What to watch on jobs day: Expected future impact of COVID-19

As COVID-19—commonly known as the coronavirus—continues to spread throughout the world, it is likely to have a direct impact on the United States through the health and well-being of our population. It is also likely to have an impact on economic activity, as workers stop working to care for themselves or their families, and people generally reduce social spending. I’ll be watching this in tomorrow’s job report from the Bureau of Labor Statistics, and keeping an eye on it in the coming months. The first order of business, however, is to make sure that workers can follow the Centers for Disease Control and Prevention (CDC)’s recommendations to stay home and seek medical care—if they are lucky enough to have paid sick days and health insurance. While there are still very few reported cases in the United States, it is expected to spread and the effects may be far-reaching.

In terms of the economy, there has already been an impact on the manufacturing sector as inputs from China are delayed because of temporary factory closures. The Federal Reserve has cut interest rates in expectation of further economic disruptions. Many employers are making contingencies for workers to telecommute rather than risk illness. Unfortunately, this isn’t an option for millions of workers in direct service professions across the economy. Another likely side effect of the pandemic is a pull-back on social consumption. Either because people become sick themselves or are avoiding public spaces, there will likely be a drop in certain types of spending across the economy.Read more

Low-wage workers saw the biggest wage growth in states that increased their minimum wage between 2018 and 2019

Twenty-three states and the District of Columbia raised their minimum wage in 2019 through legislation, referendum, or because the minimum wage was indexed to inflation in those states. Low-wage workers in these states saw much faster wage growth than low-wage workers in states that did not increase their minimum wage between 2018 and 2019, as shown in EPI’s latest State of Working America Wages report. This blog post dives a bit deeper by dispelling some tempting explanations for what might be happening, such as stronger across-the-board wage growth in those states (didn’t happen) or employment losses (not borne out in the data).

Figure A shows in green the states with minimum wage increases that occurred through legislation or referendum in 2019, while states in blue had automatic increases resulting from indexing the minimum wage to inflation. Workers in states that increased their minimum wage between 2018 and 2019 account for about 55% of the U.S. workforce. The nominal minimum wage increases ranged from $0.05 (0.5%) in Alaska to $1.00 (9.1%−10.0%) in California, Massachusetts, and Maine.

Figure A

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

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

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

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Figure B compares 10th-percentile wage growth in states with minimum wage increases compared with those without increases. Growth at the 10th percentile in states without minimum wage increases was much slower (0.9%) than in states with any kind of minimum wage increase (4.1%). This result holds true for both men and women. The 10th-percentile men’s wage grew 3.6% in states with minimum wage increases, compared with 0.7% growth in states without any minimum wage increases, while women’s 10th-percentile wages grew 2.8% in states with minimum wage increases and 1.4% in states without.

Figure B

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

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

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

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Economic policy and COVID-19—Mitigate harm and plan for the future: A list of considerations for policymakers

The direct cost that COVID-19 inflicts on human health is obviously its most important effect on society. But this direct cost can be worsened by flawed economic and policy structures. And the indirect damage the disease causes through economic ripple effects could be large, so policymakers should do everything they can to minimize them.

Past decisions that have weakened our economic policy infrastructure will hamper our response to COVID-19; this is already baked into the cake. But there are some short-run ameliorative actions we can take that might help, and there are long-run policy changes that will aid our response to future epidemics.

In technical economic terms, COVID-19 combines potential supply shocks with sector-specific demand shocks. Basically, supply shocks hamper our ability to produce goods and services, and demand shocks are sharp cutbacks in spending from households, businesses, or governments. Below I provide a list for policymakers of what could/should be considered to deal with some of these.

The supply shocks come from disrupted global value chains, as, for example, Chinese production of inputs used by U.S. manufacturing and construction firms are not delivered on time because Chinese factories have temporarily closed. In countries where schools are shut down for long periods of time, a shock to labor supply can occur as working parents have to stay home to care for kids.

The potential sector-specific demand shock is to businesses where consumption is largely social—done with other people around. Think bars, restaurants, grocery stores, and malls. As people avoid social contact to minimize disease transmission, this leads to less activity in these sectors.

These effects mean it will be hard indeed for policymakers to spare the economy any pain from this.

There’s very little that can be done about the supply-side shocks—particularly in the short run. Demand-side shocks are generally easier to address with policy (in theory—policymakers still often fumble the ball in this regard), but the specific nature of the demand shocks associated with COVID-19 make them slightly harder to address. Simply giving households more money won’t boost consumption much in the sectors likely to be affected—the pullback in consumption is not driven by income constraints, but due to concerns over catching the illness.

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EPI President Thea Lee testifies before the House Committee on Ways and Means on U.S.–China Trade and Competition (Video)

On February 26, EPI President Thea Lee testified before the House Committee on Ways and Means on the impact of the imbalanced U.S.–China economic relationship on U.S. jobs, wages, businesses, and long-term growth.

In her testimony, Lee discussed the history of U.S. trade policy toward China, problems with Trump’s “phase one” deal with China, and fundamental flaws in the U.S.–China economic relationship. According to new EPI research, the growing U.S.–China trade deficit was responsible for the loss of 3.7 million U.S. jobs between 2001 and 2018. These job losses are spread across all 50 states and the District of Columbia—and every congressional district in America.

Watch her testimony:

Lack of paid sick days and large numbers of uninsured increase risks of spreading the coronavirus

COVID-19—commonly known as the coronavirus—is now a potential threat for the United States and we all “need to be preparing for significant disruption of our lives,” warned the Centers for Disease Control and Prevention (CDC) this week.

Unfortunately, preparing for the “significant disruption” will be economically unimaginable for one group of Americans—the millions of people in the United States who do not have access to paid sick days or have health insurance with a regular health care provider.

The CDC released very clear instructions to help prevent the spread of respiratory diseases, including staying home when you are sick. Not everyone has that option.

Overall, just under three-quarters (73%) of private-sector workers in the United States have the ability to earn paid sick time at work. And, as shown in Figure A, below, access to paid sick days is vastly unequal. The highest-wage workers are more than three times as likely to have access to paid sick leave as the lowest-paid workers. Whereas 93% of the highest-wage workers had access to paid sick days, only 30% of the lowest-paid workers are able to earn sick days. In this way, access to paid sick days increases with wages among workers, disproportionately denying workers at the bottom this important security. And low-wage workers are more likely to be found in occupations where they have contact with the public—think early care and education workers, home health aides, restaurant workers, and food processors. When workers or their family members are sick, they shouldn’t have to decide between staying home from work to care for themselves or their dependents and paying rent or putting food on the table. But that is the situation our policymakers have put workers in.

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Black-white wage gaps are worse today than in 2000

This week, my colleagues hosted a discussion on the policies that the 2020 presidential candidates should focus on in order to help black workers in the economy. One of the challenges that the presidential candidates should discuss is how to reduce the black–white wage gap—which has stubbornly persisted over the last four decades. Black-white wage gaps are large and have gotten worse in the last 20 years.

