by Robert Litan
In this concluding chapter, I explore how the economics profession is changing, where it is likely to go from here, and what the future of economics means for innovation, business, and economists themselves. I begin by describing the current and continuing revolution in economics, focusing on how it is merging with other disciplines and how the data revolution, in particular, has affected economic analysis. I then discuss how these changes in the profession will likely affect businesses and society in the coming years and decades.
As I explain below, the future of economics looks far from dismal. In fact, as the field becomes more practical, it will become ever more valuable to society. The tools and methodology of economics will influence other disciplines and enhance our understanding of how the world works. As the field becomes more empirical and evidence-based, economics will become increasingly intertwined with other technical disciplines and will play a critical role in harnessing the power of information technology and Big Data analytics to help businesses exploit new markets, cut costs, and earn more revenues.
The Revolution in Economics
Whether economists realize it or not, the economics profession is in the midst of a revolution, driven by two factors. The first one is accidental and is the topic I discussed at the outset of this book: the financial crisis of 2008 and the Great Recession that followed it, here and in other developed economies. These crises have resurrected many of the age-old theoretical debates in the profession about how recessions begin and what policies can best end them quickly. As I write this, economists continue to debate the merits of the fiscal stimulus in 2009 and the unorthodox and unprecedented monetary easing of 2009 to 2103. At the same time, macroeconomics has become a hot subject in economics again, with theorists attempting to incorporate models of the financial sector into larger macroeconomic frameworks. I wish them luck because modeling abrupt turns in the economy has been the Achilles heel of macroeconomics in the past, and getting this right in the future will be no easy undertaking.
The other driver of change is technology, specifically the continually increasing power of information technology to analyze ever-larger bodies of data that allows economists and other social scientists to ask and answer new questions. As we saw in Chapter 5, many of the innovative tools and methods that economists use today depend on the use of advanced network services and software to store and analyze large quantities of data. Indeed, analyses that used to be virtually impossible to do a quarter century ago can now be performed in a matter of seconds and with much greater precision than ever before.
Because of these IT advances, economists who have computer and statistical skills, and have used their formal training in economic theory to conduct empirical research, have been in the vanguard of the profession. This trend should continue. It is noteworthy, for example, that two of the recent winners of the Clark Medal are superstars with a strong empirical bent: Esther Duflo of MIT and Raj Chetty of Harvard.
If empirical economics is largely the future of the profession, which I believe it is, what topics will economists study and how will their toolkit evolve over time? I don’t have the foresight to be able to answer this question with sufficient specificity to make you happy. But I have two relevant quasi-answers.
First, if the past is any guide to the future, then economists will continue to apply their skills to subjects not traditionally thought to be within the economic domain. The best examples come from the University of Chicago. The late Gary Becker was a pioneer in this respect, applying economic insights to crime, marriage, and education. The Chicago tradition of expanding the scope of economics continued with Richard Posner’s pioneering insights into how law and economics can and should be fused. Steven Leavitt, the author of the highly popular Freakonomics books, is the most recent example of this genre, with his studies of education, drug markets, and naming conventions followed by parents. The experimental economics of Duflo and Harvard’s Abhijit Banerjee, discussed in Chapter 6, show how out-of-the-box thinking is taking place outside of Chicago.
Second, as Tyler Cowen has persuasively argued,1 and I too believe, economics will merge with other professions in the coming decades, in which researchers from related academic disciplines who are trained to analyze large data sets (sociologists, political scientists, psychologists, anthropologists, and perhaps others) work together to ask new questions, solve new problems, and uncover new business opportunities. Some specialists in other disciplines have even won Nobel Prizes in economics, including Daniel Kahneman (psychology), Elinor Ostrom (political science), and John Nash (mathematics).
The notion that the economics of the future will continue to blend with other disciplines is nothing new.2 After all, as a social science, economics has been embedded with philosophy, political science, and mathematics for centuries. The relatively recent field of agent-based modeling, which describes and predicts the course of contagious events such as financial panics or diseases, is pioneered by my good friend Joshua Epstein of Johns Hopkins Medical School (and others). Josh was originally trained as a political scientist, but he also brings a deep knowledge of advanced mathematics, computer science, genetics, and biology to this work.3
To be sure, because it attempts to describe and predict human behavior rather than natural laws, economics and other social sciences have a tougher task than their counterparts in the physical sciences.4 Nonetheless, I agree with Harvard’s Ray Chetty who argues that, notwithstanding its difficulties, economics should be considered a science because of its sophisticated methodology, which is characterized by formulating and testing precise hypotheses.5 Understandably, economists themselves widely share this view, though they also accept the challenges the discipline faces, such as the limited ability to run experiments. As the discipline becomes more empirical, however, there is reason to believe that, as Nobel laureate Robert Shiller has noted, “it will broaden its repertory of methods and sources of evidence.”6
But as the profession becomes more evidence-based and data-driven, what will happen to the theoretical economists who create mathematical models to explain economic phenomena? The short answer is that the profession will always have a place for theorists. Theories guide the construction of empirical work, just as theoretical scientists form hypotheses for their more practical colleagues to test. Often, theorists and empiricists are one and the same, but this is not always the case.
