The Map and the Territory Read online

  Also by Alan Greenspan

  The Age of Turbulence

  THE PENGUIN PRESS

  Published by the Penguin Group

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  First published by The Penguin Press, a member of Penguin Group (USA) LLC, 2013

  Copyright © 2013 by Alan Greenspan

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  LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

  Greenspan, Alan, 1926–

  The map and the territory : risk, human nature, and the future of forecasting / Alan Greenspan.

  pages cm

  Includes bibliographical references and index.

  ISBN 978-1-101-63874-3

  1. Economic forecasting—United States. 2. Financial crises—United States. 3. United States—Economic policy. 4. Economic forecasting. 5. Risk. I. Title.

  HC106.84.G74 2013

  330.973001'12—dc23

  2013028130

  FOR MY BELOVED ANDREA

  CONTENTS

  Also by Alan Greenspan

  Title Page

  Copyright

  Dedication

  Introduction

  ONE. ANIMAL SPIRITS

  TWO. THE CRISIS BEGINS, INTENSIFIES, AND ABATES

  THREE. THE ROOTS OF CRISIS

  FOUR. STOCK PRICES AND EQUITY STIMULUS

  FIVE. FINANCE AND REGULATION

  SIX. SCHOONER INTELLIGENCE AND THEN SOME

  SEVEN. UNCERTAINTY UNDERMINES INVESTMENT

  EIGHT. PRODUCTIVITY: THE ULTIMATE MEASURE OF ECONOMIC SUCCESS

  NINE. PRODUCTIVITY AND THE AGE OF ENTITLEMENTS

  TEN. CULTURE

  ELEVEN. THE ONSET OF GLOBALIZATION, INCOME INEQUALITY, AND THE RISE OF THE GINI AND THE CRONY

  TWELVE. MONEY AND INFLATION

  THIRTEEN. BUFFERS

  FOURTEEN. THE BOTTOM LINE

  Acknowledgments

  Appendices

  Notes

  Index

  INTRODUCTION

  It was a call I never expected to receive. I had just returned home from indoor tennis on the chilly, windy Sunday afternoon of March 16, 2008. A senior official of the Federal Reserve Board was on the phone to alert me of the board’s just-announced invocation, for the first time in decades, of the obscure but explosive section 13 (3) of the Federal Reserve Act. Broadly interpreted, section 13 (3) empowered the Federal Reserve to lend nearly unlimited cash to virtually anybody.1 On March 16, it empowered the Federal Reserve Bank of New York to lend $29 billion to facilitate the acquisition of Bear Stearns by JPMorgan.

  Bear Stearns, the smallest of the major investment banks, founded in 1923, was on the edge of bankruptcy, having run through nearly $20 billion of cash just the previous week. Its demise was the beginning of a six-month erosion in global financial stability that would culminate with the Lehman Brothers failure on September 15, 2008, triggering possibly the greatest financial crisis ever. To be sure, the Great Depression of the 1930s involved a far greater collapse in economic activity. But never before had short-term financial markets, the facilitators of everyday commerce, shut down on so global a scale. The drying up of deeply liquid markets, literally overnight, as investors swung from euphoria to fear, dismantled vast financial complexes and led to a worldwide contraction in economic activity. The role of human nature in economic affairs was never more apparent than on that fateful day in September and in the weeks that followed.

  On the face of it, the financial crisis also represented an existential crisis for economic forecasting. I began my postcrisis investigations, culminating in this book, in an effort to understand how we all got it so wrong, and what we can learn from the fact that we did. At its root, then, this is a book about forecasting human nature, what we think we know about the future and what we decide we should do about it. It’s about the short term and the long term, and perhaps most important, about the foggy place where the one turns into the other. We are at this moment faced with a number of serious long-term economic problems, all in a sense having to do with underinvesting in our economic future. My most worrisome concern is our broken political system. It is that system on which we rely to manage our rule of law, defined in our Constitution (see Chapter 14). My fondest hope for this book is that some of the insights my investigations have yielded will be of some use in bolstering the case for taking action now, in the short term, which is in our long-term collective self-interest despite the unavoidable short-term pain it will bring. The only alternative is incalculably worse pain and human suffering later. There is little time to waste.

  THE FORECASTING IMPERATIVE

  As always, though we wish it were otherwise, economic forecasting is a discipline of probabilities. The degree of certainty with which the so-called hard sciences are able to identify the metrics of the physical world appears to be out of the reach of the economic disciplines. But forecasting, irrespective of its failures, will never be abandoned. It is an inbred necessity of human nature. The more we can anticipate the course of events in the world in which we live, the better prepared we are to react to those events in a manner that can improve our lives.

  Introspectively, we know that we have a limited capability to see much beyond our immediate horizon. That realization has prompted us, no doubt from before recorded history, to look for ways to compensate for this vexing human “shortcoming.” In ancient Greece, kings and generals sought out the advice of the oracle of Delphi before embarking on political or military ventures. Two millennia later, Europe was enthralled by the cryptic prognostications of Nostradamus. Today, both fortune-tellers and stock pickers continue to make a passable living. Even repeated forecasting failure will not deter the unachievable pursuit of prescience, because our nature demands it.

