Economics & Job Creation
“The Employment Situation — July 2021”
“Scientists reverse age-related memory loss in mice”
“Connective issue: AI learns by doing more with less”
“Mathematicians develop ground-breaking modeling toolkit to predict local COVID-19 impact”
“Hybrid cars are twice as vulnerable to supply chain issues as gas-powered models”
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Economics & Job Creation
“The Employment Situation – July 2021”
THE EMPLOYMENT SITUATION -- JULY 2021 Total nonfarm payroll employment rose by 943,000 in July, and the unemployment rate declined by 0.5 percentage point to 5.4 percent, the U.S. Bureau of Labor Statistics reported today. Notable job gains occurred in leisure and hospitality, in local government education, and in professional and business services. 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 The unemployment rate declined by 0.5 percentage point to 5.4 percent in July, and the number of unemployed persons fell by 782,000 to 8.7 million. These measures are down considerably from their highs at the end of the February-April 2020 recession. However, they 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 declined in July for adult men (5.4 percent), adult women (5.0 percent), Whites (4.8 percent), Blacks (8.2 percent), and Hispanics (6.6 percent). The jobless rates for teenagers (9.6 percent) and Asians (5.3 percent) showed little change over the month. (See tables A-1, A-2, and A-3.) Among the unemployed, the number of persons on temporary layoff fell by 572,000 to 1.2 million in July. This measure is down considerably from the high of 18.0 million in April 2020 but is 489,000 above the February 2020 level. The number of permanent job losers declined by 257,000 to 2.9 million in July but is 1.6 million higher than in February 2020. (See table A-11.) The number of long-term unemployed (those jobless for 27 weeks or more) decreased by 560,000 in July to 3.4 million but is 2.3 million higher than in February 2020. These long-term unemployed accounted for 39.3 percent of the total unemployed in July. The number of persons jobless less than 5 weeks increased by 276,000 to 2.3 million. (See table A-12.) The labor force participation rate was little changed at 61.7 percent in July and has remained within a narrow range of 61.4 percent to 61.7 percent since June 2020. The participation rate is 1.6 percentage points lower than in February 2020. The employment- population ratio increased by 0.4 percentage point to 58.4 percent in July and is up by 1.0 percentage point since December 2020. However, this measure is 2.7 percentage points below its February 2020 level. (See table A-1.) In July, the number of persons employed part time for economic reasons, at 4.5 million, was about unchanged. There were 4.4 million persons in this category 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 July, the number of persons not in the labor force who currently want a job was 6.5 million, about unchanged over the month but up by 1.5 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 little changed in July but is up by 435,000 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 507,000 in July, down by 110,000 from the previous month but 106,000 higher than in February 2020. (See Summary table A.) Household Survey Supplemental Data In July, 13.2 percent of employed persons teleworked because of the coronavirus pandemic, down from 14.4 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 July, 5.2 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 6.2 million in June. Among those who reported in July that they were unable to work because of pandemic-related closures or lost business, 9.1 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 July, 1.6 million persons were prevented from looking for work due to the pandemic, essentially unchanged from June. (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 www.bls.gov/cps/effects-of-the-coronavirus-covid-19-pandemic.htm. Establishment Survey Data Total nonfarm payroll employment rose by 943,000 in July, following a similar increase in June (+938,000). Nonfarm payroll employment in July is up by 16.7 million since April 2020 but is down by 5.7 million, or 3.7 percent, from its pre-pandemic level in February 2020. In July, notable job gains occurred in leisure and hospitality, in local government education, and in professional and business services. (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 July, employment in leisure and hospitality increased by 380,000. Two-thirds of the job gain was in food services and drinking places (+253,000). Employment also continued to increase in accommodation (+74,000) and in arts, entertainment, and recreation (+53,000). Despite recent growth, employment in leisure and hospitality is down by 1.7 million, or 10.3 percent, from its level in February 2020. In July, employment rose by 221,000 in local government education and by 40,000 in private education. Staffing fluctuations in education due to the pandemic have distorted the normal seasonal buildup and layoff patterns, likely contributing to the job gains in July. Without the typical seasonal employment increases earlier, there were fewer layoffs at the end of the school year, resulting in job gains after seasonal adjustment. These variations make it more challenging to discern the current employment trends in these education industries. Since February 2020, employment is down by 205,000 in local government education and 207,000 in private education. Employment in