I’ve been reading a lot about our economy and in particular, the tumultuous marriage between jobs and technology. If you believe in punctuated equilibrium, then certainly the microprocessor and the internets and the iPhones have forced a jump upward in socioeconomic development; some people jump higher than others.
Race Against the Machine [Kindle] is one of the better books I’ve read on the subject: it’s short, well-written and from experts who have a balanced point-of-view. And it’s got the usual eye-widening assortment of data and anecdotes.
So, I hope the below is informative and not boring. If the topic interests you, I’d love to hear from you. I can loan the Kindle version.
– – – – –
Employment is taking longer and longer to recover from a recession
Recessions always increase joblessness, of course, but between May 2007 and October 2009 unemployment jumped by more than 5.7 percentage points, the largest increase in the postwar period.
Just look at this chart:
A lot of people have quit the workforce…one of several reasons why I discount unemployment rates
And the workforce participation rate…fell below 64%—a level not seen since 1983 when women had not yet entered the labor force in large numbers.
Franklin D. Roosevelt put this most eloquently: No country, however rich, can afford the waste of its human resources. Demoralization caused by vast unemployment is our greatest extravagance. Morally, it is the greatest menace to our social order.
Just look at food stamp rolls, which have increased from 21M to 47M Americans in the last 5 years!
But people can’t stop, won’t stop investing in tech
And by 2010, investment in equipment and software returned to 95% of its historical peak, the fastest recovery of equipment investment in a generation.
Why has unemployment stayed high? According to the authors…
#1 cyclicality (shit happens)
Paul Krugman is one of the prime advocates of this explanation. As he writes, “All the facts suggest that high unemployment in America is the result of inadequate demand—full stop.”
#2 stagnation (we’re not innovating)
Stagnation in this context means a long-term decline in America’s ability to innovate and increase productivity. – Economist Tyler Cowen
A variant on this explanation is not that America has stagnated, but that other nations such as India and China have begun to catch up.
In particular, labor productivity can be measured as output per worker or output per hour worked. In the long run, productivity growth is almost the only thing that matters for ensuring rising living standards. Robert Solow earned his Nobel Prize for showing that economic growth does not come from people working harder but rather from working smarter.
Economists Menzie Chinn and Robert Gordon, in separate analyses, find that the venerable relationship between output and employment known as Okun’s Law has been amended. Historically, increased output meant increased employment, but the recent recovery created much less employment than predicted; GDP rebounded but jobs didn’t.
#3 “end of work” (robots are replacing us)
In it, Rifkin laid out a bold and disturbing hypothesis: that “we are entering a new phase in world history—one in which fewer and fewer workers will be needed to produce the goods and services for the global population.”
“the role of humans as the most important factor of production is bound to diminish in the same way that the role of horses in agricultural production was first diminished and then eliminated by the introduction of tractors.”
What a quote!
The authors believe it’s #3: we’re losing “the race against the machine”
The root of our problems is not that we’re in a Great Recession, or a Great Stagnation, but rather that we are in the early throes of a Great Restructuring.
Such versatility is a key feature of general purpose technologies (GPTs), a term economists assign to a small group of technological innovations so powerful that they interrupt and accelerate the normal march of economic progress. Steam power, electricity, and the internal combustion engine are examples of previous GPTs.
Vending machines now sell iPods, bathing suits, gold coins, sunglasses and razors; some will even dispense prescription drugs and medical marijuana to consumers willing to submit to a fingerprint scan.
In 1995, for example, 2.08 people were employed in “sales and related” occupations for every $1 million of real GDP generated that year. By 2002 (the last year for which consistent data are available), that number had fallen to 1.79, a decline of nearly 14 percent.
But, you know, computers still can’t manage a team or paint a Picasso…
Computers so far have proved to be great pattern recognizers but lousy general problem solvers; IBM’s supercomputers, for example, couldn’t take what they’d learned about chess and apply it to Jeopardy!
And for all their power and speed, today’s digital machines have shown little creative ability. They can’t compose very good songs, write great novels, or generate good ideas for new businesses.
