In his landmark 1776 work The Wealth of Nations, Adam Smith showed that a clever division of labor could make a commercial enterprise vastly more productive than if each worker took personal charge of constructing a finished product. Four decades later, in On the Principles of Political Economy and Taxation, David Ricardo took the argument further with his theory of comparative advantage, asserting that because it is more efficient for Portuguese workers to make wine and English workers to make cloth, each group would be better off focusing on its area of advantage and trading with the other.
These insights both reflected and drove the Industrial Revolution, which was as much about process innovations that reduced waste and increased productivity as it was about the application of new technologies. The notions that the way we organize work can influence productivity more than individual effort can and that specialization creates commercial advantage underlie the study of management to this day. In that sense Smith and Ricardo were the precursors of Frederick Winslow Taylor, who introduced the idea that management could be treated as a science—thus starting a movement that reached its apogee with W. Edwards Deming, whose Total Quality Management system was designed to eliminate all waste in the production process.
Eliminating waste sounds like a reasonable goal. Why would we not want managers to strive for an ever-more-efficient use of resources? Yet as I will argue, an excessive focus on efficiency can produce startlingly negative effects, to the extent that superefficient businesses create the potential for social disorder.
Outcomes Aren’t Really Random
When predicting economic outcomes—incomes, profits, and so forth—we often assume that any payoffs at the individual level are random: dictated by chance. Of course, this is not actually so; payoffs are determined by a host of factors, including the choices we make. But those factors are so complex that as far as we can tell, economic outcomes might as well be determined by chance. Randomness is a simplifying assumption that fits what we observe.
If economic outcomes are random, statistics tells us that they will follow a Gaussian distribution: When plotted on a graph, the vast majority of payoffs will be close to the average, with fewer and fewer occurring the further we move in either direction. This is sometimes known as a normal distribution, because many things in our world follow the pattern, including human traits such as height, weight, and intelligence. It is also called a bell curve, for its shape. As data points are added, the whole becomes ever more normally distributed.
But evidence doesn’t justify the assumption of randomness in economic outcomes. In reality, efficiency gains create an enduring advantage for some players, and the outcomes follow an entirely different type of distribution—one named for the Italian economist Vilfredo Pareto, who observed more than a century ago that 20% of Italians owned 80% of the country’s land. In a Pareto distribution, the vast majority of incidences are clustered at the low end, and the tail at the high end extends and extends. There is no meaningful mean or median; the distribution is not stable. Unlike what occurs in a Gaussian distribution, additional data points render a Pareto distribution even more extreme.
That happens because Pareto outcomes, in contrast to Gaussian ones, are not independent of one another. Consider height—a trait that, as mentioned, tracks a Gaussian distribution. One person’s shortness does not contribute to another person’s tallness, so height (within each sex) is normally distributed. Now think about what happens when someone is deciding whom to follow on Instagram. Typically, he or she looks at how many followers various users have. People with just a few don’t even get into the consideration set. Conversely, famous people with lots of followers—for example, Kim Kardashian, who had 115 million at last count—are immediately attractive candidates because they already have lots of followers. The effect—many followers—becomes the cause of more of the effect: additional followers. Instagram followership, therefore, tracks a Pareto distribution: A very few people have the lion’s share of followers, and a large proportion of people have only a few. The median number of followers is 150 to 200—a tiny fraction of what Kim Kardashian has.
The same applies to wealth. The amount of money in the world at any one moment is finite. Every dollar you have is a dollar that is not available to anyone else, and your earning a dollar is not independent of another person’s earning a dollar. Moreover, the more dollars you have, the easier it is to earn more; as the saying goes, you need money to make money. As we’re often told, the richest 1% of Americans own almost 40% of the country’s wealth, while the bottom 90% own just 23%. The richest American is 100 billion times richer than the poorest American; by contrast, the tallest American adult is less than three times as tall as the shortest—demonstrating again how much wider the spread of outcomes is in a Pareto distribution.
We find a similar polarization in the geographic distribution of wealth. The rich are increasingly concentrated in a few places. In 1975, 21% of the richest 5% of Americans lived in the richest 10 cities. By 2012 the share had increased to 29%. The same holds for incomes. In 1966 the average per capita income in Cedar Rapids, Iowa, was equal to that in New York City; now it is 37% behind. In 1978 Detroit was on a par with New York City; now it is 38% behind. San Francisco was 50% above the national average in 1980; now it is 88% above. The comparable figures for New York City are 80% and 172%.
Business outcomes also seem to be shifting toward a Pareto distribution. Industry consolidation is increasingly common in the developed world: In more and more industries, profits are concentrated in a handful of companies. For instance, 75% of U.S. industries have become more concentrated in the past 20 years. In 1978 the 100 most profitable firms earned 48% of the profits of all publicly traded companies combined, but by 2015 the figure was an incredible 84%. The success stories of the so-called new economy are in some measure responsible—the dynamics of platform businesses, where competitive advantages often derive from network effects, quickly convert Gaussian distributions to Pareto ones, as with Kim Kardashian and Instagram.