The latest findings on wage growth as documented in EPI’s State of Working America Wages 2019 report indicate wages in general are slowly improving with the growing economy, but wage inequality has grown and wage gaps have persisted, and in some cases, worsened. In this post, I will highlight the worsening black-white wage gap and look at it from multiple dimensions. Since 2000, by any way it’s measured, the wage gap between black and white workers has grown significantly.

The figure below compares wages for black and white workers over the last 19 years, highlighting the gaps in wages in 2000, the last time the economy was closest to full employment, 2007, the last business cycle peak before the Great Recession, and 2019, the latest data available. Against these benchmarks, I illustrated the growth in the average gap, the gap for low-, middle-, and high-wage workers, the gap for workers with a high school diploma, a college degree, and an advanced degree, and a regression-adjusted wage gap (controlling for age, gender, education, and region).

Figure A

Black–white wage gaps widen across multiple measures: Black–white wages gaps at different points in the wage distribution, by education, and regression-based, 2000, 2007, and 2019

2000 2007 2019
Average 21.8% 23.5% 26.5%
10th percentile 6.2% 8.7% 9.0%
Median 20.8% 22.3% 24.4%
95th percentile 28.0% 28.3% 34.7%
 High school 15.3% 17.4% 18.3%
 College 17.2% 19.2% 22.5%
 Advanced degree 12.5% 16.7% 17.6%
Regression-based 10.2% 12.2% 14.9%
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Notes: Sample based on all workers ages 16 and older. The xth-percentile wage is the wage at which x% of wage earners earn less and (100-x)% earn more. Educational attainment is based on mutually exclusive categories: e.g., high school is high school only, etc. Similar results are found for those with less than high school or some college. The regression-adjusted black–white wage gap controls for education, age, gender, and region.

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

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The black–white wage gap is smallest at the bottom of the wage distribution, where the minimum wage serves as a wage floor. The largest black–white wage gap as well as the one with the most growth since the Great Recession, is found at the top of the wage distribution, explained in part by the pulling away of top earners generally as well as continued occupational segregation, the disproportionate likelihood for white workers to occupy positions in the highest-wage professions.

It’s clear from the figure that education is not a panacea for closing these wage gaps. Again, this should not be shocking, as increased equality of educational access—as laudable a goal as it is—has been shown to have only small effects on class-based wage inequality, and racial wealth gaps have been almost entirely unmoved by a narrowing of the black–white college attainment gap, as demonstrated by William Darity Jr. and others.

Black workers can’t simply educate their way out of the gap. Across various levels of education, a significant black–white wage gap remains. Even black workers with an advanced degree experience a significant wage gap compared with their white counterparts. And after controlling for age, gender, education, and region, black workers are paid 14.9% less than white workers.

While the wage gaps differ depending on measure, what is obvious from the trends displayed is that the gaps widened in the full business cycle 2000–2007 and continued to grow in the Great Recession and its aftermath. Even though the black unemployment rate has fallen precipitously over the last several years, wage growth has remained particularly weak for black workers.

As always, it’s important to remember the historical and social contexts for differences in black and white labor market experiences and labor market outcomes (see Razza). Workers’ ability to claim higher wages rests on a host of social, political, and institutional factors outside of their control. The systematic social deprivation and economic disadvantage is maintained and reinforced by those with economic and political power. Furthermore, occupational segregation plays a significant role in these gaps, for both black men and black women. And, black women, in particularly, can face larger wages gaps with white men than the sum of their parts, meaning the black women face a double wage penalty for their race and gender. The trends in black–white wage gaps found here are supported by other important research that shows that black-white wage gaps expanded with rising inequality from 1979 to 2015.

Given a long history of excluding black Americans from social and political institutions that boost wage growth, the stubbornness of racial wage gaps is less surprising. However, the fact that they are getting worse is troubling. The good news is that policy can make a difference.

We see in the figure that the minimum wage keeps the lowest-wage black workers from even lower wages. In states that increased in the minimum wage between 2018 and 2019, low-wage workers saw stronger wage growth than in states that had no increase in their minimum wage in that period. Raising the federal minimum wage would disproportionately benefit black workers because they are overrepresented among low-wage workers and are less likely to live in states or localities that have passed a minimum wage that is higher than the current federal minimum.

Aside from strengthening and enforcing labor standards such as the minimum wage, making it easier for workers to form unions can narrow the black–white wage gap. Black workers are more likely to be in a union than white and get a bigger wage boost to being in a union than white. Therefore, unions can help shrink the black–white wage gap. Related, research has shown that the decline of unionization led to an expansion of the black–white wage gap.

Using all fiscal and monetary policy levers to achieve and maintain high-pressure labor markets can improve relative labor market outcomes for black workers, including participation in the labor force and work hours as well as wage growth. The U.S. certainly saw this stronger across the board growth in the tight labor market of the late 1990s.

In 2019, black wages exceeded their 2000 and 2007 levels across the wage distribution for the first time in this recovery. I’m hopeful that as the economy continues to move toward genuine full employment, black workers will see their wages rise. But it will take more than a couple of years of a full-employment economy to close racial wage gaps and compensate for years of lower wages, lower incomes, and lower wealth.

The U.S. federal tax and spending system is the biggest tool to combat inequality, but it could do much more

Last week, we launched the U.S. Tax & Spending Explorer on the EPI website. It’s an interactive web feature designed to shed light on how the government (mostly the federal government) raises and spends money and how changes in taxes and spending over time either increase or decrease income inequality.

There’s enough granular detail in the feature that everybody might have different takeaways from visiting it. But here’s what strikes me looking at this data:

  • Together, taxes and spending significantly reduce inequality at any given point in time relative to a world with a much smaller federal footprint. That’s the good news. The bad news is that since 1979 the inequality-reducing effect of taxes and spending hasn’t grown that much—but inequality has grown, a lot. We should use the proven inequality-fighting lever of a larger tax and spending system to combat the inequality that has risen so fast in recent decades.

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Medicare4All C-Span discussion sheds light on its impact

Economic Policy Institute’s Director of Research Josh Bivens and American Enterprise Institute’s Jospeh Antos addressed the issue of Medicare for All, and the larger role health care policy is playing in Campaign 2020 on C-Span’s Washington Journal Saturday.

The United States needs movement forward on healthcare that makes it accessible and affordable, said Bivens during the discussion.

“I think there’s a real hunger out there for something for health reform,” he explained. “Health care is something that Americans worry about the most, not just their health but would means for financial security and that worry is well-placed. We have a uniquely dysfunctional health care system.

“We spend on a per capita basis about $10,000 per person, we have some great health systems in the world, number one in terms of health outcomes as France and the Netherlands who spend literally half of what we spend. One of the reasons why a single-payer plan would be expensive is because we still have 27 million uninsured people and 60 million underinsured people. So, yeah, it would be more expensive to give health care to people who need it, but that’s the virtue of a fundamental reform. Keeping costs down by keeping people excluded, seems to me as not the way to go.”

Bivens is the author of a soon-to-be released paper on the impact of fundamental health care reform, including Medicare for All, on wages and job quality.

