The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (21 page)

In contrast, the economics of personal services (nursing) or physical work (gardening) are very different, since each provider, no matter how skilled or hard-working, can only fulfill a tiny fraction of the overall market demand. When an activity transitions from the second category to the first the way tax preparation did, the economics shift toward winner-take-all outcomes. What’s more, lowering prices, the traditional refuge for second-tier products, is of little benefit for anyone whose quality is not already at or near the world’s best. Digital goods have enormous economies of scale, giving the market leader a huge cost advantage and room to beat the price of any competitor while still making a good profit.
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Once their fixed costs are covered, each marginal unit produced costs very little to deliver.
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Improvements in Telecommunications: Reach Out and Touch More People

Secondly, winner-take-all markets have also been boosted by technological improvements in telecommunications and transportation that also expand the market individuals and companies can reach. When there are many small local markets, there can be a ‘best’ provider in each, and these local heroes frequently can all earn a good income. If these markets merge into a single global market, top performers have an opportunity to win more customers, while the next-best performers face harsher competition from all directions. A similar dynamic comes into play when technologies like Google or even Amazon’s recommendation engine reduce search costs. Suddenly second-rate producers can no longer count on consumer ignorance or geographic barriers to protect their margins.

Digital technologies have aided the transition to winner-take-all markets, even for products we wouldn’t think would have superstar status. In a traditional camera store, cameras typically are not ranked number one versus number ten. But online retailers make it easy to list products in rank order by customer ratings, or to filter results to include only products with every conceivable desirable feature. Products with lower rankings or only nine out of ten desirable features receive disproportionately lower sales from even small differences in quality, convenience, or pricing performance.
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Digital ranking and filtering create disproportional returns even in labor markets for workaday, non-superstar careers. Companies have digitized their hiring processes and use automated filters to winnow the flood of applicants. For example, companies can readily cull all the candidates that don’t have a college degree as a simple expedient even if the job does not actually require a college education.
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This can amplify a trickle of skill-biased technical change into a torrent of stardom for a lucky few. Similarly, job candidate resumes that miss the buzzword requirements might drop from consideration even if the 90-percent-qualified candidate might otherwise be a stellar employee.

Networks and Standards: The Value of Scale

Thirdly, the increased importance of networks (like the Internet or credit card networks) and interoperable products (like computer components) can also create winner-take-all markets. Just as low marginal costs create economies of scale on the production side, networks can create ‘demand side economies of scale’ that economists sometimes call
network effects
. We see them at work when users prefer products or services that other people are flocking to. If your friends keep in touch via Facebook, that makes Facebook more attractive to you, too. If you then join Facebook, the site becomes more valuable to your friends as well.

Sometimes network effects are indirect. You can make a phone call equally well to someone using an iPhone or an Android phone. But the total number of users on a given platform influences app developers: the bigger network of users will tend to attract more developers, or encourage app developers to invest more in a given platform. The more apps available for a given phone, the greater its appeal to users. Thus, your benefits from buying one or the other will be affected by the number of other users who buy the same product. When Apple’s app ecosystem is strong, buyers will want to buy into that platform, attracting even more developers. But the opposite dynamic can unravel a dominant standard, as it almost did for the Apple Macintosh platform in the mid-1990s. Like low marginal costs, network effects can create both winner-take-all markets and high turbulence.
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The Social Acceptability of Superstars

In addition to the technical changes that have increased digitization, telecommunication, networks, and other factors that create superstar products and companies, there are more aspects at work in boosting superstar compensation for individuals. In some cases, cultural barriers to very large pay packages have fallen. CEOs, financial executives, actors, and professional athletes may be more willing to demand seven- or even eight-figure compensation deals. As more people get those deals, a positive feedback loop emerges: it becomes easier for others to make similar requests.

In fact, the concentration of wealth itself can create what Frank and Cook call “deep pocket” winner-take-all markets. As the great economist Alfred Marshall noted, “a rich client whose reputation or fortune or both are at stake will scarcely count any price too high to secure the services of the best man he can get.”
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If mass-market media enables an athlete like O. J. Simpson to earn millions, then he can afford to pay a lawyer like Alan Dershowitz millions to defend him in court, even if Dershowitz’s services are not replicated to millions of people like Simpson’s are. In a sense, Dershowitz is a superstar by proxy: he benefits from the ability of his superstar clients whose labor has been more directly leveraged by digitization and networks.
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Laws and institutions have also changed in ways that often boost the incomes of superstars. The top marginal tax rate was as high as 90 percent during the Eisenhower years and over 50 percent early in Ronald Reagan’s administration, but fell to 35 percent in 2002, where it remained through 2012. While this shift obviously boosted the
after-tax
income of top earners, research suggests it can also affect reported
pre-tax
income by motivating people to work harder (because they keep more of each dollar they earn) and report more of their actual income, rather than seek ways to hide or shelter it (because the costs of reporting to tax authorities aren’t as high as before).

