April 2020 Jobs Report and Industry Update
“THE EMPLOYMENT SITUATION — March 2020”
“Validation may be best way to support stressed out friends and family”
“Designing lightweight glass for efficient cars, wind turbines”
“COVID-19 vaccine candidate shows promise, research shows”
“How coworkers impact the value of your skills”
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Economics & Job Creation:
THE EMPLOYMENT SITUATION — MARCH 2020
Total nonfarm payroll employment fell by 701,000 in March, and the unemployment
rate rose to 4.4 percent, the U.S. Bureau of Labor Statistics reported today.
The changes in these measures reflect the effects of the coronavirus (COVID-19)
and efforts to contain it. Employment in leisure and hospitality fell by 459,000,
mainly in food services and drinking places. Notable declines also occurred in
health care and social assistance, professional and business services, retail
trade, and construction.
This news release presents statistics from two monthly surveys. The household
survey measures labor force status, including unemployment, by demographic
characteristics. The establishment survey measures nonfarm employment, hours,
and earnings by industry. Note that the March survey reference periods for both
surveys predated many coronavirus-related business and school closures that
occurred in the second half of the month. For more information about the concepts
and statistical methodology used in these two surveys, see the Technical Note.
Household Survey Data
In March, the unemployment rate increased by 0.9 percentage point to 4.4 percent.
This is the largest over-the-month increase in the rate since January 1975, when
the increase was also 0.9 percentage point. The number of unemployed persons rose
by 1.4 million to 7.1 million in March. The sharp increases in these measures
reflect the effects of the coronavirus and efforts to contain it. (See table A-1.
Measures from the household survey pertain to the week of March 8th to March 14th.
For more information about how the household survey and its measures were affected
by the coronavirus, see the box note at the end of this news release.)
In March, unemployment rates rose among all major worker groups. The rate was 4.0
percent for adult men, 4.0 percent for adult women, 14.3 percent for teenagers,
4.0 percent for Whites, 6.7 percent for Blacks, 4.1 percent for Asians, and 6.0
percent for Hispanics. (See tables A-1, A-2, and A-3.)
The number of unemployed persons who reported being on temporary layoff more than
doubled in March to 1.8 million. The number of permanent job losers increased by
177,000 to 1.5 million. (See table A-11.)
The number of unemployed persons who were jobless less than 5 weeks increased by
1.5 million in March to 3.5 million, accounting for almost half of the unemployed.
The number of long-term unemployed (those jobless for 27 weeks or more), at 1.2
million, was little changed in March and represented 15.9 percent of the unemployed.
(See table A-12.)
The labor force participation rate, at 62.7 percent, decreased by 0.7 percentage
point over the month. Total employment, as measured by the household survey, fell
by 3.0 million to 155.8 million, and the employment-population ratio, at 60.0
percent, dropped by 1.1 percentage points over the month. (See table A-1.)
The number of persons employed part time for economic reasons, at 5.8 million,
increased by 1.4 million in March. These individuals, who would have preferred
full-time employment, were working part time because their hours had been reduced
or they were unable to find full-time jobs. (See table A-8.)
In March, 1.4 million persons were marginally attached to the labor force, little
changed from the previous month. These individuals were not in the labor force,
wanted and were available for work, and had looked for a job sometime in the prior
12 months but had not looked for work in the 4 weeks preceding the survey. Discouraged
workers, a subset of the marginally attached who believed that no jobs were available
for them, numbered 514,000 in March, up by 109,000 from the previous month. (See
Summary table A.)
Establishment Survey Data
Total nonfarm payroll employment fell sharply in March (-701,000), reflecting the
effects of the coronavirus and efforts to contain it. About two-thirds of the drop
occurred in leisure and hospitality, mainly in food services and drinking places.
Notable employment declines also occurred in health care and social assistance,
professional and business services, retail trade, and construction. In the prior
12 months, nonfarm employment growth had averaged 196,000 per month. (See table
B-1. Measures from the establishment survey pertain to the pay period including
March 12th; pay periods may be weekly, bi-weekly, semi-monthly, or monthly. For
more information about the establishment survey reference period and how survey
operations were affected by the coronavirus, see the box note at the end of this
In March, employment in leisure and hospitality fell by 459,000. Most of the
decline occurred in food services and drinking places (-417,000); this employment
decline nearly offset gains over the previous 2 years. Employment in the
accommodation industry also declined in March (-29,000).
