Why is productivity growth low if information technology is advancing rapidly? Prominent in the 1980s and early 1990s, this question has in recent years again become one of the hottest in economics. Its salience has grown as techies have become convinced that machine learning and artificial intelligence will soon put hordes of workers out of work. Bill Gates has called for a robot tax to deter automation, Elon Musk for a universal basic income.
A lot of economists think that a surge in productivity that would leave millions on the scrap heap is unlikely soon, if at all. Yet earlier this month at the annual meeting of the American Economic Association, participants showed they are taking the tech believers seriously. Sessions on the implications of automation were packed.
Recent history seems to support productivity pessimism. From 1995 to 2004, output per hour worked grew at an annual average pace of 2.5 percent; from 2004 to 2016 the pace was 1 percent. John Fernald, a senior research adviser of the Federal Reserve Bank of San Francisco, and his co-workers estimate that the U.S. slowdown started in 2006. This supports the popular idea that fewer transformative technologies are left to be discovered.
Yet at the economics conference, Erik Brynjolfsson of the Massachusetts Institute of Technology pointed to recent sharp gains in machines' ability to recognize patterns, which could be used in everything from self-driving cars to improving accuracy in medical diagnoses. Brynjolfsson and his colleagues forecast that such advances will eventually lead to a widespread reorganization of jobs, affecting high- and low-skilled workers alike.
Daron Acemoglu, also of MIT, and Pascual Restrepo of Boston University suggested that new technology needs to be measured in a new way: the sort that replaces labor with machines and the kind that creates more complicated tasks for workers. Historically, the authors argue, the two types of innovation seem to have been in balance, encouraged by market forces. However, if they are out of sync, it can lead to unintended consequences.
If research in automation does start yielding big payoffs, the question is what will happen to the displaced workers? The risk is that without sufficient investment in training, technology will relegate many more workers to the ranks of the low-skilled. To employ them all, pay or working conditions might have to deteriorate. If productivity optimists are right, the eventual problem may not be the quantity of available work, but its quality.
