The threat of technological unemployment is real. To understand this threat, we'll define three overlapping sets of winners and losers that technical change creates: (1) high-skilled vs. low-skilled workers, (2) superstars vs. everyone else, and (3) capital vs. labor. Each set has well-documented facts and compelling links to digital technology. What's more, these sets are not mutually exclusive. In fact, the winners in one set are more likely to be winners in the other two sets as well, which concentrates the consequences.

In each case, economic theory is clear. Even when technological progress increases productivity and overall wealth, it can also affect the division of rewards, potentially making some people worse off than they were before the innovation. In a growing economy, the gains to the winners may be larger than the losses of those who are hurt, but this is a small consolation to those who come out on the short end of the bargain.

Ultimately, the effects of technology are an empirical question--one that is best settled by looking at the data. For all three sets of winners and losers, the news is troubling. Let's look at each in turn.

1. High-Skilled vs. Low-Skilled Workers

We'll start with skill-biased technical change, which is perhaps the most carefully studied of the three phenomena. This is technical change that increases the relative demand for high-skill labor while reducing or eliminating the demand for low-skill labor. A lot of factory automation falls into this category, as routine drudgery is turned over to machines while more complex programming, management, and marketing decisions remain the purview of humans.

A recent paper by economists Daron Acemoglu and David Autor highlights the growing divergence in earnings between the most-educated and least-educated 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 (Figure 3.5).

What's more, this increase in the relative price of educated labor--their wages--comes during a period where the supply of educated workers has also increased. The combination of higher pay in the face of growing supply points unmistakably to an increase in the relative demand for skilled labor. Because those with the least education typically already had the lowest wages, this change has increased overall income inequality.

It's clear from the chart in Figure 3.5 that wage divergence accelerated in the digital era. As documented in careful studies by David Autor, Lawrence Katz, and Alan Krueger, as well as Frank Levy and Richard Murnane and many others, the increase in the relative demand for skilled labor is closely correlated with advances in technology, particularly digital technologies. Hence, the moniker "skill-biased technical change," or SBTC. There are two distinct components to recent SBTC. Technologies like robotics, numerically controlled machines, computerized inventory control, and automatic transcription have been substituting for routine tasks, displacing those workers. Meanwhile other technologies like data visualization, analytics, high-speed communications, and rapid prototyping have augmented the contributions of more abstract and data-driven reasoning, increasing the value of those jobs.