October 2019 Jobs Report and Industry Update


Economics & Job Creation:


Life Sciences:
“The fast and the curious: Fitter adults have fitter brains”

“Why should you care about AI used for hiring?'”

“The birth of vision, from the retina to the brain”

The Industrials:
“How to make online recommendations work better”

Human Capital Solutions, Inc. (HCS) www.humancs.com is a Retained Executive Search firm focused in Healthcare, Life Sciences, the Industrials, and Technology. Visit our LinkedIn Company Page to learn more about HCS and receive weekly updates.

HCS has created the Prosperity at Work proposition which focuses on creating prosperous relationships between companies and their employees (associates). HCS assists companies in improving bottom line profitability by efficiently planning, organizing and implementing optimized, practical and value-added business solutions.


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Economics & Job Creation:


The unemployment rate declined to 3.5 percent in September, and total nonfarm
payroll employment rose by 136,000, the U.S. Bureau of Labor Statistics reported
today. Employment in health care and in professional and business services continued
to trend up.

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

In September, the unemployment rate declined by 0.2 percentage point to 3.5 percent.
The last time the rate was this low was in December 1969, when it also was 3.5 percent.
Over the month, the number of unemployed persons decreased by 275,000 to 5.8 million.
(See table A-1.)

Among the major worker groups, the unemployment rate for Whites declined to 3.2
percent in September. The jobless rates for adult men (3.2 percent), adult women
(3.1 percent), teenagers (12.5 percent), Blacks (5.5 percent), Asians (2.5 percent),
and Hispanics (3.9 percent) showed little or no change over the month. (See tables A-1,
A-2, and A-3.)

Among the unemployed, the number of job losers and persons who completed temporary
jobs declined by 304,000 to 2.6 million in September, while the number of new entrants
increased by 103,000 to 677,000. New entrants are unemployed persons who never
previously worked. (See table A-11.)

In September, the number of persons unemployed for less than 5 weeks fell by 339,000
to 1.9 million. The number of long-term unemployed (those jobless for 27 weeks or more)
was little changed at 1.3 million and accounted for 22.7 percent of the unemployed.
(See table A-12.)

The labor force participation rate held at 63.2 percent in September. The employment-
population ratio, at 61.0 percent, was little changed over the month but was up by
0.6 percentage point over the year. (See table A-1.)

The number of persons employed part time for economic reasons (sometimes referred to
as involuntary part-time workers) was essentially unchanged at 4.4 million in September.
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 September, 1.3 million persons were marginally attached to the labor force, down by
278,000 from a year earlier. (Data are not seasonally adjusted.) 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. They were not counted as unemployed because they had not
searched for work in the 4 weeks preceding the survey. (See table A-16.)

Among the marginally attached, there were 321,000 discouraged workers in September,
little changed from a year earlier. (Data are not seasonally adjusted.) Discouraged
workers are persons not currently looking for work because they believe no jobs are
available for them. The remaining 978,000 persons marginally attached to the labor
force in September had not searched for work for reasons such as school attendance or
family responsibilities. (See table A-16.)

Establishment Survey Data

Total nonfarm payroll employment increased by 136,000 in September. Job growth has
averaged 161,000 per month thus far in 2019, compared with an average monthly gain
of 223,000 in 2018. In September, employment continued to trend up in health care and in
professional and business services. (See table B-1.)

In September, health care added 39,000 jobs, in line with its average monthly gain over
the prior 12 months. Ambulatory health care services (+29,000) and hospitals (+8,000)
added jobs over the month.

Employment in professional and business services continued to trend up in September
(+34,000). The industry has added an average of 35,000 jobs per month thus far in 2019,
compared with 47,000 jobs per month in 2018.

Employment in government continued on an upward trend in September (+22,000). Federal
hiring for the 2020 Census was negligible (+1,000). Government has added 147,000 jobs
over the past 12 months, largely in local government.

Employment in transportation and warehousing edged up in September (+16,000). Within the
industry, job growth occurred in transit and ground passenger transportation (+11,000)
and in couriers and messengers (+4,000).

Retail trade employment changed little in September (-11,000). Within the industry,
clothing and clothing accessories stores lost 14,000 jobs, while food and beverage stores
added 9,000 jobs. Since reaching a peak in January 2017, retail trade has lost 197,000

Employment in other major industries, including mining, construction, manufacturing,
wholesale trade, information, financial activities, and leisure and hospitality, showed
little change over the month.