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The Trump budget doesn’t spare seniors

President Trump’s proposed 2021 budget claims to help the “most vulnerable populations,” including seniors. But vulnerable older Americans are among those who would be most hurt if this draconian budget were ever enacted.

The budget would slash Medicaid and non-defense discretionary spending, eliminating or drastically shrinking programs targeted at low-income people, including programs benefiting seniors, such as the Low Income Home Energy Assistance Program. At first glance, the administration appears to spare middle-class seniors, a group with high voter turnout that tends to support the president and his party. Despite the president’s hints that Social Security and Medicare will be on the chopping block after the election, the budget would spare retirement benefits (except those for federal employees) and claims to achieve Medicare savings only by eliminating “excessive spending and distortionary payment incentives” while “preserv[ing] benefits and access to care.”

Some Medicare provisions in the president’s budget, such as site-neutral payments across different types of facilities, address genuine problems in how Medicare is administered. But the nearly half trillion in proposed savings from Medicare over 10 years includes provisions that would indirectly affect Medicare beneficiaries’ access to care, such as reducing payments to partially cover unpaid medical bills for Medicare beneficiaries. Since unlimited out-of-pocket expenses are a major cause of bankruptcy for older Americans in poor health, reducing these reimbursements would cause some providers to avoid treating Medicare patients who have expensive conditions and limited resources—and would surely lead to hospital and clinic closures in underserved areas. Middle-class seniors and providers who treat them wouldn’t be spared, since lower-middle-class seniors ineligible for Medicaid are those most likely to spend a high share of their income on health care. The problem of uncompensated care would be compounded by the administration’s attempts to roll back Medicaid expansion under the Affordable Care Act (ACA), which has helped hospitals treating low-income and uninsured patients in expansion states.

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Top five Valentine’s Day gifts ideas for U.S. workers: Nothing spells ‘romance’ like a fair wage and quality jobs

That’s why we decided to sum up what we think are the top five Valentine’s Day gifts ideas for working people across the country.

Power through collective action!

Our economy is out of balance. Corporations and CEOs hold too much power and wealth, and working people know it. Workers are mobilizing, organizing, protesting, and striking at a level not seen in decades, and they are winning pay raises and other real change by using their collective voices.

But, the fact is, it is still too difficult for working people to form a union at their workplace when they want to. The law gives employers too much power and puts too many roadblocks in the way of workers trying to organize a union. The Protecting the Right to Organize (PRO) Act will go a long way toward restoring workers’ right to join together to bargain for better wages and working conditions by streamlining the process when workers form a union, ensuring that they are successful in negotiating a first agreement, and holding employers accountable when they violate labor law. The U.S. Senate should join the House of Representatives and pass the PRO Act in order to restore power to working people.

Affording bread…and roses

The real (inflation-adjusted) minimum wage is now roughly 30 percent lower than it was in 1968, and it has been more than 10 years since congress raised the minimum wage—the longest stretch in history. To end this shameful streak, it is incumbent upon the Senate to take up and pass the Raise the Wage Act immediately. Raising the federal minimum wage to $15 by 2025 would lift wages for 33.5 million workers across the country—more than one-fifth of the wage-earning workforce. The increase would boost total annual wages for these low-wage workers by $92.5 billion, lifting annual earnings for the average affected year-round worker by $2,800. Recent survey data have shown that 74% of U.S. workers live paycheck to paycheck. Policymakers should give working people the ability to make ends meet—but also the ability to treat themselves occasionally.

Failure to raise the federal minimum wage has taken thousands of dollars out of the pockets of minimum wage workers: The real value of the minimum wage (adjusted for inflation) is 17% less than 10 years ago and 31% less than in 1968

Failure to raise the federal minimum wage has taken thousands of dollars out of the pockets of minimum wage workers: The real value of the minimum wage (adjusted for inflation) is 17% less than 10 years ago and 31% less than in 1968

Note: All values are in June 2019 dollars, adjusted using the CPI-U-RS.

Source: Adapted from Figure C in David Cooper, Elise Gould, and Ben Zipperer, Low-Wage Workers Are Suffering from a Decline in the Real Value of the Federal Minimum Wage, Economic Policy Institute, August 2019).

Source: Adapted from Figure C in David Cooper, Elise Gould, and Ben Zipperer, Low-Wage Workers Are Suffering from a Decline in the Real Value of the Federal Minimum Wage, Economic Policy Institute, August 2019). The figure reflects EPI analysis of historical minimum wage data in the Fair Labor Standards Act and amendments.

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Pay workers for their hours worked, or give them their time back

The U.S. Department of Labor announced in September its final overtime rule, which will set the salary threshold under which salaried workers are automatically entitled to overtime pay to $35,568 a year. The rule leaves behind millions of workers who would have received overtime protections under the much stronger rule, published in 2016, that Trump administration chose to abandon. A stronger overtime protection would pay more workers for working more than 40 hours a week, or allow them extra time with their families.

Seven states have already taken steps to raise the overtime threshold, but without further action, it’s estimated that 8.2 million workers who would have benefited from the 2016 rule will be left behind by the Trump administration’s rule, including 3.2 million workers who would have gotten new overtime protections under the 2016 rule and 5.0 million who would have gotten strengthened overtime protections under the 2016 rule. States should follow suit and extend the overtime protections so workers don’t continue to lose out on their hard-earned wages.

Let your workers move on

At least 36 million workers—27.8% of the private-sector workforce—are required to enter noncompete agreements. Noncompete agreements are employment provisions that ban workers at one company from going to work for, or starting, a competing business within a certain period of time after leaving a job. Establishments with high pay or high levels of education among workers are more likely to use noncompetes, but noncompetes agreements are also common in workplaces with low pay and low levels of education. More than a quarter (29.0%) of private-sector workers with an average hourly wage below $13.00 require noncompetes for all their workers. Noncompetes are part of a disturbing trend of employers requiring workers to sign away their rights. Noncompetes may be contributing to weak wage growth, given that changing jobs is how workers often get a raise. And given that noncompetes limit the ability of individuals to start businesses or take other jobs, it also is not difficult to see that noncompetes may be contributing to the declines in dynamism in the U.S. labor market. Congress should pass the bipartisan legislationthe Workforce Mobility Act of 2019to prohibit noncompete agreements.

Labor protections for Uber drivers shuttling around Valentine’s couples

The General Counsel of the National Labor Relations Board recently released a memo claiming that Uber drivers are independent contractors, not employees of Uber. The reality is that these drivers have very little entrepreneurial freedom: Drivers can’t raise revenues because they can’t control prices or expand their customer base through marketing. Unlike a typical enterprise, Uber drivers do not build earnings as they get more experience. Uber drivers are not able to choose their customers—drivers are penalized for rejecting or not accepting trips. And after accounting for Uber’s commissions and fees and vehicle expenses, and taking into account the cost of a modest package of health insurance and other benefits equivalent to those earned by W-2 workers, Uber drivers earn the equivalent of $9.21 in hourly wages—less than what is earned by 90% of all other wage and salary earners, and below the minimum wage in 13 of the 20 major urban markets where Uber operates.