professional and business services rose by 60,000 in July. Within the industry, employment in the professional and technical services component rose by 43,000 over the month and is 121,000 above its February 2020 level. (Professional and technical services includes industries such as accounting and bookkeeping services, management and technical consulting services, and scientific research and development services.) By contrast, employment in the administrative and waste services component (which includes temporary help services) changed little over the month (+20,000) and is 577,000 lower than in February 2020. Employment in the management of companies and enterprises component was also little changed over the month (-3,000) but is 100,000 lower than the level in February 2020. Employment in professional and business services overall is down by 556,000 since February 2020. Transportation and warehousing added 50,000 jobs in July. Job growth occurred in transit and ground passenger transportation (+19,000), warehousing and storage (+11,000), and couriers and messengers (+8,000). Employment in transportation and warehousing has grown by 534,000 since April 2020; the industry has recovered 92.9 percent of the jobs lost during the February-April 2020 recession (-575,000). The other services industry added 39,000 jobs in July, with gains in membership associations and organizations (+17,000) and in personal and laundry services (+15,000). Employment in other services is 236,000 lower than in February 2020. Health care added 37,000 jobs in July. Job gains in ambulatory health care services (+32,000) and hospitals (+18,000) more than offset a loss of 13,000 jobs in nursing and residential care facilities. Health care employment is down by 502,000 since February 2020. Employment in manufacturing increased by 27,000 in July, largely in durable goods manufacturing. Within durable goods, job gains occurred in machinery (+7,000) and miscellaneous durable goods manufacturing (+6,000). Manufacturing employment is 433,000 below its February 2020 level. Employment in information increased by 24,000 over the month, with three-quarters of the gain in motion picture and sound recording industries (+18,000). Employment in information is down by 172,000 since February 2020. Employment in financial activities rose by 22,000 over the month, largely in real estate and rental and leasing (+18,000). Employment in financial activities is down by 48,000 since February 2020. Employment in mining increased by 7,000 in July, reflecting a gain in support activities for mining (+6,000). Mining employment has risen by 49,000 since a trough in August 2020 but is 103,000 below a peak in January 2019. Employment in retail trade changed little in July (-6,000), following large increases in the prior 2 months. In July, job gains in gasoline stations (+14,000), miscellaneous store retailers (+7,000), and nonstore retailers (+5,000) were more than offset by a loss in building material and garden supply stores (-34,000). Since February 2020, employment in retail trade is down by 270,000. In July, employment showed little change in construction and wholesale trade. In July, average hourly earnings for all employees on private nonfarm payrolls increased by 11 cents to $30.54, following increases in the prior 3 months. Average hourly earnings for private-sector production and nonsupervisory employees also rose by 11 cents in July to $25.83. The data for recent months suggest that the rising demand for labor associated with the recovery from the pandemic may have put upward pressure on wages. However, because 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.) In July, the average workweek for all employees on private nonfarm payrolls was unchanged at 34.8 hours. In manufacturing, the average workweek increased by 0.2 hour to 40.5 hours, and overtime was unchanged at 3.2 hours. The average workweek for production and nonsupervisory employees on private nonfarm payrolls was unchanged at 34.2 hours. (See tables B-2 and B-7.) The change in total nonfarm payroll employment for May was revised up by 31,000, from +583,000 to +614,000, and the change for June was revised up by 88,000, from +850,000 to +938,000. With these revisions, employment in May and June combined is 119,000 higher 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 August is scheduled to be released on Friday, September 3, 2021, at 8:30 a.m. (ET).
Employment Situation Summary (bls.gov)
“Scientists reverse age-related memory loss in mice”
Scientists at Cambridge and Leeds have successfully reversed age-related memory loss in mice and say their discovery could lead to the development of treatments to prevent memory loss in people as they age.
In a study published today in Molecular Psychiatry, the team show that changes in the extracellular matrix of the brain — ‘scaffolding’ around nerve cells — lead to loss of memory with ageing, but that it is possible to reverse these using genetic treatments.
Recent evidence has emerged of the role of perineuronal nets (PNNs) in neuroplasticity — the ability of the brain to learn and adapt — and to make memories. PNNs are cartilage-like structures that mostly surround inhibitory neurons in the brain. Their main function is to control the level of plasticity in the brain. They appear at around five years old in humans, and turn off the period of enhanced plasticity during which the connections in the brain are optimised. Then, plasticity is partially turned off, making the brain more efficient but less plastic.
