A recent article in The Guardian dons the foreboding title “Robots will destroy our jobs—and we’re not ready for it.”
The article claims, “For every job created by robotic automation, several more will be eliminated entirely. … This disruption will have a devastating impact on our workforce.”
According to an article in MIT Technology Review, business researchers Erik Brynjolfsson and Andrew McAfee believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States.
If technology is destroying jobs faster than it’s creating them, it is the first time in human history that it’s done so. Actually, the number of jobs is unlimited, for the simple reason that human wants are unlimited—or they don’t frequently reveal their bounds.
People always want more of something that will create a job for someone. To suggest that there are a finite number of jobs commits an error known as the “lump of labor fallacy.”
That fallacy suggests that when automation or technology eliminates a job, there’s nothing that people want that would create employment for the person displaced by the automation. In other words, all human wants have been satisfied.
Let’s look at a few examples.
In 1790, farmers were 90 percent of the U.S. labor force. By 1900, only about 41 percent of our workers were employed in agriculture. Today less than 3 percent of Americans are employed in agriculture.
And it’s a good thing. If 90 percent or 41 percent of our labor force were still employed in agriculture, where in the world would we find the workforce to produce all those goods and services that weren’t around in 1790 or 1900, such as cars, aircraft, TVs, computers, aircraft carriers, etc.?
Indeed, if technology had not destroyed all of those agricultural jobs, we would be a much, much poorer nation.
What about the claim that our manufacturing jobs are going to China—a claim that’s fueling the Trump administration to impose trade barriers?
It is true that between 2001 and 2013, 3.2 million jobs were outsourced to China. However, in the same time frame, China lost about 4.5 million manufacturing jobs, compared with the loss of 3.1 million in the U.S.
Job loss is the trend among the top 10 manufacturing countries, which produce 75 percent of the world’s manufacturing output (the U.S., Japan, Germany, China, Britain, France, Italy, South Korea, Canada, and Mexico). Only Italy has managed not to lose factory jobs since 2000.
Nonetheless, the U.S. remains a major force in global manufacturing.
Because of automation, the U.S. worker is now three times as productive as in 1980 and twice as productive as in 2000. It’s productivity gains, rather than outsourcing and imports, that explain most of our manufacturing job loss.
If our manufacturing sector were its own economy and had its own gross domestic product, it would be the seventh-largest in the world. Total manufacturing value could be as high as $5.5 trillion. In other words, about 17 percent of global manufacturing activity happens in the United States, and America dominates advanced manufacturing.
According to the Alliance for American Manufacturing, U.S. manufacturing employs a large percentage of the workers who are trained in fields related to science, technology, engineering, and math. It employs 37 percent of architectural and engineering workers and 16 percent of life, physical, and social scientists.
Economist Joseph Schumpeter described this process of technological change. He called it “creative destruction.” Technology and innovation destroy some jobs while creating many others.
According to the Bureau of Labor Statistics, the U.S. labor force in 1950 was 62 million. By 2000, it was 79 million, and it’s projected to reach 192 million by 2050.
Though the “creative destruction” process works hardships on some people who lose their jobs and are forced to take lower-paying jobs, any attempt to impede the process would make all of us worse off.
This article originally appeared in the Daily Signal.
Image: Wikimedia Commons