US Economy
Decoupling of per capita GDP, productivity, private employment, and median family income in America
By Michael Hennigan, Finfacts founder and editor
Jun 12, 2015 - 5:04 AM

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Using four key measures of an economy’s health: per capita GDP, labour productivity, the number of jobs, and median household income, for more than three decades after World War II, In America all four went up steadily and in almost perfect lockstep. Job growth and wage growth, in other words, kept pace with gains in output and productivity. American workers not only created more wealth but also captured a proportional share of the gains.

Erik Brynjolfsson who with Andrew McAfee, his faculty colleague at the MIT Sloan School of Management, authored the 2014 book, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, says in a joint-interview in the current issue of the Harvard Business Review: "In the 1980s, however, the growth in median income began to sputter. In the past 15 years it’s turned negative; once you adjust for inflation, an American household at the 50th percentile of income distribution earns less today than it did in 1998, even after accounting for changes in household size.

Job growth in the private sector has also slowed—and not just because of the 2008 recession. Job gains were anemic throughout the 2000s, even when the economy was expanding. This phenomenon is what we call the Great Decoupling. The two halves of the cycle of prosperity are no longer married: Economic abundance, as exemplified by GDP and productivity, has remained on an upward trajectory, but the income and job prospects for typical workers have faltered."

Amy Bernstein, the editor, says in an interview summarising the cover story theme, 'Meet Your New Employee: How to manage the man-machine collaboration,'[It looks at the evolving role of the knowledge worker in the age of the algorithm. And I have to say, I just saw the new Avengers movie, and this Spotlight was very much on my mind. You know, artificial intelligence (AI) is moving into the workplace. It’s taking over more and more sophisticated work. And people are justifiably afraid that they’re going to lose their jobs.

So we look at this from a number of different vantage points, from the view of the individual, from the view of the firm. And really, we take kind of a longer, economic view of it. So let me just start with the first feature in the package. It’s called “Beyond Automation,” and it’s by Tom Davenport and Julia Kirby...What Tom and Julia say is, let’s reframe this. Let’s reframe this situation. What if we were able to reveal new achievements that are possible only if humans have better thinking machines to assist them. We could reframe the threat of automation, in that case, as an opportunity for augmentation. That’s a word they use a lot.

Julia and Tom have been looking at case after case in which knowledge workers collaborate with machines to do things that neither could’ve done well on its own or on his own. And they found that smart people are able to take five approaches to making peace with smart machines. They kind of package them up in a neat way. So I’ll go through that.

The framing goes like this. Some people will step up to even higher levels of cognition, where machines can’t follow. Some will step aside, trying on forms of intelligence that machines lack. Some will step in to monitor and adjust computers’ decision making. Some will step narrowly into very specialised realms of expertise. And finally, and inevitably, some will step forward, by creating next generation machines and finding new ways for them to augment the human strengths of workers...

So I think that what I’m finding so interesting about this moment in our development as managers and leaders is that we’re really grappling with how to fit the machine into our work in a way that benefits everyone. So let me describe some of the other articles in the package. We do an interview with two familiar names, Erik Brynjolfsson and Andy McAfee from MIT, who wrote The Second Machine Age over a year ago. And they are probably the leading thinkers on the effect of technology on our economy.

What’s different now from when they wrote the book is that they’re much more concerned about what they call “the Great Decoupling.” And let me describe that to you. They say that the pie is going to get bigger, but not everyone will benefit equally in this machine-augmented world we’re living in, largely because they’ll be less need for certain kinds of workers.

So what they’re saying is — and they’ve got all the data to prove it — for decades, per capita GDP, productivity, private employment, and median family income rose in almost perfect lockstep. In the 1980s, though, growth and income began to slow. And we all know what happened. Median income dropped. You know, the median family household today is earning less than it did 20 years ago, and job growth has also slowed.

So what do we do about this? Erik and Andy, in this interview, explore the implications. And they talk about who they think will win, and those are the workers with the higher technical skills and with creative skills that machines just aren’t yet able to take on; who’s going to lose, and that’s the middle class — they don’t have an answer for that — and how business should respond to the surge in technology in the workplace, and that is, develop a way to, as they say, race with machines, not against them.

We also take a look at this age of the algorithm from a really different standpoint. And that is we look at how some enterprises are actually adopting algorithmic thinking in ways that we found very, very surprising. So in the article called “The Self-Tuning Enterprise” by Martin Reeves, Ming Zeng, and Amin Venjara, the authors take a look at Alibaba, the e-commerce giant out of China, which has adopted the kind of algorithmic, innovative thinking that we see in Amazon and Netflix.

So it’s where algorithms are constantly readjusting the products and the content shown to customers. They’re doing this with the three famous learning loops. They’re experimenting. They’re modulating and shaping. So what Alibaba is doing and actually quite a few online companies, especially in emerging markets, is they’re starting to readjust their business models, their allocation processes, and their structures using the same self-directed learning loops. It’s absolutely fascinating. It made sense of a lot of the changes we’re seeing in a company like Alibaba. I highly recommend this one.

And then, the final article is really a lot of fun. It was written by one of our own editors, Walt Frick. And it looks at some of the most interesting research being done on the frontier of the man-machine relationship. And this one is called “When the Boss Wears Metal Pants.” Basically, Walt found, looking at all of this research, that in order for us to accept machines as our teammates or as our bosses in their metal pants, we got to go beyond just, quote, unquote, “adopting new technology.”

We really have to understand when computers do better than we do. We have to be able to recognize this widespread phenomenon, for example, of algorithm avoidance, in which people prefer human judgment over that of machines, even when machines demonstrably do better. That’s one set of findings.

He also looked at the ways that you can encourage trust in machines, essentially make them look like us. Right? And he’s got some great examples. And then, he suggests ways that you can reframe the relationship in the workplace. It’s a lot of fun. And you get to visit with Pleo, the little dinosaur robot that many of us remember from a few years ago, which it turns out, shows us that people really want to bond with their robots.

So in this experiment that’s gotten quite a bit of play, a bunch of participants were given a Pleo. And it’s an adorable, little pet dinosaur robot. It has facial expressions, and it has physical gestures. If you pick it up by the tail, it writhes uncomfortably, for example. They were told to play with the Pleos for about an hour, and then they were told to take a break. When the participants came back, they were given knives and hatchets, and they were told to destroy their Pleos.]

Andrew McAfee says in the interview: "Workers’ prospects are deteriorating in the developing world, too. A recent study by Loukas Karabarbounis and Brent Neiman found that labour’s share of GDP had declined in 42 out of 59 countries, including China, Mexico, and India. The researchers concluded that as advances in information technology caused the price of plants, machinery, and equipment to drop, companies shifted investment away from labour and toward capital."

Brynjolfsson added: "Over the past 30 years, as American companies moved production overseas to lower costs, manufacturing employment in the United States fell. Our MIT colleague David Autor and his co-researchers David Dorn and Gordon Hanson estimate that competition from China can explain about a quarter of the decline in manufacturing employment in the United States. But both American and Chinese workers are being made more efficient by automation."

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