May 2021 Jobs Report & Industry Update

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Economics & Job Creation
“The Employment Situation — April 2021”

Life Sciences
“Testing tool can quickly distinguish between viral and bacterial infections”

“Artificial intelligence to monitor water quality more effectively”

“Targeted methods to control SARS-CoV-2 spread”

The Industrials
“When algorithms go bad: How consumers respond”

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Economics & Job Creation
“The Employment Situation — April 2021”


Total nonfarm payroll employment rose by 266,000 in April, and the unemployment rate was
little changed at 6.1 percent, the U.S. Bureau of Labor Statistics reported today. Notable
job gains in leisure and hospitality, other services, and local government education were
partially offset by employment declines in temporary help services and in couriers and

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. For more
information about the concepts and statistical methodology used in these two surveys, see
the Technical Note.

Household Survey Data

Both the unemployment rate, at 6.1 percent, and the number of unemployed persons, at 9.8
million, were little changed in April. These measures are down considerably from their
recent highs in April 2020 but remain well above their levels prior to the coronavirus 
(COVID-19) pandemic (3.5 percent and 5.7 million, respectively, in February 2020). (See
table A-1. See the box note at the end of this news release for more information about 
how the household survey and its measures were affected by the coronavirus pandemic.)

Among the major worker groups, the unemployment rates for adult men (6.1 percent), adult
women (5.6 percent), teenagers (12.3 percent), Whites (5.3 percent), Blacks (9.7 percent),
Asians (5.7 percent), and Hispanics (7.9 percent) showed little or no change in April. 
(See tables A-1, A-2, and A-3.)

Among the unemployed, the number of persons on temporary layoff, at 2.1 million, changed
little in April. This measure is down considerably from the recent high of 18.0 million
in April 2020 but is 1.4 million higher than in February 2020. The number of permanent 
job losers, at 3.5 million, was also little changed over the month but is 2.2 million 
higher than in February 2020. (See table A-11.)

In April, the number of persons jobless less than 5 weeks increased by 237,000 to 2.4
million, while the number of persons jobless 15 to 26 weeks declined by 188,000 to 1.2
million. The number of long-term unemployed (those jobless for 27 weeks or more), at 
4.2 million, was essentially unchanged in April but is 3.1 million higher than in 
February 2020. These long-term unemployed accounted for 43.0 percent of the total 
unemployed in April. (See table A-12.)

The labor force participation rate was little changed at 61.7 percent in April and is
1.6 percentage points lower than in February 2020. The employment-population ratio was
also little changed in April at 57.9 percent but is up by 0.5 percentage point since
December 2020. However, this measure is 3.2 percentage points below its February 2020
level. (See table A-1.)

The number of persons employed part time for economic reasons decreased by 583,000 to
5.2 million in April. This decline reflected a drop in the number of people whose 
hours were cut due to slack work or business conditions. The number of persons employed
part time for economic reasons is 845,000 higher than in February 2020. 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 April, the number of persons not in the labor force who currently want a job was 6.6
million, little changed over the month but up by 1.6 million since February 2020. These
individuals were not counted as unemployed because they were not actively looking for
work during the last 4 weeks or were unavailable to take a job. (See table A-1.) 

Among those not in the labor force who currently want a job, the number of persons 
marginally attached to the labor force, at 1.9 million, was essentially unchanged in
April but is up by 419,000 since February 2020. These individuals 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. The number of discouraged workers,
a subset of the marginally attached who believed that no jobs were available for them,
was little changed at 565,000 in April but is 164,000 higher than in February 2020. 
(See Summary table A.)

Household Survey Supplemental Data

In April, 18.3 percent of employed persons teleworked because of the coronavirus 
pandemic, down from 21.0 percent in the prior month. These data refer to employed persons
who teleworked or worked at home for pay at some point in the last 4 weeks specifically 
because of the pandemic.

In April, 9.4 million persons reported that they had been unable to work because their
employer closed or lost business due to the pandemic--that is, they did not work at all
or worked fewer hours at some point in the last 4 weeks due to the pandemic. This measure
is down from 11.4 million in the previous month. Among those who reported in April that 
they were unable to work because of pandemic-related closures or lost business, 9.3 percent
received at least some pay from their employer for the hours not worked, little changed 
from the previous month.

