This summer’s biggest movie is Jurassic World. Apparently, people have an endless appetite for dinosaurs, which could also explain much about the popularity of Flintstones vitamins or, for that matter, Vladimir Putin. Fortunately for these people, there remain dinosaurs among us who are producing mayhem on a scale unimagined by even Hollywood’s CGI wizards.
We call them economists.
The term may initially evoke visions of kindly bespectacled wonks droning on about arcane theories or perhaps government big shots mumbling unintelligibly before Congress. But we know better: These are powerful women and men. They have made giant policy decisions that have affected the lives of billions, often while working behind closed doors with data and on strategies that few understand and fewer still believe in.
Economics has long been known as the dismal science. Thomas Malthus, a cleric who also wrote about economics, has become the poster child used by many to illustrate the rationale behind this label. (Thomas Carlyle actually first coined the term in reference to the study of the business of slavery.) In the very last years of the 18th century, Malthus posited the argument that population growth would ultimately derail human society’s efforts to perfect itself. “[T]he power of population is,” he wrote, “indefinitely greater than the power in the earth to produce subsistence for man.” It is indeed a grim prognosis. But it highlights another reason economics might be seen as dismal: that is, just how off the mark its predictions can be.
Being wrong has long been a special curse of economists. You might not think this would be the case in a so-called “science.” But, of course, all sciences struggle in those early years before scientists have enough data to support theories that can reflect and predict what actually happens in nature. Scientists from Galileo to Einstein have offered great discoveries but, due to the limits of their age, have labored under gross misconceptions. And in economics we are hardly in the era of Galileo quite yet. It is more like we are somewhere in the Middle Ages, where, based on some careful observation of the universe and a really inadequate view of the scope and nature of that universe, we have produced proto-science—also known today as crackpottery. (See long-standing views that the Earth was the center of the solar system or the belief that bleeding patients would cure them by ridding them of their “bad humors.”)
Modern economic approaches, theories, and techniques, the ones that policymakers fret over and to which newspapers devote barrels of ink, will someday be seen as similarly primitive. For example, economic policymakers regularly use gross estimates of national and international economic performances—largely aggregated measures based on data and models that are somewhere between profoundly flawed and crazy wrong—to assess society’s economic health, before determining whether to bleed the economic body politic by reducing the money supply or to warm it up by pumping new money into its system. Between these steps and regulating just how much the government spends and takes in taxes, we have just run through most of the commonly utilized and discussed economic policy tools—the big blunt instruments of macroeconomics.
I remember that when I was in government, those of us who dealt with trade policy or commercial issues were seen as pipsqueaks in the economic scheme of things by all the macrosauruses beneath whose feet the earth trembled, whose pronouncements echoed within the canyons of financial capitals, and who felt everything we and anyone else did was playing at the margins.
But think of the data on which those decisions were based. GDP, as it is calculated today, has roughly the same relationship to the size of the economy as estimates of the number of angels that can dance on the head of a pin do to the size of heaven. It misses vast amounts of economic activity and counts some things as value creation that aren’t at all. Even the guy who pioneered the idea in the 1930s, Simon Kuznets, warned against using it as the prime measure of national economic well-being. Trade data, such as that used in measuring national surpluses and deficits, misses a big chunk of trade in services and much Internet activity, among many other swaths of trade—and is widely reported inaccurately. Labor statistics, such as unemployment rates, are cooked and deceptive. The list goes on. The reality is that only two things are known about most of the data that policymakers use to make decisions: It is late and it is wrong.
But today the world stands at the dawn of a new era thanks to the advent of big data and enhanced computing power. Already there exist data flows that will show economic fluctuations in real time and down to an incredible level of detail: by community, by block, by family, by business, by however you want to slice it. The world will also be able to find correlations never before imagined. Old ideas, like tracking national economic performance based on geography, will give way to new ones, like tracking customizable groups that share much closer correlations than borders. There is a “you-istan” out there full of millions of people who act more like you, who respond to stimuli more like you, and who rise and fall more like you than do your neighbors. Next-generation economists will be able to target their actions more surgically.
Whereas today’s economic models rely on a relative handful of variables, future models will be able to utilize a limitless number, creating opportunities for policymakers to develop new tools. Many of these new models and tools will require not the insights of microeconomists, but those of nano-economists, superspecialists in the relationship between much smaller economic units and the larger economy as a whole. Economic policymaking will therefore devolve from central governments to state and local governments, which are not only closer to the issues and the solutions that workers, companies, investors, and citizens require, but are better equipped to work with the local private sector in real time to solve those issues.
New economic theories will also emerge based on growing sources of real-time data about every aspect of markets and the factors affecting them—and new, ever more powerful tools will be created for analyzing that data. Some will relate to the fact that soon money as we know it will be replaced by alternative bit-based and mobile-payment systems, knocking old-school monetary policies for a loop. Others will have to do with the new ways we not only create jobs, but define work. There may ultimately be a need to revisit the issue of the redistribution of wealth as big companies harness capital, technology, and data to grow rich—but in so doing, benefit comparatively few investors and employees, while displacing many. Just as the 20th century saw the advent of the weekend, the hyperproductivity of the intelligent-technology-empowered 21st century might see labor demand fall and four- or three-day weeks become the norm. Taxation will transform as methods by which we track activity and levy fees within the economy change; such processes will easily cover more kinds of activity in real time, while algorithms will constantly adjust for the economic circumstances of those being taxed. Gradually, there will be a recognition that most of the economic value in the global economy is created and exchanged in virtual rather than real space, with important consequences for the metrics and ideas we use for measuring that value.
Indeed, tomorrow’s economics will be so unlike that of today’s that it might just take a Hollywood device—like a mosquito preserved in amber, carrying, for example, the blood of Alan Greenspan, from which viable DNA can re-create this macrosaurus—for future generations to fully grasp the Jurassic Period economic thinking and approaches that have governed and guided our daily lives.