Employment in health care and social assistance fell by 61,000 in March. Health
care employment declined by 43,000, with job losses in offices of dentists
(-17,000), offices of physicians (-12,000), and offices of other health care
practitioners (-7,000). Over the prior 12 months, health care employment had
grown by 374,000. In March, social assistance saw an employment decline of 19,000,
reflecting a job loss in child day care services (-19,000). Over the prior 12
months, social assistance added 193,000 jobs.
Employment in professional and business services decreased by 52,000 in March,
with the decline concentrated in temporary help services (-50,000). Employment
also decreased in travel arrangement and reservation services (-7,000).
In March, employment in retail trade declined by 46,000. Job losses occurred
in clothing and clothing accessories stores (-16,000); furniture stores (-10,000);
and sporting goods, hobby, book, and music stores (-9,000). General merchandise
stores gained 10,000 jobs.
Employment decreased over the month in construction (-29,000). In March,
nonresidential building (-11,000) and heavy and civil engineering construction
(-10,000) lost jobs. Construction employment had increased by 211,000 over
the prior 12 months.
Employment in the other services industry declined by 24,000 in March, with
about half of the loss occurring in personal and laundry services (-13,000).
Over the prior 12 months, other services had added 89,000 jobs.
Mining lost 6,000 jobs in March, with much of the decline occurring in support
activities for mining (-5,000). Since a recent peak in January 2019, mining
employment has declined by 42,000.
In March, manufacturing employment edged down (-18,000). Over the past 12 months,
employment in the industry has shown little net change.
Federal government employment rose by 18,000 in March, reflecting the hiring of
17,000 workers for the 2020 Census.
Employment in other major industries, including wholesale trade, transportation
and warehousing, information, and financial activities, changed little over the
In March, average hourly earnings for all employees on private nonfarm payrolls
increased by 11 cents to $28.62. Over the past 12 months, average hourly earnings
have increased by 3.1 percent. Average hourly earnings of private-sector production
and nonsupervisory employees increased by 10 cents to $24.07 in March. (See tables
B-3 and B-8.)
The average workweek for all employees on private nonfarm payrolls fell by 0.2
hour to 34.2 hours in March. The decline in the average workweek was most
pronounced in leisure and hospitality, where average weekly hours dropped by
1.4 hours. In manufacturing, the workweek declined by 0.3 hour to 40.4 hours,
and overtime declined by 0.2 hour to 3.0 hours. The average workweek for production
and nonsupervisory employees on private nonfarm payrolls decreased by 0.3 hour
to 33.4 hours. (See tables B-2 and B-7.)
The change in total nonfarm payroll employment for January was revised down by
59,000 from +273,000 to +214,000, and the change for February was revised up
by 2,000 from +273,000 to +275,000. With these revisions, employment gains in
January and February combined were 57,000 lower than previously reported.
(Monthly revisions result from additional reports received from businesses and
government agencies since the last published estimates and from the recalculation
of seasonal factors.) After revisions, job gains averaged 245,000 per month for
January and February.
The Employment Situation for April is scheduled to be released on
Friday, May 8, 2020, at 8:30 a.m. (EDT).
“Validation may be best way to support stressed out friends and family”
In uncertain times, supporting your friends and family can help them make it through. But your comforting words can have different effects based on how you phrase them, according to new Penn State research.
The researchers studied how people responded to a variety of different messages offering emotional support. They found that messages that validated a person’s feelings were more effective and helpful than ones that were critical or diminished emotions.
The findings were recently published in the keystone paper of a virtual special issue of the Journal of Communication. The researchers said the results could help people provide better support to their friends and families.