  ECONOMETRICS

  A key plot point in the history of our efforts to see the future has been the development over the past eight decades of the discipline of model-based economic forecasting. That discipline has embraced many of the same mathematical tools employed in the physical sciences, tools used by almost all economic forecasters, both in government and in the private sector, largely to build models that “explain” the past, and perhaps, as a consequence, make the future more comprehensible.

  I was drawn to the sophistication of the then-new mathematical economics as a graduate student at Columbia University in the early 1950s. My professors Jacob Wolfowitz and Abraham Wald were pioneers in mathematical statistics.2 But my early fascination was increasingly tempered over the years by a growing skepticism about its relevance to a world in which the state of seemingly unmodelable animal spirits is so critical a factor in economic outcomes.

  John Maynard Keynes, in his groundbreaking 1936 opus, The General Theory of Employment, Interest and Money, set the framework for much of modern-day macromodeling. The Keynesian model, as it came to be known, to this day governs much government macroeconomic policy. Keynes’s model was a complete, though simplified version of how the major pieces of a market economy fit together. The class of models that today we still call Keynesian is widely employed in the public and private sectors, especially to judge the impact of various
governmental policies on the levels of GDP and employment.

  Keynes’s approach was a direct challenge to classical economists’ belief that market economies were always self-correcting and would, when disturbed, return to full employment in relatively short order. By contrast, Keynes argued that there were circumstances in which those self-equilibrating mechanisms became dysfunctional, creating an “underemployment equilibrium.” In those circumstances, he advocated government deficit spending to offset shortfalls in aggregate demand. Remarkably, more than seventy-five years later, economists continue to debate the pros and cons of that policy.

  Economic forecasting of all varieties, Keynesian and otherwise, has always been fraught with never-ending challenges. Models, by their nature, are vast simplifications of a complex economic reality. There are literally millions of relationships that interact every day to create aggregate GDP, even for a relatively simple market economy. Because only a very small fraction of these interactions can be represented in any model, economists are continually seeking sets of equations that, while few in number, nonetheless are presumed to capture the fundamental forces that drive modern economies.

  In practice, model builders (myself included) keep altering the set of chosen variables and equation specifications until we get a result that appears to replicate the historical record in an economically credible manner. Every forecaster must decide which relative handful of “equations” he or she believes most effectively captures the essence of an economy’s overall dynamics.

  For the most part, the modeling of the nonfinancial sectors of market economies has worked tolerably well. Vast amounts of research have increasingly enhanced our understanding of how those markets function.3 Finance, however, as we repeatedly learn, operates in a different leveraged environment where risk is of a significantly larger magnitude than in the rest of the economy. Risk taking and avoiding is at the root of almost all financial decisions. Nonfinancial business is more oriented to engineering, technology, and management organization.

  Nonfinancial businesses do factor risk into all their capital investment and other decisions, but their principal concern concentrates on, for example, how many transistors can be squeezed onto a microchip and how to ensure bridges can safely carry the traffic load they are built to carry. But that is the application of quantum mechanics and engineering, where risk has been largely, though not wholly, removed from decision making. Synthetic derivative trading and other new activities in our financial sector have levels of risk many multiples greater than exist in the physical sciences, the critical body of knowledge that supports nonfinancial business. Human nature has no role to play in how subatomic particles interact with one another.4 Our propensities related to fear, euphoria, herding, and culture, however, virtually define finance. Because finance importantly guides a nation’s savings toward investment in cutting-edge technologies, its impact on overall economic outcomes, for good or ill, is far greater than its less than 10 percent share of GDP would suggest. Moreover, financial imbalances are doubtless the major cause, directly or indirectly, of modern business cycles. Finance has always been the most difficult component of an economy to model.

  Spurred in the 1960s by the apparent success of the forecasting models of the Council of Economic Advisers (CEA) under Presidents Kennedy and Johnson, econometrics, as it came to be known, moved from the classroom to the forefront of economic policy making. By the late 1960s, econometric models had become an integral part of government and private policy making, and remain so to this day.

  But the road forward for forecasters has been rocky. Simple models do well in the classroom as tutorials, but regrettably have had less success in the world beyond. No sooner had Keynes’s paradigm gained wide acceptance within the economics profession than the American economy began to behave in a manner that contradicted some of the core tenets of the so-called Keynesian models, including the thesis that a rise in unemployment reflected increased slack in the economy that would in turn lower the rate of inflation. For much of the 1970s, the unemployment rate rose, but the inflation rate remained stubbornly elevated—a malady dubbed stagflation at the time.

