Category: Labor Economics

Men Are More Likely to Risk Their Lives for Others

(p. A15) “T” does what all superb popular science must do: It entertains as it educates.

. . .

Ultimately, “T” is a vigorous defense of the scientific method itself. Ms. Hooven summarizes: “Multiple independent sources of evidence can combine to strongly support a hypothesis, whether it’s about the cause of a rattle in your car, why your soufflé has collapsed, or why someone blocked you on Twitter. It’s just like that in science.”

. . .

. . . she’s emphatic that high T levels do not lead inexorably to rape and murder; mountains of data disprove this fallacy. She also gives testosterone its due: Men are far more likely “to put their lives on the line for others, and are massively overrepresented in the most dangerous occupations.” She lauds the men who protected her while she conducted fieldwork in the jungles; heroism, for her, thrives at the molecular level.

For the full review, see:

Hamilton Cain. “The Hormone of the Hour.” The Wall Street Journal (Tuesday, July 13, 2021): A15.

(Note: ellipses added.)

(Note: the online version of the review has the date July 12, 2021, and has the title “‘T’ Review: Hormone of the Hour.”)

The book under review is:

Hooven, Carole. T: The Story of Testosterone, the Hormone That Dominates and Divides Us. New York: Henry Holt and Co., 2021.

Firms That Discriminate Earn Lower Profits

(p. B1) Economists at the University of California, Berkeley, and the University of Chicago this week unveiled a vast discrimination audit of some of the largest U.S. companies. Starting in late 2019, they sent 83,000 fake job applications for entry-level positions at 108 companies — most of them in the top 100 of the Fortune 500 list, and (p. B6) some of their subsidiaries.

. . .

(p. B6) In the study, applicants’ characteristics — like age, sexual orientation, or work and school experience — varied at random. Names, however, were chosen purposefully to ensure applications came in pairs: one with a more distinctive white name — Jake or Molly, say — and the other with a similar background but a more distinctive Black name, like DeShawn or Imani.

. . . : On average, applications from candidates with a “Black name” get fewer callbacks than similar applications bearing a “white name.”

. . .

All told, for every 1,000 applications received, the researchers found, white candidates got about 250 responses, compared with about 230 for Black candidates. But among one-fifth of companies, the average gap grew to 50 callbacks. Even allowing that some patterns of discrimination could be random, rather than the result of racism, they concluded that 23 companies from their selection were “very likely to be engaged in systemic discrimination against Black applicants.”

. . .

“Discriminatory behavior is clustered in particular firms,” the researchers wrote. “The identity of many of these firms can be deduced with high confidence.”

The researchers also identified some overall patterns. For starters, discriminating companies tend to be less profitable, a finding consistent with the proposition by Gary Becker, who first studied discrimination in the workplace in the 1950s, that it is costly for firms to discriminate against productive workers.

For the full story, see:

Eduardo Porter. “Study Shows Which Firms Discriminate.” The New York Times (Friday, July 30, 2021): B1 & B6.

(Note: ellipses added.)

(Note: the online version of the story has the date July 29, 2021, and has the title “Who Discriminates in Hiring? A New Study Can Tell.”)

The economic study summarized in the passages quoted above is:

Kline, Patrick M., Evan K Rose, and Christopher R Walters. “Systemic Discrimination among Large U.S. Employers.” National Bureau of Economic Research Working Paper #29053, Aug. 2021.

“Unemployment Rises Like a Rocket and Falls Like a Feather”

(p. B7) Robert Hall, an economics professor at Stanford University, says the job matching process has progressed in two stages. Last year, millions of people were called back to their jobs from temporary layoffs and the unemployment rate descended quickly from 14.8% to 6.7%. This year, the progress has slowed markedly; the jobless rate fell from 6.3% in January [2021] to 5.9% in June.

Mr. Hall and Marianna Kudlyak at the Federal Reserve Bank of San Francisco studied the past 10 recoveries and concluded that U.S. job recoveries have a common pattern. In normal times, they find, “unemployment rises like a rocket and falls like a feather.”

“The easy stuff has been accomplished,” Mr. Hall said in an interview. The rest of the job recovery, he concluded, is going to take some time.

For the full story, see:

Jon Hilsenrath and Sarah Chaney Cambon. “The Mismatch That Is Hammering Job Prospects.” The Wall Street Journal (Saturday, July 10, 2021): B1 & B6-B7.

(Note: bracketed year added.)