Among those not in the labor force in April, 2.8 million persons were prevented from 
looking for work due to the pandemic. This measure is down from 3.7 million the month 
before. (To be counted as unemployed, by definition, individuals must be either actively 
looking for work or on temporary layoff.)

These supplemental data come from questions added to the household survey beginning in May
2020 to help gauge the effects of the pandemic on the labor market. The data are not
seasonally adjusted. Tables with estimates from the supplemental questions for all months 
are available online at

Establishment Survey Data

Total nonfarm payroll employment increased by 266,000 in April, following increases of 
770,000 in March and 536,000 in February. In April, nonfarm employment is down by 8.2 
million, or 5.4 percent, from its pre-pandemic level in February 2020. In April, notable
job gains in leisure and hospitality, other services, and local government education were
partially offset by losses in temporary help services and in couriers and messengers. 
(See table B-1. See the box note at the end of this news release for more information about
how the establishment survey and its measures were affected by the coronavirus pandemic.)

In April, employment in leisure and hospitality increased by 331,000, as pandemic-related
restrictions continued to ease in many parts of the country. More than half of the increase
was in food services and drinking places (+187,000). Job gains also occurred in amusements,
gambling, and recreation (+73,000) and in accommodation (+54,000). Although leisure and 
hospitality has added 5.4 million jobs over the year, employment in the industry is down
by 2.8 million, or 16.8 percent, since February 2020.

In April, employment increased by 44,000 in the other services industry, with gains in 
repair and maintenance (+14,000) and personal and laundry services (+14,000). Employment
in other services is 352,000 below its February 2020 level.

Employment in local government education increased by 31,000 in April but is 611,000 lower
than in February 2020. Federal government employment increased by 9,000 over the month. 

In April, employment in social assistance rose by 23,000, with about half of the increase
in child day care services (+12,000). Employment in social assistance is 286,000 lower 
than in February 2020. 

Employment in financial activities rose by 19,000 over the month, with most of the gain 
occurring in real estate and rental and leasing (+17,000). Employment in financial 
activities is down by 63,000 since February 2020. 

Within professional and business services, employment in temporary help services declined
by 111,000 in April and is 296,000 lower than in February 2020. Business support services
lost jobs in April (-15,000), while architectural and engineering services and scientific
research and development services added jobs (+12,000 and +7,000, respectively). 

Within transportation and warehousing, employment in couriers and messengers fell by 
77,000 in April but is up by 126,000 since February 2020. Air transportation added 7,000
jobs over the month. 

Manufacturing employment edged down in April (-18,000), following gains in the previous 2
months (+54,000 in March and +35,000 in February). In April, job losses in motor vehicles
and parts (-27,000) and in wood products (-7,000) more than offset job gains in 
miscellaneous durable goods manufacturing (+13,000) and chemicals (+4,000). Employment in
manufacturing is 515,000 lower than in February 2020.

Retail trade employment changed little in April (-15,000), following a gain in the prior 
month (+33,000). In April, employment declined in food and beverage stores (-49,000), 
general merchandise stores (-10,000), and gasoline stations (-9,000). These losses were 
partially offset by employment increases in sporting goods, hobby, book, and music stores
(+20,000); clothing and clothing accessories stores (+10,000); and health and personal 
care stores (+9,000). Employment in retail trade overall is 400,000 lower than in February

Employment in health care changed little in April (-4,000), as a job gain in ambulatory 
health care services (+21,000) was largely offset by a job loss in nursing care facilities
(-19,000). Health care employment is down by 542,000 since February 2020.

Employment in construction was unchanged over the month. Employment in the industry is up
by 917,000 over the year but is 196,000 below its February 2020 level.

In April, employment changed little in other major industries, including mining, 
wholesale trade, and information.

In April, average hourly earnings for all employees on private nonfarm payrolls 
increased by 21 cents to $30.17, following a decline of 4 cents in the prior month. In
April, average hourly earnings for private-sector production and nonsupervisory employees
rose by 20 cents to $25.45. The data for April suggest that the rising demand for labor 
associated with the recovery from the pandemic may have put upward pressure on wages. 
Since average hourly earnings vary widely across industries, the large employment 
fluctuations since February 2020 complicate the analysis of recent trends in average 
hourly earnings. (See tables B-3 and B-8.)