Recently, AB5 went into effect in California, a set of protections aimed at combatting the misclassification of workers as independent contractors, helping ensure that California’s employees have access to basic labor and employment protections denied to independent contractors including: minimum wage and overtime protections, paid sick days and family leave, workers’ compensation benefits, and unemployment insurance benefits. Policymakers across the country should take notice and provide similar protections to workers in their states.


AAPI women face a double pay penalty for race and gender

Asian American/Pacific Islander (AAPI) Equal Pay Day is February 11. It marks the number of days into 2020 that AAPI women have to work to make the same amount as their white male counterparts were paid in 2019. Put another way, the average AAPI woman needs to work almost an extra month and a half to make up for the shortfall in annual earnings relative to the average non-Hispanic white man.

The infographic below takes a closer look at the data to debunk commonly held myths about the AAPI women’s pay gap. Specifically, all AAPI women do not face a relatively small pay gap—Asian American women are paid 93 cents on the dollar, while Hawaiian/Pacific Islander women are paid only 68 cents.

Further, Asian American women can’t just educate their way out of the pay gap. Asian American women have higher levels of education than white men, and when comparing wages of workers with the same level of education, the disparities are much larger. Asian American women with a bachelor’s degree only are paid 22% less than their white male counterparts and those with an advanced degree are paid 14% less.


Declining trade balances disguise continued growth in the non-oil trade deficit

The overall goods and services trade deficit declined 1.7% ($10.9 billion) in 2019, while the total deficit in goods trade fell 2.4% ($21.4 billion). However, the U.S. trade deficit in non-oil goods, which is dominated by trade in manufactured products, increased 1.8% in 2019. Aside from petroleum, trade was a net drag on the economy in 2019 and on manufacturing, in particular.

The small decline in overall U.S. trade deficits follows an 18.3% increase in the goods trade deficit in the first two years of the Trump administration. Taken altogether, the U.S. goods trade deficit increased $116.2 billion (15.5%) in the first three years of the Trump Administration. It has proven neither quick nor easy to reduce the growing U.S. goods trade deficit.

The petroleum products deficit decreased 72.6% in 2019, masking the 1.8% increase in the non-oil goods trade deficit within the overall 2.4% decline in the U.S. goods trade balance. The fracking revolution has resulted in a significant reduction in oil imports (13.9%) and a small increase in petroleum exports (2.8%).

Recent changes in petroleum trade yield this shocking factoid: The United States became a net exporter of petroleum products for the last four months of 2019. This reflects a key element of Trump’s trade “strategy” to export liquefied natural gas (LNG) to the rest of the world, which comes at a steep cost. This will drive up U.S. prices for natural gas and oil, despite the fact that low energy prices were a key element of the mini-recovery in US manufacturing exports. Increased LNG exports will hurt U.S. consumers by increasing fuel costs, heightening risks of transport and catastrophic port explosions, and exacerbating global warming and air pollution levels in the country as a whole.

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What to watch on jobs day: Large downward revisions in employment expected

On Friday, the Bureau of Labor Statistics (BLS) will revise nonfarm payroll employment, hours, and earnings data to reflect the annual benchmark process in the establishment survey. Each year, the BLS benchmarks total nonfarm payroll employment to state unemployment insurance tax records. In August 2019, BLS released preliminary benchmark revisions to payroll employment for April 2018 through March 2019, but revisions don’t get officially incorporated into the historical numbers until the final revisions are released. While revisions in most years tend to be relatively small, this year’s preliminary revisions came in much higher. The preliminary estimate of the benchmark revision indicates a downward adjustment to March 2019 total nonfarm employment of -501,000. This means that between April 2018 and March of 2019, there were a half million fewer jobs created than initially reported. Over the last ten years, preliminary revisions averaged about -92,000, so -501,000 is very large in comparison. And, usually the difference between the preliminary revisions and the final revisions is plus or minus 40,000. Therefore, it’s likely tomorrow’s final revisions will also be around 500,000 fewer jobs in that period.

The revisions will also provide details on changes in the initial payroll employment estimates by sector. For instance, in the preliminary release, the revisions were located primarily in “leisure and hospitality”, “professional and business services”, and “retail trade” with downward revisions of -175,000, -163,000, and -146,400, respectively. On Friday, the historical data will reflect the final benchmarks overall and by sector.

Tracking trends in nominal wage growth

Turning to nominal wage growth, the most important economic indicator to watch in 2020, last month there was a large drop for production/nonsupervisory workers. The figure below charts year-over-year changes in private-sector nominal average hourly earnings for “all nonfarm employees” as well as “production/nonsupervisory workers.” After remaining consistently higher than “all nonfarm” for nearly a year and at or above 3.4% for much of that time, it fell to 3.0% in December, its lowest point since September 2018. This begs the question of whether this is simply a blip and production/nonsupervisory workers will continue to pull away or if the separation in growth rates between the two over the last year was mostly statistical noise.

At this point in the recovery—with unemployment at or below 4.0% for 22 months—wage growth remains lower than expected. As employment growth consistently remains higher than working-age population growth, more and more workers are pulled into the labor force and finding jobs. As this slack gets absorbed, workers should be getting scarcer and scarcer. Therefore, employers would typically have to pay more to attract and retain the workers they want. After increasing in 2018, wage growth for all nonfarm employees has slowed for much of 2019 and remains below target levels.

Nominal Wage Tracker

Nominal wage growth has been far below target in the recovery: Year-over-year change in private-sector nominal average hourly earnings, 2007–2019