(Note: the online version of the story has the date July 9, 2021, and has the title “Why Aren’t Millions of Unemployed Americans Finding Jobs?”)

Anderson Led NCR to Disrupt Its Own Cash Register Technology

I believe that Clayton Christensen (with Raynor) in The Innovator’s Solution, used the NCR transition from mechanical cash registers to electronic cash registers as an example of creative destruction that was NOT an example of his disruptive innovation. Alternatively, should this be considered a rare case where a firm succeeds in disrupting itself, especially rare because it was not implemented by the firm founders? (The usual case of rare self-disruption is HP disrupting its laser printer by developing the ink jet printer.)

(p. A9) The same self-belief that kept Mr. Anderson alive as a POW gave him confidence he could save NCR.

“The most important message I try to get across to our managers all over the world is that we are in trouble but we will overcome it,” he told Business Week, which reported that he had the “stance and mien of a middleweight boxer.”

Founded in 1884, NCR was comfortably entrenched as a dominant supplier of mechanical cash registers and machines used in accounting and banking. It underestimated the speed at which microelectronics and computers would wipe out its legacy product line. By the early 1970s, NCR was losing sales to more nimble rivals.

A factory complex covering 55 acres in Dayton made hundreds of exceedingly complicated machines rapidly becoming obsolete. Mr. Anderson found that NCR was using about 130,000 different parts, including more than 9,000 types and sizes of screws. For 1972, his first year as president, NCR took a $70 million charge, largely to write down the value of parts and inventory and replace outdated production equipment.

Mr. Anderson slashed the payroll and invested in new products, including automated teller machines and computers. Profitability recovered, and NCR reported record revenue of $4.07 billion for 1984, the year he retired as chairman.

For the full obituary, see:

James R. Hagerty. “Former POW Revived National Cash Register.” The Wall Street Journal (Saturday, July 10, 20211): A9.

(Note: the online version of the obituary has the date July 6, 2021, and has the title “Former Prisoner of War Saved NCR From Obsolescence.”)

The Christensen co-authored book mentioned above is:

Christensen, Clayton M., and Michael E. Raynor. The Innovator’s Solution: Creating and Sustaining Successful Growth. Boston, MA: Harvard Business School Press, 2003.

AI Algorithms Use Massive Data to Do “Narrow Tasks”

(p. B2) A funny thing happens among engineers and researchers who build artificial intelligence once they attain a deep level of expertise in their field. Some of them—especially those who understand what actual, biological intelligences are capable of—conclude that there’s nothing “intelligent” about AI at all.

. . .

. . . the muddle that the term AI creates fuels a tech-industry drive to claim that every system involving the least bit of machine learning qualifies as AI, and is therefore potentially revolutionary. Calling these piles of complicated math with narrow and limited utility “intelligent” also contributes to wild claims that our “AI” will soon reach human-level intelligence. These claims can spur big rounds of investment and mislead the public and policy makers who must decide how to prepare national economies for new innovations.

. . .

The tendency for CEOs and researchers alike to say that their system “understands” a given input—whether it’s gigabytes of text, images or audio—or that it can “think” about those inputs, or that it has any intention at all, are examples of what Drew McDermott, a computer scientist at Yale, once called “wishful mnemonics.” That he coined this phrase in 1976 makes it no less applicable to the present day.

“I think AI is somewhat of a misnomer,” says Daron Acemoglu, an economist at Massachusetts Institute of Technology whose research on AI’s economic impacts requires a precise definition of the term. What we now call AI doesn’t fulfill the early dreams of the field’s founders—either to create a system that can reason as a person does, or to create tools that can augment our abilities. “Instead, it uses massive amounts of data to turn very, very narrow tasks into prediction problems,” he says.

When AI researchers say that their algorithms are good at “narrow” tasks, what they mean is that, with enough data, it’s possible to “train” their algorithms to, say, identify a cat. But unlike a human toddler, these algorithms tend not to be very adaptable. For example, if they haven’t seen cats in unusual circumstances—say, swimming—they might not be able to identify them in that context. And training an algorithm to identify cats generally doesn’t also increase its ability to identify any other kind of animal or object. Identifying dogs means more or less starting from scratch.

For the full commentary, see:

Christopher Mims. “AI’s Big Chill.” The Wall Street Journal (Sat., July 31, 2021): B2.

(Note: ellipses added.)

(Note: the online version of the commentary has the date July 30, 2021, and has the title “Artificial Intelligence’s Big Chill.” When you click on the title in the search list internal to the WSJ, you get a different title on the page of the article itself: “Why Artificial Intelligence Isn’t Intelligent.”)