PNNs contain compounds known as chondroitin sulphates. Some of these, such as chondroitin 4-sulphate, inhibit the action of the networks, inhibiting neuroplasticity; others, such as chondroitin 6-sulphate, promote neuroplasticity. As we age, the balance of these compounds changes, and as levels of chondroitin 6-sulphate decrease, so our ability to learn and form new memories changes, leading to age-related memory decline.
Researchers at the University of Cambridge and University of Leeds investigated whether manipulating the chondroitin sulphate composition of the PNNs might restore neuroplasticity and alleviate age-related memory deficits.
To do this, the team looked at 20-month old mice — considered very old — and using a suite of tests showed that the mice exhibited deficits in their memory compared to six-month old mice.
For example, one test involved seeing whether mice recognised an object. The mouse was placed at the start of a Y-shaped maze and left to explore two identical objects at the end of the two arms. After a short while, the mouse was once again placed in the maze, but this time one arm contained a new object, while the other contained a copy of the repeated object. The researchers measured the amount of the time the mouse spent exploring each object to see whether it had remembered the object from the previous task. The older mice were much less likely to remember the object.
The team treated the ageing mice using a ‘viral vector’, a virus capable of reconstituting the amount of 6-sulphate chondroitin sulphates to the PNNs and found that this completely restored memory in the older mice, to a level similar to that seen in the younger mice.
Dr Jessica Kwok from the School of Biomedical Sciences at the University of Leeds said: “We saw remarkable results when we treated the ageing mice with this treatment. The memory and ability to learn were restored to levels they would not have seen since they were much younger.”
To explore the role of chondroitin 6-sulphate in memory loss, the researchers bred mice that had been genetically-manipulated such that they were only able to produce low levels of the compound to mimic the changes of ageing. Even at 11 weeks, these mice showed signs of premature memory loss. However, increasing levels of chondroitin 6-sulphate using the viral vector restored their memory and plasticity to levels similar to healthy mice.
Professor James Fawcett from the John van Geest Centre for Brain Repair at the University of Cambridge said: “What is exciting about this is that although our study was only in mice, the same mechanism should operate in humans — the molecules and structures in the human brain are the same as those in rodents. This suggests that it may be possible to prevent humans from developing memory loss in old age.”
The team have already identified a potential drug, licensed for human use, that can be taken by mouth and inhibits the formation of PNNs. When this compound is given to mice and rats it can restore memory in ageing and also improves recovery in spinal cord injury. The researchers are investigating whether it might help alleviate memory loss in animal models of Alzheimer’s disease.
The approach taken by Professor Fawcett’s team — using viral vectors to deliver the treatment — is increasingly being used to treat human neurological conditions. A second team at the Centre recently published research showing their use for repairing damage caused by glaucoma and dementia.
The study was funded by Alzheimer’s Research UK, the Medical Research Council, European Research Council and the Czech Science Foundation.
“Connective issue: AI learns by doing more with less”
Brains have evolved to do more with less. Take a tiny insect brain, which has less than a million neurons but shows a diversity of behaviors and is more energy-efficient than current AI systems. These tiny brains serve as models for computing systems that are becoming more sophisticated as billions of silicon neurons can be implemented on hardware.
The secret to achieving energy-efficiency lies in the silicon neurons’ ability to learn to communicate and form networks, as shown by new research from the lab of Shantanu Chakrabartty, the Clifford W. Murphy Professor in the Preston M. Green Department of Electrical & Systems Engineering at Washington University in St. Louis’ McKelvey School of Engineering.
Their results were published July 28, 2021 in the journal Frontiers in Neuroscience.
For several years, his research group studied dynamical systems approaches to address the neuron-to-network performance gap and provide a blueprint for AI systems as energy efficient as biological ones.
Previous work from his group showed that in a computational system, spiking neurons create perturbations which allow each neuron to “know” which others are spiking and which are responding. It’s as if the neurons were all embedded in a rubber sheet formed by energy constraints; a single ripple, caused by a spike, would create a wave that affects them all. Like all physical processes, systems of silicon neurons tend to self-optimize to their least-energetic states, while also being affected by the other neurons in the network. These constraints come together to form a kind of secondary communication network, where additional information can be communicated through the dynamic but synchronized topology of spikes. It’s like the rubber sheet vibrating in a synchronized rhythm in response to multiple spikes.