Date All nonfarm employees Production/nonsupervisory workers
Mar-2007 3.44% 4.11%
Apr-2007 3.08% 3.79%
May-2007 3.48% 4.14%
Jun-2007 3.56% 4.19%
Jul-2007 3.25% 4.05%
Aug-2007 3.35% 3.98%
Sep-2007 3.14% 4.09%
Oct-2007 3.08% 3.78%
Nov-2007 3.07% 3.83%
Dec-2007 2.97% 3.81%
Jan-2008 2.91% 3.80%
Feb-2008 2.80% 3.79%
Mar-2008 3.04% 3.83%
Apr-2008 2.84% 3.76%
May-2008 3.07% 3.69%
Jun-2008 2.77% 3.56%
Jul-2008 3.05% 3.67%
Aug-2008 3.33% 3.89%
Sep-2008 3.23% 3.64%
Oct-2008 3.27% 3.81%
Nov-2008 3.60% 3.91%
Dec-2008 3.59% 3.90%
Jan-2009 3.63% 3.72%
Feb-2009 3.43% 3.65%
Mar-2009 3.28% 3.47%
Apr-2009 3.42% 3.35%
May-2009 2.93% 3.06%
Jun-2009 2.83% 2.88%
Jul-2009 2.69% 2.71%
Aug-2009 2.44% 2.70%
Sep-2009 2.44% 2.75%
Oct-2009 2.53% 2.68%
Nov-2009 2.19% 2.67%
Dec-2009 1.91% 2.50%
Jan-2010 2.05% 2.66%
Feb-2010 2.09% 2.55%
Mar-2010 1.81% 2.27%
Apr-2010 1.76% 2.38%
May-2010 1.90% 2.59%
Jun-2010 1.76% 2.53%
Jul-2010 1.85% 2.42%
Aug-2010 1.75% 2.36%
Sep-2010 1.84% 2.19%
Oct-2010 1.93% 2.45%
Nov-2010 1.65% 2.13%
Dec-2010 1.79% 2.02%
Jan-2011 1.92% 2.28%
Feb-2011 1.87% 2.06%
Mar-2011 1.87% 2.06%
Apr-2011 1.91% 2.16%
May-2011 2.04% 2.10%
Jun-2011 2.13% 2.05%
Jul-2011 2.30% 2.26%
Aug-2011 1.99% 1.94%
Sep-2011 1.94% 1.99%
Oct-2011 2.07% 1.88%
Nov-2011 2.02% 1.82%
Dec-2011 1.98% 1.77%
Jan-2012 1.71% 1.35%
Feb-2012 1.79% 1.45%
Mar-2012 2.10% 1.76%
Apr-2012 2.09% 1.65%
May-2012 1.78% 1.39%
Jun-2012 2.00% 1.54%
Jul-2012 1.77% 1.44%
Aug-2012 1.86% 1.33%
Sep-2012 2.03% 1.54%
Oct-2012 1.51% 1.18%
Nov-2012 1.90% 1.43%
Dec-2012 2.24% 1.69%
Jan-2013 2.24% 1.84%
Feb-2013 2.15% 2.04%
Mar-2013 1.93% 1.88%
Apr-2013 2.05% 1.83%
May-2013 2.14% 1.93%
Jun-2013 2.13% 2.03%
Jul-2013 2.00% 1.97%
Aug-2013 2.26% 2.23%
Sep-2013 2.04% 2.22%
Oct-2013 2.25% 2.32%
Nov-2013 2.24% 2.37%
Dec-2013 1.85% 2.21%
Jan-2014 1.89% 2.31%
Feb-2014 2.27% 2.55%
Mar-2014 2.10% 2.30%
Apr-2014 1.93% 2.29%
May-2014 2.09% 2.44%
Jun-2014 1.96% 2.24%
Jul-2014 2.04% 2.33%
Aug-2014 2.16% 2.43%
Sep-2014 2.08% 2.22%
Oct-2014 2.03% 2.27%
Nov-2014 2.03% 2.22%
Dec-2014 1.99% 1.92%
Jan-2015 2.19% 2.01%
Feb-2015 1.93% 1.66%
Mar-2015 2.22% 1.95%
Apr-2015 2.26% 2.00%
May-2015 2.34% 2.14%
Jun-2015 2.25% 2.14%
Jul-2015 2.17% 2.04%
Aug-2015 2.20% 1.98%
Sep-2015 2.28% 2.08%
Oct-2015 2.52% 2.37%
Nov-2015 2.43% 2.12%
Dec-2015 2.47% 2.51%
Jan-2016 2.55% 2.40%
Feb-2016 2.42% 2.50%
Mar-2016 2.45% 2.49%
Apr-2016 2.61% 2.58%
May-2016 2.40% 2.33%
Jun-2016 2.60% 2.48%
Jul-2016 2.76% 2.62%
Aug-2016 2.55% 2.51%
Sep-2016 2.63% 2.46%
Oct-2016 2.66% 2.41%
Nov-2016 2.61% 2.50%
Dec-2016 2.65% 2.50%
Jan-2017 2.40% 2.39%
Feb-2017 2.72% 2.34%
Mar-2017 2.55% 2.29%
Apr-2017 2.47% 2.24%
May-2017 2.54% 2.33%
Jun-2017 2.50% 2.32%
Jul-2017 2.57% 2.32%
Aug-2017 2.57% 2.31%
Sep-2017 2.83% 2.59%
Oct-2017 2.32% 2.16%
Nov-2017 2.47% 2.35%
Dec-2017 2.74% 2.48%
Jan-2018 2.81% 2.47%
Feb-2018 2.57% 2.47%
Mar-2018 2.80% 2.74%
Apr-2018 2.79% 2.78%
May-2018 2.94% 2.91%
Jun-2018 2.93% 2.91%
Jul-2018 2.85% 2.85%
Aug-2018 3.18% 3.12%
Sep-2018 2.98% 3.02%
Oct-2018 3.32% 3.25%
Nov-2018 3.31% 3.37%
Dec-2018 3.34% 3.50%
Jan-2019 3.18% 3.35%
Feb-2019 3.40% 3.44%
Mar-2019 3.24% 3.38%
Apr-2019 3.16% 3.33%
May-2019 3.08% 3.36%
Jun-2019 3.18% 3.35%
Jul-2019 3.25% 3.52%
Aug-2019 3.23% 3.51%
Sep-2019 3.00% 3.54%
Oct-2019 3.11% 3.62%
Nov-2019 3.14% 3.39%
Dec-2019 2.87% 3.03%
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Nominal wage growth consistent with the Federal Reserve Board’s 2 percent inflation target, 1.5 percent productivity growth, and a stable labor share of income

Source: EPI analysis of Bureau of Labor Statistics Current Employment Statistics public data series

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On Friday, the BLS will also be employing new population controls in the Current Population Survey (CPS) starting in January 2020. Unlike the establishment survey, these changes to the CPS are not updated historically so caution should be exercised when making comparisons with data for December 2019 or earlier periods. The BLS is also making some changes to their methodology in terms of providing new seasonally adjusted series for measures of labor market underutilization as well as beginning to include both those in opposite-sex and same-sex marriages in estimates of married persons.

The new benchmarks to the establishment survey as well as revisions to the household survey will provide much fodder for thought on Friday morning. And, wage growth continues to be the most important indicator to watch as it lags behind overall improvements in the labor market.

Trump’s ‘blue-collar boom’ is likely a dud

In his State of the Union address tonight, President Trump plans to extol the “blue-collar boom” in the economy along with his purported “great American comeback.” He’ll claim this based on two recent signature trade deals—the United States-Mexico-Canada Agreement (USMCA) and a “phase one” deal with China. Unfortunately, both agreements will likely to lead to more outsourcing and job loss for U.S. workers, and the facts just don’t support Trump’s claims about the broader economy.

Trump comes from a world that has ardently championed globalization, like many of his predecessors. However, that approach has decimated U.S. manufacturing over the past 20 years, eliminating nearly 5 million good factory jobs as shown in Figure A, below. Nearly 90,000 U.S. factories have been lost as well.