The Walt Disney Company Cheats Scarlett Johansson

Walt Disney is one of my heroes. The current Walt Disney Company is not.

(p. B1) The fight between actress Scarlett Johansson and Walt Disney Co. over her contract for the movie “Black Widow” has a new participant: the powerful Creative Artists Agency, which represents Ms. Johansson and many of Hollywood’s biggest stars.

On Thursday, Ms. Johansson filed a lawsuit against Disney alleging her contract was breached when the company decided to put “Black Widow” on its Disney+ streaming service at the same time it was released in movie theaters.

. . .

“They have shamelessly and falsely accused Ms. Johansson of being insensitive to the global Covid pandemic,” CAA Co-Chairman Bryan Lourd said in a statement.

Mr. Lourd blasted Disney for releasing details of Ms. Johansson’s salary and for attempting to tie (p. B2) her lawsuit to the pandemic. Disney “included her salary in their press statement in an attempt to weaponize her success as an artist and businesswoman, as if that were something she should be ashamed of,” he added.

Mr. Lourd said Disney’s response to Ms. Johansson is an attack on her character that is “beneath the company that many of us in the creative community have worked with successfully for decades.”

. . .

“They have very deliberately moved the revenue stream and profits to the Disney+ side of the company, leaving artistic and financial partners out of their new equation. That’s it, pure and simple,” Mr. Lourd said.

For the full story, see:

Joe Flint. “Johansson’s Agent Rips Disney Over Film Flap.” The Wall Street Journal (Sat., July 31, 2021): B1-B2.

(Note: ellipses added.)

(Note: the online version of the story has the date July 30, 2021, and has the title “Scarlett Johansson’s Agent Rips Disney Over ‘Black Widow’ Dispute.”)

Musk Pushed Hard to Achieve Sustainable Scale at Tesla

(p. B1) This was Mr. Hunter’s big moment: His team had scheduled 1,700 people to pick up their Model 3s in the coming days—a record—and he was proud to announce the achievement. The compact Model 3 was Mr. Musk’s bet-the-company shot at transforming Tesla into a mainstream auto maker and ushering in a new era of electric vehicles—and at that moment, Tesla needed to move thousands of them to stay afloat.

Mr. Hunter had set a record, but Mr. Musk wasn’t happy. The Tesla chief executive ordered Mr. Hunter to more than double the number the next day or else he’d personally take over.

There was more. Mr. Musk said he’d heard that Mr. Hunter’s team had been relying on phone calls to schedule car pickups. That stopped now. Nobody likes talking on (p. B6) the phone, Mr. Musk said; it takes up too much time. Text customers instead. That would be faster. If he heard about any calls being made the next day, Mr. Hunter was fired.

Mr. Hunter’s wife and children had only recently joined him in Las Vegas; they had just finished unpacking their boxes. Now Mr. Musk was threatening to fire him if he didn’t do the impossible in 24 hours.

Tesla was 15 years old, and it was running out of time and money.

. . .

The sales organization didn’t have hundreds of company cellphones that Mr. Hunter’s sales team could use to send text messages, as Mr. Musk demanded, and they didn’t want their employees using their own personal phones.

Overnight, Mr. Hunter and other managers pieced together a solution, employing software that allowed his team to text from their computers. They stopped the practice of walking customers through the reams of sales paperwork that would eventually need to be completed and signed. If Mr. Musk’s goal was to have people in a queue to pick up their cars, then that’s what they would do. They’d just start assigning pickup times for customers: Can you come in at 4 p.m. on Friday to get your new Model 3?

Often, Mr. Hunter didn’t even wait for any response before putting a customer on the list for pickup. If the customer couldn’t make it, she might be told she would lose her spot in line for a car that quarter. Customers became more motivated to complete the tedious paperwork needed to complete a sale when there was a Model 3 dangled in front of them. Mr. Hunter’s team began telling customers to have it all completed 48 hours before delivery.

The team raced through their list of customers, assigning times at pickup centers around the U.S. By 6 p.m. the next day, they had reached 5,000 appointments. Mr. Hunter gathered the team to thank them for their work. He fought back tears. He hadn’t told them that his job was on the line; all they knew was that it was super-important to schedule a bunch of deliveries. That night on the call, Mr. Hunter reported the results to Mr. Musk.

“Wow,” Mr. Musk said.

. . .