The average workweek for all employees on private nonfarm payrolls increased by 0.1 hour 
to 35.0 hours in April. In manufacturing, the workweek and overtime were both unchanged 
over the month, at 40.5 hours and 3.2 hours, respectively. The average workweek for 
production and nonsupervisory employees on private nonfarm payrolls was unchanged at 34.4 
hours. (See tables B-2 and B-7.)

The change in total nonfarm payroll employment for February was revised up by 68,000, from
+468,000 to +536,000, and the change for March was revised down by 146,000, from +916,000
to +770,000. With these revisions, employment in February and March combined is 78,000 
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.)

The Employment Situation for May is scheduled to be released on Friday, June 4, 2021, at
8:30 a.m. (ET).

Employment Situation Summary (

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Life Sciences
“Testing tool can quickly distinguish between viral and bacterial infections”

When patients complain of coughing, runny nose, sneezing and fever, doctors are often stumped because they have no fundamental tool to identify the source of the respiratory symptoms and guide appropriate treatments.

That tool might finally be on its way. In a study proving feasibility, researchers at Duke Health showed that their testing technology can accurately distinguish between a viral and a bacterial infection for respiratory illness – a critical difference that determines whether antibiotics are warranted. And, importantly, the test provided results in under an hour.

“This is exciting progress,” said study lead Ephraim Tsalik, associate professor in the departments of Medicine and Molecular Genetics and Microbiology at Duke University School of Medicine.

“We’ve been working on this for over a decade,” Tsalik said. “We knew in 2016 that our test worked in the research setting, but it’s always been our goal to have a test that could produce results rapidly, while patients are at their doctor’s office. It’s important that the distinction can be made quickly to ensure that antibiotics are not inappropriately prescribed.”

Tsalik and colleagues published results of their study in the journal Critical Care Medicine, which confirm the test’s accuracy with results available in under an hour.

The researchers have developed a gene expression method that diverges from current diagnostic strategies, which focus on identifying specific pathogens. The current tests are time-consuming and can only identify a pathogen if it’s specifically targeted by the test in the first place.

Host gene expression, however, looks for a distinct immune signal that is unique to the type of infection the body is fighting. The immune system activates one set of genes when fighting bacterial infections and a different set of genes in response to a viral infection. After the team discovered these gene expression signatures for bacterial and viral infection, they collaborated with BioFire Diagnostics, a company that specializes in molecular diagnostics, to develop this first-of-its kind test.

In a multisite study of more than 600 patients presenting to hospital emergency departments with respiratory infections, the tests identified bacterial infections with 80% accuracy and viral infections with nearly 87% accuracy. The current standard tests have about 69-percent accuracy. Tests provided results in less than an hour, and their accuracy was confirmed retrospectively using two different methods.

“Acute respiratory illness is the most common reason that people visit a health care provider when feeling sick,” Tsalik said. “Patients with these symptoms are inappropriately treated with antibiotics far too often due to challenges in discriminating the cause of illness, fueling antibiotic resistance. Our study shows that a rapid test to distinguish between these two sources of illness is possible and could improve clinical care.”

Tsalik said additional studies are underway to validate this approach in additional groups of patients. The researchers are also working to adapt the technology to produce more specific information, including whether the virus causing illness is influenza or SARS-CoV-2.

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“Artificial intelligence to monitor water quality more effectively”

Artificial intelligence that enhances remote monitoring of water bodies — highlighting quality shifts due to climate change or pollution — has been developed by researchers at the University of Stirling.

A new algorithm — known as the ‘meta-learning’ method — analyses data directly from satellite sensors, making it easier for coastal zone, environmental and industry managers to monitor issues such as harmful algal blooms (HABs) and possible toxicity in shellfish and finfish.

Environmental protection agencies and industry bodies currently monitor the ‘trophic state’ of water — its biological productivity — as an indicator of ecosystem health. Large clusters of microscopic algae, or phytoplankton, is called eutrophication and can turn into HABs, an indicator of pollution and which pose risk to human and animal health.