Restrictions on trade have also decreased. Like cheaper telecommunications and transportation, this makes markets more global, allowing international superstars to more easily compete with, and drive out, local producers. When Kia poached Peter Schreyer from Audi in 2006, it was a signal that the market for talented automobile designers was increasingly global, not local.

Although the top 1 percent and 0.01 percent have seen record increases in their earnings, the superstar economy has faced a few headwinds. Perhaps the most important among these is the growth of the
long tail
—the increased availability of niche products and services. Technology has not just lowered marginal costs; in many cases it has also lowered fixed costs, inventory costs, and the costs of searching. Each of these changes makes it more attractive to offer a greater variety of products and services, filling small niches that previously went unfilled.

Instead of going head-to-head with a superstar, some individuals and businesses are instead finding ways to differentiate their products, to find or create an alternative niche where they can be the world’s best. J. K. Rowling is a billion-dollar author, but there are also millions of other authors who now have a chance to publish for more specialized audiences of a few thousand or even a few hundred readers. Amazon will stock their books and make them accessible to people across the planet. That will be profitable for Amazon even if it would have been unprofitable for any physical bookstore, with a much smaller set of customers, to stock the book. Even as the technology destroys geography—a barrier that used to protect authors from worldwide competition—it opens up specialization as a source of differentiation.

Instead of being the thousandth-best children’s book author in the world, it may be more profitable to be the number-one author in Science-Based Advice for Ecological Entrepreneurs, or Football Clock Management.
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Following this principle, developers have created over seven hundred thousand apps for the iPhone and Android, while Amazon offers over twenty-five million songs. An even larger number of blog posts, Facebook stories, and YouTube videos have been created in the sharing economy, creating economic value if not necessarily direct income for their creators. As we’ve seen, however, opportunities to create new products don’t necessarily come with big paychecks. A superstar or long-tail economy with low barriers to entry is still one with far more inequality.

The Power Curve Nation

An economy dominated by winner-take-all markets has very different dynamics than the industrial economy to which we are accustomed. As we discussed at the beginning of the chapter, the earnings of bricklayers will vary a lot less than the winner-take-all earnings of app developers, but that’s not the only difference. Instead of stable market shares, where revenues and income correspond proportionally to differences in talent and effort, competition in winner-take-all markets will be much more unstable and asymmetrical. The great economist Joseph Schumpeter wrote of “creative destruction,” where each innovation not only created value for consumers but also wiped out the previous incumbent. The winners scaled up and dominated their markets, but were in turn vulnerable to the next generation of innovators. Schumpeter’s observation describes markets in software, media, and the Internet much better than traditional markets in manufacturing and services. But as more and more industries become increasingly digitized and networked, we can expect the Schumpeterian dynamic to spread.
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In a superstar economy, the distribution of income isn’t just more spread out; it has a very different shape. It’s not just that a small group at the top sees big increases. It’s also a change in the fundamental structure of the distribution. When revenues are roughly proportional to absolute performance, as in the example of the bricklayer, the earnings distribution is likely to roughly match the distribution of aptitude and effort. For many characteristics, humans fall roughly along a
normal distribution
, also known as the
Gaussian distribution
or the
bell curve
. That’s the approximate distribution for height, strength, speed, general IQ, and in all likelihood many other characteristics such as emotional intelligence, management savvy, and even diligence.

Normal distributions are very common (hence the name), and they have an intuitive pattern. As you move further and further into either tail, the number of participants drops precipitously. What’s more, the mean, median, and mode of the distribution are all the same number. An ‘average’ person is also the one in the middle of the distribution, as well as the most typical or common type of person. If the income distribution of the United States followed a normal distribution, then median income would have risen along with average income—but of course, it didn’t. Another characteristic of the normal distribution is that as you diverge from the mean, the probability of finding anyone with extreme characteristics drops rapidly, and at an increasing rate. The ratio of people who are seven feet tall to people who are six and a half feet tall is much less than the ratio people who are six and a half feet tall to people who are six feet tall. Thus, there are very few people at the extremes.

In contrast, superstar (and long tail) markets are often better described by a power law, or Pareto curve, in which a small number of people reap a disproportionate share of sales. This is often characterized as the 80/20 rule, where 20 percent of the participants get 80 percent of the gains, but it can be more extreme than that.
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For instance, research by Erik and his coauthors found that book sales at Amazon were characterized by a power law distribution.
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Power law distributions have a ‘fat tail,’ which means the likelihood of extreme events is much greater than one would expect to see in a normal distribution.
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They are also ‘scale invariant,’ which means that the top-selling book accounts for about the same share of the top ten books’ sales as the top ten books do for the top one hundred, or the top one hundred do for the top one thousand. Power laws describe many phenomena, from frequency of earthquakes to the frequency of words in most languages. They also describe the sales distribution of books, DVD, apps, and other information products.

Other markets are mixtures of different types of distributions. The U.S. economy as a whole can be described as a mixture of a log-normal distribution (a variant of the classical normal distribution) and power law, with the power law fitting the incomes at the top best.
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Some of our current research at MIT is trying to better understand the causes and consequences of this mixture, and how it may be evolving over time.