Robots likely to steal jobs from poor, middle class

nnttttt

WASHINGTON – Computers and cyborgs aren’t about to render the American worker obsolete. But they are tilting the U.S. economy more and more in favor of the rich and away from the poor and the middle class, new economic research contends.

Despite rising fears of technology displacing huge swaths of the U.S. workforce, there remain huge classes of jobs that robots — and low-wage foreign workers — still can’t replace in the nation, and won’t replace any time soon.

n

To land the best of those jobs, workers need sophisticated vocabularies, advanced problem-solving abilities and other high-value skills that the U.S. economy does a good job of bestowing on young people from wealthy families — but cannot seem to deliver to poor and middle-class youths.

That is the alternatively optimistic and bleak picture of the American labor market sketched by economists Frank Levy of the Massachusetts Institute of Technology and Richard Murnane of Harvard, who conducted a detailed study of the kind of jobs have been lost to automation in recent years and which are likely to be shed as technology keeps advancing.

They wrapped their findings into a new paper for the centrist Democratic think tank Third Way, in which they argue, “For the foreseeable future, the challenge of ‘cybernation’ is not mass unemployment but the need to educate many more young people for the jobs computers cannot do.”

It’s a challenge other countries are solving better than the United States, Levy and Murnane say. It’s one American policymakers will need to solve if they hope to retain their global economic edge, and to keep lower-income Americans from falling further and further behind.

“We’ve been fearing this technological change since LBJ (former President Lyndon B. Johnson),” said Jim Kessler, Third Way’s senior vice president for policy.

Murnane and Levy, he added, “are saying: ‘Look, understand what these technological changes are. There are things we do better, there are (things) the machines do better. Know that, and we can prepare ourselves for it.’ “

Experts have warned for decades that technological advancements could eventually muscle people out of the workforce. In 1964, a group of high-profile scientists and economists called the Ad Hoc Committee on the Triple Revolution told Johnson that computers would soon create a massive unemployment problem, Kessler noted.

More recently, MIT professors Erik Brynjolfsson and Andrew McAfee penned an ebook, “Race Against the Machine,” blaming automation for the sluggish job growth of the last decade-plus.

They predict worse employment effects to come, citing the recent — and historically anomalous — “decoupling” of productivity and growth in the United States: The economy is producing more per worker, but that extra efficiency is not translating into a corresponding rate of hiring.

“Race Against the Machine” argues that all sorts of jobs people once imagined could never be undertaken by robots are on the cusp of automation. The authors cite truck drivers, whose jobs are threatened by the advent of Google’s driverless car.

Levy and Murnane do not believe such breakthroughs in artificial intelligence are anywhere close to at hand, based on extensive conversations with experts in the field at MIT. They’ve convened several lunches with those experts; Brynjolfsson and McAfee have attended some of them.

They’re not convinced driverless cars will replace truckers — computers still have a “common-sense” problem, Levy and Murnane say: A driverless 18-wheeler won’t know to hit the brakes pre-emptively if a ball bounces into the street, knowing from experience that a small child may likely chase after it.

The more optimistic view holds that computers will continue to struggle, for a long time, with several types of tasks — and in those tasks lie the United States’ employment future.

Levy and Murnane looked back over 50 years of employment in the U.S. and sorted jobs into five broad categories: routine manual tasks, routine cognitive tasks, non-routine manual tasks, working with new information and solving unstructured problems.

In the last 20 years, almost all the net job gains were in the two areas computers struggle with the most: working with new information — for example, figuring out a customer’s Internet service issues — and solving unstructured problems — such as repairing cars when computer diagnostics cannot pinpoint what is wrong.

Put another way, computers have grown very good at doing things that require plugging in formulas, or simply following directions. Humans are still much better at talking to one another to figure out where problems lie and strategize how to solve them.

There are still a lot of jobs in the economy that require those human skills. But wealthy kids have a huge advantage in getting those jobs, thanks to their schooling — data by the Pew Research Center shows the lowest-achieving wealthy child is more likely to finish college than the highest-achieving poor student — and, maybe more importantly, their home environments.

“With the constant need to acquire and work with new information,” Levy and Murnane wrote, “literacy requires not only the ability to sound out words phonetically, but also the background knowledge and vocabulary to make sense of newly encountered words and concepts.”

On this, studies show wealthier children have a big edge, hearing their parents speak nearly four times as many words in their infancy than the children of welfare recipients do. More affluent parents send their children to preschool and science camp and all sorts of other enrichment activities that supplement their basic educations.

When it comes to the skills most prized in the future job market, Murnane said, “kids from affluent families get a lot of that at home, and poor kids don’t.”

ntnttnttntntnttnttntntnttnttntnnnt

In a time of both misinformation and too much information, quality journalism is more crucial than ever.
By subscribing, you can help us get the story right.

ntSUBSCRIBE NOWnnnntnttKEYWORDSntntU.S., Employment, robotsnn n n tn tttt