“One recommendation is for people to avoid using language that conveys control or uses arguments without sound justification,” said Xi Tian, a graduate assistant in communication arts and sciences. “For example, instead of telling a distressed person how to feel, like ‘don’t take it so hard’ or ‘don’t think about it,’ you could encourage them to talk about their thoughts or feelings so that person can come to their own conclusions about how to change their feelings or behaviors.”
Tian said that previous research has shown that social support can help alleviate emotional distress, increase physical and psychological well-being, and improve personal relationships. But — depending on how support is phrased or worded — it could be counterproductive, such as actually increasing stress or reducing a person’s confidence that they can manage their stressful situation.
Denise Solomon, department head and professor of communication arts and sciences, said they were trying to learn more about why well-intentioned attempts to comfort others are sometimes seen as insensitive or unhelpful.
“We wanted to examine the underlying mechanism that explains why some supportive messages may produce unintended consequences,” Solomon said. “We also wanted to understand how people cognitively and emotionally respond to insensitive social support.”
For the study, the researchers recruited 478 married adults who had recently experienced an argument with their spouse. Before completing an online questionnaire, participants were asked to think about someone with whom they had previously discussed their marriage or spouse. Then, they were presented with one of six possible supportive messages and were asked to imagine that person giving them that message.
Lastly, the participants were asked to rate their given message on a variety of characteristics.
“We manipulated the messages based on how well the support message validates, recognizes, or acknowledges the support recipients’ emotions, feelings, and experiences,” Tian said. “Essentially, the messages were manipulated to exhibit low, moderate, or high levels of person-centeredness, and we created two messages for each level of person-centeredness.”
According to the researchers, a highly person-centered message recognizes the other person’s feelings and helps the person explore why they might be feeling that way. For example, “Disagreeing with someone you care about is always hard. It makes sense that you would be upset about this.” Meanwhile, a low person-centered message is critical and challenges the person’s feelings. For example, “Nobody is worth getting so worked up about. Stop being so depressed.”
After analyzing the data, the researchers found that low person-centered support messages did not help people manage their marital disagreement in a way that reduced emotional distress.
“In fact, those messages were perceived as dominating and lacking argument strength,” Tian said. “Those messages induced more resistance to social support, such that the participants reported feeling angry after receiving the message. They also reported actually criticizing the message while reading it.”
In contrast, high person-centered messages produced more emotional improvement and circumvented reactance to social support.
“Another recommendation that can be taken from this research is that people may want to use moderately to highly person-centered messages when helping others cope with everyday stressors,” said Solomon.
The researchers said people can try using language that expresses sympathy, care and concern. For example, “I’m sorry you are going through this. I’m worried about you and how you must be feeling right now.” Acknowledging the other person’s feelings or offering perspective — like saying “It’s understandable that you are stressed out since it’s something you really care about” — may also be helpful.
Kellie St.Cyr Brisini, postdoctoral teaching fellow in communication arts and sciences, also participated in this work.
Penn State’s Department of Communication Arts and Sciences helped support this research.
“Designing lightweight glass for efficient cars, wind turbines”
A new machine-learning algorithm for exploring lightweight, very stiff glass compositions can help design next-gen materials for more efficient vehicles and wind turbines. Glasses can reinforce polymers to generate composite materials that provide similar strengths as metals but with less weight.
Liang Qi, a professor of materials science and engineering at the University of Michigan, answered questions about his group’s new paper in npj Computational Materials.
What is elastic stiffness? Elastic and glass don’t seem to be two words that go together.
All solid materials, including glass, have a property called elastic stiffness — also known as elastic modulus. It’s a measure of how much force per unit area is needed to make the material bend or stretch. If that change is elastic, the material can totally recover its original shape and size once you stop the force.
Why do we want light and very stiff glasses?
Elastic stiffness is critical for any materials in structural applications. Higher stiffness means that you can sustain the same force loading with a thinner material. For example, the structural glass in car windshields, and in touch screens on smartphones and other screens, can be made thinner and lighter if the glasses are stiffer. Glass fiber composites are widely used lightweight materials for cars, trucks and wind turbines, and we can make these parts even lighter.