The Pressure to Consolidate
In business outcomes, gravity’s equivalent is efficiency. Consider the U.S. waste-management industry. At one time there were thousands of little waste-management companies—garbage collectors—across the country. Each had one to several trucks serving customers on a particular route. The profitability of those thousands of companies was fairly normally distributed. Most clustered around the mean, with some highly efficient and bigger companies earning higher profits, and some weaker ones earning lower profits.
The 100 most profitable U.S. firms earn 84% of the profits of all public firms.
Then along came Wayne Huizenga, the founder of Waste Management (WM). Looking at the cost structure of the business, he saw that two big factors were truck acquisition (the vehicles were expensive, and because they were used intensively, they needed to be replaced regularly) and maintenance and repair (intensive use made this both critical and costly). Each small player bought trucks one or maybe a handful at a time and ran a repair depot to service its little fleet.
Huizenga realized that if he acquired a number of routes in a given region, two things would be possible. First, he would have much greater purchasing leverage with truck manufacturers and could acquire vehicles more cheaply. Second, he could close individual maintenance facilities and build a single, far more efficient one. As he proceeded, the effect—greater efficiency—became the cause of more of the effect. Huizenga generated the resources to keep buying small garbage companies and expanding into new territories, which made WM bigger and more efficient still. This put competitive pressure on all small operators, because WM could come into their territories and underbid them. Those smaller firms could either lose money or sell to WM. Huizenga’s success represented a huge increase in pressure on the system.
Like a collapsing sandpile, the industry quickly consolidated, with WM as the dominant player, earning the highest profits; fellow consolidator Republic Services as the second-largest player, earning decent profits; several considerably smaller would-be consolidators earning few returns; and lots of tiny companies mainly operating at subsistence levels. The industry today is structured as a Pareto distribution, with WM as winner-take-most. The company earned more than $14 billion in 2017; Huizenga died (in March 2018) a multibillionaire.
If WM is so highly efficient, why should we object? Don’t all consumers benefit, and does it matter whether WM or a collection of small firms issues sanitation workers’ paychecks? The answer is that a superefficient dominant model elevates the risk of catastrophic failure. To understand why, we’ll turn to an example from agriculture.
The Problem with Monocultures
Almonds were once grown in a number of places in America. But some locations proved better than others, and as in most production contexts, economies of scale could be had from consolidation. As it turns out, California’s Central Valley is perfect for almond growing, and today more than 80% of the world’s almonds are produced there. This is a classic business example of what biologists call a monoculture: A single factory produces a product, a single company holds sway in an industry, a single piece of software dominates all systems.
Such efficiency comes at a price. The almond industry designed away its redundancies, or slack, and in the process it lost the insurance that redundancy provides. One extreme local weather event or one pernicious virus could wipe out most of the world’s production.
And consolidation has knock-on effects. California’s almond blossoms all need to be pollinated in the same narrow window of time, because the trees grow in the same soil and experience the same weather. This necessitates shipping in beehives from all over America. At the same time, widespread bee epidemics have created concern about the U.S. population’s ability to pollinate all the plants that need the bees’ work. One theory about the epidemics is that because hives are being trucked around the country as never before for such monoculture pollinations, the bees’ resistance has been weakened.
Power and Self-Interest
As we saw with WM, another result of efficient systems is that the most efficient player inevitably becomes the most powerful one. Given that people operate substantially out of self-interest, the more efficient a system becomes, the greater the likelihood that efficient players will game it—and when that happens, the goal of efficiency ceases to be the long-term maximization of overall societal value. Instead, efficiency starts to be construed as that which delivers the greatest immediate value to the dominant player.
You can see this dynamic in the capital markets, where key corporate decision makers make common cause with the largest shareholders. It goes like this: Institutional investors support stock-based compensation for senior executives. The executives then take actions to reduce payroll and cut back on R&D and capital expenditures, all in the name of efficiency. The immediate savings boost cash flow and consequently cause the stock price to spike. Those investors—especially actively trading hedge funds—and executives then sell their holdings to realize short-term gains, almost certainly moving back in after the resulting decline in price. Their gains come at a cost. The most obvious losers are employees who are laid off because of the company’s flagging fortunes. But long-term shareholders also lose, because the company’s future is imperiled. And customers suffer in terms of product quality, which is threatened as the company reduces its investment in making improvements.
The pension fund business provides a particularly egregious case of abuse by dominant insiders. In theory, fund managers should compete on the quality of their long-term investment decisions, because that is what delivers value to pensioners. But 19 of the 25 biggest U.S. pension funds, accounting for more than 50% of the assets of the country’s 75 largest pension funds, are government-created and -regulated monopolies. Their customers have no choice of provider. If you are a teacher in Texas, the government mandates that the Teacher Retirement System of Texas—a government agency—manage your retirement assets. Fund managers’ jobs, therefore, are relatively secure as long as they don’t screw up in some obvious and public way. They are well placed to game the system.
If a system is highly efficient, odds are that efficient players will game it.