  The forecasting tools that had made government economists seem so prescient a decade earlier now appeared flawed. Milton Friedman of the University of Chicago gained intellectual traction by arguing that our inflationary economic policies, most notably the rapid expansion of the money supply, were raising inflation expectations that overcame the disinflationary effect of slack in the labor market. Friedman and his followers developed a theory, monetarism, and a forecasting tool based on the growth in money supply that, for a while, appeared to forecast the developments of the late 1970s far more accurately than any of the variations of the Keynesian model. By the end of the 1970s, the weekly issuance by the Federal Reserve of its money supply figures soon drew as much attention as today’s unemployment numbers.

  By the 1980s, with inflation under control—thanks, in part, to the Federal Reserve’s restraint of money supply growth—a rejuvenated but somewhat chastened Keynesianism, with a stagflation fix to reflect the importance of inflation expectations, reemerged. Such models worked reasonably well for the next two decades, largely as a consequence of an absence of any serious structural breakdown in markets. The model constructed by Federal Reserve staff, combining the elements of Keynesianism, monetarism, and other more recent contributions to economic theory, seemed particularly impressive, and was particularly helpful to the Fed’s Board of Governors over the years of my tenure.

  THE WORLD CHANGED

  But leading up to the almost universally unanticipated crisis of September 2008, macromodeling unequivocally failed when it was needed most, much to the chagrin of the economics profession. The Federal Reserve Board’s highly sophisticated forecasting system did not foresee a recession until the crisis hit. Nor did the model developed by the prestigious International Monetary Fund, which concluded as late as the spring of 2007 that “global economic risks [have] declined since . . . September 2006. . . . The overall U.S. economy is holding up well . . . [and] the signs elsewhere are very encouraging.”5 JPMorgan, arguably America’s premier financial institution, projected on September 12, 2008—three days before the crisis hit—that the U.S. GDP growth rate would be accelerating into the first half of 2009.

  Most analysts and forecasters, both public and private, agreed with the view expressed by the Economist in December 2006 that “market capitalism, the engine that runs most of the world economy, seems to be doing its job well.” As late as the day before the crash, September 14, 2008, the outlook was still sufficiently equivocal that I was asked on ABC’s Sunday morning show This Week if “the chances of escaping a recession [were] greater than fifty percent.”6 With the crisis less than twenty-four hours away, conventional wisdom had not yet coalesced around even the possibility of a typical recession, to say nothing of the worst economic crisis in eight decades.

  Moreover, even after the crash, in January 2009, the unemployment rate, then at 7.8 percent, was forecast by the chairman designate of the President’s Council of Economic Advisers to fall to 7.0 percent by the end of 2010 and to 6.5 percent by the end of 2011.7 In December 2011, the rate was 8.5 percent.

  What went wrong? Why was virtually every economist and policy maker of note so off about so large an issue?

  My inquiry begins with an examination of “animal spirits,” the term John Maynard Keynes famously coined to refer to “a spontaneous urge to action rather than inaction, and not as the [rational] outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities.”8 Keynes was talking about the spirit that impels economic activity, but we now amend his notion of animal spirits to include its obverse, fear-driven risk aversion. I had long been aware of such “spirits” and their quirkiness; in 1959, as a young economist, I had my first taste of being impressively wrong in a public prediction when I worried in the pages of Fortune magazine of investors’ “over-exuberance” at what would pr
ove to be very far from the top of a roaring bull market.9, 10 The point isn’t that I and other economic forecasters didn’t understand that markets are prone to wild and even deranging mood swings that are uncoupled from any underlying rational basis. The point is rather that such “irrational” behavior is hard to measure, and stubbornly resistant to any reliable systematic analysis.

  But now, after the past several years of closely studying the manifestations of animal spirits during times of severe crisis, I have come around to the view that there is something more systematic about the way people behave irrationally, especially during periods of extreme economic stress, than I had previously contemplated. In other words, this behavior can be measured and made an integral part of the economic forecasting process and the formulation of economic policy.

  In a change of my perspective, I have recently come to appreciate that “spirits” do in fact display “consistencies” that can importantly enhance our ability to identify emerging asset price bubbles in equities, commodities, and exchange rates—and even to anticipate the economic consequences of their ultimate collapse and recovery.

  In Chapter 1 in particular, I seek to identify specific behavioral imperatives—spirits—such as euphoria, fear, panic, optimism, and many more—and explore how they, and the cultures they foster, interact with rational economic behavior and spur important market outcomes. This isn’t to say that we should throw Homo economicus out with his dirty bathwater: Despite ample evidence of persistent irrational market behavior, the data indicate that over the long run, rational economic judgments still guide free economies. But, of course, the long run can, famously, take a very long time.

  Nonetheless, it is essential to take both a long-term and a short-term perspective when we examine the roots of the 2008 crisis and the tepid recovery that followed. The rise and fall from 1994 to 2008 of two asset price bubbles, the data indicate, did reflect in part real improvements in productivity, but the bubbles were also carried by a wave of irrational exuberance and bubble euphoria. Those waves, when they inevitably collapsed, produced widespread fear that disabled markets.