In the latest research result, Chakrabartty and doctoral student Ahana Gangopadhyay showed how the neurons learn to pick the most energy-efficient perturbations and wave patterns in the rubber sheet. They show that if the learning is guided by sparsity (less energy), it’s like the electrical stiffness of the rubber sheet is adjusted by each neuron so that the entire network vibrates in a most energy-efficient way. The neuron does this using only local information which is communicated more efficiently. Communications between the neurons then become an emergent phenomenon guided by the need to optimize energy use.
This result could have significant implications on how neuromorphic AI systems might be designed. “We want to learn from neurobiology,” Chakrabartty said. “But we want to be able to exploit the best principles from both neurobiology and silicon engineering.”
Historically, neuromorphic engineering — modeling AI systems on biology — has been based on a relatively straightforward model of the brain. Take some neurons, a few synapses, connect everything together and, voila, it’s… if not alive, at least it’s able to perform a simple task (recognizing images, for example) as efficiently, or moreso, than a biological brain. These systems are built by connecting memory (synapses) and processors (neurons). Each performing its single task, as it was presumed to work in the brain. But this one-structure-to-one-function approach, though it is easy to understand and model, misses the full complexity and flexibility of the brain.
Recent brain research has shown tasks are not so neatly divided, and there may be instances in which the same function is being performed by different brain structures, or multiple structures working together. “There is more and more information showing that this reductionist approach we’ve followed might not be complete,” Chakrabartty said.
The key to building an efficient system that can learn new things is the use of energy and structural constraints as a medium for computing and communications or, as Chakrabartty said, “Optimization using sparsity.”
The situation is reminiscent of the theory of six-degrees of Kevin Bacon: The challenge — or constraint — is to make connections to the actor by connecting six or fewer people.
For a neuron that is physically located on one chip to be its most efficient: The challenge — or constraint — is completing the task within the allotted amount of energy. It might be more efficient for one neuron to communicate through intermediaries to get to the destination neuron. The challenge is how to pick the right set of “friend” neurons among many choices that might be available. Enter energy constraints and sparsity.
Like a tired professor, a system in which energy has been constrained also will seek the least resistant way to complete an assigned task. Unlike the professor, an AI system can test all of its options at once, thanks to the superposition techniques developed in Chakrabartty’s lab, which uses analog computing methods. In essence, a silicon neuron can attempt all communication routes at once, finding the most efficient way to connect in order to complete the assigned task.
The current paper shows that a network of 1,000 silicon neurons can accurately detect odors with very few training examples. The long-term goal is to look for analogs in the brain of a locust which has also been shown to be adept in classifying odors. Chakrabartty has been collaborating with Barani Raman, a professor in Department of Biomedical Engineering, and Srikanth Singamaneni, The Lilyan & E. Lisle Hughes Professor in the Department of Mechanical Engineering & Materials Science, to create a sort of cyborg locust — one with two brains, a silicon one connected to the biological one.
“This would be the most interesting and satisfactory aspect of this research if and when we can start connecting the two realms,” Chakrabartty said. “Not just physically, but also functionally.”
“Mathematicians develop ground-breaking modeling toolkit to predict local COVID-19 impact”
A Sussex team — including university mathematicians — have created a new modelling toolkit which predicts the impact of COVID-19 at a local level with unprecedented accuracy. The details are published in the International Journal of Epidemiology, and are available for other local authorities to use online, just as the UK looks as though it may head into another wave of infections.
The study used the local Sussex hospital and healthcare daily COVID-19 situation reports, including admissions, discharges, bed occupancy and deaths.
Through the pandemic, the newly-published modelling has been used by local NHS and public health services to predict infection levels so that public services can plan when and how to allocate health resources — and it has been conclusively shown to be accurate. The team are now making their modelling available to other local authorities to use via the Halogen toolkit.
Anotida Madzvamuse, professor of mathematical and computational biology within the School of Mathematical and Physical Sciences at the University of Sussex, who led of the study, said:
“We undertook this study as a rapid response to the COVID-19 pandemic. Our objective was to provide support and enhance the capability of local NHS and Public Health teams to accurately predict and forecast the impact of local outbreaks to guide healthcare demand and capacity, policy making, and public health decisions.”
“Working with outstanding mathematicians, Dr James Van Yperen and Dr Eduard Campillo-Funollet, we formulated an epidemiological model and inferred model parameters by fitting the model to local datasets to allow for short, and medium-term predictions and forecasts of the impact of COVID-19 outbreaks.
