Labor Economics – Page 19 – artdiamondblog.com

Half of Today’s 36-Year-Olds Earn Less Than Their Parents Did at Same Age

Source of graph: http://www.equality-of-opportunity.org/

(p. 2) These days, people are arguably more worried about the American dream than at any point since the Depression. But there has been no real measure of it, despite all of the data available. No one has known how many Americans are more affluent than their parents were — and how the number has changed.

The beginnings of a breakthrough came several years ago, when a team of economists led by Raj Chetty received access to millions of tax records that stretched over decades. The records were anonymous and came with strict privacy rules, but nonetheless allowed for the linking of generations.
The resulting research is among the most eye-opening economics work in recent years.
. . .
After the research began appearing, I mentioned to Chetty, a Stanford professor, and his colleagues that I thought they had a chance to do something no one yet had: create an index of the American dream. It took them months of work, using old Census data to estimate long-ago decades, but they have done it. They’ve constructed a data set that shows the percentage of American children who earn more money — and less money — than their parents earned at the same age.
The index is deeply alarming. It’s a portrait of an economy that disappoints a huge number of people who have heard that they live in a country where life gets better, only to experience something quite different.
. . .
About 92 percent of 1940 babies had higher pretax inflation-adjusted household earnings at age 30 than their parents had at the same age.
. . .
For babies born in 1980 — today’s 36-year-olds — the index of the American dream has fallen to 50 percent: Only half of them make as much money as their parents did.

For the full commentary, see:
Leonhardt, David. “The American Dream, Quantified at Last.” The New York Times, SundayReview Section (Sun., DEC. 11, 2016): 2.
(Note: ellipses added.)
(Note: the online version of the commentary has the date DEC. 8, 2016.)

The Chetty co-authored paper mentioned above, is:
Chetty, Raj, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. “The Fading American Dream: Trends in Absolute Income Mobility since 1940.” Science 356, no. 6336 (2017): 398-406.

More Workers Benefit from Driverless Cars, Than Are Hurt

(p. A2) Self-driving vehicles have the potential to reshape a wide range of occupations held by roughly one in nine American workers, according to a new U.S. government report.
About 3.8 million people drive taxis, trucks, ambulances and other vehicles for a living. An additional 11.7 million workers drive as part of their work, including personal care aides, police officers, real-estate agents and plumbers. In all, that’s roughly 11.3% of total U.S. employment based on 2015 occupational data, according to the analysis by three Commerce Department economists.
If businesses embrace autonomous vehicles on a large scale, workers in the first category are “more likely to be displaced” from their jobs, while workers in the latter group “may be more likely to benefit from greater productivity and better working conditions,” wrote David Beede, Regina Powers and Cassandra Ingram in the report, released Friday.

For the full story, see:
Ben Leubsdorf. “Driverless Cars May Alter 1 in 9 Jobs.” The Wall Street Journal (Tues., Aug 15, 2017): A2.
(Note: the online version of the story has the date Aug 14, 2017, and has the title “Self-Driving Cars Could Transform Jobs Held by 1 in 9 U.S. Workers.”)

The report summarized in the passages quoted above, is:
Beede, David, Regina Powers, and Cassandra Ingram. “The Employment Impact of Autonomous Vehicles.” ESA Issue Brief, #05-17, Aug. 11, 2017.

“Achievement Is a Magnet to Mentors and a Beacon to Backers”

(p. 7) It’s true that networking can help you accomplish great things. But this obscures the opposite truth: Accomplishing great things helps you develop a network.
Look at big breaks in entertainment. For George Lucas, a turning point was when Francis Ford Coppola hired him as a production assistant and went on to mentor him. Mr. Lucas didn’t schmooze his way into the relationship, though. As a film student he’d won first prize at a national festival and a scholarship to be an apprentice on a Warner Bros. film — he picked one of Mr. Coppola’s.
Or take Justin Bieber’s career: Although it took off after Usher signed him, he didn’t network his way into that meeting. Mr. Bieber taught himself to sing and play four instruments, put a handful of videos on YouTube, and a manager ended up clicking on one. Adele was discovered that way, too: She wrote and recorded a three-song demo, a friend posted it on Myspace, and a music exec heard it. Developing talent — and sharing it — catapulted them into those connections.
For entrepreneurs, too, achievement is a magnet to mentors and a beacon to backers. Spanx took off when Oprah Winfrey chose it as one of her favorite things of the year — but not because she was stalked by the company’s founder, Sara Blakely. For two and a half years, Ms. Blakely sold fax machines by day so that she could build her prototype of footless pantyhose by night. She sent one from the first batch to Ms. Winfrey.
Networks help, of course. In a study of internet security start-ups, having a previous connection to an investor increased the odds of getting funded by that investor in the first year. But it was pretty much irrelevant afterward. Accomplishments were the dominant driver of who invested over time.
Similarly, researchers found that in hospitals, the radiologists who ended up with the most desirable networks were the ones with the highest performance nine months earlier. And in banks, star performers attracted bigger networks and were more likely to maintain those ties. Achievements don’t just help us make connections; they also help sustain those connections.
. . .
So stop fretting about networking. Take a page out of the George Lucas and Sara Blakely playbooks: Make an intriguing film, build a useful product.
And don’t feel pressure to go to networking events. No one really mixes at mixers. Although we plan to meet new people, we usually end up hanging out with old friends. The best networking happens when people gather for a purpose other than networking, to learn from one another or help one another.