As the clock ticked down to the end of September [2020] and Tesla’s outrageous sales goal seemed out of reach, Mr. Musk turned to Twitter to make an unusual request to his loyal customers: Help us deliver vehicles.

Longtime owners showed up at stores around the country. They focused on showing customers how to operate their new cars, and explained life with an electric vehicle, freeing up paid staff to handle the overflow of paperwork. Mr. Musk and his new girlfriend, pop musician Grimes, worked at the Fremont delivery center, joined by board member Antonio Gracias. Mr. Musk’s brother, Kimbal, also a member of the board, showed up at a store in Colorado. It was truly an all-hands-on-deck moment. Surrounded by friends and kin, Musk seemed at his happiest, one manager recalled: “It was like a big family event…. He likes that—he likes loyalty.”

The company was ready to tabulate the quarter’s final delivery results. It was close. Deliveries reached 83,500—a record that exceeded Wall Street’s expectations but that was more than 15% shy of the internal goal of 100,000. (It was also uncannily close to the estimate by the head of customer experience, who had seemingly been ousted for suggesting it.) Almost 12,000 vehicles were still en route to customers, missing the deadline for the third quarter.

For the full essay, see:

Tim Higgins. “The Race to Rescue Tesla.” The Wall Street Journal (Sat., July 31, 2021): B1 & B6.

(Note: ellipses, and bracketed year, added.)

(Note: the online version of the essay has the date July 30, 2021, and has the title “Elon Musk’s ‘Delivery Hell’.”)

The essay quoted above is based on Higgins’s book:

Higgins, Tim. Power Play: Tesla, Elon Musk, and the Bet of the Century. New York: Doubleday, 2021.

SAS Entrepreneurs Forgo Billions in Order to Maintain Firm’s Tightknit Culture

Profits provide valuable information about whether a firm is doing something that consumers like, at a price they are willing to pay. But I think it is OK for a firm’s owners to seek less profits in return for more of some other values. Though personally, I would not forgo billions in order to preserve a yoga studio and a disc golf course.

(p. B1) Talks for Broadcom Inc. to buy SAS Institute Inc. have ended after the founders of the closely held software company changed their minds about a sale, people familiar with the matter said.

The Wall Street Journal reported Monday that the companies were discussing a deal that would value SAS in the range of $15 billion to $20 billion, including any debt. Following the report, Jim Goodnight and John Sall, who co-founded SAS decades ago and still run the company, had a change of heart and decided not to sell to Broadcom, the people said. Whether another suitor for SAS could emerge isn’t clear.

Some SAS employees saw the company as a strange fit for efficiency-focused Broadcom, some of the people familiar with the matter said. SAS is known for a tightknit culture and has a sprawling North Carolina campus with amenities including a yoga studio and a disc golf course.

For the full story, see:

Cara Lombardo. “SAS Calls Off Broadcom Talks.” The Wall Street Journal (Weds., July 14, 2021): B1.

(Note: ellipses added.)

(Note: the online version of the story has the date July 13, 2021, and has the title “Broadcom No Longer in Talks to Buy SAS.” The words “and a disc golf course” appear in the online version, but not in the print version.)

Highly Praised Robot Is Replaced by Humans in a Variety of Jobs

(p. A1) TOKYO—Having a robot read scripture to mourners seemed like a cost-effective idea to the people at Nissei Eco Co., a plastics manufacturer with a sideline in the funeral business.

The company hired child-sized robot Pepper, clothed it in the vestments of Buddhist clergy and programmed it to chant several sutras, or Buddhist scriptures, depending on the sect of the deceased.

Alas, the robot, made by SoftBank Group Corp., kept breaking down during practice runs. “What if it refused to operate in the middle of a ceremony?” said funeral-business manager Osamu Funaki. “It would be such a disaster.”

Pepper was fired. The company ended its lease of the robot and sent it back to the manufacturer. After a rash of similar mishaps across Japan, in which Pepper botched its job at (p. A10) a nursing home and gave baseball fans a creepy feeling, some people are saying the humanoid itself will need a funeral soon.

. . .

. . . , a Japanese hotel chain created a robot-operated hotel, with dinosaur-shaped robots handling front-desk duties, only to reverse course after the plan failed to save money and created more work for humans.

Pepper was given a perky demeanor and programmed to grasp human emotions and engage in basic conversation. It starred in some early demonstrations. But like a candidate who puts on a fine performance at his job interview only to drive his bosses crazy later, Pepper lacked the skills it said it had, say some of his managers.