At the same time, theorists can also become too abstract, distant from the realities of the actual economy. I think many agree that this is what happened to economics prior to the financial crisis. Nobel Prize winner and New York Times columnist Paul Krugman hit the nail on the head when he asserted that in the run-up to the financial crisis, economists “mistook beauty, clad in impressive-looking mathematics, for truth.”7 The challenge facing theorists in the future who are busy incorporating finance into their macroeconomic models will be to avoid this danger.
How Economics Will Continue to Affect Business
The future of economics is here. Economics has begun merging with other disciplines, and the traditional domain of what economists study has dramatically expanded over a relatively short period of time. In addition, the Big Data revolution has vastly improved economists’ toolkit, enabling economists to perform more powerful empirical research.
To paraphrase a popular political phrase, what dog does business have in this hunt? Well, if you buy the thesis of the preceding chapters that economists and their ideas have had powerful direct and indirect effects on business, many and perhaps most of them not immediately evident at the time, then there is no reason for believing that this won’t be true in the future. And if the economics of the future (and present) is increasingly about formulating hypotheses to test with large data sets, then some of those hypotheses and their results cannot help but be useful in business: in identifying new markets, better targeting of customers, and customizing products and services for customers. Indeed, many firms already in the vanguard of the Big Data revolution are engaged in
these activities.
But firms tend not to be as persnickety as economists about hatching a theory first and then testing it. In business, the temptation is to take whatever Big Data set exists and then hunt for any and all correlations, without identifying or structuring any theory. That approach is fine if the correlations remain valid in the future, but as any economist will tell you, correlation is not causation. Just because sunspots may be correlated with sales of a particular item (you choose it) does not mean that sunspots cause the variation in sales. Ideally, firms should want to know the underlying causes before trusting their correlations too blindly. Want proof ? Then look no further than the housing and later the financial crisis of the last decade. Lenders kept lending, and the ratings agencies kept handing out AAA ratings, on the basis of statistical relationships that the sudden decline in housing prices destroyed. In all fairness, as I said at the outset of this book, not many economists foresaw this problem in advance either. But that is not a reason to abandon the quest for underlying structures in relationships between economic variables, and that is what economists are trained to do. Business ignores this quest at its peril.
Implications for Economists
For a long time, economists outside of academia have found lucrative jobs in industries as diverse as banking, public policy, and litigation consulting. In recent years, however, the Big Data revolution has opened the doors to a much broader set of career opportunities. Indeed, as a recent McKinsey Global Institute report on the Big Data revolution found, in the next several years, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions.”8
As sophisticated data analysts, economists stand to benefit from the Big Data revolution, and companies in turn stand to gain immensely from application of economic ideas and tools to solve new business challenges. As an example, let’s consider the health-care industry, where Big Data is already generating enormous value and will continue to do so in the coming decades. Three of the biggest challenges in the health IT space today—detecting fraud, delivering the best care, and keeping costs low—have economic components. The work of the best software engineers and computer scientists mining large bodies of data would benefit from the insights of the best empirical health-care economists in addressing these challenges.
Since the Big Data revolution is only in its infancy, there is tremendous scope for innovation and cross-disciplinary work between empirical economists, computer scientists, statisticians, and perhaps trained specialists in other academic disciplines in the years ahead. To be most in demand, however, economists will need to improve their empirical techniques, and to acquire other technical skills. As economists Lirav Einav and Jonathan Levin of Stanford University recently noted, “our expectation is that future economists who want to work with large datasets will have to acquire at least some of the new tools of the computer scientists, so they can combine the conceptual framework with the ability to actually implement ideas quickly and efficiently on large-scale data.”9
If this sounds vaguely familiar, it’s because in a way it is. Some of the best economists of tomorrow, armed with the conceptual framework and technical aptitude to conduct sophisticated analyses of large-scale datasets, will resemble some of the great empirical economists of the past: Lawrence Klein, Otto Eckstein, and others who had the ability to translate economic theory into the construction of large-scale econometric models. Those models may no longer be in use, at least in the form in which they were constructed then, but the skills it took to build them were analogous to the skills that economists working with Big Data will need in the future. In the process, the economists of tomorrow may have a greater impact on innovation and business than those macroeconomic forecasters.