For the full commentary, see:
Grant, Adam. “Networking Is Overrated.” The New York Times, SundayReview Section (Sun., AUG. 27, 2017): 7.
(Note: ellipsis added.)
(Note: the online version of the commentary has the date AUG. 24, 2017, and has the title “Good News for Young Strivers: Networking Is Overrated.”)

Higher-Paid Finance Jobs Moving from NYC and San Francisco to Phoenix, Salt Lake City, and Dallas

Source of graph: online version of the WSJ article quoted and cited below.

(p. B1) Traditional finance hubs have yet to recover all the jobs lost during the recession, but the industry is booming in places like Phoenix, Salt Lake City and Dallas. The migration has accelerated as investment firms face declining profitability and soaring real estate costs.
. . .
“San Francisco is a wonderful place, but unfortunately it’s an expensive place from a real estate standpoint,” said Brian McDonald, a senior vice president for Schwab. “So we had to identify other places where we could make things work.”
While the finance industry has been relocating entry-level jobs since the late 1980s, today’s moves are claiming higher-paid jobs in human resources, compliance and asset management, chipping away at New York City’s middle class, said (p. B2) Kathryn Wylde, president and chief executive of the Partnership for New York City, a nonprofit that represents the city’s business leadership.
“This industry isn’t just a bunch of rich Wall Street guys,” Ms. Wylde said. “It’s a big source of employment that’s disappearing from New York.”

For the full story, see:
Asjylyn Loder. “Wall Street’s New Frontier.” The Wall Street Journal (Thurs., JULY 27, 2017): B1-B2.
(Note: ellipsis added.)
(Note: the online version of the story has the date JULY 26, 2017, and has the title “Passive Migration: Denver Wins Big as Financial Firms Relocate to Cut Costs.”)

Seattle Increase in Minimum Wage Results in Fewer Hours Worked, and Lower Incomes

(p. A13) By now you have read 15 articles on the Seattle minimum-wage fiasco. Since the city boosted its local minimum from $9.47 in 2014 to $13 last year (on its way to $15), a detailed investigation by University of Washington economists finds that beneficiaries actually saw their incomes fall by a net $125 a month because employers cut their hours.
. . .
The impetus came from people who don’t actually earn the minimum wage–labor-union leaders and think-tankers and activist organizations.
. . .
Organizers look fondly to Denmark, where a McDonald’s line worker receives $41,000 a year and five weeks of paid vacation. As the Atlantic put it two years ago, “Unionizing workers at McDonald’s and other fast-food chains might be a long shot, but if it succeeds, it might help lift a million or more workers into the middle class (or at least into the lower middle class) and create a model for low-wage workers in other industries.”
This sounds pretty but is misleading in a fundamental way. The workers a McDonald’s franchise would hire at $15 an hour are different from those it would hire at $8.29, the average earned by a fast-food worker today.
Costs would go up. The industry would likely shrink, it would likely replace workers with automation, but it would still create jobs at $15 an hour for people whose productivity can justify $15 an hour. The people who work at McDonald’s today, typically, would already be earning $15 an hour somewhere else if their productivity could justify $15 an hour.
Everybody needs to start somewhere, including the unskilled and those who lack a work history. Some need a job that doesn’t demand much of them. They have other obligations. They accept less pay to maximize flexibility and freedom from responsibility. They don’t plan to make a career of it. The fast-food industry in America is built on such people.

For the full commentary, see:
Holman W. Jenkins, Jr. “Seattle Aims at McDonald’s, Hits Workers.” The Wall Street Journal (Sat., July 1, 2017): A13.
(Note: ellipses added.)
(Note: the online version of the commentary has the date June 30, 2017.)

The Seattle minimum wage paper, mentioned above, is:
Jardim, Ekaterina, Mark C. Long, Robert Plotnick, Emma van Inwegen, Jacob Vigdor, and Hilary Wething. “Minimum Wage Increases, Wages, and Low-Wage Employment: Evidence from Seattle.” National Bureau of Economic Research Working Paper Series, # 23532, June 2017.

