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.

Read more

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%
ChartData Download data

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

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.

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

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.

Read more

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.

Sign up here for EPI’s newsletter so you don’t miss the report’s release, or any other essential labor research, analysis and trends from EPI’s research team.

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.

Read more

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.

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

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.

Tagged

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.

Read more

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%
ChartData Download data

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

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

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

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
ChartData Download data

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

Source: EPI analysis of Bureau of Labor Statistics 2020 Manufacturing Employment data series [CES3000000001].

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

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.

Read more