0% 4.0% 8.1% 23.0%
1986 1.1% 6.4% 12.5% 32.6%
1987 2.1% 7.4% 15.0% 53.5%
1988 2.2% 8.2% 18.4% 68.7%
1989 1.8% 8.1% 18.2% 63.3%
1990 1.1% 7.1% 16.5% 64.8%
1991 0.0% 6.9% 15.5% 53.6%
1992 1.5% 9.0% 19.2% 74.3%
1993 0.9% 9.2% 20.6% 67.9%
1994 2.0% 11.2% 21.0% 63.4%
1995 2.8% 12.2% 24.1% 70.2%
1996 4.1% 13.6% 27.0% 79.0%
1997 7.0% 16.9% 32.3% 100.6%
1998 11.0% 21.3% 38.2% 113.1%
1999 13.2% 25.0% 42.9% 129.7%
2000 15.3% 26.8% 48.0% 144.8%
2001 15.7% 29.0% 46.4% 130.4%
2002 15.6% 29.0% 43.2% 109.3%
2003 15.7% 30.3% 44.9% 113.9%
2004 15.6% 30.8% 47.1% 127.2%
2005 15.0% 30.8% 48.6% 135.3%
2006 15.7% 32.5% 52.1% 143.4%
2007 16.6625450273242% 34.0650819079098% 55.3586221137521% 156.174314731946%
2008 16.0% 34.2% 53.8% 137.5%
2009 16.0% 35.3% 53.5% 116.2%
2010 15.2% 35.7% 55.7% 130.8%
2011 14.5% 36.2% 56.9% 134.0%
2012 14.6% 36.3% 58.3% 148.3%
2013 15.1% 37.1% 59.4% 137.5%
2014 16.6% 38.7% 62.3% 149.0%
2015 20.5% 43.1% 67.9% 156.2%
2016 21.0% 43.5% 68.1% 148.1%
2017 22.2% 44.2% 69.3% 157.3%
2018 23.9% 45.7% 71.3% 157.8%
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Source: EPI analysis of Kopczuk, Saez, and Song (2010, Table A3) and Social Security Administration wage statistics

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As Figure A shows, the top 1.0% of earners are now paid 157.8% more than they were in 1979. Even more impressive is that those in the top 0.1% had more than double that wage growth, up 340.7% since 1979 (Table 1). In contrast, wages for the bottom 90% only grew 23.9% in that time. Since the Great Recession, the bottom 90%, in contrast, experienced very modest wage growth, with annual wages—reflecting growing annual hours as well as higher hourly wages—up just 6.8% from 2009 to 2018. In contrast, the wages of the top 0.1% grew 19.2% during those nine years.Read more