Figure A

U.S. manufacturing employment, January 1970–December 2019 (millions of jobs)

Date Manufacturing employment (millions of jobs)
1970-01-01 18.424
1970-02-01 18.361
1970-03-01 18.36
1970-04-01 18.207
1970-05-01 18.029
1970-06-01 17.93
1970-07-01 17.877
1970-08-01 17.779
1970-09-01 17.692
1970-10-01 17.173
1970-11-01 17.024
1970-12-01 17.309
1971-01-01 17.28
1971-02-01 17.216
1971-03-01 17.154
1971-04-01 17.149
1971-05-01 17.225
1971-06-01 17.139
1971-07-01 17.126
1971-08-01 17.115
1971-09-01 17.154
1971-10-01 17.126
1971-11-01 17.166
1971-12-01 17.202
1972-01-01 17.283
1972-02-01 17.361
1972-03-01 17.447
1972-04-01 17.508
1972-05-01 17.602
1972-06-01 17.641
1972-07-01 17.556
1972-08-01 17.741
1972-09-01 17.774
1972-10-01 17.893
1972-11-01 18.005
1972-12-01 18.158
1973-01-01 18.276
1973-02-01 18.41
1973-03-01 18.493
1973-04-01 18.53
1973-05-01 18.564
1973-06-01 18.606
1973-07-01 18.598
1973-08-01 18.629
1973-09-01 18.609
1973-10-01 18.702
1973-11-01 18.773
1973-12-01 18.82
1974-01-01 18.788
1974-02-01 18.727
1974-03-01 18.7
1974-04-01 18.702
1974-05-01 18.688
1974-06-01 18.69
1974-07-01 18.656
1974-08-01 18.57
1974-09-01 18.492
1974-10-01 18.364
1974-11-01 18.077
1974-12-01 17.693
1975-01-01 17.344
1975-02-01 17.004
1975-03-01 16.853
1975-04-01 16.759
1975-05-01 16.746
1975-06-01 16.69
1975-07-01 16.678
1975-08-01 16.824
1975-09-01 16.904
1975-10-01 16.984
1975-11-01 17.025
1975-12-01 17.14
1976-01-01 17.287
1976-02-01 17.384
1976-03-01 17.47
1976-04-01 17.541
1976-05-01 17.513
1976-06-01 17.521
1976-07-01 17.524
1976-08-01 17.596
1976-09-01 17.665
1976-10-01 17.548
1976-11-01 17.682
1976-12-01 17.719
1977-01-01 17.803
1977-02-01 17.843
1977-03-01 17.941
1977-04-01 18.024
1977-05-01 18.107
1977-06-01 18.192
1977-07-01 18.259
1977-08-01 18.276
1977-09-01 18.334
1977-10-01 18.356
1977-11-01 18.419
1977-12-01 18.531
1978-01-01 18.593
1978-02-01 18.639
1978-03-01 18.699
1978-04-01 18.772
1978-05-01 18.848
1978-06-01 18.919
1978-07-01 18.951
1978-08-01 19.006
1978-09-01 19.068
1978-10-01 19.142
1978-11-01 19.257
1978-12-01 19.334
1979-01-01 19.388
1979-02-01 19.409
1979-03-01 19.453
1979-04-01 19.45
1979-05-01 19.509
1979-06-01 19.553
1979-07-01 19.531
1979-08-01 19.406
1979-09-01 19.442
1979-10-01 19.39
1979-11-01 19.299
1979-12-01 19.301
1980-01-01 19.282
1980-02-01 19.219
1980-03-01 19.217
1980-04-01 18.973
1980-05-01 18.726
1980-06-01 18.49
1980-07-01 18.276
1980-08-01 18.414
1980-09-01 18.445
1980-10-01 18.506
1980-11-01 18.601
1980-12-01 18.64
1981-01-01 18.639
1981-02-01 18.613
1981-03-01 18.647
1981-04-01 18.711
1981-05-01 18.766
1981-06-01 18.789
1981-07-01 18.785
1981-08-01 18.748
1981-09-01 18.712
1981-10-01 18.566
1981-11-01 18.409
1981-12-01 18.223
1982-01-01 18.047
1982-02-01 17.981
1982-03-01 17.857
1982-04-01 17.683
1982-05-01 17.588
1982-06-01 17.43
1982-07-01 17.278
1982-08-01 17.16
1982-09-01 17.074
1982-10-01 16.853
1982-11-01 16.722
1982-12-01 16.69
1983-01-01 16.705
1983-02-01 16.706
1983-03-01 16.711
1983-04-01 16.794
1983-05-01 16.885
1983-06-01 16.96
1983-07-01 17.059
1983-08-01 17.118
1983-09-01 17.255
1983-10-01 17.367
1983-11-01 17.479
1983-12-01 17.551
1984-01-01 17.63
1984-02-01 17.728
1984-03-01 17.806
1984-04-01 17.872
1984-05-01 17.916
1984-06-01 17.967
1984-07-01 18.013
1984-08-01 18.034
1984-09-01 18.019
1984-10-01 18.024
1984-11-01 18.016
1984-12-01 18.023
1985-01-01 18.009
1985-02-01 17.966
1985-03-01 17.939
1985-04-01 17.886
1985-05-01 17.855
1985-06-01 17.819
1985-07-01 17.776
1985-08-01 17.756
1985-09-01 17.718
1985-10-01 17.708
1985-11-01 17.697
1985-12-01 17.693
1986-01-01 17.686
1986-02-01 17.663
1986-03-01 17.624
1986-04-01 17.616
1986-05-01 17.593
1986-06-01 17.53
1986-07-01 17.497
1986-08-01 17.489
1986-09-01 17.498
1986-10-01 17.477
1986-11-01 17.472
1986-12-01 17.478
1987-01-01 17.465
1987-02-01 17.499
1987-03-01 17.507
1987-04-01 17.525
1987-05-01 17.542
1987-06-01 17.537
1987-07-01 17.593
1987-08-01 17.63
1987-09-01 17.691
1987-10-01 17.729
1987-11-01 17.775
1987-12-01 17.809
1988-01-01 17.79
1988-02-01 17.823
1988-03-01 17.844
1988-04-01 17.874
1988-05-01 17.892
1988-06-01 17.916
1988-07-01 17.926
1988-08-01 17.891
1988-09-01 17.914
1988-10-01 17.966
1988-11-01 18.003
1988-12-01 18.025
1989-01-01 18.057
1989-02-01 18.055
1989-03-01 18.06
1989-04-01 18.055
1989-05-01 18.04
1989-06-01 18.013
1989-07-01 17.98
1989-08-01 17.964
1989-09-01 17.922
1989-10-01 17.895
1989-11-01 17.886
1989-12-01 17.881
1990-01-01 17.797
1990-02-01 17.893
1990-03-01 17.868
1990-04-01 17.845
1990-05-01 17.797
1990-06-01 17.776
1990-07-01 17.704
1990-08-01 17.649
1990-09-01 17.609
1990-10-01 17.577
1990-11-01 17.428
1990-12-01 17.395
1991-01-01 17.33
1991-02-01 17.211
1991-03-01 17.14
1991-04-01 17.093
1991-05-01 17.07
1991-06-01 17.044
1991-07-01 17.015
1991-08-01 17.025
1991-09-01 17.01
1991-10-01 16.999
1991-11-01 16.961
1991-12-01 16.916
1992-01-01 16.839
1992-02-01 16.829
1992-03-01 16.805
1992-04-01 16.831
1992-05-01 16.835
1992-06-01 16.826
1992-07-01 16.819
1992-08-01 16.783
1992-09-01 16.761
1992-10-01 16.751
1992-11-01 16.758
1992-12-01 16.769
1993-01-01 16.791
1993-02-01 16.805
1993-03-01 16.795
1993-04-01 16.772
1993-05-01 16.766
1993-06-01 16.742
1993-07-01 16.739
1993-08-01 16.741
1993-09-01 16.769
1993-10-01 16.778
1993-11-01 16.8
1993-12-01 16.815
1994-01-01 16.855
1994-02-01 16.862
1994-03-01 16.897
1994-04-01 16.933
1994-05-01 16.962
1994-06-01 17.01
1994-07-01 17.026
1994-08-01 17.081
1994-09-01 17.115
1994-10-01 17.144
1994-11-01 17.186
1994-12-01 17.217
1995-01-01 17.262
