In Towns Already Hit by Steel Mill Closings, a New Casualty: Retail Jobs

The New York Times reports another sad story of how the small-town America with its local stores that we grew up in is determinedly being efaced and replaced by the new on-line shopping world of Amazon that has no jobs to offer it in return for the ones it has stolen. These all go to the larger metropolitan centers, where the bulk of Amazon’s customers live.

Old Bethlehem Steel buildings that still hug stretches of the Little Conemaugh River bear witness to the loss of Johnstown’s industrial base. CreditGeorge Etheredge for The New York Times

By RACHEL ABRAMS and ROBERT GEBELOFF, June 25, 2017, for The New York Times

 

JOHNSTOWN, Pa. — Dawn Nasewicz comes from a family of steelworkers, with jobs that once dominated the local economy. She found her niche in retail.

She manages a store, Ooh La La, that sells prom dresses and embroidered jeans at a local mall. But just as the jobs making automobile springs and rail anchors disappeared, local retail jobs are now vanishing.

“I need my income,” said Ms. Nasewicz, who was told that her store will close as early as August. “I’m 53. I have no idea what I’m going to do.”

Ms. Nasewicz is another retail casualty, one of tens of thousands of workers facing unemployment nationwide as the industry struggles to adapt to online shopping.

Small cities in the Midwest and Northeast are particularly vulnerable. When major industries left town, retail accounted for a growing share of the job market in places like Johnstown, Decatur, Ill., and Saginaw, Mich. Now, the work force is getting hit a second time, and there is little to fall back on.

Moreover, while stores in these places are shedding jobs because of e-commerce, e-commerce isn’t absorbing these workers. Growth in e-commerce jobs like marketing and engineering, while strong, is clustered around larger cities far away. Rural counties and small metropolitan areas account for about 23 percent of traditional American retail employment, but they are home to just 13 percent of e-commerce positions.

E-commerce has also fostered a boom in other industries, including warehouses. But most of those jobs are being created in larger metropolitan areas, an analysis of Census Bureau business data shows.

Almost all customer fulfillment centers run by the online shopping behemoth Amazon are in metropolitan areas with more than 250,000 people — close to the bulk of its customers — according to a list of locations compiled by MWPVL International, a logistics consulting firm. An Amazon spokeswoman noted, however, that the company had recently opened warehouses in two distressed cities in larger metropolitan areas, Fall River, Mass., and Joliet, Ill.

Emptied stores tell the tale of retailing in Johnstown, where the metropolitan area has lost 19 percent of its retail jobs since 2001, and the future is uncertain. CreditGeorge Etheredge for The New York Times

 The Johnstown metropolitan area, in western Pennsylvania, has lost 19 percent of its retail jobs since 2001, and the future is uncertain. At least a dozen of Ooh La La’s neighbors at the mall have closed, and a “Going out of business” banner hangs across the front of the sporting goods store Gander Mountain.

“Every time you lose a corner store, every time you lose a restaurant, every time you lose a small clothing store, it detracts from the quality of life, as well as the job loss,” said John McGrath, a professor of management at the University of Pittsburgh Johnstown.

This city is perhaps still best known for a flood that ravaged it nearly 130 years ago. After rebuilding, Johnstown eventually became prosperous from its steel and offered a clear path to the middle class. For generations, people could walk out of high school and into a steady factory job.

But today, the area bears the marks of a struggling town. Its population has dwindled, and addiction treatment centers and Dollar Generals stand in place of corner grocers and department stores like Glosser Brothers, once owned by the family of Stephen Miller, President Trump’s speechwriter and a policy adviser.

When Mr. Trump spoke about “rusted-out factories scattered like tombstones across the landscape of our nation” in his Inaugural Address, people like Donald Bonk, a local economic development consultant, assumed that Mr. Miller — who grew up in California but spent summers in Johnstown — was writing about the old Bethlehem Steel buildings that still hug long stretches of the Little Conemaugh River.

The county voted overwhelmingly for Mr. Trump, eight years after it helped to elect Barack Obama. (It also voted for Mitt Romney in 2012, but not by as wide a margin.)

Here and in similar towns, when the factory jobs left, a greater share of the work force ended up in retail.

Sometimes that meant big-box retailers like Walmart, which were often blamed for destroying mom-and-pop stores but at least created other jobs for residents. The damage from e-commerce plays out differently. Digital firms may attract customers from small towns, but they are unlikely to employ them.

Some remaining retailers are straining for solutions.

Randy Clark remembers when his Miller’s Clothing Store, a family-run men’s wear shop, employed twice as many people and sold 20 pairs of pants a day. He knows he needs a website, but attracting digital customers is the least of his concerns. Brands that he sells, like Tommy Bahama and Southern Tide, will not even let him sell their products online, where he would compete with their own e-commerce operations, he said.