“I’m really pleased that our modelling has been of such value to local health services and people. The modelling approach can be used by local authorities to predict the dynamics of other conditions such as winter flu and mental health problems.”
Professor Anjum Memon, Chair in Epidemiology and Public Health Medicine at BSMS and co-author of the study, said:
“The world is in the cusp of experiencing local and regional hotspots and spikes of COVID-19 infections. Our epidemiological model, which is based on local data, can be used by all local authorities in the UK and other countries to inform healthcare demand and capacity, emergency planning and response to the supply of medications and oxygen, formulation, tightening or lifting of legal restrictions and implementation of preventive measures.”
“The model will also serve as an excellent tool to monitor the situation after the legal COVID-19 restrictions are lifted in England on 19 July, and during winter months with competing respiratory infections.”
Kate Gilchrist, Head of Public Health Intelligence at Brighton & Hove City Council and co-author of the study, said:
“This unique piece of work demonstrated that by using local datasets, model predictions and forecasting allowed us to plan adequately the healthcare demand and capacity, as well as policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes and waves could possibly affect the local populations empowers us to ensure that contingency measures are in place and the timely commissioning and organisation of services.”
Dr Sue Baxter, Director of Innovations and Business Partnerships at the University of Sussex, said:
“The University is delighted that this innovative modelling approach and philosophy has been translated from the mathematical drawing board into a web-based tool-kit called Halogen, which can be used by NHS hospitals, local authorities and public health departments locally and across the UK to help save lives and improve capability for hard pressed public health workers. The successful commercialisation of this kind of innovation illustrates just one of the transformational impacts that the Higher Education Innovation Fund can make when applied in a targeted way.”
The study is published in the International Journal of Epidemiology. It was supported by the Higher Education Innovation Fund (University of Sussex); Global Challenges Research Fund (Engineering and Physical Sciences Research Council); UK-Africa Postgraduate Advanced Study Institute in Mathematical Sciences; Wellcome Trust; Health Foundation; the NIHR; and Dr Perry James (Jim) Browne Research Centre on Mathematics and its Applications (University of Sussex).
“Hybrid cars are twice as vulnerable to supply chain issues as gas-powered models”
The global computer chip shortage has hit car manufacturers especially hard, indicating the importance of supply chain resilience. Yet, for hybrid electric vehicles, it isn’t clear how their production could be impacted by fluctuating supplies and high prices. To get a grasp of these vulnerabilities compared to those for gas-powered models, researchers reporting in ACS’ Environmental Science & Technology conducted a thorough analysis, finding that hybrid models have twice the vulnerability to supply chain disruptions.
Supply chain weaknesses were brought to the forefront during the COVID-19 pandemic, especially for industries relying on electronics, as the flow of raw materials slowed or sometimes stopped. On top of that, shifting consumer values and tougher environmental regulations have resulted in more people buying hybrid vehicles. The batteries in these cars require rare metals that, depending on their supplies, can have volatile and unpredictable prices. But there are other scarce elements and materials that may be used in smaller amounts in hybrid models versus conventional gas vehicles, raising the question of how these vehicles really compare with regard to supply chain vulnerabilities. Although previous studies reported lists of the elements used in conventional cars’ parts, similar information on the parts used in hybrid vehicles is lacking. So, Randolph Kirchain and colleagues wanted to develop a comprehensive comparison of the elements and compounds that go into all the parts in gas-powered, self-charging hybrid and plug-in hybrid cars, calculating each of the three vehicles’ materials cost vulnerability.
The researchers collected information on the compounds in the more than 350,000 parts used to build seven vehicles from the same manufacturer with different levels of electrification, including four sedans and three sport utility vehicles (SUVs). Then, they calculated the amount of the 76 chemical elements present, as well as a few other materials, in each car type. To develop a monetary metric for vulnerability, the team considered the weight of each component, along with its average price and price volatility between 1998 and 2015. The results showed that self-charging hybrid and plug-in hybrid vehicles have twice the raw material cost risks, which equates to an increase of $1 billion for a fleet of a million sedans and SUVs, compared to conventional models. The largest contributors to the increase in cost risks were battery-related elements, such as cobalt, nickel, graphite and neodymium; however, changes to the exhaust and transmission systems in hybrid vehicles reduced the impact of palladium and aluminum, respectively. The researchers suggest that as manufacturers ramp up electric vehicle production to meet demand, they can reduce raw material cost risks with long-term supplier contracts, substitute some materials or recycle others.