In 2016, a Tokyo-area nursing-home operator called Ittokai introduced three units of Pepper, each at a cost of around $900 a month, to lead singing and exercises for elderly people at the home.

“Users got excited to have it early on because of its novelty,” said Masataka Iida, an executive at the company. “But they lost interest sooner than expected.” Mr. Iida said Pepper’s repertoire of exercise moves was limited and, owing to mechanical errors, it sometimes took unplanned breaks in the middle of its shift. After three years, the company pulled the plug.

. . .

SoftBank also touted Pepper as a companion for the home. The initial batch of 1,000 units sold out in a minute despite the hefty price tag.

Technology journalist Tsutsumu Ishikawa said he “fell in love at first sight” after seeing Mr. Son, the SoftBank chief, present a futuristic picture of living with a chatty Pepper.

After arriving at the Ishikawa home, however, Pepper couldn’t recognize the faces of family members or carry on a proper conversation, said Mr. Ishikawa. The robot, connected to the cloud, is supposed to remember the family even after a breakdown, Mr. Ishikawa says, but when Pepper returned home after the repair of a sensor, Pepper greeted him, “Nice to meet you!”

He shipped the robot back to SoftBank in 2018 after spending at least $9,000 over the three-year life of his subscription services agreement; he wasn’t eligible for any form of refund.

“It was such a waste of money. I still regret it,” he said.

For the full story, see:

Miho Inada. “Humanoid Robot Keeps Getting Fired From Jobs.” The Wall Street Journal (Wednesday, July 14, 2021): A1 & A10.

(Note: ellipses added.)

(Note: the online version of the story has the date July 13, 2021, and has the title “Humanoid Robot Keeps Getting Fired From His Jobs.”)

Serendipitous Water Cooler Collaboration “Is More Fairy Tale Than Reality”

(p. B1) When Yahoo banned working from home in 2013, the reason was one often cited in corporate America: Being in the office is essential for spontaneous collaboration and innovation.

. . .

Yet people who study the issue say there is no evidence that working in person is essential for creativity and collaboration. It may even hurt innovation, they say, because the demand for doing office work at a prescribed time and place is a big reason the American workplace has been inhospitable for many people.

“That’s led to a lot of the outcomes we see in the modern office environment — long hours, burnout, the lack of representation — because that office culture is set up for the advantage of the few, not the many,” said Dan Spaulding, chief people officer at Zillow, the real estate market-(p. B7)place.

“The idea you can only be collaborative face-to-face is a bias,” he said. “And I’d ask, how much creativity and innovation have been driven out of the office because you weren’t in the insider group, you weren’t listened to, you didn’t go to the same places as the people in positions of power were gathering?”

“All of this suggests to me that the idea of random serendipity being productive is more fairy tale than reality,” he said.

. . .

“There’s credibility behind the argument that if you put people in spaces where they are likely to collide with one another, they are likely to have a conversation,” said Ethan S. Bernstein, who teaches at Harvard Business School and studies the topic. “But is that conversation likely to be helpful for innovation, creativity, useful at all for what an organization hopes people would talk about? There, there is almost no data whatsoever.”

“All of this suggests to me that the idea of random serendipity being productive is more fairy tale than reality,” he said.

. . .

. . . Professor Bernstein found that contemporary open offices led to 70 percent fewer face-to-face interactions. People didn’t find it helpful to have so many spontaneous conversations, so they wore headphones and avoided one another.

. . .

. . . some creative professionals, like architects and designers, have been surprised at how effective remote work has been during the pandemic, while scientists and academic researchers have long worked on projects with colleagues in other places.

Requiring people to be in the office can drive out innovation, some researchers and executives said, because for many people, in-person office jobs were never a great fit. They include many women, racial minorities and people with caregiving responsibilities or disabilities. Also, people who are shy; who need to live far from the office; who are productive at odd hours; or who were excluded from golf games or happy hours.

For the full commentary, see:

Claire Cain Miller. “THE UPSHOT;Returning to the Office? The Myth of Serendipity.” The New York Times, SundayBusiness Section (Sunday, July 2, 2021): B1 & B7.

(Note: the online version of the commentary was updated July 1, 2021, and has the title “THE UPSHOT; Do Chance Meetings at the Office Boost Innovation? There’s No Evidence of It.”)

The Bernstein research mentioned above is:

Bernstein, Ethan, and Ben Waber. “The Truth About Open Offices.” Harvard Business Review 97, no. 6 (Nov./Dec. 2019): 82-91.