In September, average hourly earnings for all employees on private nonfarm payrolls,
at $28.09, were little changed (-1 cent), after rising by 11 cents in August. Over the
past 12 months, average hourly earnings have increased by 2.9 percent. In September, average
hourly earnings of private-sector production and nonsupervisory employees rose by 4 cents
to $23.65. (See tables B-3 and B-8.)

The average workweek for all employees on private nonfarm payrolls was unchanged at 34.4
hours in September. In manufacturing, the average workweek and overtime remained at 40.5
hours and 3.2 hours, respectively. The average workweek of private-sector production and
nonsupervisory employees held at 33.6 hours. (See tables B-2 and B-7.)

The change in total nonfarm payroll employment for July was revised up by 7,000 from
+159,000 to +166,000, and the change for August was revised up by 38,000 from +130,000 to
+168,000. With these revisions, employment gains in July and August combined were 45,000
more 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 have averaged 157,000 per
month over the last 3 months.



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Life Sciences:

“The fast and the curious: Fitter adults have fitter brains”

In a large study, German scientists have shown that physical fitness is associated with better brain structure and brain functioning in young adults. This opens the possibility that increasing fitness levels may lead to improved cognitive ability, such as memory and problem solving, as well as improved structural changes in the brain. This work is presented for the first time at the ECNP Congress in Copenhagen, with simultaneous publication in the peer-reviewed journal Scientific Reports.

Scientists have previously shown that “exercise is good for the brain,” but most studies have not controlled for underlying causes which might give distorted results, such as body weight, blood glucose levels, education status, age and other factors, making it difficult to take an overall view of the benefits. In addition, studies have rarely looked at fitness in relations to both brain structure and mental functioning.

The scientists used a publicly available database of 1206 MRI brain scans from the Human Connectome Project, which had been contributed by volunteers who wanted to contribute to scientific research. The volunteers (average age 30 years old) underwent some additional testing. The first test was a “two-minute walking test,” where each person was asked to walk as fast as possible for 2 minutes and the distance was then measured. The volunteers then underwent a series of cognitive tests, to measure such things as memory, sharpness, judgement, and reasoning.

As team leader, Dr Jonathan Repple (University Hospital Muenster, Germany) said “The great strength of this work is the size of the database. Normally when you are dealing with MRI work, a sample of 30 is pretty good, but the existence of this large MRI database allowed us to eliminate possibly misleading factors, and strengthened the analysis considerably.”

The tests were able to show two main points: better performance on a 2-minute walking test in young healthy adults is associated with better cognitive performance, and with structural integrity of the white matter in the brain: healthy white matter is known to improve the speed and quality of nerve connections in the brain.

Repple continued, “It surprised us to see that even in a young population cognitive performance decreases as fitness levels drops. We knew how this might be important in an elderly population which does not necessarily have good health, but to see this happening in 30 year olds is surprising. This leads us to believe that a basic level of fitness seems to be a preventable risk factor for brain health.

This type of study raises an important question. We see that fitter people have better brain health, so we now need to ask whether actually making people fitter will improve their brain health. Finding this out is our next step. There are some trials which point in that direction, but if we can prove this using such a large database, this would be very significant.”

Commenting, Professor Peter Falkai (University Clinic, Munich, Germany) said:

“This is an important cross-sectional study demonstrating a robust correlation between physical health and cognitive functioning in a large cohort of healthy young adults. This correlation was backed by changes in the white matter status of the brain supporting the notion that better macro-connectivity is related to better brain functioning. It stresses the importance of physical activity at all stages of life and as preliminary recent evidence suggests one can start improving physical health even in later life even if one has never trained before (see reference). These findings however need to be replicated in longitudinal studies and translated for the use in mental illness.”




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“Why should you care about AI used for hiring?””

Artificial intelligence has become much more prominent in business processes recently, and was voted the number one trend in SIOP’s Top 10 Workplace Trends for 2019. SIOP is currently celebrating Smarter Workplace Awareness Month to highlight trends like AI in an effort to help organizations grow and thrive in ways that may not be possible without the help of I-O psychology.

As AI continues to gain traction, it will be critical for organizations to include I-O psychologists on their data science teams to leverage expertise in psychological theory and methods in ensuring optimal outcomes for organizations.

“Artificial Intelligence in Talent Assessment and Selection” is the latest in the Society for Industrial and Organizational Psychology (SIOP) white paper series. Written by Neil Morelli, PhD, VP of Product and Assessment Science at Berke, this paper provides an overview of artificial intelligence in the workplace, provides practical to-dos for organizations considering AI tools for their hiring process, and explains how I-O psychologists can help along the way.