So instead, Mr. Clark has focused on the store itself. He renovated the first floor to attract customers from farther away, customers who might have more money to spend and more places to go than Johnstown. He bought new furniture and new floors, installed a coffee machine, and donated old sports coats and corduroy jackets to make room for fresh inventory. He wears a suit and tie to work six days a week, and says he does not own a pair of jeans.

“Not a lot of people dress up anymore,” Mr. Clark said. “If I don’t dress the part, who will?”

Tom Apryle IV takes the opposite approach at his jewelry store.

Metalworkers, office clerks and executives — thousands of workers used to stream in and out of the factories here every day. When they got engaged or celebrated anniversaries or just wanted a nice diamond bracelet, they often went to Apryle’s, a jewelry store that Mr. Apryle’s great-grandfather opened in 1902.

But fewer people can afford his products now that the good jobs are long gone, and Mr. Apryle has had to make adjustments.

A cash-for-gold sign hangs in the window. He started selling knickknacks on eBay. Eventually, he stopped wearing a tie.

“I might as well be comfortable,” Mr. Apryle, 46, said, gesturing to his wrinkled T-shirt and tennis shoes. “There’s no one here to impress.”

The story of America’s Johnstowns is not just the story of retail, or e-commerce, or how men don’t buy suits and ties at Miller’s the way they used to. It’s also about men like Mr. Apryle, who wouldn’t even have a place to wear them.

“I was the last generation to see it booming and prosperous, and people were employed,” said Mr. Bonk, 53, the economic development consultant, who grew up in Johnstown. “It disappeared in my lifetime.”

Just as Johnstown scrambled to adapt to the decline in manufacturing that began a generation ago, local leaders are now looking at how to navigate a future with a much diminished retail economy.

To help revitalize the area, the county hired Mr. Bonk, whose parents ran a corner grocery store here for more than 40 years and made enough money to send him and his brother to college.

Mr. Bonk has returned, determined to make downtown thrive again. But he does not have dreams of bringing back the department stores of his youth. He knows that consumers these days want to spend their money more on experiences than things, and that neighborhood stores are competing against digital upstarts that do not need as many workers and often have far more resources.

As he walks briskly down Main Street, Mr. Bonk points out the new businesses that stand shoulder-to-shoulder with empty storefronts. There is The Vault, a day spa in an old bank building, and the Press Bistro, which, he excitedly points out, has an area for live music.

These places are evidence, he says, that other people are committed to restoring Johnstown.

“They want to see it be a healthy, thriving community, like where they grew up,” he said.

Mr. Bonk is inspired by Pittsburgh, another former steel town that revived its economy by attracting new businesses, including an Amazon distribution center and the fleet of trucks that came with it. But he knows that the Pittsburgh metro area, with a population of 2.4 million, is 17 times as large as Johnstown.

“I’m thinking about what’s next,” he said. “We’re essentially thinking of Johnstown as an economic development laboratory.”

Mr. Bonk isn’t counting on Amazon coming here.

Rachel Abrams reported from Johnstown, Pa., and Robert Gebeloff from New York.

 

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The Real Threat of Artificial Intelligence

We have been out of commission for some time, due to the breakdown of our internet system. But this morning we are back in business with a smashing article from the Sunday New York Times on artificial intelligence and the devastating effect it will have for the future of our economy by a man who is at the center of this world game-changer.

By KAI-FU LEE, June 25, 2017, for The New York Times

. . . the A.I. products that now exist are improving faster than most people realize and promise to radically transform our world, not always for the better. They are only tools, not a competing form of intelligence. But they will reshape what work means and how wealth is created, leading to unprecedented economic inequalities and even altering the global balance of power.

It is imperative that we turn our attention to these imminent challenges.

What is artificial intelligence today? Roughly speaking, it’s technology that takes in huge amounts of information from a specific domain (say, loan repayment histories) and uses it to make a decision in a specific case (whether to give an individual a loan) in the service of a specified goal (maximizing profits for the lender). Think of a spreadsheet on steroids, trained on big data. These tools can outperform human beings at a given task.

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs — mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools aren’t good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and “cross-domain” thinking — for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the “people skills” that A.I. lacks: social workers, bartenders, concierges — professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve “service jobs of love.” These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous — or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future.

Other volunteer jobs may be higher-paying and professional, such as compassionate medical service providers who serve as the “human interface” for A.I. programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.

Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.

As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of “service of love” voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most people’s lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals.

This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes. But what about other countries?

They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. It’s a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech-recognition company iFlytek and several Chinese face-recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies — Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent — are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones.

So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s A.I. companies continue to profit from the dependent country’s users. Such economic arrangements would reshape today’s geopolitical alliances.

One way or another, we are going to have to start thinking about how to minimize the looming A.I.-fueled gap between the haves and the have-nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far-ranging in their effects for any nation to isolate itself from the rest of the world.

Kai-Fu Lee is the chairman and chief executive of Sinovation Ventures, a venture capital firm, and the president of its Artificial Intelligence Institute.