HABs are estimated to cost the Scottish shellfish industry £1.4 million per year, and a single HAB event in Norway killed eight million salmon in 2019, with a direct value of over £74 million.

Lead author Mortimer Werther, a PhD Researcher in Biological and Environmental Sciences at Stirling’s Faculty of Natural Sciences, said: “Currently, satellite-mounted sensors, such as the Ocean and Land Instrument (OLCI), measure phytoplankton concentrations using an optical pigment called chlorophyll-a. However, retrieving chlorophyll-a across the diverse nature of global waters is methodologically challenging.

“We have developed a method that bypasses the chlorophyll-a retrieval and enables us to estimate water health status directly from the signal measured at the remote sensor.”

Eutrophication and hypereutrophication is often caused by excessive nutrient input, for example from agricultural practices, waste discharge, or food and energy production. In impacted waters, HABs are common, and cyanobacteria may produce cyanotoxins which affect human and animal health. In many locations, these blooms are of concern to the finfish and shellfish aquaculture industries.

Mr Werther said: “To understand the impact of climate change on freshwater aquatic environments such as lakes, many of which serve as drinking water resources, it is essential that we monitor and assess key environmental indicators, such as trophic status, on a global scale with high spatial and temporal frequency.

“This research, funded by the European Union’s Horizon 2020 programme, is the first demonstration that trophic status of complex inland and nearshore waters can be learnt directly by machine learning algorithms from OLCI reflectance measurements. Our algorithm can produce estimates for all trophic states on imagery acquired by OLCI over global water bodies.

“Our method outperforms a comparable state-of-the-art approach by 5-12% on average across the entire spectrum of trophic states, as it also eliminates the need to choose the right algorithm for water observation. It estimates trophic status with over 90% accuracy for highly affected eutrophic and hypereutrophic waters.”

The collaborative study was carried out with five external partners from research and industry: Dr. Stefan G.H. Simis from Plymouth Marine Laboratory; Harald Krawczyk from the German Aerospace Center; Dr. Daniel Odermatt from the Swiss Federal Institute of Aquatic Science and Technology; Kerstin Stelzer from Brockmann Consult and Oberon Berlage from Appjection (Amsterdam).

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“Targeted methods to control SARS-CoV-2 spread”

At the beginning of the COVID-19 pandemic, intense social distancing and lockdown measures were the primary weapon in the fight against the spread of SARS-CoV-2, but they came with a monumental societal burden. New research from the Center for the Ecology of Infectious Diseases and the College of Public Health at the University of Georgia explores if there could have been a better way.

Published in the journal Proceedings of the Royal Society B, the research analyzes more palatable alternatives to the kind of social distancing mandates that threw a wrench at how businesses, schools and even family gatherings work. The alternatives — widespread testing, contact tracing, quarantines, certification for non-infected people and other public health policy measures — can slow the spread when combined together, but only with significant investments and broad public compliance.

“I understand why government leaders quickly enacted strict social distancing mandates as the COVID-19 pandemic was rapidly spreading in 2020,” said lead author John Drake, director of the Center for the Ecology of Infectious Diseases and Distinguished Research Professor in the Odum School of Ecology. “This was the best that we could do at the time. However, school and workplace closures, gathering limits and shelter-in-place orders have had extreme economic consequences. These are harsh, and we really need to find alternative solutions.”

Drake worked with other researchers to develop two models. One targeted how to find infected people to limit transmission through active case finding (through testing of at-risk individuals), thorough contact tracing when cases arise, and quarantines for people infected and their traced contacts.

The second model focused on a strategy of limiting exposure by certifying healthy individuals.

“Each model was tested independently and in combination with general non-pharmaceutical interventions (NPIs),” said co-author Kyle Dahlin, a postdoctoral associate with the center.

For this study, those interventions were defined as behavioral or generalized interventions that can be broadly adopted, such as wearing a face mask, hand washing, enhanced sick leave, micro distancing and contactless transactions.

“When we ran the model to evaluate the effectiveness of only using social distancing measures, like workplace closures, after the onset of the first wave, approximately half of the population eventually became infected,” said study co-author Andreas Handel, associate professor of biostatistics and epidemiology in UGA’s College of Public Health who helped design the models. “When we combined social distancing with general interventions, SARS-CoV-2 transmission was slowed, but not enough for complete suppression.”