David Ricardo, who initially thought that advances in technology would benefit all, developed an abstract model that showed the possibility of technological unemployment. The basic idea was that at some point, the equilibrium wages for workers might fall below the level needed for subsistence. A rational human would see no point in taking a job at a wage that low, so the worker would go unemployed and the work would be done by a machine instead.
Not everyone is losing the race; 3 types of runners are going to win (but they were in the lead to begin with)
In fact, economist Ed Wolff found that over 100% of all the wealth increase in America between 1983 and 2009 accrued to the top 20% of households. The other four-fifths of the population saw a net decrease in wealth over nearly 30 years.
Instead, the stagnation of median incomes primarily reflects a fundamental change in how the economy apportions income and wealth. The median worker is losing the race against the machine.
Runner #1: High-skilled workers
Over the past 40 years, weekly wages for those with a high school degree have fallen and wages for those with a high school degree and some college have stagnated. On the other hand, college-educated workers have seen significant gains, with the biggest gains going to those who have completed graduate training
Even the low wages earned by factory workers in China have not insulated them from being undercut by new machinery and the complementary organizational and institutional changes. For instance, Terry Gou, the founder and chairman of the electronics manufacturer Foxconn, announced this year a plan to purchase 1 million robots over the next three years to replace much of his workforce. The robots will take over routine jobs like spraying paint, welding, and basic assembly. Foxconn currently has 10,000 robots, with 300,000 expected to be in place by next year.
Runner #2: Superstars
Income has grown faster for the top 1% than the rest of the top decile. In turn, the top 0.1% and top 0.01% have seen their income grow even faster. This is not run-of-the-mill skill-biased technical change but rather reflects the unique rewards of superstardom. Sherwin Rosen, himself a superstar economist, laid out the economics of superstars in a seminal 1981 article. In many markets, consumers are willing to pay a premium for the very best. If technology exists for a single seller to cheaply replicate his or her services, then the top-quality provider can capture most—or all—of the market.
Um, that’s the internet. Bits and bytes.
Runner #3: Capital(ists)
In particular, if technology replaces labor, you might expect that the shares of income earned by equipment owners would rise relative to laborers—the classic bargaining battle between capital and labor.
Similarly, corporate profits as a share of GDP are at 50-year highs. Meanwhile, compensation to labor in all forms, including wages and benefits, is at a 50-year low.
But if humans and machines work together, everyone wins!
As Kasparov writes, it instead consisted of a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. … Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.
Technology enables more and more opportunities for what Google chief economist Hal Varian calls “micromultinationals”—businesses with less than a dozen employees that sell to customers worldwide and often draw on worldwide supplier and partner networks.
wonderful study by economist Robert Jensen found, for example, that as soon as mobile telephones became available in the fishing regions of Kerala, India, the price of sardines dropped and stabilized, yet fishermen’s profits actually went up.
We need to incentivize better teachers…
Start by simply paying teachers more so that more of the best and the brightest sign up for this profession, as they do in many other nations. American teachers make 40% less than the average college graduate.
…and encourage immigration…
Increase the ratio of skilled workers in the United States by encouraging skilled immigrants.
…and support entrepreneurs! :P
Foster a broader class of mid-tech, middle-class entrepreneurs by training them in the fundamentals of business creation and management.
In the 19th and 20th centuries, as each successive wave of automation eliminated jobs in some sectors and occupations, entrepreneurs identified new opportunities where labor could be redeployed and workers learned the necessary skills to succeed. Millions of people left agriculture, but an even larger number found employment in manufacturing and services.
Trivia: productivity is hard to measure
Compounding this measurement problem is the fact that free digital goods like Facebook, Wikipedia, and YouTube are essentially invisible to productivity statistics.
Reminds me of Tim O’Reilly and the clothesline paradox.
Health care productivity is poorly measured and often assumed to be stagnant, yet Americans live on average about 10 years longer today than they did in 1960.
That’s it, folks! Thank you for reading. I plan to do Cal Newport’s So Good They Can’t Ignore You next. It’s the best career advice book for 20-somethings that I’ve read. The irony is not lost on me as 30 is right around the bend…