Lighter vehicles can go further on a gallon of gas — 6-8% further for a 10% reduction in weight, according to the U.S. Office of Energy Efficiency and Renewable Energy. Weight reduction can also significantly increase the range of electric vehicles.
Lighter, stiffer glass can enable wind turbine blades to transfer wind power into electricity more efficiently because less wind power is “wasted” to make the blades rotate. It can also enable longer wind turbine blades, which can generate more electricity under the same wind speed.
What are the challenges in trying to design light but resilient glasses?
Because glasses are amorphous — or disordered — materials, it’s hard to predict their atomistic structures and the corresponding physical/chemical properties. We use computer simulations to speed up the study of glasses, but they require so much computing time that it is impossible to investigate each possible glass composition.
The other problem is that we don’t have enough data about glass compositions for machine learning to be effective at predicting glass properties for new glass compositions. Machine learning algorithms are fed data, and they find patterns in the data that enable them to make predictions. But without enough of the right training data, their predictions aren’t reliable — just like a political poll conducted in Ohio cannot predict the election in Michigan.
How did you overcome these barriers?
First, we used existing high-throughput computer simulations to generate data on the densities and elastic stiffnesses of various glasses. Second, we developed the machine learning model that is more suitable for a small amount of data — because we still didn’t have a lot of data by machine learning standards. We designed it so that the key thing it pays attention to is the strength of the interaction between atoms. In essence, we used physics to give it hints about what was important in the data, and that improves the quality of its predictions for new compositions.
What can your model do?
While we trained our machine learning model with glasses made of silicon dioxide and one or two other additives, we found that it could accurately predict the lightness and elastic stiffness of more complex glasses, with more than 10 different components. It can screen as many as 100,000 different compositions at once.
What are the next steps?
Lightness and elastic stiffness are only two properties that are important in designing glasses. We also need to know their strength, toughness, and their melting temperatures. By openly sharing our data and methods, we hope to inspire the development of new models in the glass research community.
“COVID-19 vaccine candidate shows promise, research shows”
University of Pittsburgh School of Medicine scientists today announced a potential vaccine against SARS-CoV-2, the new coronavirus causing the COVID-19 pandemic. When tested in mice, the vaccine, delivered through a fingertip-sized patch, produces antibodies specific to SARS-CoV-2 at quantities thought to be sufficient for neutralizing the virus.
The paper appeared today in EBioMedicine, which is published by The Lancet, and is the first study to be published after critique from fellow scientists at outside institutions that describes a candidate vaccine for COVID-19. The researchers were able to act quickly because they had already laid the groundwork during earlier coronavirus epidemics.
“We had previous experience on SARS-CoV in 2003 and MERS-CoV in 2014. These two viruses, which are closely related to SARS-CoV-2, teach us that a particular protein, called a spike protein, is important for inducing immunity against the virus. We knew exactly where to fight this new virus,” said co-senior author Andrea Gambotto, M.D., associate professor of surgery at the Pitt School of Medicine. “That’s why it’s important to fund vaccine research. You never know where the next pandemic will come from.”
“Our ability to rapidly develop this vaccine was a result of scientists with expertise in diverse areas of research working together with a common goal,” said co-senior author Louis Falo, M.D., Ph.D., professor and chair of dermatology at Pitt’s School of Medicine and UPMC.
Compared to the experimental mRNA vaccine candidate that just entered clinical trials, the vaccine described in this paper — which the authors are calling PittCoVacc, short for Pittsburgh Coronavirus Vaccine — follows a more established approach, using lab-made pieces of viral protein to build immunity. It’s the same way the current flu shots work.
The researchers also used a novel approach to deliver the drug, called a microneedle array, to increase potency. This array is a fingertip-sized patch of 400 tiny needles that delivers the spike protein pieces into the skin, where the immune reaction is strongest. The patch goes on like a Band-Aid and then the needles — which are made entirely of sugar and the protein pieces — simply dissolve into the skin.
“We developed this to build on the original scratch method used to deliver the smallpox vaccine to the skin, but as a high-tech version that is more efficient and reproducible patient to patient,” Falo said. “And it’s actually pretty painless — it feels kind of like Velcro.”