Some New Jobs Require Same Skills as Old Jobs Did

(p. B1) . . . many of the skills needed to do fading jobs are applicable to growing jobs.
. . .
(p. B2) A New York Times review of the activities and skills that jobs entail, based on the Labor Department’s O*Net database, shows how much overlap there is between many seemingly dissimilar occupations. Service industry jobs, for example, require social skills and experience working with customers — which also apply to sales and office jobs.
. . .
. . . , employers hire based on credentials that job applicants can’t change — a college degree or previous job title — rather than assessing the skills an applicant has developed, said Mr. Auguste, who was an economic adviser in the Obama administration. He said the approach should instead be, “If you learned it at Harvard or Cal State Northridge or on the job as a secretary or in the Navy or as a volunteer, awesome.”

For the full commentary, see:
CLAIRE CAIN MILLER and QUOCTRUNG BUI. “The Upshot; Old Skills, New Career.” The New York Times (Fri., JULY 28, 2017): B1-B2.
(Note: ellipses added.)
(Note: the online version of the commentary has the date JULY 27, 2017, and has the title “The Upshot; Switching Careers Doesn’t Have to Be Hard: Charting Jobs That Are Similar to Yours.”)

A.I. “Continues to Struggle in the Real World”

The passages quoted below are authored by an NYU professor of psychology and neural science.

(p. 6) Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go. Sure, A.I. systems have mastered an array of games, from chess and Go to “Jeopardy” and poker, but the technology continues to struggle in the real world. Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has yet designed a machine that can read reliably at the level of a sixth grader, let alone a college student. Computers that can educate themselves — a mark of true intelligence — remain a dream.

Even the trendy technique of “deep learning,” which uses artificial neural networks to discern complex statistical correlations in huge amounts of data, often comes up short. Some of the best image-recognition systems, for example, can successfully distinguish dog breeds, yet remain capable of major blunders, like mistaking a simple pattern of yellow and black stripes for a school bus. Such systems can neither comprehend what is going on in complex visual scenes (“Who is chasing whom and why?”) nor follow simple instructions (“Read this story and summarize what it means”).
Although the field of A.I. is exploding with microdiscoveries, progress toward the robustness and flexibility of human cognition remains elusive. Not long ago, for example, while sitting with me in a cafe, my 3-year-old daughter spontaneously realized that she could climb out of her chair in a new way: backward, by sliding through the gap between the back and the seat of the chair. My daughter had never seen anyone else disembark in quite this way; she invented it on her own — and without the benefit of trial and error, or the need for terabytes of labeled data.
Presumably, my daughter relied on an implicit theory of how her body moves, along with an implicit theory of physics — how one complex object travels through the aperture of another. I challenge any robot to do the same. A.I. systems tend to be passive vessels, dredging through data in search of statistical correlations; humans are active engines for discovering how things work.

For the full commentary, see:
GARY MARCUS. “Gray Matter; A.I. Is Stuck. Let’s Unstick It.” The New York Times, SundayReview Section (Sun., JULY 30, 2017): 6.
(Note: the online version of the commentary has the date JULY 29, 2017, and has the title “Gray Matter; Artificial Intelligence Is Stuck. Here’s How to Move It Forward.”)

Code Schools Provide Intense 12 Week Training, and Jobs

(p. B1) Across the U.S., change is coming for the ecosystem of employers, educational institutions and job-seekers who confront the increasingly software-driven nature of work. A potent combination–a yawning skills gap, stagnant middle-class wages and diminished career prospects for millennials–is bringing about a rapid shift (p. B4) in the labor market for coders and other technical professionals.
Riding into the breach are “code schools,” a kind of vocational training that rams students through intense 12-week crash courses in precisely the software-development skills employers need.

For the full commentary, see:
Christopher Mims. “Code-School Boot Camps Offer Fast Track to Jobs.” The Wall Street Journal (Mon., Feb. 27, 2017): B1 & B4.
(Note: the online version of the commentary has the date Feb. 26, 2017, and has the title “A New Kind of Jobs Program for Middle America.”)

Petsitting Is Illegal Without a License

“Raul Cordero with his Rhodesian ridgeback, Viuty. Mr. Cordero operates a dog-care business in East Harlem that appears to run afoul of city rules regarding the care of pets for pay in homes.” Source of caption and photo: online version of the NYT article quoted and cited below.