1995-02-01 17.265
1995-03-01 17.263
1995-04-01 17.278
1995-05-01 17.259
1995-06-01 17.247
1995-07-01 17.218
1995-08-01 17.24
1995-09-01 17.247
1995-10-01 17.216
1995-11-01 17.209
1995-12-01 17.231
1996-01-01 17.208
1996-02-01 17.229
1996-03-01 17.193
1996-04-01 17.204
1996-05-01 17.222
1996-06-01 17.226
1996-07-01 17.223
1996-08-01 17.255
1996-09-01 17.252
1996-10-01 17.268
1996-11-01 17.277
1996-12-01 17.284
1997-01-01 17.297
1997-02-01 17.316
1997-03-01 17.34
1997-04-01 17.349
1997-05-01 17.362
1997-06-01 17.387
1997-07-01 17.389
1997-08-01 17.452
1997-09-01 17.465
1997-10-01 17.513
1997-11-01 17.556
1997-12-01 17.588  
1998-01-01 17.619
1998-02-01 17.627
1998-03-01 17.637
1998-04-01 17.637
1998-05-01 17.624
1998-06-01 17.608
1998-07-01 17.422
1998-08-01 17.563
1998-09-01 17.557
1998-10-01 17.512
1998-11-01 17.465
1998-12-01 17.449
1999-01-01 17.427
1999-02-01 17.395
1999-03-01 17.368
1999-04-01 17.344
1999-05-01 17.333
1999-06-01 17.295
1999-07-01 17.308
1999-08-01 17.287
1999-09-01 17.281
1999-10-01 17.272
1999-11-01 17.282
1999-12-01 17.28
2000-01-01 17.284
2000-02-01 17.285
2000-03-01 17.302
2000-04-01 17.298
2000-05-01 17.279
2000-06-01 17.296
2000-07-01 17.322
2000-08-01 17.287
2000-09-01 17.23
2000-10-01 17.217
2000-11-01 17.202
2000-12-01 17.181
2001-01-01 17.104
2001-02-01 17.028
2001-03-01 16.938
2001-04-01 16.802
2001-05-01 16.661
2001-06-01 16.515
2001-07-01 16.382
2001-08-01 16.232
2001-09-01 16.117
2001-10-01 15.972
2001-11-01 15.825
2001-12-01 15.711
2002-01-01 15.587
2002-02-01 15.515
2002-03-01 15.443
2002-04-01 15.392
2002-05-01 15.337
2002-06-01 15.298
2002-07-01 15.256
2002-08-01 15.171
2002-09-01 15.119
2002-10-01 15.06
2002-11-01 14.992
2002-12-01 14.912
2003-01-01 14.866
2003-02-01 14.781
2003-03-01 14.721
2003-04-01 14.609
2003-05-01 14.557
2003-06-01 14.493
2003-07-01 14.402
2003-08-01 14.376
2003-09-01 14.347
2003-10-01 14.334
2003-11-01 14.316
2003-12-01 14.3
2004-01-01 14.29
2004-02-01 14.279
2004-03-01 14.287
2004-04-01 14.315
2004-05-01 14.342
2004-06-01 14.332
2004-07-01 14.33
2004-08-01 14.345
2004-09-01 14.331
2004-10-01 14.332
2004-11-01 14.307
2004-12-01 14.287
2005-01-01 14.257
2005-02-01 14.273
2005-03-01 14.269
2005-04-01 14.25
2005-05-01 14.256
2005-06-01 14.227
2005-07-01 14.226
2005-08-01 14.203
2005-09-01 14.175
2005-10-01 14.192
2005-11-01 14.187
2005-12-01 14.193
2006-01-01 14.21
2006-02-01 14.209
2006-03-01 14.214
2006-04-01 14.226
2006-05-01 14.203
2006-06-01 14.213
2006-07-01 14.188
2006-08-01 14.159
2006-09-01 14.125
2006-10-01 14.075
2006-11-01 14.041
2006-12-01 14.015
2007-01-01 14.008
2007-02-01 13.997
2007-03-01 13.97
2007-04-01 13.945
2007-05-01 13.929
2007-06-01 13.911
2007-07-01 13.889
2007-08-01 13.828
2007-09-01 13.79
2007-10-01 13.764
2007-11-01 13.757
2007-12-01 13.746
2008-01-01 13.725
2008-02-01 13.696
2008-03-01 13.659
2008-04-01 13.599
2008-05-01 13.564
2008-06-01 13.504
2008-07-01 13.43
2008-08-01 13.358
2008-09-01 13.275
2008-10-01 13.147
2008-11-01 13.034
2008-12-01 12.85
2009-01-01 12.561
2009-02-01 12.38
2009-03-01 12.208
2009-04-01 12.03
2009-05-01 11.862
2009-06-01 11.726
2009-07-01 11.668
2009-08-01 11.626
2009-09-01 11.591
2009-10-01 11.538
2009-11-01 11.509
2009-12-01 11.475
2010-01-01 11.46
2010-02-01 11.453
2010-03-01 11.453
2010-04-01 11.489
2010-05-01 11.525
2010-06-01 11.545
2010-07-01 11.561
2010-08-01 11.553
2010-09-01 11.563
2010-10-01 11.562
2010-11-01 11.585
2010-12-01 11.595
2011-01-01 11.618
2011-02-01 11.653
2011-03-01 11.67
2011-04-01 11.7
2011-05-01 11.712
2011-06-01 11.724
2011-07-01 11.742
2011-08-01 11.766
2011-09-01 11.771
2011-10-01 11.776
2011-11-01 11.774
2011-12-01 11.799
2012-01-01 11.834
2012-02-01 11.857
2012-03-01 11.899
2012-04-01 11.916
2012-05-01 11.93
2012-06-01 11.941
2012-07-01 11.965
2012-08-01 11.961
2012-09-01 11.948
2012-10-01 11.951
2012-11-01 11.947
2012-12-01 11.961
2013-01-01 11.98
2013-02-01 12.002
2013-03-01 12.006
2013-04-01 12.006
2013-05-01 12.007
2013-06-01 12.005
2013-07-01 11.983
2013-08-01 12.011
2013-09-01 12.022
2013-10-01 12.04
2013-11-01 12.072
2013-12-01 12.086
2014-01-01 12.102
2014-02-01 12.122
2014-03-01 12.131
2014-04-01 12.142
2014-05-01 12.154
2014-06-01 12.177
2014-07-01 12.191
2014-08-01 12.205
2014-09-01 12.214
2014-10-01 12.237
2014-11-01 12.282
2014-12-01 12.301
2015-01-01 12.295
2015-02-01 12.303
2015-03-01 12.311
2015-04-01 12.317
2015-05-01 12.334
2015-06-01 12.338
2015-07-01 12.357
2015-08-01 12.343
2015-09-01 12.35
2015-10-01 12.361
2015-11-01 12.357
2015-12-01 12.362
2016-01-01 12.384
2016-02-01 12.369
2016-03-01 12.344
2016-04-01 12.351
2016-05-01 12.333
2016-06-01 12.353
2016-07-01 12.37
2016-08-01 12.347
2016-09-01 12.344
2016-10-01 12.341
2016-11-01 12.341
2016-12-01 12.355
2017-01-01 12.368
2017-02-01 12.386
2017-03-01 12.395
2017-04-01 12.403
2017-05-01 12.405
2017-06-01 12.42
2017-07-01 12.417
2017-08-01 12.459
2017-09-01 12.467
2017-10-01 12.487
2017-11-01 12.517
2017-12-01 12.545
2018-01-01 12.561
2018-02-01 12.592
2018-03-01 12.612
2018-04-01 12.634
2018-05-01 12.655
2018-06-01 12.687
2018-07-01 12.707
2018-08-01 12.715
2018-09-01 12.733
2018-10-01 12.762
2018-11-01 12.789
2018-12-01 12.809
2019-01-01 12.826
2019-02-01 12.834
2019-03-01 12.831
2019-04-01 12.834
2019-05-01 12.836
2019-06-01 12.846
2019-07-01 12.85
2019-08-01 12.852
2019-09-01 12.854
2019-10-01 12.809
2019-11-01 12.867
2019-12-01 12.855
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Source: EPI analysis of Bureau of Labor Statistics 2020 Manufacturing Employment data series [CES3000000001].