The system also is highly scalable. The protein pieces are manufactured by a “cell factory” — layers upon layers of cultured cells engineered to express the SARS-CoV-2 spike protein — that can be stacked further to multiply yield. Purifying the protein also can be done at industrial scale. Mass-producing the microneedle array involves spinning down the protein-sugar mixture into a mold using a centrifuge. Once manufactured, the vaccine can sit at room temperature until it’s needed, eliminating the need for refrigeration during transport or storage.
“For most vaccines, you don’t need to address scalability to begin with,” Gambotto said. “But when you try to develop a vaccine quickly against a pandemic that’s the first requirement.”
When tested in mice, PittCoVacc generated a surge of antibodies against SARS-CoV-2 within two weeks of the microneedle prick.
Those animals haven’t been tracked long term yet, but the researchers point out that mice who got their MERS-CoV vaccine produced a sufficient level of antibodies to neutralize the virus for at least a year, and so far the antibody levels of the SARS-CoV-2 vaccinated animals seem to be following the same trend.
Importantly, the SARS-CoV-2 microneedle vaccine maintains its potency even after being thoroughly sterilized with gamma radiation — a key step toward making a product that’s suitable for use in humans.
The authors are now in the process of applying for an investigational new drug approval from the U.S. Food and Drug Administration in anticipation of starting a phase I human clinical trial in the next few months.
“Testing in patients would typically require at least a year and probably longer,” Falo said. “This particular situation is different from anything we’ve ever seen, so we don’t know how long the clinical development process will take. Recently announced revisions to the normal processes suggest we may be able to advance this faster.”
Additional authors on the study are Eun Kim, Geza Erdos, Ph.D., Shaohua Huang, Thomas Kenniston, Stephen Balmert, Ph.D., Cara Donahue Carey, Michael Epperly, Ph.D., William Klimstra, Ph.D., and Emrullah Korkmaz, Ph.D., all of Pitt; and Bart Haagmans, of Erasmus Medical Center.
Funding for this study was provided by National Institute of Allergy and Infectious Diseases grant R21-AI114264, National Institute of Arthritis and Musculoskeletal and Skin Diseases grants R01-AR074285, R01-AR071277 and R01-AR068249, and National Cancer Institute grant T32-CA175294.
“How coworkers impact the value of your skills”
In today’s world, most workers are highly specialized, but this specialization can come at a cost — especially for those on the wrong team. New research by Harvard’s Growth Lab uncovers the importance of teams and coworkers when it comes to one’s productivity, earning potential, and stays of employment.
The research — recently published in the journal Science Advances — analyzed administrative data on the 9 million inhabitants of Sweden. By constructing networks of complementarity and substitutability among specific educational tracks, the research assessed the importance of the skills of coworkers. It found that to earn high wages and returns on education, workers must find coworkers who complement, but not substitute, them. The returns to having complementary coworkers are large: the impact is comparable to having a college degree.
The research offers a tool to assess the right and wrong coworkers in fields of expertise. The right coworkers are those with skills you lack, yet needed to complete a team. The wrong coworkers are those who replicate your skillset and ultimately lower your value to the employer. For example, those with a degree in Architecture are best complemented by workers with engineering, construction, or surveying degrees, and negatively impacted by those with landscape or interior design degrees.
“We tend to think of skills as being something personal that individuals can market to a company,’ said Frank Neffke, Growth Lab Research Director. “However, this vision of skills is too simplistic. One person’s skills connect to another person’s skills, etc., and the better these connections, the more productive workers will be, and the more they will earn.”
Complementarity also drives careers. The research shows that people tend to stay longer in organizations with many complementary workers and tend to leave those with many workers who substitute them. These results hold true for up to 20 years of one’s career.
The research also supports several well-known facts, such as cities and large firms pay higher wages. Workers are more likely to find better fitting teams in cities, and large firms often allow workers to specialize.
Neffke adds that the benefits of working with complementary coworkers are not the same for all workers. Those with higher levels of education seem to benefit much more from working in complementary teams than workers with lower levels. Over the past 20 years, workers with college degrees or higher have been increasingly able to find better matching coworkers.