When they tested the model that actively looked for infection, they found that active case-finding had to identify approximately 95% of infected persons to stop viral spread. When combined with NPIs, like face masks, the fraction of active cases that needed to be located dropped to 80%. Considering that during the first wave of the pandemic in 2020, only 1% to 10% of positive cases were found, such an approach by itself wouldn’t work.

The researchers also determined that adding contact tracing and quarantine to active case finding and general NPIs did not drastically change the model’s success.

The model that targeted healthy people to limit exposure determined that to successfully control viral spread, SARS-CoV-2 test validity had to occur within a very narrow window of seven to 10 days with a waiting time of three days or less, and NPIs had to be strictly adopted. Otherwise, a large outbreak would occur.

Pej Rohani, Regents’ and Georgia Athletic Association Professor of Ecology and Infectious Diseases in the Odum School and College of Veterinary Medicine, said that the models’ conclusions indicated the need for continued research.

“These models are important because infectious disease ecologists and epidemiologists need to understand how SARS-CoV-2 transmission can be reduced using measures that do not have extreme societal consequences,” he said.

The CEID’s research highlighted the importance of a robust and widespread testing program, the general adoption of NPIs like face masks, and targeted measures to globally control the ongoing pandemic. These approaches are still extremely important as vaccines continue to be distributed.

This research was funded by the National Institutes of Health under Award Numbers U01GM110744 and R01GM123007 and R01 GM 12480-03S1.

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The Industrials

“When algorithms go bad: How consumers respond”

Researchers from University of Texas-Austin and Copenhagen Business School published a new paper in the Journal of Marketing that offers actionable guidance to managers on the deployment of algorithms in marketing contexts.

The study, forthcoming in the Journal of Marketing, is titled “When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors” and is authored by Raji Srinivasan and Gulen Sarial-Abi.

Marketers increasingly rely on algorithms to make important decisions. A perfect example is the Facebook News Feed. You do not know why some of your posts show up on some people’s News Feeds or not, but Facebook does. Or how about Amazon recommending books and products for you? All of these are driven by algorithms. Algorithms are software and are far from perfect. Like any software, they can fail, and some do fail spectacularly. Add in the glare of social media and a small glitch can quickly turn into a brand harm crisis, and a massive PR nightmare. Yet, we know little about consumers’ responses to brands following such brand harm crises.

First, the research team finds that consumers penalize brands less when an algorithm (vs. human) causes an error that causes a brand harm crisis. In addition, consumers’ perceptions of the algorithm’s lower agency for the error and resultant lower responsibility for the harm caused mediate their less negative responses to a brand following such a crisis.

Second, when the algorithm is more humanized — when it is anthropomorphized (e.g., Alexa, Siri) (vs. not) or machine learning (vs. not), it is used in a subjective (vs. objective) task, or an interactive (vs. non-interactive) task — consumers’ responses to the brand are more negative following a brand harm crisis caused by an algorithm error. Srinivasan says that “Marketers must be aware that in contexts where the algorithm appears to be more human, it would be wise to have heightened vigilance in the deployment and monitoring of algorithms and provides resources for managing the aftermath of brand harm crises caused by algorithm errors.”

This study also generates insights about how to manage the aftermath of brand harm crises caused by algorithm errors. Managers can highlight the role of the algorithm and the lack of agency of the algorithm for the error, which may reduce consumers’ negative responses to the brand. However, highlighting the role of the algorithm will consumers’ negative responses to the brand for an anthropomorphized algorithm, a machine learning algorithm, or if the algorithm error occurs in a subjective or in an interactive task, all of which tend to humanize the algorithm.

Finally, insights indicate that marketers should not publicize human supervision of algorithms (which may actually be effective in fixing the algorithm) in communications with customers following brand harm crises caused by algorithm errors. However, they should publicize the technological supervision of the algorithm when they use it. The reason? Consumers are less negative when there is technological supervision of the algorithm following a brand harm crisis.

“Overall, our findings suggest that people are more forgiving of algorithms used in algorithmic marketing when they fail than they are of humans. We see this as a silver lining to the growing usage of algorithms in marketing and their inevitable failures in practice,” says Sarial-Abi.

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