(p. A18) Raul Cordero and his Rhodesian ridgeback, Viuty, often have canine houseguests overnight at their East Harlem home, part of Mr. Cordero’s dog-care business, for which he carries special petsitter’s insurance that costs about $800 a year.
Yet despite Mr. Cordero’s efforts to do everything by the book, he was shocked to discover that his petsitting business — and in fact, any of the ubiquitous, your-home-or-mine variety — is against New York City’s rules.
According to long-established but little-noticed regulations of the city’s Department of Health and Mental Hygiene, anyone offering petsitting for pay must be licensed to board animals, and do so in a permitted kennel. Running such a kennel out of a home is not allowed in the city.
. . .
The newcomers are large, app-based pet-care businesses, with names like Wag and Rover, that operate in a similar style to Airbnb, enabling New Yorkers to open their apartments and dog beds as à la carte dog hostels.
. . . Rover and its ilk have run afoul of similar stipulations in places like California and Colorado, and John Lapham, Rover’s general counsel, said that Rover was currently embroiled in similar concerns in several cities in New Jersey.
. . .
The department’s rule “deprives dog owners of the most obvious, safe and affordable care,” Mr. Lapham said.
“And it deprives sitters of the opportunity to make ends meet,” he said.
Mr. Lapham noted that in New York City, babysitting, for example, is permitted, no license necessary.
. . .
. . . to Mr. Cordero, 27, regulating small-time dogsitters like him and his Rhodesian sidekick feels like government overreach. Petsitting “is like taking care of you own pet in your house,” he said, adding: “So if you have a license, that means you are certified to feed a dog or a cat? That’s crazy.”

For the full story, see:
SARAH MASLIN NIR. “Paid Petsitting in Homes Is Illegal in New York. That’s News to Some Sitters.” The New York Times (Sat., JULY 22, 2017): A18.
(Note: ellipses added.)
(Note: the online version of the story has the date JULY 21, 2017.)

Employment Grows as Productivity Rises

(p. C3) In a recent paper prepared for a European Central Bank conference, the economists David Autor of MIT and Anna Salomons of Utrecht University looked at data for 19 countries from 1970 to 2007. While acknowledging that advances in technology may hurt employment in some industries, they concluded that “country-level employment generally grows as aggregate productivity rises.”
The historical record provides strong support for this view. After all, despite centuries of progress in automation and recurrent warnings of a jobless future, total employment has continued to increase relentlessly, even with bumps along the way.
More remarkable is the fact that today’s most dire projections of jobs lost to automation fall short of historical norms. A recent analysis by Robert Atkinson and John Wu of the Information Technology & Innovation Foundation quantified the rate of job destruction (and creation) in each decade since 1850, based on census data. They found that an incredible 57% of the jobs that workers did in 1960 no longer exist today (adjusted for the size of the workforce).
Workers suffering some of the largest losses included office clerks, secretaries and telephone operators. They found similar levels of displacement in the decades after the introduction of railroads and the automobile. Who is old enough to remember bowling alley pin-setters? Elevator operators? Gas jockeys? When was the last time you heard a manager say, “Take a memo”?
. . .
. . . , if artificial intelligence is getting so smart that it can recognize cats, drive cars, beat world-champion Go players, identify cancerous lesions and translate from one language to another, won’t it soon be capable of doing just about anything a person can?
Not by a long shot. What all of these tasks have in common is that they involve finding subtle patterns in very large collections of data, a process that goes by the name of machine learning.
. . .
But it is misleading to characterize all of this as some extraordinary leap toward duplicating human intelligence. The selfie app in your phone that places bunny ears on your head doesn’t “know” anything about you. For its purposes, your meticulously posed image is just a bundle of bits to be strained through an algorithm that determines where to place Snapchat face filters. These programs present no more of a threat to human primacy than did automatic looms, phonographs and calculators, all of which were greeted with astonishment and trepidation by the workers they replaced when first introduced.
. . .
The irony of the coming wave of artificial intelligence is that it may herald a golden age of personal service. If history is a guide, this remarkable technology won’t spell the end of work as we know it. Instead, artificial intelligence will change the way that we live and work, improving our standard of living while shuffling jobs from one category to another in the familiar capitalist cycle of creation and destruction.

For the full commentary, see:
Kaplan, Jerry. “Don’t Fear the Robots.” The Wall Street Journal (Sat., June 22, 2017): C3.
(Note: ellipses added.)
(Note: the online version of the commentary has the date June 21, 2017.)

The David Autor paper, mentioned above, is:

Autor, David, and Anna Salomons. “Does Productivity Growth Threaten Employment?” Working Paper. (June 19, 2017).

The Atkinson and Wu report, mentioned above, is:
Atkinson, Robert D., and John Wu. “False Alarmism: Technological Disruption and the U.S. Labor Market, 1850-2015.” (May 8, 2017).

The author’s earlier book, somewhat related to his commentary quoted above, is:
Kaplan, Jerry. Artificial Intelligence: What Everyone Needs to Know. New York: Oxford University Press, 2016.