Media channels often feature stories on the “future of work,” “the skills gap,” “income inequality,” and “globalization.” What these stories have in common is a focus on the work people will do in the future and how they will be placed in those jobs. In other words, how people are hired and managed are interests among everyday people. AI is a driving force behind the workforce changes occurring and is a tool that can help hire people into jobs.

However, HR is late to the AI game. Anyone interested in the future of work, HR, or hiring should read this whitepaper to get up to speed on this evolving topic. Artificial intelligence is changing the way businesses operate and has the potential to revolutionize the way we select and retain talent. For businesses to take advantage of new technology, they must first understand it. AI is complex topic, but Morelli breaks it down in a relatable manner so that HR practitioners can take action.

In addition to providing an overview and background on the topic, Morelli discusses three next steps for consumers to consider before using an AI hiring tool. He explains the options of getting I-O psychologists involved, pairing AI-based tools with human decision makers, and applying a healthy amount of skepticism to marketing materials provided by vendors.




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“The birth of vision, from the retina to the brain”

How is the retina formed? And how do neurons differentiate to become individual components of the visual system? By focusing on the early stages of this complex process, researchers at the University of Geneva (UNIGE), Switzerland, in collaboration with the École Polytechnique Fédérale de Lausanne (EPFL), have identified the genetic programmes governing the birth of different types of retinal cells and their capacity to wire to the correct part of the brain, where they transmit visual information. In addition, the discovery of several genes regulating nerve growth allows for the possibility of a boost to optic nerve regeneration in the event of neurodegenerative disease. These results can be discovered in the journal Development.

The visual system of mammals is composed of different types of neurons, each of which must find its place in the brain to enable it to transform stimuli received by the eye into images. There are photoreceptors, which detect light, optic nerve neurons, which send information to the brain, cortical neurons, which form images, or interneurons, which make connections between other cells. Though not yet differentiated in the early stages of embryonic development, these neurons are all produced by progenitor cells that, are capable of giving rise to different categories of specialized neurons. To better understand the exact course of this mechanism and identify the genes at work during retinal construction, researchers studied the dynamics of gene expression in individual cells. “To monitor gene activity in cells and understand the early specification of retinal neurons, we sequenced more than 6,000 cells during retinal development and conducted large-scale bioinformatic analyses,” explains Quentin Lo Giudice, PhD student in the Department of Basic Neurosciences at the UNIGE Faculty of Medicine and first author of this article.

Mapping a system under construction

In collaboration with Gioele La Manno and Marion Leleu of EPFL, the researchers studied progenitor’s behaviour during the cell cycle as well as during their progressive differentiation. The scientists then mapped very accurately the different cell types of the developing retina and the genetic changes that occur during the early stages of this process. “Beyond their “age” — that is, when they were generated during their embryonic life — the diversity of neurons stems from their position in the retina, which predestines them for a specific target in the brain,” explains Pierre Fabre, senior researcher in the Department of Basic Neurosciences at the UNIGE Faculty of Medicine, who directed this work. “In addition, by predicting the sequential activation of neural genes, we were able to reconstruct several differentiation programs, similar to lineage trees, showing us how the progenitors progress to one cell type or another after their last division.”

The researchers also conducted a second analysis. If the right eye mainly connects essentially to the left side of the brain, and vice versa, a small fraction of neurons in the right eye make connections in the right side of the brain. Indeed, all species with two eyes with overlapping visual fields, such as mammals, must be able to mix information from both eyes in the same part of the brain. This convergence makes it possible to see binocularly and perceive depths or distances. “Knowing this phenomenon, we have genetically and individually “tagged” the cells in order to follow each of them as they progress to their final place in the visual system,” says Quentin Lo Giudice. By comparing the genetic diversity of these two neural populations, researchers discovered 24 genes that could play a key role in three-dimensional vision. “The identification of these gene expression patterns may represent a new molecular code orchestrating retinal wiring to the brain,” adds Dr. Fabre.

Towards regenerative medicine

Even before the neurons reach the brain, they must leave the retina through the optic nerve. The last part of this study identified the molecules that guide neurons on the right path. Moreover, these same molecules also allow the initial growth of axons, the part of neurons that transmits electrical signals to the synapses and thus ensures the passage of information from one neuron to another, as well as about twenty genes that control this process. This discovery is a fundamental step forward for regenerative medicine.