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Trump has not brought these jobs back, nor will his present policies change the status quo. Globalization, and China trade in particular, have also hurt countless communities throughout the country, especially in the upper Midwest, mid-Atlantic, and Northeast regions. The nation has lost a generation of skilled manufacturing workers, many of whom have dropped out of the labor force and never returned. All of this globalized trade has reduced the wages of roughly 100 million Americans, all non-college educated workers, by roughly $2,000 per year.

In addition, more than half of the U.S. manufacturing jobs lost in the past two decades were due to the growing trade deficit with China, which eliminated 3.7 million U.S. jobs, including 2.8 million manufacturing jobs, between 2001 and 2018. In fact, the United States lost 700,000 jobs to China in the first two years of the Trump administration, as shown in our recent report. The phase one trade deal will not bring those jobs back, either.

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As investment continues to decline, the Trump tax cuts remain nothing but a handout to the rich

President Trump is likely to tout the benefits of the 2017 Tax Cuts and Jobs Act (TCJA) during his annual State of the Union Address. The centerpiece of the TCJA was a corporate rate cut that proponents claimed would eventually trickle down to workers’ wages—boosting the average American household’s wages by $4,000. We pointed out at the time that there was a lot wrong about this economic theory in practice. Even so, key to the theory is that investment would surge after the tax cuts were enacted. And without a substantial uptick in investment, the typical worker has no chance of benefiting from the TCJA’s corporate rate cuts. Instead, investment has cratered since the TCJA passed. In fact, last week’s GDP data showed that for the first time since the Great Recession, investment has declined for three straight quarters. Given that boosting business investment was the primary stated goal of the TCJA, this seems like an unambiguous policy failure for working people, benefiting only the rich and corporations.

Figure A

No evidence the TCJA is working as advertised: Year-over-year change in real, nonresidential fixed investment, 2003Q1–2019Q4

Quarter Real, nonresidential fixed investment
2003Q1 -2.3%
2003Q2 1.6%
2003Q3 4.0%
2003Q4 6.8%
2004Q1 5.2%
2004Q2 4.9%
2004Q3 5.7%
2004Q4 6.5%
2005Q1 9.2%
2005Q2 8.2%
2005Q3 7.4%
2005Q4 6.1%
2006Q1 8.0%
2006Q2 8.2%
2006Q3 7.8%
2006Q4 8.1%
2007Q1 6.5%
2007Q2 7.0%
2007Q3 6.8%
2007Q4 7.3%
2008Q1 5.8%
2008Q2 3.8%
2008Q3 0.2%
2008Q4 -7.0%
2009Q1 -14.4%
2009Q2 -17.1%
2009Q3 -16.1%
2009Q4 -10.3%
2010Q1 -2.3%
2010Q2 4.1%
2010Q3 7.5%
2010Q4 8.9%
2011Q1 8.0%
2011Q2 7.3%
2011Q3 9.3%
2011Q4 10.0%
2012Q1 12.9%
2012Q2 12.6%
2012Q3 7.2%
2012Q4 5.6%
2013Q1 4.3%
2013Q2 2.3%
2013Q3 4.4%
2013Q4 5.4%
2014Q1 5.5%
2014Q2  8.1%
2014Q3 8.4%
2014Q4 6.9%
2015Q1 5.0%
2015Q2 2.5%
2015Q3 0.8%
2015Q4 -0.9%
2016Q1 -0.7%
2016Q2 0.0%
2016Q3 1.1%
2016Q4 2.4%
2017Q1 4.2%
2017Q2 4.3%
2017Q3 3.5%
2017Q4  5.4%
2018Q1 6.0%
2018Q2 6.9%
2018Q3 6.8%
2018Q4 5.9%
2019Q1 4.8%
2019Q2 2.6%
2019Q3 1.4%
2019Q4 -0.1%
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Source: EPI analysis of data from table 1.1.6 from the National Income and Product Accounts (NIPA) from the Bureau of Economic Analysis (BEA).

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The state of the union for black workers: Myths and facts

As President Trump prepares to deliver his State of the Union address, here are three charts that show why the economy is still not “working great” for all black workers in America.

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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Notes: 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 with Jan.–Dec. 2019 data from Black Workers Endure Persistent Racial Disparities in Employment Outcomes (EPI, 2019)

EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau. Updated with Jan.–Dec. 2019 data 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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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

  • 2019 had solid job growth, but wage growth slowed. Average monthly job creation has held remarkably steady for the past nine years, but it did soften in the last year, from 223,000 in 2018 to 176,000 in 2019. Wage growth slowed for much of the year, providing further evidence that we are not yet at genuine full employment. After hitting a recent high point of 3.4% year-over-year wage growth, the growth rate has measurably decelerated and wage growth closed out the year at only 2.9% in December.
  • Wage growth for low-wage workers has been strongest in states with minimum wage increases
  • More on longer wage trends in our Nominal Wage Tracker.

Manufacturing and trade


Read more

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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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.

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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.

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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

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

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

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

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

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
ChartData Download data

The data below can be saved or copied directly into Excel.

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
ChartData Download data

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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

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

The data below can be saved or copied directly into Excel.

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%
ChartData Download data

The data below can be saved or copied directly into Excel.

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

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.

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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 [https://www.epi.org/publication/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

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