There is some irony in all this. As discussed in Chapter 5, the IT revolution of the past thirty years and the transition from mainframe computers to PCs greatly diminished the demand for large macroeconomic modeling. Although economics certainly remained an empirical discipline, the arrival of the PC meant that economists could now run their own models, and the days of large-scale macroeconomic forecasting gave way to an era of individual empirical research on a smaller and more focused scale. Now, the Big Data revolution has called for more computing power, greater technical skills, and more cross-disciplinary work in order to tackle the most difficult problems facing businesses and society. Empirical economics is back with a vengeance, but with much more powerful personal computers that made one major economic endeavor obsolete.
Concluding Thoughts
There is another irony. The same innovations in IT and automation that are reducing the need for low- and mid-skilled labor in many occupations throughout all economies, but especially advanced ones, are also likely to do the same to economics.10 There will be less demand for economics teachers, for example, as the kinks in online education get ironed out. The melding of economics with other disciplines in the age of Big Data also will reduce the demand for pure economists.
Yet if the discipline becomes more empirical and evidence-based, it will adapt to the needs of the business world and continue to foster innovation, and thereby become even more practical and thus more valuable to society. The trillion-dollar economists of today may be superseded by the more well-rounded and skilled economists of tomorrow, whose social and economic worth may have even more zeroes at the end than the trillion dollar economists of recent vintage.
Notes
1. Tyler Cowen, Average Is Over: Powering America Beyond the Age of the Great Stagnation (New York: Dutton, 2013): 221–224.
2. See David Collander, “The Future of Economics: The Appropriately Educated in Pursuit of the Knowable,” Cambridge Journal of Economics 29 (2005): 927–941.
3. One early example of this work, which is now much more advanced, can be found in Robert Axtell and Joshua Epstein, Growing Artificial Societies from the Bottom Up (Cambridge, MA: MIT Press, 1996).
4. Robert J. Shiller, “Is Economics a Science?” Project Syndicate, November 6, 2013, www.project-syndicate.org/commentary/robert-j–shilleron-whether-he-is-a-scientist.
5. Raj Chetty, “Yes, Economics Is a Science,” New York Times, October 20, 2013, www.nytimes.com/2013/10/21/opinion/yes-economics-is-a-science.html.
6. Shiller, “Is Economics a Science?”
7. Paul Krugman, “How Did Economics Get It So Wrong?” New York Times, September 6, 2009, www.nytimes.com/2009/09/06/magazine/06Economic-t.html.
8. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers, “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute, May 2011, www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
9. Liran Einav and Jonathan Levin, “The Data Revolution and Economic Analysis,” NBER, April 2013, www.stanford.edu/∼jdlevin/Papers/BigData.pdf.
10. The Economist, “The Onrushing Wave,” January 18, 2014, www.economist.com/news/briefing/21594264-previous-technological-innovation-has-always-delivered-more-long-run-employment-not-less.
Appendix
Prizes in Economics
Like other academic disciplines, economics has its share of prizes for outstanding research work. Three are briefly described here; readers may know two well, the third less so (but it is one I am proud to say I had a hand in creating and I believe its importance will grow over time).
For its winners, the Nobel Prize is probably the most prestigious accolade they receive in their lives. The money that comes with the prize, currently about $1.2 million, which must be split when multiple winners in the same year are announced, is surely a welcome surprise for those who get that unforgettable phone call on an October morning (when the prize is announced each year). But for most winners, the prize’s money is not its main virtue. At least for a time, the winners become famous,
while giving them satisfaction that the years they may have been working in relative obscurity have finally been validated and given worldwide recognition beyond the technical fields in which they have labored.
The prize has other advantages. If they wish to take advantage of it, the Nobel gives the winners a platform they may not have had before to speak out not only about subjects in their field but often outside it. Op-ed editorial chiefs solicit or will more readily accept their pieces. Journalists will seek out Nobel winners for their opinions and quotes. All of the attention can lead to speaking engagements, with honoraria that can handsomely supplement the money from the prize itself. And some Nobel winners may find themselves heavily recruited by other academic institutions (if they are still teaching), at higher salaries and with even more prestige than those they had before winning the prize.
The Nobel Prizes in general were established in 1895 with an endowment from Alfred Nobel, a Swedish entrepreneur who made his fortune primarily in the armaments industry. Nobel is most famous for his invention of dynamite—ironic, since one of the prizes in his name is awarded for promoting peace. The other fields initially eligible for the prize have been in the hard sciences, medicine, and literature.
Economists were not added to the list of recipients until 1969, and even then, as an appendage. Whereas the winners in the other fields received their monetary awards from the Nobel Foundation, the Swedish central bank gave the Foundation funds in 1968 to establish a separate Nobel Memorial Prize in Economic Sciences. The winners of this particular prize are selected by the Royal Swedish Academy of Sciences rather than by the Nobel Committees organized for the other fields. The economics award is thus technically not a Nobel Prize, though it is announced in the fall of each year when the other Nobel awards also are named, and the economics winners are commonly referred to in the media as Nobel recipients.