The more we know about the molecules needed to appropriately guide axons, the more likely we are to develop a therapy to treat nerves trauma. “If the optic nerve is cut or damaged, for example by glaucoma, we could imagine reactivating those genes that are usually only active during the embryonic development phase. By stimulating axon growth, we could allow neurons to stay connected and survive,” explains Dr. Fabre, who plans to launch a research project on this theme. Although the regeneration capacities of neurons are very low, they do exist and techniques to encourage their development must be found. Genetic stimulation of the damaged spinal cord after an accident is based on the same idea and is beginning to show its first successes.




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The Industrials:

“How to make online recommendations work better”

Researchers from Erasmus University published a new paper in the Journal of Marketing that explores online recommendations and their effectiveness, providing marketers with tools to maximize this important engagement tool.

The study, forthcoming in the November issue of the Journal of Marketing, is titled “Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs” and is authored by Phyliss Jia Gai and Anne-Kathrin Klesse.

Algorithm-based recommendations are everywhere. Imagine you are browsing news articles on the website of The New York Times. You see a piece in the “Science” section, find it interesting, click on the title, and start reading. Once you finish the article, the webpage automatically generates other article recommendations for you so that you extend your engagement with the platform’s content. The recommendations are branded with the tagline: “More in Science,” the section you have already been reading.

While most companies provide explanations for why customers receive recommendations, they differ in the specific strategies they adopt. Some companies, like the aforementioned The New York Times, emphasize that recommendations are item-based: That is, they are based on common attributes across products (e.g., “More in Science” by The New York Times, and “Similar to [what you have listened to]” by Spotify). In contrast, other companies highlight that their recommendations are user-based by focusing on the overlap in customer preferences (e.g., “Customers who viewed this item also viewed…” by Amazon and “Customers also watched…” by Netflix). Importantly, companies can explain the same recommendation as either item-based or user-based, because today’s recommender systems frequently adopt a hybrid approach that accounts for both common attributes across products and common preferences across customers.

The study investigates which of the two explanations (hereafter referred to as item-based and user-based framings) is more effective at triggering clicks on a recommendation. The research team suggests that item-based and user-based framings differ in terms of the information they provide to customers regarding how a recommendation is made. Both framings tell customers that the recommendation is based on a product matching of the focal item that customers have shown interest in to the recommended item: Item-based framing matches products by their attributes, whereas user-based framing matches products by their consumers. Critically, user-based framing also suggests to customers that the recommendation is based on taste matching among users who shared interested in the focal item. By providing information on taste matching beyond product matching, user-based framing serves as a sort of “double guarantee” for customers liking the recommended product.

To test whether user-based framing outperforms item-based framing in terms of recommendation click-throughs, the researchers conducted two field studies within WeChat, the top social media app in China. They collaborated with a media company that publishes popular science articles and summaries of academic research on WeChat and embedded a pair of recommendations at the end of each day’s focal article. One article was recommended using user-based framing and the other using item-based framing. Gai explains that “In both studies, user-based framing increased the click-through rates of recommended articles compared to item-based framing. When asked about their understanding of the two framings, subscribers responded that they see that both suggest product matching as the basis for recommendations, but that user-based framing also signals taste matching. This confirms that user-based framing provides additional information.”

“However, customers do not always see taste matching as successful” adds Klesse. “When taste matching is perceived as inaccurate, user-based framing is no longer more advantageous than item-based framing or even becomes disadvantageous.” One critical factor that contributes to the perceived success of taste matching is how much experience customers already accumulated within a consumption domain. More experienced individuals tend to see their own tastes as idiosyncratic. As a result, it is more difficult for them to believe that their tastes can be accurately matched with other people’s tastes based on a single focal item. Another critical factor is the presence of other users’ profiles. Companies sometimes display the information of other users who are interested in the recommendation, but this information backfires when it indicates to customers that they are different from other users. Dissimilarity cues, such as age and gender, make people infer that their tastes diverge from other users and lead to customers avoiding the user-based recommendations.

These novel findings have relevance for companies that use product recommendations. The research suggests that the explanation matters for why customers see a recommendation. Importantly, adapting the explanation for a recommendation comes with almost zero cost and, thus, constitutes an effective tool that can help companies maximize the return on recommender systems. Importantly, the study highlights situations in which user-based framing is more effective than item-based framing and in which situations it becomes disadvantageous. By leveraging these findings, managers can tailor the framing of their recommendations for different customers and products and thereby boost click-through rates.



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